WO2019000017A1

WO2019000017A1 - Intracellular microrna signatures of insulin- producing cells

Info

Publication number: WO2019000017A1
Application number: PCT/AU2018/000108
Authority: WO
Inventors: Anandwardhan Awadhoot HARDIKAR; Mugdha Vinay JOGLEKAR; Andrzej Szczesny JANUSZEWSKI
Original assignee: University of Sydney
Current assignee: University of Sydney
Priority date: 2017-06-29
Filing date: 2018-06-29
Publication date: 2019-01-03
Anticipated expiration: 2019-12-29

Abstract

The present invention relates generally to the field of medicine and more specifically to insulin-related diseases and conditions. Described herein are microRNA signatures of cells that naturally produce insulin which are relevant, for example, in processes involving differentiation of stem/progenitor/precursor cells to insulin-producing cells, to predicting the level of insulin in cells based on microRNA expression, and in diagnosing and/or prognosing the development of diseases and conditions associated with the loss of insulin-producing cells (e.g. diabetes).

Description

Intracellular microRNA Signatures of Insulin- Producing Cells

Incorporation by Cross-Reference

The present invention claims priority from Australian provisional application number 2017902522 filed on 29 June 2017, the entire contents of which are incorporated herein by cross-reference.

Technical Field

Background

Insulin is a hormone generated in the pancreas. Clusters of cells within the pancreas known as the islets of Langerhans contain beta cells, which make insulin and release it into the circulation. Insulin plays a major role in metabolism, assisting cells throughout the body to absorb glucose and use it for energy. For example, it lowers blood glucose levels by assisting muscle, fat, and liver cells absorb glucose from the bloodstream, it stimulates the liver and muscle tissue to store excess glucose (glycogen), and it lowers blood glucose levels by reducing glucose production in the liver

There are numerous diseases and conditions arising from or associated with loss of insulin-producing cells in humans and other mammals. For example, diabetes is characterized by loss of beta-cell function. Inadequate production (in Type 1 diabetes/TID) or use of insulin (as in Type 2 diabetes/T2D) affects glucose-insulin metabolism resulting in abnormally higher concentrations of glucose in the blood. Insulin lowers blood glucose levels by increasing its uptake into cells of the liver, muscle or fat and storing this in form of glycogen for its use as an energy source in future. Type 1 diabetes is characterized by autoimmune destruction of pancreatic islet beta-cells, while Type 2 diabetes is characterized by insulin resistance and impaired glucose tolerance where insulin is not efficiently used (or produced). Individuals with Type 2 diabetes may eventually require exogenous insulin to regulate blood glucose levels. Individuals with diabetes need to regularly monitor their glucose and inject exogenous insulin for several times in a day so as to maintain normal circulating concentrations of glucose.

Hyperglycemia, as well as the more life-threatening hypoglycemia, are common outcomes of uncontrolled diabetes and over time can lead to serious damage to nerves, blood vessels, heart, eyes, and kidneys. The World Health Organization (WHO) and the International Diabetes Federation (IDF) estimate that more than 420 million adults are currently suffering from diabetes and its global prevalence has risen considerably in recent years.

Standard clinical tests for diagnosing abnormalities in the production of insulin and/or insulin metabolism typically rely on measurements of glucose (e.g. AIC, FPG and OGTT tests), insulin, or c-peptide in the blood. Extended time, expense, inaccuracies in measurement, and/or poor standardisation are just a few of the issues associated with these tests. Blood glucose tests also lack predictive power due to the capacity of pancreatic β-cells to produce the desired level of insulin even when the majority of the β-cells are dead/dying.

Alternative methods for detecting the loss of insulin-producing cells are hence desirable in the context of diagnosing and/or prognosing diseases and conditions associated with loss of insulin-producing cells such as diabetes.

Insulin therapy is a common means of treating diseases and conditions arising from or associated with low insulin production. Exogenous insulin administration therapy typically requires the regular and long-term application of insulin with patient compliance of paramount importance. Insulin therapy also carries a high risk of achieving very low blood glucose concentrations ("hypoglycemia") if over-administered, and may be fatal. Moreover, subjects are additionally required to undertake frequent blood glucose monitoring and carefully control carbohydrate intake. Although insulin therapy can in some cases manage the clinical symptoms of diabetes, cell replacement therapy, often carried out by the transplantation of pancreas or the islets of Langerhans is another therapeutic option. However, there is an increasing scarcity of transplantable human donor islets/pancreas, and rejection issues are still commonly encountered necessitating the use of immunosuppressive drugs. The use of progenitor-/stem-/pluripotent-/precursor-cells to develop new islet cells offers a potential alternative, but achieving efficient differentiation of human progenitor cells to insulin-producing cells has proven challenging. Molecules capable of enhancing the production of insulin and/or promoting the development of surrogate insulin-producing cells for cell replacement therapy are clearly desirable in the context of diseases and conditions associated with loss of insulin-producing cells (e.g. diabetes).

Summary of the Invention

The present invention addresses existing need/s in the field by providing a set of intracellular microRNA molecules that are indicative of loss of insulin-producing cells, and/or which may be used to induce the differentiation of progenitor-/precursor-cells into insulin-producing cells.

The intracellular microRNA signatures of the present invention may be used, without limitation,: (i) to predict the presence/absence or relative abundance (cycle threshold- / Ct- value) of insulin gene transcripts in cells and/or tissues; (ii) to induce insulin gene expression in islet progenitor/precursor cells which, for example, can be used for cell replacement therapy in diseases and conditions associated with loss of insulin-producing cells (e.g. diabetes); (iii) as a biomarker to determine the death or function of insulin-producing cells in subjects with or progressing to diseases and conditions associated with loss of insulin production (e.g. diabetes); and/or (iv) to identify the tissue of origin, based on the signature and levels of microRNA expression.

In general, the intracellular microRNA signatures of the present invention may be: i) associated with insulin-producing cells (including naturally-occurring insulin-producing cells); ii) associated with different levels of insulin expression in insulin-producing cells (including naturally-occurring insulin-producing cells); iii) causal to induction of insulin expression; and/or iv) biomarkers of pancreatic beta-cell death.

The present invention relates at least in part to the following embodiments:

Embodiment 1. A method for predicting a level of insulin production in cells of a subject, the method comprising:

determining expression levels of six or more microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa- miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof, in a sample of the cells obtained from the subject;

wherein: elevated expression levels of the microRNAs in the sample of cells compared to expression levels of the microRNAs in control cells that do not produce insulin is indicative of insulin production in the sample of cells, and

reduced or absent expression levels of the microRNAs in the sample of cells compared to expression level/s of the microRNAs in control cells that do not produce insulin is indicative of reduced or absent insulin production in the sample of cells.

Embodiment 2. The method of embodiment 1, comprising or consisting of determining expression levels of seven or more of the microRNAs, eight or more of the microRNAs, nine or more of the microRNAs, or ten of the microRNAs, in the sample of the cells obtained from the subject.

Embodiment s. The method of embodiment 1, comprising or consisting of determining expression levels of:

(i) six microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, and hsa-miR-217; or

(ii) seven microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa- miR-375, hsa-miR-183#, hsa-miR-217, and hsa-miR-429; or

(iii) eight microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, and hsa-miR-7-2#; or(iv) nine microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR- 217, hsa-miR-429, hsa-miR-7-2#, and mmu-miR-129-3p; or

(v) ten microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, and hsa- miR-433;

in the sample of the cells obtained from the subject.

Embodiment 4. The method of any one of embodiments 1 to 3, further comprising or consisting of determining expression levels of any one or more microRNAs selected from the group consisting of: miR216a, miR144#, miR124a, miR-935, miR133b, miR206, miR200b#, miR625# and miR1271 ;

in the sample of the cells obtained from the subject.

Embodiment 5. The method of any one of embodiments 1 to 4, comprising or consisting of determining expression levels of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, miR216a, miR144#, and miR124a;

in the sample of the cells obtained from the subject. Embodiment 6. The method of any one of embodiments 1 to 5, wherein said elevated expression levels of the microRNAs in the sample of cells is indicative of production of insulin gene transcripts in the sample of cells, and

said reduced or absent expression level/s of the microRNA/s in the sample of cells is indicative of reduced or absent insulin production of insulin gene transcripts in the sample of cells.

Embodiment 7. The method of any one of embodiments 1 to 6, wherein the control cells that do not produce insulin are from the subject.

Embodiment 8. The method of any one of embodiments 1 to 7, wherein the sample of cells comprises any one or more of: pancreatic cells, brain cells, gall bladder cells.

Embodiment 9. The method of any one of embodiments 1 to 8, wherein the sample of cells comprises beta-islet cells.

Embodiment 10. The method of any one of embodiments 1 to 9, wherein said reduced or absent insulin production is diagnostic or prognostic of a disease or condition associated with or arising from a loss of insulin-producing cells in the subject.

Embodiment 11. The method of embodiment 10, wherein the condition or disease is selected from the group consisting of: diabetes, pancreatitis, insulinoma, and pancreatic cancer.

Embodiment 12. The method of any one of embodiments 1 to 11, wherein the subject has or is progressing toward a disease or condition associated with or arising from a loss of insulin-producing cells, and the expression levels of one or more microRNA/s is a marker of death and/or loss of insulin-producing function in the cells of the subject.

Embodiment 13. The method of any one of embodiments 1 to 12, further comprising an initial step of obtaining the sample of cells from the subject.

Embodiment 14. A method for inducing insulin production in pancreatic lineage cells, the method comprising treating the pancreatic lineage cells six one or more microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR- 433, and any combination thereof.

Embodiment 15. The method of embodiment 14, comprising or consisting of treating the pancreatic lineage cells with seven or more of the microRNAs, eight or more of the microRNAs, nine or more of the microRNAs, or ten of the microRNAs, in the sample of the cells obtained from the subject. Embodiment 16. The method of embodiment 14, comprising or consisting of treating the pancreatic lineage cells with:

(iii) eight microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, and hsa-miR-7-2#; or

(iv) nine microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, and mmu-miR-129-3p; or

(v) ten microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, and hsa- miR-433.

Embodiment 17. The method of any one of embodiments 14 to 16, further comprising or consisting of treating the pancreatic lineage cells with any one or more microRNAs selected from the group consisting of: miR216a, miR144#, miR124a, miR-935, miR133b, miR206, miR200b#, miR625# and miR1271.

Embodiment 18. The method of any one of embodiments 14 to 17, comprising or consisting of treating the pancreatic lineage cells with: hsa-miR-183, hsa-miR-216b, dme- miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR- 129-3p, hsa-miR-433, miR216a, miR144#, and miR124a.

Embodiment 19. The method of any one of embodiments 14 to 18, wherein said treating comprises overexpressing the one or more microRNA/s in the pancreatic lineage cells.

Embodiment 20. The method of any one of embodiments 14 to 19, wherein the pancreatic lineage cells comprise any one or more of: pancreatic islet precursor cells, endocrine progenitor cells, pancreatic islet progenitor cells, pancreatic islet stem cells, induced pluripotent pancreatic islet cells.

Embodiment 21. The method of any one of embodiments 14 to 20, wherein the pancreatic lineage cells are beta-islet precursor cells, beta-islet cell pro-precursors, "betalike" cells, "islet-like" cells.

Embodiment 22. The method of any one of embodiments 14 to 21, further comprising differentiating the pancreatic lineage cells into mature pancreatic cells. Embodiment 23. The method of embodiment 22, wherein the mature pancreatic cells are beta-islet cells.

Embodiment 24. The method of any one of embodiments 14 to 23, wherein the treating is conducted in vitro or ex vivo.

Embodiment 25. A method for preventing treating a disease or condition associated with or arising from a loss of insulin-producing cells in a subject, the method comprising treating pancreatic lineage cells according to the method of any one of embodiments 14 to 24, and transplanting the treated cells into a subject.

Embodiment 26. The method of embodiment 25, wherein the cells transplanted are autologous for the subject.

Embodiment 27. The method of embodiment 25 or embodiment 26, wherein the subject is at risk of developing the disease or condition.

Embodiment 28. The method of any one of embodiments 25 to 27, wherein the condition or disease is selected from the group consisting of: diabetes, pancreatitis, insulinoma, and pancreatic cancer.

Embodiment 29. The method of embodiment 11 or embodiment 28, wherein the disease is Type 1 diabetes or insulin-requiring Type 2 diabetes.

Embodiment 30. A method for identifying a tissue of origin of a sample of cells obtained from a subject, the method comprising:

determining expression levels of six or more microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa- miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof, in the sample of cells; and

comparing expression levels the microRNAs in the sample of cells to control expression level/s of the microRNA/s generated from control cells equivalent to the sample of cells,

wherein substantially equivalent expression levels of the microRNAs in the sample of cells compared to the expression level/s of the microRNAs generated from the control cells is indicative that the sample of cells are of the same type as the control cells, and

substantially different expression levels of the microRNAs in the sample of cells compared to the expression level/s of the microRNAs generated from the control cells are indicative that the sample of cells are not of the same type as the control cells. Embodiment 31. The method of embodiment 30, comprising or consisting of determining expression levels of seven or more of the microRNAs, eight or more of the microRNAs, nine or more of the microRNAs, or ten of the microRNAs, in the sample of the cells obtained from the subject.

Embodiment 32. The method of embodiment 31 , comprising or consisting of determining expression levels of:

(iii) eight microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, and hsa-miR-7-2#; or(iv) nine microRNAs which are: hsa- miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR- 217, hsa-miR-429, hsa-miR-7-2#, and mmu-miR-129-3p; or

in the sample of the cells obtained from the subject.

Embodiment 33. The method of any one of embodiments 30 to 32, further comprising or consisting of determining expression levels of any one or more microRNAs selected from the group consisting of: miR216a, miR144#, miR124a, miR-935, miR133b, miR206, miR200b#, miR625# and miR1271 ;

in the sample of the cells obtained from the subject.

Embodiment 34. The method of any one of embodiments 30 to 33, comprising or consisting of determining expression levels of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, miR216a, miR144#, and miR124a;

in the sample of the cells obtained from the subject.

Embodiment 35. The method of any one of embodiments 30 to 34, further comprising an initial step of obtaining the sample of cells from the subject.

Embodiment 36. The method of any one of embodiments 30 to 35, wherein the sample of cells is from the pancreas, brain, or gall bladder of the subject. Embodiment 37. The method of any one of embodiments 4, 17 or 33, wherein the six or more microRNAs comprise or consist of any: 11, 12, 13, 14, 15, 16, 17, 18, or 19 of the microRNAs.

Embodiment 38. A kit comprising primers, probes and/or other binding agents for use in detecting expression of at least six microRNAs selected from the group consisting of: hsa- miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR- 429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof, in a sample of cells.

Embodiment 39. The kit of embodiment 38, comprising or consisting of primers, probes and/or other binding agents for use in detecting expression of seven or more of the microRNAs, eight or more of the microRNAs, nine or more of the microRNAs, or ten of the microRNAs, in the sample of the cells obtained from the subject.

Embodiment 40. The kit of embodiment 39, comprising or consisting of primers, probes and/or other binding agents for use in detecting expression of:

Embodiment 41. The kit of any one of embodiments 38 to 40, further comprising or consisting of primers, probes and/or other binding agents for use in detecting expression of any one or more microRNAs selected from the group consisting of: miR216a, miR144#, miR124a, miR-935, miR133b, miR206, miR200b#, miR625# and miR1271.

Embodiment 42. The kit of any one of embodiments 38 to 41, comprising or consisting of primers, probes and/or other binding agents for use in detecting expression of: hsa-miR- 183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, miR216a, miR144#, and miR124a. Embodiment 43. A microRNA signature comprising at least six microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa- miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof.

Embodiment 44. The microRNA signature of embodiment 43, comprising or consisting of seven or more of the microRNAs, eight or more of the microRNAs, nine or more of the microRNAs, or ten of the microRNAs, in the sample of the cells obtained from the subject.

Embodiment 45. The microRNA signature of embodiment 44, comprising or consisting of:

Embodiment 46. The microRNA signature of any one of embodiments 43 to 45, further comprising or consisting of any one or more microRNAs selected from the group consisting of: miR216a, miR144#, miR124a, miR-935, miR133b, miR206, miR200b#, miR625# and miR1271 .

Embodiment 47. The microRNA signature of any one of embodiments 43 to 46, comprising or consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa- miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, miR216a, miR144#, and miR124a.

Embodiment 48. Use of the kit of any one of embodiments 38 to 42, or the microRNA signature of any one of embodiments 43 to 47, for predicting, diagnosing, and/or prognosing a disease or condition associated with or arising from a loss of insulin-producing cells in a subject, in a subject, wherein the disease or condition is selected from the group consisting of: diabetes, pancreatitis, insulinoma, and pancreatic cancer. Embodiment 49. The method of any one of embodiments 11, 28 or 29, or the use of embodiment 48, wherein the disease is Type 1 diabetes.

Embodiment 50. Use of six or more agents for determining the expression levels of one or more microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu- miR-129-3p, hsa-miR-433, and any combination thereof, for the preparation of a medicament for predicting a level of insulin production in cells of a subject.

Embodiment 51. The use of embodiment 50, wherein elevated expression levels of the microRNA/s in the sample of cells compared to expression level/s of the microRNAs in control cells that do not produce insulin is indicative of insulin production in the sample of cells, and

reduced or absent expression level/s of the microRNAs in the sample of cells compared to expression level/s of the microRNAs in control cells that do not produce insulin is indicative of reduced or absent insulin production in the sample of cells.

Embodiment 52. Use of six or more microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa- miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof, for the preparation of a medicament for inducing insulin production in pancreatic lineage cells.

Embodiment 53. Use of six or more agents capable of inducing overexpression of one or more microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu- miR-129-3p, hsa-miR-433, and any combination thereof, in cells, for the preparation of a medicament for inducing insulin production in pancreatic lineage cells.

Embodiment 54. The use of any one of embodiments 50 to 53, wherein the cells comprise any one or more of: pancreatic cells, brain cells, gall bladder cells, beta-islet cells, pancreatic lineage cells, pancreatic islet precursor cells, endocrine progenitor cells, pancreatic islet progenitor cells, pancreatic islet stem cells, induced pluripotent pancreatic islet cells, and/or beta-islet precursor cells.

Embodiment 55. Use of six or more microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa- miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof, for the preparation of a medicament for preventing treating a disease or condition associated with or arising from a loss of insulin-producing cells in a subject. Embodiment 56. Use of six or more agents capable of inducing overexpression of microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof, in cells, for the preparation of a medicament for preventing treating a disease or condition associated with or arising from a loss of insulin- producing cells in a subject.

Embodiment 57. The use of embodiment 55 or embodiment 56, wherein the condition or disease is selected from the group consisting of: diabetes, pancreatitis, insulinoma, pancreatic cancer, Type 1 diabetes and insulin-requiring Type 2 diabetes.

Embodiment 58. Use of six or more agents for determining expression levels of six or more microRNA/s selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme- miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR- 129-3p, hsa-miR-433, and any combination thereof, in cells, for the preparation of a medicament for identifying a tissue of origin of a sample of cells obtained from a subject.

Embodiment 59. The use of embodiment 58, the sample of cells is from: pancreas, brain, or gall bladder.

Embodiment 60. The use of any one of embodiments 48 to 59, comprising or consisting of the use of agents for detecting expression of seven or more of the microRNAs, eight or more of the microRNAs, nine or more of the microRNAs, or ten of the microRNAs.

Embodiment 61. The use of embodiment 58 or embodiment 59, comprising or consisting of the use of:

(i) six or more agents to detect the microRNAs which are: hsa-miR-183, hsa-miR- 216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, and hsa-miR-217; or

(ii) seven or more agents to detect the microRNAs which are: hsa-miR-183, hsa- miR-216b, dme-miR-7, hsa- miR-375, hsa-miR-183#, hsa-miR-217, and hsa-miR-429; or

(iii) eight or more agents to detect microRNAs which are: hsa-miR-183, hsa-miR- 216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, and hsa-miR-7- 2#; or

(iv) nine or more agents to detect the microRNAs which are: hsa-miR-183, hsa-miR- 216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, and mmu-miR-129-3p; or

(v) ten or more agents to detect the microRNAs which are: hsa-miR-183, hsa-miR- 216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, and hsa-miR-433. Embodiment 62. The use of any one of embodiments 58 to 61, further comprising or consisting of primers, probes and/or other binding agents for use in detecting expression of any six or more microRNAs selected from the group consisting of: miR216a, miR144#, miR124a, miR-935, miR133b, miR206, miR200b#, miR625# and miR1271.

Embodiment 63. The use of any one of embodiments 58 to 62, comprising or consisting of primers, probes and/or other binding agents for use in detecting expression of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa- miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, miR216a, miR144#, and miR124a.

The present invention also relates at least in part to the following embodiments:

determining expression levels of one or more microRNA/s selected from the group consisting of: hsa-miR-216b, hsa-miR-519b-3p, hsa-miR-520e, hsa-miR-183, hsa-miR-520c- 3p, mmu-miR-187, hsa-miR-147b, hsa-miR-34b, hsa-miR-375, hsa-let-7e#, hsa-miR-636, hsa-miR-183#, hsa-miR-30d#, hsa-miR-433, hsa-miR-663b, hsa-miR-335#, hsa-miR-429, hsa-miR-200c, hsa-miR-98, hsa-miR-424#, hsa-miR-329, dme-miR-7, mmu-miR-129-3p, hsa-miR-139-3p, hsa-miR-485-5p, hsa-miR-363, hsa-miR-21#, hsa-miR-184, hsa-miR-661, hsa-miR-655, hsa-miR-135b#, hsa-miR-142-5p, hsa-miR-222#, hsa-miR-382, hsa-miR-141, hsa-miR-367, hsa-miR-1285, hsa-miR-217, hsa-miR-215, hsa-miR-485-3p, hsa-miR-512-3p, hsa-miR-639, hsa-miR-7-2#, and any combination thereof, in a sample of the cells obtained from the subject;

wherein:

elevated expression levels of the microRNA/s in the sample of cells compared to expression level/s of the microRNA/s in control cells that do not produce insulin is indicative of insulin production in the sample of cells, and

reduced or absent expression level/s of the microRNA/s in the sample of cells compared to expression level/s of the microRNA/s in control cells that do not produce insulin is indicative of reduced or absent insulin production in the sample of cells.

Embodiment 2. The method of embodiment 1 , wherein the one or more microRNA/s is or are selected from the group consisting of: hsa-miR-216b, hsa-miR-183, hsa-miR-375, hsa-miR-183#, mmu-miR-129-3p, hsa-miR-184, hsa-miR-7-2#, hsa-miR-433, hsa-miR-429, hsa-miR-141, hsa-miR-512-3p, hsa-miR-382, hsa-miR-329, hsa-miR-217, dme-miR-7, hsa- miR-485-3p, hsa-miR-335#, hsa-miR-200c, hsa-miR-655, and any combination thereof. Embodiment 3. The method of embodiment 1 or embodiment 2, wherein said elevated expression levels of the microRNA/s in the sample of cells is indicative of production of insulin gene transcripts in the sample of cells, and

Embodiment 4. The method of any one of embodiments 1 to 3, wherein the control cells that do not produce insulin are from the subject.

Embodiment s. The method of any one of embodiments 1 to 4, further comprising determining an expression level of hsa miR-452 microRNA in the sample of cells, wherein: an elevated expression level of the hsa miR-452 in the sample of cells compared to expression level/s of the hsa miR-452 microRNA in control cells that do not produce insulin is indicative of reduced or absent insulin production in the sample of cells, and

a reduced or absent expression level of the hsa miR-452 microRNA in the sample of cells compared to expression level/s of the hsa miR-452 microRNA in control cells that do not produce insulin is indicative of insulin production in the sample of cells.

Embodiment 6. The method of any one of embodiments 1 to 5, wherein the sample of cells comprises any one or more of: pancreatic cells, brain cells, gall bladder cells.

Embodiment 7. The method of any one of embodiments 1 to 6, wherein the sample of cells comprises beta-islet cells.

Embodiment 8. The method of any one of embodiments 1 to 7, wherein said reduced or absent insulin production is diagnostic or prognostic of a disease or condition associated with or arising from a loss of insulin-producing cells in the subject.

Embodiment 9. The method of embodiment 8, wherein the condition or disease is selected from the group consisting of: diabetes, pancreatitis, insulinoma, and pancreatic cancer.

Embodiment 10. The method of any one of embodiments 1 to 7, wherein the subject has or is progressing toward a disease or condition associated with or arising from a loss of insulin-producing cells, and the expression levels of one or more microRNA/s is a marker of death and/or loss of insulin-producing function in the cells of the subject.

Embodiment 11. The method of any one of embodiments 1 to 10, further comprising an initial step of obtaining the sample of cells from the subject.

Embodiment 12. A method for inducing insulin production in pancreatic lineage cells, the method comprising treating the pancreatic lineage cells with one or more microRNA/s selected from the group consisting of: hsa-miR-216b, hsa-miR-519b-3p, hsa-miR-520e, hsa- miR-183, hsa-miR-520c-3p, mmu-miR-187, hsa-miR-147b, hsa-miR-34b, hsa-miR-375, hsa- let-7e#, hsa-miR-636, hsa-miR-183#, hsa-miR-30d#, hsa-miR-433, hsa-miR-663b, hsa-miR- 335#, hsa-miR-429, hsa-miR-200c, hsa-miR-98, hsa-miR-424#, hsa-miR-329, dme-miR-7, mmu-miR-129-3p, hsa-miR-139-3p, hsa-miR-485-5p, hsa-miR-363, hsa-miR-21#, hsa-miR- 184, hsa-miR-661, hsa-miR-655, hsa-miR-135b#, hsa-miR-142-5p, hsa-miR-222#, hsa-miR- 382, hsa-miR-141, hsa-miR-367, hsa-miR-1285, hsa-miR-217, hsa-miR-215, hsa-miR-485- 3p, hsa-miR-512-3p, hsa-miR-639, hsa-miR-7-2#, and any combination thereof.

Embodiment 13. The method of embodiment 12, wherein the one or more micro RNA/s is or are selected from the group consisting of: hsa-miR-216b, hsa-miR-183, hsa-miR-375, hsa-miR-183#, mmu-miR-129-3p, hsa-miR-184, hsa-miR-7-2#, hsa-miR-433, hsa-miR-429, hsa-miR-141, hsa-miR-512-3p, hsa-miR-452, hsa-miR-382, hsa-miR-329, hsa-miR-217, dme-miR-7, hsa-miR-485-3p, hsa-miR-335#, hsa-miR-200c, hsa-miR-655, and any combination thereof.

Embodiment 14. The method of embodiment 12 or embodiment 13, wherein said treating comprises overexpressing the one or more microRNA/s in the pancreatic lineage cells.

Embodiment 15. The method of embodiment 12 or embodiment 13, comprising inhibiting expression of hsa miR-452 microRNA in the pancreatic lineage cells.

Embodiment 16. The method of any one of embodiments 12 to 15, wherein the pancreatic lineage cells comprise any one or more of: pancreatic islet precursor cells, endocrine progenitor cells, pancreatic islet progenitor cells, pancreatic islet stem cells, induced pluripotent pancreatic islet cells.

Embodiment 17. The method of any one of embodiments 12 to 16, wherein the pancreatic lineage cells are beta-islet precursor cells, beta-islet cell pro-precursors, "betalike" cells, "islet-like" cells.

Embodiment 18. The method of any one of embodiments 12 to 17, further comprising differentiating the pancreatic lineage cells into mature pancreatic cells.

Embodiment 19. The method of embodiment 18, wherein the mature pancreatic cells are beta-islet cells.

Embodiment 20. The method of any one of embodiments 12 to 19, wherein the treating is conducted in vitro or ex vivo.

Embodiment 21. A method for preventing treating a disease or condition associated with or arising from a loss of insulin-producing cells in a subject, the method comprising treating pancreatic lineage cells according to the method of any one of embodiments 12 to 20, and transplanting the treated cells into a subject.

Embodiment 22. The method of embodiment 21, wherein the cells transplanted are autologous for the subject.

Embodiment 23. The method of embodiment 21 or embodiment 22, wherein the subject is at risk of developing the disease or condition.

Embodiment 24. The method of any one of embodiments 21 to 23, wherein the condition or disease is selected from the group consisting of: diabetes, pancreatitis, insulinoma, and pancreatic cancer.

Embodiment 25. The method of embodiment 9 or embodiment 24, wherein the disease is Type 1 diabetes or insulin-requiring Type 2 diabetes.

Embodiment 26. A method for identifying a tissue of origin of a sample of cells obtained from a subject, the method comprising:

determining expression levels of one or more microRNA/s selected from the group consisting of: hsa-miR-216b, hsa-miR-519b-3p, hsa-miR-520e, hsa-miR-183, hsa-miR-520c- 3p, mmu-miR-187, hsa-miR-147b, hsa-miR-34b, hsa-miR-375, hsa-let-7e#, hsa-miR-636, hsa-miR-183#, hsa-miR-30d#, hsa-miR-433, hsa-miR-663b, hsa-miR-335#, hsa-miR-429, hsa-miR-200c, hsa-miR-98, hsa-miR-424#, hsa-miR-452, hsa-miR-329, dme-miR-7, mmu- miR-129-3p, hsa-miR-139-3p, hsa-miR-485-5p, hsa-miR-363, hsa-miR-21#, hsa-miR-184, hsa-miR-661 , hsa-miR-655, hsa-miR-135b#, hsa-miR-142-5p, hsa-miR-222#, hsa-miR-382, hsa-miR-141, hsa-miR-367, hsa-miR-1285, hsa-miR-217, hsa-miR-215, hsa-miR-485-3p, hsa-miR-512-3p, hsa-miR-639, hsa-miR-7-2#, and any combination thereof, in the sample of cells; and

comparing expression levels the microRNA/s in the sample of cells to control expression level/s of the microRNA/s generated from control cells equivalent to the sample of cells,

wherein substantially equivalent expression levels of the microRNA/s in the sample of cells compared to the expression level/s of the microRNA/s generated from the control cells is indicative that the sample of cells are of the same type as the control cells, and

substantially different expression levels of the microRNA/s in the sample of cells compared to the expression level/s of the microRNA/s generated from the control cells are indicative that the sample of cells is not of the same type as the control cells.

Embodiment 27. The method of embodiment 26, wherein the one or more microRNA/s is or are selected from the group consisting of: hsa-miR-216b, hsa-miR-183, hsa-miR-375, hsa-miR-183#, mmu-miR-129-3p, hsa-miR-184, hsa-miR-7-2#, hsa-miR-433, hsa-miR-429, hsa-miR-141, hsa-miR-512-3p, hsa-miR-452, hsa-miR-382, hsa-miR-329, hsa-miR-217, dme-miR-7, hsa-miR-485-3p, hsa-miR-335#, hsa-miR-200c, hsa-miR-655, and any combination thereof.

Embodiment 28. The method of embodiment 26 or embodiment 27, further comprising an initial step of obtaining the sample of cells from the subject.

Embodiment 29. The method of any one of embodiments 26 to 28, wherein the sample of cells is from the pancreas, brain, or gall bladder of the subject.

Embodiment 30. The method of any one of embodiments 1 to 29, wherein the one or more microRNA/s comprise or consist of any: 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44 or 45 of the microRNA/s.

Embodiment 31. The method of any one of embodiments 1 to 30, wherein the one or more microRNA/s are a microRNA signature comprising or consisting of: hsa-miR-216b, hsa-miR-183, hsa-miR-375, hsa-miR-183#, mmu-miR-129-3p, hsa-miR-184, hsa-miR-7-2#, and any combination thereof.

Embodiment 32. A kit comprising primers, probes and/or other binding agents for use in detecting expression of at least two microRNAs selected from the group consisting of: hsa- miR-216b, hsa-miR-519b-3p, hsa-miR-520e, hsa-miR-183, hsa-miR-520c-3p, mmu-miR- 187, hsa-miR-147b, hsa-miR-34b, hsa-miR-375, hsa-let-7e#, hsa-miR-636, hsa-mi -183#, hsa-miR-30d#, hsa-miR-433, hsa-miR-663b, hsa-miR-335#, hsa-miR-429, hsa-miR-200c, hsa-miR-98, hsa-miR-424#, has-miR-452, hsa-miR-329, dme-miR-7, mmu-miR-129-3p, hsa- miR-139-3p, hsa-miR-485-5p, hsa-miR-363, hsa-miR-21#, hsa-miR-184, hsa-miR-661, hsa- miR-655, hsa-miR-135b#, hsa-miR-142-5p, hsa-miR-222#, hsa-miR-382, hsa-miR-141 , hsa- miR-367, hsa-miR-1285, hsa-miR-217, hsa-miR-215, hsa-miR-485-3p, hsa-miR-512-3p, hsa- miR-639, hsa-miR-7-2#, and any combination thereof, in a sample of cells.

Embodiment 33. The kit of embodiment 32, wherein the at least two microRNA/s is or are selected from the group consisting of: hsa-miR-216b, hsa-miR-183, hsa-miR-375, hsa- miR-183#, mmu-miR-129-3p, hsa-miR-184, hsa-miR-7-2#, hsa-miR-433, hsa-miR-429, hsa- miR-141, hsa-miR-512-3p, hsa-miR-452, hsa-miR-382, hsa-miR-329, hsa-miR-217, dme- miR-7, hsa-miR-485-3p, hsa-miR-335#, hsa-miR-200c, hsa-miR-655, and any combination thereof.

Embodiment 34. A microRNA signature comprising least two microRNAs selected from the group consisting of: hsa-miR-216b, hsa-miR-519b-3p, hsa-miR-520e, hsa-miR-183, hsa-miR-520c-3p, mmu-miR-187, hsa-miR-147b, hsa-miR-34b, hsa-miR-375, hsa-let-7e#, hsa-miR-636, hsa-miR-183#, hsa-miR-30d#, hsa-miR-433, hsa-miR-663b, hsa-miR-335#, hsa-miR-429, hsa-miR-200c, hsa-miR-98, hsa-miR-424#, has-miR-452, hsa-miR-329, dme- miR-7, mmu-miR-129-3p, hsa-miR-139-3p, hsa-miR-485-5p, hsa-miR-363, hsa-miR-21#, hsa-miR-184, hsa-miR-661 , hsa-miR-655, hsa-miR-135b#, hsa-miR-142-5p, hsa-miR-222#, hsa-miR-382, hsa-miR-141, hsa-miR-367, hsa-miR-1285, hsa-miR-217, hsa-miR-215, hsa- miR-485-3p, hsa-miR-512-3p, hsa-miR-639, hsa-miR-7-2#, and any combination thereof.

Embodiment 35. The microRNA signature of embodiment 34 wherein the at least two microRNA/s is or are selected from the group consisting of: hsa-miR-216b, hsa-miR-183, hsa-miR-375, hsa-miR-183#, mmu-miR-129-3p, hsa-miR-184, hsa-miR-7-2#, hsa-miR-433, hsa-miR-429, hsa-miR-141, hsa-miR-512-3p, hsa-miR-452, hsa-miR-382, hsa-miR-329, hsa- miR-217, dme-miR-7, hsa-miR-485-3p, hsa-miR-335#, hsa-miR-200c, hsa-miR-655, and any combination thereof.

Embodiment 36. Use of the kit of embodiment 32 or 33, or the microRNA signature of embodiment 34 or 35, for predicting, diagnosing, and/or prognosing a disease or condition associated with or arising from a loss of insulin-producing cells in a subject, in a subject, wherein the disease or condition is selected from the group consisting of: diabetes, pancreatitis, insulinoma, and pancreatic cancer.

Embodiment 37. The method of any one of embodiments 9, 24 or 25, or the use of embodiment 36, wherein the disease is Type 1 diabetes.

Embodiment 38. Use of one or more agents for determining the expression levels of one or more microRNA/s selected from the group consisting of: hsa-miR-216b, hsa-miR- 519b-3p, hsa-miR-520e, hsa-miR-183, hsa-miR-520c-3p, mmu-miR-187, hsa-miR-147b, hsa- miR-34b, hsa-miR-375, hsa-let-7e#, hsa-miR-636, hsa-miR-183#, hsa-miR-30d#, hsa-miR- 433, hsa-miR-663b, hsa-miR-335#, hsa-miR-429, hsa-miR-200c, hsa-miR-98, hsa-miR-424#, hsa-miR-329, dme-miR-7, mmu-miR-129-3p, hsa-miR-139-3p, hsa-miR-485-5p, hsa-miR- 363, hsa-miR-21#, hsa-miR-184, hsa-miR-661, hsa-miR-655, hsa-miR-135b#, hsa-miR-142- 5p, hsa-miR-222#, hsa-miR-382, hsa-miR-141, hsa-miR-367, hsa-miR-1285, hsa-miR-217, hsa-miR-215, hsa-miR-485-3p, hsa-miR-512-3p, hsa-miR-452, hsa-miR-639, hsa-miR-7-2#, and any combination thereof, for the preparation of a medicament for predicting a level of insulin production in cells of a subject.

Embodiment 39. The use of embodiment 38, wherein elevated expression levels of the microRNA/s in the sample of cells compared to expression level/s of the microRNA/s in control cells that do not produce insulin is indicative of insulin production in the sample of cells, and

Embodiment 40. Use of one or more microRNA/s selected from the group consisting of: hsa-miR-216b, hsa-miR-519b-3p, hsa-miR-520e, hsa-miR-183, hsa-miR-520c-3p, mmu- miR-187, hsa-miR-147b, hsa-miR-34b, hsa-miR-375, hsa-let-7e#, hsa-miR-636, hsa-miR- 183#, hsa-miR-30d#, hsa-miR-433, hsa-miR-663b, hsa-miR-335#, hsa-miR-429, hsa-miR- 200c, hsa-miR-98, hsa-miR-424#, hsa-miR-329, dme-miR-7, mmu-miR-129-3p, hsa-miR- 139-3p, hsa-miR-485-5p, hsa-miR-363, hsa-miR-21#, hsa-miR-184, hsa-miR-661, hsa-miR- 655, hsa-miR-135b#, hsa-miR-142-5p, hsa-miR-222#, hsa-miR-382, hsa-miR-141, hsa-miR- 367, hsa-miR-1285, hsa-miR-217, hsa-miR-215, hsa-miR-485-3p, hsa-miR-512-3p, hsa-miR- 452, hsa-miR-639, hsa-miR-7-2#, and any combination thereof, for the preparation of a medicament for inducing insulin production in pancreatic lineage cells.

Embodiment 41. Use of one or more agents capable of inducing overexpression of one or more microRNA/s selected from the group consisting of: hsa-miR-216b, hsa-miR-519b- 3p, hsa-miR-520e, hsa-miR-183, hsa-miR-520c-3p, mmu-miR-187, hsa-miR-147b, hsa-miR- 34b, hsa-miR-375, hsa-let-7e#, hsa-miR-636, hsa-miR-183#, hsa-miR-30d#, hsa-miR-433, hsa-miR-663b, hsa-miR-335#, hsa-miR-429, hsa-miR-200c, hsa-miR-98, hsa-miR-424#, hsa- miR-329, dme-miR-7, mmu-miR-129-3p, hsa-miR-139-3p, hsa-miR-485-5p, hsa-miR-363, hsa-miR-21#, hsa-miR-184, hsa-miR-661, hsa-miR-655, hsa-miR-135b#, hsa-miR-142-5p, hsa-miR-222#, hsa-miR-382, hsa-miR-141, hsa-miR-367, hsa-miR-1285, hsa-miR-217, hsa- miR-215, hsa-miR-485-3p, hsa-miR-512-3p, hsa-miR-639, hsa-miR-7-2#, and any combination thereof, in cells, for the preparation of a medicament for inducing insulin production in pancreatic lineage cells.

Embodiment 42. The use of any one of embodiments 38 to 41, wherein the cells comprise any one or more of: pancreatic cells, brain cells, gall bladder cells, beta-islet cells, pancreatic lineage cells, pancreatic islet precursor cells, endocrine progenitor cells, pancreatic islet progenitor cells, pancreatic islet stem cells, induced pluripotent pancreatic islet cells, and/or beta-islet precursor cells.

Embodiment 43. Use of one or more microRNA/s selected from the group consisting of: hsa-miR-216b, hsa-miR-519b-3p, hsa-miR-520e, hsa-miR-183, hsa-miR-520c-3p, mmu- miR-187, hsa-miR-147b, hsa-miR-34b, hsa-miR-375, hsa-let-7e#, hsa-miR-636, hsa-miR- 183#, hsa-miR-30d#, hsa-miR-433, hsa-miR-663b, hsa-miR-335#, hsa-miR-429, hsa-miR- 200c, hsa-miR-98, hsa-miR-424#, hsa-miR-329, dme-miR-7, mmu-miR-129-3p, hsa-miR- 139-3p, hsa-miR-485-5p, hsa-miR-363, hsa-miR-21#, hsa-miR-184, hsa-miR-661, hsa-miR- 655, hsa-miR-135b#, hsa-miR-142-5p, hsa-miR-222#, hsa-miR-382, hsa-miR-141, hsa-miR- 367, hsa-miR-1285, hsa-miR-217, hsa-miR-215, hsa-miR-485-3p, hsa-miR-512-3p, hsa-miR- 452, hsa-miR-639, hsa-miR-7-2#, and any combination thereof, for the preparation of a medicament for preventing treating a disease or condition associated with or arising from a loss of insulin-producing cells in a subject.

Embodiment 44. Use of one or more agents capable of inducing overexpression of microRNA/s selected from the group consisting of: hsa-miR-216b, hsa-miR-519b-3p, hsa- miR-520e, hsa-miR-183, hsa-miR-520c-3p, mmu-miR-187, hsa-miR-147b, hsa-miR-34b, hsa-miR-375, hsa-let-7e#, hsa-miR-636, hsa-miR-183#, hsa-miR-30d#, hsa-miR-433, hsa- miR-663b, hsa-miR-335#, hsa-miR-429, hsa-miR-200c, hsa-miR-98, hsa-miR-424#, hsa- miR-329, dme-miR-7, mmu-miR-129-3p, hsa-miR-139-3p, hsa-miR-485-5p, hsa-miR-363, hsa-miR-21#, hsa-miR-184, hsa-miR-661, hsa-miR-655, hsa-miR-135b#, hsa-miR-142-5p, hsa-miR-222#, hsa-miR-382, hsa-miR-141, hsa-miR-367, hsa-miR-1285, hsa-miR-217, hsa- miR-215, hsa-miR-485-3p, hsa-miR-512-3p, hsa-miR-639, hsa-miR-7-2#, and any combination thereof, in cells, for the preparation of a medicament for preventing treating a disease or condition associated with or arising from a loss of insulin-producing cells in a subject.

Embodiment 45. The use of embodiment 43 or embodiment 44, wherein the condition or disease is selected from the group consisting of: diabetes, pancreatitis, insulinoma, pancreatic cancer, Type 1 diabetes and insulin-requiring Type 2 diabetes.

Embodiment 46. Use of one or more agents for determining expression levels of one or more microRNA/s selected from the group consisting of: hsa-miR-216b, hsa-miR-519b-3p, hsa-miR-520e, hsa-miR-183, hsa-miR-520c-3p, mmu-miR-187, hsa-miR-147b, hsa-miR-34b, hsa-miR-375, hsa-let-7e#, hsa-miR-636, hsa-miR-183#, hsa-miR-30d#, hsa-miR-433, hsa- miR-663b, hsa-miR-335#, hsa-miR-429, hsa-miR-200c, hsa-miR-98, hsa-miR-424#, hsa- miR-329, dme-miR-7, mmu-miR-129-3p, hsa-miR-139-3p, hsa-miR-485-5p, hsa-miR-363, hsa-miR-21#, hsa-miR-184, hsa-miR-661, hsa-miR-655, hsa-miR-135b#, hsa-miR-142-5p, hsa-miR-222#, hsa-miR-382, hsa-miR-141, hsa-miR-367, hsa-miR-1285, hsa-miR-217, hsa- miR-215, hsa-miR-485-3p, hsa-miR-512-3p, hsa-miR-452, hsa-miR-639, hsa-miR-7-2#, and any combination thereof, in cells, for the preparation of a medicament for identifying a tissue of origin of a sample of cells obtained from a subject. Embodiment 47. The use of embodiment 46, the sample of cells is from: pancreas, brain, or gall bladder.

Embodiment 48. The use of any one of embodiments 38 to 47, wherein the one or more microRNA/s is or are selected from the group consisting of: hsa-miR-216b, hsa-miR-183, hsa-miR-375, hsa-miR-183#, mmu-miR-129-3p, hsa-miR-184, hsa-miR-7-2#, hsa-miR-433, hsa-miR-429, hsa-miR-141, hsa-miR-512-3p, hsa-miR-452, hsa-miR-382, hsa-miR-329, hsa- miR-217, dme-miR-7, hsa-miR-485-3p, hsa-miR-335#, hsa-miR-200c, hsa-miR-655, and any combination thereof.

Embodiment 49. The use of any one of embodiments 38 to 48, the kit of embodiment 32 or 33, or the microRNA signature of embodiment 34 or 35, wherein the one or more microRNA/s are a microRNA signature comprising or consisting of: hsa-miR-216b, hsa- miR-183, hsa-miR-375, hsa-miR-183#, mmu-miR-129-3p, hsa-miR-184, hsa-miR-7-2#, and any combination thereof.

Embodiment 50. The use of embodiment 41 or 44, wherein the medicament further comprises one or more agents capable of inhibiting expression of has-miR-452 microRNA in the subject.

Definitions

As used in this application, the singular form "a", "an" and "the" include plural references unless the context clearly dictates otherwise. For example, the term "microRNA" also includes a plurality of microRNAs.

As used herein, the term "comprising" means "including." Variations of the word "comprising", such as "comprise" and "comprises," have correspondingly varied meanings. Thus, for example, a composition "comprising" microRNA type A may consist exclusively of microRNA X or may include one or more additional components (e.g. microRNA type B).

The term "therapeutically effective amount" as used herein, includes within its meaning a non-toxic but sufficient amount of an agent or composition for use in the present invention to provide the desired therapeutic effect. The exact amount required will vary from subject to subject depending on factors such as the species being treated, the age and general condition of the subject, the severity of the condition being treated, the particular agent being administered, the mode of administration and so forth. Thus, it is not possible to specify an exact "effective amount" applicable to all embodiments. However, for any given case, an appropriate "effective amount" may be determined by one of ordinary skill in the art using only routine experimentation. As used herein, a disease or condition that is "associated with aberrant insulin production" will be understood to encompass any ailment that arises directly and/or indirectly from aberrant insulin production, and/or that causes aberrant insulin production in a subject, "aberrant insulin production" meaning levels of insulin production that lie outside of a standard range for a population of individuals of the same species as the subject. The population may also be of the same or similar: race, gender, sex, and/or age as the subject. The determination of aberrant insulin production in a given subject may be achieved using standard tests known in the art.

As used herein, a disease or condition that is "associated with or arising from a loss of insulin-producing cells" will be understood to encompass any ailment that arises directly and/or indirectly from a reduction in the number of insulin-producing cells in a subject (e.g. a reduction in the number of insulin-producing pancreatic cells (including beta-islet cells), brain cells, and/or gall bladder cells in the subject). Non-limiting examples of such diseases or conditions include diabetes (e.g. Type 1 , Type 2), pancreatitis, insulinoma, and some forms of pancreatic cancer).

As used herein, a disease or condition that is "associated with aberrant insulin metabolism" will be understood to encompass any ailment that arises directly and/or indirectly from aberrant insulin metabolism, and/or that causes aberrant insulin metabolism in a subject, "aberrant insulin metabolism" encompassing insulin resistance and insulin sensitivity, as can be determined using standard tests known in the art.

As used herein, the term "subject" includes any animal of economic, social or research importance including bovine, equine, ovine, primate, avian and rodent species. Hence, a "subject" may be a mammal such as, for example, a human or a non-human mammal.

As used herein, the term "kit" refers to any delivery system for delivering materials. Such delivery systems include systems that allow for the storage, transport, or delivery of reaction reagents (for example labels, reference samples, supporting material, etc. in the appropriate containers) and/or supporting materials (for example, buffers, written instructions for performing an assay etc.) from one location to another. For example, kits may include one or more enclosures, such as boxes, containing the relevant reaction reagents and/or supporting materials. The term "kit" includes both fragmented and combined kits. A "fragmented kit" refers to a delivery system comprising two or more separate containers that each contains a sub-portion of the total kit components. The containers may be delivered to the intended recipient together or separately. Any delivery system comprising two or more separate containers that each contains a sub-portion of the total kit components are included within the meaning of the term "fragmented kit". A "combined kit" refers to a delivery system containing all of the components of a reaction assay in a single container (e.g. in a single box housing each of the desired components).

It will be understood that use the term "about" herein in reference to a recited numerical value includes the recited numerical value and numerical values within plus or minus ten percent of the recited value.

It will be understood that use of the term "between" herein when referring to a range of numerical values encompasses the numerical values at each endpoint of the range. For example, a polypeptide of between 10 residues and 20 residues in length is inclusive of a polypeptide of 10 residues in length and a polypeptide of 20 residues in length.

Any description of prior art documents herein, or statements herein derived from or based on those documents, is not an admission that the documents or derived statements are part of the common general knowledge of the relevant art.

For the purposes of description, all documents referred to herein are hereby incorporated by reference in their entirety unless otherwise stated.

Brief Description of the Figures

Preferred embodiments of the present invention will now be described by way of example only, with reference to the accompanying figures wherein:

Figure One shows fluorescent microscopy images demonstrating the presence of insulin-producing cells in human pancreatic islets, gallbladder and brain tissue.

Figure Two is a schematic of a study design to characterize the expression of microRNAs in human islet tissues, other insulin-producing tissues (gallbladder, brain), and non-insulin-producing tissues (endothelium/hUVECs, blood/Bl, muscle, liver/Liv, skin/Sk), for expression of microRNAs.

Figure Three shows a series of graphs indicating the level of expression of islet hormones (Insulin, Glucagon and Somatostatin), islet-specific transcription factors (MafA, Ngn3, Pdxl) and Hesl (inhibitor of NGN3) in human islet tissues (A), gallbladder (B), brain (C) and endothelial cells (D), derived from open array analyses.

Figure Four shows the results of an unsupervised hierarchical cluster analysis designed to group tissues based on low and high insulin production.

Figure Five shows the results of penalized regression analyses conducted on microRNA dataset obtained after carrying out the molecular assays outlined in Figure Two. These analyses identified a signature of microRNAs that are highly associated with insulin gene transcript abundance.

Figure Six shows a frequency table for the microRNAs generated by resampling validation of data using bootstrapping.

Figure Seven shows the results of a validation analysis used to distinguish insulin positive tissues from insulin-negative tissues (A) and predict the level of insulin gene expression (in terms of the actual Cycle-threshold value/Ct-valiie) as assessed by TaqMan- based real-time PCR (B).

Figure Eight is a schematic outlining an assay designed to assess if the microRNAs identified via bootstrap analysis were mere associations with insulin expression or causal to insulin expression. Expression of the bootstrapped microRNAs or insulin itself was forced in human islet-derived progenitor cells (hlPCs) in two separate sets of experiments and the differentiation of these progenitor cells was assessed on day 4.

Figure Nine shows the levels of insulin expressed in hlPCs with forced expression of insulin (A) or the microRNAs (B) in accordance with the assay depicted in Figure Eight.

Figure 10 presents a Receiver Operating Characteristic (ROC) curve analysis for detecting a high level of "insulin expression" in cells (those with real-time qPCR value <16.8) as compared to the insulin-negative tissues (Ct value >16.8). True positive rate (sensitivity) is plotted as a function of the false positive rate (indicated in the figure as 100% specificity) for different miRNAs. The AUC is a measure of how well a miRNA can distinguish between cell samples that produce good (Ct value <16.8) or bad (Ct value >16.8) levels of insulin.

Figure 11 shows immunostaining of insulin (green), glucagon (red) and somatostatin (pink; not detected in the brain) on freshly isolated human (A) non-diabetic islet, (B) gallbladder epithelium and (C) brain neurospheres. Nuclei (DNA) are shown in blue. Realtime TaqMan qPCR expression profile for pancreatic islet (pro-) hormones and transcription factors in human (D) non-diabetic islets (N=86) (E) gallbladders (N=68), (F) brains (N=14) and (G) the negative control (endothelial cells; N=8). Results are presented as cycle threshold (Ct)-values normalized to 18s rRNA. As a lower Ct-value represents the higher abundance of gene transcripts, the Y-axis is reversed. The brown rectangles indicate the undetectable (nonlinear) part of the qPCR data. Absolute copy number for insulin transcripts, as measured by digital droplet (dd)PCR (H). Panel (I) represents the type of insulin-positive (left side) and insulin negative (right side) samples in the human tissue biobank and the bidirectional hierarchical plot for profiling of 754 microRNAs in the discovery set of 507 samples (J). Normalized Ct values obtained from qPCR were used for the cluster analysis. Lower Ct- values (higher abundance) appear in shades of red whereas higher Ct-values (low abundance / no expression) appear in the range from yellow to white. Spearman distance metric and McQuitty linkage were applied on the unsupervised hierarchical cluster analysis. The discovery set was split into quartiles of insulin Ct-values and the highest quartile samples are shown by a black color along the Y2-axis of the heatmap. Samples that represent low or no expression of insulin are marked in gray color along the Y2 axis. Individual samples are color-coded and plotted along the Yl -axis. Data presented as mean+S.D. Bar=2C^m.

Figure 12 provides real-time TaqMan qPCR data for the seven pancreatic islet mRNAs in(A) non-diabetic pancreas (n=l 8) and (B) human negative solid tissues (n=40). Results are presented as normalized Ct-values, with their mean+SD. The brown rectangles indicate the undetectable (non-linear) part of the qPCR data. As a low Ct-value indicates a higher abundance of gene transcripts, the Y-axes in panels A and B are reversed.

Figure 13 shows Volcano plot presenting TaqMan qRT-PCR Ct-value difference between negative tissues and (A) non-diabetic islets, (B) gallbladder and (C) brain for the 754 microRNAs profiles. Volcano plots for the same 754 miRNAs presenting the difference between top quartile of high vs low/no insulin-expressing (D) islets, (E) gallbladders and (F) brains. The Ct-value differences are depicted on the X-axis and the -loglO p-value is depicted on the Y-axis. The dashed horizontal line represents the significant P value = 0.05 while the vertical dashed lines represent a difference of 2-fold (1 Ct value). (G-I) Circos plots for the miRNA expression dataset are presented for each of the insulin-producing tissues. Each panel of circos plots is divided into six sections (al, a2, b, c, d, e). Section al represents the larger part of the outermost circle with each gray rectangle representing one of the measured miRNAs. Section a2 contains colored rectangles representing each of the seven (pro-) endocrine genes or transcription factors labeled next to them. Section b of the ideogram presents Ct-value data (ranges 6.5 to 39; greater values are closer to the center and smaller values closer to the periphery). The glyphs are color coded; the greater the value the darker the red, the smaller the value, the darker the green. The thick white line is at the median of the data: 22.5. The thin gray lines are placed at every 5% of the data. Section c presents Z-scores (for each tissue; range from -3.4 to 3.5; smaller values are closer to the center of the circle). The thick white line is at 0. The thin gray lines are placed at every 5% of the data. Section d is a histogram plot presenting Z-scores data (relative to negative tissues). The data ranges from -37 to 1 19. Lesser values are at the bottom (closer to the center of the circle). The thick white line is at 0. The thin gray lines are placed at every 5% of the data. Section e presents correlations between miRNAs and the seven endocrine pancreas related mRNAs in the form of colored links. A link drawn between a miRNA and mRNA indicates a significant correlation between the two. The cutoff was set at r=0.6. The color of the link depends on the mRNA that it leads to and the thickness indicates the strength of correlation. Solid thickest lines have correlation r>0.9.

Figure 14 shows Volcano plots for the 754 miRNAs in the human negative-insulin solid tissues (excluding blood) vs (A) non-diabetic islets, (B) gallbladders and (C) brains. The Ct-value differences are depicted on the X-axis and the -log 10 P-value is depicted on the Y- axis. The dashed horizontal line represents the significant P value = 0.05 while the vertical dashed lines represent a difference of 2-fold (1 Ct value). The values for each volcano plot are presented in Table 10.

Figure 15 (A) Penalized linear regression analysis on the 754 microRNAs in human insulin-producing (positive) tissues relative to the non-insulin producing (negative) tissues. Linear regression involves comparison of the actual Ct-value of insulin to the actual Ct-value of each of the 754 microRNAs in each of the discovery set samples. A penalty applied to microRNAs is represented by the lambda on the X-axis. The lambda value is increased until coefficients for all microRNAs are reduced to zero (Y-axis). MicroRNAs that withstand a high penalty were selected for further validation. (B) Penalized logistic regression analysis for the same 754 microRNAs in the same human insulin-producing (positive) tissues (1) to non-insulin producing (negative) tissues (0). MicroRNAs that withstand a high penalty (above the threshold lambda) were selected as the signature microRNAs associated with high insulin expression. (C) A schematic for bootstrapping workflow (a resampling validation process) that was applied to validate this set of microRNA identified through the Penalized logistic regression model is shown here. Bootstrapping involves drawing multiple random samples with replacement from the dataset (green dots). A number of samples would be duplicated (red dots) in each bootstrap analysis followed by regression analyses. At the end of several resampling (1000 in this case), a frequency table (D), representing the number of times that a microRNA was present in the bootstraps, is generated. This random sampling validation workflow allows the user to validate their microRNA signature that was generated through the penalized logistic/linear regression method. A "-" sign in the table indicates higher abundance of that particular microRNA in insulin-positive tissue, whereas a "+" sign represents lower abundance.

Figure 16 (A) Penalized logistic regression (PLR) analysis on the 754 microRNAs in human non-diabetic (high insulin-producing) islets (with a normalized insulin Ct value < 16.8) (0) to (low insulin-producing) islets (with a normalized insulin Ct value >16.8) (1). A penalty was applied to each microRNA as represented by the lambda on the X-axis. The lambda value was increased until the coefficient (Y-axis) obtained is Zero. MicroRNAs that withstand a high penalty had a stronger association in distinguishing the two groups (1 and 0) analyzed. Similar penalized regression analyses were carried out for gallbladder and brain samples (not shown here). (B) Venn diagram of the microRNA signatures obtained from analyses presented in panel A for islets, gallbladder and brain samples. The three different tissue types were analyzed within their own specific tissue sets. MicroRNAs represented from this Venn diagram were obtained from the Penalized logistic regression bootstrapped analysis. The Venn diagram contains only the miRNAs with a bootstrapped frequency score > 25%, with the exception of brain analysis. Due to the limited number of brain samples used for PLR analysis all miRNAs identified are shown in the Venn diagram. (C) An Euler diagram illustrating that the signature of microRNAs obtained through penalized logistic regression analysis are a subset of the microRNA signature obtained from penalized linear regression analysis.

Figure 17 shows violin plots generated for microRNAs that were identified through penalized (linear and logistic) regression analysis. The box plots show the three quartile values of the distribution with whiskers extending to points that lie within 1.5 IQRs of the lower and upper quartile. Polygons (violins) represent kernel density estimates of data and extend to extreme values. The width of each of the violins is scaled by the number of observations in that bin. Y-axis depicts cycle threshold (Ct)-values of samples from the insulin positive (green; N=30) and insulin negative (red; N=299) tissues. **** represent PO.0001, ***: PO.001, ns: non-significant.

Figure 18 shows an analysis of a subset of the biobank samples that did not meet the desired quality or required quantity for analysis (Validation set 1 ; N=91). The abundance of bootstrapped microRNAs was measured using qRT-PCR. These microRNA Ct-values were used to predict the Ct-value of insulin mRNA (A) using coefficients obtained through penalized regression analysis (see table S3). As insulin mRNA could not be assessed in these samples using qRT-PCR, the predicted Ct-values for these 91 samples are plotted with reference to their tissue of origin on the X-axis. Samples from the discovery set (N=507) showed a similar range of insulin Ct-values for these six different tissue types (B). In a set of samples from the biobank that had good quality and the desired quantity (Validation set 2; N=100), the bootstrapped microRNAs were measured and provided these data to the biostatistician for predicting the insulin Ct-value. Another researcher performed qRT-PCR analysis for insulin mRNA on the same samples. Predicted and observed insulin Ct values (normalized to 18s rRNA) showed high correlation (r=0.87; P<0.0001, N=100) (C). The receiver operating characteristic (ROC) curve using the set of bootstrapped microRNAs obtained through the penalized linear regression analysis categorized the tissues in this set with an accuracy of 98.1 % (D)

Figure 19 shows receiver operating characteristic (ROC) curves for each of the microRNAs obtained in the bootstrapped microRNA signature associated with insulin gene expression (Figure 4D) using the validation set 2 of N = 100 human cell/tissue samples. The AUC and microRNA names are presented at the top of each ROC curve. Figure 20 shows the results of an investigation as to whether bootstrapped microRNAs from the analysis could drive insulin gene expression. Either i) the insulin gene, or ii) candidate mature microRNAswere overexpressed in human islet-derived progenitor cells (hIPCs). (A) Lentiviral transduction of ins-fur vector increased insulin gene expression by 100- to ~100,000-fold (N=5) relative to empty vector transduced hIPCs (B). Box plots show mean values (indicated by "+"), line at the median and whiskers extending to the minimum and maximum values in the set. **:P<0.01. However, this did not significantly change the expression of insulin-associated (bootstrapped) microRNAs (C). The Ct-value differences are depicted on the X-axis and the -log 10 P-value is depicted on the Y-axis. The dotted horizontal line represents the significant P value = 0.05 whereas the dotted vertical lines represent a difference of one Ct-value (2-fold). Quantitative real-time PCR data after transient transfection of miRNAs is presented as violin plots (D-G). The box plots show the three quartile values of the distribution with whiskers extending to points that lie within 1.5 IQRs of the lower and upper quartile. Polygons (violins) represent kernel density estimates of data and extend to extreme values. The width of each of the violins is scaled by the number of observations in that bin. Forced expression of either of the three insulin-associated miRNAs (or their combination; "combo") significantly increased endocrine pancreatic hormone expression in just 4 days of induction for differentiation (D-F). The expression of Hesl did not change significantly (G).

Figure 21 shows real-time TaqMan qPCR data for three (pro-) endocrine hormones and four pancreatic transcription factors in human (A) T2D pancreas (n=8) and (B) T2D islets (n=7). The brown rectangles indicate the undetectable (non-linear) part of the qPCR data. As a low Ct-value indicates a higher abundance of gene transcripts, the Y-axes in panels A and B are reversed. Volcano plots for the 754 validated miRNAs in human (C) T2D pancreas vs non-diabetic pancreas, and (D) T2D islets vs non-diabetic islets. The Ct-value differences are depicted on the X-axis and the -loglO P-value is depicted on the Y-axis. The dashed horizontal line represents the significant P value=0.05 while the vertical dashed lines represent a difference of 2-fold (1 Ct value). Real-time TaqMan qPCR data of six candidate microRNAs identified from the microRNA bootstrapped signature demonstrate a difference between non-diabetic islets and T2D islets (E-J). Indeed, insulin transcripts were significantly less abundant in islets (K) and pancreas (L) from the individuals with type 2 diabetes. Results are presented as transcript abundance calculated using the 2ΔΔ Ct-value methodl . Data are presented as mean+SEM. **** represents PO.0001, **: P<0.01, *:P<0.05.

Figure 22 shows correlation plots of the bootstrapped miRNA signature within (A) high insulin expression (insulin Ct-value < 16.8) human non-diabetic islets and T2D islets, (B) human non-diabetic pancreas and T2D pancreas and (C) human non-diabetic islets (insulin Ct-value < 16.8) and splenocyte samples. The correlation was computed using Pearson pair-wise (complete) analysis. Results are presented as correlation coefficient values. Dark blue/red areas bindicate highest positive and negative correlation between the two sample sets compared. White areas indicate no correlation. Circles indicate the pie chart for individual correlation coefficient value.

Figure 23 shows (A) Correlation analysis of total RNA (in ng, as quantitated using Nanodrop) and Small RNA content (in pg, assessed using the Agilent Bioanalyzer smallRNA chip) for human biobank samples. The line of best fit using linear regression are plotted for islets (Green line), and gallbladder (Blue line). The amount of smallRNA and the total RNA in each of the samples was higher than the amount and concentrations desired for carrying out the proposed OpenArray microRNA and the TaqMan mRNA qPCRs. (B) Unsupervised bidirectional hierarchical cluster analysis for the three pancreatic islet (pro-)hormones and four transcription factors in different human solid tissues of the discovery set. Lower Ct- values (higher abundance) appear in shades of red whereas higher Ct-values (low abundance / no expression) appear in the range from yellow to white. Euclidean distance metric and McQuitty linkage were applied on the unsupervised hierarchical cluster analysis. The set was split into pancreatic tissues (all islets and pancreas) and non-pancreatic tissues shown by a black or gray color respectively along the top of the heatmap. The individual tissue sources are color-coded and plotted along the bottom of the X-axis.

Detailed Description

The present invention relates to intracellular microRNA signatures that are biomarkers for tissue-specific insulin production capability, and which can also be used to induce insulin production in subjects in need thereof. Without limitation, the microRNA signatures described herein may be used: (i) to predict the presence/absence or relative abundance (cycle threshold- / Ct-value) of insulin gene transcripts in cells and/or tissues; (ii) to induce insulin gene expression in islet progenitor/precursor cells which, for example, can be used for cell replacement therapy in subjects with diseases and conditions associated with aberrant insulin production and/or aberrant insulin metabolism (e.g. diabetes); (iii) as a biomarker to determine the death or function of insulin-producing cells in subjects with or progressing to diseases and conditions associated with aberrant insulin production and/or aberrant insulin metabolism (e.g. diabetes); and/or (iv) to identify the tissue of origin, based on the signature and levels of microRNA expression.

The microRNA signatures of the present invention and non-limiting examples of their applications are described in detail as follows. microRNA Signatures

The present invention provides microRNA signatures indicative of beta-cell insulin production capacity. The microRNA signatures may be obtained from an insulin-producing tissue (e.g. pancreas, gallbladder, brain). The microRNA signatures may be intracellular microRNA signatures.

The microRNA signatures may comprise or consist of any one or more of the following microRNA/s, in any combination: hsa-miR-216b, hsa-miR-519b-3p, hsa-miR-520e, hsa-miR- 183, hsa-miR-520c-3p, mmu-miR-187, hsa-miR-147b, hsa-miR-34b, hsa-miR-375, hsa-let- 7e#, hsa-miR-636, hsa-miR-183#, hsa-miR-30d#, hsa-miR-433, hsa-miR-663b, hsa-miR- 335#, hsa-miR-429, hsa-miR-200c, hsa-miR-98, hsa-miR-424#, hsa-miR-452, hsa-miR-329, dme-miR-7, mmu-miR-129-3p, hsa-miR-139-3p, hsa-miR-485-5p, hsa-miR-363, hsa-miR- 21#, hsa-miR-184, hsa-miR-661, hsa-miR-655, hsa-miR-135b#, hsa-miR-142-5p, hsa-miR- 222#, hsa-miR-382, hsa-miR-141, hsa-miR-367, hsa-miR-1285, hsa-miR-217, hsa-miR-215, hsa-miR-485-3p, hsa-miR-512-3p, hsa-miR-452, hsa-miR-639 and/or hsa-miR-7-2#.

For example, the microRNA signatures may comprise or consist of any 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44 or 45 of the microRNA/s. of the identified microRNAs

In some embodiments the microRNA signatures may comprise or consist of: hsa-miR- 216b, hsa-miR-183, hsa-miR-375, hsa-miR-183#, mmu-miR-129-3p, hsa-miR-184, hsa-miR- 7-2#, hsa-miR-433, hsa-miR-429, hsa-miR-141 , hsa-miR-512-3p, hsa-miR-452, hsa-miR-382, hsa-miR-329, hsa-miR-217, dme-miR-7, hsa-miR-485-3p, hsa-miR-335#, hsa-miR-200c and hsa-miR-655.

In some embodiments the microRNA signatures may comprise or consist of: hsa-miR- 216b, hsa-miR-183, hsa-miR-375, hsa-miR-183#, mmu-miR-129-3p, hsa-miR-184, and hsa- miR-7-2#.

In some embodiments the microRNA signatures may comprise or consist of six or more microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof, in a sample of the cells obtained from the subject;

In some embodiments the microRNA signatures may comprise or consist of six microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR- 183#, and hsa-miR-217.

In some embodiments the microRNA signatures may comprise or consist of seven microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR- 183#, hsa-miR-217, and hsa-miR-429.

In some embodiments the microRNA signatures may comprise or consist of eight microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR- 183#, hsa-miR-217, hsa-miR-429, and hsa-miR-7-2#.

In some embodiments the microRNA signatures may comprise or consist of nine microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR- 183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, and mmu-miR-129-3p.

In some embodiments the microRNA signatures may comprise or consist of ten microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR- 183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, and hsa-miR-433.

In some embodiments the microRNA signatures may further comprise or consist of any one or more microRNAs selected from the group consisting of: miR216a, miR144#, miR124a, miR-935, miR133b, miR206, miR200b#, miR625# and miR1271.

In some embodiments the microRNA signatures may further comprise or consist of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa- miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, miR216a, miR144#, and miR124a.

Detecting microRNA Signatures

The microRNA signatures of the present invention can be detected in a biological sample using standard methods known in the art. Methods of RNA extraction suitable for use in generating microRNA signatures of the present invention are well known in the art. Without limitation, suitable methods are disclosed in the Examples of the present application, as well as standard textbooks including Ausubel et al., Ed., "Current Protocols in Molecular Biology ", John Wiley & Sons, New York 1987-1999. Methods suitable for RNA extraction from paraffin embedded tissues are disclosed, for example, in De Andres et al. (1995) Biotechniques 18: 42-44, and Rupp & Locker (1987), Lab Invest. 56: A67. RNA isolation may be performed using commercially available purification kits, buffer sets and proteases according to the manufacturer's recommended instructions (see for example, commercial kits available from Thermo Fisher Scientific, Sigma-Aldrich, Roche, Promega and Qiagen). Non-limiting examples of suitable commercial RNA extraction kits include the masterpure™ Complete DNA and RNA Purification Kit (epicentre), Maxwell® RSC miRNA Tissue Kits (Promega), RNeasy mini- columns (Qiagen), and Paraffin Block RNA Isolation Kits (Ambion, Inc.). Total RNA from Formaldehyde Fixed Paraffin Embedded samples (FFPE) can be isolated, for example, using Maxwell* CSC RNA FFPE Kit (Promega). Total RNA from tissue samples can be isolated, for example, using RNA Stat-60 (Tel-Test). High numbers of tissue samples may be processed using methods known to those of ordinary skill in the art (e.g. by use of the single- step RNA isolation method described in US patent no. 4,843,155).

Expression levels of specific microRNAs that in combination make up the microRNA signatures of the present invention can be determined using conventional methods known in the art (e.g. polymerase-based assays, hybridisation-based assays, flap endonuclease-based assays, direct RNA capture with branched DNA, and the like). Non-limiting methods suitable for detecting the level of expression of a given microRNA in a biological sample include microarray profiling, RT-PCR, Northern blotting, differential display, reporter gene matrix assays, nuclease protection, slot or dot blots, ICAT, 2D gel electrophoresis, SELDI-TOF, assays using MNAzymes/PlexZymes, enzyme assays, and antibody assays. Although not required, microRNAs under analysis for expression may be amplified using known techniques including, for example, any one or more of: the polymerase chain reaction (PCR), reverse transcription-polym erase chain reaction (RT-PCR), nucleic acid sequence-based amplification (NASBA), loop-mediated isothermal amplification (LAMP), self-sustained sequence replication (3SR), rolling circle amplification (RCA), transcription-mediated amplification (TMA), and strand displacement amplification (SDA).

Suitable high throughput methods suitable for microRNA quantification may include those involving physical or logical arrays. Non-limiting examples include assays which utilise solid phase arrays. Exemplary formats include membrane or filter arrays (e.g. nylon, nitrocellulose), bead arrays, and pin arrays. In general, the solid phase assays may utilise probes that specifically interact with (e.g. bind or hybridise to) a microRNA expression product may be immobilised, to a solid support (e.g. by indirect or direct cross-linking). Any solid support compatible with assay reagents and conditions may be utilised (e.g. silicon, modified silicon, silicon dioxide, various polymers (e.g. polystyrene, polycarbonate, (poly)tetrafliioroethylene, (poly)vinylidenedifluoride, or combinations thereof) or functionalised glass). In some embodiments, the solid support may be a chip composed wholly or partially of any one or more of silicon, modified silicon, silicon dioxide, various polymers (e.g. polystyrene, polycarbonate, (poly)tetrafluoroethylene, (poly)vinylidenedifluoride, or combinations thereof) or functionalised glass). Binding proteins (e.g. antibodies, antigen-binding fragments, or derivatives thereof) or polynucleotide probes, (e.g. DNA, RNA, cDNA, synthetic oligonucleotides, and the like) which specifically interact with target microRNA/s may be immobilised on the chip in an array (i.e. a logically-ordered manner) for detection of any microRNAs in a sample applied thereto.

Microarray expression may be detected by scanning the microarray using any of a variety of CCD-based or laser scanners, and analysing output using any suitable software, (e.g. GENEPIX™ (Axon Instruments), nCounter^®1 (NanoString Technologies), IMAGENE™ (Biodiscovery), Feature Extraction Software (Agilent)).

Non-limiting examples include assays which utilise liquid phase arrays (e.g. for hybridisation of nucleic acids, binding of antibodies or other receptors to a ligand) in microtiter or multiwell plates. Non-limiting examples of suitable systems include, xMAP^® (Luminex), ORCA™ (Beckman-Coulter, Inc.) SECTOR* Imager with MULTI -ARRAY^® and MULTI-SPOT^® systems (Meso Scale Discovery), miRCURY LNA™ microRNA Arrays (Exiqon), and ZYMATE™ (Zymark Corporation).

Reverse transcription PCR and real-time PCR may be employed to determine levels of microRNA expression in accordance with the invention. Two commonly used quantitative RT-PCR techniques are the Lightcycler assay (Roche, USA) and the TaqMan RT-PCR assay (ABI, Foster City, USA). Commercial RT-PCR products for assessing microRNA levels include the TaqMan Low-Density miRNA Array card (Applied Biosystems). Art-known methods of expression profiling of microRNAs using real-time quantitative PCR are described, for example, in Chen et al. (2009), BMC Genomics, 10:407, and Benes and Castaldi (2010), Methods, 50:244-249. Data indicative of microRNA expression levels may be normalised against the expression level of a suitable control RNA. The normalised data may then be processed using appropriate software to generate a microRNA signature (e.g. represented by a numeric number) representative of the expression level profile of the microRNAs. This signature may be compared with a reference value to assess whether it is indicative of a low expression or a high expression of the microRNAs in question. The reference value can be determined based on miRNA signatures (including the same miRNA signature) obtained from control patient/s (e.g. those with non-aberrant insulin production) via computational analysis. For example, the reference value may be the middle point between the signature of subject/s determined to have aberrant insulin production and subject/s determined to have non-aberrant insulin production. Alternatively, the reference value may be the middle point between the signature of subject/s determined to have aberrant insulin metabolism and subject/s determined to have non-aberrant insulin production.

Various software may be utilised in determining a microRNA signature of the present invention. Non-limiting examples include Plausible Neural Network (PNN) (see, for example, US patent no. 7,287,014), PNN Solution software (PNN Technologies Inc.), Prediction Analysis of Microarray (PAM) (see, for example, Tibshirani et al. (2002), PNAS 99(10):6567-6572,), and Significance Analysis of Microarray (SAM).

The skilled person will recognise that methods disclosed above are exemplary and any suitable method of determining microRNA expression may be utilised.

Methods for Predicting, Diagnosis, Prognosis

The microRNA signatures of the present invention may be used to predict the presence, absence, or relative abundance of insulin gene transcripts in cells and tissues. Given the central role of reduced beta-cell insulin-production in various diseases and conditions, the microRNA signatures disclosed herein may be used as biomarkers to inform for predicting, diagnosing, and/or prognosing the development of diseases and conditions associated with reduced or excessive insulin production. Accordingly, the microRNA signatures described herein can be used, for example, to identify and/or monitor a subject suspected to be at risk of developing a disease or condition associated with reduced or excessive insulin production. Alternatively, they may be used to diagnosis a subject with a disease or condition associated with reduced or excessive insulin production. Alternatively, the microRNA signatures may be used to predict the progression of the disease or condition associated with reduced or excessive insulin production in a subject. Without any particular limitation, the microRNA signatures may be used to predict, diagnose, and/or prognose the development of diseases and conditions associated with reduced insulin production including, for example, diabetes, pancreatitis, insulinoma, and pancreatic cancer. In some embodiments, the disease is diabetes (e.g. Type 1 diabetes, insulin-requiring Type 2 Diabetes). Without any particular limitation, the microRNA signatures may be used to predict, diagnose, and/or prognose the development of diseases and conditions associated with reduced insulin production including, for example, diabetes, pancreatitis, insulinoma, and pancreatic cancer. In some embodiments, the disease is diabetes (e.g. Type 1 diabetes, insulin-requiring Type 2 Diabetes).

By virtue of their capacity to predict the presence, absence, or relative abundance of insulin gene transcripts in cells and tissues, the microRNA signatures described herein may be used to monitor the response of a subject to a treatment administered for the purpose of alleviating, curing, and/or reducing the symptoms associated with a disease or condition associated with aberrant (e.g. reduced or increased) insulin production. For example and without limitation, a determination that the subject is undergoing an increased expression of a given microRNA signature described herein in response to a given treatment or therapeutic intervention may be indicative of a positive response to the treatment or therapeutic intervention by the subject. Alternatively, a determination that the subject does not have an increased expression, or has a reduced expression, of a given microRNA signature described herein in response to a given treatment or therapeutic intervention may be indicative of a negative or absent response to the treatment or therapeutic intervention by the subject. Without any particular limitation, the microRNA signatures may be used to monitor the response of the subject to treatments and therapeutic interventions for diseases and conditions including, for example, diabetes, pancreatitis, insulinoma, and pancreatic cancer. In some embodiments, the disease is diabetes (e.g. Type 1 diabetes, insulin-requiring Type 2 Diabetes). The treatment or therapeutic intervention may comprise any one or more of administering pharmaceutical agents (e.g. vaccines, drugs) to the subject, the grafting of cells (beta-islet cell transplantation), and the like.

Additionally or alternatively, the microRNA signatures described herein may be used to identify and/or test the efficacy of a treatment or therapeutic intervention. For example and without limitation, a determination that the subject is undergoing an increased expression of a given microRNA signature described herein in response to a given candidate treatment or therapeutic intervention may be indicative that the treatment or therapeutic intervention is effective against the targeted disease or condition associated with reduced insulin production. Alternatively, a determination that the subject does not have an increased expression, or has a reduced expression, of a given microRNA signature described herein in response to a given candidate treatment or therapeutic intervention may be indicative that the treatment or therapeutic intervention is ineffective against the targeted disease or condition associated with reduced insulin production. Without any particular limitation, the targeted disease or condition may include, for example, diabetes, pancreatitis, insulinoma, and pancreatic cancer. In some embodiments, the disease is diabetes (e.g. Type 1 diabetes, insulin-requiring Type 2 Diabetes).

Additionally or alternatively, the microRNA signatures described herein may be used to induce insulin gene expression in islet progenitor/precursor cells which, for example, can be used for cell replacement therapy in subjects suffering from diseases and conditions associated with aberrant insulin production and/or aberrant insulin metabolism (e.g. diabetes). The islet progenitor/precursor cells may, for example, be human embryonic stem cells (hESCs), induced pluripotent cells (iPSCs), endocrine progenitor cells, pancreatic progenitor cells (e.g. Ngn3+/NeuroD+/IAl+/Isll+/Pax6+ cells), or beta cell pro-precursors (e.g. MafB+/Pdxl+/Nkx2.2+ cells) pancreatic lineage cells, pancreatic islet precursor cells, endocrine progenitor cells, pancreatic islet progenitor cells, pancreatic islet stem cells, induced pluripotent pancreatic islet cells, and/or beta-islet precursor cells. Suitable methods for use in cell replacement therapy in subjects suffering from diseases and conditions associated with aberrant insulin production and/or aberrant insulin metabolism (e.g. diabetes) are known in the art, and a review of some approaches is provided in Hayek & King, (2016), Clinical Diabetes and Endocrinology, 2:4; and Niclauss et al. (2016), Novelties in Diabetes. Endocr Dev. Basel, Karger vol 31, pp 146-162 Stettler et al. (Eds)).

Additionally or alternatively, the microRNA signatures described herein may be used as a biomarker to determine the death or function of insulin-producing cells in subjects with or progressing to diseases and conditions associated with aberrant insulin production and/or aberrant insulin metabolism (e.g. diabetes). For example and without limitation, a determination that the subject is undergoing an increased expression of a given microRNA signature described herein may be indicative that the insulin-producing cells of the subject under analysis were functional in terms of insulin production. Alternatively, a determination that the subject does not have an increased expression, or has a reduced expression, of a given microRNA signature described herein may be indicative that the insulin-producing cells of the subject under analysis were not functional in terms of insulin or at least a reduced capacity to produce insulin. Without any particular limitation, the targeted disease or condition may include, for example, diabetes, pancreatitis, insulinoma, and pancreatic cancer. In some embodiments, the disease is diabetes (e.g. Type 1 diabetes, insulin-requiring Type 2 Diabetes). In some embodiments, the disease is diabetes (e.g. Type 1 diabetes, Type 2 Diabetes).

Additionally or alternatively, the microRNA signatures described herein may be used to identify the tissue of origin, based on the signature and levels of microRNA expression.

In general, detection of an increased expression of a microRNA signature as described herein is indicative of an increased abundance of insulin gene transcripts in the cells or tissue of interest. Alternatively, detection of a reduced expression of a microRNA signature as described herein in a test subject is indicative of a reduced abundance of insulin gene transcripts in the cells or tissue of interest. Determination of whether expression of a given microRNA signature is increased or reduced is generally made by comparison of the subject expression levels to the expression levels of the same microRNA signature (or expression levels of individual microRNAs within the signature) in cells obtained from a control subject, or a population of control subjects. The control cells may be known to not produce insulin. The control subject population may be of the same or similar: race, gender, sex, and/or age as the test subject. The determination of increased insulin transcript production or increased insulin production in a given subject may be achieved using standard tests known in the art. In some embodiments, more than a 2-, 3-, 4-, 5-, 6-, 7-, 8-, 9- or 10- fold increase in the expression level of a given microRNA signature in a sample from the subject tested as compared to the control is indicative of increased insulin transcript production, and a consequent indication of reduced insulin production capacity in the subject.

The expression microRNA signatures may be tested in a biological sample from the subject comprising cells. Typically, the cells within the biological sample may be isolated from the biological sample prior to determining microRNA expression, to ensure that the microRNA measured is predominantly/substantially intracellular. The biological sample may comprise cells from one or more tissue/s of the subject. Preferably the tissues are capable of insulin production, non-limiting examples of which include pancreatic tissue (e.g. pancreatic tissue comprising beta-islet cells), gallbladder tissue and brain tissue.

The subject from which the biological sample is derived may be a mammalian subject, such as, for example, a human or a non-human mammal. The human subject may be, for example, a Caucasian, an Asian, an African, or a Hispanic. The subject may be of any age.

Kits Disclosed herein are kits for performing the methods of the present invention. The kits may be fragmented kits or combined kits. The kits may comprise reagents sufficient for determining the level of expression of a given microRNA signature disclosed herein.

The kits may comprise primers, probes, and/or binding agents for detecting expression of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44 or 45 of any one or more of the following microRNA/s, in any combination: hsa-miR-216b, hsa-miR-519b-3p, hsa- miR-520e, hsa-miR-183, hsa-miR-520c-3p, mmu-miR-187, hsa-miR-147b, hsa-miR-34b, hsa-miR-375, hsa-let-7e#, hsa-miR-636, hsa-miR-183#, hsa-miR-30d#, hsa-miR-433, hsa- miR-663b, hsa-miR-335#, hsa-miR-429, hsa-miR-200c, hsa-miR-98, hsa-miR-424#, hsa miR-452, hsa-miR-329, dme-miR-7, mmu-miR-129-3p, hsa-miR-139-3p, hsa-miR-485-5p, hsa-miR-363, hsa-miR-21#, hsa-miR-184, hsa-miR-661, hsa-miR-655, hsa-miR-135b#, hsa- miR-142-5p, hsa-miR-222#, hsa-miR-382, hsa-miR-141, hsa-miR-367, hsa-miR-1285, hsa- miR-217, hsa-miR-215, hsa-miR-485-3p, hsa-miR-512-3p, hsa-miR-452, hsa-miR-639 and/or hsa-miR-7-2#.

In some embodiments the kits may comprise primers, probes, and/or binding agents for detecting expression of any one or more of the following microRNA/s, in any combination: hsa-miR-216b, hsa-miR-183, hsa-miR-375, hsa-miR-183#, mmu-miR-129-3p, hsa-miR-184, hsa-miR-7-2#, hsa-miR-433, hsa-miR-429, hsa-miR-141, hsa-miR-512-3p, hsa-miR-452, hsa-miR-382, hsa-miR-329, hsa-miR-217, dme-miR-7, hsa-miR-485-3p, hsa-miR-335#, hsa- miR-200c and hsa-miR-655.

In some embodiments the kits may comprise primers, probes, and/or binding agents for detecting expression of any one or more of the following microRNA/s, in any combination: hsa-miR-216b, hsa-miR-183, hsa-miR-375, hsa-miR-183#, mmu-miR-129-3p, hsa-miR-184, and hsa-miR-7-2#.

In some embodiments the kits may comprise primers, probes, and/or binding agents for detecting expression of six or more microRNAs selected from the group consisting of: hsa- miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR- 429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof, in a sample of the cells obtained from the subject;

In some embodiments the kits may comprise primers, probes, and/or binding agents for detecting expression of six microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, and hsa-miR-217. In some embodiments the kits may comprise primers, probes, and/or binding agents for detecting expression of seven microRNAs which are: hsa-miR-183, hsa-miR-216b, dme- miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, and hsa-miR-429.

In some embodiments the kits may comprise primers, probes, and/or binding agents for detecting expression of eight microRNAs which are: hsa-miR-183, hsa-miR-216b, dme- miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, and hsa-miR-7-2#.

In some embodiments the kits may comprise primers, probes, and/or binding agents for detecting expression of nine microRNAs which are: hsa-miR-183, hsa-miR-216b, dme- miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, and mmu- miR-129-3p.

In some embodiments the kits may comprise primers, probes, and/or binding agents for detecting expression of ten microRNAs which are: hsa-miR-183, hsa-miR-216b, dme- miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR- 129-3p, and hsa-miR-433.

In some embodiments the kits may comprise primers, probes, and/or binding agents for detecting expression of any one or more microRNAs selected from the group consisting of: miR216a, miR144#, miR124a, miR-935, miR133b, miR206, miR200b#, miR625# and miR1271.

In some embodiments the kits may comprise primers, probes, and/or binding agents for detecting expression of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR- 183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, miR216a, miR144#, and miR124a.

Additionally or alternatively, the kits may comprise means for extracting RNA from a biological sample.

Additionally or alternatively, the kits may comprise means for reverse-transcribing RNA into cDNA and optionally means for amplifying cDNA. The means for amplifying cDNA may facilitate real-time quantification of the cDNA.

Additionally or alternatively, the kits may comprise control standards to allow normalisation of microRNA signature expression data and/or comparison of microRNA signature expression data to determine whether expression of the microRNA signature is increased, reduced, or in a normal/standard range.

Additionally or alternatively, the kits may comprise buffers, washing reagents, and/or RNAse inhibitors. It will be appreciated by persons of ordinary skill in the art that numerous variations and/or modifications can be made to the present invention as disclosed in the specific embodiments without departing from the spirit or scope of the present invention as broadly described. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive.

It will be appreciated by persons of ordinary skill in the art that numerous variations and/or modifications can be made to the present invention as disclosed in the specific embodiments without departing from the spirit or scope of the present invention as broadly described. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive.

Examples

The present invention will now be described with reference to specific Example(s), which should not be construed as in any way limiting.

Example One: experimental procedures

The following protocols were employed to generate the experimental results provided in the Examples below:

(a) Cellular RNA Isolation

Reagents:

1) 1.7 ml microcentrifuge tubes (Axygen, MCT-175-C)

2) TRIzol (Ambion, 15596018)

3) Chloroform (Sigma, 2432-500ml)

4) Isopropyl alcohol, IPA (Sigma, 59304-1L-F)

5) 100% ethanol (Sigma, E7023-500ml)

6) Fresh aliquot of nuclease-free water

7) P 10, P20, P200 and P 1000 filtered tips

8) RNase AWAY (highly preferred but not essential)

NB: All plastic ware should be nuclease-free. Never stick your hands into containers of nuclease-free plastic ware. Always pour out what is required. Equipment:

) Refrigerated centrifuge for Eppendorf tubes set at 4°C

) Vortex mixer

) Well calibrated micropipettes designated for (pre-PCR) nucleic acid isolation Reagent Setup:

) Allow samples to thaw thoroughly on ice prior to commencing.

a. Due to the time involved in each stage of processing, a maximum of 8 samples should be processed at a time.

) Set temperature on a centrifuge to 4°C

a. Run "Fast Temp" program if changing rotor or a quick cool down is required. b. Use a dedicated centrifuge with appropriate rotor

) Clean gloves with RNase Away prior to commencing.

Procedure

) Lyse cells by adding 1 ml of Trizol to each sample in a 1.7 ml Eppendorf tube. For this protocol use 1 ml of Trizol. If a different volume of Trizol is used, the volume of all other reagents must be altered accordingly to maintain the correct ratios.

) Add 200 μΐ of chloroform to each sample and shake vigorously for 1 min. This ensures correct mixing. Do not vortex at this stage.

) Immediately centrifuge the tubes at 12,000 xg for 15 mins at 4°C.

) Carefully remove the upper aqueous (clear) layer (400-500 μΐ) into a fresh 1.7 ml microcentrifuge tube. Use a 200 μΐ pipette and tips to remove this gradually. Note: There will be a large amount of white precipitate at the interface between the organic and aqueous layers. Do not disturb this layer. After the removal of 400-500 μΐ there will still be some of the aqueous layer left; try to remove as much as possible without contamination.

) Add 500 μΐ of IPA.

) Mix gently by inverting 7-10 times. Do not vortex or shake tubes.

) Incubate for 10 mins at RT.

) Centrifuge at 12,000 xg for 15 mins at 4°C. A pellet may not always be visible at this stage, so always orient the tubes with the hinge facing outwards to allow estimation of the pellet location - in the bottom towards the hinge. 9) Prepare a fresh volume of 75% ethanol while the samples are spinning. For 8 samples, add 6.75 ml of 100% ethanol to 2.25 ml of nuclease-free water (9 ml in total).

10) Carefully aspirate and discard the supernatant by placing the pipette tip along the wall of the tube opposite to the hinge. When removing the supernatant, reduce the size of the pipette as you go to minimise disturbance to the pellet. Ideally, use a P200 initially, then a P20 when getting closer to the pellet.

11) Add 1 ml of freshly prepared 75% ethanol to each tube and briefly vortex.

12) Centrifuge at 12,000 xg for 15 mins at 4°C. As in #8, orient tubes with the hinge facing outwards.

13) Carefully aspirate and discard the supernatant by placing the pipette tip along the wall of the tube opposite to the hinge. As before (# 10), reduce the size of the pipette when removing the supernatant. Ideally, use a P200 initially then a PI 0 to remove as much ethanol as possible.

14) Allow the tubes to dry at RT for 5 minutes. Lay the tubes flat with their lids open. If there are large droplets, gently twist the tube to spread out the liquid, allowing it to dry faster. Note: Do not over dry the samples as this will reduce the solubility of the RNA.

15) Add 15 μΐ of nuclease-free water to each tube and resuspend. Always store RNA on ice after this stage. The volume may vary depending on the pellet size.

16) Measure concentration of RNA using Nanodrop. If you are not going to proceed with downstream analysis immediately, skip this step and store your RNA. Always measure (Nanodrop, Qubit or Bioanalyzer) before downstream processing.

17) Store RNA at -80°C. Always log your sample details in the -80°C freezer log on the networked lab drive.

(b) Automated RNA Isolation

Reagents

1) Collection microtubes (Qiagen, 19560)

2) Lids for collection microtubes (Qiagen, 19566)

3) Glycogen (nuclease-free) 10 μg/μl (Sigma)

4) TRIzol (Ambion, 15596018)

5) Chloroform (Sigma, 2432-500ml)

6) Isopropyl alcohol, I PA (Sigma, 59304-1 L-F)

7) 50 nM ath-miR-172a (8-well strips, stored in -80°C freezer) 8) RNeasy 96 QI Acube HT kit (Qiagen, CAT 74171 )

9) QIAcube HT Plasticware (Qiagen, CAT 950067)

10) Tip disposal box (Qiagen, CAT 990550)

11) Reagent trough with lid, 70 ml (Qiagen, CAT 990554)

12) Reagent trough with lid, 170 ml (Qiagen, CAT 990556)

13) Filter tips, OnCor C, 200 μΐ (Qiagen, CAT 990610)

14) P10, P20, P200 and PI 000 filtered tips

15) RNase AWAY (highly preferred but not essential)

NB: All plastic ware should be nuclease-free. Never stick your hands into containers of nuclease-free plastic ware. Always pour out whatever is required.

Equipment

1) QIAcube HT

2) Refrigerated centrifuge for plates set at 4°C

3) Vortex mixer

4) Well calibrated micropipettes designated for (pre-PCR) nucleic acid isolation Reagent Setup

1) Allow samples to thaw thoroughly on ice prior to commencing.

2) Clean gloves with RNase Away prior to commencing.

Procedure

1) Mix sample by gently pipetting up and down, then aliquot Ι ΟΟμΙ into one collection microtube (96 racked).

2) Vortex and briefly spin glycogen (10 g/μl stock) to mix, then add Ιμΐ to each sample.

3) Add 500 μΐ of Trizol to each tube. Yellow globules appear in the solution after adding Trizol. These dissipate following vortexing and do not seem to affect downstream processing. Wear appropriate PPE and be careful when vortexing or shaking tubes containing Trizol.

4) Vortex the microtubes for 40 sees or until the yellow globules disappear.

5) Incubate at RT (22-24°C) for 10 minutes.

6) Briefly centrifuge (1 min, 1500 rpm) the racked microtubes at 4°C to remove Trizol from the caps. ) Add 2.5 μΐ of 50 nM ath-miR-172a spike in control to each tube. One 8-well strip contains enough for an entire 96-well plate.

) Add 100 μΐ of chloroform to each tube and shake vigorously for 40 seconds. Secure the caps using an upside down plastic lid and elastic bands as shown below.

NB: DO NOT vortex the tubes at this stage.

) Incubate at RT for 15 minutes.

0) Centrifuge 3,200 x g for 25 minutes at 4°C.

1) Turn on QIAcube HT system. Remove tip eject cover (red). Open the pre -treatment run file.

2) Place microtubes into position CI of the QIAcube HT. The trough holder in this position must be removed first.

3) Load an empty S-block into position Bl. Load tips.

4) Begin the run. This program will transfer 300 μΐ of aqueous phase into the S-block.5) Remove the microtubes. For protein/DNA analysis, store these samples at -80°C. Place the trough holder back into this position.

6) Open the purification run file.

7) Load the appropriate reagents in their respective troughs. See software for specific volumes. Ensure there is a little excess volume to avoid running out mid-cycle.

8) Load RNeasy plate and Elution plate in Al (left/right respectively). Ensure that Al is top left. The left section of Al is for waste, while the right is for elution.

9) Load tips (2x96). Start the run and complete the pre-run checklist.

0) Remove the Elution plate. Cap these tubes and then store at -80°C. Always log your sample details on the networked database.

1 ) Remove the reagent troughs and discard remaining liquid. All troughs, except the Top Elute, may be rinsed with nuclease-free water and left to dry. The Top Elute trough must be wiped out with a Kim wipe. Reagent troughs can be used for a maximum of one month or one full RNeasy kit before being discarded.

2) Remove channel block (three pieces) and pour 20 ml of RO water down the waste chute.

Replace the tip eject cover (red). Ensure that the rubber gasket is removed from the filter carriage for cleaning.

3) Soak channel block in 1% Trigene for 15 - 30 mins. Rinse with RO water. Dry.

4) Run cleaning cycle, including UV if this is the last run of the day. Select to turn off after the cleaning cycle is completed. (c) Open Array Low Sample Input

Reagents

1) Custom OpenArray Slides (Applied Biosystems, 4470813)

2) Custom RT primers (included with custom slides)

3) Custom PreAmp primers (Applied Biosystems, 4441856)

4) TaqMan microRNA reverse transcription kit (Applied Biosystems, 200 rxn 4366596, 1000 rxn 4366597)

5) 15 nM ath-miR-l 59a synthetic miRNA (Sigma)

6) OpenArray 384-well sample plate (Applied Biosystems, 4406947)

7) Aluminium Seal (Beckman-Coulter, 538619)

8) TaqMan PreAmp mastermix (Applied Biosystems, 1 ml 4391128, 5 ml 4488593)

9) TE Buffer (Invitrogen, 12090015)

10) OpenArray accessories kit (Applied Biosystems, 4453975)

11) TaqMan OpenArray real-time PCR mastermix (Applied Biosystems, 1.5 ml 4462159, 5 ml 4462164)

12) AccuFill tips (Applied Biosystems, 1 box 4457246, 10 boxes 4458107)

13) Filtered pipette tips

14) Nuclease-free water (Qiagen, 129117)

15) 0.2 ml 96-well plates (Axygen, PCR-96M2-HS-C)

NB: All plastic ware should be nuclease free. Never stick your hands into containers of nuclease-free plastic ware. Always pour out whatever is required.

Equipment

1) Thermocycler

2) QuantStudio™ 12K Flex Accufill System

3) QuantStudio™ 12K Flex Real-Time PCR System

4) Vortex

5) Centrifuge

6) Axymat Silicon Seals (Axygen, AM-96-PCR-RD) Reagent Setup

1) Download the relevant run file/s (.tpf) from the Life Technologies website (https://www.thermofisher.com/au/en/home/products-and-services/product- types/download-openarray-tpf-and-spf-plate-files.html) using the lot and serial number of the slide.

2) Thaw all reagents on ice, except for the RT enzyme, which must remain at -20°C until use.

Procedure

Pt 1 : Reverse Transcription

1. Dilute the RNA samples to <10 ng/μΐ. This protocol is designed for samples with low RNA concentrations. Diluting to <10 ng/μΐ (around 8.5 ng/μΐ is sufficient) allows for >1μ1 to be taken in the next step, increasing accuracy.

2. Add 10 ng of RNA to the respective well of a 96-well plate and then bring the volume to 3 μΐ.

3. Create an RT mastermix using the components from the reverse transcription kit, as detailed below. It is recommended to add 5% excess to account for pipette error.

Table 1

4. Pipette to mix and then centrifuge briefly at 10,000 x g for 10 sec (quick spin).

5. Aliquot 4.5 μΐ of the mastermix into each well. Seal with a silicon seal. 6. Invert to mix and then quick spin.

7. Incubate on ice for 5 min.

8. Place into the thermocycler and run the following program: 40 cycles (16 °C for 2 min, 42 °C for 1 min, 50 °C for 1 sec), 85 °C for 5 min, hold at 4 °C.

9. cDNA can be stored at -20°C or used immediately.

Pt 2: Preamplification

10. Create a preamp mastermix using the components listed below. Swirl the PreAmp mastermix prior to use. It is recommended to add 5% excess to account for pipette error.

Table 2

11. Invert to mix, and then quick spin.

12. Aliquot 32.5 μΐ of the mastermix into each well.

13. Invert to mix, and then quick spin.

14. Incubate on ice for 5 mins.

15. Place into thermocycler and run the following program: 95°C for 10 mins, 55°C for 2 mins, 72°C for 2 mins, 16 cycles (95°C for 15 sees, 60 °C for 4 mins), 99°C for 10 mins, hold at 4°C.

16. Invert preamplified cDNA and then quick spin.

17. Dilute 1 :20 by adding 4 μΐ of preamplified cDNA to 76 μΐ of 0.1X TE buffer.

18. Both diluted and undiluted preamplified cDNA can be stored at -20°C for up to 1 week or used immediately.

Pt3: Loading OpenArray Slides and Performing qPCR Combine 5 μΐ of diluted, preamplified cDNA to 5 μΐ of TaqMan OpenArray real-time PCR mastermix in a new 96-well plate. Seal with a silicon seal.

Vortex and quick spin.

Aliquot 5 μΐ of each sample into 1 well of the 384-well sample plate. The position of each sample will depend on the configuration of the custom slide. Each well of the sample plate corresponds to one subarray of the OpenArray slide. An entire slide will take 48 wells (4 rows, 12 columns).

Seal the samples plate. It is advisable to pre-cut the seal into the required sections, so the sections may be sealed/unsealed individually to reduce evaporation. Alternatively, the plate may be sealed with an intact seal, and then sections can be individually cut out when loading.

Centrifuge the sample plate at 490 xg for 1 min at 4°C. Load the OpenArray slides within 1 hr.

Remove the required slides from the freezer and allow them to come to room temperature (~15 mins). As these slides work on hydrophobic/hydrophilic interactions, condensation should be avoided.

Set up consumables from the accessory kit.

25.1. Gently pull on the plunger of the immersion fluid syringe to loosen. Remove cap, place tip on and flush air from the tip.

25.2. Removed slide lid and plug from packaging.

25.3. Place the loading system tips within the machine and remove lid.

25.4. Place sample plate within PCR system.

25.5. Put gloves on. Use a size lower than normal to minimise the risk of accidentally marking the slide lid.

25.6. Carefully open slide packaging. Slowly tip slide into hand. Do not touch the top of the slide. Place slide into the AccuFill, with the barcode on the left.

25.7. Remove seal from the portion of the sample plate intended for loading. Use the loading system software to enter the slide barcode, slide position, sample position and tip configuration.

When all relevant checks are completed, press load slide. While the PCR system is loading the slide, remove the clear and red plastic from the bottom of the slide lid. When finished loading, carefully remove and seal the slide within 90 sec. 26.1. Place the slide within the plate clamp. Place the slide lid onto the slide. Clamp for 30 sec. Ensure the lid is positioned so that barcode is correctly displayed. Remove the assembly from the plate clamp.

26.2. Position immersion fluid syringe within the slide so that the tip is pressing against the lid. Slowly fill slide with immersion fluid, ensuring the fluid runs along the lid. Once full, seal the slide with the plug, turning the screw until the handle breaks off.

26.3. Remove the plastic cover on the top of the slide lid, and then carefully place into the slide carrier of the real-time PCR system. Ensure there is support on the bottom of the slide as it is being lowered, so it does not drop suddenly, and do not touch the top of the slide. It is OK to touch the sides of the slide/cassette.

27. Initialise the QuantStudio 12K Flex and start the qPCR run.

27.1. Select "OpenArray" within the PCR-system software. Press "Find Slide

IDs". This will take a few mins. If the software cannot find the plate ID, it will ask for it to be entered manually.

27.2. Press "Confirm Plate Centres". Again, this will take a few mins. Check that the red dot is within the centre and that there are no fingerprints/marks on the top of the slide. Load the respective tpf file for each slide and specify a result file name and location. Press "Start Run". The program will take approximately 2 hr to complete.

Data obtained from these custom microRNA panels was assessed to remove nonspecific amplification (results with an AMP score of <1.24 and/or a Cq confidence score of <0.6 were omitted). Data were normalised to the RNA isolation and RT ath-miR spike-in controls and transcript abundance was calculated using the fold over detectable method (limit of detection = 39) described earlier (Hardikar A et al 2014 J Am Heart Assoc).

(d) Immunofluorescent Staining of Paraffin-Embedded Tissues

Reagents:

1) Normal donkey serum (Thermo Fisher, 14190250)

2) 100% ethanol (Sigma, E7023-500ml)

3) Distilled (or MilliQ) water 4) Tissue paper or Kimwipes

5) Xylene (Thermo Fisher, AJA2342-5L)

6) Primary antibody/ies

7) Dulbecco's phosphate buffered saline, DPBS (Gibco, 14190-250)

8) Secondary antibody/ies

9) Vectashield (Vector Laboratories, H-1000)

10) Hoechst 33342 nuclear stain (10 mg/ml)

11) Coverslips

12) Nail polish

Equipment

1) Convection oven set at 85-90°C.

2) Coplin Jars

3) Forceps

4) Hydrophobic marker

5) Moist chamber

Reagent Setup

1) Fill two Coplin jars with Xylene.

2) Fill four Coplin jars decreasing concentrations of ethanol in this order: 100%, 90%, 70% and 50% (50 ml in distilled water).

3) Fill one Coplin Jar with distilled water.

4) Dilute normal donkey serum (NDS) with dPBS to 4% NDS

5) Dilute primary antibodies to a working stock in 4% NDS. The actual dilution factor with depend on the antibody used [for example the Guinea Pig anti-insulin polyclonal (from DA O, catalogue number-A0564(01 )) insulin is usually 1 : 100]. Multiple antibodies can be combined into one stock ONLY if they were raised in different animals.

6) Dilute secondary antibodies 1 : 100 to a working stock in 4% NDS. Ensure the antibodies target the animal in which the primary antibody was raised

7) Set up moist chamber by placing moist paper towel within the chamber. Create the mounting solution by adding 10 μΐ of Hoechst to 1 ml of Vectashield. Procedure

Pt 1 : Primary Antibody

Place slide at 85-90°C for 2-5 mins or until the paraffin wax becomes translucent (check every minute after the first 2 minutes). The tissue will still be opaque.

Immediately place slide into the first xylene Coplin jar for 2-3 mins. When adding the slide to a Coplin jar, gently wash the slide by dipping into the liquid 3-4 times.

Transfer slide to the second xylene Coplin jar for 5 mins. When transferring the slide between jars, gently dab the edge of the slide onto tissue paper or imwipes to remove excess liquid.

Transfer the slide to 100% ethanol for 5 mins.

Transfer the slide to 90% ethanol for 5 mins.

Transfer the slide to 70% ethanol for 5 mins.

Transfer the slide to 50% ethanol for 5 mins.

Transfer the slide to distilled water for 5 mins.

Remove slide and dab off excess liquid.

Using the hydrophobic marker, draw around your tissue section. Ensure that the line is close to your sample without touching it.

Add enough 4% NDS to cover the section.

Place slide into moist chamber and incubate at room temperature for 20 mins.

Tilt the slide and use a pipette to remove and discard the liquid. Do not remove the slides from the moist chamber.

Add enough of the selected primary antibody (working stock in 4% NDS) to cover the tissue.

Seal the moist chamber with parafilm and incubate at 4°C overnight.

Pt 2: Secondary Antibody

Tilt the slide and use a pipette to remove and discard the primary antibody.

Add DPBS and incubate at room temperature for 3-5 mins. Add as much PBS as possible within the confines of the hydrophobic marker. This will create a large bubble of fluid over the tissue section.

Remove liquid.

Repeat steps 17 and 18 at least 4 more times. 20. Add enough secondary antibody (working stock diluted in 4% NDS) to cover the step, and all subsequent steps, MUST be completed in the dark to ensure that the reporter dyes to not degrade.

21. Seal the moist chamber with parafilm and incubate at 37°C for 1 hour. Ensure there is ample liquid in the tissue paper used in the moist chamber.

22. Remove the secondary antibody and then repeat steps 17 and 18 at least five times to remove any unbound secondary antibodies.

23. Tilting the slide and place folded tissue or Kimwipe at the bottom of the sample to soak up the remaining PBS. It is important not to touch the tissue section or wipe the liquid.

24. Add 20 μΐ of the mounting solution (Vectashield + Hoechst).

25. Add a coverslip and seal using nail polish. Place one edge of the coverslip to the side of the sample, in contact with the mounting solution. Slowly lower the other edge using a scalpel blade (or another suitable thin, flat utensil), allowing the solution to spread along the coverslip. This method minimises the introduction of bubbles.

26. Store slide(s) at RT in the dark until the slide is dry (-30 minutes).

27. Examine slide(s) using a fluorescent microscope.

28. Place the slide(s) at 4°C for long-term storage.

(e) Data analysis and validation

Data were analysed using Statistica for Windows ver. 13 (Dell Inc. Tulsa, OK), XLStat (Adinsoft,Paris, France) and R software (ver. 3.3.1). The ability of microRNAses panel to distinguish between insulin production status in various tissues was assessed using unsupervised bidirectional (cases-tissues/variables-microRNAses) hierarchical clustering analysis (Everitt, B. S., Landau, S., & Leese, M. (2001)). Cluster analysis. London: Arnold press). Complete linkage and Spearman correlation distance between cases/variables were chosen to draw the heatmap using R package "Heatplus".

In order to identify the subset of tissue microRNAses with the strongest association with insulin production status or insulin mRNA transcript level in the tissues LI -penalized logistic and linear regression techniques were used (Goeman, J. J., LI penalized estimation in the Cox proportional hazards model. Biometrical Journal 2010, 52(1), 70-84). This approach prevents overfitting of collinear and high-dimensional data and utlilize the LASSO (least absolute shrinkage and selection operator) algorithm. This procedure reduces the regression coefficients to zero relative to the maximum likelihood estimates. The level of reduction is determined by the tuning parameter λΐ, which is increased progressively up to the value that reduces all regression coefficients to zero. Optimal value of the tuning λΐ parameter was calculated from 5-fold likelihood cross-validation. Using bootstrapping (n=1000) the frequency at which particular microRNAses were selected was calculated and those miRNAs that were presented in at least 50% of the bootstraps were chosen.

Plots of beta coefficients (Y-axis) versus λΐ (X-axis) were generated using R package "penalized". Beta coefficients of the selected microRNAses from the discovery set were then applied to the same microRNAs in the validation set to assess the accuracy of i) insulin production status and ii) the insulin mRNA transcript level. In order to assess the accuracy of individual microRNAs to discriminate between insulin-producing and -non-producing tissues, we performed ROC analysis (Hajian-Tilaki . Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation . Caspian Journal of Internal Medicine. 2013;4(2):627-635, Swets JA. ROC analysis applied to the evaluation of medical imaging techniques. Invest Radiol. 1979;14: 109-21, Metz CE. ROC methodology in radiological imaging. Invest Radiol. 1986;21 :720-33). ROC curves were based on the concept of a scale, on which the CT values of a particular microRNA for the insulin- producing/non-producing tissue formed a pair of overlapping distributions sets. The complete separation of the two sets implies that a microRNA offers perfectly discriminating ability while complete overlap implies no discrimination. The ROC curve shows the trade off between the true positive fraction (TPF, sensitivity) and false positive fraction (FPF, 1- specificity) as the separation criterion is changing.

For wet-lab validation, candidate microRNAs were transfected using the manufacturer's protocol for overexpression of microRNAs. siPORT NeoFX was diluted in Opti-MEM 1 medium and incubated at room temperature for 10 min. Synthetic microRNAs were diluted in Opti-MEM to a final concentration of 30 nM. Diluted RNA and diluted siPORT NeoFX were mixed by gentle pipetting and incubated at room temperature for 10 min. The RNA/siPORT NeoFX complexes were then distributed to each well and overlaid with cell suspension. Differentiation was carried out as described earlier (Hardikar AA et al. Proc Natl Acad Sci USA. 2003 Jun 10;100(12):7117-22, Gershengorn MC et al. Science. 2004 Dec 24;306(5705):2261 -4). Cells were harvested on day 4 of transfection and assessed for gene expression analysis using TaqMan-based real-time PCR.

Example Two: insulin production and microRNA expression in different human tissues

To better understand the role of microRNAs in maintenance of insulin gene expression amongst tissues, a set of 754 microRNAs in 526 different human tissues was assessed - 240 non-diabetic insulin-producing tissues; including 155 normal human donor islets, over 70 human gallbladders and 14 brains all tissues naturally expressing variable levels of insulin. In addition, the profile of the same 754 microRNAs in 250 tissues that do not produce insulin and 36 pancreas/islets from individuals with or without Type 2 diabetes was compared.

An overview of the study design is shown in Figure 1.

Immunofluorescent staining of human pancreatic islets, gallbladder and brain tissue confirmed the presence of insulin-producing cells in each tissue (see Figure 2).

Islet hormones (Insulin, Glucagon and Somatostatin) and islet-specific transcription factors (MafA, Ngn3, Pdxl ) and Hesl (inhibitor of NGN3) were observed to be expressed in islets, gallbladders and brains - all naturally occurring insulin-producing cells (Figures 3A- 3C). Endothelial cells do not show any of these transcripts (except the negative regulator Hesl) (Figure 3D). The data shown in Figure 3 is presented as actual Cycle threshold (Ct) values as obtained by real-time TaqMan®-based quantitative PCR (low number = high expression) and the Y-axis is therefore reversed. The dotted line represents the limit of detection and the shaded area represents un-detectable transcripts. The endothelial cells shown in Figure 3D are an example of the non-insulin-producing tissue.

Using bioinformatics workflows implementing penalized regression analyses followed by resampling validation using bootstrapping, a set of microRNAs that is highly associated with insulin expression was identified. Independent analyses involving forced expression of insulin-associated microRNAs (vs insulin itself) identified the role of microRNAs to regulate the expression of insulin.

An unsupervised hierarchical cluster analysis was used to sort samples into two major clusters - one that is composed of tissues that produce low levels of insulin ("insulin- negative" - grey color) and the other, of tissues that produce large amounts of insulin ("insulin-positive") (Figure 4).

Penalized regression analyses of the datasets identified a signature of microRNAs that is highly associated with insulin gene transcript abundance (measured in Figure 3 as presented on a cycle-threshold scale). A logistic regression analysis was carried out to identify microRNAs that are most associated with high (low Ct value) vs. low (high Ct value) insulin gene expression (left panel below). A linear regression analysis compares the actual expression (Ct value) level of microRNAs to the actual expression (Ct value) levels of insulin gene in each tissue (Figure 5B). These analyses led to identification of a microRNA signature that is highly associated with insulin gene expression. Resampling validation of data was carried out using bootstrapping. Here, the computing workflow eliminates five samples at a time from the entire dataset and carries out the regression analysis on the remaining set of samples. The whole process is repeated 1,000 times with five different sets of samples eliminated at each time. Finally, a frequency table is achieved for the microRNAs (Figure 6). The microRNAs occurring at higher frequency in the bootstrap analyses were selected for validation.

Table 3 below represents the results from n= 1,000 bootstrap repeats showing the frequency of specific microRNAs being included in the logistic regression model. A positive or negative sign (in column 3) indicates the direction of the logistic regression coefficient.

Table 3 microRNA sign frequency

002269 hsa-miR-183 A -1 94.9

000268 dme-miR-7 B -1 71.1

000564 hsa-miR-375 A -1 64

000463 hsa-miR-141 A -1 58.2

002314 hsa-miR-7-2# B -1 57.7

002326 hsa-miR-216b A -1 55

002337 hsa-miR-217 A -1 53.9

000485_hsa-miR-184_A -1 41.1

001024 hsa-miR-429 A -1 33.3

000400 hsa-miR-23b A -1 22.8

002270 hsa-miR-183# B -1 20.7

002298 hsa-miR-129# B -1 19.8

001184 mmu-miR-129- -1 19

002220 hsa-miR-216a A -1 18.6

002159 hsa-miR-135b# B 1 15.3

002245 hsa-miR-122 A 1 14.2

001119 hsa-miR-520e A -1 9.1

002300 hsa-miR-200c A -1 8

000186 mmu-miR-96 A -1 5.1

002384 hsa-miR-519b- 1 4.8

002263 hsa-miR-190b B -1 4.6

001028 hsa-miR-433 A -1 4.4

002415 hsa-miR-519a A 1 3.2

000477 hsa-miR-154 A -1 1.7

000509 hsa-miR-205 A -1 1.5

000583 hsa-miR-9 A 1 1.2

001141 mmu-miR-451 A 1 1

000502 hsa-miR-200a A -1 1 A validation analysis was conducted using a de-identified (masked) set of 35 samples; six of these were from insulin-producing tissues while the remaining 29 were from tissues that do not produce insulin. Using the signature derived from the logistic regression analysis (see Figure 3), the insulin positive tissues from insulin-negative tissues were identified (Figure 7A). Using the microRNAs analysed through the linear regression analysis, the level of insulin gene expression was predicted (in terms of the actual Cycle -threshold value/Ct- value) as assessed by TaqMan-based real time PCR. This value of the predicted insulin expression analysis was within few cycles of the actual insulin Ct -value as analysed using real-time PCR. These data indicate that the signature of selected microRNAs can accurately predict insulin gene expression in human tissues.

Example Three: relationship between microRNA signature and insulin production

In order to assess if the microRNAs identified via the bootstrap analysis were merely associated with insulin expression or causal to insulin expression, a pre-defined in vitro differentiation system of human islet-derived progenitor cells (hIPCs) was used. Here, expression of the bootstrapped microRNAs or insulin itself was forced and the differentiation process followed over the next 4 days (Figure 8).

When the expression of insulin gene in hIPCs was forced, the levels of microRNAs identified in the bootstrap signature (above) did not change (Figure 9A - forced Insulin). Interestingly, when the expression of selected microRNAs identified in the bootstrap analysis was forced (Figure 9B - forced microRNAs) in hIPCs, then the level of insulin gene expression (transcript abundance), increases significantly (p<0.001) in just four days following induction of differentiation. These data show that the signature of bootstrapped microRNAs can induce insulin expression from 2- to 200-fold in islet progenitor cells.

The data presented indicates that differential expression of microRNAs tunes insulin production in tissues. The microRNAs that are identified to be highly associated with insulin gene expression can

i) predict the presence / absence or relative abundance (cycle threshold- / Ct-value) of insulin gene transcripts in cells / tissues;

ii) induce insulin gene expression in islet progenitor/precursor cells, which can be used for cell replacement therapy in diabetes;

iii) be used as a biomarker to determine the death (or function) of insulin-producing cells in individuals with or progressing to diabetes; and iv) identif the tissue of origin, based on the signature and levels of microRNA expression.

In order to estimate the predictive power of all microRNAs, all available samples were grouped into tissue subsets of insulin-expressing versus no insulin expressing cells (Table 4) and carried out multiple univariate comparisons via logistic regression analyses (Table 5) followed by bootstrapping for re-sampling validation. Finally, we derived a set of microRNAs that were represented across all the comparisons carried out in Table 5 and were selected for at least 50% or more of the times in the bootstrap analysis (Table 6). Receiver operator characteristic curves were generated (Figure 10) for each of these microRNAs using the validation set (N=35) and the area under the curve calculated for all these insulin- associated microRNAs (Tables 7 and 8).

Table 4

Table 5

Table 6 miRNA frequency hsa-miR-216b 98.1 hsa-miR-519b-3p 95.4 hsa-miR-520e 95.1 hsa-miR-183 90.3 hsa-miR-520c-3p 89.7 mmu-miR-187 86.8 hsa-miR-147b 82.2 hsa-miR-34b 79.9 hsa-miR-375 76.8 hsa-let-7e# 76.3 hsa-miR-636 76.1 hsa-miR-183# 75 hsa-miR-30d# 74.1

sa-m - - 50.6

Table 7

Bootstrap

AUC

miRNA frequency hsa-miR-216b 98.1 1 hsa-miR-183 90.3 1 hsa-miR-375 76.8 1 hsa-miR-183# 75 1 mmu-miR-129-3p 64.7 1 hsa-miR-184 62.1 1 hsa-miR-7-2# 50.6 1 hsa-miR-433 73.5 0.99 hsa-miR-429 70.1 0.98 hsa-miR-141 53.6 0.969 hsa-miR-512-3p 51.6 0.964 hsa-miR-452 50.8 0.954 hsa-miR-382 55.4 0.949 hsa-miR-329 65.4 0.929 hsa-miR-217 52.8 0.923 dme-miR-7 64.9 0.888 hsa-miR-485-3p 52.1 0.885 hsa-miR-335# 72.6 0.852 hsa-miR-200c 67.5 0.806 hsa-miR-655 59.1 0.801

Table 8 miRNA frequency hsa-miR-216b 98.1

hsa-miR-183 90.3

hsa-miR-375 76.8

hsa-miR-183# 75

mmu-miR-129-3p 64.7

hsa-miR-184 62.1

hsa-miR-7-2# 50.6

hsa-miR-129

hsa-miR-433 73.5

hsa-miR-429 70.1

hsa-miR-141 53.6

hsa-miR-512-3p 51.6

hsa-miR-452 50.8

hsa-miR-382 55.4

hsa-miR-147b 82.2

hsa-miR-329 65.4

hsa-miR-217 52.8

hsa-miR-98 67.3

dme-miR-7 64.9

hsa-miR-485-3p 52.1

hsa-miR-335# 72.6

hsa-miR-200c 67.5

hsa-miR-655 59.1

hsa-let-7e# 76.3

hsa-miR-485-5p 64.4

hsa-miR-30d# 74.1

hsa-miR-424# 67.1

hsa-miR-520c-3p 89.7

Table 9

S

Example Four: determination of a microRNA signature associated with, predictive of and necessary for insulin transcription

4.1 Overview

Expression of the (pro-)insulin gene in pancreatic islet β-cells is tightly regulated. The the microRNAs that specifically associate with/regulate human (pro-)insulin gene transcription are not yet identified. This study presents an analysis of microRNAs in 507 human tissue samples, identifying microRNAs associated with insulin expression. Furthermore, using two validation sets (N=91 and N=100), it is demonstrate that the levels of insulin-associated microRNAs can predict the abundance of human insulin transcript. Finally, it was shown that forced expression of the insulin gene in human islet-derived progenitor cells (hIPCs) does not influence microRNA expression, but forced expression of candidate microRNAs in hIPCs, increased insulin transcript abundance. microRNAs associated with, predictive of and promoting human insulin gene transcription, were identified.

Precursor and mature microRNA sequences used in these studies are widely published and can be accessed on numerous websites (e.g. miRBase- http://www.mirbase.org/ ; NCBI https://www.ncbi.nlm.nih.gov/ ; and the like).

4.2 Expression of pancreatic (pro-) hormones, transcription factors and microRNAs in human tissues

This study confirmed that human pancreatic islets (Figure 11 A) as well as human gallbladder epithelial cells and brain neurospheres (Figures 11B and 11C), contain islet hormone-positive cells. The gallbladder and the brain tissues demonstrated hormone co- expressing cells (Figures 11B and 11C). The abundance of islet (pro-) hormone gene transcripts (insulin/Ins, glucagon/Gcg and somatostatin/Sst) and pancreatic transcription factors (MafA, Ngn3, Hesl and Pdxl) was assessed in islet, gallbladder and brain cells (Figures 11D-11F and Figure 12A) as well as in insulin negative "solid" (excluding blood) tissues; endothelial cells (Figure 11 G), muscle, spleen, liver and skin (Figure 12B). Realtime qPCR data are presented as the normalized cycle-threshold (Ct) value as measured by TaqMan-based real-time quantitative (q)PCR (Figures 11D - 11G and Figure 12A). As expected, the level of pro-insulin gene expression in the pancreatic islets (mean Ct-value+SD; 16.5 + 1.8) was significantly higher than in the gallbladder (mean Ct-value+SD; 34.1 + 3.4, P<0.0001 vs islet), which was significantly higher than the insulin abundance in the brain (mean Ct-value+SD; 37.8 + 1.6, PO.0001 vs islet and vs gallbladder). This difference of 17.6 Ct-values (between islets and gallbladders) represents a 198,668-fold higher pro-insulin transcript expression in islets, while the difference of 3.7 Ct-values between gallbladders and brains represents 13-fold higher insulin transcript abundance in gallbladder cells. Significant differences in the absolute copy number of pro-insulin gene transcripts were confirmed using digital droplet (dd)PCR platform (Figure 11H). Pro-insulin transcripts were not detectable in samples from the insulin-negative tissues (Figures 11G and 11H and Figure 12B). Thus, the discovery set of 507 human tissues provides a unique resource to assess microRNAs in naturally occurring insulin-producing cells with high (islets), intermediate (gallbladder), low (brain) or undetectable (blood, spleen, muscle, endothelium, liver, skin) levels of insulin gene expression. Pancreatic transcription factors were detectable in most of the insulin-expressing tissues (Figures 11D-11F). Transcripts of Hesl, the negative regulator of the pro-endocrine transcription factor Ngn3, were detected in endothelial cells (Figure 11G) and in other "insulin-negative" tissues (Figure 12B).

Total RNA was then isolated from the biobank of 698 human tissues (Figure 111) and 754 microRNAs and eight different mRNAs were measured (Ins, Gcg, Sst, Pdxl, MafA, Ngn3, Hesl, 18s rRNA) in these tissues, as part of this study. Ninety one of the 698 biobank samples that did not have either sufficient (>60%) microRNA content, desired (>^g total RNA) amount/concentration or acceptable (Ct <10) 18s rRNA were retained as a categorical validation set ("validation set 1 "), while those that met the desired high quality and required quantity were saved as the "validation set 2". All but 91 of the 698 tissue samples surpassed the desired high quality and quantity (Figure 23A), necessary for this study.

Unsupervised hierarchical clustering of microRNA expression data was then carries out from the discovery set (Figure 11 J), so as to identify groups of samples with different microRNA expression profiles. The majority (97%; N=110 of 114) of the human islets clustered together (Figure 11 J), suggesting the presence of a unique profile that separated these high insulin-expressing tissues from other tissues in the set. As expected, pancreatic tissues also cluster with the islets, supporting the existence of a tissue-specific expression profile. All blood tissues formed a single large cluster (Figure 11J), which was very different from all solid tissues. The brain and the gallbladder samples formed two sub-matrices, with the brain samples clustering closer to the islets (Figure 11J). The mRNA expression for seven different genes (Ins, Gcg, Sst, Pdxl, MafA, Ngn3, Hesl; normalized to 18s rRNA) was then compared to assess the similarities and differences between these tissues. Unsupervised hierarchical cluster analysis indicated that insulin-producing tissues clustered in two sub- matrices, with the gallbladder and brain samples clustering close to the insulin-producing pancreatic groups (Figure 23B).

4.3 Tissue-specific microRNA expression profile

To identify dysregulated microRNAs in the discovery set, we compared expression profiles of insulin-producing (N=114 islet, 18 pancreas, 61 gallbladders, and 14 brain samples) and insulin non-producing (insulin-negative tissue) samples (N=300). Relative to the insulin-negative tissues, islets expressed 438 different microRNAs at significantly higher levels (Figure 13A, p<0.05), whilst just seven miRNAs were present at higher abundance in the insulin-negative tissues (Figure 13A).

Similarly, a higher number of microRNAs were expressed in other insulin-producing tissues when all insulin-negative tissues were compared with either gallbladder (Figure 13B) 369 microRNAs vs 6 microRNAs, p<0.05) or brain (Figure 13C; 364 microRNAs vs 7 microRNAs, p<0.05). These data indicate that a larger number of microRNAs (listed in Table 10) are associated with higher levels of insulin gene expression. It was observed that up to seven microRNAs were expressed at higher abundance in insulin-negative tissues as compared to any of the three insulin-producing tissues (Figures 13A-13C, Table 10). Amongst these seven microRNAs, two microRNAs (miR-326 and miR-34a) were common across all comparisons (Figures 13A-13C, Table 10).

Table 10

Difference

[Negative

tissues- Non- miR p value -moGio p

diabetic

islets]

000542_hsa-mi R-326_A -8.271138825 1.80699E-33 32.74304379

001606_hsa-mi R-661_B -4.737038902 7.48533E-10 9.125789117

002316 hsa-miR-34a# B -2.111562357 4.44122E-10 9.352497709

02658_HSA-MIR-338-5P_ -1.751244279 0.00310363 2.508130124

002098_hsa-miR-223#_B -1.500070175 0.011838419 1.926706303

000514_hsa-mi R-211_A -1.054365942 4.60893E-07 6.336399996

002173 hsa-miR-15b# B -1.026794813 0.004064006 2.391045622 Difference

[Negative

miR tissues - p value -l*LOG10 p

Gallbladder]

000542_h sa- m i - 326_A -10.00191994 1.18795E-29 28.92520048

001597_hsa-miR-645_B -4.134050897 2.31131E-08 7.636142221

002316_hsa-miR-34a#_B -3.472077812 9.65914E-13 12.01506147

002088_h sa- m i R- 636_A -1.712629917 0.002497575 2.602481441

002173_hsa-mi R-15b#_B -1.335557971 3.2033E-06 5.494402719

002783_HSA-MI R-548J_B -1.099777088 2.60362E-05 4.584421785

Table 10 lists all microRNAs that show significantly higher expression in the insulin- negative tissues compared to each of the insulin-producing tissues as see in the volcano plots presented in Figure 3A-C. The cycle difference and P values for each of the microRNA are provided in this table. Please note: Solid tissues refers to tissues other than blood while negative solid tissues refers to solid tissues other than islets, pancreas, gallbladder and the brain. When expression of microRNAs in the islets, gallbladders, and brains was compared only with those in the insulin-negative solid tissues (Figures 14A-14C), several microRNAs were again detected at higher levels in insulin-producing tissues). The levels of insulin expression within each insulin-producing tissue were then compared. For examples, within the islets, identified "good" islets were identified as those with an insulin Ct-value<16.8 and compared the microRNA expression of these "good" islets with those of islets containing lower levels (Ct-value>16.8) of insulin (Figure 13D). Similarly, a few other microRNAs were higher in abundance in the insulin -producing gallbladders (Ct-value < 36.2 Vs Ct-value >36.2) and insulin-producing brains (Ct-value < 39 vs Ct-value >39; Figure 13E and 13F). Taken together, these data suggest that dozens to hundreds of microRNAs are differentially expressed within tissues that produce insulin. Most importantly, all of these microRNAs were expressed at 2- to a million-fold higher abundance (Ct value difference of 1 to 20 cycles; Figure 2A-F) and at significantly low P-value (P<0.05; dashed line in Figures 13A-F to P<2.8E-262 as in Figure 13A), relative to the insulin-negative tissues.

It was then decided to focus on microRNAs that correlate with the expression of genes, which define endocrine pancreatic lineage. The expression of islet (pro-) hormones and the key pancreatic transcription factors - Pdxl , MafA, Ngn3 as well as Ngn3's negative regulator Hesl was assessed and correlations with each of the 754 microRNAs assessed in these samples were determined. Figures 13G-13I present circos plots of the microRNA expression data for islets, gallbladders, and brains respectively. In each of these panels, the normalized expression of the seven different islet (pro-) hormones and transcription factors is indicated by the colored boxes on the rim of the circos plot, while the gray color on the rim indicates each of the 754 microRNAs measured. The outermost segment of the circos presents the normalized Ct-values, the adjacent inner segment their individual Z-scores and the innermost segment presents their Z-score relative to insulin-negative tissues. The lines in the center link the genes (Ins, Gcg, Sst, Pdxl, Ngn3, MafA, Hesl) with the microRNAs with which they correlate. A significantly higher number of correlations between pancreatic mRNAs and all the microRNAs were observed in the brain as compared to the gallbladder or the islets (Figures 13G-13I). Although insightful, all of these analyses did not lead to the identification of a small set of insulin-associated microRNAs; several dozens to hundreds of microRNAs were seen to be expressed at very high levels (1000- to a million-fold) and at a very low p- value (p<0.05 to p<2.8E-262; Figures 13A-13F). It was therefore decided to use penalization algorithms that perform variable selection and offer an improved predictive performance by balancing the fit to the data and the stability of the estimates.

Identifying microRNAs that are highly associated with (pro-) insulin gene expression Penalized regression (Goeman, J. J. LI penalized estimation in the Cox proportional hazards model. Biom J 52, 70-84, doi: 10.1002/bimj.200900028 (2010)) was used in order to derive a microRNA signature that is associated with insulin expression. Model selection was performed using the LASSO (Least Absolute Shrinkage and Selection Operator) method. Penalty applied to the regression coefficients allows for improving the predictive power and interpretability of regression models by selecting only a subset of all the available independent variables rather than using all of them. Penalized regression analysis was carried out using a linear (actual Ct-value of pro-insulin gene and of microRNA transcripts; Figure 15A) or logistic (dependent variable defined as high level (1 ) vs. low level/none (0) of insulin expression; Figure 15B, Figure 16A) regression analysis workflow. Here, we used human islets (insulin Ct-value <16.8) as the high level (1) group and all other solid tissues (insulin Ct-value >39) as the low level (0) group. Validation of the model was carried out using bootstrapping to confirm the signature of microRNAs that are highly associated with insulin expression. Validation of penalized logistic regression included resampling 1000 times (Figure 15C). Here, 20% of the samples are eliminated at random, whilst a subset of 20% samples from the remaining dataset were duplicated to make a complete set for each resampling procedure. After 1000 rounds of resampling, a frequency table representing the number of times a specific microRNA was found to be highly associated with insulin expression was derived (Figure 15D). The microRNA signature identified through a penalized logistic regression analysis (Figure 15B) was represented with >43% frequency in the bootstrap analysis. It was found that the microRNA signature obtained after 1000 bootstraps was indifferent than the signature obtained after 10,000 bootstraps (data not shown). Therefore all of the resampling validations were carried out using 1000 bootstraps. Similar analyses were carried out in a tissue-specific manner so as to identify microRNAs associated with insulin expression separately in islets, gallbladders or brains (Figures 16A and 16B). Overall, these studies led to the identification of tissue-specific and overlapping sets of microRNAs that are highly associated with insulin expression (Table 11, Figure 16C). Indeed, almost all of these candidate microRNAs were significantly different in their abundance between insulin-producing (islets) and the insulin non-producing (insulin-negative) tissues (Figure 17). Table 11 (A)

Type the Ct-value for each microRNA (only values between 1 and 39)

ALL values are required for the calculation of predicted insulin Ct Only BLUE values are required for the prediction

of the probability of insulin production

Probability that the cell will

99.87% produce insulin: S3

Predicted insulin Ct-value: 13.22

Table 11 (B)

Type the Ct-value for each microRNA (only values between 1 and 39)

of the probability of insulin production

Probability that the cell will

produce insulin:

Predicted insulin Ct-value: 16.82 ill] Table 11 (C)

Type the Ct-value for each microRNA

(only values between 1 and 39)

ALL values are required for the calculation of predicted insulin Ct

Only BLUE values are required for the prediction

of the probability of insulin production

Probability that the cell will

produce insulin:

Predicted insulin Ct-value:

Table 12 shows exemplary precursor and mature microRNA sequences of 19 microRNAs identified in these studies to be associated with insulin production and relevant to insulin gene expression. Table 12

* precursor and mature microRNA sequences of 19 miRNAs identified to be associated with insulin production and relevant to insulin gene experession.

Insulin-associated microRNAs predict and promote insulin-sene expression The coefficients derived from the penalized regression analysis (Figure 15) were used to obtain the odds ratios that constitute the predictive formula to determine presence of insulin gene expression (penalized logistic regression), or predict the insulin Ct-value (penalized linear regression). It was first tested if the microRNA signature identified from penalized linear regression analysis (Figure 15A) could classify a set of 91 different tissues that were originally eliminated from discovery set as they did not meet the desired quality/quantity criteria; very low RNA concentration or a higher 18s rRNA Ct-value. The microRNA expression levels were assessed using real-time PCR and provided to the statistical team for calculation of the (predicted) insulin Ct-values (Table 11) in all of these 91 samples from validation set 1. Since we did not have the actual insulin Ct-values for any of these 91 (validation set 1) tissue samples, we classified these based on their known tissue of origin (Figure 18A). Islet samples (N=41) within the validation set 1 showed some of the lowest predicted insulin Ct-values (highest expression). Similarly, splenocytes were predicted to contain the highest Ct-values (very low/no insulin transcripts; Figure 168A). Indeed, when (pro-)insulin gene transcripts were profiled from samples within the discovery set, actual Ct- values (Figure 18B) for these tissue types (islets, pancreas, gallbladder, splenocyte, endothelium, muscle) were comparable to the predicted Ct-values (Figure 18A) for the 91 samples in validation set 1. Receiver operating characteristic (ROC) curve analysis for the microRNA signature (Figure 18C) indicated that this microRNA signature offered the highest positive predictive value of 100%, a high negative predictive value of 93.9% with an overall accuracy of 96.7% in classification of this validation sample set to their tissue of origin. The potential of this microRNA signature to predict insulin expression (or lack of expression) was further examined in a separate validation set of 100 high quality tissue samples ("validation set 2"). Two wet lab biologists independently carried out the measurements of microRNAs and (pro-) insulin mRNA on randomized sets of these de- identified tissue samples. This de-identified sample dataset was provided for biostatistical prediction of insulin Ct-values based on the microRNA Ct-values. The output obtained was placed into a trackable database before the sample identifiers were revealed. When assessed individually, the microRNAs demonstrated AUCs ranging from 0.5 to 1.0 in this validation set (Figure 19). Intriguingly, eight microRNAs from the penalized logistic regression signature (Figure 16C) collectively offered 100% predictive power to identify the insulin producing (positive) and insulin non-producing (negative) tissues (Figure 18D) within samples from the validation set 2. Furthermore, using the coefficients from the penalized linear regression analysis workflow, we could predict the insulin Ct-value (from the measured microRNA Ct-values) (Figure 18C, Table 11). A very high correlation (Pearson R=0.95, P<0.0001) was observed between the predicted and actual (measured) insulin Ct- values in validation set 2 (Figure 18C). The numerical estimation of insulin Ct-values (Figure 18C) is in agreement with the qualitative assessment of sample categories and quantitative/wet-lab measurement of insulin Ct-values using the penalized linear regression workflow. These data demonstrate that the microRNA signature was not only associated with (pro-) insulin expression but also predictive of (pro-) insulin transcript abundance in these de-identified sets of human tissues.

Since associations do not prove causality, it was further probed if microRNAs identified through the bootstrap analyses can promote insulin gene expression. In order to address this question, we used a set of human islet-derived progenitor cells (hIPCs). forced The expression of the insulin gene in five biological replicates of hIPCs and the expression of candidate bootstrapped microRNAs was forced in a parallel set of the same five biological replicates of hIPCs (Figure 20A). Although overexpression of the insulin gene led to a 100- to -50,000-fold increase in insulin mRNA in these cells as compared to the empty vector transduced cells (Figure 20B), no significant changes were seen in the level of expression of the bootstrapped microRNAs (Figure 20C). On the other hand, forced expression of each of the three candidate microRNAs from the bootstrapped signature, or a combination of these three together, induced insulin expression by 2- to 64-fold in just three days from transient transfection of these microRNAs (Figure 20D), with modest increase in glucagon and somatostatin gene transcripts (Figures 20E and 20F). Hesl, the negative regulator of Ngn3, did not change significantly post-transfection (Figure 20G).

Discussion:

The goal of this study was to identify associations between microRNA expression and insulin gene transcription using a large biobank of 698 human tissues. The strategy also involved analysis of tissues that are known to naturally produce insulin, albeit in lower amounts. Blood, as well as other insulin-negative solid tissues, were included to compare differences between the tissues, whilst within-tissue comparisons of low vs high insulin gene expression samples of microRNA profiles allowed us to eliminate tissue-specific effects. These experiments led to the identification of several microRNAs that showed strong correlation with the expression of the seven (pro-)endocrine pancreatic genes that we assessed. Within the insulin-producing tissues, brain pro-endocrine gene expression correlated with the largest number of microRNAs (Figure 131), while such correlations became limited in the gallbladder (Figure 13H) and the islets (Figure 13G). Although these comparisons help to identify candidate microRNAs that correlate with (pro-) endocrine gene expression, such comparisons do not provide us with a set of insulin-associated microRNAs. Penalization strategies allowed us to model the expression profiles within this large group of human tissues so as to obtain a microRNA signature that is the most highly associated with insulin gene expression (Table 11). Validation of such a signature using bootstrapping offered mathematical confirmation of the association of selected microRNAs with insulin (transcript) abundance.

The results of the analysis were independently validated in two sets of differing quality; validation set 1 included samples (N=91) that did not make a designated QC cut-off values for the desired quality/quantity, while validation set 2 (N=100) included samples that met or exceeded the QC indicators. Intriguingly, the microRNA signature was able to identify the presence or absence of insulin gene transcripts irrespective of the sample quality, and in addition, was also able to mathematically estimate the Ct-value of insulin transcripts at a level that was very close to the Ct-value observed through wet lab evaluation (Figure 18C). Finally, using forced overexpression of microRNAs (vs insulin), we demonstrated that some of the top candidate microRNAs could increase insulin gene transcript abundance in an in vitro differentiation model of human islet-derived progenitor cells (Figure 20). Taken together, these studies identify a unique signature of microRNAs that are not only associated with insulin gene expression but also predictive of and directly promoting insulin gene transcription.

It is well-recognized that progressive loss of pancreatic islet β-cell function is a major cause leading to the decline in glucose tolerance during the development of type 2 diabetes44,45. All of the (pro-) endocrine hormones and transcription factors measured were mostly detectable in islets and pancreas of individuals with Type 2 diabetes (Figures 21A and 21B). It was observed howeverthat several microRNAs are expressed at significantly higher levels in non-diabetic human pancreas/islets as compared to T2D human pancreas/islets (Figures 21A-21C) The levels of the signature microRNAs show a higher correlation between non-diabetic and T2D islets (Figure 22A) and between non-diabetic and T2D pancreas (Figure 22B), than between non-diabetic islets and solid insulin-negative tissues such as splenocytes, as expected (Figure 22C). Interestingly, several of these candidate microRNAs were expressed at lower levels in T2D islets relative to non-diabetic (ND) islets and associated with lower levels of insulin in these samples (Figures 21 E-L). These data demonstrate a potential role of the microRNAs in maintenance of insulin gene expression in Type 2 diabetes.

These microRNAs are involved in core processes associated with T2DM, such as carbohydrate and lipid metabolism, insulin signaling pathway and the adipocytokine signaling pathway. In stem cell biology, the study offers an algorithm that enabling the estimation of cell differentiation to insulin-production based on the level of microRNA expression. This calculator can be used as a guide, optionally along with other commonly used transcription factor expression analyses, to assess the differentiation of stem cells towards an insulin-producing lineage even when insulin gene transcript cannot be detected due to sample quality/RNA quantity issues.

Claims

1. A method for predicting a level of insulin production in cells of a subject, the method comprising:

wherein:

elevated expression levels of the microRNAs in the sample of cells compared to expression levels of the microRNAs in control cells that do not produce insulin is indicative of insulin production in the sample of cells, and

2. The method of claim 1, comprising or consisting of determining expression levels of seven or more of the microRNAs, eight or more of the microRNAs, nine or more of the microRNAs, or ten of the microRNAs, in the sample of the cells obtained from the subject.

3. The method of claim 1, comprising or consisting of determining expression levels of:

in the sample of the cells obtained from the subject.

4. The method of any one of claims 1 to 3, further comprising or consisting of determining expression levels of any one or more microRNAs selected from the group consisting of: miR216a, miR144#, miR124a, miR-935, miR133b, miR206, miR200b#, miR625# and miR1271 ;

in the sample of the cells obtained from the subject.

5. The method of any one of claims 1 to 4, comprising or consisting of determining expression levels of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, miR216a, miR144#, and miR124a;

in the sample of the cells obtained from the subject.

6. The method of any one of claims 1 to 5, wherein said elevated expression levels of the microRNAs in the sample of cells is indicative of production of insulin gene transcripts in the sample of cells, and

7. The method of any one of claims 1 to 6, wherein the control cells that do not produce insulin are from the subject.

8. The method of any one of claims 1 to 7, wherein the sample of cells comprises any one or more of: pancreatic cells, brain cells, gall bladder cells.

9. The method of any one of claims 1 to 8, wherein the sample of cells comprises beta- islet cells.

10. The method of any one of claims 1 to 9, wherein said reduced or absent insulin production is diagnostic or prognostic of a disease or condition associated with or arising from a loss of insulin-producing cells in the subject.

1 1. The method of claim 10, wherein the condition or disease is selected from the group consisting of: diabetes, pancreatitis, insulinoma, and pancreatic cancer.

12. The method of any one of claims 1 to 11, wherein the subject has or is progressing toward a disease or condition associated with or arising from a loss of insulin-producing cells, and the expression levels of one or more microRNA/s is a marker of death and/or loss of insulin-producing function in the cells of the subject.

13. The method of any one of claims 1 to 12, further comprising an initial step of obtaining the sample of cells from the subject.

14. A method for inducing insulin production in pancreatic lineage cells, the method comprising treating the pancreatic lineage cells six one or more microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR- 183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof.

15. The method of claim 14, comprising or consisting of treating the pancreatic lineage cells with seven or more of the microRNAs, eight or more of the microRNAs, nine or more of the microRNAs, or ten of the microRNAs, in the sample of the cells obtained from the subject.

16. The method of claim 14, comprising or consisting of treating the pancreatic lineage cells with:

17. The method of any one of claims 14 to 16, further comprising or consisting of treating the pancreatic lineage cells with any one or more microRNAs selected from the group consisting of: miR216a, miR144#, miR124a, miR-935, miR133b, miR206, miR200b#, miR625# and miR1271.

18. The method of any one of claims 14 to 17, comprising or consisting of treating the pancreatic lineage cells with: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa- miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, miR216a, miR144#, and miR124a.

19. The method of any one of claims 14 to 18, wherein said treating comprises overexpressing the one or more microRNA/s in the pancreatic lineage cells.

20. The method of any one of claims 14 to 19, wherein the pancreatic lineage cells comprise any one or more of: pancreatic islet precursor cells, endocrine progenitor cells, pancreatic islet progenitor cells, pancreatic islet stem cells, induced pluripotent pancreatic islet cells.

21. The method of any one of claims 14 to 20, wherein the pancreatic lineage cells are beta- islet precursor cells, beta-islet cell pro-precursors, "beta-like" cells, "islet-like" cells.

22. The method of any one of claims 14 to 21, further comprising differentiating the pancreatic lineage cells into mature pancreatic cells.

23. The method of claim 22, wherein the mature pancreatic cells are beta-islet cells.

24. The method of any one of claims 14 to 23, wherein the treating is conducted in vitro or ex vivo.

25. A method for preventing treating a disease or condition associated with or arising from a loss of insulin-producing cells in a subject, the method comprising treating pancreatic lineage cells according to the method of any one of claims 14 to 24, and transplanting the treated cells into a subject.

26. The method of claim 25, wherein the cells transplanted are autologous for the subject.

27. The method of claim 25 or claim 26, wherein the subject is at risk of developing the disease or condition.

28. The method of any one of claims 25 to 27, wherein the condition or disease is selected from the group consisting of: diabetes, pancreatitis, insulinoma, and pancreatic cancer.

29. The method of claim 1 1 or claim 28, wherein the disease is Type 1 diabetes or insulin- requiring Type 2 diabetes.

30. A method for identifying a tissue of origin of a sample of cells obtained from a subject, the method comprising:

substantially different expression levels of the microRNAs in the sample of cells compared to the expression level/s of the microRNAs generated from the control cells are indicative that the sample of cells are not of the same type as the control cells.

31. The method of claim 30, comprising or consisting of determining expression levels of seven or more of the microRNAs, eight or more of the microRNAs, nine or more of the microRNAs, or ten of the microRNAs, in the sample of the cells obtained from the subject.

32. The method of claim 31, comprising or consisting of determining expression levels of:

(i) six microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, and hsa-miR-217; or (ii) seven microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa- miR-375, hsa-miR-183#, hsa-miR-217, and hsa-miR-429; or

in the sample of the cells obtained from the subject.

33. The method of any one of claims 30 to 32, further comprising or consisting of determining expression levels of any one or more microRNAs selected from the group consisting of: miR216a, miR144#, miR124a, miR-935, miR133b, miR206, miR200b#, miR625# and miR1271 ;

in the sample of the cells obtained from the subject.

34. The method of any one of claims 30 to 33, comprising or consisting of determining expression levels of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, miR216a, miR144#, and miR124a;

in the sample of the cells obtained from the subject.

35. The method of any one of claims 30 to 34, further comprising an initial step of obtaining the sample of cells from the subject.

36. The method of any one of claims 30 to 35, wherein the sample of cells is from the pancreas, brain, or gall bladder of the subject.

37. The method of any one of claims 4, 17 or 33, wherein the six or more microRNAs comprise or consist of any: 11, 12, 13, 14, 15, 16, 17, 18, or 19 of the microRNAs.

38. A kit comprising primers, probes and/or other binding agents for use in detecting expression of at least six microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa- miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof, in a sample of cells.

39. The kit of claim 38, comprising or consisting of primers, probes and/or other binding agents for use in detecting expression of seven or more of the microRNAs, eight or more of the microRNAs, nine or more of the microRNAs, or ten of the microRNAs, in the sample of the cells obtained from the subject.

40. The kit of claim 39, comprising or consisting of primers, probes and/or other binding agents for use in detecting expression of:

41 . The kit of any one of claims 38 to 40, further comprising or consisting of primers, probes and/or other binding agents for use in detecting expression of any one or more microRNAs selected from the group consisting of: miR216a, miR144#, miR124a, miR-935, miR133b, miR206, miR200b#, miR625# and miR1271.

42. The kit of any one of claims 38 to 41 , comprising or consisting of primers, probes and/or other binding agents for use in detecting expression of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu- miR-129-3p, hsa-miR-433, miR216a, miR144#, and miR124a.

43. A microRNA signature comprising at least six microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa- miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof.

44. The microRNA signature of claim 43, comprising or consisting of seven or more of the microRNAs, eight or more of the microRNAs, nine or more of the microRNAs, or ten of the microRNAs, in the sample of the cells obtained from the subject.

45. The microRNA signature of claim 44, comprising or consisting of:

46. The microRNA signature of any one of claims 43 to 45, further comprising or consisting of any one or more microRNAs selected from the group consisting of: miR216a, miR144#, miR124a, miR-935, miR133b, miR206, miR200b#, miR625# and miR1271.

47. The microRNA signature of any one of claims 43 to 46, comprising or consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa- miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, miR216a, miR144#, and miR124a.

48. Use of the kit of any one of claims 38 to 42, or the microRNA signature of any one of claims 43 to 47, for predicting, diagnosing, and/or prognosing a disease or condition associated with or arising from a loss of insulin-producing cells in a subject, in a subject, wherein the disease or condition is selected from the group consisting of: diabetes, pancreatitis, insulinoma, and pancreatic cancer.

49. The method of any one of claims 11, 28 or 29, or the use of claim 48, wherein the disease is Type 1 diabetes.

50. Use of six or more agents for determining the expression levels of one or more microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof, for the preparation of a medicament for predicting a level of insulin production in cells of a subject.

51. The use of claim 50, wherein elevated expression levels of the microRNA/s in the sample of cells compared to expression level/s of the microRNAs in control cells that do not produce insulin is indicative of insulin production in the sample of cells, and

52. Use of six or more microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa- miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof, for the preparation of a medicament for inducing insulin production in pancreatic lineage cells.

53. Use of six or more agents capable of inducing overexpression of one or more microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof, in cells, for the preparation of a medicament for inducing insulin production in pancreatic lineage cells.

54. The use of any one of claims 50 to 53, wherein the cells comprise any one or more of: pancreatic cells, brain cells, gall bladder cells, beta-islet cells, pancreatic lineage cells, pancreatic islet precursor cells, endocrine progenitor cells, pancreatic islet progenitor cells, pancreatic islet stem cells, induced pluripotent pancreatic islet cells, and/or beta-islet precursor cells.

55. Use of six or more microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa- miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof, for the preparation of a medicament for preventing treating a disease or condition associated with or arising from a loss of insulin-producing cells in a subject.

56. Use of six or more agents capable of inducing overexpression of microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa- miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof, in cells, for the preparation of a medicament for preventing treating a disease or condition associated with or arising from a loss of insulin-producing cells in a subject.

57. The use of claim 55 or claim 56, wherein the condition or disease is selected from the group consisting of: diabetes, pancreatitis, insulinoma, pancreatic cancer, Type 1 diabetes and insulin-requiring Type 2 diabetes.

58. Use of six or more agents for determining expression levels of six or more microRNA/s selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR- 433, and any combination thereof, in cells, for the preparation of a medicament for identifying a tissue of origin of a sample of cells obtained from a subject.

59. The use of claim 58, the sample of cells is from: pancreas, brain, or gall bladder.

60. The use of any one of claims 48 to 59, comprising or consisting of the use of agents for detecting expression of seven or more of the microRNAs, eight or more of the microRNAs, nine or more of the microRNAs, or ten of the microRNAs.

61. The use of claim 58 or claim 59, comprising or consisting of the use of: (i) six or more agents to detect the microRNAs which are: hsa-miR-183, hsa-miR- 216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, and hsa-miR-217; or

(iv) nine or more agents to detect the microRNAs which are: hsa-miR-183, hsa-miR- 216b, dme-miR-7. hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, and mmu-miR-129-3p; or

(v) ten or more agents to detect the microRNAs which are: hsa-miR-183, hsa-miR- 216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, and hsa-miR-433.

62. The use of any one of claims 58 to 61, further comprising or consisting of primers, probes and/or other binding agents for use in detecting expression of any six or more microRNAs selected from the group consisting of: miR216a, miR144#, miR124a, miR-935, miR133b, miR206, miR200b#, miR625# and miR1271.

63. The use of any one of claims 58 to 62, comprising or consisting of primers, probes and/or other binding agents for use in detecting expression of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu- miR-129-3p, hsa-miR-433, miR216a, miR144#, and miR124a.