Dario Veneziano

Juvenile myelomonocytic leukemia (JMML) is an aggressive leukemia of early childhood characterized by aberrant proliferation of myelomonocytic cells and hypersensitivity to GM-CSF stimulation. Mutually exclusive mutations in the RAS/ ERK pathway genes such as PTPN11, NRAS, KRAS, CBL, or NF1 are found in ~90% of the cases. These mutations give rise to disease at least in part by activating STAT5 through phosphorylation and by promoting cell growth. MicroRNAs (miRs) are small non-coding RNAs that regulate gene expression, which are often deregulated in leukemia. However, little is known about their role in JMML. Here, we report distinctive miR expression signatures associated with the molecular subgroups of JMML. Among the downregulated miRs in JMML, miR-150-5p was found to target STAT5b, a gene which is often over-activated in JMML, and contributes to the characteristic aberrant signaling of this disorder. Moreover, loss of miR-150-5p and upregulation of STAT5b expression were also identified in a murine model of JMML. Ectopic overexpression of miR-150-5p in mononuclear cells from three JMML patients significantly decreased cell proliferation. Altogether, our data indicate that miR expression is deregulated in JMML and may play a role in the pathogenesis of this disorder by modulating key effectors of cytokine receptor pathways.

RNA editing is a finely tuned, dynamic mechanism for post-transcriptional gene regulation that has been thoroughly investigated in the last decade. Nevertheless , RNA editing in non-coding RNA, such as microRNA (miRNA), have caused great debate and have called for deeper investigation. Until recently, in fact, inadequate methodologies and experimental contexts have been unable to provide detailed insights for further elucidation of RNA editing affecting miRNAs, especially in cancer. In this work, we leverage on recent innovative bioinformatics approaches applied to a more informative experimental context in order to analyze the variations in miRNA seed region editing activity during a time course of a hypoxia-exposed breast cancer cell line. By investigating its behavior in a dynamic context, we found that miRNA editing events in the seed region are not depended on miRNA expression, unprecedentedly providing insights on the targetome shifts derived from these modifications. This reveals that miRNA editing acts under the influence of environmentally induced stimuli. Our results show a miRNA editing activity trend aligning with cellular pathways closely associated to hypoxia, such as the VEGF and PI3K/Akt pathways, providing important novel insights on this poorly elucidated phenomenon.

Chronic lymphocytic leukemia (CLL) is the most common human leukemia, and transgenic mouse studies indicate that activation of the T-cell leukemia/lymphoma 1 (TCL1) oncogene is a contributing event in the pathogenesis of the aggressive form of this disease. While studying the regulation of TCL1 expression, we identified the microRNA cluster miR-4521/3676 and discovered that these two microRNAs are associated with tRNA sequences and that this region can produce two small RNAs, members of a recently identified class of small noncoding RNAs, tRNA-derived small RNAs (tsRNAs). We further proved that miR-3676 and miR-4521 are tsRNAs using Northern blot analysis. We found that, like ts-3676, ts-4521 is down-regulated and mutated in CLL. Analysis of lung cancer samples revealed that both ts-3676 and ts-4521 are down-regulated and mutated in patient tumor samples. Because tsRNAs are similar in nature to piRNAs [P-element–induced wimpy testis (Piwi)-interacting small RNAs], we investigated whether ts-3676 and ts-4521 can interact with Piwi proteins and found these two tsRNAs in complexes containing Piwi-like protein 2 (PIWIL2). To determine whether other tsRNAs are involved in cancer, we generated a custom microarray chip containing 120 tsRNAs 16 bp or more in size. Microarray hybridization experiments revealed tsRNA signatures in CLL and lung cancer, indicating that, like microRNAs, tsRNAs may have an oncogenic and/or tumor-suppressor function in he-matopoietic malignancies and solid tumors. Thus, our results show that tsRNAs are dysregulated in human cancer.

RNA editing is a dynamic mechanism for gene regulation attained through the alteration of the sequence of primary RNA transcripts. A-to-I (adenosine-to-inosine) RNA editing, which is catalyzed by members of the adenosine deaminase acting on RNA (ADAR) family of enzymes, is the most common post-transcriptional modification in humans. The ADARs bind double-stranded regions and deaminate adenosine (A) into inosine (I), which in turn is interpreted by the translation and splicing machineries as guanosine (G). In recent years, this modification has been discovered to occur not only in coding RNAs but also in non-coding RNAs (ncRNA), such as microRNAs, small interfering RNAs, transfer RNAs, and long non-coding RNAs. This may have several consequences, such as the creation or disruption of microRNA/mRNA binding sites, and thus affect the biogenesis, stability, and target recognition properties of ncRNAs. The malfunction of the editing machinery is not surprisingly associated with various human diseases, such as neurodegenerative, cardiovascular, and carcinogenic diseases. Despite the enormous efforts made so far, the real biological function of this phenomenon, as well as the features of the ADAR substrate, in particular in non-coding RNAs, has still not been fully understood. In this work, we focus on the current knowledge of RNA editing on ncRNA molecules and provide a few examples of computational approaches to elucidate its biological function.

The majority of the human transcriptome is defined as non-coding RNA (ncRNA), since
only a small fraction of human DNA encodes for proteins, as reported by the ENCODE
project. Several distinct classes of ncRNAs, such as transfer RNA, microRNA, and long
non-coding RNA, have been classified, each with its own three-dimensional folding and
specific function. As ncRNAs are highly abundant in living organisms and have been
discovered to play important roles in many biological processes, there has been an ever
increasing need to investigate the entire ncRNAome in further unbiased detail. Recently,
the advent of next-generation sequencing (NGS) technologies has substantially increased
the throughput of transcriptome studies, allowing an unprecedented investigation of
ncRNAs, as regulatory pathways and novel functions involving ncRNAs are now also
emerging. The huge amount of transcript data produced by NGS has progressively
required the development and implementation of suitable bioinformatics workflows,
complemented by knowledge-based approaches, to identify, classify, and evaluate the
expression of hundreds of ncRNAs in normal and pathological conditions, such as
cancer. In this mini-review, we present and discuss current bioinformatics advances in
the development of such computational approaches to analyze and classify the ncRNA
component of human transcriptome sequence data obtained from NGS technologies.