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20 pages, 3837 KiB  
Article
NF-κB Transcription Factors: Their Distribution, Family Expansion, Structural Conservation, and Evolution in Animals
by Siphesihle Msweli, Suresh B. Pakala and Khajamohiddin Syed
Int. J. Mol. Sci. 2024, 25(18), 9793; https://doi.org/10.3390/ijms25189793 - 10 Sep 2024
Viewed by 375
Abstract
The Nuclear Factor Kappa B (NF-κB) transcription factor family consists of five members: RelA (p65), RelB, c-Rel, p50 (p105/NF-κB1), and p52 (p100/NF-κB2). This family is considered a master regulator of classical biochemical pathways such as inflammation, immunity, cell proliferation, and cell death. The [...] Read more.
The Nuclear Factor Kappa B (NF-κB) transcription factor family consists of five members: RelA (p65), RelB, c-Rel, p50 (p105/NF-κB1), and p52 (p100/NF-κB2). This family is considered a master regulator of classical biochemical pathways such as inflammation, immunity, cell proliferation, and cell death. The proteins in this family have a conserved Rel homology domain (RHD) with the following subdomains: DNA binding domain (RHD-DBD) and dimerization domain (RHD-DD). Despite the importance of the NF-κB family in biology, there is a lack of information with respect to their distribution patterns, evolution, and structural conservation concerning domains and subdomains in animals. This study aims to address this critical gap regarding NF-κB proteins. A comprehensive analysis of NF-κB family proteins revealed their distinct distribution in animals, with differences in protein sizes, conserved domains, and subdomains (RHD-DBD and RHD-DD). For the first time, NF-κB proteins with multiple RHD-DBDs and RHD-DDs have been identified, and in some cases, this is due to subdomain duplication. The presence of RelA/p65 exclusively in vertebrates shows that innate immunity originated in fishes, followed by amphibians, reptiles, aves, and mammals. Phylogenetic analysis showed that NF-κB family proteins grouped according to animal groups, signifying structural conservation after speciation. The evolutionary analysis of RHDs suggests that NF-κB family members p50/p105 and c-Rel may have been the first to emerge in arthropod ancestors, followed by RelB, RelA, and p52/p100. Full article
(This article belongs to the Special Issue Advances in Endoplasmic Reticulum Stress and Apoptosis)
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Figure 1

Figure 1
<p>Schematic diagram illustrating the classification and characteristic domains of NF-κB proteins, along with additional DNA binding and dimerization domains identified in this study.</p>
Full article ">Figure 2
<p>Genome-wide data mining, identification, and classification of animal NF-κB family proteins. (<b>A</b>) Comparative analysis of five NF-κB family members in animals. (<b>B</b>) Heat-map representation of NF-κB family members conservation in animals. The animal groups are depicted vertically, while NF-κB families are represented horizontally. Comparative analysis of c-Rel (<b>C</b>), p50/p105 (<b>D</b>), RelA/p65 (<b>E</b>), and p52/p100 (<b>F</b>) NF-κB family members in different animal groups. Detailed information on the number of species and NF-κB proteins in animals is presented in <a href="#app1-ijms-25-09793" class="html-app">Table S1</a>. The complete NF-κB protein sequences without duplicates identified and annotated in this study are presented in <a href="#app1-ijms-25-09793" class="html-app">Supplementary Dataset S1</a>.</p>
Full article ">Figure 3
<p>Statistical analysis of NF-κB family members protein sizes and the sizes of Rel homology domains (RHDs) and its subdomains, DNA binding domains (RHD-DBDs), and Dimerization domains (RHD-DDs). Statistical analysis was carried out as mentioned in the methods <a href="#sec3dot8-ijms-25-09793" class="html-sec">Section 3.8</a>, and the corresponding <span class="html-italic">p</span>-values comparing the different NF-κB family members are presented in the figure. Detailed information on protein and domain sizes, as well as the results of Welch’s <span class="html-italic">t</span>-test, can be found in <a href="#app1-ijms-25-09793" class="html-app">Tables S2 and S3</a>. The statistical representation uses the following symbols: ns for <span class="html-italic">p</span> &gt; 0.05; * for <span class="html-italic">p</span> ≤ 0.05; **** for <span class="html-italic">p</span> ≤ 0.0001.</p>
Full article ">Figure 4
<p>Evolutionary analysis of Rel proteins (c-Rel, RelA/p65, and RelB). The phylogenetic tree was constructed using 2461 Rel protein sequences (<a href="#app1-ijms-25-09793" class="html-app">Supplementary Dataset S1</a>). A high-resolution phylogenetic tree with individual node information is provided in <a href="#app1-ijms-25-09793" class="html-app">Supplementary Figure S1</a>.</p>
Full article ">Figure 5
<p>Evolutionary analysis of p50/p105 and p52/p100 proteins. The phylogenetic tree was constructed with 1683 protein sequences (<a href="#app1-ijms-25-09793" class="html-app">Supplementary Dataset S1</a>). A high-resolution phylogenetic tree is provided in <a href="#app1-ijms-25-09793" class="html-app">Supplementary Figure S2</a>, where one can see individual node information.</p>
Full article ">Figure 6
<p>Phylogenetic analysis of Rel Homology Domain (RHD) of different NF-κB family members. 4097 NF-κB family members RHDs (<a href="#app1-ijms-25-09793" class="html-app">Supplementary Dataset S1</a>) were used to construct the tree. A high-resolution phylogenetic tree is provided in <a href="#app1-ijms-25-09793" class="html-app">Supplementary Figure S3</a>, where one can see information for each tree node.</p>
Full article ">Figure 7
<p>Analysis of the type and pattern of domains in protein families of NF-κB family members using NCBI Batch Web CD-search [<a href="#B55-ijms-25-09793" class="html-bibr">55</a>] and HMMSCAN [<a href="#B58-ijms-25-09793" class="html-bibr">58</a>]. An asterisk denotes the domain in invertebrate NF-κB family members for families c-Rel and p50/p105. For a detailed analysis of domains, including their names, please refer to <a href="#ijms-25-09793-t004" class="html-table">Table 4</a>.</p>
Full article ">
20 pages, 3009 KiB  
Article
Mathematical Structure of RelB Dynamics in the NF-κB Non-Canonical Pathway
by Toshihito Umegaki, Naoya Hatanaka and Takashi Suzuki
Math. Comput. Appl. 2024, 29(4), 62; https://doi.org/10.3390/mca29040062 - 5 Aug 2024
Viewed by 463
Abstract
This study analyzed the non-canonical NF-κB pathway, which controls functions distinct from those of the canonical pathway. Although oscillations of NF-κB have been observed in the non-canonical pathway, a detailed mechanism explaining the observed behavior remains elusive, owing to [...] Read more.
This study analyzed the non-canonical NF-κB pathway, which controls functions distinct from those of the canonical pathway. Although oscillations of NF-κB have been observed in the non-canonical pathway, a detailed mechanism explaining the observed behavior remains elusive, owing to the different behaviors observed across cell types. This study demonstrated that oscillations cannot be produced by the experimentally observed pathway alone, thereby suggesting the existence of an unknown reaction pathway. Assuming this pathway, it became evident that the oscillatory structure of the non-canonical pathway was caused by stable periodic orbits. In addition, we demonstrated that altering the expression levels of specific proteins reproduced various behaviors. By fitting 14 parameters, excluding those measured in previous studies, this study successfully reproduce nuclear retention (saturation), oscillation, and singular events that had been experimentally confirmed. The analysis also provided a comprehensive understanding of the dynamics of the RelB protein and suggested a potential inhibitory role for the unknown factor. These findings indicate that the unknown factor may be an isoform of IκB, contributing to the regulation of NF-κB signaling. Based on these models, we gained invaluable understanding of biological systems, paving the way for the development of new strategies to manipulate specific biological processes. Full article
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Figure 1

Figure 1
<p>Model diagram and a simulation result of p100KO-M. (<b>A</b>) Model diagram based on experimental data and one assumption. A feedback loop from p50/RelB to I<math display="inline"><semantics> <mi>κ</mi> </semantics></math>B<math display="inline"><semantics> <mi>α</mi> </semantics></math> is required to obtain the self-sustained oscillation of RelB. (<b>B</b>) Simulation result of p100KO-M. The equilibrium points of p100KO-M are unstable, and the self-sustained oscillation of RelB is caused by a stable limit cycle.</p>
Full article ">Figure 2
<p>NC-FM model diagram. (<b>A</b>) Full pathway models of the NF-<math display="inline"><semantics> <mi>κ</mi> </semantics></math>B non-canonical pathway extended from p100KO-M. (<b>B</b>) Time evolutions of <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mi>l</mi> <mi>B</mi> <mi>r</mi> <mi>a</mi> <mi>t</mi> <mi>i</mi> <mi>o</mi> </mrow> </semantics></math> with parameters of (<math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>R</mi> <mi>e</mi> <mi>l</mi> <mi>B</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>I</mi> <mi>κ</mi> <mi>B</mi> <mi>α</mi> </mrow> </semantics></math>) = (<b>left</b>) (4, 2.5), (<b>center</b>) (0.4, 2.5), and (<b>right</b>) (0.4, 25) [<math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>M]. Three events, namely saturation, oscillation, and single events, were reproduced by sensitivity analysis, changing <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>R</mi> <mi>e</mi> <mi>l</mi> <mi>B</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>I</mi> <mi>κ</mi> <mi>B</mi> <mi>α</mi> </mrow> </semantics></math> in NC-FM.</p>
Full article ">Figure 3
<p>Experimental results of time evolutions of RelB nuclear translocation [<a href="#B20-mca-29-00062" class="html-bibr">20</a>], which show three types of dynamics, namely (<b>left</b>) nuclear retention (saturation), (<b>center</b>) oscillation, and (<b>right</b>) single events.</p>
Full article ">Figure 4
<p>Time evolutions of <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mi>l</mi> <mi>B</mi> <mi>r</mi> <mi>a</mi> <mi>t</mi> <mi>i</mi> <mi>o</mi> </mrow> </semantics></math> with values of (<math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>R</mi> <mi>e</mi> <mi>l</mi> <mi>B</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>I</mi> <mi>κ</mi> <mi>B</mi> <mi>α</mi> </mrow> </semantics></math>) = (<b>left</b>) (4, 2.5), (<b>center</b>) (0.4, 2.5), and (<b>right</b>) (0.4, 25) [<math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>M]. The three legends mean that <math display="inline"><semantics> <msub> <mi>e</mi> <mi>r</mi> </msub> </semantics></math> is set to <math display="inline"><semantics> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>1.9</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>7</mn> </mrow> </msup> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mn>3</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>8</mn> </mrow> </msup> </mrow> </semantics></math> [<math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>M/min].</p>
Full article ">Figure 5
<p>Time evolutions of <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mi>l</mi> <mi>B</mi> <mi>r</mi> <mi>a</mi> <mi>t</mi> <mi>i</mi> <mi>o</mi> </mrow> </semantics></math> with parameters of (<math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>R</mi> <mi>e</mi> <mi>l</mi> <mi>B</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>I</mi> <mi>κ</mi> <mi>B</mi> <mi>α</mi> </mrow> </semantics></math>) = (<b>left</b>) (4, 2.5), (<b>center</b>) (0.4, 2.5), and (<b>right</b>) (0.4, 25) [<math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>M]. Legend 0 of the red line represents the case where all parameters are set to the PAV, while legends 1, 3, 4, 6, 10, 12, and 14 indicate cases where one of the parameters (<math display="inline"><semantics> <msub> <mi>s</mi> <mi>q</mi> </msub> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>t</mi> <msub> <mi>r</mi> <mi>a</mi> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>d</mi> <mi>e</mi> <msub> <mi>g</mi> <mn>1</mn> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <msub> <mi>s</mi> <mi>p</mi> </msub> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>t</mi> <msub> <mi>r</mi> <mrow> <mi>r</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <msub> <mi>d</mi> <mrow> <mi>r</mi> <mi>m</mi> </mrow> </msub> </semantics></math>, or <math display="inline"><semantics> <mrow> <mi>d</mi> <mi>e</mi> <msub> <mi>g</mi> <mn>2</mn> </msub> </mrow> </semantics></math>) is set to the EMV.</p>
Full article ">Figure 6
<p>Time evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>X</mi> <mi>i</mi> </msub> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>⋯</mo> <mn>8</mn> </mrow> </semantics></math> of the p100KO model: (<b>upper left</b>) (<math display="inline"><semantics> <msub> <mi>X</mi> <mn>3</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>X</mi> <mn>6</mn> </msub> </semantics></math>), (<b>right-upper</b>) (<math display="inline"><semantics> <msub> <mi>X</mi> <mn>1</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>X</mi> <mn>4</mn> </msub> </semantics></math>), (<b>lower left</b>) (<math display="inline"><semantics> <msub> <mi>X</mi> <mn>2</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>X</mi> <mn>5</mn> </msub> </semantics></math>), and (<b>lower right</b>) (<math display="inline"><semantics> <msub> <mi>X</mi> <mn>2</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>X</mi> <mn>5</mn> </msub> </semantics></math>) [<math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>M].</p>
Full article ">Figure 7
<p>Time evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>X</mi> <mi>i</mi> </msub> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>⋯</mo> <mn>14</mn> </mrow> </semantics></math> of the NC-FM; (<b>left-upper</b>) (<math display="inline"><semantics> <msub> <mi>X</mi> <mn>3</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>X</mi> <mn>6</mn> </msub> </semantics></math>), (<b>right-upper</b>) (<math display="inline"><semantics> <msub> <mi>X</mi> <mn>1</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>X</mi> <mn>4</mn> </msub> </semantics></math>), (<b>left-middle</b>) (<math display="inline"><semantics> <msub> <mi>X</mi> <mn>2</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>X</mi> <mn>5</mn> </msub> </semantics></math>), (<b>right-middle</b>) (<math display="inline"><semantics> <msub> <mi>X</mi> <mn>7</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>X</mi> <mn>14</mn> </msub> </semantics></math>), (<b>left-lower</b>) (<math display="inline"><semantics> <msub> <mi>X</mi> <mn>9</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>X</mi> <mn>11</mn> </msub> </semantics></math>), (<b>right-lower</b>) (<math display="inline"><semantics> <msub> <mi>X</mi> <mn>8</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>X</mi> <mn>10</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>X</mi> <mn>12</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>X</mi> <mn>13</mn> </msub> </semantics></math>) [<math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>M], respectively.</p>
Full article ">Figure A1
<p>Time evolutions of <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mi>l</mi> <mi>B</mi> <mi>r</mi> <mi>a</mi> <mi>t</mi> <mi>i</mi> <mi>o</mi> </mrow> </semantics></math> with parameters of (<math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>R</mi> <mi>e</mi> <mi>l</mi> <mi>B</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>I</mi> <mi>κ</mi> <mi>B</mi> <mi>α</mi> </mrow> </semantics></math>) = (<b>left</b>) (4, 2.5), (<b>center</b>) (0.4, 2.5), and (<b>right</b>) (0.4, 25) [<math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>M]. The legend 0 of the red line in <a href="#mca-29-00062-f0A1" class="html-fig">Figure A1</a> represents the case where all parameters are set to the PAV, while (1)–(14) indicate cases where one of parameters (1)–(14) is set to the EMV. (<b>Upper</b>) time evolutions with the EMVs of parameters (1) <math display="inline"><semantics> <msub> <mi>s</mi> <mi>q</mi> </msub> </semantics></math>, (3) <math display="inline"><semantics> <mrow> <mi>t</mi> <msub> <mi>r</mi> <mi>a</mi> </msub> </mrow> </semantics></math>, (4) <math display="inline"><semantics> <mrow> <mi>d</mi> <mi>e</mi> <msub> <mi>g</mi> <mn>1</mn> </msub> </mrow> </semantics></math>, (6) <math display="inline"><semantics> <msub> <mi>s</mi> <mi>p</mi> </msub> </semantics></math>, (10) <math display="inline"><semantics> <mrow> <mi>t</mi> <msub> <mi>r</mi> <mrow> <mi>r</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>, (12) <math display="inline"><semantics> <msub> <mi>d</mi> <mrow> <mi>r</mi> <mi>m</mi> </mrow> </msub> </semantics></math>, and (14) <math display="inline"><semantics> <mrow> <mi>d</mi> <mi>e</mi> <msub> <mi>g</mi> <mn>2</mn> </msub> </mrow> </semantics></math>; (<b>lower</b>) time evolutions with the EMVs of parameters (2) <math display="inline"><semantics> <msub> <mi>k</mi> <mi>a</mi> </msub> </semantics></math>, (5) <math display="inline"><semantics> <msub> <mi>k</mi> <mi>p</mi> </msub> </semantics></math>, (7) <math display="inline"><semantics> <msub> <mi>u</mi> <mi>p</mi> </msub> </semantics></math>, (8) <math display="inline"><semantics> <msub> <mi>s</mi> <mi>r</mi> </msub> </semantics></math>, (9) <math display="inline"><semantics> <msub> <mi>u</mi> <mi>r</mi> </msub> </semantics></math>, (11) <math display="inline"><semantics> <msub> <mi>d</mi> <mi>r</mi> </msub> </semantics></math>, and (13) <math display="inline"><semantics> <msub> <mi>e</mi> <mi>r</mi> </msub> </semantics></math>.</p>
Full article ">Figure A2
<p>Time evolutions of <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mi>l</mi> <mi>B</mi> <mi>r</mi> <mi>a</mi> <mi>t</mi> <mi>i</mi> <mi>o</mi> </mrow> </semantics></math> for various (<math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>R</mi> <mi>e</mi> <mi>l</mi> <mi>B</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>I</mi> <mi>κ</mi> <mi>B</mi> <mi>α</mi> </mrow> </semantics></math>) of the p100KO-M with time ranges of (<b>left</b>) 0–10 and (<b>right</b>) 90–100 h.</p>
Full article ">Figure A3
<p>Bifurcation diagrams for <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>R</mi> <mi>e</mi> <mi>l</mi> <mi>B</mi> </mrow> </semantics></math> (<b>A</b>) and <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>I</mi> <mi>κ</mi> <mi>B</mi> <mi>α</mi> </mrow> </semantics></math> (<b>B</b>) in p100KO-M for the calibrated parameters. Dependencies of the <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>R</mi> <mi>e</mi> <mi>l</mi> <mi>B</mi> </mrow> </semantics></math> (<b>A</b>) and <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>I</mi> <mi>κ</mi> <mi>B</mi> <mi>α</mi> </mrow> </semantics></math> (<b>B</b>) are fixed at <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>I</mi> <mi>κ</mi> <mi>B</mi> <mi>α</mi> </mrow> </semantics></math> = 2.5 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>M and <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>R</mi> <mi>e</mi> <mi>l</mi> <mi>B</mi> </mrow> </semantics></math> = 0.4 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>M, respectively.</p>
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<p><math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mi>l</mi> <mi>B</mi> <mi>r</mi> <mi>a</mi> <mi>t</mi> <mi>i</mi> <mi>o</mi> </mrow> </semantics></math>—(<b>left</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>R</mi> <mi>e</mi> <mi>l</mi> <mi>B</mi> </mrow> </semantics></math> and (<b>right</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>I</mi> <mi>κ</mi> <mi>B</mi> <mi>α</mi> </mrow> </semantics></math> dependencies of p100KO-M for the calibrated parameters. The dependencies of the <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>R</mi> <mi>e</mi> <mi>l</mi> <mi>B</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>I</mi> <mi>κ</mi> <mi>B</mi> <mi>α</mi> </mrow> </semantics></math> are fixed at <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>I</mi> <mi>κ</mi> <mi>B</mi> <mi>α</mi> </mrow> </semantics></math> = 2.5 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>M and <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>R</mi> <mi>e</mi> <mi>l</mi> <mi>B</mi> </mrow> </semantics></math> = 0.4 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>M, respectively.</p>
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<p>Loss function—(<b>left</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>R</mi> <mi>e</mi> <mi>l</mi> <mi>B</mi> </mrow> </semantics></math> and (<b>right</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>I</mi> <mi>κ</mi> <mi>B</mi> <mi>α</mi> </mrow> </semantics></math> dependencies of p100KO-M for the calibrated parameters. The dependencies of the <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>R</mi> <mi>e</mi> <mi>l</mi> <mi>B</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>I</mi> <mi>κ</mi> <mi>B</mi> <mi>α</mi> </mrow> </semantics></math> are fixed at <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>I</mi> <mi>κ</mi> <mi>B</mi> <mi>α</mi> </mrow> </semantics></math> = 2.5 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>M and <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> <mi>R</mi> <mi>e</mi> <mi>l</mi> <mi>B</mi> </mrow> </semantics></math> = 0.4 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>M, respectively.</p>
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26 pages, 13349 KiB  
Article
Anomaly Detection and Artificial Intelligence Identified the Pathogenic Role of Apoptosis and RELB Proto-Oncogene, NF-kB Subunit in Diffuse Large B-Cell Lymphoma
by Joaquim Carreras and Rifat Hamoudi
BioMedInformatics 2024, 4(2), 1480-1505; https://doi.org/10.3390/biomedinformatics4020081 - 7 Jun 2024
Cited by 2 | Viewed by 1046
Abstract
Background: Diffuse large B-cell lymphoma (DLBCL) is one of the most frequent lymphomas. DLBCL is phenotypically, genetically, and clinically heterogeneous. Aim: We aim to identify new prognostic markers. Methods: We performed anomaly detection analysis, other artificial intelligence techniques, and conventional statistics using gene [...] Read more.
Background: Diffuse large B-cell lymphoma (DLBCL) is one of the most frequent lymphomas. DLBCL is phenotypically, genetically, and clinically heterogeneous. Aim: We aim to identify new prognostic markers. Methods: We performed anomaly detection analysis, other artificial intelligence techniques, and conventional statistics using gene expression data of 414 patients from the Lymphoma/Leukemia Molecular Profiling Project (GSE10846), and immunohistochemistry in 10 reactive tonsils and 30 DLBCL cases. Results: First, an unsupervised anomaly detection analysis pinpointed outliers (anomalies) in the series, and 12 genes were identified: DPM2, TRAPPC1, HYAL2, TRIM35, NUDT18, TMEM219, CHCHD10, IGFBP7, LAMTOR2, ZNF688, UBL7, and RELB, which belonged to the apoptosis, MAPK, MTOR, and NF-kB pathways. Second, these 12 genes were used to predict overall survival using machine learning, artificial neural networks, and conventional statistics. In a multivariate Cox regression analysis, high expressions of HYAL2 and UBL7 were correlated with poor overall survival, whereas TRAPPC1, IGFBP7, and RELB were correlated with good overall survival (p < 0.01). As a single marker and only in RCHOP-like treated cases, the prognostic value of RELB was confirmed using GSEA analysis and Kaplan–Meier with log-rank test and validated in the TCGA and GSE57611 datasets. Anomaly detection analysis was successfully tested in the GSE31312 and GSE117556 datasets. Using immunohistochemistry, RELB was positive in B-lymphocytes and macrophage/dendritic-like cells, and correlation with HLA DP-DR, SIRPA, CD85A (LILRB3), PD-L1, MARCO, and TOX was explored. Conclusions: Anomaly detection and other bioinformatic techniques successfully predicted the prognosis of DLBCL, and high RELB was associated with a favorable prognosis. Full article
(This article belongs to the Special Issue Feature Papers in Applied Biomedical Data Science)
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Graphical abstract

Graphical abstract
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<p>Histological heterogeneity of DLBCL. Despite the fact that DLBCL is a unique lymphoma subtype, its morphological characteristics are heterogeneous, including the neoplastic B lymphocytes and variable content of the tumor immune microenvironment. Hematoxylin and eosin stain (scale bar = 50 μm). The histological cases were retrieved from the lymphoma database of the Department of Pathology, Tokai University, School of Medicine.</p>
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<p>Types of artificial intelligence methods.</p>
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<p>Types of machine learning methods for predictive data analysis. In addition to anomaly detection analysis, there are many other types of machine learning that can be classified as supervised (<b>A</b>), unsupervised (<b>B</b>), and reinforcement learning (<b>C</b>). Of note, this figure includes methods usually used in predictive data analysis, but it does not focus on deep learning and reinforcement learning (please refer to popular deep learning frameworks such as tensorflow, keras, and pytorch, for documentation).</p>
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<p>Segmentation analysis. This figure shows example images of the K-Means cluster (<b>A</b>), Kohonen clustering analysis (<b>B</b>), and anomaly detection (<b>C</b>).</p>
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<p>Aim and methodology. The discovery set was the Lymphoma/Leukemia Molecular Profiling Project (LLMPP) GSE10846 gene expression dataset (last update 25 March 2019) of 414 cases.</p>
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<p>Anomaly index values. Anomaly detection analysis identifies outliners, or unusual cases, in the data. It records information on what normal behavior looks like and identifies outliers even if they do not conform to any known pattern. It is an unsupervised method that examines large numbers of variables to identify clusters or peer groups. Then, each record is compared to others in its peer group to identify possible anomalies. Each record (blue circle) is assigned an abnormality index. High index implies a higher average of the case than the average. In the setup, several options can be specified, such as the adjustment of coefficient, number of peer groups, noise level, and noise ratio.</p>
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<p>Machine learning and artificial neural networks using the LLMPP gene expression dataset. Abnormality detection analysis identified 12 genes. The prognostic value of these genes for overall survival was tested using several artificial intelligence analysis techniques. XGBoost tree (<b>A</b>), random forest (<b>B</b>), C5 tree (<b>C</b>), and neural network (<b>D</b>). Of note, the prognostic value of <span class="html-italic">RELB</span> was confirmed in the RCHOP-like cases of the LLMPP series using conventional overall survival analysis of Kaplan–Meier with log-rank tests (<b>E</b>). High gene expression of <span class="html-italic">RELB</span> was associated with favorable overall survival (<b>E</b>).</p>
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<p>Protein−protein interaction analysis and gene set enrichment analysis (GSEA) of <span class="html-italic">RELB</span> gene and pathway. First, a functional network association analysis (protein−protein interaction network) focused on RELB created a pathway. Later, this RELB pathway was used in the GSEA analysis. The GSEA analysis confirmed the association of the RELB gene and pathway with a favorable overall survival of patients with DLBCL treated with R-CHOP therapy. Functional network association analysis (<b>A</b>), GSEA (<b>B</b>).</p>
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<p>Immunohistochemical analysis of RELB in reactive tonsils and DLBCL. The protein expression of RELB was analyzed in 10 reactive tonsils (tissue control) and 30 cases of DLBCL not otherwise specified (NOS). In reactive tonsils, RELB expression was mainly present in the germinal centers of the follicles, with strong staining in macrophage/dendritic cells and weak in the B-lymphocytes. In DLBCL NOS, the staining was heterogeneous, ranging from 0 to 3+, and expressed by neoplastic B-lymphocytes and cells of the microenvironment.</p>
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<p>Immunohistochemical analysis of RELB in relationship with other immune microenvironment markers in DLBCL NOS. The expression of RELB in DLBCL was heterogeneous, with a pattern compatible with mixture of macrophage/dendritic cells and B-lymphocytes. Correlation with other macrophage-associated and immune microenvironment/immune checkpoint markers was performed using HLA DP-DR, SIRPA, CD85A, PD-L1, MARCO, and TOX (TOX1). Original magnification 400×.</p>
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<p>Validation of the association between <span class="html-italic">RELB</span> gene expression and overall survival in other series.</p>
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17 pages, 2814 KiB  
Article
Effects of Berberine on Lipid Metabolism, Antioxidant Status, and Immune Response in Liver of Tilapia (Oreochromis niloticus) under a High-Fat Diet Feeding
by Rui Jia, Yiran Hou, Liqiang Zhang, Bing Li and Jian Zhu
Antioxidants 2024, 13(5), 548; https://doi.org/10.3390/antiox13050548 - 29 Apr 2024
Cited by 2 | Viewed by 1254
Abstract
Berberine, a natural alkaloid found abundantly in various medicinal plants, exhibits antioxidative, anti-inflammatory, and lipid metabolism-regulatory properties. Nonetheless, its protective effects and the molecular mechanisms underlying liver injury in fish have not been fully elucidated. The aims of this study were to investigate [...] Read more.
Berberine, a natural alkaloid found abundantly in various medicinal plants, exhibits antioxidative, anti-inflammatory, and lipid metabolism-regulatory properties. Nonetheless, its protective effects and the molecular mechanisms underlying liver injury in fish have not been fully elucidated. The aims of this study were to investigate the antioxidative, anti-inflammatory, and lipid metabolism-regulating effects of berberine against high-fat diet (HFD)-induced liver damage and to clarify the underlying molecular mechanisms. Tilapia were fed diets containing two doses of berberine (50 and 100 mg/kg diet) alongside high fat for 60 days. The results showed that berberine treatments (50 and/or 100 mg/kg) significantly reduced elevated aminotransferases, triglycerides (TG), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-c) in the plasma. In the liver, berberine treatments significantly increased the expression of peroxisome proliferator-activated receptor α (pparα) and carnitine palmitoyltransferase 1 (cpt-1) genes, leading to a reduction in lipid accumulation. Meanwhile, berberine treatment suppressed lipid peroxidation formation and enhanced antioxidant capacity. Berberine upregulated the mRNA levels of erythroid 2-related factor 2 (nrf2) and its downstream genes including heme oxygenase 1 (ho-1) and glutathione-S-transferase (gstα). Additionally, berberine attenuated the inflammation by inhibiting the expression of toll-like receptor 2 (tlr2), myeloid differential protein-88 (myd88), relb, and inflammatory cytokines such as interleukin-1β (il-1β), tumor necrosis factor-α (tnf-α), and il-8. In summary, this study suggested that berberine offers protection against HFD-induced liver damage in tilapia via regulating lipid metabolism, antioxidant status, and immune response. This protective effect may be attributed to the modulation of the Nrf2, TLR2/MyD88/NF-κB, and PPARα signaling pathways. Full article
(This article belongs to the Special Issue Natural Antioxidants and Aquatic Animal Health)
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<p>Changes in plasma hepatic damage parameters in tilapia fed berberine-inclusive high-fat diet. Different lowercase letters indicate significant differences between groups. (<b>A</b>) Glutamate pyruvate transaminase (GPT). (<b>B</b>) Glutamate oxaloacetate transaminase (GOT). (<b>C</b>) Total protein (TP) (<b>D</b>) Albumin (Alb). (<b>E</b>) Alkaline phosphatase (AKP). (<b>F</b>) Acid phosphatase (ACP).</p>
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<p>Changes in plasma lipid metabolism parameters in tilapia fed berberine-inclusive high-fat diet. Different lowercase letters indicate significant differences between groups. (<b>A</b>) Total triacylglycerol (TG). (<b>B</b>) Total cholesterol (TC). (<b>C</b>) Low-density lipoprotein cholesterol (LDL-c). (<b>D</b>) High-density lipoprotein cholesterol (HDL-c).</p>
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<p>Relative expression of genes related to metabolism function in liver of tilapia fed berberine-inclusive high-fat diet. Different lowercase letters indicate significant differences between groups. (<b>A</b>) Peroxisome proliferator activated receptor alpha (<span class="html-italic">pparα</span>). (<b>B</b>) Acyl-CoA oxidase 1 (<span class="html-italic">acox1</span>). (<b>C</b>) Carnitine O-palmitoyltransferase 1 (<span class="html-italic">cpt-1</span>). (<b>D</b>) Glutamine synthase a (<span class="html-italic">gs</span>). (<b>E</b>) UDP-glucuronosyltransferase 2A2 (<span class="html-italic">ugt2a2</span>). (<b>F</b>) NADH-cytochrome b5 reductase 2 (<span class="html-italic">cbr2</span>).</p>
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<p>Changes in antioxidant status in liver of tilapia fed berberine-inclusive high-fat diet. Different lowercase letters indicate significant differences between groups. (<b>A</b>) Superoxide dismutase (SOD). (<b>B</b>) Glutathione (GSH). (<b>C</b>) Total antioxidant capacity (T-AOC). (<b>D</b>) Malondialdehyde (MDA).</p>
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<p>Relative expression of genes related to antioxidant status in liver of tilapia fed berberine-inclusive high-fat diet. Different lowercase letters indicate significant differences between groups. (<b>A</b>) Nuclear factor erythroid 2-related factor 2 (<span class="html-italic">nrf2</span>) (<b>B</b>) Heme oxygenase (<span class="html-italic">ho-1</span>). (<b>C</b>) Glutathione S-transferase (<span class="html-italic">gsta</span>). (<b>D</b>) NAD(P)H dehydrogenase 1 (<span class="html-italic">nqo1</span>).</p>
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<p>Relative expression of genes related to antioxidant status in liver of tilapia fed berberine-inclusive high-fat diet. Different lowercase letters indicate significant differences between groups. (<b>A</b>) Nuclear factor erythroid 2-related factor 2 (<span class="html-italic">nrf2</span>) (<b>B</b>) Heme oxygenase (<span class="html-italic">ho-1</span>). (<b>C</b>) Glutathione S-transferase (<span class="html-italic">gsta</span>). (<b>D</b>) NAD(P)H dehydrogenase 1 (<span class="html-italic">nqo1</span>).</p>
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<p>Relative expression of genes related to inflammatory response in liver of tilapia fed berberine-inclusive high-fat diet. Different lowercase letters indicate significant differences between groups. (<b>A</b>) Toll-like receptor 2 (<span class="html-italic">tlr2</span>). (<b>B</b>) Myloid differentiation factor 88 (<span class="html-italic">myd88</span>). (<b>C</b>) NF-kB subunit (<span class="html-italic">relb</span>). (<b>D</b>) Tumor necrosis factor-alpha (<span class="html-italic">tnf-α</span>). (<b>E</b>) Interleukin-1 beta (<span class="html-italic">il-1β</span>). (<b>F</b>) Interleukin-8 (<span class="html-italic">il-8</span>).</p>
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<p>Relative expression of genes related to immune function in liver of tilapia fed berberine-inclusive high-fat diet. Different lowercase letters indicate significant differences between groups. (<b>A</b>) Complement C3 (<span class="html-italic">c3</span>). (<b>B</b>) Lysozyme C (<span class="html-italic">lzm</span>). (<b>C</b>) Immunoglobulin (<span class="html-italic">igm</span>). (<b>D</b>) Hepcidin (<span class="html-italic">hep</span>).</p>
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<p>Possible mechanisms of berberine in ameliorating liver injury induced by HFD in tilapia. Red arrows indicate stimulatory modification, green arrows indicate inhibitory modification.</p>
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13 pages, 1728 KiB  
Article
Inflammation in Hypervolemic Hemodialysis Patients: The Roles of RelB and Caspase-4
by Christof Ulrich, Zeynep Canim, Eva Herberger, Matthias Girndt and Roman Fiedler
Int. J. Mol. Sci. 2023, 24(24), 17550; https://doi.org/10.3390/ijms242417550 - 16 Dec 2023
Cited by 1 | Viewed by 1018
Abstract
Hypervolemia is associated with inflammation in hemodialysis (HD) patients. How hypervolemia triggers inflammation is not entirely known. We initiated a cross-sectional study enrolling 40 hemodialysis patients who were categorized into normovolemic (N; 23) and hypervolemic (H; 17) groups by bioimpedance measurement. A caspase [...] Read more.
Hypervolemia is associated with inflammation in hemodialysis (HD) patients. How hypervolemia triggers inflammation is not entirely known. We initiated a cross-sectional study enrolling 40 hemodialysis patients who were categorized into normovolemic (N; 23) and hypervolemic (H; 17) groups by bioimpedance measurement. A caspase activity assay in combination with a specific caspase-4 inhibitor was used to detect caspase-4 activity in isolated peripheral blood mononuclear cells (PBMCs). Transcription factors RelA (pS529) and RelB (pS552) were analyzed by phospho-flow cytometry. Serum endotoxins were detected by an amebocyte lysate-based assay, and IL-6 (interleukin-6) and TNF-α (Tumor necrosis factor-α) gene expression were detected using the ELISA technique. Hypervolemic patients were older, more frequently had diabetes and showed increased CRP and IL-6 levels. Caspase-4 activity, which is linked to intracellular endotoxin detection, was significantly elevated in H patients. While the frequency of RelA-expressing immune cells and the expression density in these cells did not differ, the monocytic frequency of cells positively stained for RelB (pS552) was significantly decreased in H patients. Increased caspase-4 activity in H patients may indicate a cause of inflammation in H patients. The post-translational modification of RelB (pS552) is linked to downregulation of NF-kB activity and may indicate the resolution of inflammation, which is more distinct in N patients compared to H patients. Therefore, both higher inflammatory loads and lower inflammatory resolution capacities are characteristics of H patients. Full article
(This article belongs to the Special Issue Renal Dysfunction, Uremic Compounds, and Other Factors 2.0)
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<p>mRNA expression in PBMCs of normo- (N) and hypervolemic (H) HD patients: TNF-α mRNA expression under basal (<b>a</b>) and stimulated conditions (<b>b</b>). Comparison of IL-6 mRNA expression in unstimulated PBMCs (<b>c</b>). TNFRSF1a expression in unstimulated PBMCs (<b>d</b>) and upon LPS stimulation (<b>e</b>). TNFRSF1b mRNA expression under basal (<b>f</b>) and stimulated conditions (<b>g</b>). Data are presented as box plots with medians and 25/75 percentiles. Data were analyzed using Mann–Whitney tests. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>IL-6 and TNF-α protein expression in normovolemic (N) and hypervolemic (H) HD patients. IL-6 protein expression in serum (<b>a</b>) and supernatants of unstimulated PBMCs of N and H patients (<b>b</b>). TNF-α protein levels in serum (<b>c</b>) or PBMC supernatants of N and H patients (<b>d</b>). TNF-α expression upon LPS stimulation (<b>e</b>). Frequency of monocytes (<b>f</b>) and lymphocytes (<b>g</b>) positively stained for TNF-α. Data were analyzed using Mann–Whitney tests. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Expression of Rel B but not the phosphorylation of Rel A differs between N and H patients. The frequency of unstimulated monocytes (CD14+) positively stained for RelA phosphorylated at serine P-529 (<b>a</b>), as well as the median fluorescence intensity (MFI) of phosphorylated RelA (pS529), did not differ between the PBMCs of N and H patients (<b>c</b>). This was also true when PBMCs were stimulated with LPS (<b>b</b>,<b>d</b>). In contrast to RelA, the frequency of monocytes positively stained for RelB (pS552) was significantly enhanced under basal conditions (<b>e</b>) and upon LPS stimulation (<b>f</b>). RelB expression per cell (MFI) did not differ between N and H patients (<b>g</b>,<b>h</b>). Data were analyzed using Mann–Whitney tests. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Endotoxin levels and caspase-4 activity in normovolemic (N) and hypervolemic (H) patients: Endotoxin level in serum of N and H patients (<b>a</b>). Caspase-4 activity in N (N+LEVD) and H (H+LEVD) patients (<b>b</b>). Caspase-8 activity in N (N+IETD) and H (H+IETD) patients (<b>c</b>). Induction of caspase activity by indoxyl sulfate (IS) and specific inhibition of this caspase activity with caspase-4 specific inhibitor Ac-LEVD-CHO in N and H patients (<b>d</b>). Data were analyzed using Mann–Whitney tests or one-way ANOVA. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001.</p>
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14 pages, 1867 KiB  
Article
Expression and Activity of the NF-κB Subunits in Chronic Lymphocytic Leukaemia: A Role for RelB and Non-Canonical Signalling
by Evan A. Mulligan, Susan J. Tudhope, Jill E. Hunter, Arabella E. G. Clift, Sarah L. Elliott, Geoffrey P. Summerfield, Jonathan Wallis, Chris J. Pepper, Barabara Durkacz, Stephany Veuger and Elaine Willmore
Cancers 2023, 15(19), 4736; https://doi.org/10.3390/cancers15194736 - 26 Sep 2023
Cited by 1 | Viewed by 1287
Abstract
Background: Canonical NF-κB signalling by p65 (RelA) confers chemo-resistance and poor survival in chronic lymphocytic leukaemia (CLL). The role of non-canonical NF-κB signalling (leading to RelB and p52 subunit activation) in CLL is less understood, but given its importance in other B-cell tumour [...] Read more.
Background: Canonical NF-κB signalling by p65 (RelA) confers chemo-resistance and poor survival in chronic lymphocytic leukaemia (CLL). The role of non-canonical NF-κB signalling (leading to RelB and p52 subunit activation) in CLL is less understood, but given its importance in other B-cell tumour types, we theorised that RelB and p52 may also contribute to the pathology of CLL. Methods: DNA binding activity of all five NF-kB subunits, p65, p50, RelB, p52, and c-Rel, was quantified using ELISA and correlated to ex vivo chemoresistance, CD40L-stimulated signalling (to mimic the lymph node microenvironment), and clinical data. Results: Importantly, we show for the first time that high basal levels of RelB DNA binding correlate with nuclear RelB protein expression and are associated with del(11q), ATM dysfunction, unmutated IGHV genes, and shorter survival. High levels of nuclear p65 are prevalent in del(17p) cases (including treatment-naïve patients) and also correlate with the outcome. CD40L-stimulation resulted in rapid RelB activation, phosphorylation and processing of p100, and subsequent CLL cell proliferation. Conclusions: These data highlight a role for RelB in driving CLL cell tumour growth in a subset of patients and therefore strategies designed to inhibit non-canonical NF-κB signalling represent a novel approach that will have therapeutic benefit in CLL. Full article
(This article belongs to the Special Issue Targeting Signal Transduction Pathways in Cancer)
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<p>Expression of NF-kB subunits in CLL. Nuclear extracts prepared from selected CLL cases were examined using both ELISA and Western blotting. The scattergram (<b>A</b>) shows DNA binding levels of NF-κB p65 and p50 subunits (n = 80) and p52, c-Rel, and RelB subunits (n = 52) as measured using ELISA (arbitrary units AU, see methods). (<b>B</b>) Western blotting confirmed the heterogeneity in RelB and p52 expression in CLL cases. Nuclear extracts prepared from IR-treated MDA-MB-231 cells containing increasing amounts of protein (5, 10, 20 μg) were used as controls for increasing subunit expression and PARP was used as a loading control. (<b>C</b>) For both RelB and p52 there was a strong correlation between DNA binding activity and protein expression. (<b>D</b>) Expression of RelB, p100, and p52 in cytoplasmic and nuclear fractions prepared from cells from 4 CLL cases. (<b>E</b>) Correlation between p65 and p50 subunits in CLL samples (n = 80), and lack of correlation between p52 and RelB (n = 52).</p>
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<p>Unmutated IGHV is associated with increased RelB activation. (<b>A</b>) Kaplan–Meier analysis shows the expected shorter survival for patients with unmutated <span class="html-italic">IGHV</span> genes (hazard ratio 2.7). A total of 52 cases were separated into two groups according to whether they had mutated or unmutated <span class="html-italic">IGHV</span> genes, and the p65 and RelB DNA binding level of both groups is shown (<b>B</b>). DNA binding levels of p65 and RelB separated according to cytogenetic abnormalities: del(17p), del(11q), and del(13q) (<b>C</b>). ** denotes <span class="html-italic">p</span> value of &lt;0.01 (ns = not significant).</p>
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<p>Ex vivo chemoresistance is significantly associated with high p65, p50, and p52 activity but not RelB. Cells from CLL cases were treated with fludarabine for 48 h and viability was assessed using the XTT assay. The LC<sub>50</sub> values (concentration of drug that reduced viability to 50% of solvent controls) were calculated and are summarised in (<b>A</b>). LC<sub>50</sub> values from fludarabine-treated cells were analysed to study relationships with p65 (<b>B</b>), p50 (<b>C</b>), p52 (<b>D</b>), and RelB (<b>E</b>).</p>
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<p>Increased activity of RelB in ATM dysfunctional cases. (<b>A</b>) Cases with ATM dysfunction tended to have higher RelB DNA binding levels than those with functional ATM; (<b>B</b>) ATM dysfunctional cases had significantly lower levels of p65; (<b>C</b>) RelB and p65 DNA binding levels showed an inverse relationship, suggesting that high levels of the RelB subunit tend to be associated with low levels of the p65 subunit. * denotes <span class="html-italic">p</span> value of &lt; 0.05.</p>
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<p>RelB and p52 are activated following CD40L-induced stimulation. (<b>A</b>) Nuclear extracts were prepared from CLL cells at specific times following co-culture on CD40L-expressing cells (n = 6, representative example shown). CLL cells with IL-4 alone, or cells lacking CD40L expression were used as controls. (<b>B</b>) Phosphorylation of cytoplasmic p100 was measured over 0.5 to 24 h after CD40 stimulation (and compared to the control (“—CD40L” conditions). (<b>C</b>) CLL cells were stained with CFSE and co-cultured (+10 ng/mL interleukin 4) on CD40L-expressing fibroblast cells or on the non-CD40L-expressing (NTL) cells (both of which had been growth-arrested with 75 Gy IR). Cell samples were removed daily and quantification of CFSE in CD19+ve CLL cells (flow cytometry), shows CLL cell proliferation (seen by sub-peaks of CFSE fluorescence) that continued throughout the period studied. CLL cells cultured on the non-CD40L-expressing cells (shown by overlaying black lines) have no CFSE sub-peaks (histograms from one case representative of 5 others).</p>
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<p>High levels of nuclear RelB activity are associated with shorter time to treatment and poorer survival in CLL. Kaplan–Meier analysis was used to examine p65 and RelB activity in relation to time from diagnosis to first treatment and overall survival (OS); cases were stratified according to whether their DNA binding levels for p65 were higher or lower than the median level. (<b>A</b>) p65 in relation to time to treatment or to OS (<b>B</b>). RelB subunit DNA binding activity in relation to (<b>C</b>) TTT or (<b>D</b>) OS. Cases were then segregated according to the Binet stage at the time of sample collection, and p65 DNA binding activity for Binet B and C cases (n = 47) was compared to that for stage A cases (n = 33) (<b>E</b>). Similarly, RelB was compared according to Binet stage (<b>F</b>) (solid line on scatter plot shows median value). * denotes a <span class="html-italic">p</span> value of &lt;0.05. (ns = not significant).</p>
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13 pages, 2888 KiB  
Article
Exploitation of a Type 1 Toxin–Antitoxin System as an Inducible Counter-Selective Marker for Genome Editing in the Acetogen Eubacterium limosum
by James Millard, Alexander Agius, Ying Zhang, Philippe Soucaille and Nigel Peter Minton
Microorganisms 2023, 11(5), 1256; https://doi.org/10.3390/microorganisms11051256 - 10 May 2023
Cited by 2 | Viewed by 1800
Abstract
Targeted mutations in the anaerobic methylotroph Eubacterium limosum have previously been obtained using CRISPR-based mutagenesis methods. In this study, a RelB-family toxin from Eubacterium callanderi was placed under the control of an anhydrotetracycline-sensitive promoter, forming an inducible counter-selective system. This inducible system was [...] Read more.
Targeted mutations in the anaerobic methylotroph Eubacterium limosum have previously been obtained using CRISPR-based mutagenesis methods. In this study, a RelB-family toxin from Eubacterium callanderi was placed under the control of an anhydrotetracycline-sensitive promoter, forming an inducible counter-selective system. This inducible system was coupled with a non-replicative integrating mutagenesis vector to create precise gene deletions in Eubacterium limosum B2. The genes targeted in this study were those encoding the histidine biosynthesis gene hisI, the methanol methyltransferase and corrinoid protein mtaA and mtaC, and mtcB, encoding an Mttb-family methyltransferase which has previously been shown to demethylate L-carnitine. A targeted deletion within hisI brought about the expected histidine auxotrophy, and deletions of mtaA and mtaC both abolished autotrophic growth on methanol. Deletion of mtcB was shown to abolish the growth of E. limosum on L-carnitine. After an initial selection step to isolate transformant colonies, only a single induction step was required to obtain mutant colonies for the desired targets. The combination of an inducible counter-selective marker and a non-replicating integrative plasmid allows for quick gene editing of E. limosum. Full article
(This article belongs to the Special Issue Physiology, Genetic and Industrial Applications of Clostridia)
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Figure 1

Figure 1
<p>(<b>A</b>) The RelBE type 1 toxin–antitoxin (TA) system from <span class="html-italic">E. callanderi</span> KIST612. (<b>B</b>) The toxicity assay plasmid pMTL-JM101, with the expression of <span class="html-italic">relE</span> driven by an anhydrotetracycline (ATc)-inducible promoter system. Expression of <span class="html-italic">tetR</span> and <span class="html-italic">relE</span> is via Ptet01/02 which are inhibited by TetR until ATc is added. Expression of <span class="html-italic">relB</span> remains under the control of the native promoter of the TA system.</p>
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<p>(<b>A</b>) Growth of <span class="html-italic">E. limosum</span> B2 on glucose (20 mM) in a medium supplemented with varying concentrations of anhydrotetracycline (ATc). Points represent the mean average (<span class="html-italic">n</span> = 3). Error bars represent the standard deviation. (<b>B</b>) <span class="html-italic">E. limosum</span> pMTL-JM101 growth on solid medium ± ATc. (<b>C</b>) Growth of <span class="html-italic">E. limosum</span> pMTL-JM101 in the presence and absence of ATc. Cultures were grown to the mid-exponential phase and then divided in half. One-half of each replicate was spiked with ATc to a final concentration of 100 ng/mL (indicated by the red line). Data points represent mean average values (<span class="html-italic">n</span> = 3). Error bars represent the standard deviation. (<b>D</b>) Fluorescence at 490 nm of cells harvested from (<b>C</b>) at 96 h and stained with thioflavin T. Bars represent the mean average of measurements (<span class="html-italic">n</span> = 3). Fluorescence measurements were normalised based on the sample optical density at 600 nm.</p>
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<p>Isolation and characterisation of <span class="html-italic">E. limosum</span> B2 Δ<span class="html-italic">hisI</span> mutants. (<b>A</b>) Scheme for mutant generation. Following the integration of the non-replicating knockout vector, toxin induction will prevent cell growth on a non-selective medium unless the integrated plasmid is excised. This can occur due to (i) a recombination event at the original locus of recombination (resulting in a wild-type revertant) or (ii) recombination at the adjacent homology arm (resulting in the deletion of the region between the homology arms. (<b>B</b>) The <span class="html-italic">hisI</span> knockout vector pMTL-JM201-<span class="html-italic">hisI</span>. (<b>C</b>) Screening of hisI deletion mutants. Mutants were screened using primers <span class="html-italic">hisI</span>_ext_fwd and <span class="html-italic">hisI</span>_ext_rev. The ladder is NEB Quick-Load® Purple 1 kb Plus. (<b>D</b>) Plates showing growth of <span class="html-italic">E. limosum</span> B2 ATcR colonies on defined medium in the presence (+) and absence (−) of histidine. Plate sectors 1–8 correspond to the sample lanes in the gel image above.</p>
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<p>Screening of (<b>A</b>) <span class="html-italic">mtaA</span> and (<b>B</b>) <span class="html-italic">mtaC</span> deletion mutants. ATc<sup>R</sup> colonies were screened using primer pairs <span class="html-italic">mtaA</span>-ext-fwd/rev and <span class="html-italic">mtaC</span>-ext-fwd/rev, respectively. Ladder: Generuler 1 kb Plus (Thermo Fisher, Waltham, MA, USA). Sample lanes are indicated using brackets. (<b>C</b>) Schematic representation of the <span class="html-italic">mta</span> operon, showing primer binding sites and homology arm locations. LHA/RHA: left/right homology arms. Refer to <a href="#microorganisms-11-01256-t002" class="html-table">Table 2</a> for primer nucleotide sequences.</p>
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<p>Growth of wild-type <span class="html-italic">E. limosum</span> and deletion mutants Δ<span class="html-italic">mtaA</span> and Δ<span class="html-italic">mtaC</span> on (<b>A</b>) 60 mM glucose and (<b>B</b>) 200 mM methanol. Points represent mean average OD600 readings (<span class="html-italic">n</span> = 3). Error bars represent the standard deviation.</p>
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<p>Diagnostic PCR of eight ATcR colonies using primers <span class="html-italic">mtcB</span>_ext_fwd and <span class="html-italic">mtcB</span>_ext_rev. Samples 2, 3 and 5 show band sizes consistent with the deletion of <span class="html-italic">mtcB</span> (wild-type 3637 bp; deletion 2182 bp). L: Generuler 1 kb Plus (Thermo Fisher).</p>
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<p>Phenotypic characterisation of <span class="html-italic">E. limosum</span> Δ<span class="html-italic">mtcB</span>. Two biologically independent Δ<span class="html-italic">mtcB</span> mutants are shown. (<b>A</b>) Growth of <span class="html-italic">E. limosum</span> wild-type and Δ<span class="html-italic">mtcB</span> using glucose (60 mM) as substrate. (<b>B</b>) Growth of <span class="html-italic">E. limosum</span> wild-type and Δ<span class="html-italic">mtcB</span> using L-carnitine (50 mM) as a substrate. The red line indicates the spiking of all cultures with an additional 50 mM L-carnitine at 73 h. (<b>C</b>) Maximum OD600 values attained in (<b>A</b>,<b>B</b>). All measurements shown are mean average (<span class="html-italic">n</span> = 3). Error bars represent the standard deviation.</p>
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17 pages, 2204 KiB  
Article
NF-κB Signaling Modulates miR-452-5p and miR-335-5p Expression to Functionally Decrease Epithelial Ovarian Cancer Progression in Tumor-Initiating Cells
by Rahul D. Kamdar, Brittney S. Harrington, Emma Attar, Soumya Korrapati, Jyoti Shetty, Yongmei Zhao, Bao Tran, Nathan Wong, Carrie D. House and Christina M. Annunziata
Int. J. Mol. Sci. 2023, 24(9), 7826; https://doi.org/10.3390/ijms24097826 - 25 Apr 2023
Cited by 4 | Viewed by 1696
Abstract
Epithelial ovarian cancer (EOC) remains the fifth leading cause of cancer-related death in women worldwide, partly due to the survival of chemoresistant, stem-like tumor-initiating cells (TICs) that promote disease relapse. We previously described a role for the NF-κB pathway in promoting TIC chemoresistance [...] Read more.
Epithelial ovarian cancer (EOC) remains the fifth leading cause of cancer-related death in women worldwide, partly due to the survival of chemoresistant, stem-like tumor-initiating cells (TICs) that promote disease relapse. We previously described a role for the NF-κB pathway in promoting TIC chemoresistance and survival through NF-κB transcription factors (TFs) RelA and RelB, which regulate genes important for the inflammatory response and those associated with cancer, including microRNAs (miRNAs). We hypothesized that NF-κB signaling differentially regulates miRNA expression through RelA and RelB to support TIC persistence. Inducible shRNA was stably expressed in OV90 cells to knockdown RELA or RELB; miR-seq analyses identified differentially expressed miRNAs hsa-miR-452-5p and hsa-miR-335-5p in cells grown in TIC versus adherent conditions. We validated the miR-seq findings via qPCR in TIC or adherent conditions with RELA or RELB knocked-down. We confirmed decreased expression of hsa-miR-452-5p when either RELA or RELB were depleted and increased expression of hsa-miR-335-5p when RELA was depleted. Either inhibiting miR-452-5p or mimicking miR-335-5p functionally decreased the stem-like potential of the TICs. These results highlight a novel role of NF-κB TFs in modulating miRNA expression in EOC cells, thus opening a better understanding toward preventing recurrence of EOC. Full article
(This article belongs to the Special Issue NF-κB and Disease 3.0)
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Figure 1
<p>Volcano plot of differentially expressed miRNAs plotted with respect to the log<sub>2</sub> Fold change (log<sub>2</sub>FC) and −log<sub>10</sub> False discovery rate (−log<sub>10</sub>FDR) in OV90 cells with: (<b>A</b>) <span class="html-italic">RELA</span>-knockdown cells grown adherently, (<b>B</b>) <span class="html-italic">RELB</span>-knockdown cells grown adherently, (<b>C</b>) <span class="html-italic">RELA</span>-knockdown cells grown in spheroid conditions, (<b>D</b>) <span class="html-italic">RELB</span>-knockdown cells grown in spheroid conditions. The candidate miRNAs for this study, hsa-miR-452-5p and hsa-miR-335-5p, are identified as red and green circles, respectively. (<b>E</b>) Venn diagram of differentially expressed miRNAs in each condition.</p>
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<p>Examining the differential expression of candidate miRNAs in epithelial ovarian cancer cell lines. qRT-PCR analysis showed the expression of hsa-miR-452-5p across adherent (Adh) or tumor-initiating cell (TIC)-enriched spheroid conditions containing either the <span class="html-italic">RELA</span> or <span class="html-italic">RELB</span> knockdown in OV90 (<b>A</b>), ACI23 (<b>B</b>), and OVCAR8 (<b>C</b>) cell lines. qRT-PCR analysis also showed the expression of hsa-miR-335-5p across Adh or TIC conditions containing either <span class="html-italic">RELA</span> or <span class="html-italic">RELB</span> knockdown in OV90 (<b>D</b>), ACI23 (<b>E</b>), or OVCAR8 (<b>F</b>) cell lines. Data were collected in triplicate and plotted as the mean with standard deviation (SD). Analysis was performed by using an unpaired <span class="html-italic">t</span>-test. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001, n.s.—non-significant as compared to Adh or TIC shNeg control.</p>
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<p>Sphere-formation capability of OV90 and OVCAR8 with either <span class="html-italic">RELA</span> or <span class="html-italic">RELB</span> silenced. Spheres with an area greater than 1000 square microns were quantified. (<b>A</b>) OV90 spheres were transfected with either negative control (NC) or hsa-miR-452-5p inhibitor (452 ib) at 90 pmol. (<b>B</b>) OVCAR8 spheres were transfected with either NC or 452 ib. (<b>C</b>) Addition of NC or hsa-miR-335-5p mimic (335 mimic) at 1 pmol to OV90 spheres or (<b>D</b>) OVCAR8 spheres. Data are plotted as mean with SEM. Analysis was performed by using an unpaired <span class="html-italic">t</span>-test. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, n.s.—non-significant as compared to shNeg, shRelA, or shRelB negative control.</p>
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<p>Measured cell viability of EOC cell lines containing silenced <span class="html-italic">RELA</span> or <span class="html-italic">RELB</span> with modulated miR-452-5p expression. (<b>A</b>) Viability of OV90 spheres silenced for either <span class="html-italic">RELA</span> or <span class="html-italic">RELB</span> and transfected with either 90 pmol of miR-452-5p inhibitor or 0.1 pmol miR-452-5p mimic. (<b>B</b>) Viability of OVCAR8 spheres that were silenced for either <span class="html-italic">RELA</span> or <span class="html-italic">RELB</span> and transfected with either 90 pmol of miR-452-5p inhibitor or 0.1 pmol miR-452-5p mimic. Graphs represent mean with standard error of mean (SEM). Analysis was performed by using a two-way ANOVA and Dunnett’s multiple comparisons test. ** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt; 0.0001, n.s.—non-significant as compared to the negative control (NC) within each cell line (shNeg, shRelA, shRelB).</p>
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<p>ALDH activity in OV90 EOC cells with silenced <span class="html-italic">RELA</span> or <span class="html-italic">RELB</span> after miRNA modulation. (<b>A</b>) Quantified percentages of ALDH+ cells in <span class="html-italic">RELA</span>- or <span class="html-italic">RELB</span>-silenced OV90 spheres after adding 90 pmol of either negative control (NC) or hsa-miR-452-5p inhibitor (452 Ib). (<b>B</b>) Quantified percentages of ALDH+ OV90 cells with <span class="html-italic">RELB</span> silenced and transfected with 0.1 pmol miR-452-5p inhibitor (452 Ib). Graphs represent data from three independent experiments (<span class="html-italic">n</span> = 3) and are plotted to indicate mean with SD. Analysis was performed by using an unpaired <span class="html-italic">t</span>-test. ** <span class="html-italic">p</span> &lt; 0.01, n.s.—non-significant as compared to negative control.</p>
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<p><span class="html-italic">SOX7</span> is a putative target downstream of miR-452-5p. (<b>A</b>) Table of potential targets downstream of miR-452-5p and their respective integration score from the miRNA Data Integration Portal (mirDIP). qRT-PCR analysis of downstream targets in OV90 and OVCAR8 cells transfected with 0.1 pmol of miR-452-5p mimic also listed as the Mean ± SEM relative to the shNeg + Negative control (NC) transfected cells. N.D. = not detectable. (<b>B</b>) Predicted consequential pairing of <span class="html-italic">SOX7</span> and miR-452-5p from TargetScanHuman miRNA prediction software. (<b>C</b>) Human Protein Atlas immunohistochemistry data displaying <span class="html-italic">SOX7</span> expression level by number of cases. (<b>D</b>) qRT-PCR analysis of relative <span class="html-italic">SOX7</span> expression using OVCAR8 and (<b>E</b>) OV90 TIC cDNA transfected with miR-452-5p mimic. Data were collected in triplicate and plotted as mean with SD. ** <span class="html-italic">p</span> &lt; 0.01 as compared to shNeg negative control.</p>
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14 pages, 7567 KiB  
Article
Interleukin 26 Induces Macrophage IL-9 Expression in Rheumatoid Arthritis
by Yi-Hsun Wang, Yi-Jen Peng, Feng-Cheng Liu, Gu-Jiun Lin, Shing-Hwa Huang, Huey-Kang Sytwu and Chia-Pi Cheng
Int. J. Mol. Sci. 2023, 24(8), 7526; https://doi.org/10.3390/ijms24087526 - 19 Apr 2023
Viewed by 1967
Abstract
Rheumatoid arthritis (RA) is an autoimmune disease with chronic inflammation, bone erosion, and joint deformation. Synovial tissue in RA patients is full of proinflammatory cytokines and infiltrated immune cells, such as T help (Th) 9, Th17, macrophages, and osteoclasts. Recent reports emphasized a [...] Read more.
Rheumatoid arthritis (RA) is an autoimmune disease with chronic inflammation, bone erosion, and joint deformation. Synovial tissue in RA patients is full of proinflammatory cytokines and infiltrated immune cells, such as T help (Th) 9, Th17, macrophages, and osteoclasts. Recent reports emphasized a new member of the interleukin (IL)-10 family, IL-26, an inducer of IL-17A that is overexpressed in RA patients. Our previous works found that IL-26 inhibits osteoclastogenesis and conducts monocyte differentiation toward M1 macrophages. In this study, we aimed to clarify the effect of IL-26 on macrophages linking to Th9 and Th17 in IL-9 and IL-17 expression and downstream signal transduction. Murine and human macrophage cell lines and primary culture cells were used and stimulated by IL26. Cytokines expressions were evaluated by flow cytometry. Signal transduction and transcription factors expression were detected by Western blot and real time-PCR. Our results show that IL-26 and IL-9 colocalized in macrophage in RA synovium. IL-26 directly induces macrophage inflammatory cytokines IL-9 and IL-17A expression. IL-26 increases the IL-9 and IL-17A upstream mechanisms IRF4 and RelB expression. Moreover, the AKT-FoxO1 pathway is also activated by IL-26 in IL-9 and IL-17A expressing macrophage. Blockage of AKT phosphorylation enhances IL-26 stimulating IL-9-producing macrophage cells. In conclusion, our results support that IL-26 promotes IL-9- and IL-17-expressing macrophage and might initiate IL-9- and IL-17-related adaptive immunity in rheumatoid arthritis. Targeting IL-26 may a potential therapeutic strategy for rheumatoid arthritis or other IL-9 plus IL-17 dominant diseases. Full article
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Figure 1
<p>IL-26<sup>+</sup> and IL-9<sup>+</sup> macrophages were highly expressed in synovial tissue in RA. Synovial tissue from RA patients was stained by H&amp;E or immunofluorescence. DAPI (blue) was used to mark the location of cell nuclei. (<b>A</b>) CD68 antibody (green) was used to identify macrophage cells and was counterstained with IL-9 or IL-26 antibody (red). (<b>B</b>) Counterstaining of IL-9 (green) and IL-26 (red) antibodies showed the overlapping sites of two cytokines in RA synovial tissue.</p>
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<p>Effect of IL-26 on IL-9 cytokines expression and CD80 macrophage differentiation in RAW264.7. RAW264.7 cells were treated with IFN-γ (20 ng/mL), IL4 (20 ng/mL), or IL26 (60 ng/mL) for 18 h. After incubation, representative results (<b>A</b>) and summarized percentages of CD80, IL-9, and IL17A (<b>B</b>) were measured by flow cytometry. Results are the means ± SD of three independent experiments (<span class="html-italic">n</span> = 3) (** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, as multiple comparison significance between normal (N) control, IL-26, or IFN-γ group).</p>
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<p>Effect of IL-26 on IL-9- and IL17A-related transcription factor expression in RAW264.7. RAW264.7 cells were treated with IFN-γ (20 ng/mL), IL-4 (20 ng/mL), or IL-26 (60 ng/mL). After 2 or 4 h incubation, mRNA gene expression level of IRF4, PU.1, RelB, and RORγt was measured by real-time PCR (<b>A</b>). After 6 or 12 h incubation, protein levels of IRF4, PU.1, and RelB were measured by Western blot (<b>B</b>,<b>C</b>). GAPDH was the loading control for checking equal amounts of cDNA or protein in each sample. Results are the means ± SD of three independent experiments (<span class="html-italic">n</span> = 3) and normalized to N group (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, as multiple comparison significance between each group).</p>
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<p>Effect of IL-26 on AKT and FoxO1 activation in RAW264.7 and THP-1. RAW264.7 cells were serum-starved for 12 h then treated with IFN-γ (20 ng/mL), IL-4 (20 ng/mL), or IL-26 (60 ng/mL) for 1 or 3 h to detect phosphorylated or nonphosphorylated AKT and FoxO1 protein by Western blot (<b>A</b>,<b>B</b>). RAW264.7 (<b>C</b>,<b>D</b>) and PMA-stimulated THP-1 cells (<b>E</b>,<b>F</b>) were serum-starved for 12 h and consecutively treated with p-Akt inhibitor (MK2206, 5 uM) or solvent (0.1% DMSO) for 1 h then concurrently treated with or without IL-26 for 1 or 3 h to detect phosphorylated or nonphosphorylated AKT and FoxO1 protein by Western blot. GAPDH was the loading control for checking equal amounts of protein in each lane. Results are the means ± SD of three independent experiments (<span class="html-italic">n =</span> 3) and normalized to N group (* <span class="html-italic">p &lt;</span> 0.05, ** <span class="html-italic">p &lt;</span> 0.01, *** <span class="html-italic">p &lt;</span> 0.001, as multiple comparison significance between each group).</p>
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<p>Effect of IL-26 plus MK2206 on IL-9 and IL17A regulated transcriptional gene expression in RAW264.7 and THP-1 cells. RAW264.7 (<b>A</b>,<b>C</b>,<b>D</b>) and PMA-stimulated THP-1 cells (<b>B</b>,<b>E</b>,<b>F</b>) were pretreated with p-Akt inhibitor (MK2206, 5 uM) or solvent (0.1% DMSO) for 1 h then concurrently treated with or without IL-26 (60 ng/mL). After 2 or 4 h incubation, gene expression levels of IRF4, PU.1, RelB, and RORγt were measured by real-time PCR (<b>A</b>,<b>B</b>). After 6 or 12 h incubation, protein levels of IRF4, PU.1, and RelB were measured by Western blot (<b>C</b>–<b>F</b>). GAPDH was the loading control for checking equal amounts of cDNA or protein in each sample. Results are the means ± SD of three independent experiments (<span class="html-italic">n =</span> 3) and normalized to N group (* <span class="html-italic">p &lt;</span> 0.05, ** <span class="html-italic">p &lt;</span> 0.01, *** <span class="html-italic">p &lt;</span> 0.001, as multiple comparison significance between each group).</p>
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<p>Effects of IL-26 plus MK2206 on IL-9 and IL-17A cytokines expression in RAW264.7 and THP-1. RAW264.7 (<b>A</b>,<b>B</b>) and PMA-stimulated THP-1 cells (<b>C</b>,<b>D</b>) were pretreated with p-Akt inhibitor (MK2206, 5 uM) or solvent (0.1% DMSO) for 1 h then concurrently treated with or without IL-26 (60 ng/mL) for 24 h. After incubation, representative results (<b>A</b>,<b>C</b>) and summarized percentages of IL-9, IL17A, and CD80 (<b>B</b>,<b>D</b>) were measured by flow cytometry. Results are the means ± SD of three independent experiments (<span class="html-italic">n =</span> 3) (* <span class="html-italic">p &lt;</span> 0.05, ** <span class="html-italic">p &lt;</span> 0.01, *** <span class="html-italic">p &lt;</span> 0.001, as multiple comparison significance between each group).</p>
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<p>Effects of IL-26 plus MK2206 IL-9 and IL-17A cytokines expression in murine BMDM and human PBMC. BMDM (<b>A</b>,<b>B</b>) and PBMC cells (<b>C</b>,<b>D</b>) were pretreated with p-Akt inhibitor (MK2206, 5 uM) or solvent (0.1% DMSO) for 1 h then concurrently treated with or without IL-26 (60 ng/mL) for 24 h. After incubation, representative results (<b>A</b>,<b>C</b>) and summarized percentages of IL-9 and IL17A (<b>B</b>,<b>D</b>) were measured by flow cytometry. Results are the means ± SD of four independent experiments (<span class="html-italic">n =</span> 4) (* <span class="html-italic">p &lt;</span> 0.05, ** <span class="html-italic">p &lt;</span> 0.01, *** <span class="html-italic">p</span> &lt; 0.001, as multiple comparison significance between each group).</p>
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25 pages, 3453 KiB  
Article
Human Brain Microvascular Endothelial Cells Exposure to SARS-CoV-2 Leads to Inflammatory Activation through NF-κB Non-Canonical Pathway and Mitochondrial Remodeling
by Carolline Soares Motta, Silvia Torices, Barbara Gomes da Rosa, Anne Caroline Marcos, Liandra Alvarez-Rosa, Michele Siqueira, Thaidy Moreno-Rodriguez, Aline da Rocha Matos, Braulia Costa Caetano, Jessica Santa Cruz de Carvalho Martins, Luis Gladulich, Erick Loiola, Olivia R. M. Bagshaw, Jeffrey A. Stuart, Marilda M. Siqueira, Joice Stipursky, Michal Toborek and Daniel Adesse
Viruses 2023, 15(3), 745; https://doi.org/10.3390/v15030745 - 14 Mar 2023
Cited by 17 | Viewed by 3700
Abstract
Neurological effects of COVID-19 and long-COVID-19, as well as neuroinvasion by SARS-CoV-2, still pose several questions and are of both clinical and scientific relevance. We described the cellular and molecular effects of the human brain microvascular endothelial cells (HBMECs) in vitro exposure [...] Read more.
Neurological effects of COVID-19 and long-COVID-19, as well as neuroinvasion by SARS-CoV-2, still pose several questions and are of both clinical and scientific relevance. We described the cellular and molecular effects of the human brain microvascular endothelial cells (HBMECs) in vitro exposure by SARS-CoV-2 to understand the underlying mechanisms of viral transmigration through the blood–brain barrier. Despite the low to non-productive viral replication, SARS-CoV-2-exposed cultures displayed increased immunoreactivity for cleaved caspase-3, an indicator of apoptotic cell death, tight junction protein expression, and immunolocalization. Transcriptomic profiling of SARS-CoV-2-challenged cultures revealed endothelial activation via NF-κB non-canonical pathway, including RELB overexpression and mitochondrial dysfunction. Additionally, SARS-CoV-2 led to altered secretion of key angiogenic factors and to significant changes in mitochondrial dynamics, with increased mitofusin-2 expression and increased mitochondrial networks. Endothelial activation and remodeling can further contribute to neuroinflammatory processes and lead to further BBB permeability in COVID-19. Full article
(This article belongs to the Special Issue SARS-CoV-2 Research in Brazil)
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Figure 1
<p>Characterization of infectivity profile of HBMECs and Vero cells by SARS-CoV-2. (<b>A</b>) Cells were exposed to different MOIs of SARS-CoV-2 (variant D614G) and viral production, and release to supernatant was analyzed by RT-qPCR for Envelope (E) gene from 0 to 72 h post-infection (hpi). As compared to Vero cells, HBMECs showed a non-productive infection. (<b>B</b>) At desired time points (6 and 24 hpi), total RNA from HBMEC cultures and expression of Spike1 and E genes were analyzed by RT-qPCR. HBMECs exposed to MOI 0.1 showed an increase in the expression of these two transcripts at 24 hpi (<span class="html-italic">p</span> &gt; 0.05). (<b>C</b>) Evaluation of SARS-CoV-2 receptors expression in HBMECs after SARS-CoV-2 challenge. ACE2 mRNA had a significant decrease at 24 hpi with the MOI 0.1, which did not translate to protein levels (right panel). TMPRSS2 had a slight increase in protein content at 24 hpi. (<b>D</b>) Exposure to SARS-CoV-2 increased immunoreactivity for cleaved caspase-3 in Vero cells and HBMECs at 24 hpi. *: <span class="html-italic">p</span> &lt; 0.05; ****: <span class="html-italic">p</span> &lt; 0.0001, Two-Way ANOVA with Bonferroni post-test of at least five independent experiments.</p>
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<p>Effects of SARS-CoV-2 on tight junctional proteins in Vero and HBMECs. (<b>A</b>)<b>:</b> Cells were stained for tight junction adaptor protein ZO-1 (red) and SARS-CoV-2 Spike1 (in green). ZO-1 was affected in infected cultures at 24 hpi, as shown in higher magnification in the insets. (<b>B</b>): Morphometrical analyses of ZO-1 fluorescence intensity and TiJOR in HBMECs (<b>B</b>) showed increased ZO-1 signal and TiJOR index 6 h after exposure to the MOI 0.01. Levels of mRNA encoding for ZO-1 and claudin-5 TJ genes remained unaffected by SARS-CoV-2 challenge (<b>C</b>), but a significant increase 6 h after exposure to MOI 0.1 was observed at the protein level (<b>D</b>). *: <span class="html-italic">p</span> &lt; 0.05 one-way ANOVA with Bonferroni post-test (in (<b>D</b>)) or two-way ANOVA with Bonferroni post-test (in (<b>B</b>,<b>C</b>)); ***: <span class="html-italic">p</span> &lt; 0.001; ****: <span class="html-italic">p</span> &lt; 0.0001, Two-Way ANOVA with Bonferroni post-test. Each symbol in (<b>C</b>,<b>D</b>) corresponds to independent cultures, and in (<b>B</b>) corresponds to microscopic field from four independent cultures. Representative blots in (<b>D</b>) from 3–4 independent experiments.</p>
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<p>Transcriptomic profiling of SARS-CoV-2 challenge on HBMECs. Cells were exposed to MOIs 0.01 and 0.1 and analyzed by RNA-Seq. (<b>A</b>) Volcano plot depicting the overall profile of differentially expressed genes in cultures after 24 h exposure to the MOI 0.1, with up-regulated genes shown in purple and down-regulated—in green. (<b>B</b>) Heatmap diagram depicting expression levels of the most significantly altered genes by MOI 0.1 (3 right columns), as compared to uninfected controls (3 left columns). (<b>C</b>) Cnetplot visualization of functional enrichment results with up-regulated genes, depicting the functional correlation of genes with the most significant GO terms. (<b>D</b>) Enrichment functional analysis of GO terms most affected by SARS-CoV-2 challenge in HBMECs indicates inflammatory endothelial activation, as well as mitochondrial dysfunction and ribosomal-related gene expression. (<b>E</b>) RT-qPCR validation of most significantly altered genes detected in the RNA-Seq indicates activation of non-canonical NF-κB pathway, with massive increase in TNF-α, lymphotoxin B (LTB, or TNF-C), and downstream target genes, such as IL-6, CXCL1, -2, and -8. NFKB1 (p105/p50) and NFKB2 (p100/p52), as well as JUNB, showed no significant alteration in SARS-CoV-2-exposed cultures. *: <span class="html-italic">p</span> &lt; 0.05; **: <span class="html-italic">p</span> &lt; 0.01; ****: <span class="html-italic">p</span> &lt; 0.0001, two-way ANOVA with Bonferroni post-test of at least 5 independent experiments. MOI: multiplicity of infection; GO: gene ontology.</p>
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<p>Production of angiogenic-related molecules is modulated by SARS-CoV-2 in HBMECs. (<b>A</b>) Conditioned medium from Mock and SARS-CoV-2-exposed HBMEC cultures (both with MOI 0.01 and 0.1) were analyzed via Proteome Profiler Human Angiogenic Antibody Array and detected by chemoluminescence, each protein detected in duplicated spots. (<b>B</b>) Densitometric analysis of membranes in (<b>A</b>) revealed the analytes with the strongest signal and which were affected by the SARS-CoV-2 challenge. Spots labelled 1-15 in (<b>A</b>) correspond to the analytes depicted in (<b>B</b>). (<b>C</b>) RT-qPCR analysis of angiogenesis-related genes in HBMECs revealed that PTX3 and HIF-1α were increased following SARS-CoV-2 exposure. *: <span class="html-italic">p</span> &lt; 0.05; **: <span class="html-italic">p</span> &lt; 0.01, two-way ANOVA with Bonferroni post-test of at least five independent experiments.</p>
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<p>SARS-CoV-2-induced mitochondrial remodeling in HBMECs. Mitochondrial networks were detected by TOMM20 immunostaining (<b>A</b>) and TEM (<b>B</b>). MiNA analysis of TOMM20 revealed that exposure to SARS-CoV-2 induced an increase in mitochondrial footprint, branch length mean, and summed branch length (<b>C</b>). Mitochondrial density was calculated by TEM images (<b>D</b>), which also revealed increased fusion and association with multivesicular bodies (<b>B</b>). (<b>E</b>): RT-qPCR (<b>upper panel</b>) and western blotting (<b>lower panel</b>) analyses revealed that although fission-related genes (Fis1 and Drp1) were up-regulated in MOI 0.01-exposed cultures, only Mfn2 protein levels were increased in MOI 0.1-exposed cultures. TOMM20 protein levels also remained unaltered. *: <span class="html-italic">p</span> &lt; 0.05; **: <span class="html-italic">p</span> &lt; 0.01; ****: <span class="html-italic">p</span> &lt; 0.0001, two-way ANOVA with Bonferroni post-test. Each symbol in graphs represents one cell (<b>C</b>), one mitochondrion (<b>D</b>), or one independent experiment (<b>E</b>). Bottom right panels depict blots from (<b>E</b>). Scale bars: 50 µm for (<b>A</b>) and 500 nm for (<b>B</b>).</p>
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19 pages, 4898 KiB  
Article
Transcriptome Analysis Reveals Immunomodulatory Effect of Spore-Displayed p75 on Human Intestinal Epithelial Caco-2 Cells
by Soo-Ji Kang, Ji-Su Jun and Kwang-Won Hong
Int. J. Mol. Sci. 2022, 23(23), 14519; https://doi.org/10.3390/ijms232314519 - 22 Nov 2022
Cited by 4 | Viewed by 1866
Abstract
Lacticaseibacillus rhamnosus GG (LGG) can promote intestinal health by modulating the immune responses of the gastrointestinal tract. However, knowledge about the immunomodulatory action of LGG-derived soluble factors is limited. In our previous study, we have displayed LGG-derived p75 protein on the spore surface [...] Read more.
Lacticaseibacillus rhamnosus GG (LGG) can promote intestinal health by modulating the immune responses of the gastrointestinal tract. However, knowledge about the immunomodulatory action of LGG-derived soluble factors is limited. In our previous study, we have displayed LGG-derived p75 protein on the spore surface of Bacillus subtilis. The objective of this study was to determine the effect of spore-displayed p75 (CotG-p75) on immune system by investigating transcriptional response of Caco-2 cells stimulated by CotG-p75 through RNA-sequencing (RNA-seq). RNA-seq results showed that CotG-p75 mainly stimulated genes involved in biological processes, such as response to stimulus, immune regulation, and chemotaxis. KEGG pathway analysis suggested that many genes activated by CotG-p75 were involved in NF-ĸB signaling and chemokine signaling pathways. CotG-p75 increased cytokines and chemokines such as CXCL1, CXCL2, CXCL3, CXCL8, CXCL10, CCL20, CCL22, and IL1B essential for the immune system. In particular, CotG-p75 increased the expression levels of NF-ĸB-related genes such as NFKBIA, TNFAIP3, BIRC3, NFKB2, and RELB involved in immune and inflammatory responses. This study provides genes and pathways involved in immune responses influenced by CotG-p75. These comprehensive transcriptome profiling could be used to elucidate the immunomodulatory action of CotG-p75. Full article
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<p>Volcano plot of differentially expressed genes (DEGs) in wild-type spores-stimulated cells versus control cells (<b>A</b>) and CotG-p75-stimulated cells versus control cells (<b>B</b>). Scattered yellow and blue dots indicate up-regulated and down-regulated DEGs with significant differences corresponding to an adjusted <span class="html-italic">p</span>-value &lt; 0.05, respectively. Gray dots indicate non-differentially expressed genes.</p>
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<p>Numbers of common and unique differentially expressed genes (DEGs) in Caco-2 cells treated with wild-type spores and CotG-p75 compared to control. The overlapping region indicates the number of DEGs expressed commonly in two groups. The non-overlapping regions indicate the number of DEGs expressed only in comparison groups.</p>
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<p>Hierarchical clustering of differentially expressed genes (DEGs) in Caco-2 cells unstimulated (CON) and stimulated by wild-type spores (WT) or CotG-p75 (G75). Each row represents one of the common genes. Each column represents each sample. The color scale shows the gene expression standard deviations from the mean represented as Z-score, with yellow indicating up-regulation and blue indicating down-regulation.</p>
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<p>Gene ontology (GO) term networks of differentially expressed genes between CotG-p75 treated Caco-2 and control. Networks were generated by Cytoscape BiNGO. All significantly enriched GO terms in biological process (<b>A</b>), molecular function (<b>B</b>), and cellular component (<b>C</b>) are presented. Circle size represents GO hierarchy. Yellow shade represents enrichment level. Threshold of hypergeometric distribution of functional annotation was set at corrected <span class="html-italic">p</span>-value &lt; 0.00005.</p>
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<p>Network visualization of top four KEGG pathways and genes enriched in CotG-p75 stimulated Caco-2 cells. Yellow circles represent KEGG pathways. Red and blue circles represent the enriched genes colored according to their fold change values. Fold change value is expressed based on the color key.</p>
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<p>TNF signaling pathway (KEGG map, has04668) of differentially expressed genes from CotG-p75 stimulated Caco-2 cells. Fold change value is expressed based on the color key. Modules with significantly selected DEGs are marked with a red star symbol. Blue indicates modules to which down-regulated genes are mapped. Red indicates modules to which up-regulated genes are mapped. Green indicates modules made up of genes present in a species whose expression cannot be determined.</p>
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<p>NF-κB signaling pathway (KEGG map, has04064) of differentially expressed genes from CotG-p75 stimulated Caco-2 cells. Fold change value is expressed based on the color key. Modules with significantly selected DEGs are marked with a red star symbol. Blue indicates modules in which down-regulated genes are mapped. Red indicates modules in which up-regulated genes are mapped. Green indicates modules made up of genes present in a species whose expression cannot be determined.</p>
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<p>Real-time quantitative PCR validation of RNA-seq data for selected genes associated with EGFR signaling from Caco-2 cells stimulated by CotG-p75. Right and left <span class="html-italic">y</span>-axis represent fold change of RNA-seq data and relative expression of RT-qPCR data, respectively. Relative mRNA expression was compared with spore-untreated control. RT-qPCR analysis for mRNA expression of selected genes was normalized against <span class="html-italic">GAPDH</span>. RT-qPCR data are presented as mean ± standard deviation (<span class="html-italic">n</span> = 3).</p>
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<p>Real-time quantitative PCR validation of RNA-seq data for selected genes associated with NF-κB signaling from Caco-2 cell. Right and left <span class="html-italic">y</span>-axis represent fold change of RNA-seq data and relative expression of RT-qPCR data, respectively. Relative mRNA expression was compared with spore-untreated control. RT-qPCR analysis for mRNA expression of selected genes was normalized against <span class="html-italic">GAPDH</span>. RT-qPCR data are presented as mean ± standard deviation (<span class="html-italic">n</span> = 3).</p>
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<p>Real-time quantitative PCR validation of RNA-seq data for selected genes associated with immune response from Caco-2 cells stimulated by CotG-p75. Right and left <span class="html-italic">y</span>-axis represent fold change of RNA-seq data and relative expression of RT-qPCR data, respectively. Relative mRNA expression was compared with spore-untreated control. RT-qPCR analysis for mRNA expression of selected genes was normalized against <span class="html-italic">GAPDH</span>. RT-qPCR data are presented as mean ± standard deviation (<span class="html-italic">n</span> = 3).</p>
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<p>Effects of Cotable 75. on <span class="html-italic">HBEGF</span> mRNA expression (<b>A</b>) and HBEGF protein production (<b>B</b>) in Caco-2 cells. In panel (A), cells were treated with various concentrations (10<sup>5</sup>, 10<sup>6</sup>, and 10<sup>7</sup> spores/mL) of CotG-p75 for 3 h. In panel (<b>B</b>), cells were treated with CotG-p75 (10<sup>7</sup> spores/mL) for various time periods (0, 3, 6, 12, and 24 h). Both mRNA expression levels and protein production levels in treated groups were compared with those of the control group. All data are expressed as mean ± standard deviation (<span class="html-italic">n</span> = 3). Asterisks (***) indicate a significance difference from the control (***, <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Effects of CotG-p75 on <span class="html-italic">CCL20</span> mRNA expression (<b>A</b>) and CCL20 protein production (<b>B</b>) in Caco-2 cells. In panel (<b>A</b>), cells were treated with various concentrations (10<sup>5</sup>, 10<sup>6</sup>, and 10<sup>7</sup> spores/mL) of CotG-p75 for 3 h. In panel (<b>B</b>), cells were treated with CotG-p75 (10<sup>7</sup> spores/mL) for various time periods (0, 3, 6, 12, and 24 h). Both mRNA expression levels and protein production levels in treated groups were compared with those of the control group. All data are expressed as mean ± standard deviation (<span class="html-italic">n</span> = 3). Asterisks (*) indicate a significance difference from the control (*, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Numbers of common and unique DEGs in HT-29 and Caco-2 cells stimulated with CotG-p75. Top 10 gene ontology terms of common and unique DEGs in HT-29 and Caco-2 are presented in the green, red, and blue boxes.</p>
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12 pages, 1400 KiB  
Review
Critical Roles of NF-κB Signaling Molecules in Bone Metabolism Revealed by Genetic Mutations in Osteopetrosis
by Eijiro Jimi and Takenobu Katagiri
Int. J. Mol. Sci. 2022, 23(14), 7995; https://doi.org/10.3390/ijms23147995 - 20 Jul 2022
Cited by 22 | Viewed by 3018
Abstract
The nuclear factor-κB (NF-κB) transcription factor family consists of five related proteins, RelA (p65), c-Rel, RelB, p50/p105 (NF-κB1), and p52/p100 (NF-κB2). These proteins are important not only for inflammation and the immune response but also for bone metabolism. Activation of NF-κB occurs via [...] Read more.
The nuclear factor-κB (NF-κB) transcription factor family consists of five related proteins, RelA (p65), c-Rel, RelB, p50/p105 (NF-κB1), and p52/p100 (NF-κB2). These proteins are important not only for inflammation and the immune response but also for bone metabolism. Activation of NF-κB occurs via the classic and alternative pathways. Inflammatory cytokines, such as tumor necrosis factor (TNF)-α and interleukin (IL)-1β, activate the former, and cytokines involved in lymph node formation, such as receptor activator of NF-κB ligand (RANKL) and CD40L, activate the latter. p50 and p52 double-knockout mice revealed severe osteopetrosis due to the total lack of osteoclasts, which are specialized cells for bone resorption. This finding suggests that the activation of NF-κB is required for osteoclast differentiation. The NF-κB signaling pathway is controlled by various regulators, including NF-κB essential modulator (NEMO), which is encoded by the IKBKG gene. In recent years, mutant forms of the IKBKG gene have been reported as causative genes of osteopetrosis, lymphedema, hypohidrotic ectodermal dysplasia, and immunodeficiency (OL-EDA-ID). In addition, a mutation in the RELA gene, encoding RelA, has been reported for the first time in newborns with high neonatal bone mass. Osteopetrosis is characterized by a diffuse increase in bone mass, ranging from a lethal form observed in newborns to an asymptomatic form that appears in adulthood. This review describes the genetic mutations in NF-κB signaling molecules that have been identified in patients with osteopetrosis. Full article
(This article belongs to the Special Issue Bone and Cartilage Biology)
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<p>Schematic representation of the NF-κB family proteins. Members of the NF-κB protein family are shown. The total number of amino acids in each protein is indicated on the right. Presumed sites of cleavage in p105 (amino acid 433) and p100 (amino acid 447) are indicated by dotted lines. RHD: Rel homology domain; TAD: transcriptional activation domain [<a href="#B8-ijms-23-07995" class="html-bibr">8</a>]; LZ: leucine zipper; GRR: glycine-rich repeat; ANK: ankyrin repeat. An arrow indicates the position of a genetic mutation associated with osteopetrosis.</p>
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<p>Two different NF-κB signaling pathways. The classic (canonical) pathway (<b>left</b>) is activated by a large number of agonists, such as TNF-α, IL-1, lipopolysaccharide, and T-cell receptors. The activation of this pathway depends on the inhibitor of the κB (IκB) kinase (IKK) complex (IKKα/β and NEMO), which phosphorylates IκBα (Ser32, 36) to induce rapid degradation. This pathway is essential for immune responses, inflammation, tumorigenesis, and cell survival. The alternative (noncanonical) pathway (<b>right</b>) is activated by a limited number of agonists, which are involved in secondary lymphoid organogenesis. This pathway requires NF-κB-inducing kinase (NIK) and IKKα. These kinases induce the slow processing of p100 to generate p52, resulting in the dimerization and activation of the p52/RelB heterodimer. The activation of NF-κB signaling stimulates osteoclastic bone resorption and suppresses bone formation.</p>
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<p>Schematic structure of NEMO and the genetic mutations in the <span class="html-italic">IKBKG</span> gene associated with osteopetrosis. The nucleotide and amino acid sequences of the <span class="html-italic">IKBKG</span> gene and the NEMO protein, respectively, are indicated in black (normal), blue (c.1,259A &gt; G; p.X420W) [<a href="#B50-ijms-23-07995" class="html-bibr">50</a>], red (c.1,182_1,183del_insTT; OL-EDA-ID) [<a href="#B51-ijms-23-07995" class="html-bibr">51</a>], and green (c.1,238A &gt; G; p.H413R) [<a href="#B52-ijms-23-07995" class="html-bibr">52</a>]. Dots indicate deletions, and open boxes indicate “stop codons”. CC: coiled-coil domain; NUB: NEMO ubiquitin binding; LZ: leucine zipper; ZF: zinc finger.</p>
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15 pages, 2915 KiB  
Article
Edwardsiella ictaluri T3SS Effector EseN Modulates Expression of Host Genes Involved in the Immune Response
by Lidiya P. Dubytska, Ranjan Koirala, Azhia Sanchez and Ronald Thune
Microorganisms 2022, 10(7), 1334; https://doi.org/10.3390/microorganisms10071334 - 1 Jul 2022
Cited by 4 | Viewed by 2303
Abstract
The type III secretion system (T3SS) effector EseN is encoded on the Edwardsiella ictaluri chromosome and is homologous to a family of T3SS effector proteins with phosphothreonine lyase activity. Previously we demonstrated that E. ictaluri invasion activates extracellular signal-regulated kinases 1 and 2 [...] Read more.
The type III secretion system (T3SS) effector EseN is encoded on the Edwardsiella ictaluri chromosome and is homologous to a family of T3SS effector proteins with phosphothreonine lyase activity. Previously we demonstrated that E. ictaluri invasion activates extracellular signal-regulated kinases 1 and 2 (ERK1/2) early in the infection, which are subsequently inactivated by EseN. Comparative transcriptomic analysis showed a total of 753 significant differentially expressed genes in head-kidney-derived macrophages (HKDM) infected with an EseN mutant (∆EseN) compared to HKDM infected with wild-type (WT) strains. This data strongly indicates classical activation of macrophages (the M1 phenotype) in response to E. ictaluri infection and a significant role for EseN in the manipulation of this process. Our data also indicates that E. ictaluri EseN is involved in the modulation of pathways involved in the immune response to infection and expression of several transcription factors, including NF-κβ (c-rel and relB), creb3L4, socs6 and foxo3a. Regulation of transcription factors leads to regulation of proinflammatory interleukins (IL-8, IL-12a, IL-15, IL-6) and cyclooxygenase-2 (COX-2) expression. Inhibition of COX-2 mRNA by WT E. ictaluri leads to decreased production of prostaglandin E2 (PGE2), which is the product of COX-2 activity. Collectively, our results indicate that E. ictaluri EseN is an important player in the modulation of host immune responses to E.ictaluri infection. Full article
(This article belongs to the Section Molecular Microbiology and Immunology)
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<p>Differentially expressed genes from three independent RNAseq experiments using HKDM infected with WT <span class="html-italic">E. ictaluri</span> (HKDM-WT) as well as an <span class="html-italic">E. ictaluri</span> ΔEseN mutant (HKDM-ΔEseN). (<b>A</b>) Cluster analysis of 753 significant differentially expressed genes (by <span class="html-italic">p</span> and q values less than 0.05) in HKDM-∆EseN compared to HKDM-WT. 1. Uninfected HKDM, 2. HKDM-WT, 3. HKDM-∆EseN. (<b>B</b>) Comparative transcriptomic analysis of genes that were significantly differentially expressed in HKDM-WT compared to uninfected HKDM (WT), and HKDM-∆EseN compared to HKDM-WT (EseN mutant). A total of 494 of these genes were common for HKDM-∆EseN and HKDM-WT.</p>
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<p>Gene ontology enrichment analysis of DEG. 753 DEG were analyzed for enrichment in three GO ontologies: biological process, cellular component, and molecular function. The number of genes enriched in individual GO terms is indicated on top of the individual bars. Molecular function: (1) translation regulator activity; (2) molecular transducer activity; (3) molecular adaptor activity; (4) binding; (5) structural molecule activity; (6) molecular function regulator; (7) catalytic activity; (8) transporter activity. Biological process: (1) cellular process; (2) reproductive process; (3) localization; (4) interspecies interaction between organisms; (5) reproduction; (6) biological regulation; (7) response to stimulus; (8) signaling; (9) developmental process; (10) multicellular organismal process; (11) locomotion; (12) biological adhesion; (13) metabolic process; (14) growth; (15) immune system process. Cellular component: (1) cellular anatomical entity; (2) protein-containing complex; (3) intracellular.</p>
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<p>Toll-like receptor signaling pathway. The KEGG pathway enrichment analysis was performed by DAVID. Genes that differently expressed in HKDM-∆EseN mutant compared to HKDM infected by WT indicated by red star and are significant (<span class="html-italic">p</span> &lt; 0.05 and q &lt; 0.05). Red arrow indicates up-regulation. Blue arrow indicates downregulation. Numbers indicate log2 fold change in HKDM-∆EseN vs. HKDM-WT.</p>
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<p>Transcription factor expression. RNAseq data from three independent replicates. (<b>A</b>) Not NFκ-β transcription factors. (<b>B</b>) Subunits of NFκ-β. Each column represents the mean fold change compared to uninfected HKDM from three independent biological replicates. (WT) HKDM infected with WT, (∆EseN) HKDM infected with ∆EseN. The <span class="html-italic">p</span> and q values indicate significance between a: HKDM infected with WT against not infected HKDM, b: HKDM infected with WT, and HKDM infected with ∆EseN mutant.</p>
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<p>Cytokines expression detected by RT-qPCR. Data were analyzed by Rosh LightCycler<sup>®</sup> 96 qPCR software using relative expression method. CanX and SDHA were used as reference genes. Comparison between groups was based on One-way ANOVA with the Bonferroni procedure for comparison of group means. Each column represents the mean ± SE of 3 to 4 independent experiments. Comparison between two groups was also analyzed by <span class="html-italic">t</span>-test (t); a: indicates significant difference between HKDM-WT (WT) and not infected HKDM (HKDM), b: indicates significant difference between HKDM-WT (WT) and HKDM-∆EseN (∆EseN). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0001.</p>
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<p>Expression of COX-2 mRNA. Data were analyzed by Rosh LightCycler<sup>®</sup> 96 qPCR software using relative expression method. CanX and SDHA were used as reference genes. Comparison between groups was based on One-way ANOVA with the Bonferroni procedure for comparison of group means. Comparison between two groups was also analyzed by <span class="html-italic">t</span>-test (t); Each column represents the mean ± SE of 3 to 4 independent experiments (depend on time points of infection); a: indicates significant difference between HKDM-WT (WT) and uninfected HKDM (HKDM), b: indicates significant difference between HKDM-WT (WT) and HKDM-∆EseN (∆EseN) * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0001.</p>
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<p>Expression of PGE2. Representative experiment from three independent experiments. Data represents levels of PGE2 expression in uninfected HKDM (HKDM), HKDM-WT (WT), and HKDM-∆EseN mutant (∆EseN). Comparison between groups was based on One-way ANOVA with the Bonferroni procedure. Each column represents the mean ± SE; a: indicates significant difference between HKDM-WT (WT) and uninfected HKDM (HKDM), b: indicates significant difference between HKDM-WT (WT) and HKDM-∆EseN (∆EseN) * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0001.</p>
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21 pages, 7896 KiB  
Article
The RelB-BLNK Axis Determines Cellular Response to a Novel Redox-Active Agent Betamethasone during Radiation Therapy in Prostate Cancer
by Luksana Chaiswing, Fangfang Xu, Yanming Zhao, Jon Thorson, Chi Wang, Daheng He, Jinpeng Lu, Sally R. Ellingson, Weixiong Zhong, Kristy Meyer, Wei Luo, William St. Clair and Daret St. Clair
Int. J. Mol. Sci. 2022, 23(12), 6409; https://doi.org/10.3390/ijms23126409 - 8 Jun 2022
Cited by 2 | Viewed by 3950
Abstract
Aberrant levels of reactive oxygen species (ROS) are potential mechanisms that contribute to both cancer therapy efficacy and the side effects of cancer treatment. Upregulation of the non-canonical redox-sensitive NF-kB family member, RelB, confers radioresistance in prostate cancer (PCa). We screened FDA-approved compounds [...] Read more.
Aberrant levels of reactive oxygen species (ROS) are potential mechanisms that contribute to both cancer therapy efficacy and the side effects of cancer treatment. Upregulation of the non-canonical redox-sensitive NF-kB family member, RelB, confers radioresistance in prostate cancer (PCa). We screened FDA-approved compounds and identified betamethasone (BET) as a drug that increases hydrogen peroxide levels in vitro and protects non-PCa tissues/cells while also enhancing radiation killing of PCa tissues/cells, both in vitro and in vivo. Significantly, BET increases ROS levels and exerts different effects on RelB expression in normal cells and PCa cells. BET induces protein expression of RelB and RelB target genes, including the primary antioxidant enzyme, manganese superoxide dismutase (MnSOD), in normal cells, while it suppresses protein expression of RelB and MnSOD in LNCaP cells and PC3 cells. RNA sequencing analysis identifies B-cell linker protein (BLNK) as a novel RelB complementary partner that BET differentially regulates in normal cells and PCa cells. RelB and BLNK are upregulated and correlate with the aggressiveness of PCa in human samples. The RelB-BLNK axis translocates to the nuclear compartment to activate MnSOD protein expression. BET promotes the RelB-BLNK axis in normal cells but suppresses the RelB-BLNK axis in PCa cells. Targeted disruptions of RelB-BLNK expressions mitigate the radioprotective effect of BET on normal cells and the radiosensitizing effect of BET on PCa cells. Our study identified a novel RelB complementary partner and reveals a complex redox-mediated mechanism showing that the RelB-BLNK axis, at least in part, triggers differential responses to the redox-active agent BET by stimulating adaptive responses in normal cells but pushing PCa cells into oxidative stress overload. Full article
(This article belongs to the Section Molecular Pathology, Diagnostics, and Therapeutics)
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<p>Library screening of FDA-approved drugs for compounds that induce PCa cell death while increasing non-cancer prostate cell viability through mediation of H<sub>2</sub>O<sub>2</sub> production. PC3 cells and PZ cells were treated with library FDA-approved drugs for 24 h. (<b>A</b>) Cell viability. (<b>B</b>) Extracellular H<sub>2</sub>O<sub>2</sub> based on Amplex Red assay. (<b>C</b>) Cell viability after drugs and radiation (4 Gy) treatment. Duplicate wells were tested per compound. (<b>D</b>) Drug candidates were chosen from a 786-compound library based on H<sub>2</sub>O<sub>2</sub> production and the ability to exert opposite cytotoxic effects on non-cancer and cancer cells. One dot represents one compound. Please see supplementary excel file for details of each compound.</p>
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<p>BET induces PCa cell death while increasing non-cancer prostate cell viability through mediation of H<sub>2</sub>O<sub>2</sub> production. Cells were treated with 5 µM BET at various time points. (<b>A</b>) Cell viability based on MTT assay. (<b>B</b>) Extracellular H<sub>2</sub>O<sub>2</sub> production (µM) based on Amplex Red method (24 h). (<b>C</b>) Intracellular H<sub>2</sub>O<sub>2</sub> production was measured using the aminotriazole-(3-AT)-mediated inactivation of CAT method (6 h). (<b>D</b>,<b>E</b>) Cell viability after treatment with PEG-CAT prior to BET. * <span class="html-italic">p</span>-value ≤ 0.05 when compared with vehicle. <span class="html-italic">n</span> = 3.</p>
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<p>Repurposing BET as a radioprotector for normal tissues while enhancing radiation efficacy for PCa in vivo. (<b>A</b>) Non-cancer nude mice were I.P. with BET alone or RT (2 Gy) 1 h prior, daily, for 5 days. Prostate, rectum, and blood were collected 1 week after treatment was completed. <span class="html-italic">n</span> = 5/group. (<b>B</b>) 4HNE protein adducts in serum. RU = Relative arbitrary unit. (<b>C</b>) Ultrastructural analysis of damaged prostate. (<b>D</b>) Representative photographs of damaged prostate. <span class="html-italic">n</span> = Nucleus. Star = Damaged mitochondria as indicated with loss of cristae and vacuolization of mitochondria. Bar = 6 µm. (<b>E</b>) PC3 xenograft tumor mice were I.P. with BET alone or RT (2 Gy) 1 h prior, daily, for 5 days. <span class="html-italic">n</span> = 10/group. (<b>F</b>) PC3 tumor size was measured every 2–3 days. (<b>G</b>) Survival fraction of PC3 xenograft tumor mice. * <span class="html-italic">p</span>-value ≤ 0.05 when compared to vehicle.</p>
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<p>RelB expression determines radioprotector effect of BET in non-PCa cells and radio-killing effect of BET. PZ cells were treated with BET and/or RT (2 Gy) for 24 h. NF-kB transcription factor family expression levels and function were measured. (<b>A</b>) RT-PCR of RelB. (<b>B</b>) RelB binding activity. (<b>C</b>) Chip assay with RelB antibody to I2E promoter of MnSOD. (<b>D</b>) Representative Western blots and quantitative analysis of protein expression for PrEC cells and PZ cells. * <span class="html-italic">p</span>-value ≤ 0.05 when compared with vehicle. PCa cells (LNCaP, PC3, DU145) were treated with either BET and/or RT (2 Gy) for 24 h. NF-kB transcription factor family expression levels and function were measured. (<b>E</b>) RT-PCR of RelB of PC3 cells. (<b>F</b>) RelB binding activity of PCa cells. (<b>G</b>) Chip assay with RelB antibody to I2E promoter of MnSOD of PC3 cells. (<b>H</b>) Representative Western blots and quantitative analysis of protein expression of PCa cells. <span class="html-italic">n</span> = 3. * <span class="html-italic">p</span>-value ≤ 0.05 when compared with vehicle.</p>
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<p>RNA sequencing identifies BLNK as a novel complementary protein that is upregulated by BET-mediated RelB expression. (<b>A</b>) Upregulated molecules that were most significantly changed (threshold exp-value 10). The rank order is determined by the indicated <span class="html-italic">p</span>-values. (<b>B</b>) 5 regulator pathways that were most significantly changed (threshold <span class="html-italic">p</span>-value 1.3). All these top regulators contain NF-kB family, and regulator star (*) contains BLNK. (<b>C</b>) Venn diagrams identified BLNK as the one of the proteins that is upregulated in PZ cells and downregulated in PC3 cells. (<b>D</b>) Heat map demonstrated ~2000 common genes that are diversely expressed in PC3 and PZ cells by all the treatments. Red = Upregulation vs. vehicle; Blue = Downregulation vs. vehicle, in PZ vs. PC3 cells. (<b>E</b>) RelB and (<b>F</b>) BLNK in PCa (<span class="html-italic">n</span> = 499) vs. normal prostate (<span class="html-italic">n</span> = 52) from TCGA database vs. vehicle. PARD = Prostate Adenocarcinoma. RELB and BLNK expressions are presented in log2-transformed transcripts per kilobase million (TPM) values. <span class="html-italic">p</span>-value calculated from linear mixed model. (<b>G</b>) PPI between RELB (magenta) and BLNK (orange). Contacts (1–4) given following: (1) Hydrogen bond at Cys 343 (BLNK) and Leu127 (RelB); (2) Ionic bond at Arg427 (BLNK) and Glu290 (RelB); (3) Hydrogen bond at His431 (BLNK) and Arg136 (RelB); and (4) Hydrogen bond at Arg448 (BLNK) and Cys389 (RelB). (<b>H</b>) Docking and binding analysis with ZDOCK and PyMol suggesting the interaction at the Y300 residue of RelB and the conserved R32 and R51 residues of BLNK.</p>
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<p>RelB and BLNK exert their roles reversely in non-PCa cells vs. PCa cells with BET treatment. Cells were treated with BET for 24 h and then harvested for analysis. (<b>A</b>) Western blots of BLNK, RelB, and MnSOD upon BET treatment. (<b>B</b>) IP with RelB antibody in whole cells nuclear fraction (NE) after treatment with BET. R = Anti-rabbit antibody. G = Anti-goat antibody. (<b>C</b>) Proximity Ligation assay. Red = Binding of RelB and BLNK. Blue = Nucleus. Inserts indicate the binding of RelB and BLNK in the nuclei of PZ cells (yellow) but not in the nuclei of PC3 cells (yellow). Bar = 50 μm. (<b>D</b>) Representation of double immunogold electron microscopy of cells and quantification of gold bead number in nuclei. PZ cells or PC3 cells were labeled with anti-rabbit RelB antibody (6 nm gold beads, arrow heads) and anti-goat BLNK antibody (10 nm gold beads, arrow). The gold beads were localized primarily in the nuclei of PZ cells after BET treatment but less in the nuclei of PC3 cells. * <span class="html-italic">p</span>-value ≤ 0.05 when compared with vehicle. Twenty nuclei were counted per group.</p>
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<p>BLNK interaction with RelB is a signal for BET protection of RT-induced injury in normal tissues. Cells were transfected with siRNA (Santa Cruz biotechnology) for 48 h and then treated with BET for 24 h. Western blot analysis (<b>A</b>) and protein quantification of (<b>B</b>) PC3 and (<b>C</b>) PZ cells. <span class="html-italic">n</span> = 2. (<b>D</b>) IP analysis confirmed a decrease in RelB:BLNK axis with RelB knockdown. WC = Whole cell lysates. R = Anti-rabbit antibody. G = Anti-goat antibody. (<b>E</b>) Trypan Blue assay and (<b>F</b>) representative photograph indicates a decrease in cell viability with siRNA against RelB, BLNK, or RelB + BLNK with or without BET treatment. <span class="html-italic">n</span> = 3. * <span class="html-italic">p</span>-value &lt; 0.05 vs. non-BET. Scale bar = 50 µm.</p>
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<p>Schematic of how BET-mediated ROS production sensitizes PCa cells toward death caused by RT while protecting non-PCa cells against injury from RT off-target effects. Aberrant redox homeostasis of cancer cells enables redox-modifying agent BET to enhance radiation therapy efficacy by selective sensitization. In normal cells under physiologic conditions, cellular redox status is kept at a low oxidizing level. A shift in cell redox status toward an oxidizing condition, from BET + RT, will stimulate the expression of RelB-BLNK and translocation to the nucleus, which leads to upregulation of the antioxidant system, including MnSOD. The upregulation of MnSOD maintains redox status in normal cells and promotes cell survival. To the contrary, cancer cells are usually under high oxidizing conditions. A comparable shift in ROS levels modulated by BET + RT to an extreme oxidizing condition will cause cell death. Green arrows = activation; Red line = inhibition.</p>
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24 pages, 1648 KiB  
Review
The Role of Nuclear Factor Kappa B (NF-κB) in the Immune Response against Parasites
by Piotr Bąska and Luke J. Norbury
Pathogens 2022, 11(3), 310; https://doi.org/10.3390/pathogens11030310 - 2 Mar 2022
Cited by 22 | Viewed by 5135
Abstract
The immune system consists of various cells, organs, and processes that interact in a sophisticated manner to defend against pathogens. Upon initial exposure to an invader, nonspecific mechanisms are raised through the activation of macrophages, monocytes, basophils, mast cells, eosinophils, innate lymphoid cells, [...] Read more.
The immune system consists of various cells, organs, and processes that interact in a sophisticated manner to defend against pathogens. Upon initial exposure to an invader, nonspecific mechanisms are raised through the activation of macrophages, monocytes, basophils, mast cells, eosinophils, innate lymphoid cells, or natural killer cells. During the course of an infection, more specific responses develop (adaptive immune responses) whose hallmarks include the expansion of B and T cells that specifically recognize foreign antigens. Cell to cell communication takes place through physical interactions as well as through the release of mediators (cytokines, chemokines) that modify cell activity and control and regulate the immune response. One regulator of cell states is the transcription factor Nuclear Factor kappa B (NF-κB) which mediates responses to various stimuli and is involved in a variety of processes (cell cycle, development, apoptosis, carcinogenesis, innate and adaptive immune responses). It consists of two protein classes with NF-κB1 (p105/50) and NF-κB2 (p100/52) belonging to class I, and RelA (p65), RelB and c-Rel belonging to class II. The active transcription factor consists of a dimer, usually comprised of both class I and class II proteins conjugated to Inhibitor of κB (IκB). Through various stimuli, IκB is phosphorylated and detached, allowing dimer migration to the nucleus and binding of DNA. NF-κB is crucial in regulating the immune response and maintaining a balance between suppression, effective response, and immunopathologies. Parasites are a diverse group of organisms comprised of three major groups: protozoa, helminths, and ectoparasites. Each group induces distinct effector immune mechanisms and is susceptible to different types of immune responses (Th1, Th2, Th17). This review describes the role of NF-κB and its activity during parasite infections and its contribution to inducing protective responses or immunopathologies. Full article
(This article belongs to the Special Issue Immune Response of the Host and Vaccine Development)
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<p>Schematic model of NF-κB activation through canonical and non-canonical pathways. Canonical activation involves TGF-β activated kinase-1 (TAK1) which phosphorylates Inhibitory Kappa B Kinase β (IKKβ) complexed with IKKα and IKKγ (NEMO). This leads to phosphorylation of the α Inhibitor of κB (IκBα), its detachment from the p56/p50 dimer, ubiquitination, and proteasomal degradation. Released p65/p50 dimer migrates to the nucleus and binds to DNA sequences leading to transcription of appropriate genes. During the noncanonical pathway, NF-κB-inducing kinase (NIK) phosphorylates the IKKα dimer which phosphorylates p100 leading to its disruption and release of the RelB/p52 dimer. The dimer migrates to the nucleus and regulates the transcription of particular genes.</p>
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<p>The impact of <span class="html-italic">Plasmodium</span> spp., <span class="html-italic">Trypanosoma cruzi, Toxoplasma gondii,</span> and <span class="html-italic">Leishmania</span> spp. on NF-κB activity and outcomes. (<b>A</b>) <span class="html-italic">Plasmodium</span> spp. increase NF-κB activity in specific cell populations which is associated with pathology in the brain, inducing cerebral malaria symptoms (apoptosis in brain endothelial cells and intravascular leukocytes), and may facilitate hidden parasite populations in the spleen. <span class="html-italic">Plasmodium</span> spp. also trigger an inflammatory response in monocytes, but patients with decreased NF-κB activity in PBMCs show more severe malaria symptoms. (<b>B</b>) Reduced NF-κB activity facilitates infection of <span class="html-italic">T. cruzi</span>. Enhanced NF-κB activity in heart tissue during <span class="html-italic">T. cruzi</span> infections leads to heart failure. (<b>C</b>) RelB-deprived mice do not survive <span class="html-italic">T. gondii</span> infection. <span class="html-italic">T. gondii</span> deactivate NF-κB signaling, reducing the immune response in macrophages and neutrophils. (<b>D</b>) <span class="html-italic">Leishmania</span> spp. reduce NF-κB activity in infected macrophages and DC, facilitating parasite survival.</p>
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<p>The impact of helminths on NF-κB activity and outcomes. (<b>A</b>) <span class="html-italic">B. malayi</span> infection results in decreased NF-κB activity and induces M<sub>2</sub> and eventually M<sub>reg</sub> macrophages, while patients with lymphatic pathology show increased angiogenesis associated with NF-κB activation. <span class="html-italic">H. polygyrus</span> induces semi-maturation of DCs and induces Th<sub>2</sub> and regulatory events through modulation of NF-κB activity. Products released by <span class="html-italic">T. spiralis</span> affect NF-κB activity in LPS-activated macrophages, significantly reducing proinflammatory cytokine production. (<b>B</b>) <span class="html-italic">T. solium</span> larval antigens activate the NF-κB pathway in monocytes inducing chemokine release. <span class="html-italic">M. corti</span> antigens inhibit LPS-induced inflammatory phenotypes in microglia cells via NF-κB modulation. (<b>C</b>) <span class="html-italic">F. hepatica</span> tegumental antigens temporarily prevent LPS-induced NF-κB in DC, suppressing maturation. <span class="html-italic">S. mansoni</span> induces NF-κB activation in human hepatic stellate cells which is associated with liver fibrosis; a similar situation occurs in <span class="html-italic">S. Japonicum-</span>infected mice.</p>
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