Improving biocuration of microRNAs in diseases: a case study in idiopathic pulmonary fibrosis

MicroRNAs (miRNAs) are small and non-coding RNA molecules that inhibit gene expression posttranscriptionally. They play important roles in several biological processes, and in recent years there has been an interest in studying how they are related to the pathogenesis of diseases. Although there are already some databases that contain information for miRNAs and their relation with illnesses, their curation represents a significant challenge due to the amount of information that is being generated every day. In particular, respiratory diseases are poorly documented in databases, despite the fact that they are of increasing concern regarding morbidity, mortality and economic impacts. In this work, we present the results that we obtained in the BioCreative Interactive Track (IAT), using a semiautomatic approach for improving biocuration of miRNAs related to diseases. Our procedures will be useful to complement databases that contain this type of information. We adapted the OntoGene text mining pipeline and the ODIN curation system in a full-text corpus of scientific publications concerning one specific respiratory disease: idiopathic pulmonary fibrosis, the most common and aggressive of the idiopathic interstitial cases of pneumonia. We curated 823 miRNA text snippets and found a total of 246 miRNAs related to this disease based on our semiautomatic approach with the system OntoGene/ODIN. The biocuration throughput improved by a factor of 12 compared with traditional manual biocuration. A significant advantage of our semiautomatic pipeline is that it can be applied to obtain the miRNAs of all the respiratory diseases and offers the possibility to be used for other illnesses. Database URL:http://odin.ccg.unam.mx/ODIN/bc2015-miRNA

CD4+ T Cell Regulatory Network Underlies the Decrease in Th1 and the Increase in Anergic and Th17 Subsets in Severe COVID-19

In this model we use a dynamic and multistable Boolean regulatory network to provide a mechanistic explanation of the lymphopenia and dysregulation of CD4+ T cell subsets in COVID-19 and provide therapeutic targets. Using a previous model, the cytokine micro-environments found in mild, moderate, and severe COVID-19 with and without TGF-β and IL-10 was we simulated. It shows that as the severity of the disease increases, the number of antiviral Th1 cells decreases, while the the number of Th1-like regulatory and exhausted cells and the proportion between Th1 and Th1R cells increases. The addition of the regulatory cytokines TFG-β and IL-10 makes the Th1 attractor unstable and favors the Th17 and regulatory subsets. This is associated with the contradictory signals in the micro-environment that activate SOCS proteins that block the signaling pathways. Furthermore, it determined four possible therapeutic targets that increase the Th1 compartment in severe COVID-19: the activation of the IFN-γ pathway, or the inhibition of TGF-β or IL-10 pathways or SOCS1 protein; from these, inhibiting SOCS1 has the lowest number of predicted collateral effects. Finally, a tool is provided that allows simulations of specific cytokine environments and predictions of CD4 T cell subsets and possible interventions, as well as associated secondary effects

Lack of ZNF365 Drives Senescence and Exacerbates Experimental Lung Fibrosis

Idiopathic pulmonary fibrosis (IPF) is characterized by aberrant activation of the alveolar epithelium, the expansion of the fibroblast population, and the accumulation of extracellular matrix. Global gene expression of human lung fibroblasts stimulated with TGFβ-1, a strong fibrotic mediator revealed the overexpression of ZNF365, a zinc finger protein implicated in cell cycle control and telomere stabilization. We evaluated the expression and localization of ZNF365 in IPF lungs and in the fibrotic response induced by bleomycin in WT and deficient mice of the orthologous gene Zfp365. In IPF, ZNF365 was overexpressed and localized in fibroblasts/myofibroblasts and alveolar epithelium. Bleomycin-induced lung fibrosis showed an upregulation of Zfp365 localized in lung epithelium and stromal cell populations. Zfp365 KO mice developed a significantly higher fibrotic response compared with WT mice by morphology and hydroxyproline content. Silencing ZNF365 in human lung fibroblasts and alveolar epithelial cells induced a significant reduction of growth rate and increased senescence markers, including Senescence Associated β Galactosidase activity, p53, p21, and the histone variant γH2AX. Our findings demonstrate that ZNF365 is upregulated in IPF and experimental lung fibrosis and suggest a protective role since its absence increases experimental lung fibrosis mechanistically associated with the induction of cell senescence

Transcription Factors in <i>Escherichia coli</i> Prefer the <i>Holo</i> Conformation

<div><p>The transcriptional regulatory network of <i>Escherichia coli</i> K-12 is among the best studied gene networks of any living cell. Transcription factors bind to DNA either with their effector bound (<i>holo</i> conformation), or as a free protein (<i>apo</i> conformation) regulating transcription initiation. By using RegulonDB, the functional conformations (<i>holo</i> or <i>apo</i>) of transcription factors, and their mode of regulation (activator, repressor, or dual) were exhaustively analyzed. We report a striking discovery in the architecture of the regulatory network, finding a strong under-representation of the <i>apo</i> conformation (without allosteric metabolite) of transcription factors when binding to their DNA sites to activate transcription. This observation is supported at the level of individual regulatory interactions on promoters, even if we exclude the promoters regulated by global transcription factors, where three-quarters of the known promoters are regulated by a transcription factor in <i>holo</i> conformation. This genome-scale analysis enables us to ask what are the implications of these observations for the physiology and for our understanding of the ecology of <i>E. coli</i>. We discuss these ideas within the framework of the demand theory of gene regulation.</p></div

Gene classification based on MultiFun and TF conformational bias.

<p>Each gene was classified by its regulation, on the function of the TF (activator or repressor), the conformation (apo or holo), and functional class (T: transport; O: others; C: catabolic; A: anabolic). This plot represents a contingency table, with each rectangle corresponding to a piece of the plot, with their sizes proportional to the cell entry. The Pearson residuals indicate the fit of a log-linear model. Blue represents the maximum significance of the corresponding residual, and red shows the minimum.</p

Conformational asymmetries.

<p>(a) Conformational asymmetries of TFs. TFs were classified based on the mode of control (activators: green; repressors: red; dual regulators: blue) and the functional conformation (holo, apo, or holo-apo). Pearson’s chi-squared test: χ² = 29.0212, df = 4, P = 7.74×10⁻⁰⁶. (b) Conformational asymmetries in TF functional conformation-promoter pairs. TF functional conformation-promoter pairs were classified according to the mode of control (activation: green, repression: red, dual: blue) and the functional conformation (holo, apo, or holo-apo) of the TF. Activating interaction pairs may come from TFs that are either activators or from promoters that are activated by dual TFs; repressing interaction pairs may come from repressor TFs or from promoters negatively regulated by dual TFs. Dual interaction pairs refer here exclusively to interactions by a TF with a dual effect on the same promoter. Pearson’s chi-squared test: χ² = 76.3451, df = 2, P<2.2×10⁻¹⁶. (c) Effect of excluding global TFs on conformational asymmetries of TF functional conformation-promoter pairs. Only TF-promoter interactions where the TF is local are here counted. If a promoter is subject both to local and global regulation, those interactions with local TFs contribute to this counting. Interactions, excluding those by global regulators. They were classified according to the mode of control (activation: green; repression: red; dual: blue) and functional conformation (holo, apo, or holo-apo) of the TF. Pearson’s chi-squared test: χ² = 79.4576, df = 4, P = 2.269×10⁻¹⁶.</p

Predicted gene control circuits for simple cases.

<p><b>Case 1.</b> Inducible catabolic high-demand system. a) Expression of the regulated genes is OFF, because the activator is in a nonfunctional state. b) In the presence of the effector, it binds to the activator, changing it to the holo conformation, which facilitates transcription, e.g., maltotriose binds to MalT and this induces maltose operon expression. Case 2. Inducible catabolic low-demand system. a) The repressor is functional in the apo conformation, so the system is repressed in the absence of the effector. b) In the presence of the effector, it binds to the TF, changing it to a nonfunctional conformation, which allows induction of the system, e.g., allolactose binds to LacI and this induces lactose operon expression. Case 3. Repressible anabolic high-demand system. a) In the absence of the effector, the system is ON, with the activator in the apo conformation. b) When the effector is present, the activator is nonfunctional and the system is deactivated, e.g., Cbl activates the tau and ssi operons when it is unbound to the adenosyl 5′-phosphosulfate compound. Case 4. Repressible anabolic low-demand system. a) The repressor is nonfunctional in the absence of effector, so gene expression is turned ON. b) In the presence of effector, it binds to the TF, converting it to the holo conformation, which binds DNA and represses transcription, e.g., TrpR bound to tryptophan in the holo conformation represses the tryptophan operon. Symbols: ON and OFF show gene expression and a lack of gene expression, respectively. Activator: green oval; repressor: red oval; RNA polymerase: purple bean shape; effector: yellow triangles; mRNA: blue line.</p

Mir-155-5p targets TP53INP1 to promote proliferative phenotype in hypersensitivity pneumonitis lung fibroblasts

Background: Hypersensitivity pneumonitis (HP) is an inflammatory disorder affecting lung parenchyma and often evolves into fibrosis (fHP). The altered regulation of genes involved in the pathogenesis of the disease is not well comprehended, while the role of microRNAs in lung fibroblasts remains unexplored. Methods: We used integrated bulk RNA-Seq and enrichment pathway bioinformatic analyses to identify differentially expressed (DE)-miRNAs and genes (DEGs) associated with HP lungs. In vitro, we evaluated the expression and potential role of miR-155-5p in the phenotype of fHP lung fibroblasts. Loss and gain assays were used to demonstrate the impact of miR-155-5p on fibroblast functions. In addition, mir-155-5p and its target TP53INP1 were analyzed after treatment with TGF-β, IL-4, and IL-17A. Results: We found around 50 DEGs shared by several databases that differentiate HP from control and IPF lungs, constituting a unique HP lung transcriptional signature. Additionally, we reveal 18 DE-miRNAs that may regulate these DEGs. Among the candidates likely associated with HP pathogenesis was miR-155-5p. Our findings indicate that increased miR-155-5p in fHP fibroblasts coincides with reduced TP53INP1 expression, high proliferative capacity, and a lack of senescence markers compared to IPF fibroblasts. Induced overexpression of miR-155-5p in normal fibroblasts remarkably increases the proliferation rate and decreases TP53INP1 expression. Conversely, miR-155-5p inhibition reduces proliferation and increases senescence markers. TGF-β, IL-4, and IL-17A stimulated miR-155-5p overexpression in HP lung fibroblasts. Conclusion: Our findings suggest a distinctive signature of 53 DEGs in HP, including CLDN18, EEF2, CXCL9, PLA2G2D, and ZNF683, as potential targets for future studies. Likewise, 18 miRNAs, including miR-155-5p, could be helpful to establish differences between these two pathologies. The overexpression of miR-155-5p and downregulation of TP53INP1 in fHP lung fibroblasts may be involved in his proliferative and profibrotic phenotype. These findings may help differentiate and characterize their pathogenic features and understand their role in the disease