Master regulator genes and their impact on major diseases

Identification of master regulator genes controlling pathogenic CD4+ T cell fate in inflammatory bowel disease through transcriptional network analysis

Inflammatory bowel diseases (IBD) are a group of chronic inflammatory conditions of the gastrointestinal tract associated with multiple pathogenic factors, including dysregulation of the immune response. Effector CD4+ T cells and regulatory CD4+ T cells (Treg) are central players in maintaining the balance between tolerance and inflammation. Interestingly, genetic modifications in these cells have been implicated in regulating the commitment of specific phenotypes and immune functions. However, the transcriptional program controlling the pathogenic behavior of T helper cells in IBD progression is still unknown. In this study, we aimed to find master transcription regulators controlling the pathogenic behavior of effector CD4+ T cells upon gut inflammation. To achieve this goal, we used an animal model of IBD induced by the transfer of naïve CD4+ T cells into recombination-activating gene 1 (Rag1) deficient mice, which are devoid of lymphocytes. As a control, a group of Rag1-/- mice received the transfer of the whole CD4+ T cells population, which includes both effector T cells and Treg. When gut inflammation progressed, we isolated CD4+ T cells from the colonic lamina propria and spleen tissue, and performed bulk RNA-seq. We identified differentially up- and down-regulated genes by comparing samples from both experimental groups. We found 532 differentially expressed genes (DEGs) in the colon and 30 DEGs in the spleen, mostly related to Th1 response, leukocyte migration, and response to cytokines in lamina propria T-cells. We integrated these data into Gene Regulatory Networks to identify Master Regulators, identifying four up-regulated master gene regulators (Lef1, Dnmt1, Mybl2, and Jup) and only one down-regulated master regulator (Foxo3). The altered expression of master regulators observed in the transcriptomic analysis was confirmed by qRT-PCR analysis and found an up-regulation of Lef1 and Mybl2, but without differences on Dnmt1, Jup, and Foxo3. These two master regulators have been involved in T cells function and cell cycle progression, respectively. We identified two master regulator genes associated with the pathogenic behavior of effector CD4+ T cells in an animal model of IBD. These findings provide two new potential molecular targets for treating IBD.

Complexation of histone deacetylase inhibitor belinostat to Cu(II) prevents premature metabolic inactivation in vitro and demonstrates potent anti-cancer activity in vitro and ex vivo in colon cancer

PURPOSE

The histone deacetylase inhibitor (HDACi), belinostat, has had limited therapeutic impact in solid tumors, such as colon cancer, due to its poor metabolic stability. Here we evaluated a novel belinostat prodrug, copper-bis-belinostat (Cubisbel), in vitro and ex vivo, designed to overcome the pharmacokinetic challenges of belinostat.

METHODS

The in vitro metabolism of each HDACi was evaluated in human liver microsomes (HLMs) using mass spectrometry. Next, the effect of belinostat and Cubisbel on cell growth, HDAC activity, apoptosis and cell cycle was assessed in three colon cancer cell lines. Gene expression alterations induced by both HDACis were determined using RNA-Seq, followed by in silico analysis to identify master regulators (MRs) of differentially expressed genes (DEGs). The effect of both HDACis on the viability of colon cancer patient-derived tumor organoids (PDTOs) was also examined.

RESULTS

Belinostat and Cubisbel significantly reduced colon cancer cell growth mediated through HDAC inhibition and apoptosis induction. Interestingly, the in vitro half-life of Cubisbel was significantly longer than belinostat. Belinostat and its Cu derivative commonly dysregulated numerous signalling and metabolic pathways while genes downregulated by Cubisbel were potentially controlled by VEGFA, ERBB2 and DUSP2 MRs. Treatment of colon cancer PDTOs with the HDACis resulted in a significant reduction in cell viability and downregulation of stem cell and proliferation markers.

CONCLUSIONS

Complexation of belinostat to Cu(II) does not alter the HDAC activity of belinostat, but instead significantly enhances its metabolic stability in vitro and targets anti-cancer pathways by perturbing key MRs in colon cancer. Complexation of HDACis to a metal ion might improve the efficacy of clinically used HDACis in patients with colon cancer.

Use of gene regulatory network analysis to repurpose drugs to treat bipolar disorder

BACKGROUND

Bipolar disorder (BD) presents significant challenges in drug discovery, necessitating alternative approaches. Drug repurposing, leveraging computational techniques and expanding biomedical data, holds promise for identifying novel treatment strategies.

METHODS

This study utilized gene regulatory networks (GRNs) to identify significant regulatory changes in BD, using network-based signatures for drug repurposing. Employing the PANDA algorithm, we investigated the variations in transcription factor-GRNs between individuals with BD and unaffected individuals, incorporating binding motifs, protein interactions, and gene co-expression data. The differences in edge weights between BD and controls were then used as differential network signatures to identify drugs potentially targeting the disease-associated gene signature, employing the CLUEreg tool in the GRAND database.

RESULTS

Using a large RNA-seq dataset of 216 post-mortem brain samples from the CommonMind consortium, we constructed GRNs based on co-expression for individuals with BD and unaffected controls, involving 15,271 genes and 405 TFs. Our analysis highlighted significant influences of these TFs on immune response, energy metabolism, cell signalling, and cell adhesion pathways in the disorder. By employing drug repurposing, we identified 10 promising candidates potentially repurposed as BD treatments.

LIMITATIONS

Non-drug-naïve transcriptomics data, bulk analysis of BD samples, potential bias of GRNs towards well-studied genes.

CONCLUSIONS

Further investigation into repurposing candidates, especially those with preclinical evidence supporting their efficacy, like kaempferol and pramocaine, is warranted to understand their mechanisms of action and effectiveness in treating BD. Additionally, novel targets such as PARP1 and A2b offer opportunities for future research on their relevance to the disorder.

PBX1 as a novel master regulator in cancer: Its regulation, molecular biology, and therapeutic applications

PBX1 is a critical transcription factor at the top of various cell fate-determining pathways. In cancer, PBX1 stands at the crossroads of multiple oncogenic signaling pathways and mediates responses by recruiting a broad repertoire of downstream targets. Research thus far has corroborated the involvement of PBX1 in cancer proliferation, resisting apoptosis, tumor-associated neoangiogenesis, epithelial-mesenchymal transition (EMT) and metastasis, immune evasion, genome instability, and dysregulating cellular metabolism. Recently, our understanding of the functional regulation of the PBX1 protein has advanced, as increasing evidence has depicted a regulatory network consisting of transcriptional, post-transcriptional, and post-translational levels of control mechanisms. Furthermore, accumulating studies have supported the clinical utilization of PBX1 as a prognostic or therapeutic target in cancer. Preliminary results showed that PBX1 entails vast potential as a targetable master regulator in the treatment of cancer, particularly in those with high-risk features and resistance to other therapeutic strategies. In this review, we will explore the regulation, protein-protein interactions, molecular pathways, clinical application, and future challenges of PBX1.

Transcriptional Signatures and Network-Based Approaches Identified Master Regulators Transcription Factors Involved in Experimental Periodontitis Pathogenesis

Periodontitis is a chronic inflammatory disease characterized by the progressive and irreversible destruction of the periodontium. Its aetiopathogenesis lies in the constant challenge of the dysbiotic biofilm, which triggers a deregulated immune response responsible for the disease phenotype. Although the molecular mechanisms underlying periodontitis have been extensively studied, the regulatory mechanisms at the transcriptional level remain unclear. To generate transcriptomic data, we performed RNA shotgun sequencing of the oral mucosa of periodontitis-affected mice. Since genes are not expressed in isolation during pathological processes, we disclose here the complete repertoire of differentially expressed genes (DEG) and co-expressed modules to build Gene Regulatory Networks (GRNs) and identify the Master Transcriptional Regulators of periodontitis. The transcriptional changes revealed 366 protein-coding genes and 42 non-coding genes differentially expressed and enriched in the immune response. Furthermore, we found 13 co-expression modules with different representation degrees and gene expression levels. Our GRN comprises genes from 12 gene clusters, 166 nodes, of which 33 encode Transcription Factors, and 201 connections. Finally, using these strategies, 26 master regulators of periodontitis were identified. In conclusion, combining the transcriptomic analyses with the regulatory network construction represents a powerful and efficient strategy for identifying potential periodontitis-therapeutic targets.

Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection

Despite extensive research efforts, reconstruction of gene regulatory networks (GRNs) from transcriptomics data remains a pressing challenge in systems biology. While non-linear approaches for reconstruction of GRNs show improved performance over simpler alternatives, we do not yet have understanding if joint modelling of multiple target genes may improve performance, even under linearity assumptions. To address this problem, we propose two novel approaches that cast the GRN reconstruction problem as a blend between regularized multivariate regression and graphical models that combine the L2,1-norm with classical regularization techniques. We used data and networks from the DREAM5 challenge to show that the proposed models provide consistently good performance in comparison to contenders whose performance varies with data sets from simulation and experiments from model unicellular organisms Escherichia coli and Saccharomyces cerevisiae. Since the models' formulation facilitates the prediction of master regulators, we also used the resulting findings to identify master regulators over all data sets as well as their plasticity across different environments. Our results demonstrate that the identified master regulators are in line with experimental evidence from the model bacterium E. coli. Together, our study demonstrates that simultaneous modelling of several target genes results in improved inference of GRNs and can be used as an alternative in different applications.

A Mesp1-dependent developmental breakpoint in transcriptional and epigenomic specification of early cardiac precursors

Transcriptional networks governing cardiac precursor cell (CPC) specification are incompletely understood owing, in part, to limitations in distinguishing CPCs from non-cardiac mesoderm in early gastrulation. We leveraged detection of early cardiac lineage transgenes within a granular single-cell transcriptomic time course of mouse embryos to identify emerging CPCs and describe their transcriptional profiles. Mesp1, a transiently expressed mesodermal transcription factor, is canonically described as an early regulator of cardiac specification. However, we observed perdurance of CPC transgene-expressing cells in Mesp1 mutants, albeit mislocalized, prompting us to investigate the scope of the role of Mesp1 in CPC emergence and differentiation. Mesp1 mutant CPCs failed to robustly activate markers of cardiomyocyte maturity and crucial cardiac transcription factors, yet they exhibited transcriptional profiles resembling cardiac mesoderm progressing towards cardiomyocyte fates. Single-cell chromatin accessibility analysis defined a Mesp1-dependent developmental breakpoint in cardiac lineage progression at a shift from mesendoderm transcriptional networks to those necessary for cardiac patterning and morphogenesis. These results reveal Mesp1-independent aspects of early CPC specification and underscore a Mesp1-dependent regulatory landscape required for progression through cardiogenesis.

Modeling and cleaning RNA-seq data significantly improve detection of differentially expressed genes

Background

RNA-seq has become a standard technology to quantify mRNA. The measured values usually vary by several orders of magnitude, and while the detection of differences at high values is statistically well grounded, the significance of the differences for rare mRNAs can be weakened by the presence of biological and technical noise.

Results

We have developed a method for cleaning RNA-seq data, which improves the detection of differentially expressed genes and specifically genes with low to moderate transcription. Using a data modeling approach, parameters of randomly distributed mRNA counts are identified and reads, most probably originating from technical noise, are removed. We demonstrate that the removal of this random component leads to the significant increase in the number of detected differentially expressed genes, more significant pvalues and no bias towards low-count genes.

Conclusion

Application of RNAdeNoise to our RNA-seq data on polysome profiling and several published RNA-seq datasets reveals its suitability for different organisms and sequencing technologies such as Illumina and BGI, shows improved detection of differentially expressed genes, and excludes the subjective setting of thresholds for minimal RNA counts. The program, RNA-seq data, resulted gene lists and examples of use are in the supplementary data and at https://github.com/Deyneko/RNAdeNoise.

SNP-to-gene linking strategies reveal contributions of enhancer-related and candidate master-regulator genes to autoimmune disease

We assess contributions to autoimmune disease of genes whose regulation is driven by enhancer regions (enhancer-related) and genes that regulate other genes in trans (candidate master-regulator). We link these genes to SNPs using several SNP-to-gene (S2G) strategies and apply heritability analyses to draw three conclusions about 11 autoimmune/blood-related diseases/traits. First, several characterizations of enhancer-related genes using functional genomics data are informative for autoimmune disease heritability after conditioning on a broad set of regulatory annotations. Second, candidate master-regulator genes defined using trans-eQTL in blood are also conditionally informative for autoimmune disease heritability. Third, integrating enhancer-related and master-regulator gene sets with protein-protein interaction (PPI) network information magnified their disease signal. The resulting PPI-enhancer gene score produced >2-fold stronger heritability signal and >2-fold stronger enrichment for drug targets, compared with the recently proposed enhancer domain score. In each case, functionally informed S2G strategies produced 4.1- to 13-fold stronger disease signals than conventional window-based strategies.

Counteracting Health Risks by Modulating Homeostatic Signaling

Homeostasis was initially conceptualized by Bernard and Cannon around a century ago as a steady state of physiological parameters that vary within a certain range, such as blood pH, body temperature, and heart rate^1,2. The underlying mechanisms that maintain homeostasis are explained by negative feedbacks that are executed by the neuronal, endocrine, and immune systems. At the cellular level, homeostasis, such as that of redox and energy steady state, also exists and is regulated by various cell signaling pathways. The induction of homeostatic mechanism is critical for human to adapt to various disruptive insults (stressors); while on the other hand, adaptation occurs at the expense of other physiological processes and thus runs the risk of collateral damages, particularly under conditions of chronic stress. Conceivably, anti-stress protection can be achieved by stressor-mimicking medicinals that elicit adaptive responses prior to an insult and thereby serve as health risk countermeasures; and in situations where maladaptation may occur, downregulating medicinals could be used to suppress the responses and prevent subsequent pathogenesis. Both strategies are preemptive interventions particularly suited for individuals who carry certain lifestyle, environmental, or genetic risk factors. In this article, we will define and characterize a new modality of prophylactic intervention that forestalls diseases via modulating homeostatic signaling. Moreover, we will provide evidence from the literature that support this concept and distinguish it from other homeostasis-related interventions such as adaptogen, hormesis, and xenohormesis.

Cancer progression as a learning process

Drug resistance and metastasis—the major complications in cancer—both entail adaptation of cancer cells to stress, whether a drug or a lethal new environment. Intriguingly, these adaptive processes share similar features that cannot be explained by a pure Darwinian scheme, including dormancy, increased heterogeneity, and stress-induced plasticity. Here, we propose that learning theory offers a framework to explain these features and may shed light on these two intricate processes. In this framework, learning is performed at the single-cell level, by stress-driven exploratory trial-and-error. Such a process is not contingent on pre-existing pathways but on a random search for a state that diminishes the stress. We review underlying mechanisms that may support this search, and show by using a learning model that such exploratory learning is feasible in a high-dimensional system as the cell. At the population level, we view the tissue as a network of exploring agents that communicate, restraining cancer formation in health. In this view, disease results from the breakdown of homeostasis between cellular exploratory drive and tissue homeostasis.

A Stochastic Binary Model for the Regulation of Gene Expression to Investigate Responses to Gene Therapy

Simple Summary

Gene editing technologies reached a turning point toward epigenetic modulation for cancer treatment. Gene networks are complex systems composed of multiple non-trivially coupled elements capable of reliably processing dynamical information from the environment despite unavoidable randomness. However, this functionality is lost when the cells are in a diseased state. Hence, gene-editing-based therapeutic design can be viewed as a gene network dynamics modulation toward a healthy state. Enhancement of this control relies on mathematical models capable of effectively describing the regulation of stochastic gene expression. We use a two-state stochastic model for gene expression to investigate treatment response with a switching target gene. We show the necessity of modulating multiple gene-expression-related processes to reach a heterogeneity-reduced specific response using epigenetic-targeting cancer treatment designs. Our approach can be used as an additional tool for developing epigenetic-targeting treatments.

Abstract

In this manuscript, we use an exactly solvable stochastic binary model for the regulation of gene expression to analyze the dynamics of response to a treatment aiming to modulate the number of transcripts of a master regulatory switching gene. The challenge is to combine multiple processes with different time scales to control the treatment response by a switching gene in an unavoidable noisy environment. To establish biologically relevant timescales for the parameters of the model, we select the RKIP gene and two non-specific drugs already known for changing RKIP levels in cancer cells. We demonstrate the usefulness of our method simulating three treatment scenarios aiming to reestablish RKIP gene expression dynamics toward a pre-cancerous state: (1) to increase the promoter’s ON state duration; (2) to increase the mRNAs’ synthesis rate; and (3) to increase both rates. We show that the pre-treatment kinetic rates of ON and OFF promoter switching speeds and mRNA synthesis and degradation will affect the heterogeneity and time for treatment response. Hence, we present a strategy for reaching increased average mRNA levels with diminished heterogeneity while reducing drug dosage by simultaneously targeting multiple kinetic rates that effectively represent the chemical processes underlying the regulation of gene expression. The decrease in heterogeneity of treatment response by a target gene helps to lower the chances of emergence of resistance. Our approach may be useful for inferring kinetic constants related to the expression of antimetastatic genes or oncogenes and for the design of multi-drug therapeutic strategies targeting the processes underpinning the expression of master regulatory genes.

Computational Identification of Master Regulators Influencing Trypanotolerance in Cattle

African Animal Trypanosomiasis (AAT) is transmitted by the tsetse fly which carries pathogenic trypanosomes in its saliva, thus causing debilitating infection to livestock health. As the disease advances, a multistage progression process is observed based on the progressive clinical signs displayed in the host’s body. Investigation of genes expressed with regular monotonic patterns (known as Monotonically Expressed Genes (MEGs)) and of their master regulators can provide important clue for the understanding of the molecular mechanisms underlying the AAT disease. For this purpose, we analysed MEGs for three tissues (liver, spleen and lymph node) of two cattle breeds, namely trypanosusceptible Boran and trypanotolerant N’Dama. Our analysis revealed cattle breed-specific master regulators which are highly related to distinguish the genetic programs in both cattle breeds. Especially the master regulators MYC and DBP found in this study, seem to influence the immune responses strongly, thereby susceptibility and trypanotolerance of Boran and N’Dama respectively. Furthermore, our pathway analysis also bolsters the crucial roles of these master regulators. Taken together, our findings provide novel insights into breed-specific master regulators which orchestrate the regulatory cascades influencing the level of trypanotolerance in cattle breeds and thus could be promising drug targets for future therapeutic interventions.