A novel statistical approach for identification of the master regulator transcription factor

Pharmacological inhibition of RUNX1 reduces infarct size after acute myocardial infarction in rats and underlying mechanism revealed by proteomics implicates repressed cathepsin levels

Myocardial infarction (MI) results in prolonged ischemia and the subsequent cell death leads to heart failure which is linked to increased deaths or hospitalizations. New therapeutic targets are urgently needed to prevent cell death and reduce infarct size among patients with MI. Runt-related transcription factor-1 (RUNX1) is a master-regulator transcription factor intensively studied in the hematopoietic field. Recent evidence showed that RUNX1 has a critical role in cardiomyocytes post-MI. The increased RUNX1 expression in the border zone of the infarct heart contributes to decreased cardiac contractile function and can be therapeutically targeted to protect against adverse cardiac remodelling. This study sought to investigate whether pharmacological inhibition of RUNX1 function has an impact on infarct size following MI. In this work we demonstrate that inhibiting RUNX1 with a small molecule inhibitor (Ro5-3335) reduces infarct size in an in vivo rat model of acute MI. Proteomics study using data-independent acquisition method identified increased cathepsin levels in the border zone myocardium following MI, whereas heart samples treated by RUNX1 inhibitor present decreased cathepsin levels. Cathepsins are lysosomal proteases which have been shown to orchestrate multiple cell death pathways. Our data illustrate that inhibition of RUNX1 leads to reduced infarct size which is associated with the suppression of cathepsin expression. This study demonstrates that pharmacologically antagonizing RUNX1 reduces infarct size in a rat model of acute MI and unveils a link between RUNX1 and cathepsin-mediated cell death, suggesting that RUNX1 is a novel therapeutic target that could be exploited clinically to limit infarct size after an acute MI.

Comprehensive understanding of glioblastoma molecular phenotypes: classification, characteristics, and transition

Among central nervous system-associated malignancies, glioblastoma (GBM) is the most common and has the highest mortality rate. The high heterogeneity of GBM cell types and the complex tumor microenvironment frequently lead to tumor recurrence and sudden relapse in patients treated with temozolomide. In precision medicine, research on GBM treatment is increasingly focusing on molecular subtyping to precisely characterize the cellular and molecular heterogeneity, as well as the refractory nature of GBM toward therapy. Deep understanding of the different molecular expression patterns of GBM subtypes is critical. Researchers have recently proposed tetra fractional or tripartite methods for detecting GBM molecular subtypes. The various molecular subtypes of GBM show significant differences in gene expression patterns and biological behaviors. These subtypes also exhibit high plasticity in their regulatory pathways, oncogene expression, tumor microenvironment alterations, and differential responses to standard therapy. Herein, we summarize the current molecular typing scheme of GBM and the major molecular/genetic characteristics of each subtype. Furthermore, we review the mesenchymal transition mechanisms of GBM under various regulators.

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.

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.

Validating methods for testing natural molecules on molecular pathways of interest in silico and in vitro

Differentially expressed genes can serve as drug targets and are used to predict drug response and disease progression. In silico drug analysis based on the expression of these genetic biomarkers allows the detection of putative therapeutic agents, which could be used to reverse a pathological gene expression signature. Indeed, a set of bioinformatics tools can increase the accuracy of drug discovery, helping in biomarker identification. Once a drug target is identified, in vitro cell line models of disease are used to evaluate and validate the therapeutic potential of putative drugs and novel natural molecules. This study describes the development of efficacious PCR primers that can be used to identify gene expression of specific genetic pathways, which can lead to the identification of natural molecules as therapeutic agents in specific molecular pathways. For this study, genes involved in health conditions and processes were considered. In particular, the expression of genes involved in obesity, xenobiotics metabolism, endocannabinoid pathway, leukotriene B4 metabolism and signaling, inflammation, endocytosis, hypoxia, lifespan, and neurotrophins were evaluated. Exploiting the expression of specific genes in different cell lines can be useful in in vitro to evaluate the therapeutic effects of small natural molecules.

MicroRNA-326 impairs chemotherapy resistance in non small cell lung cancer by suppressing histone deacetylase SIRT1-mediated HIF1α and elevating VEGFA

Compelling evidence has implicated the role of microRNAs (miRs or miRNAs) in lung cancer. Sirtuin-1 (SIRT1) is key contributor to the progression of non small cell lung cancer (NSCLC). This study was intended to investigate whether miR-326 affected NSCLC associated with SIRT1. miR-326 and SIRT1 expression in H460 cells and chemoresistant cells H460-R was measured by RT-qPCR. Dual luciferase reporter gene assay and RIP assay were used to identify and validate the relationship between miR-326 and SIRT1. Using gain- and loss-of-function approaches, we evaluated their effects on the chemoresistance of NSCLC cells. ChIP assay was used to detect binding of SIRT1 to the promoter of HIF1α gene, and the binding H3K9Ac to HIF1α, binding of H3K9Ac and HIF1α after silencing SIRT1, and binding HIF1α to VEGFA promoter. In vivo experiments were performed to validate the in vitro findings. MiR-326 expression was decreased while SIRT1 expression was increased in NSCLC cells. SIRT1 was a target of miR-326. MiR-326 inhibited the proliferation of chemotherapy-resistant NSCLC cells and promoted their apoptosis by suppressing SIRT1. In addition, SIRT1 promoted chemoresistance of NSCLC cell by elevating VEGFA expression. Through this mechanism, miR-326 reduced the chemoresistance, which was validated in vivo. Taken together, miR-326 represses SIRT1 through impeding HIF1α expression, thus hindering chemotherapy resistance in lung cancer. These findings provide an exquisite therapeutic target for NSCLC.

Histone demethylase KDM4A plays an oncogenic role in nasopharyngeal carcinoma by promoting cell migration and invasion

Compelling evidence has indicated the vital role of lysine-specific demethylase 4 A (KDM4A), hypoxia-inducible factor-1α (HIF1α) and the mechanistic target of rapamycin (mTOR) signaling pathway in nasopharyngeal carcinoma (NPC). Therefore, we aimed to investigate whether KDM4A affects NPC progression by regulating the HIF1α/DDIT4/mTOR signaling pathway. First, NPC and adjacent tissue samples were collected, and KDM4A protein expression was examined by immunohistochemistry. Then, the interactions among KDM4A, HIF1α and DDIT4 were assessed. Gain- and loss-of-function approaches were used to alter KDM4A, HIF1α and DDIT4 expression in NPC cells. The mechanism of KDM4A in NPC was evaluated both in vivo and in vitro via RT-qPCR, Western blot analysis, MTT assay, Transwell assay, flow cytometry and tumor formation experiments. KDM4A, HIF1α, and DDIT4 were highly expressed in NPC tissues and cells. Mechanistically, KDM4A inhibited the enrichment of histone H3 lysine 9 trimethylation (H3K9me3) in the HIF1α promoter region and thus inhibited the methylation of HIF1α to promote HIF1α expression, thus upregulating DDIT4 and activating the mTOR signaling pathway. Overexpression of KDM4A, HIF1α, or DDIT4 or activation of the mTOR signaling pathway promoted SUNE1 cell proliferation, migration, and invasion but inhibited apoptosis. KDM4A silencing blocked the mTOR signaling pathway by inhibiting the HIF1α/DDIT4 axis to inhibit the growth of SUNE1 cells in vivo. Collectively, KDM4A silencing could inhibit NPC progression by blocking the activation of the HIF1α/DDIT4/mTOR signaling pathway by increasing H3K9me3, highlighting a promising therapeutic target for NPC.

Perspective of mesenchymal transformation in glioblastoma

Despite aggressive multimodal treatment, glioblastoma (GBM), a grade IV primary brain tumor, still portends a poor prognosis with a median overall survival of 12–16 months. The complexity of GBM treatment mainly lies in the inter- and intra-tumoral heterogeneity, which largely contributes to the treatment-refractory and recurrent nature of GBM. By paving the road towards the development of personalized medicine for GBM patients, the cancer genome atlas classification scheme of GBM into distinct transcriptional subtypes has been considered an invaluable approach to overcoming this heterogeneity. Among the identified transcriptional subtypes, the mesenchymal subtype has been found associated with more aggressive, invasive, angiogenic, hypoxic, necrotic, inflammatory, and multitherapy-resistant features than other transcriptional subtypes. Accordingly, mesenchymal GBM patients were found to exhibit worse prognosis than other subtypes when patients with high transcriptional heterogeneity were excluded. Furthermore, identification of the master mesenchymal regulators and their downstream signaling pathways has not only increased our understanding of the complex regulatory transcriptional networks of mesenchymal GBM, but also has generated a list of potent inhibitors for clinical trials. Importantly, the mesenchymal transition of GBM has been found to be tightly associated with treatment-induced phenotypic changes in recurrence. Together, these findings indicate that elucidating the governing and plastic transcriptomic natures of mesenchymal GBM is critical in order to develop novel and selective therapeutic strategies that can improve both patient care and clinical outcomes. Thus, the focus of our review will be on the recent advances in the understanding of the transcriptome of mesenchymal GBM and discuss microenvironmental, metabolic, and treatment-related factors as critical components through which the mesenchymal signature may be acquired. We also take into consideration the transcriptomic plasticity of GBM to discuss the future perspectives in employing selective therapeutic strategies against mesenchymal GBM.

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.

Motifs enable communication efficiency and fault-tolerance in transcriptional networks

Analysis of the topology of transcriptional regulatory networks (TRNs) is an effective way to study the regulatory interactions between the transcription factors (TFs) and the target genes. TRNs are characterized by the abundance of motifs such as feed forward loops (FFLs), which contribute to their structural and functional properties. In this paper, we focus on the role of motifs (specifically, FFLs) in signal propagation in TRNs and the organization of the TRN topology with FFLs as building blocks. To this end, we classify nodes participating in FFLs (termed motif central nodes) into three distinct roles (namely, roles A, B and C), and contrast them with TRN nodes having high connectivity on the basis of their potential for information dissemination, using metrics such as network efficiency, path enumeration, epidemic models and standard graph centrality measures. We also present the notion of a three tier architecture and how it can help study the structural properties of TRN based on connectivity and clustering tendency of motif central nodes. Finally, we motivate the potential implication of the structural properties of motif centrality in design of efficient protocols of information routing in communication networks as well as their functional properties in global regulation and stress response to study specific disease conditions and identification of drug targets.

Discovering master regulators in hepatocellular carcinoma: one novel MR, SEC14L2 inhibits cancer cells

Identification of master regulator (MR) genes offers a relatively rapid and efficient way to characterize disease-specific molecular programs. Since strong consensus regarding commonly altered MRs in hepatocellular carcinoma (HCC) is lacking, we generated a compendium of HCC datasets from 21 studies and identified a comprehensive signature consisting of 483 genes commonly deregulated in HCC. We then used reverse engineering of transcriptional networks to identify the MRs that underpin the development and progression of HCC. After cross-validation in different HCC datasets, systematic assessment using patient-derived data confirmed prognostic predictive capacities for most HCC MRs and their corresponding regulons. Our HCC signature covered well-established liver cancer hallmarks, and network analyses revealed coordinated interaction between several MRs. One novel MR, SEC14L2, exerted an anti-proliferative effect in HCC cells and strongly suppressed tumor growth in a mouse model. This study advances our knowledge of transcriptional MRs potentially involved in HCC development and progression that may be targeted by specific interventions.

Integrating gene regulatory pathways into differential network analysis of gene expression data

The advent of next-generation sequencing has introduced new opportunities in analyzing gene expression data. Research in systems biology has taken advantage of these opportunities by gleaning insights into gene regulatory networks through the analysis of gene association networks. Contrasting networks from different populations can reveal the many different roles genes fill, which can lead to new discoveries in gene function. Pathologies can also arise from aberrations in these gene-gene interactions. Exposing these network irregularities provides a new avenue for understanding and treating diseases. A general framework for integrating known gene regulatory pathways into a differential network analysis between two populations is proposed. The framework importantly allows for any gene-gene association measure to be used, and inference is carried out through permutation testing. A simulation study investigates the performance in identifying differentially connected genes when incorporating known pathways, even if the pathway knowledge is partially inaccurate. Another simulation study compares the general framework with four state-of-the-art methods. Two RNA-seq datasets are analyzed to illustrate the use of this framework in practice. In both examples, the analysis reveals genes and pathways that are known to be biologically significant along with potentially novel findings that may be used to motivate future research.

Bioinformatic approaches to interrogating vitamin D receptor signaling

Bioinformatics applies unbiased approaches to develop statistically-robust insight into health and disease. At the global, or "20,000 foot" view bioinformatic analyses of vitamin D receptor (NR1I1/VDR) signaling can measure where the VDR gene or protein exerts a genome-wide significant impact on biology; VDR is significantly implicated in bone biology and immune systems, but not in cancer. With a more VDR-centric, or "2000 foot" view, bioinformatic approaches can interrogate events downstream of VDR activity. Integrative approaches can combine VDR ChIP-Seq in cell systems where significant volumes of publically available data are available. For example, VDR ChIP-Seq studies can be combined with genome-wide association studies to reveal significant associations to immune phenotypes. Similarly, VDR ChIP-Seq can be combined with data from Cancer Genome Atlas (TCGA) to infer the impact of VDR target genes in cancer progression. Therefore, bioinformatic approaches can reveal what aspects of VDR downstream networks are significantly related to disease or phenotype.