TRED: a transcriptional regulatory element database, new entries and other development

Multi-objective context-guided consensus of a massive array of techniques for the inference of Gene Regulatory Networks

BACKGROUND AND OBJECTIVE

Gene Regulatory Network (GRN) inference is a fundamental task in biology and medicine, as it enables a deeper understanding of the intricate mechanisms of gene expression present in organisms. This bioinformatics problem has been addressed in the literature through multiple computational approaches. Techniques developed for inferring from expression data have employed Bayesian networks, ordinary differential equations (ODEs), machine learning, information theory measures and neural networks, among others. The diversity of implementations and their respective customization have led to the emergence of many tools and multiple specialized domains derived from them, understood as subsets of networks with specific characteristics that are challenging to detect a priori. This specialization has introduced significant uncertainty when choosing the most appropriate technique for a particular dataset. This proposal, named MO-GENECI, builds upon the basic idea of the previous proposal GENECI and optimizes consensus among different inference techniques, through a carefully refined multi-objective evolutionary algorithm guided by various objective functions, linked to the biological context at hand.

METHODS

MO-GENECI has been tested on an extensive and diverse academic benchmark of 106 gene regulatory networks from multiple sources and sizes. The evaluation of MO-GENECI compared its performance to individual techniques using key metrics (AUROC and AUPR) for gene regulatory network inference. Friedman's statistical ranking provided an ordered classification, followed by non-parametric Holm tests to determine statistical significance.

RESULTS

MO-GENECI's Pareto front approximation facilitates easy selection of an appropriate solution based on generic input data characteristics. The best solution consistently emerged as the winner in all statistical tests, and in many cases, the median precision solution showed no statistically significant difference compared to the winner.

CONCLUSIONS

MO-GENECI has not only demonstrated achieving more accurate results than individual techniques, but has also overcome the uncertainty associated with the initial choice due to its flexibility and adaptability. It is shown intelligently to select the most suitable techniques for each case. The source code is hosted in a public repository at GitHub under MIT license: https://github.com/AdrianSeguraOrtiz/MO-GENECI. Moreover, to facilitate its installation and use, the software associated with this implementation has been encapsulated in a Python package available at PyPI: https://pypi.org/project/geneci/.

Clustering of RNA co-expression network identifies novel long non-coding RNA biomarkers in squamous cell carcinoma

Long non-coding RNAs (lncRNAs) have emerged as important players in cancer progression. Cutaneous squamous cell carcinoma (cSCC) is the most common metastatic skin cancer with increasing incidence worldwide. The prognosis of the metastatic cSCC is poor, and currently there are no established biomarkers to predict metastasis risk or specific therapeutic targets for advanced or metastatic cSCC. To elucidate the role of lncRNAs in cSCC, RNA sequencing of patient derived cSCC cell lines and normal human epidermal keratinocytes was performed. The correlation analysis of differentially expressed lncRNAs and protein-coding genes revealed six distinct gene clusters with one of the upregulated clusters featuring genes associated with cell motility. Upregulation of the expression of lncRNAs linked to cSCC cell motility in cSCC and head and neck SCC (HNSCC) cells was confirmed using qRT-PCR. Elevated expression of HOTTIP and LINC00543 was also noted in SCC tumors in vivo and was associated with poorer prognosis in HNSCC and lung SCC cohorts within TCGA data, respectively. Altogether, these findings uncover a novel set of lncRNAs implicated in cSCC cell locomotion. These lncRNAs may serve as potential novel biomarkers and as putative therapeutic targets for locally advanced and metastatic cSCC.

De-obstruction of bladder outlet in humans reverses organ remodelling by normalizing the expression of key transcription factors

BACKGROUND

Benign prostatic hyperplasia in elderly males often causes bladder outlet obstruction termed benign prostatic obstruction (BPO). BPO induces lower urinary tract symptoms and quantifiable urodynamic alterations in bladder function. When conservative medical treatments are exhausted, surgical interventions like transurethral resection of the prostate (TURP) are employed for bladder outlet de-obstruction. Elucidating the molecular changes in the human bladder resulting from BPO and their reversal post-de-obstruction is pivotal for defining the "point of no return", when the organ deterioration becomes irreversible. In this study we carried out a comprehensive molecular and urodynamic characterization of the bladders in men with BPO before TURP and 3 months after the relief of obstruction.

METHODS

We report integrated transcriptome and proteome analysis of bladder samples from male patients with BPO before and 3 months after de-obstruction surgery (TURP). mRNA and protein profiles were correlated with urodynamic findings, specifically voiding detrusor pressure (PdetQmax) before TURP. We delineated the molecular classifiers of each group, pointing at the different pre-TURP bladder status.

RESULTS

Age-matched patients with BPO without DO were divided into two groups based on the PdetQmax values recorded by UDI before de-obstruction: high and medium pressure (HP and MP) groups. Three months after de-obstruction surgery, the voiding parameters PdetQmax, Qmax and RV were significantly improved in both groups, without notable inter-group differences in the values after TURP. Patients with high PdetQmax showed less advanced remodeling and inflammatory changes than those with lower values. We detected significant dysregulation of gene expression, which was at least partially reversed by de-obstruction in both patients' groups. Transcription factor SOX21 and its target thrombospondin 4 (THBS4) demonstrated normalization post-TURP.

CONCLUSIONS

Our findings reveal substantial yet incomplete reversal of cell signalling pathways three months after TURP, consistent with improved urodynamic parameters. We propose a set of biomarker genes, indicative of BPO, and possibly contributing to the bladder changes. This study unveils the stages of progressive obstruction-induced bladder decompensation and offers insights into selecting an optimal intervention point to mitigate loss of contractility.

Context-specific synthetic T cell promoters from assembled transcriptional elements

Genetic engineering of human lymphocytes for therapeutic applications is constrained by a lack of transgene transcriptional control, resulting in a compromised therapeutic index. Incomplete understanding of transcriptional logic limits the rational design of contextually responsive genetic modules1. Here, we juxtaposed rationally curated transcriptional response element (TRE) oligonucleotides by random concatemerization to generate a library from which we selected context-specific inducible synthetic promoters (iSynPros). Through functional selection, we screened an iSynPro library for "IF-THEN" logic-gated transcriptional responses in human CD8+ T cells expressing a 4-1BB second generation chimeric antigen receptor (CAR). iSynPros exhibiting stringent off-states in quiescent T cells and CAR activation-dependent transcriptional responsiveness were cloned and subjected to TRE composition and pattern analysis, as well as performance in regulating candidate antitumor potency enhancement modules. These data reveal synthetic TRE grammar can mediate logic-gated transgene transcription in human T cells that, when applied to CAR T cell engineering, enhance potency and improve therapeutic indices.

A Bayesian Noisy Logic Model for Inference of Transcription Factor Activity from Single Cell and Bulk Transcriptomic Data

The advent of high-throughput sequencing has made it possible to measure the expression of genes at relatively low cost. However, direct measurement of regulatory mechanisms, such as Transcription Factor (TF) activity is still not readily feasible in a high-throughput manner. Consequently, there is a need for computational approaches that can reliably estimate regulator activity from observable gene expression data. In this work, we present a noisy Boolean logic Bayesian model for TF activity inference from differential gene expression data and causal graphs. Our approach provides a flexible framework to incorporate biologically motivated TF-gene regulation logic models. Using simulations and controlled over-expression experiments in cell cultures, we demonstrate that our method can accurately identify TF activity. Moreover, we apply our method to bulk and single cell transcriptomics measurements to investigate transcriptional regulation of fibroblast phenotypic plasticity. Finally, to facilitate usage, we provide user-friendly software packages and a web-interface to query TF activity from user input differential gene expression data: https://umbibio.math.umb.edu/nlbayes/.

cRel and Wnt5a/Frizzled 5 Receptor-Mediated Inflammatory Regulation Reveal Novel Neuroprotectin D1 Targets for Neuroprotection

Wnt5a triggers inflammatory responses and damage via NFkB/p65 in retinal pigment epithelial (RPE) cells undergoing uncompensated oxidative stress (UOS) and in experimental ischemic stroke. We found that Wnt5a-Clathrin-mediated uptake leads to NFkB/p65 activation and that Wnt5a is secreted in an exosome-independent fashion. We uncovered that docosahexaenoic acid (DHA) and its derivative, Neuroprotectin D1 (NPD1), upregulate c-Rel expression that, as a result, blunts Wnt5a abundance by competing with NFkB/p65 on the Wnt5a promoter A. Wnt5a increases in ischemic stroke penumbra and blood, while DHA reduces Wnt5a abundance with concomitant neuroprotection. Peptide inhibitor of Wnt5a binding, Box5, is also neuroprotective. DHA-decreased Wnt5a expression is concurrent with a drop in NFkB-driven inflammatory cytokine expression, revealing mechanisms after stroke, as in RPE cells exposed to UOS. Limiting the Wnt5a activity via Box5 reduces stroke size, suggesting neuroprotection pertinent to onset and progression of retinal degenerations and stroke consequences. NPD1 disrupts Wnt5a feedback loop at two sites: (1) decreasing FZD5, thus Wnt5a internalization, and (2) by enhancing cREL activity, which competes with p65/NFkB downstream endocytosis. As a result, Wnt5a expression is reduced, and so is its inflammatory signaling in RPE cells and neurons in ischemic stroke.

Functional impact of multi-omic interactions in breast cancer subtypes

Multi-omic approaches are expected to deliver a broader molecular view of cancer. However, the promised mechanistic explanations have not quite settled yet. Here, we propose a theoretical and computational analysis framework to semi-automatically produce network models of the regulatory constraints influencing a biological function. This way, we identified functions significantly enriched on the analyzed omics and described associated features, for each of the four breast cancer molecular subtypes. For instance, we identified functions sustaining over-representation of invasion-related processes in the basal subtype and DNA modification processes in the normal tissue. We found limited overlap on the omics-associated functions between subtypes; however, a startling feature intersection within subtype functions also emerged. The examples presented highlight new, potentially regulatory features, with sound biological reasons to expect a connection with the functions. Multi-omic regulatory networks thus constitute reliable models of the way omics are connected, demonstrating a capability for systematic generation of mechanistic hypothesis.

The ChIP-Hub Resource: Toward plantEncode

Recent advances in sequencing technologies lead to the generation of an enormous amount of regulome and epigenome data in a variety of plant species. However, a comprehensive standardized resource is so far not available. In this chapter, we present ChIP-Hub, an integrative platform that has been developed based on the ENCODE standards by collecting and reanalyzing regulatory genomic datasets from 41 plant species. The ChIP-hub website is introduced in this chapter, including information on detailed steps of searching, data download, and online analyses, which facilitates users to explore ChIP-Hub. We also provide a cross-species comparison of chromatin accessibility information that gives a thorough view of evolutionary regulatory networks in plants.

Effects of allicin on human Simpson-Golabi-Behmel syndrome cells in mediating browning phenotype

INTRODUCTION

Obesity is a major health problem because it is associated with increased risk of cardiovascular disease, diabetes, hypertension, and some cancers. Strategies to prevent or reduce obesity focus mainly on the possible effects of natural compounds that can induce a phenotype of browning adipocytes capable of releasing energy in the form of heat. Allicin, a bioactive component of garlic with numerous pharmacological functions, is known to stimulate energy metabolism.

METHODS

In the present study, the effects of allicin on human Simpson-Golabi-Behmel Syndrome (SGBS) cells were investigated by quantifying the dynamics of lipid droplets (LDs) and mitochondria, as well as transcriptomic changes after six days of differentiation.

RESULTS

Allicin significantly promoted the reduction in the surface area and size of LDs, leading to the formation of multilocular adipocytes, which was confirmed by the upregulation of genes related to lipolysis. The increase in the number and decrease in the mean aspect ratio of mitochondria in allicin-treated cells indicate a shift in mitochondrial dynamics toward fission. The structural results are confirmed by transcriptomic analysis showing a significant arrangement of gene expression associated with beige adipocytes, in particular increased expression of T-box transcription factor 1 (TBX1), uncoupling protein 1 (UCP1), PPARG coactivator 1 alpha (PPARGC1A), peroxisome proliferator-activated receptor alpha (PPARA), and OXPHOS-related genes. The most promising targets are nuclear genes such as retinoid X receptor alpha (RXRA), retinoid X receptor gamma (RXRG), nuclear receptor subfamily 1 group H member 3 (NR1H3), nuclear receptor subfamily 1 group H member 4 (NR1H4), PPARA, and oestrogen receptor 1 (ESR1).

DISCUSSION

Transcriptomic data and the network pharmacology-based approach revealed that genes and potential targets of allicin are involved in ligand-activated transcription factor activity, intracellular receptor signalling, regulation of cold-induced thermogenesis, and positive regulation of lipid metabolism. The present study highlights the potential role of allicin in triggering browning in human SGBS cells by affecting the LD dynamics, mitochondrial morphology, and expression of brown marker genes. Understanding the potential targets through which allicin promotes this effect may reveal the underlying signalling pathways and support these findings.

NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity

A major question in systems biology is how to identify the core gene regulatory circuit that governs the decision-making of a biological process. Here, we develop a computational platform, named NetAct, for constructing core transcription factor regulatory networks using both transcriptomics data and literature-based transcription factor-target databases. NetAct robustly infers regulators' activity using target expression, constructs networks based on transcriptional activity, and integrates mathematical modeling for validation. Our in silico benchmark test shows that NetAct outperforms existing algorithms in inferring transcriptional activity and gene networks. We illustrate the application of NetAct to model networks driving TGF-β-induced epithelial-mesenchymal transition and macrophage polarization.

The transcription factor c-Jun inhibits RBM39 to reprogram pre-mRNA splicing during genotoxic stress

Genotoxic agents, that are used in cancer therapy, elicit the reprogramming of the transcriptome of cancer cells. These changes reflect the cellular response to stress and underlie some of the mechanisms leading to drug resistance. Here, we profiled genome-wide changes in pre-mRNA splicing induced by cisplatin in breast cancer cells. Among the set of cisplatin-induced alternative splicing events we focused on COASY, a gene encoding a mitochondrial enzyme involved in coenzyme A biosynthesis. Treatment with cisplatin induces the production of a short isoform of COASY lacking exons 4 and 5, whose depletion impedes mitochondrial function and decreases sensitivity to cisplatin. We identified RBM39 as a major effector of the cisplatin-induced effect on COASY splicing. RBM39 also controls a genome-wide set of alternative splicing events partially overlapping with the cisplatin-mediated ones. Unexpectedly, inactivation of RBM39 in response to cisplatin involves its interaction with the AP-1 family transcription factor c-Jun that prevents RBM39 binding to pre-mRNA. Our findings therefore uncover a novel cisplatin-induced interaction between a splicing regulator and a transcription factor that has a global impact on alternative splicing and contributes to drug resistance.

Widespread hypertranscription in aggressive human cancers

Cancers are often defined by the dysregulation of specific transcriptional programs; however, the importance of global transcriptional changes is less understood. Hypertranscription is the genome-wide increase in RNA output. Hypertranscription's prevalence, underlying drivers, and prognostic significance are undefined in primary human cancer. This is due, in part, to limitations of expression profiling methods, which assume equal RNA output between samples. Here, we developed a computational method to directly measure hypertranscription in 7494 human tumors, spanning 31 cancer types. Hypertranscription is ubiquitous across cancer, especially in aggressive disease. It defines patient subgroups with worse survival, even within well-established subtypes. Our data suggest that loss of transcriptional suppression underpins the hypertranscriptional phenotype. Single-cell analysis reveals hypertranscriptional clones, which dominate transcript production regardless of their size. Last, patients with hypertranscribed mutations have improved response to immune checkpoint therapy. Our results provide fundamental insights into gene dysregulation across human cancers and may prove useful in identifying patients who would benefit from novel therapies.

The Sall2 transcription factor promotes cell migration regulating focal adhesion turnover and integrin β1 expression

SALL2/Sall2 is a transcription factor associated with development, neuronal differentiation, and cancer. Interestingly, SALL2/Sall2 deficiency leads to failure of the optic fissure closure and neurite outgrowth, suggesting a positive role for SALL2/Sall2 in cell migration. However, in some cancer cells, SALL2 deficiency is associated with increased cell migration. To further investigate the role of Sall2 in the cell migration process, we used immortalized Sall2 knockout (Sall2−/−) and Sall2 wild-type (Sall2+/+) mouse embryonic fibroblasts (iMEFs). Our results indicated that Sall2 positively regulates cell migration, promoting cell detachment and focal adhesions turnover. Sall2 deficiency decreased cell motility and altered focal adhesion dynamics. Accordingly, restoring Sall2 expression in the Sall2−/− iMEFs by using a doxycycline-inducible Tet-On system recovered cell migratory capabilities and focal adhesion dynamics. In addition, Sall2 promoted the autophosphorylation of Focal Adhesion Kinase (FAK) at Y397 and increased integrin β1 mRNA and its protein expression at the cell surface. We demonstrated that SALL2 increases ITGB1 promoter activity and binds to conserved SALL2-binding sites at the proximal region of the ITGB1 promoter, validated by ChIP experiments. Furthermore, the overexpression of integrin β1 or its blockade generates a cell migration phenotype similar to that of Sall2+/+ or Sall2−/− cells, respectively. Altogether, our data showed that Sall2 promotes cell migration by modulating focal adhesion dynamics, and this phenotype is associated with SALL2/Sall2-transcriptional regulation of integrin β1 expression and FAK autophosphorylation. Since deregulation of cell migration promotes congenital abnormalities, tumor formation, and spread to other tissues, our findings suggest that the SALL2/Sall2-integrin β1 axis could be relevant for those processes.

TFLink: an integrated gateway to access transcription factor-target gene interactions for multiple species

Analysis of transcriptional regulatory interactions and their comparisons across multiple species are crucial for progress in various fields in biology, from functional genomics to the evolution of signal transduction pathways. However, despite the rapidly growing body of data on regulatory interactions in several eukaryotes, no databases exist to provide curated high-quality information on transcription factor-target gene interactions for multiple species. Here, we address this gap by introducing the TFLink gateway, which uniquely provides experimentally explored and highly accurate information on transcription factor-target gene interactions (∼12 million), nucleotide sequences and genomic locations of transcription factor binding sites (∼9 million) for human and six model organisms: mouse, rat, zebrafish, fruit fly, worm and yeast by integrating 10 resources. TFLink provides user-friendly access to data on transcription factor-target gene interactions, interactive network visualizations and transcription factor binding sites, with cross-links to several other databases. Besides containing accurate information on transcription factors, with a clear labelling of the type/volume of the experiments (small-scale or high-throughput), the source database and the original publications, TFLink also provides a wealth of standardized regulatory data available for download in multiple formats. The database offers easy access to high-quality data for wet-lab researchers, supplies data for gene set enrichment analyses and facilitates systems biology and comparative gene regulation studies. Database URL https://tflink.net/.

Structural, molecular hybridization and network based identification of miR-373-3p and miR-520e-3p as regulators of NR4A2 human gene involved in neurodegeneration

MicroRNAs (miRNAs) are short non-coding RNAs with a 22 nucleotide sequence length and docks to the 3'UTR/5'UTR of the gene to regulate their mRNA translation to play a vital role in neurodegenerative diseases. The Nuclear Receptor gene (NR4A2), a transcription factor, and a steroid-thyroid hormone retinoid receptor is involved in neural development, memory formation, dopaminergic neurotransmission, and cellular protection from inflammatory damage. Therefore, recognizing the miRNAs is essential to efficiently target the 3'UTR/5'UTR of the NR4A2 gene and regulate neurodegeneration. Highly stabilized top miRNA-mRNA hybridized structures, their homologs, and identification of the best structures based on their least free energy were evaluated using in silico techniques. The miR-gene, gene-gene network analysis, miR-disease association, and transcription factor binding sites were also investigated. Results suggest top 166 miRNAs targeting the NR4A2 mRNA, but with a total of 10 miRNAs bindings with 100% seed sequence identity (both at 3' and 5'UTR) at the same position on the NR4A2 mRNA region. The miR-373-3p and miR-520e-3p are considered the best candidate miRNAs hybridizing with high efficiency at both 3' and 5'UTR of NR4A2 mRNA. This could be due to the most significant seed sequence length complementary, supplementary pairing, and absence of non-canonical base pairs. Furthermore, the miR-gene network, target gene-gene interaction analysis, and miR-disease association provide an understanding of the molecular, cellular, and biological processes involved in various pathways regulated by four transcription factors (PPARG, ZNF740, NRF1, and RREB1). Therefore, miR-373-3p, 520e-3p, and four transcription factors can regulate the NR4A2 gene involved in the neurodegenerative process.

Bronchial epithelium epithelial-mesenchymal plasticity forms aberrant basaloid-like cells in vitro

Although epithelial-mesenchymal transition (EMT) is a common feature of fibrotic lung disease, its role in fibrogenesis is controversial. Recently, aberrant basaloid cells were identified in fibrotic lung tissue as a novel epithelial cell type displaying a partial EMT phenotype. The developmental origin of these cells remains unknown. To elucidate the role of EMT in the development of aberrant basaloid cells from the bronchial epithelium, we mapped EMT-induced transcriptional changes at the population and single-cell levels. Human bronchial epithelial cells grown as submerged or air-liquid interface (ALI) cultures with or without EMT induction were analyzed by bulk and single-cell RNA-Sequencing. Comparison of submerged and ALI cultures revealed differential expression of 8,247 protein coding (PC) and 1,621 long noncoding RNA (lncRNA) genes and revealed epithelial cell-type-specific lncRNAs. Similarly, EMT induction in ALI cultures resulted in robust transcriptional reprogramming of 6,020 PC and 907 lncRNA genes. Although there was no evidence for fibroblast/myofibroblast conversion following EMT induction, cells displayed a partial EMT gene signature and an aberrant basaloid-like cell phenotype. The substantial transcriptional differences between submerged and ALI cultures highlight that care must be taken when interpreting data from submerged cultures. This work supports that lung epithelial EMT does not generate fibroblasts/myofibroblasts and confirms ALI cultures provide a physiologically relevant system to study aberrant basaloid-like cells and mechanisms of EMT. We provide a catalog of PC and lncRNA genes and an interactive browser (https://bronc-epi-in-vitro.cells.ucsc.edu/) of single-cell RNA-Seq data for further exploration of potential roles in the lung epithelium in health and lung disease.

Impaired CD4+ T cell differentiation in HIV-1 infected patients receiving early anti-retroviral therapy

Differentiation of CD4+ T naïve (T_N) into central memory (T_CM) cells involves extensive molecular processes. We compared the transcriptomes of CD4+ T_N and T_CM cells from HIV-1 infected patients receiving early anti-retroviral therapy (ART; EA; n = 13) and controls (n = 15). Comparison of protein coding genes between T_CM and T_N revealed 533 and 82 differentially expressed genes (DEGs) in controls and EA, respectively. A high degree of transcriptional complexity was detected during transition of CD4+ T_N to T_CM cells in controls involving 70 TFs, 20 master regulators of T cell differentiation (TBX21, GATA3, RARA, FOXP3, RORC); in EA only 7 TFs were modulated with expression of several master regulators remaining unchanged during differentiation. Analysis of interactions between modulated TFs and target genes revealed important regulatory interactions missing in EA group. We conclude that T cell differentiation in EA patients is impaired due to reduced modulation of genes involved in transition from CD4+ T_N to T_CM cells.

Construction Immune Related Feed-Forward Loop Network Reveals Angiotensin II Receptor Blocker as Potential Neuroprotective Drug for Ischemic Stroke

Ischemic stroke (IS) accounts for the leading cause of disability and mortality in China. Increasing researchers are studying the effects of neuroprotective agents on IS. However, the molecular mechanisms of feed-forward loops (FFLs) associated with neuroprotection in the pathogenesis of IS need to be further studied. A protein-protein interaction (PPI) network of IS immune genes was constructed to decipher the characters and excavate 3 hub genes (PI3K, IL6, and TNF) of immunity. Then, we identified two hub clusters of IS immune genes, and the cytokine-cytokine receptor interaction pathway was discovered on the pathway enrichment results of both clusters. Combined with GO enrichment analysis, the cytokines participate in the inflammatory response in the extracellular space of IS patients. Next, a transcription factor (TF)-miRNA-immune gene network (TMIGN) was established by extracting four regulatory pairs (TF-miRNA, TF-gene, miRNA-gene, and miRNA-TF). Then, we detected 3-node regulatory motif types in the TMIGN network. According to the criteria we set for defining 3-node motifs, the motif with the highest Z-score (3-node composite FFL) was picked as the statistically evident motif, which was merged to construct an immune-associated composite FFL motif-specific sub-network (IA-CFMSN), which contained 21 3-node FFLs composed of 13 miRNAs, 4 TFs, 9 immune genes, and 1 TF& immune gene, among which TP53 and VEGFA were prominent TF and immune gene, respectively. In addition, the immune genes in IA-CFMSN were used for identifying associated pathways and drugs to further clarify the immune regulation mechanism and neuroprotection after IS. As a result, 5 immune genes targeted by 20 drugs were identified and the Angiotensin II Receptor Blockers (ARBs) target AGTR1 was found to be a neuroprotective drug for IS. In the present study, the construction of IA-CFMSN provides IS immune-associated FFLs for further experimental studies, providing new prospects for the discovery of new biomarkers and potential drugs for IS.

The Cannabinoid Receptor Agonist, WIN-55212-2, Suppresses the Activation of Proinflammatory Genes Induced by Interleukin 1 Beta in Human Astrocytes

Background: Alterations of astrocyte function play a crucial role in neuroinflammatory diseases due to either the loss of their neuroprotective role or the gain of their toxic inflammatory properties. Accumulating evidence highlights that cannabinoids and cannabinoid receptor agonists, such as WIN55,212-2 (WIN), reduce inflammation in cellular and animal models. Thus, the endocannabinoid system has become an attractive target to attenuate chronic inflammation in neurodegenerative diseases. However, the mechanism of action of WIN in astrocytes remains poorly understood. Objective: We studied the immunosuppressive property of WIN by examining gene expression patterns that were modulated by WIN in reactive astrocytes. Materials and Methods: Transcriptomic analysis by RNA-seq was carried out using primary human astrocyte cultures stimulated by the proinflammatory cytokine interleukin 1 beta (IL1β) in the presence or absence of WIN. Real-time quantitative polymerase chain reaction analysis was conducted on selected transcripts to characterize the dose-response effects of WIN, and to test the effect of selective antagonists of cannabinoid receptor 1 (CB1) and peroxisome proliferator-activated receptors (PPAR). Results: Transcriptomic analysis showed that the IL1β-induced inflammatory response is robustly inhibited by WIN pretreatment. WIN treatment alone also induced substantial gene expression changes. Pathway analysis revealed that the anti-inflammatory properties of WIN were linked to the regulation of kinase pathways and gene targets of neuroprotective transcription factors, including PPAR and SMAD (mothers against decapentaplegic homolog). The inhibitory effect of WIN was dose-dependent, but it was not affected by selective antagonists of CB1 or PPAR. Conclusions: This study suggests that targeting the endocannabinoid system may be a promising strategy to disrupt inflammatory pathways in reactive astrocytes. The anti-inflammatory activity of WIN is independent of CB1, suggesting that alternative receptors mediate the effects of WIN. These results provide mechanistic insights into the anti-inflammatory activity of WIN and highlight that astrocytes are a potential therapeutic target to ameliorate neuroinflammation in the brain.

MYC dosage compensation is mediated by miRNA-transcription factor interactions in aneuploid cancer

We hypothesize that dosage compensation of critical genes arises from systems-level properties for cancer cells to withstand the negative effects of aneuploidy. We identified several candidate genes in cancer multiomics data and developed a biocomputational platform to construct a mathematical model of their interaction network with micro-RNAs and transcription factors, where the property of dosage compensation emerged for MYC and was dependent on the kinetic parameters of its feedback interactions with three micro-RNAs. These circuits were experimentally validated using a genetic tug-of-war technique to overexpress an exogenous MYC, leading to overexpression of the three microRNAs involved and downregulation of endogenous MYC. In addition, MYC overexpression or inhibition of its compensating miRNAs led to dosage-dependent cytotoxicity in MYC-amplified colon cancer cells. Finally, we identified negative correlation of MYC dosage compensation with patient survival in TCGA breast cancer patients, highlighting the potential of this mechanism to prevent aneuploid cancer progression.

Identification of Novel Kinase-Transcription Factor-mRNA-miRNA Regulatory Network in Nasopharyngeal Carcinoma by Bioinformatics Analysis

Purpose

Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumors of the head and neck. This study aimed to investigate the crucial genes and regulatory networks involved in the carcinogenesis of NPC using a bioinformatics approach.

Methods

Five mRNA and two miRNA expression datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and miRNAs (DEMs) between NPC and normal samples were analyzed using R software. The WebGestalt tool was used for functional enrichment analysis, and protein-protein interaction (PPI) network analysis of DEGs was performed using STRING database. Transcription factors (TFs) were predicted using TRRUST and Transcriptional Regulatory Element Database (TRED). Kinases were identified using X2Kgui. The miRNAs of DEGs were predicted using miRWalk database. A kinase-TF-mRNA-miRNA integrated network was constructed, and hub nodes were selected. The hub genes were validated using NPC datasets from the GEO and Oncomine databases. Finally, candidate small-molecule agents were predicted using CMap.

Results

A total of 122 DEGs and 44 DEMs were identified. DEGs were associated with the immune response, leukocyte activation, endoplasmic reticulum stress in GO analysis, and the NF-κB signaling pathway in KEGG analysis. Four significant modules were identified using PPI network analysis. Subsequently, 26 TFs, 73 kinases, and 2499 miRNAs were predicted. The predicted miRNAs were cross-referenced with DEMs, and seven overlapping miRNAs were selected. In the kinase-TF-mRNA-miRNA integrated network, eight genes (PTGS2, FN1, MMP1, PLAU, MMP3, CD19, BMP2, and PIGR) were identified as hub genes. Hub genes were validated with consistent results, indicating the reliability of our findings. Finally, six candidate small-molecule agents (phenoxybenzamine, luteolin, thioguanosine, reserpine, blebbistatin, and camptothecin) were predicted.

Conclusion

We identified DEGs and an NPC regulatory network involving kinases, TFs, mRNAs, and miRNAs, which might provide promising insight into the pathogenesis, treatment, and prognosis of NPC.

CircGSK3B promotes RORA expression and suppresses gastric cancer progression through the prevention of EZH2 trans-inhibition

BACKGROUND

Circular RNAs (circRNAs) are a class of non-coding RNA that play critical roles in the development and pathogenesis of various cancers. The circRNA circGSK3B (hsa_circ_0003763) has been shown to enhance cell proliferation, migration, and invasion in hepatocellular carcinoma. However, the specific functions and underlying mechanistic involvement of circGSK3B in gastric cancer (GC) have not yet been explored. Our study aimed to investigate the effect of circGSK3B on the progression of GC and to identify any potential mechanisms underlying this process.

METHODS

CircRNA datasets associated with GC were obtained from the PubMed, GEO, and ArrayExpress databases, and circRNAs were validated via RT-qPCR and Sanger sequencing. Biotin-labeled RNA pull-down, mass spectrometry, RNA immunoprecipitation, and in vitro binding assays were employed to determine proteins demonstrating interactions with circGSK3B. Gene expression regulation was assessed through RT-qPCR, chromatin immunoprecipitation, and western blot assays. Gain- and loss-of-function assays were used to analyze any effects of circGSK3B and its partner regulatory molecule (EZH2) on the proliferation, invasion, and migration abilities of GC cells both in vitro and in vivo.

RESULTS

CircGSK3B was mainly identified in the nucleus. This circRNA was present at a reduced concentration in GC tissues and cell lines. Overexpression of circGSK3B was shown to inhibit the growth, invasion, and metastasis of GC cells both in vitro and in vivo. Mechanistically, circGSK3B directly interacted with EZH2, acting to suppress the binding of EZH2 and H3K27me3 to the RORA promoter, and leading to an elevation in RORA expression and ultimately the suppression of GC progression.

CONCLUSIONS

CircGSK3B acts as a tumor suppressor, reducing EZH2 trans-inhibition and GC progression. This demonstrates the potential use of this RNA as a therapeutic target for GC.

Making use of transcription factor enrichment to identify functional microRNA-regulons

microRNAs (miRNAs) are important modulators of messenger RNA stability and translation, controlling wide gene networks. Albeit generally modest on individual targets, the regulatory effect of miRNAs translates into meaningful pathway modulation through concurrent targeting of regulons with functional convergence. Identification of miRNA-regulons is therefore essential to understand the function of miRNAs and to help realise their therapeutic potential, but it remains challenging due to the large number of false positive target sites predicted per miRNA. In the current work, we investigated whether genes regulated by a given miRNA were under the transcriptional control of a predominant transcription factor (TF). Strikingly we found that for ~50% of the miRNAs analysed, their targets were significantly enriched in at least one common TF. We leveraged such miRNA-TF co-regulatory networks to identify pathways under miRNA control, and demonstrated that filtering predicted miRNA-target interactions (MTIs) relying on such pathways significantly enriched the proportion of predicted true MTIs. To our knowledge, this is the first description of an in- silico pipeline facilitating the identification of miRNA-regulons, to help understand miRNA function.

Transcriptional Pausing and Activation at Exons-1 and -2, Respectively, Mediate the MGMT Gene Expression in Human Glioblastoma Cells

Background: The therapeutically important DNA repair gene O⁶-methylguanine DNA methyltransferase (MGMT) is silenced by promoter methylation in human brain cancers. The co-players/regulators associated with this process and the subsequent progression of MGMT gene transcription beyond the non-coding exon 1 are unknown. As a follow-up to our recent finding of a predicted second promoter mapped proximal to the exon 2 [Int. J. Mol. Sci.2021, 22(5), 2492], we addressed its significance in MGMT transcription. Methods: RT-PCR, RT q-PCR, and nuclear run-on transcription assays were performed to compare and contrast the transcription rates of exon 1 and exon 2 of the MGMT gene in glioblastoma cells. Results: Bioinformatic characterization of the predicted MGMT exon 2 promoter showed several consensus TATA box and INR motifs and the absence of CpG islands in contrast to the established TATA-less, CpG-rich, and GAF-bindable exon 1 promoter. RT-PCR showed very weak MGMT-E1 expression in MGMT-proficient SF188 and T98G GBM cells, compared to active transcription of MGMT-E2. In the MGMT-deficient SNB-19 cells, the expression of both exons remained weak. The RT q-PCR revealed that MGMT-E2 and MGMT-E5 expression was about 80- to 175-fold higher than that of E1 in SF188 and T98G cells. Nuclear run-on transcription assays using bromo-uridine immunocapture followed by RT q-PCR confirmed the exceptionally lower and higher transcription rates for MGMT-E1 and MGMT-E2, respectively. Conclusions: The results provide the first evidence for transcriptional pausing at the promoter 1- and non-coding exon 1 junction of the human MGMT gene and its activation/elongation through the protein-coding exons 2 through 5, possibly mediated by a second promoter. The findings offer novel insight into the regulation of MGMT transcription in glioma and other cancer types.

Mucosal acidosis elicits a unique molecular signature in epithelia and intestinal tissue mediated by GPR31-induced CREB phosphorylation

Metabolic changes associated with tissue inflammation result in significant extracellular acidosis (EA). Within mucosal tissues, intestinal epithelial cells (IEC) have evolved adaptive strategies to cope with EA through the up-regulation of SLC26A3 to promote pH homeostasis. We hypothesized that EA significantly alters IEC gene expression as an adaptive mechanism to counteract inflammation. Using an unbiased RNA sequencing approach, we defined the impact of EA on IEC gene expression to define molecular mechanisms by which IEC respond to EA. This approach identified a unique gene signature enriched in cyclic AMP response element-binding protein (CREB)-regulated gene targets. Utilizing loss- and gain-of-function approaches in cultured epithelia and murine colonoids, we demonstrate that EA elicits prominent CREB phosphorylation through cyclic AMP-independent mechanisms that requires elements of the mitogen-activated protein kinase signaling pathway. Further analysis revealed that EA signals through the G protein-coupled receptor GPR31 to promote induction of FosB, NR4A1, and DUSP1. These studies were extended to an in vivo murine model in conjunction with colonization of a pH reporter Escherichia coli strain that demonstrated significant mucosal acidification in the TNFΔARE model of murine ileitis. Herein, we observed a strong correlation between the expression of acidosis-associated genes with bacterial reporter sfGFP intensity in the distal ileum. Finally, the expression of this unique EA-associated gene signature was increased during active inflammation in patients with Crohn's disease but not in the patient control samples. These findings establish a mechanism for EA-induced signals during inflammation-associated acidosis in both murine and human ileitis.

multiSLIDE is a web server for exploring connected elements of biological pathways in multi-omics data

Quantitative multi-omics data are difficult to interpret and visualize due to large volume of data, complexity among data features, and heterogeneity of information represented by different omics platforms. Here, we present multiSLIDE, a web-based interactive tool for the simultaneous visualization of interconnected molecular features in heatmaps of multi-omics data sets. multiSLIDE visualizes biologically connected molecular features by keyword search of pathways or genes, offering convenient functionalities to query, rearrange, filter, and cluster data on a web browser in real time. Various querying mechanisms make it adaptable to diverse omics types, and visualizations are customizable. We demonstrate the versatility of multiSLIDE through three examples, showcasing its applicability to a wide range of multi-omics data sets, by allowing users to visualize established links between molecules from different omics data, as well as incorporate custom inter-molecular relationship information into the visualization. Online and stand-alone versions of multiSLIDE are available at https://github.com/soumitag/multiSLIDE.

The integration and interpretation of different omics data types is an ongoing challenge for biologists. Here, the authors present a web-based, interactive tool called multiSLIDE for the visualization of protein, phosphoprotein, and RNA data presented as interlinked heatmaps.

The Genetic and Epigenetic Mechanisms Involved in Irreversible Pulp Neural Inflammation

AIM

To identify the critical genetic and epigenetic biomarkers by constructing the long noncoding RNA- (lncRNA-) related competing endogenous RNA (ceRNA) network involved in irreversible pulp neural inflammation (pulpitis).

MATERIALS AND METHODS

The public datasets regarding irreversible pulpitis were downloaded from the gene expression omnibus (GEO) database. The differential expression analysis was performed to identify the differentially expressed genes (DEGs) and DElncRNAs. Functional enrichment analysis was performed to explore the biological processes and signaling pathways enriched by DEGs. By performing a weighted gene coexpression network analysis (WGCNA), the significant gene modules in each dataset were identified. Most importantly, DElncRNA-DEmRNA regulatory network and DElncRNA-associated ceRNA network were constructed. A transcription factor- (TF-) DEmRNA network was built to identify the critical TFs involved in pulpitis.

RESULT

Two datasets (GSE92681 and GSE77459) were selected for analysis. DEGs involved in pulpitis were significantly enriched in seven signaling pathways (i.e., NOD-like receptor (NLR), Toll-like receptor (TLR), NF-kappa B, tumor necrosis factor (TNF), cell adhesion molecules (CAMs), chemokine, and cytokine-cytokine receptor interaction pathways). The ceRNA regulatory relationships were established consisting of three genes (i.e., LCP1, EZH2, and NR4A1), five miRNAs (i.e., miR-340-5p, miR-4731-5p, miR-27a-3p, miR-34a-5p, and miR-766-5p), and three lncRNAs (i.e., XIST, MIR155HG, and LINC00630). Six transcription factors (i.e., GATA2, ETS1, FOXP3, STAT1, FOS, and JUN) were identified to play pivotal roles in pulpitis.

CONCLUSION

This paper demonstrates the genetic and epigenetic mechanisms of irreversible pulpitis by revealing the ceRNA network. The biomarkers identified could provide research direction for the application of genetically modified stem cells in endodontic regeneration.

A CRISPR/Cas9-based method for targeted DNA methylation enables cancer initiation in B lymphocytes

Targeted DNA methylation is important for understanding transcriptional modulation and epigenetic diseases. Although CRISPR-Cas9 has potential for this purpose, it has not yet been successfully used to efficiently introduce DNA methylation and induce epigenetic diseases. We herein developed a new system that enables the replacement of an unmethylated promoter with a methylated promoter through microhomology-mediated end joining-based knock-in. We successfully introduced an approximately 100% DNA methylation ratio at the cancer-associated gene SP3 in HEK293 cells. Moreover, engineered SP3 promoter hypermethylation led to transcriptional suppression in human B lymphocytes and induced B-cell lymphoma. Our system provides a promising framework for targeted DNA methylation and cancer initiation through epimutations.

"All-In-One" Genetic Tool Assessing Endometrial Receptivity for Personalized Screening of Female Sex Steroid Hormones

Endometrium is the uterine lining that undergoes hundreds of cycles of proliferation, differentiation, and desquamation throughout a woman's reproductive life. Recently, much attention is paid to the appropriate endometrial functioning, as decreased endometrial receptivity is stated to be one of the concerns heavily influencing successes of embryo implantation rates and the efficacy of in vitro fertilization (IVF) treatment. In order to acquire and maintain the desired endometrial receptivity during IVF cycles, luteal phase support by various progestagens or other hormonal combinations is generally recommended. However, today, the selection of the specific hormonal therapy during IVF seems to be empirical, mainly due to a lack of appropriate tools for personalized approach. Here, we designed the genetic tool for patient-specific optimization of hormonal supplementation schemes required for the maintenance of endometrial receptivity during luteal phase. We optimized and characterized in vitro endometrial stromal cell (ESC) decidualization model as the adequate physiological reflection of endometrial sensitivity to steroid hormones. Based on the whole transcriptome RNA sequencing and the corresponding bioinformatics, we proposed that activation of the decidual prolactin (PRL) promoter containing ancient transposons MER20 and MER39 may reflect functioning of the core decidual regulatory network. Furthermore, we cloned the sequence of decidual PRL promoter containing MER20 and part of MER39 into the expression vector to estimate the effectiveness of ESC decidual response and verified sensitivity of the designed system. We additionally confirmed specificity of the generated tool using human diploid fibroblasts and adipose-derived human mesenchymal stem cells. Finally, we demonstrated the possibility to apply our tool for personalized hormone screening by comparing the effects of natural progesterone and three synthetic analogs (medroxyprogesterone 17-acetate, 17α-hydroxyprogesterone caproate, dydrogesterone) on decidualization of six ESC lines obtained from patients planning to undergo the IVF procedure. To sum up, we developed the "all-in-one" genetic tool based on the MER20/MER39 expression cassette that provides the ability to predict the most appropriate hormonal cocktail for endometrial receptivity maintenance specifically and safely for the patient, and thus to define the personal treatment strategy prior to the IVF procedure.

Structured reviews for data and knowledge-driven research

Hypothesis generation is a critical step in research and a cornerstone in the rare disease field. Research is most efficient when those hypotheses are based on the entirety of knowledge known to date. Systematic review articles are commonly used in biomedicine to summarize existing knowledge and contextualize experimental data. But the information contained within review articles is typically only expressed as free-text, which is difficult to use computationally. Researchers struggle to navigate, collect and remix prior knowledge as it is scattered in several silos without seamless integration and access. This lack of a structured information framework hinders research by both experimental and computational scientists. To better organize knowledge and data, we built a structured review article that is specifically focused on NGLY1 Deficiency, an ultra-rare genetic disease first reported in 2012. We represented this structured review as a knowledge graph and then stored this knowledge graph in a Neo4j database to simplify dissemination, querying and visualization of the network. Relative to free-text, this structured review better promotes the principles of findability, accessibility, interoperability and reusability (FAIR). In collaboration with domain experts in NGLY1 Deficiency, we demonstrate how this resource can improve the efficiency and comprehensiveness of hypothesis generation. We also developed a read–write interface that allows domain experts to contribute FAIR structured knowledge to this community resource. In contrast to traditional free-text review articles, this structured review exists as a living knowledge graph that is curated by humans and accessible to computational analyses. Finally, we have generalized this workflow into modular and repurposable components that can be applied to other domain areas. This NGLY1 Deficiency-focused network is publicly available at http://ngly1graph.org/.

Availability and implementation

Database URL: http://ngly1graph.org/. Network data files are at: https://github.com/SuLab/ngly1-graph and source code at: https://github.com/SuLab/bioknowledge-reviewer.

Contact

asu@scripps.edu

MKL-GRNI: A parallel multiple kernel learning approach for supervised inference of large-scale gene regulatory networks

High throughput multi-omics data generation coupled with heterogeneous genomic data fusion are defining new ways to build computational inference models. These models are scalable and can support very large genome sizes with the added advantage of exploiting additional biological knowledge from the integration framework. However, the limitation with such an arrangement is the huge computational cost involved when learning from very large datasets in a sequential execution environment. To overcome this issue, we present a multiple kernel learning (MKL) based gene regulatory network (GRN) inference approach wherein multiple heterogeneous datasets are fused using MKL paradigm. We formulate the GRN learning problem as a supervised classification problem, whereby genes regulated by a specific transcription factor are separated from other non-regulated genes. A parallel execution architecture is devised to learn a large scale GRN by decomposing the initial classification problem into a number of subproblems that run as multiple processes on a multi-processor machine. We evaluate the approach in terms of increased speedup and inference potential using genomic data from Escherichia coli, Saccharomyces cerevisiae and Homo sapiens. The results thus obtained demonstrate that the proposed method exhibits better classification accuracy and enhanced speedup compared to other state-of-the-art methods while learning large scale GRNs from multiple and heterogeneous datasets.

Construction of a TF-miRNA-gene feed-forward loop network predicts biomarkers and potential drugs for myasthenia gravis

Myasthenia gravis (MG) is an autoimmune disease and the most common type of neuromuscular disease. Genes and miRNAs associated with MG have been widely studied; however, the molecular mechanisms of transcription factors (TFs) and the relationship among them remain unclear. A TF–miRNA–gene network (TMGN) of MG was constructed by extracting six regulatory pairs (TF–miRNA, miRNA–gene, TF–gene, miRNA–TF, gene–gene and miRNA–miRNA). Then, 3/4/5-node regulatory motifs were detected in the TMGN. Then, the motifs with the highest Z-score, occurring as 3/4/5-node composite feed-forward loops (FFLs), were selected as statistically significant motifs. By merging these motifs together, we constructed a 3/4/5-node composite FFL motif-specific subnetwork (CFMSN). Then, pathway and GO enrichment analyses were performed to further elucidate the mechanism of MG. In addition, the genes, TFs and miRNAs in the CFMSN were also utilized to identify potential drugs. Five related genes, 3 TFs and 13 miRNAs, were extracted from the CFMSN. As the most important TF in the CFMSN, MYC was inferred to play a critical role in MG. Pathway enrichment analysis showed that the genes and miRNAs in the CFMSN were mainly enriched in pathways related to cancer and infections. Furthermore, 21 drugs were identified through the CFMSN, of which estradiol, estramustine, raloxifene and tamoxifen have the potential to be novel drugs to treat MG. The present study provides MG-related TFs by constructing the CFMSN for further experimental studies and provides a novel perspective for new biomarkers and potential drugs for MG.

Gene-regulatory network analysis of ankylosing spondylitis with a single-cell chromatin accessible assay

A detailed understanding of the gene-regulatory network in ankylosing spondylitis (AS) is vital for elucidating the mechanisms of AS pathogenesis. Assaying transposase-accessible chromatin in single cell sequencing (scATAC-seq) is a suitable method for revealing such networks. Thus, scATAC-seq was applied to define the landscape of active regulatory DNA in AS. As a result, there was a significant change in the percent of CD8+ T cells in PBMCs, and 37 differentially accessible transcription factor (TF) motifs were identified. T cells, monocytes-1 and dendritic cells were found to be crucial for the IL-17 signaling pathway and TNF signaling pathway, since they had 73 potential target genes regulated by 8 TF motifs with decreased accessibility in AS. Moreover, natural killer cells were involved in AS by increasing the accessibility to TF motifs TEAD1 and JUN to induce cytokine-cytokine receptor interactions. In addition, CD4+ T cells and CD8+ T cells may be vital for altering host immune functions through increasing the accessibility of TF motifs NR1H4 and OLIG (OLIGI and OLIG2), respectively. These results explain clear gene regulatory variation in PBMCs from AS patients, providing a foundational framework for the study of personal regulomes and delivering insights into epigenetic therapy.

Globally learning gene regulatory networks based on hidden atomic regulators from transcriptomic big data

Background

Genes are regulated by various types of regulators and most of them are still unknown or unobserved. Current gene regulatory networks (GRNs) reverse engineering methods often neglect the unknown regulators and infer regulatory relationships in a local and sub-optimal manner.

Results

This paper proposes a global GRNs inference framework based on dictionary learning, named dlGRN. The method intends to learn atomic regulators (ARs) from gene expression data using a modified dictionary learning (DL) algorithm, which reflects the whole gene regulatory system, and predicts the regulation between a known regulator and a target gene in a global regression way. The modified DL algorithm fits the scale-free property of biological network, rendering dlGRN intrinsically discern direct and indirect regulations.

Conclusions

Extensive experimental results on simulation and real-world data demonstrate the effectiveness and efficiency of dlGRN in reverse engineering GRNs. A novel predicted transcription regulation between a TF TFAP2C and an oncogene EGFR was experimentally verified in lung cancer cells. Furthermore, the real application reveals the prevalence of DNA methylation regulation in gene regulatory system. dlGRN can be a standalone tool for GRN inference for its globalization and robustness.

Preeclampsia-Associated lncRNA INHBA-AS1 Regulates the Proliferation, Invasion, and Migration of Placental Trophoblast Cells

Preeclampsia is believed to be caused by impaired placentation with insufficient trophoblast invasion, leading to impaired uterine spiral artery remodeling and angiogenesis. However, the underlying molecular mechanism remains unknown. We recently carried out transcriptome profiling of placental long noncoding RNAs (lncRNAs) and identified 383 differentially expressed lncRNAs in early-onset severe preeclampsia. Here, we are reporting our identification of lncRNA INHBA-AS1 as a potential causal factor of preeclampsia and its downstream pathways that may be involved in placentation. We found that INHBA-AS1 was upregulated in patients and positively correlated with clinical severity. We systematically searched for potential INHBA-AS1-binding transcription factors and their targets in databases and found that the targets were enriched with differentially expressed genes in the placentae of patients. We further demonstrated that the lncRNA INHBA-AS1 inhibited the invasion and migration of trophoblast cells through restraining the transcription factor CENPB from binding to the promoter of TNF receptor-associated factor 1 (TRAF1). Therefore, we have identified the dysregulated pathway "INHBA-AS1-CENPB-TRAF1" as a contributor to the pathogenesis of preeclampsia through prohibiting the proliferation, invasion, and migration of trophoblasts during placentation.

Integrating binding and expression data to predict transcription factors combined function

BACKGROUND

Transcription factor binding to the regulatory region of a gene induces or represses its gene expression. Transcription factors share their binding sites with other factors, co-factors and/or DNA-binding proteins. These proteins form complexes which bind to the DNA as one-units. The binding of two factors to a shared site does not always lead to a functional interaction.

RESULTS

We propose a method to predict the combined functions of two factors using comparable binding and expression data (target). We based this method on binding and expression target analysis (BETA), which we re-implemented in R and extended for this purpose. target ranks the factor's targets by importance and predicts the dominant type of interaction between two transcription factors. We applied the method to simulated and real datasets of transcription factor-binding sites and gene expression under perturbation of factors. We found that Yin Yang 1 transcription factor (YY1) and YY2 have antagonistic and independent regulatory targets in HeLa cells, but they may cooperate on a few shared targets.

CONCLUSION

We developed an R package and a web application to integrate binding (ChIP-seq) and expression (microarrays or RNA-seq) data to determine the cooperative or competitive combined function of two transcription factors.

Association of lncRNA SH3PXD2A-AS1 with preeclampsia and its function in invasion and migration of placental trophoblast cells

Accumulating evidence suggests that the pathogenesis of preeclampsia involves poor placentation caused by insufficient trophoblast invasion and impaired uterine spiral artery remodeling, yet the underlying molecular mechanism remains unclear. We carried out transcriptome profiling on placentae from preeclamptic patients and normal subjects, and identified about four hundred long non-coding RNAs differentially expressed in placentae of patients with early-onset severe preeclampsia. Here, we report our identification of lncRNA SH3PXD2A-AS1 as a potential causal factor for this disease and its downstream pathways involved in placentation. We found that expression level of SH3PXD2A-AS1 in the placentae is positively correlated with clinical severity of the patients. We demonstrated that SH3PXD2A-AS1 inhibited invasion and migration through recruiting CCCTC-binding factor (CTCF) to the promoters of SH3PXD2A and CCR7 to inhibit their transcription. Therefore, we conclude that the upregulation of lncRNA SH3PXD2A-AS1 may contribute to the pathogenesis of preeclampsia through prohibiting trophoblast invasion during placentation.

Transcriptomic Hallmarks of Tumor Plasticity and Stromal Interactions in Brain Metastasis

The brain is a major site of relapse for several cancers, yet deciphering the mechanisms of brain metastasis remains a challenge because of the complexity of the brain tumor microenvironment (TME). To define the molecular landscape of brain metastasis from intact tissue in vivo, we employ an RNA-sequencing-based approach, which leverages the transcriptome of xenografts and distinguishes tumor cell and stromal gene expression with improved sensitivity and accuracy. Our data reveal shifts in epithelial and neuronal-like lineage programs in malignant cells as they adapt to the brain TME and the reciprocal neuroinflammatory response of the stroma. We identify several transcriptional hallmarks of metastasis that are specific to particular regions of the brain, induced across multiple tumor types, and confirmed in syngeneic models and patient biopsies. These data may serve as a resource for exploring mechanisms of TME co-adaptation within, as well as across, different subtypes of brain metastasis.

Contributions of HOX genes to cancer hallmarks: Enrichment pathway analysis and review

Homeobox genes function as master regulatory transcription factors during development, and their expression is often altered in cancer. The HOX gene family was initially studied intensively to understand how the expression of each gene was involved in forming axial patterns and shaping the body plan during embryogenesis. More recent investigations have discovered that HOX genes can also play an important role in cancer. The literature has shown that the expression of HOX genes may be increased or decreased in different tumors and that these alterations may differ depending on the specific HOX gene involved and the type of cancer being investigated. New studies are also emerging, showing the critical role of some members of the HOX gene family in tumor progression and variation in clinical response. However, there has been limited systematic evaluation of the various contributions of each member of the HOX gene family in the pathways that drive the common phenotypic changes (or "hallmarks") and that underlie the transformation of normal cells to cancer cells. In this review, we investigate the context of the engagement of HOX gene targets and their downstream pathways in the acquisition of competence of tumor cells to undergo malignant transformation and tumor progression. We also summarize published findings on the involvement of HOX genes in carcinogenesis and use bioinformatics methods to examine how their downstream targets and pathways are involved in each hallmark of the cancer phenotype.

Systematic Identification of Druggable Epithelial-Stromal Crosstalk Signaling Networks in Ovarian Cancer

Background

Bulk tumor tissue samples are used for generating gene expression profiles in most research studies, making it difficult to decipher the stroma–cancer crosstalk networks. In the present study, we describe the use of microdissected transcriptome profiles for the identification of cancer–stroma crosstalk networks with prognostic value, which presents a unique opportunity for developing new treatment strategies for ovarian cancer.

Methods

Transcriptome profiles from microdissected ovarian cancer–associated fibroblasts (CAFs) and ovarian cancer cells from patients with high-grade serous ovarian cancer (n = 70) were used as input data for the computational systems biology program CCCExplorer to uncover crosstalk networks between various cell types within the tumor microenvironment. The crosstalk analysis results were subsequently used for discovery of new indications for old drugs in ovarian cancer by computational ranking of candidate agents. Survival analysis was performed on ovarian tumor–bearing Dicer/Pten double-knockout mice treated with calcitriol, a US Food and Drug Administration–approved agent that suppresses the Smad signaling cascade, or vehicle control (9–11 mice per group). All statistical tests were two-sided.

Results

Activation of TGF-β-dependent and TGF-β-independent Smad signaling was identified in a particular subtype of CAFs and was associated with poor patient survival (patients with higher levels of Smad-regulated gene expression by CAFs: median overall survival = 15 months, 95% confidence interval [CI] = 12.7 to 17.3 months; vs patients with lower levels of Smad-regulated gene expression: median overall survival = 26 months, 95% CI = 15.9 to 36.1 months, P = .02). In addition, the activated Smad signaling identified in CAFs was found to be targeted by repositioning calcitriol. Calcitriol suppressed Smad signaling in CAFs, inhibited tumor progression in mice, and prolonged the median survival duration of ovarian cancer–bearing mice from 36 to 48 weeks (P = .04).

Conclusions

Our findings suggest the feasibility of using novel multicellular systems biology modeling to identify and repurpose known drugs targeting cancer–stroma crosstalk networks, potentially leading to faster and more effective cures for cancers.

Natural Killer cell transcriptome during primary EBV infection and EBV associated Hodgkin Lymphoma in children-A preliminary observation

Epstein Barr Viral infection is a common childhood infection in India and is also nearly 100 % etiologically associated with pediatric Hodgkin Lymphoma (HL). The main question in EBV immunobiology has been, why only a small subset of infected individuals develop EBV associated malignancies, while the vast majority carry this virus asymptomatically for life. Natural Killer (NK) cells, with a phenotype of CD56dim CD16+ exhibit potent cytotoxicity towards both virus infected cells and transformed cells and hence have been considered to be crucial in preventing the development of symptomatic EBV infection and lymphoma. In order to get an insight into the various possible molecular aspects of NK cells, in the pathogenesis of both these EBV mediated diseases in children we studied the whole transcriptome of MACS sorted CD56dim CD16 + NK cells from four patients from each of the three groups of children viz. Infectious Mononucleosis (IM), HL and age matched controls by using a massively parallel sequencing approach. NK cells from both IM and HL had down-regulated innate immunity and chemokine signaling genes. While down-regulation of genes responsible for polarization of the secretory apparatus, activated NK cell signaling and MAP kinase signaling were exclusive to NK cells in patients with IM, in NK cells of HL, specifically, genes involved in extracellular matrix (ECM) - receptor interaction, cytokine-cytokine receptor interaction, TNF signaling, Toll-like receptor signaling pathway and cytosolic DNA-sensing pathways were significantly down-regulated. Enrichment analysis showed STAT3 to be the most significant transcription factor (TF) for the down-regulated genes in IM, whereas, GATA1 was found to be the most significant TF for the genes down-regulated in HL. Analysis of protein interaction network identified functionally important protein clusters. Top clusters, comprised of down-regulated genes, involved in signaling and ubiquitin-related processes and pathways. These may perhaps be responsible for the hypo-responsiveness of NK cells in both diseases. These possibly point to different deficiencies in NK cell activation, loss of activating receptor signaling and degranulation in IM, versus loss of cytokine and chemokine signaling in HL, in the two EBV associated pathologies investigated. Various suppressed molecules and pathways were novel, which have not been reported earlier and could therefore be potential targets for immunotherapy of NK cell reactivation in both the diseases in future.

ModEx: A text mining system for extracting mode of regulation of transcription factor-gene regulatory interaction

BACKGROUND

Transcription factors (TFs) are proteins that are fundamental to transcription and regulation of gene expression. Each TF may regulate multiple genes and each gene may be regulated by multiple TFs. TFs can act as either activator or repressor of gene expression. This complex network of interactions between TFs and genes underlies many developmental and biological processes and is implicated in several human diseases such as cancer. Hence deciphering the network of TF-gene interactions with information on mode of regulation (activation vs. repression) is an important step toward understanding the regulatory pathways that underlie complex traits. There are many experimental, computational, and manually curated databases of TF-gene interactions. In particular, high-throughput ChIP-Seq datasets provide a large-scale map or transcriptional regulatory interactions. However, these interactions are not annotated with information on context and mode of regulation. Such information is crucial to gain a global picture of gene regulatory mechanisms and can aid in developing machine learning models for applications such as biomarker discovery, prediction of response to therapy, and precision medicine.

METHODS

In this work, we introduce a text-mining system to annotate ChIP-Seq derived interaction with such meta data through mining PubMed articles. We evaluate the performance of our system using gold standard small scale manually curated databases.

RESULTS

Our results show that the method is able to accurately extract mode of regulation with F-score 0.77 on TRRUST curated interaction and F-score 0.96 on intersection of TRUSST and ChIP-network. We provide a HTTP REST API for our code to facilitate usage. Availibility: Source code and datasets are available for download on GitHub: https://github.com/samanfrm/modex.

Causal Inference Engine: a platform for directional gene set enrichment analysis and inference of active transcriptional regulators

Inference of active regulatory mechanisms underlying specific molecular and environmental perturbations is essential for understanding cellular response. The success of inference algorithms relies on the quality and coverage of the underlying network of regulator-gene interactions. Several commercial platforms provide large and manually curated regulatory networks and functionality to perform inference on these networks. Adaptation of such platforms for open-source academic applications has been hindered by the lack of availability of accurate, high-coverage networks of regulatory interactions and integration of efficient causal inference algorithms. In this work, we present CIE, an integrated platform for causal inference of active regulatory mechanisms form differential gene expression data. Using a regularized Gaussian Graphical Model, we construct a transcriptional regulatory network by integrating publicly available ChIP-seq experiments with gene-expression data from tissue-specific RNA-seq experiments. Our GGM approach identifies high confidence transcription factor (TF)-gene interactions and annotates the interactions with information on mode of regulation (activation vs. repression). Benchmarks against manually curated databases of TF-gene interactions show that our method can accurately detect mode of regulation. We demonstrate the ability of our platform to identify active transcriptional regulators by using controlled in vitro overexpression and stem-cell differentiation studies and utilize our method to investigate transcriptional mechanisms of fibroblast phenotypic plasticity.

In silico identification of EP400 and TIA1 as critical transcription factors involved in human hepatocellular carcinoma relapse

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-associated mortality worldwide. Transcription factors (TFs) are crucial proteins that regulate gene expression during cancer progression; however, the roles of TFs in HCC relapse remain unclear. To identify the TFs that drive HCC relapse, the present study constructed co-expression network and identified the Tan module the most relevant to HCC relapse. Numerous hub TFs (highly connected) were subsequently obtained from the Tan module according to the intra-module connectivity and the protein-protein interaction network connectivity. Next, E1A-binding protein p400 (EP400) and TIA1 cytotoxic granule associated RNA binding protein (TIA1) were identified as hub TFs differentially connected between the relapsed and non-relapsed subnetworks. In addition, zinc finger protein 143 (ZNF143) and Yin Yang 1 (YY1) were also identified by using the plugin iRegulon in Cytoscape as master upstream regulatory elements, which could potentially regulate expression of the genes and TFs of the Tan module, respectively. The Kaplan-Meier (KM) curves obtained from KMplot and Gene Expression Profiling Interactive Analysis tools confirmed that the high expression of EP400 and TIA1 were significantly associated with shorter relapse-free survival and disease-free survival of patients with HCC. Furthermore, the KM curves from the UALCAN database demonstrated that high EP400 expression significantly reduced the overall survival of patients with HCC. EP400 and TIA1 may therefore serve as potential prognostic and therapeutic biomarkers.

Identification of candidate genes and miRNAs for sensitizing resistant colorectal cancer cells to oxaliplatin and irinotecan

Drug resistance to irinotecan and oxaliplatin, two widely used chemotherapeutic, has become a common problem in cancerous patients. Despite numerous valuable studies, distinct molecular mechanisms involved in the acquisition of resistance to these anti-cancer drugs have remained a challenge. In this study, we studied the possible resistance mechanisms to irinotecan and oxaliplatin in three CRC cell lines (HCT116, HT29, and LoVo) via integration of microarray data with gene regulatory networks. After determination of hub genes, corresponding miRNAs were predicted using several databases and used in construction and subsequent analysis of miRNA-gene networks. Following to preparation of chemo-resistance CRC cells, a standard real-time PCR was conducted for validation of in silico findings. Topological and functional enrichment analyses of the resulted networks introduced several previously reported drug-resistance genes as well as novel biomarkers as hub genes which seem to be crucial in resistance of colon cancer cells to irinotecan and oxaliplatin. Furthermore, results of the functional annotation revealed the essential role of different signaling pathways like metabolic pathways in drug resistance of CRC cell lines to these drugs. A part of in silico findings was also validated in vitro using oxaliplatin-resistant cell lines. While FOXC1 and NFIC were upregulated in cell lines which were resistant to oxaliplatin, silencing FOXC1 decreased the resistance of SW480 cell line to oxaliplatin. In conclusion, our comparative in silico and in vitro study introduces several novel genes and miRNAs as the resistance-mediators which can be used for sensitizing resistant CRC cells to oxaliplatin and irinotecan.

Integrative approaches to reconstruct regulatory networks from multi-omics data: A review of state-of-the-art methods

Data generation using high throughput technologies has led to the accumulation of diverse types of molecular data. These data have different types (discrete, real, string, etc.) and occur in various formats and sizes. Datasets including gene expression, miRNA expression, protein-DNA binding data (ChIP-Seq/ChIP-ChIP), mutation data (copy number variation, single nucleotide polymorphisms), annotations, interactions, and association data are some of the commonly used biological datasets to study various cellular mechanisms of living organisms. Each of them provides a unique, complementary and partly independent view of the genome and hence embed essential information about the regulatory mechanisms of genes and their products. Therefore, integrating these data and inferring regulatory interactions from them offer a system level of biological insight in predicting gene functions and their phenotypic outcomes. To study genome functionality through regulatory networks, different methods have been proposed for collective mining of information from an integrated dataset. We survey here integration methods that reconstruct regulatory networks using state-of-the-art techniques to handle multi-omics (i.e., genomic, transcriptomic, proteomic) and other biological datasets.

Rare, functional, somatic variants in gene families linked to cancer genes: GPCR signaling as a paradigm

Oncodriver genes are usually identified when mutations recur in multiple tumours. Different drivers often converge in the activation or repression of key cancer-relevant pathways. However, as many pathways contain multiple members of the same gene family, individual mutations might be overlooked, as each family member would necessarily have a lower mutation frequency and thus not identified as significant in any one-gene-at-a-time analysis. Here, we looked for mutated, functional sequence positions in gene families that were mutually exclusive (in patients) with another gene in the same pathway, which identified both known and new candidate oncodrivers. For instance, many inactivating mutations in multiple G-protein (particularly Gi/o) coupled receptors, are mutually exclusive with Gαs oncogenic activating mutations, both of which ultimately enhance cAMP signalling. By integrating transcriptomics and interaction data, we show that the Gs pathway is upregulated in multiple cancer types, even those lacking known GNAS activating mutations. This suggests that cancer cells may develop alternative strategies to activate adenylate cyclase signalling in multiple cancer types. Our study provides a mechanistic interpretation for several rare somatic mutations in multi-gene oncodrivers, and offers possible explanations for known and potential off-label cancer treatments, suggesting new therapeutic opportunities.

Benchmark and integration of resources for the estimation of human transcription factor activities

The prediction of transcription factor (TF) activities from the gene expression of their targets (i.e., TF regulon) is becoming a widely used approach to characterize the functional status of transcriptional regulatory circuits. Several strategies and data sets have been proposed to link the target genes likely regulated by a TF, each one providing a different level of evidence. The most established ones are (1) manually curated repositories, (2) interactions derived from ChIP-seq binding data, (3) in silico prediction of TF binding on gene promoters, and (4) reverse-engineered regulons from large gene expression data sets. However, it is not known how these different sources of regulons affect the TF activity estimations and, thereby, downstream analysis and interpretation. Here we compared the accuracy and biases of these strategies to define human TF regulons by means of their ability to predict changes in TF activities in three reference benchmark data sets. We assembled a collection of TF-target interactions for 1541 human TFs and evaluated how different molecular and regulatory properties of the TFs, such as the DNA-binding domain, specificities, or mode of interaction with the chromatin, affect the predictions of TF activity. We assessed their coverage and found little overlap on the regulons derived from each strategy and better performance by literature-curated information followed by ChIP-seq data. We provide an integrated resource of all TF-target interactions derived through these strategies, with confidence scores, as a resource for enhanced prediction of TF activities.