Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis

A genome scale transcriptional regulatory model of the human placenta

Gene regulation is essential to placental function and fetal development. We built a genome-scale transcriptional regulatory network (TRN) of the human placenta using digital genomic footprinting and transcriptomic data. We integrated 475 transcriptomes and 12 DNase hypersensitivity datasets from placental samples to globally and quantitatively map transcription factor (TF)-target gene interactions. In an independent dataset, the TRN model predicted target gene expression with an out-of-sample R2 greater than 0.25 for 73% of target genes. We performed siRNA knockdowns of four TFs and achieved concordance between the predicted gene targets in our TRN and differences in expression of knockdowns with an accuracy of >0.7 for three of the four TFs. Our final model contained 113,158 interactions across 391 TFs and 7712 target genes and is publicly available. We identified 29 TFs which were significantly enriched as regulators for genes previously associated with preterm birth, and eight of these TFs were decreased in preterm placentas.

Progressive senescence programs induce intrinsic vulnerability to aging-related female breast cancer

Cancer incidence escalates exponentially with advancing age; however, the underlying mechanism remains unclear. In this study, we build a chronological molecular clock at single-cell transcription level with a mammary stem cell-enriched population to depict physiological aging dynamics in female mice. We find that the mammary aging process is asynchronous and progressive, initiated by an early senescence program, succeeded by an entropic late senescence program with elevated cancer associated pathways, vulnerable to cancer predisposition. The transition towards senescence program is governed by a stem cell factor Bcl11b, loss of which accelerates mammary ageing with enhanced DMBA-induced tumor formation. We have identified a drug TPCA-1 that can rejuvenate mammary cells and significantly reduce aging-related cancer incidence. Our findings establish a molecular portrait of progressive mammary cell aging and elucidate the transcriptional regulatory network bridging mammary aging and cancer predisposition, which has potential implications for the management of cancer prevalence in the aged.

Gene regulatory network topology governs resistance and treatment escape in glioma stem-like cells

Poor prognosis and drug resistance in glioblastoma (GBM) can result from cellular heterogeneity and treatment-induced shifts in phenotypic states of tumor cells, including dedifferentiation into glioma stem-like cells (GSCs). This rare tumorigenic cell subpopulation resists temozolomide, undergoes proneural-to-mesenchymal transition (PMT) to evade therapy, and drives recurrence. Through inference of transcriptional regulatory networks (TRNs) of patient-derived GSCs (PD-GSCs) at single-cell resolution, we demonstrate how the topology of transcription factor interaction networks drives distinct trajectories of cell-state transitions in PD-GSCs resistant or susceptible to cytotoxic drug treatment. By experimentally testing predictions based on TRN simulations, we show that drug treatment drives surviving PD-GSCs along a trajectory of intermediate states, exposing vulnerability to potentiated killing by siRNA or a second drug targeting treatment-induced transcriptional programs governing nongenetic cell plasticity. Our findings demonstrate an approach to uncover TRN topology and use it to rationally predict combinatorial treatments that disrupt acquired resistance in GBM.

LINC00606 promotes glioblastoma progression through sponge miR-486-3p and interaction with ATP11B

BACKGROUND

LncRNAs regulate tumorigenesis and development in a variety of cancers. We substantiate for the first time that LINC00606 is considerably expressed in glioblastoma (GBM) patient specimens and is linked with adverse prognosis. This suggests that LINC00606 may have the potential to regulate glioma genesis and progression, and that the biological functions and molecular mechanisms of LINC00606 in GBM remain largely unknown.

METHODS

The expression of LINC00606 and ATP11B in glioma and normal brain tissues was evaluated by qPCR, and the biological functions of the LINC00606/miR-486-3p/TCF12/ATP11B axis in GBM were verified through a series of in vitro and in vivo experiments. The molecular mechanism of LINC00606 was elucidated by immunoblotting, FISH, RNA pulldown, CHIP-qPCR, and a dual-luciferase reporter assay.

RESULTS

We demonstrated that LINC00606 promotes glioma cell proliferation, clonal expansion and migration, while reducing apoptosis levels. Mechanistically, on the one hand, LINC00606 can sponge miR-486-3p; the target gene TCF12 of miR-486-3p affects the transcriptional initiation of LINC00606, PTEN and KLLN. On the other hand, it can also regulate the PI3K/AKT signaling pathway to mediate glioma cell proliferation, migration and apoptosis by binding to ATP11B protein.

CONCLUSIONS

Overall, the LINC00606/miR-486-3p/TCF12/ATP11B axis is involved in the regulation of GBM progression and plays a role in tumor regulation at transcriptional and post-transcriptional levels primarily through LINC00606 sponging miR-486-3p and targeted binding to ATP11B. Therefore, our research on the regulatory network LINC00606 could be a novel therapeutic strategy for the treatment of GBM.

MEIS2 suppresses breast cancer development by downregulating IL10

BACKGROUND

Breast cancer (BC) is the most commonly diagnosed female cancer. Homeobox protein MEIS2, a key transcription factor, is involved in the regulation of many developmental and cellular processes. However, the role of MEIS2 in the development of breast cancer is still unclear.

AIMS

We aimed to examine the role of myeloid ecotropic insertion site (MEIS2) in breast cancer and the association of MEIS2 with breast cancer clinical stages and pathological grades. We revealed the underlying mechanism by which MEIS2 affected breast cancer cell growth and tumor development.

METHODS AND RESULTS

Using human BC cell lines, clinical samples and animal xenograft model, we reveal that MEIS2 functions as a tumor suppressor in breast cancer. The expression of MEIS2 is inversely correlated with BC clinical stages and pathological grades. MEIS2 knockdown (MEIS2-KD) promotes while MEIS2 overexpression suppresses breast cancer cell proliferation and tumor development in vitro and in animal xenograft models, respectively. To determine the biological function of MEIS2, we screen the expression of a group of MEIS2 potential targeting genes in stable-established cell lines. Results show that the knockdown of MEIS2 in breast cancer cells up-regulates the IL10 expression, but MEIS2 overexpression opposed the effect on IL10 expression. Furthermore, the suppressive role of MEIS2 in breast cancer cell proliferation is associated with the IL10 expression and myeloid cells infiltration.

CONCLUSION

Our study demonstrates that the tumor suppressor of MEIS2 in breast cancer progression is partially via down regulating the expression of IL10 and promoting myeloid cells infiltration. Targeting MEIS2 would be a potentially therapeutic avenue for BC.

3D Hydrogel Culture System Recapitulates Key Tuberculosis Phenotypes and Demonstrates Pyrazinamide Efficacy

The mortality caused by tuberculosis (TB) infections is a global concern, and there is a need to improve understanding of the disease. Current in vitro infection models to study the disease have limitations such as short investigation durations and divergent transcriptional signatures. This study aims to overcome these limitations by developing a 3D collagen culture system that mimics the biomechanical and extracellular matrix (ECM) of lung microenvironment (collagen fibers, stiffness comparable to in vivo conditions) as the infection primarily manifests in the lungs. The system incorporates Mycobacterium tuberculosis (Mtb) infected human THP-1 or primary monocytes/macrophages. Dual RNA sequencing reveals higher mammalian gene expression similarity with patient samples than 2D macrophage infections. Similarly, bacterial gene expression more accurately recapitulates in vivo gene expression patterns compared to bacteria in 2D infection models. Key phenotypes observed in humans, such as foamy macrophages and mycobacterial cords, are reproduced in the model. This biomaterial system overcomes challenges associated with traditional platforms by modulating immune cells and closely mimicking in vivo infection conditions, including showing efficacy with clinically relevant concentrations of anti-TB drug pyrazinamide, not seen in any other in vitro infection model, making it reliable and readily adoptable for tuberculosis studies and drug screening.

An atlas of causal and mechanistic drivers of interpatient heterogeneity in glioma

Grade IV glioma, formerly known as glioblastoma multiforme (GBM) is the most aggressive and lethal type of brain tumor, and its treatment remains challenging in part due to extensive interpatient heterogeneity in disease driving mechanisms and lack of prognostic and predictive biomarkers. Using mechanistic inference of node-edge relationship (MINER), we have analyzed multiomics profiles from 516 patients and constructed an atlas of causal and mechanistic drivers of interpatient heterogeneity in GBM (gbmMINER). The atlas has delineated how 30 driver mutations act in a combinatorial scheme to causally influence a network of regulators (306 transcription factors and 73 miRNAs) of 179 transcriptional "programs", influencing disease progression in patients across 23 disease states. Through extensive testing on independent patient cohorts, we share evidence that a machine learning model trained on activity profiles of programs within gbmMINER significantly augments risk stratification, identifying patients who are super-responders to standard of care and those that would benefit from 2 nd line treatments. In addition to providing mechanistic hypotheses regarding disease prognosis, the activity of programs containing targets of 2 nd line treatments accurately predicted efficacy of 28 drugs in killing glioma stem-like cells from 43 patients. Our findings demonstrate that interpatient heterogeneity manifests from differential activities of transcriptional programs, providing actionable strategies for mechanistically characterizing GBM from a systems perspective and developing better prognostic and predictive biomarkers for personalized medicine.

Large-scale phosphoproteomics reveals activation of the MAPK/GADD45β/P38 axis and cell cycle inhibition in response to BMP9 and BMP10 stimulation in endothelial cells

BACKGROUND

BMP9 and BMP10 are two major regulators of vascular homeostasis. These two ligands bind with high affinity to the endothelial type I kinase receptor ALK1, together with a type II receptor, leading to the direct phosphorylation of the SMAD transcription factors. Apart from this canonical pathway, little is known. Interestingly, mutations in this signaling pathway have been identified in two rare cardiovascular diseases, hereditary hemorrhagic telangiectasia and pulmonary arterial hypertension.

METHODS

To get an overview of the signaling pathways modulated by BMP9 and BMP10 stimulation in endothelial cells, we employed an unbiased phosphoproteomic-based strategy. Identified phosphosites were validated by western blot analysis and regulated targets by RT-qPCR. Cell cycle analysis was analyzed by flow cytometry.

RESULTS

Large-scale phosphoproteomics revealed that BMP9 and BMP10 treatment induced a very similar phosphoproteomic profile. These BMPs activated a non-canonical transcriptional SMAD-dependent MAPK pathway (MEKK4/P38). We were able to validate this signaling pathway and demonstrated that this activation required the expression of the protein GADD45β. In turn, activated P38 phosphorylated the heat shock protein HSP27 and the endocytosis protein Eps15 (EGF receptor pathway substrate), and regulated the expression of specific genes (E-selectin, hyaluronan synthase 2 and cyclooxygenase 2). This study also highlighted the modulation in phosphorylation of proteins involved in transcriptional regulation (phosphorylation of the endothelial transcription factor ERG) and cell cycle inhibition (CDK4/6 pathway). Accordingly, we found that BMP10 induced a G1 cell cycle arrest and inhibited the mRNA expression of E2F2, cyclinD1 and cyclinA1.

CONCLUSIONS

Overall, our phosphoproteomic screen identified numerous proteins whose phosphorylation state is impacted by BMP9 and BMP10 treatment, paving the way for a better understanding of the molecular mechanisms regulated by BMP signaling in vascular diseases.

Gene regulatory network topology governs resistance and treatment escape in glioma stem-like cells

Poor prognosis and drug resistance in glioblastoma (GBM) can result from cellular heterogeneity and treatment-induced shifts in phenotypic states of tumor cells, including dedifferentiation into glioma stem-like cells (GSCs). This rare tumorigenic cell subpopulation resists temozolomide, undergoes proneural-to-mesenchymal transition (PMT) to evade therapy, and drives recurrence. Through inference of transcriptional regulatory networks (TRNs) of patient-derived GSCs (PD-GSCs) at single-cell resolution, we demonstrate how the topology of transcription factor interaction networks drives distinct trajectories of cell state transitions in PD-GSCs resistant or susceptible to cytotoxic drug treatment. By experimentally testing predictions based on TRN simulations, we show that drug treatment drives surviving PD-GSCs along a trajectory of intermediate states, exposing vulnerability to potentiated killing by siRNA or a second drug targeting treatment-induced transcriptional programs governing non-genetic cell plasticity. Our findings demonstrate an approach to uncover TRN topology and use it to rationally predict combinatorial treatments that disrupts acquired resistance in GBM.

Revealing the biology behind MRI signatures in high grade glioma

Magnetic resonance imaging (MRI) measurements are routinely collected during the treatment of high-grade gliomas (HGGs) to characterize tumor boundaries and guide surgical tumor resection. Using spatially matched MRI and transcriptomics we discovered HGG tumor biology captured by MRI measurements. We strategically overlaid the spatially matched omics characterizations onto a pre-existing transcriptional map of glioblastoma multiforme (GBM) to enhance the robustness of our analyses. We discovered that T1+C measurements, designed to capture vasculature and blood brain barrier (BBB) breakdown and subsequent contrast extravasation, also indirectly reveal immune cell infiltration. The disruption of the vasculature and BBB within the tumor creates a permissive infiltrative environment that enables the transmigration of anti-inflammatory macrophages into tumors. These relationships were validated through histology and enrichment of genes associated with immune cell transmigration and proliferation. Additionally, T2-weighted (T2W) and mean diffusivity (MD) measurements were associated with angiogenesis and validated using histology and enrichment of genes involved in neovascularization. Furthermore, we establish an unbiased approach for identifying additional linkages between MRI measurements and tumor biology in future studies, particularly with the integration of novel MRI techniques. Lastly, we illustrated how noninvasive MRI can be used to map HGG biology spatially across a tumor, and this provides a platform to develop diagnostics, prognostics, or treatment efficacy biomarkers to improve patient outcomes.

The Typical tRNA Co-Expresses Multiple 5' tRNA Halves Whose Sequences and Abundances Depend on Isodecoder and Isoacceptor and Change with Tissue Type, Cell Type, and Disease

Transfer RNA-derived fragments (tRFs) are noncoding RNAs that arise from either mature transfer RNAs (tRNAs) or their precursors. One important category of tRFs comprises the tRNA halves, which are generated through cleavage at the anticodon. A given tRNA typically gives rise to several co-expressed 5'-tRNA halves (5'-tRHs) that differ in the location of their 3' ends. These 5'-tRHs, even though distinct, have traditionally been treated as indistinguishable from one another due to their near-identical sequences and lengths. We focused on co-expressed 5'-tRHs that arise from the same tRNA and systematically examined their exact sequences and abundances across 10 different human tissues. To this end, we manually curated and analyzed several hundred human RNA-seq datasets from NCBI's Sequence Run Archive (SRA). We grouped datasets from the same tissue into their own collection and examined each group separately. We found that a given tRNA produces different groups of co-expressed 5'-tRHs in different tissues, different cell lines, and different diseases. Importantly, the co-expressed 5'-tRHs differ in their sequences, absolute abundances, and relative abundances, even among tRNAs with near-identical sequences from the same isodecoder or isoacceptor group. The findings suggest that co-expressed 5'-tRHs that are produced from the same tRNA or closely related tRNAs have distinct, context-dependent roles. Moreover, our analyses show that cell lines modeling the same tissue type and disease may not be interchangeable when it comes to experimenting with tRFs.

Obtaining Functional Proteomics Insights From Thermal Proteome Profiling Through Optimized Melt Shift Calculation and Statistical Analysis With InflectSSP

Thermal proteome profiling (TPP) is an invaluable tool for functional proteomics studies that has been shown to discover changes associated with protein-ligand, protein-protein, and protein-RNA interaction dynamics along with changes in protein stability resulting from cellular signaling. The increasing number of reports employing this assay has not been met concomitantly with new approaches leading to advancements in the quality and sensitivity of the corresponding data analysis. The gap between data acquisition and data analysis tools is important to fill as TPP findings have reported subtle melt shift changes related to signaling events such as protein posttranslational modifications. In this study, we have improved the Inflect data analysis pipeline (now referred to as InflectSSP, available at https://CRAN.R-project.org/package=InflectSSP) to increase the sensitivity of detection for both large and subtle changes in the proteome as measured by TPP. Specifically, InflectSSP now has integrated statistical and bioinformatic functions to improve objective functional proteomics findings from the quantitative results obtained from TPP studies through increasing both the sensitivity and specificity of the data analysis pipeline. InflectSSP incorporates calculation of a "melt coefficient" into the pipeline with production of average melt curves for biological replicate studies to aid in identification of proteins with significant melts. To benchmark InflectSSP, we have reanalyzed two previously reported datasets to demonstrate the performance of our publicly available R-based program for TPP data analysis. We report new findings following temporal treatment of human cells with the small molecule thapsigargin that induces the unfolded protein response as a consequence of inhibition of sarcoplasmic/endoplasmic reticulum calcium ATPase 2A. InflectSSP analysis of our unfolded protein response study revealed highly reproducible and statistically significant target engagement over a time course of treatment while simultaneously providing new insights into the possible mechanisms of action of the small molecule thapsigargin.

MtrA modulates Mycobacterium tuberculosis cell division in host microenvironments to mediate intrinsic resistance and drug tolerance

The success of Mycobacterium tuberculosis (Mtb) is largely attributed to its ability to physiologically adapt and withstand diverse localized stresses within host microenvironments. Here, we present a data-driven model (EGRIN 2.0) that captures the dynamic interplay of environmental cues and genome-encoded regulatory programs in Mtb. Analysis of EGRIN 2.0 shows how modulation of the MtrAB two-component signaling system tunes Mtb growth in response to related host microenvironmental cues. Disruption of MtrAB by tunable CRISPR interference confirms that the signaling system regulates multiple peptidoglycan hydrolases, among other targets, that are important for cell division. Further, MtrA decreases the effectiveness of antibiotics by mechanisms of both intrinsic resistance and drug tolerance. Together, the model-enabled dissection of complex MtrA regulation highlights its importance as a drug target and illustrates how EGRIN 2.0 facilitates discovery and mechanistic characterization of Mtb adaptation to specific host microenvironments within the host.

Systematic integration of protein-affecting mutations, gene fusions, and copy number alterations into a comprehensive somatic mutational profile

Somatic mutations occur as random genetic changes in genes through protein-affecting mutations (PAMs), gene fusions, or copy number alterations (CNAs). Mutations of different types can have a similar phenotypic effect (i.e., allelic heterogeneity) and should be integrated into a unified gene mutation profile. We developed OncoMerge to fill this niche of integrating somatic mutations to capture allelic heterogeneity, assign a function to mutations, and overcome known obstacles in cancer genetics. Application of OncoMerge to TCGA Pan-Cancer Atlas increased detection of somatically mutated genes and improved the prediction of the somatic mutation role as either activating or loss of function. Using integrated somatic mutation matrices increased the power to infer gene regulatory networks and uncovered the enrichment of switch-like feedback motifs and delay-inducing feedforward loops. These studies demonstrate that OncoMerge efficiently integrates PAMs, fusions, and CNAs and strengthens downstream analyses linking somatic mutations to cancer phenotypes.

Long-range regulatory effects of Neandertal DNA in modern humans

The admixture between modern humans and Neandertals has resulted in ∼2% of the genomes of present-day non-Africans being composed of Neandertal DNA. Introgressed Neandertal DNA has been demonstrated to significantly affect the transcriptomic landscape in people today and via this molecular mechanism influence phenotype variation as well. However, little is known about how much of that regulatory impact is mediated through long-range regulatory effects that have been shown to explain ∼20% of expression variation. Here we identified 60 transcription factors (TFs) with their top cis-eQTL SNP in GTEx being of Neandertal ancestry and predicted long-range Neandertal DNA-induced regulatory effects by screening for the predicted target genes of those TFs. We show that the TFs form a significantly connected protein-protein interaction network. Among them are JUN and PRDM5, two brain-expressed TFs that have their predicted target genes enriched in regions devoid of Neandertal DNA. Archaic cis-eQTLs for the 60 TFs include multiple candidates for local adaptation, some of which show significant allele frequency increases over the last ∼10,000 years. A large proportion of the cis-eQTL-associated archaic SNPs have additional associations with various immune traits, schizophrenia, blood cell type composition and anthropometric measures. Finally, we demonstrate that our results are consistent with those of Neandertal DNA-associated empirical trans-eQTLs. Our results suggest that Neandertal DNA significantly influences regulatory networks, that its regulatory reach goes beyond the 40% of genomic sequence it still covers in present-day non-Africans and that via the investigated mechanism Neandertal DNA influences the phenotypic variation in people today.

Identify potential driver genes for PAX-FOXO1 fusion-negative rhabdomyosarcoma through frequent gene co-expression network mining

Background

Rhabdomyosarcoma (RMS) is a soft tissue sarcoma usually originated from skeletal muscle. Currently, RMS classification based on PAX-FOXO1 fusion is widely adopted. However, compared to relatively clear understanding of the tumorigenesis in the fusion-positive RMS, little is known for that in fusion-negative RMS (FN-RMS).

Methods

We explored the molecular mechanisms and the driver genes of FN-RMS through frequent gene co-expression network mining (fGCN), differential copy number (CN) and differential expression analyses on multiple RMS transcriptomic datasets.

Results

We obtained 50 fGCN modules, among which five are differentially expressed between different fusion status. A closer look showed 23% of Module 2 genes are concentrated on several cytobands of chromosome 8. Upstream regulators such as MYC, YAP1, TWIST1 were identified for the fGCN modules. Using in a separate dataset we confirmed that, comparing to FP-RMS, 59 Module 2 genes show consistent CN amplification and mRNA overexpression, among which 28 are on the identified chr8 cytobands. Such CN amplification and nearby MYC (also resides on one of the above cytobands) and other upstream regulators (YAP1, TWIST1) may work together to drive FN-RMS tumorigenesis and progression. Up to 43.1% downstream targets of Yap1 and 45.8% of the targets of Myc are differentially expressed in FN-RMS vs. normal comparisons, which also confirmed the driving force of these regulators.

Discussion

We discovered that copy number amplification of specific cytobands on chr8 and the upstream regulators MYC, YAP1 and TWIST1 work together to affect the downstream gene co-expression and promote FN-RMS tumorigenesis and progression. Our findings provide new insights for FN-RMS tumorigenesis and offer promising targets for precision therapy. Experimental investigation about the functions of identified potential drivers in FN-RMS are in progress.

An integrated analysis of the cancer genome atlas data discovers a hierarchical association structure across thirty three cancer types

Cancer cells harbor molecular alterations at all levels of information processing. Genomic/epigenomic and transcriptomic alterations are inter-related between genes, within and across cancer types and may affect clinical phenotypes. Despite the abundant prior studies of integrating cancer multi-omics data, none of them organizes these associations in a hierarchical structure and validates the discoveries in extensive external data. We infer this Integrated Hierarchical Association Structure (IHAS) from the complete data of The Cancer Genome Atlas (TCGA) and compile a compendium of cancer multi-omics associations. Intriguingly, diverse alterations on genomes/epigenomes from multiple cancer types impact transcriptions of 18 Gene Groups. Half of them are further reduced to three Meta Gene Groups enriched with (1) immune and inflammatory responses, (2) embryonic development and neurogenesis, (3) cell cycle process and DNA repair. Over 80% of the clinical/molecular phenotypes reported in TCGA are aligned with the combinatorial expressions of Meta Gene Groups, Gene Groups, and other IHAS subunits. Furthermore, IHAS derived from TCGA is validated in more than 300 external datasets including multi-omics measurements and cellular responses upon drug treatments and gene perturbations in tumors, cancer cell lines, and normal tissues. To sum up, IHAS stratifies patients in terms of molecular signatures of its subunits, selects targeted genes or drugs for precision cancer therapy, and demonstrates that associations between survival times and transcriptional biomarkers may vary with cancer types. These rich information is critical for diagnosis and treatments of cancers.

Atlas of regulated target genes of transcription factors (ART-TF) in human ES cells

Background

Transcription factors (TFs) play central roles in maintaining “stemness” of embryonic stem (ES) cells and their differentiation into several hundreds of adult cell types. The regulatory competence of TFs is routinely assessed by detecting target genes to which they bind. However, these data do not indicate which target genes are activated, repressed, or not affected by the change of TF abundance. There is a lack of large-scale studies that compare the genome binding of TFs with the expression change of target genes after manipulation of each TF.

Results

In this paper we associated human TFs with their target genes by two criteria: binding to genes, evaluated from published ChIP-seq data (n = 1868); and change of target gene expression shortly after induction of each TF in human ES cells. Lists of direction- and strength-specific regulated target genes are generated for 311 TFs (out of 351 TFs tested) with expected proportion of false positives less than or equal to 0.30, including 63 new TFs not present in four existing databases of target genes. Our lists of direction-specific targets for 152 TFs (80.0%) are larger that in the TRRUST database. In average, 30.9% of genes that respond greater than or equal to twofold to the induction of TFs are regulated targets. Regulated target genes indicate that the majority of TFs are either strong activators or strong repressors, whereas sets of genes that responded greater than or equal to twofold to the induction of TFs did not show strong asymmetry in the direction of expression change. The majority of human TFs (82.1%) regulated their target genes primarily via binding to enhancers. Repression of target genes is more often mediated by promoter-binding than activation of target genes. Enhancer-promoter loops are more abundant among strong activator and repressor TFs.

Conclusions

We developed an atlas of regulated targets of TFs (ART-TF) in human ES cells by combining data on TF binding with data on gene expression change after manipulation of individual TFs. Sets of regulated gene targets were identified with a controlled rate of false positives. This approach contributes to the understanding of biological functions of TFs and organization of gene regulatory networks. This atlas should be a valuable resource for ES cell-based regenerative medicine studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04924-3.

Cigarette Smoke and Morphine Promote Treg Plasticity to Th17 via Enhancing Trained Immunity

CD4+ regulatory T cells (Tregs) respond to environmental cues to permit or suppress inflammation, and atherosclerosis weakens Treg suppression and promotes plasticity. However, the effects of smoking plus morphine (SM + M) on Treg plasticity remain unknown. To determine whether SM + M promotes Treg plasticity to T helper 17 (Th17) cells, we analyzed the RNA sequencing data from SM, M, and SM + M treated Tregs and performed knowledge-based and IPA analysis. We demonstrated that (1) SM + M, M, and SM upregulated the transcripts of cytokines, chemokines, and clusters of differentiation (CDs) and modulated the transcripts of kinases and phosphatases in Tregs; (2) SM + M, M, and SM upregulated the transcripts of immunometabolism genes, trained immunity genes, and histone modification enzymes; (3) SM + M increased the transcripts of Th17 transcription factor (TF) RORC and Tfh factor CXCR5 in Tregs; M increased the transcripts of T helper cell 1 (Th1) TF RUNX3 and Th1-Th9 receptor CXCR3; and SM inhibited Treg TGIF1 transcript; (4) six genes upregulated in SM + M Tregs were matched with the top-ranked Th17 pathogenic genes; and 57, 39 genes upregulated in SM + M Tregs were matched with groups II and group III Th17 pathogenic genes, respectively; (5) SM + M upregulated the transcripts of 70 IPA-TFs, 11 iTregs-specific TFs, and 4 iTregs-Th17 shared TFs; and (6) SM + M, M, and SM downregulated Treg suppression TF Rel (c-Rel); and 35 SM + M downregulated genes were overlapped with Rel-/- Treg downregulated genes. These results provide novel insights on the roles of SM + M in reprogramming Treg transcriptomes and Treg plasticity to Th17 cells and novel targets for future therapeutic interventions involving immunosuppression in atherosclerotic cardiovascular diseases, autoimmune diseases, transplantation, and cancers.

A single-cell based precision medicine approach using glioblastoma patient-specific models

Glioblastoma (GBM) is a heterogeneous tumor made up of cell states that evolve over time. Here, we modeled tumor evolutionary trajectories during standard-of-care treatment using multi-omic single-cell analysis of a primary tumor sample, corresponding mouse xenografts subjected to standard of care therapy, and recurrent tumor at autopsy. We mined the multi-omic data with single-cell SYstems Genetics Network AnaLysis (scSYGNAL) to identify a network of 52 regulators that mediate treatment-induced shifts in xenograft tumor-cell states that were also reflected in recurrence. By integrating scSYGNAL-derived regulatory network information with transcription factor accessibility deviations derived from single-cell ATAC-seq data, we developed consensus networks that modulate cell state transitions across subpopulations of primary and recurrent tumor cells. Finally, by matching targeted therapies to active regulatory networks underlying tumor evolutionary trajectories, we provide a framework for applying single-cell-based precision medicine approaches to an individual patient in a concurrent, adjuvant, or recurrent setting.

Evolution of miRNA binding sites and regulatory networks in cichlids

The divergence of regulatory regions and gene regulatory network (GRN) rewiring is a key driver of cichlid phenotypic diversity. However, the contribution of miRNA binding site turnover has yet to be linked to GRN evolution across cichlids. Here, we extend our previous studies by analysing the selective constraints driving evolution of miRNA and transcription factor (TF) binding sites of target genes, to infer instances of cichlid GRN rewiring associated with regulatory binding site turnover. Comparative analyses identified increased species-specific networks that are functionally associated to traits of cichlid phenotypic diversity. The evolutionary rewiring is associated with differential models of miRNA and TF binding site turnover, driven by a high proportion of fast-evolving polymorphic sites in adaptive trait genes compared to subsets of random genes. Positive selection acting upon discrete mutations in these regulatory regions is likely to be an important mechanism in rewiring GRNs in rapidly radiating cichlids. Regulatory variants of functionally associated miRNA and TF binding sites of visual opsin genes differentially segregate according to phylogeny and ecology of Lake Malawi species, identifying both rewired e.g. clade-specific and conserved network motifs of adaptive trait associated GRNs. Our approach revealed several novel candidate regulators, regulatory regions and three-node motifs across cichlid genomes with previously reported associations to known adaptive evolutionary traits.

Modulation of Lymphocyte Functions in the Microenvironment by Tumor Oncogenic Pathways

Despite the broad application of different immunotherapeutic strategies for the treatment of solid as well as hematopoietic cancers, the efficacy of these therapies is still limited, with only a minority of patients having a long-term benefit resulting in an improved survival rate. In order to increase the response rates of patients to the currently available immunotherapies, a better understanding of the molecular mechanisms underlying the intrinsic and/or extrinsic resistance to treatment is required. There exist increasing evidences that activation of different oncogenic pathways as well as inactivation of tumor suppressor genes (TSG) in tumor cells inhibit the immune cell recognition and influegnce the composition of the tumor microenvironment (TME), thus leading to an impaired anti-tumoral immune response. A deeper understanding of the link between the tumor milieu and genomic alterations of TSGs and oncogenes is indispensable for the optimization of immunotherapies and to predict the patients’ response to these treatments. This review summarizes the role of different cancer-related, oncogene- and TSG-controlled pathways in the context of anti-tumoral immunity and response to different immunotherapies.

Fast and accurate inference of gene regulatory networks through robust precision matrix estimation

MOTIVATION

Transcriptional regulation mechanisms allow cells to adapt and respond to external stimuli by altering gene expression. The possible cell transcriptional states are determined by the underlying gene regulatory network (GRN), and reliably inferring such network would be invaluable to understand biological processes and disease progression.

RESULTS

In this article, we present a novel method for the inference of GRNs, called PORTIA, which is based on robust precision matrix estimation, and we show that it positively compares with state-of-the-art methods while being orders of magnitude faster. We extensively validated PORTIA using the DREAM and MERLIN+P datasets as benchmarks. In addition, we propose a novel scoring metric that builds on graph-theoretical concepts.

AVAILABILITY AND IMPLEMENTATION

The code and instructions for data acquisition and full reproduction of our results are available at https://github.com/AntoinePassemiers/PORTIA-Manuscript. PORTIA is available on PyPI as a Python package (portia-grn).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

scREMOTE: Using multimodal single cell data to predict regulatory gene relationships and to build a computational cell reprogramming model

Cell reprogramming offers a potential treatment to many diseases, by regenerating specialized somatic cells. Despite decades of research, discovering the transcription factors that promote cell reprogramming has largely been accomplished through trial and error, a time-consuming and costly method. A computational model for cell reprogramming, however, could guide the hypothesis formulation and experimental validation, to efficiently utilize time and resources. Current methods often cannot account for the heterogeneity observed in cell reprogramming, or they only make short-term predictions, without modelling the entire reprogramming process. Here, we present scREMOTE, a novel computational model for cell reprogramming that leverages single cell multiomics data, enabling a more holistic view of the regulatory mechanisms at cellular resolution. This is achieved by first identifying the regulatory potential of each transcription factor and gene to uncover regulatory relationships, then a regression model is built to estimate the effect of transcription factor perturbations. We show that scREMOTE successfully predicts the long-term effect of overexpressing two key transcription factors in hair follicle development by capturing higher-order gene regulations. Together, this demonstrates that integrating the multimodal processes governing gene regulation creates a more accurate model for cell reprogramming with significant potential to accelerate research in regenerative medicine.

Intricate crosstalk between MYB and noncoding RNAs in cancer

MYB is often overexpressed in malignant tumors and plays a carcinogenic role in the initiation and development of cancer. Deletion of the MYB regulatory C-terminal domain may be a driving mutation leading to tumorigenesis, therefore, different tumor mechanisms produce similar MYB proteins. As MYB is a transcription factor, priority has been given to identifying the genes that it regulates. All previous attention has been focused on protein-coding genes. However, an increasing number of studies have suggested that MYB can affect the complexity of cancer progression by regulating tumor-associated noncoding RNAs (ncRNAs), such as microRNAs, long-non-coding RNAs and circular RNAs. ncRNAs can regulate the expression of numerous downstream genes at the transcription, RNA processing and translation levels, thereby having various biological functions. Additionally, ncRNAs play important roles in regulating MYB expression. This review focuses on the intricate crosstalk between oncogenic MYB and ncRNAs, which play a pivotal role in tumorigenesis, including proliferation, apoptosis, angiogenesis, metastasis, senescence and drug resistance. In addition, we discuss therapeutic strategies for crosstalk between MYB and ncRNAs to prevent the occurrence and development of cancer.

Multi-scale Pan-cancer Integrative Analyses Identify the STAT3-VSIR Axis as a key Immunosuppressive Mechanism in Head and Neck Cancer

PURPOSE

VSIR is a novel immune checkpoint protein whose expression on tumor cells across cancers remains largely uncharacterized. Here we purposed to decode the pan-cancer biologic and clinical significance of VSIR over-expression in the tumor compartment.

EXPERIMENTAL DESIGN

We performed multi-omics integrative analyses of 9735 tumor samples to identify cancers with non-leukocytic expression of VSIR (VSIR-High), followed by association with overall survival and immune cell infiltration levels. Orthogonal assessments of VSIR protein expression and lymphocytic infiltration were performed using Quantitative immunofluorescence.

RESULTS

Integrative modeling identified a subset of cancer types as being enriched for VSIR-High tumors. VSIR-High tumors were associated with significantly poorer overall survival in immunogenic Ovarian Serous Adenocarcinoma (SA) and Oral Cavity Squamous Cell Carcinoma (SCC). QIF assessments in an independent validation cohort confirmed over-expression of VSIR as being associated with poorer overall survival within immunogenic Oral Cavity SCC. VSIR over-expression was associated with lower CD4 Helper T-cell infiltration in both Ovarian SA and Oral Cavity SCC, but did not impact CD8 T-cell infiltration. VSIR over-expressing tumors in both cancer types exhibited significantly higher STAT3 signaling activity. Pharmacologic inhibition of STAT3 signaling resulted in dose-dependent reduction of VSIR expression in Ovarian SA & Oral Cavity SCC cells.

CONCLUSIONS

The STAT3-VSIR axis is a potentially significant immuno-modulatory mechanism in oral cavity and ovarian cancers, whose activation is associated with poorer survival and an immune microenvironment marked by decreased CD4 helper T-cell activity. The role of VSIR as a tumor-intrinsic modulator of resistance to immunotherapy warrants further exploration.

Predictive regulatory and metabolic network models for systems analysis of Clostridioides difficile

We present predictive models for comprehensive systems analysis of Clostridioides difficile, the etiology of pseudomembranous colitis. By leveraging 151 published transcriptomes, we generated an EGRIN model that organizes 90% of C. difficile genes into a transcriptional regulatory network of 297 co-regulated modules, implicating genes in sporulation, carbohydrate transport, and metabolism. By advancing a metabolic model through addition and curation of metabolic reactions including nutrient uptake, we discovered 14 amino acids, diverse carbohydrates, and 10 metabolic genes as essential for C. difficile growth in the intestinal environment. Finally, we developed a PRIME model to uncover how EGRIN-inferred combinatorial gene regulation by transcription factors, such as CcpA and CodY, modulates essential metabolic processes to enable C. difficile growth relative to commensal colonization. The C. difficile interactive web portal provides access to these model resources to support collaborative systems-level studies of context-specific virulence mechanisms in C. difficile.

Nonredundant, isoform-specific roles of HDAC1 in glioma stem cells

Glioblastoma (GBM) is characterized by an aberrant yet druggable epigenetic landscape. One major family of epigenetic regulators, the histone deacetylases (HDACs), are considered promising therapeutic targets for GBM due to their repressive influences on transcription. Although HDACs share redundant functions and common substrates, the unique isoform-specific roles of different HDACs in GBM remain unclear. In neural stem cells, HDAC2 is the indispensable deacetylase to ensure normal brain development and survival in the absence of HDAC1. Surprisingly, we find that HDAC1 is the essential class I deacetylase in glioma stem cells, and its loss is not compensated for by HDAC2. Using cell-based and biochemical assays, transcriptomic analyses, and patient-derived xenograft models, we find that knockdown of HDAC1 alone has profound effects on the glioma stem cell phenotype in a p53-dependent manner. We demonstrate marked suppression in tumor growth upon targeting of HDAC1 and identify compensatory pathways that provide insights into combination therapies for GBM. Our study highlights the importance of HDAC1 in GBM and the need to develop isoform-specific drugs.

Chromatin insulation dynamics in glioblastoma: challenges and future perspectives of precision oncology

Glioblastoma (GBM) is the most aggressive primary brain tumor, having a poor prognosis and a median overall survival of less than two years. Over the last decade, numerous findings regarding the distinct molecular and genetic profiles of GBM have led to the emergence of several therapeutic approaches. Unfortunately, none of them has proven to be effective against GBM progression and recurrence. Epigenetic mechanisms underlying GBM tumor biology, including histone modifications, DNA methylation, and chromatin architecture, have become an attractive target for novel drug discovery strategies. Alterations on chromatin insulator elements (IEs) might lead to aberrant chromatin remodeling via DNA loop formation, causing oncogene reactivation in several types of cancer, including GBM. Importantly, it is shown that mutations affecting the isocitrate dehydrogenase (IDH) 1 and 2 genes, one of the most frequent genetic alterations in gliomas, lead to genome-wide DNA hypermethylation and the consequent IE dysfunction. The relevance of IEs has also been observed in a small population of cancer stem cells known as glioma stem cells (GSCs), which are thought to participate in GBM tumor initiation and drug resistance. Recent studies revealed that epigenomic alterations, specifically chromatin insulation and DNA loop formation, play a crucial role in establishing and maintaining the GSC transcriptional program. This review focuses on the relevance of IEs in GBM biology and their implementation as a potential theranostic target to stratify GBM patients and develop novel therapeutic approaches. We will also discuss the state-of-the-art emerging technologies using big data analysis and how they will settle the bases on future diagnosis and treatment strategies in GBM patients.

Contextualization of causal regulatory networks from toxicogenomics data applied to drug-induced liver injury

In recent years, network-based methods have become an attractive analytical approach for toxicogenomics studies. They can capture not only the global changes of regulatory gene networks but also the relationships between their components. Among them, a causal reasoning approach depicts the mechanisms of regulation that connect upstream regulators in signaling networks to their downstream gene targets. In this work, we applied CARNIVAL, a causal network contextualisation tool, to infer upstream signaling networks deregulated in drug-induced liver injury (DILI) from gene expression microarray data from the TG-GATEs database. We focussed on six compounds that induce observable histopathologies linked to DILI from repeated dosing experiments in rats. We compared responses in vitro and in vivo to identify potential cross-platform concordances in rats as well as network preservations between rat and human. Our results showed similarities of enriched pathways and network motifs between compounds. These pathways and motifs induced the same pathology in rats but not in humans. In particular, the causal interactions "LCK activates SOCS3, which in turn inhibits TFDP1" was commonly identified as a regulatory path among the fibrosis-inducing compounds. This potential pathology-inducing regulation illustrates the value of our approach to generate hypotheses that can be further validated experimentally.

Genetic program activity delineates risk, relapse, and therapy responsiveness in multiple myeloma

Despite recent advancements in the treatment of multiple myeloma (MM), nearly all patients ultimately relapse and many become refractory to multiple lines of therapies. Therefore, we not only need the ability to predict which patients are at high risk for disease progression but also a means to understand the mechanisms underlying their risk. Here, we report a transcriptional regulatory network (TRN) for MM inferred from cross-sectional multi-omics data from 881 patients that predicts how 124 chromosomal abnormalities and somatic mutations causally perturb 392 transcription regulators of 8549 genes to manifest in distinct clinical phenotypes and outcomes. We identified 141 genetic programs whose activity profiles stratify patients into 25 distinct transcriptional states and proved to be more predictive of outcomes than did mutations. The coherence of these programs and accuracy of our network-based risk prediction was validated in two independent datasets. We observed subtype-specific vulnerabilities to interventions with existing drugs and revealed plausible mechanisms for relapse, including the establishment of an immunosuppressive microenvironment. Investigation of the t(4;14) clinical subtype using the TRN revealed that 16% of these patients exhibit an extreme-risk combination of genetic programs (median progression-free survival of 5 months) that create a distinct phenotype with targetable genes and pathways.

A Systems Approach to Brain Tumor Treatment

Brain tumors are among the most lethal tumors. Glioblastoma, the most frequent primary brain tumor in adults, has a median survival time of approximately 15 months after diagnosis or a five-year survival rate of 10%; the recurrence rate is nearly 90%. Unfortunately, this prognosis has not improved for several decades. The lack of progress in the treatment of brain tumors has been attributed to their high rate of primary therapy resistance. Challenges such as pronounced inter-patient variability, intratumoral heterogeneity, and drug delivery across the blood-brain barrier hinder progress. A comprehensive, multiscale understanding of the disease, from the molecular to the whole tumor level, is needed to address the intratumor heterogeneity resulting from the coexistence of a diversity of neoplastic and non-neoplastic cell types in the tumor tissue. By contrast, inter-patient variability must be addressed by subtyping brain tumors to stratify patients and identify the best-matched drug(s) and therapies for a particular patient or cohort of patients. Accomplishing these diverse tasks will require a new framework, one involving a systems perspective in assessing the immense complexity of brain tumors. This would in turn entail a shift in how clinical medicine interfaces with the rapidly advancing high-throughput (HTP) technologies that have enabled the omics-scale profiling of molecular features of brain tumors from the single-cell to the tissue level. However, several gaps must be closed before such a framework can fulfill the promise of precision and personalized medicine for brain tumors. Ultimately, the goal is to integrate seamlessly multiscale systems analyses of patient tumors and clinical medicine. Accomplishing this goal would facilitate the rational design of therapeutic strategies matched to the characteristics of patients and their tumors. Here, we discuss some of the technologies, methodologies, and computational tools that will facilitate the realization of this vision to practice.

Neural G0: a quiescent-like state found in neuroepithelial-derived cells and glioma

Single‐cell RNA sequencing has emerged as a powerful tool for resolving cellular states associated with normal and maligned developmental processes. Here, we used scRNA‐seq to examine the cell cycle states of expanding human neural stem cells (hNSCs). From these data, we constructed a cell cycle classifier that identifies traditional cell cycle phases and a putative quiescent‐like state in neuroepithelial‐derived cell types during mammalian neurogenesis and in gliomas. The Neural G0 markers are enriched with quiescent NSC genes and other neurodevelopmental markers found in non‐dividing neural progenitors. Putative glioblastoma stem‐like cells were significantly enriched in the Neural G0 cell population. Neural G0 cell populations and gene expression are significantly associated with less aggressive tumors and extended patient survival for gliomas. Genetic screens to identify modulators of Neural G0 revealed that knockout of genes associated with the Hippo/Yap and p53 pathways diminished Neural G0 in vitro, resulting in faster G1 transit, down‐regulation of quiescence‐associated markers, and loss of Neural G0 gene expression. Thus, Neural G0 represents a dynamic quiescent‐like state found in neuroepithelial‐derived cells and gliomas.

MicroRNA-206 suppresses mesothelioma progression via the Ras signaling axis

Malignant pleural mesothelioma (MPM) is an incurable surface neoplasm with peculiar pathobiology. MPM proliferates by using the tyrosine-kinase-Ras pathway. Despite representing an attractive therapeutic target, there are no standard agent(s) specifically inhibiting Ras signaling adopted in clinical settings. We posited that biologic effects of microRNA (miRNA) can disrupt this molecular network. Using patient samples, cell lines, and murine tumor xenograft models, we confirmed specific genes in the Ras pathway are targeted by an MPM-associated miRNA and then examined its therapeutic effects. We verified significant and consistent downregulation of miR-206 in MPM tissues. When miR-206 is ectopically re-expressed in MPM cells and delivered to tumor xenografts in mice, it exerted significant cell killing by suppressing multiple components of the receptor-tyrosine-kinase-Ras-cell-cycle-signaling network; some of which were prognostic when overexpressed and/or have not been druggable. Of note, we validated CDK6 as a novel target of miR-206. Overall, this miR-206-targeting mechanism manifested as induced G1/S cell cycle arrest. In addition, we identified a novel MPM therapeutic combination by adding systemic-route abemaciclib with local-route miR-206, which showed additive efficacy translating to improved survival. Our pre-clinical study suggests a potential pathophysiologic role for, and therapeutic relevance of, miR-206 in MPM.

A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning

Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient’s quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount of information. To compensate, researchers have tried to increase the amount of information available from a single test using high-throughput technologies. This approach, referred to as single-omics analysis, has only been partially successful as one type of data may not be able to appropriately describe all the characteristics of a tumor. It is presently unclear what type of data can describe a particular clinical situation. One way to solve this problem is to use multi-omics data. When using many types of data, a selected data type or a combination of them may effectively resolve a clinical question. Hence, we conducted a comprehensive survey of papers in the field of neuro-oncology that used multi-omics data for analysis and found that most of the papers utilized machine learning techniques. This fact shows that it is useful to utilize machine learning techniques in multi-omics analysis. In this review, we discuss the current status of multi-omics analysis in the field of neuro-oncology and the importance of using machine learning techniques.

Establishing a consensus for the hallmarks of cancer based on gene ontology and pathway annotations

BACKGROUND

The hallmarks of cancer provide a highly cited and well-used conceptual framework for describing the processes involved in cancer cell development and tumourigenesis. However, methods for translating these high-level concepts into data-level associations between hallmarks and genes (for high throughput analysis), vary widely between studies. The examination of different strategies to associate and map cancer hallmarks reveals significant differences, but also consensus.

RESULTS

Here we present the results of a comparative analysis of cancer hallmark mapping strategies, based on Gene Ontology and biological pathway annotation, from different studies. By analysing the semantic similarity between annotations, and the resulting gene set overlap, we identify emerging consensus knowledge. In addition, we analyse the differences between hallmark and gene set associations using Weighted Gene Co-expression Network Analysis and enrichment analysis.

CONCLUSIONS

Reaching a community-wide consensus on how to identify cancer hallmark activity from research data would enable more systematic data integration and comparison between studies. These results highlight the current state of the consensus and offer a starting point for further convergence. In addition, we show how a lack of consensus can lead to large differences in the biological interpretation of downstream analyses and discuss the challenges of annotating changing and accumulating biological data, using intermediate knowledge resources that are also changing over time.

The Role of Network Science in Glioblastoma

Network science has long been recognized as a well-established discipline across many biological domains. In the particular case of cancer genomics, network discovery is challenged by the multitude of available high-dimensional heterogeneous views of data. Glioblastoma (GBM) is an example of such a complex and heterogeneous disease that can be tackled by network science. Identifying the architecture of molecular GBM networks is essential to understanding the information flow and better informing drug development and pre-clinical studies. Here, we review network-based strategies that have been used in the study of GBM, along with the available software implementations for reproducibility and further testing on newly coming datasets. Promising results have been obtained from both bulk and single-cell GBM data, placing network discovery at the forefront of developing a molecularly-informed-based personalized medicine.

Inference of phenotype-relevant transcriptional regulatory networks elucidates cancer type-specific regulatory mechanisms in a pan-cancer study

Reconstruction of transcriptional regulatory networks (TRNs) is a powerful approach to unravel the gene expression programs involved in healthy and disease states of a cell. However, these networks are usually reconstructed independent of the phenotypic (or clinical) properties of the samples. Therefore, they may confound regulatory mechanisms that are specifically related to a phenotypic property with more general mechanisms underlying the full complement of the analyzed samples. In this study, we develop a method called InPheRNo to identify "phenotype-relevant" TRNs. This method is based on a probabilistic graphical model that models the simultaneous effects of multiple transcription factors (TFs) on their target genes and the statistical relationship between the target genes' expression and the phenotype. Extensive comparison of InPheRNo with related approaches using primary tumor samples of 18 cancer types from The Cancer Genome Atlas reveals that InPheRNo can accurately reconstruct cancer type-relevant TRNs and identify cancer driver TFs. In addition, survival analysis reveals that the activity level of TFs with many target genes could distinguish patients with poor prognosis from those with better prognosis.

Single-cell lineage analysis reveals extensive multimodal transcriptional control during directed beta-cell differentiation

The in vitro differentiation of insulin-producing beta-like cells can model aspects of human pancreatic development. Here, we generate 95,308 single-cell transcriptomes and reconstruct a lineage tree of the entire differentiation process from human embryonic stem cells to beta-like cells to study temporally regulated genes during differentiation. We identify so-called 'switch genes' at the branch point of endocrine/non-endocrine cell fate choice, revealing insights into the mechanisms of differentiation-promoting reagents, such as NOTCH and ROCKII inhibitors, and providing improved differentiation protocols. Over 20% of all detectable genes are activated multiple times during differentiation, even though their enhancer activation is usually unimodal, indicating extensive gene reuse driven by different enhancers. We also identify a stage-specific enhancer at the TCF7L2 locus for diabetes, uncovered by genome-wide association studies, that drives a transient wave of gene expression in pancreatic progenitors. Finally, we develop a web app to visualize gene expression on the lineage tree, providing a comprehensive single-cell data resource for researchers studying islet biology and diabetes.

Prediction of transcription factors binding events based on epigenetic modifications in different human cells

Aim: We aim to predict transcription factor (TF) binding events from knowledge of gene expression and epigenetic modifications. Materials & methods: TF-binding events based on the Encode project and The Cancer Genome Atlas data were analyzed by the random forest method. Results: We showed the high performance of TF-binding predictive models in GM12878, HeLa, HepG2 and K562 cell lines and applied them to other cell lines and tissues. The genes bound by the top TFs (MAX and MAZ) were significantly associated with cancer-related processes such as cell proliferation and DNA repair. Conclusion: We successfully constructed TF-binding predictive models in cell lines and applied them in tissues.

Integrated regulatory models for inference of subtype-specific susceptibilities in glioblastoma

Glioblastoma multiforme (GBM) is a highly malignant form of cancer that lacks effective treatment options or well-defined strategies for personalized cancer therapy. The disease has been stratified into distinct molecular subtypes; however, the underlying regulatory circuitry that gives rise to such heterogeneity and its implications for therapy remain unclear. We developed a modular computational pipeline, Integrative Modeling of Transcription Regulatory Interactions for Systematic Inference of Susceptibility in Cancer (inTRINSiC), to dissect subtype-specific regulatory programs and predict genetic dependencies in individual patient tumors. Using a multilayer network consisting of 518 transcription factors (TFs), 10,733 target genes, and a signaling layer of 3,132 proteins, we were able to accurately identify differential regulatory activity of TFs that shape subtype-specific expression landscapes. Our models also allowed inference of mechanisms for altered TF behavior in different GBM subtypes. Most importantly, we were able to use the multilayer models to perform an in silico perturbation analysis to infer differential genetic vulnerabilities across GBM subtypes and pinpoint the MYB family member MYBL2 as a drug target specific for the Proneural subtype.

Crippling life support for SARS-CoV-2 and other viruses through synthetic lethality

With the rapid global spread of SARS-CoV-2, we have become acutely aware of the inadequacies of our ability to respond to viral epidemics. Although disrupting the viral life cycle is critical for limiting viral spread and disease, it has proven challenging to develop targeted and selective therapeutics. Synthetic lethality offers a promising but largely unexploited strategy against infectious viral disease; as viruses infect cells, they abnormally alter the cell state, unwittingly exposing new vulnerabilities in the infected cell. Therefore, we propose that effective therapies can be developed to selectively target the virally reconfigured host cell networks that accompany altered cellular states to cripple the host cell that has been converted into a virus factory, thus disrupting the viral life cycle.

IL4I1 Is a Metabolic Immune Checkpoint that Activates the AHR and Promotes Tumor Progression

Aryl hydrocarbon receptor (AHR) activation by tryptophan (Trp) catabolites enhances tumor malignancy and suppresses anti-tumor immunity. The context specificity of AHR target genes has so far impeded systematic investigation of AHR activity and its upstream enzymes across human cancers. A pan-tissue AHR signature, derived by natural language processing, revealed that across 32 tumor entities, interleukin-4-induced-1 (IL4I1) associates more frequently with AHR activity than IDO1 or TDO2, hitherto recognized as the main Trp-catabolic enzymes. IL4I1 activates the AHR through the generation of indole metabolites and kynurenic acid. It associates with reduced survival in glioma patients, promotes cancer cell motility, and suppresses adaptive immunity, thereby enhancing the progression of chronic lymphocytic leukemia (CLL) in mice. Immune checkpoint blockade (ICB) induces IDO1 and IL4I1. As IDO1 inhibitors do not block IL4I1, IL4I1 may explain the failure of clinical studies combining ICB with IDO1 inhibition. Taken together, IL4I1 blockade opens new avenues for cancer therapy.

LncRNA TPT1-AS1 Sponges miR-23a-5p in Glioblastoma to Promote Cancer Cell Proliferation

Background: Long noncoding RNA (LncRNA) TPT1-AS1 is an oncogene in ovarian cancer and cervical cancer, while its role in glioblastoma (GBM) is unknown. The bioinformatics analysis in this study showed that miR-23a-5p may bind to TPT1-AS1. This study was performed to investigate the interactions between miR-23a-5p and TPT1-AS1 in GBM. Materials and Methods: A total of 60 GBM patients (40 males and 20 females, 24 to 60 years old, mean age 41.7 ± 7.8 years old) were enrolled at the First Hospital of Jilin University between April 2016 and April 2018. Gene expression levels were determined by qPCR and Western blot. Cell transfections were performed to analyze the interactions between TPT1-AS1, miR-23a-5p, and extracellular matrix protein 1 (ECM1). Cell proliferation was detected by cell proliferation assay. Results: The authors found miR-23a-5p was downregulated in GBM and TPT1-AS1 was upregulated in GBM, whereas the expression of these two was not significantly correlated. In GBM cells, overexpression of TPT1-AS1 did not affect the expression of miR-23a-5p, but upregulated ECM1. In cell proliferation assay, overexpression of TPT1-AS1 and ECM1 resulted in increased proliferation rate of GBM cells. Overexpression of miR-23a-5p attenuated the effects of overexpressing TPT1-AS1. Conclusions: TPT1-AS1 may sponge miR-23a-5p in GBM to promote cancer cell proliferation by upregulating ECM1.

CEACAM3 decreases asthma exacerbations and modulates respiratory syncytial virus latent infection in children

BACKGROUND

Respiratory syncytial virus (RSV) is associated with childhood asthma. Nevertheless, not all children exposed to RSV develop asthma symptoms, possibly because genes modulate the effects of RSV on asthma exacerbations.

OBJECTIVE

The purpose of this study was to identify genes that modulate the effect of RSV latent infection on asthma exacerbations.

METHODS

We performed a meta-analysis to investigate differentially expressed genes (DEGs) of RSV infection from Gene Expression Omnibus datasets. Expression quantitative trait loci (eQTL) methods were applied to select single nucleotide polymorphisms (SNPs) that were associated with DEGs. Gene-based analysis was used to identify SNPs that were significantly associated with asthma exacerbations in the Taiwanese Consortium of Childhood Asthma Study (TCCAS), and validation was attempted in an independent cohort, the Childhood Asthma Management Program (CAMP). Gene-RSV interaction analyses were performed to investigate the association between the interaction of SNPs and RSV latent infection on asthma exacerbations.

RESULTS

A total of 352 significant DEGs were found by meta-analysis of RSV-related genes. We used 38 123 SNPs related to DEGs to investigate the genetic main effects on asthma exacerbations. We found that eight RSV-related genes (GADD45A, GYPB, MS4A3, NFE2, RNASE3, EPB41L3, CEACAM6 and CEACAM3) were significantly associated with asthma exacerbations in TCCAS and also validated in CAMP. In TCCAS, rs7251960 (CEACAM3) significantly modulated the effect of RSV latent infection on asthma exacerbations (false-discovery rate <0.05). The rs7251960 variant was associated with CEACAM3 mRNA expression in lung tissue (p for trend=1.2×10^-7). CEACAM3 mRNA was reduced in nasal mucosa from subjects with asthma exacerbations in two independent datasets.

CONCLUSIONS

rs7251960 is an eQTL for CEACAM3, and CEACAM3 mRNA expression is reduced in subjects experiencing asthma exacerbations. CEACAM3 may be a modulator of RSV latent infection on asthma exacerbations.

TransmiR v2.0: an updated transcription factor-microRNA regulation database

MicroRNAs (miRNAs) are important post-transcriptional regulators of gene expression and play vital roles in various biological processes. It has been reported that aberrant regulation of miRNAs was associated with the development and progression of various diseases, but the underlying mechanisms are not fully deciphered. Here, we described our updated TransmiR v2.0 database for more comprehensive information about transcription factor (TF)-miRNA regulations. 3730 TF–miRNA regulations among 19 species from 1349 reports were manually curated by surveying >8000 publications, and more than 1.7 million tissue-specific TF–miRNA regulations were further incorporated based on ChIP-seq data. Besides, we constructed a ‘Predict’ module to query the predicted TF–miRNA regulations in human based on binding motifs of TFs. To facilitate the community, we provided a ‘Network’ module to visualize TF–miRNA regulations for each TF and miRNA, or for a specific disease. An ‘Enrichment analysis’ module was also included to predict TFs that are likely to regulate a miRNA list of interest. In conclusion, with improved data coverage and webserver functionalities, TransmiR v2.0 would be a useful resource for investigating the regulation of miRNAs. TransmiR v2.0 is freely accessible at http://www.cuilab.cn/transmir.

CeRNASeek: an R package for identification and analysis of ceRNA regulation

Competitive endogenous RNA (ceRNA) represents a novel layer of gene regulation that controls both physiological and pathological processes. However, there is still lack of computational tools for quickly identifying ceRNA regulation. To address this problem, we presented an R-package, CeRNASeek, which allows identifying and analyzing ceRNA-ceRNA interactions by integration of multiple-omics data. CeRNASeek integrates six widely used computational methods to identify ceRNA-ceRNA interactions, including two global and four context-specific ceRNA regulation prediction methods. In addition, it provides several downstream analyses for predicted ceRNA-ceRNA pairs, including regulatory network analysis, functional annotation and survival analysis. With examples of cancer-related ceRNA prioritization and cancer subtyping, we demonstrate that CeRNASeek is a valuable tool for investigating the function of ceRNAs in complex diseases. In summary, CeRNASeek provides a comprehensive and efficient tool for identifying and analysis of ceRNA regulation. The package is available on the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=CeRNASeek.

Overexpression miR-486-3p Promoted by Allicin Enhances Temozolomide Sensitivity in Glioblastoma Via Targeting MGMT

Glioblastoma is the most common primary tumor of the central nervous system that develops chemotherapy resistance. Previous studies showed that Allicin could inhibit multiple cancer cells including glioblastoma, but the function of Allicin in glioblastoma is still unclear. Our work aimed to investigate the underlying molecular mechanism. The results showed that miR-486-3p levels were greatly increased in glioblastoma during Allicin treatment. Overexpression of miR-486-3p increased chemosensitivity to temozolomide (TMZ) in vitro and in vivo. O6-methylguanine-DNA methyltransferase (MGMT) was identified as a direct target of miR-486-3p, and miR-486-3p overexpression prevented the protein translation of MGMT. Moreover, overexpression of MGMT restored miR-486-3p-induced chemosensitivity to TMZ. Taken together, our studies revealed that Allicin could upregulate miR-486-3p and enhance TMZ sensitivity in glioblastoma. The results suggested that in the future, Allicin can be used as an adjuvant therapy with TMZ to improve the prognosis of patients, and miR-486-3p may be a potential target for glioblastoma treatment to improve the curative effects.

Integrative Bayesian Analysis Identifies Rhabdomyosarcoma Disease Genes

Identifying oncogenic drivers and tumor suppressors remains a challenge in many forms of cancer, including rhabdomyosarcoma. Anticipating gene expression alterations resulting from DNA copy-number variants to be particularly important, we developed a computational and experimental strategy incorporating a Bayesian algorithm and CRISPR/Cas9 "mini-pool" screen that enables both genome-scale assessment of disease genes and functional validation. The algorithm, called iExCN, identified 29 rhabdomyosarcoma drivers and suppressors enriched for cell-cycle and nucleic-acid-binding activities. Functional studies showed that many iExCN genes represent rhabdomyosarcoma line-specific or shared vulnerabilities. Complementary experiments addressed modes of action and demonstrated coordinated repression of multiple iExCN genes during skeletal muscle differentiation. Analysis of two separate cohorts revealed that the number of iExCN genes harboring copy-number alterations correlates with survival. Our findings highlight rhabdomyosarcoma as a cancer in which multiple drivers influence disease biology and demonstrate a generalizable capacity for iExCN to unmask disease genes in cancer.