Interpreting transcriptional changes using causal graphs: new methods and their practical utility on public networks

Integrated multi-omics analysis identifies features that predict human pluripotent stem cell-derived progenitor differentiation to cardiomyocytes

Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) are advancing cardiovascular development and disease modeling, drug testing, and regenerative therapies. However, hPSC-CM production is hindered by significant variability in the differentiation process. Establishment of early quality markers to monitor lineage progression and predict terminal differentiation outcomes would address this robustness and reproducibility roadblock in hPSC-CM production. An integrated transcriptomic and epigenomic analysis assesses how attributes of the cardiac progenitor cell (CPC) affect CM differentiation outcome. Resulting analysis identifies predictive markers of CPCs that give rise to high purity CM batches, including TTN, TRIM55, DGKI, MEF2C, MAB21L2, MYL7, LDB3, SLC7A11, and CALD1. Predictive models developed from these genes provide high accuracy in determining terminal CM purities at the CPC stage. Further, insights into mechanisms of batch failure and dominant non-CM cell types generated in failed batches are elucidated. Namely EMT, MAPK, and WNT signaling emerge as significant drivers of batch divergence, giving rise to off-target populations of fibroblasts/mural cells, skeletal myocytes, epicardial cells, and a non-CPC SLC7A11+ subpopulation. This study demonstrates how integrated multi-omic analysis of progenitor cells can identify quality attributes of that progenitor and predict differentiation outcomes, thereby improving differentiation protocols and increasing process robustness.

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/.

Shared Gene Targets of the ATF4 and p53 Transcriptional Networks

The master tumor suppressor p53 regulates multiple cell fate decisions, such as cell cycle arrest and apoptosis, via transcriptional control of a broad gene network. Dysfunction in the p53 network is common in cancer, often through mutations that inactivate p53 or other members of the pathway. Induction of tumor-specific cell death by restoration of p53 activity without off-target effects has gained significant interest in the field. In this study, we explore the gene regulatory mechanisms underlying a putative anticancer strategy involving stimulation of the p53-independent integrated stress response (ISR). Our data demonstrate the p53 and ISR pathways converge to independently regulate common metabolic and proapoptotic genes. We investigated the architecture of multiple gene regulatory elements bound by p53 and the ISR effector ATF4 controlling this shared regulation. We identified additional key transcription factors that control basal and stress-induced regulation of these shared p53 and ATF4 target genes. Thus, our results provide significant new molecular and genetic insight into gene regulatory networks and transcription factors that are the target of numerous antitumor therapies.

Identifying molecular and functional similarities and differences between human primary cardiac valve interstitial cells and ventricular fibroblasts

Introduction: Fibroblasts are mesenchymal cells that predominantly produce and maintain the extracellular matrix (ECM) and are critical mediators of injury response. In the heart, valve interstitial cells (VICs) are a population of fibroblasts responsible for maintaining the structure and function of heart valves. These cells are regionally distinct from myocardial fibroblasts, including left ventricular cardiac fibroblasts (LVCFBs), which are located in the myocardium in close vicinity to cardiomyocytes. Here, we hypothesize these subpopulations of fibroblasts are transcriptionally and functionally distinct. Methods: To compare these fibroblast subtypes, we collected patient-matched samples of human primary VICs and LVCFBs and performed bulk RNA sequencing, extracellular matrix profiling, and functional contraction and calcification assays. Results: Here, we identified combined expression of SUSD2 on a protein-level, and MEOX2, EBF2 and RHOU at a transcript-level to be differentially expressed in VICs compared to LVCFBs and demonstrated that expression of these genes can be used to distinguish between the two subpopulations. We found both VICs and LVCFBs expressed similar activation and contraction potential in vitro, but VICs showed an increase in ALP activity when activated and higher expression in matricellular proteins, including cartilage oligomeric protein and alpha 2-Heremans-Schmid glycoprotein, both of which are reported to be linked to calcification, compared to LVCFBs. Conclusion: These comparative transcriptomic, proteomic, and functional studies shed novel insight into the similarities and differences between valve interstitial cells and left ventricular cardiac fibroblasts and will aid in understanding region-specific cardiac pathologies, distinguishing between primary subpopulations of fibroblasts, and generating region-specific stem-cell derived cardiac fibroblasts.

Single-cell transcriptomics of hepatic stellate cells uncover crucial pathways and key regulators involved in non-alcoholic steatohepatitis

Background

Fibrosis is an important pathological process in the development of non-alcoholic steatohepatitis (NASH), and the activation of hepatic stellate cell (HSC) is a central event in liver fibrosis. However, the transcriptomic change of activated HSCs (aHSCs) and resting HSCs (rHSCs) in NASH patients has not been assessed. This study aimed to identify transcriptomic signature of HSCs during the development of NASH and the underlying key functional pathways.

Methods

NASH-associated transcriptomic change of HSCs was defined by single-cell RNA-sequencing (scRNA-seq) analysis, and those top upregulated genes were identified as NASH-associated transcriptomic signatures. Those functional pathways involved in the NASH-associated transcriptomic change of aHSCs were explored by weighted gene co-expression network analysis (WGCNA) and functional enrichment analyses. Key regulators were explored by upstream regulator analysis and transcription factor enrichment analysis.

Results

scRNA-seq analysis identified numerous differentially expressed genes in both rHSCs and aHSCs between NASH patients and healthy controls. Both scRNA-seq analysis and in-vivo experiments showed the existence of rHSCs (mainly expressing a-SMA) in the normal liver and the increased aHSCs (mainly expressing collagen 1) in the fibrosis liver tissues. NASH-associated transcriptomic signature of rHSC (NASHrHSCsignature) and NASH-associated transcriptomic signature of aHSC (NASHaHSCsignature) were identified. WGCNA revealed the main pathways correlated with the transcriptomic change of aHSCs. Several key upstream regulators and transcription factors for determining the functional change of aHSCs in NASH were identified.

Conclusion

This study developed a useful transcriptomic signature with the potential in assessing fibrosis severity in the development of NASH. This study also identified the main pathways in the activation of HSCs during the development of NASH.

Long non-coding RNA PCAT19 safeguards DNA in quiescent endothelial cells by preventing uncontrolled phosphorylation of RPA2

In healthy vessels, endothelial cells maintain a stable, differentiated, and growth-arrested phenotype for years. Upon injury, a rapid phenotypic switch facilitates proliferation to restore tissue perfusion. Here we report the identification of the endothelial cell-enriched long non-coding RNA (lncRNA) PCAT19, which contributes to the proliferative switch and acts as a safeguard for the endothelial genome. PCAT19 is enriched in confluent, quiescent endothelial cells and binds to the full replication protein A (RPA) complex in a DNA damage- and cell-cycle-related manner. Our results suggest that PCAT19 limits the phosphorylation of RPA2, primarily on the serine 33 (S33) residue, and thereby facilitates an appropriate DNA damage response while slowing cell cycle progression. Reduction in PCAT19 levels in response to either loss of cell contacts or knockdown promotes endothelial proliferation and angiogenesis. Collectively, PCAT19 acts as a dynamic guardian of the endothelial genome and facilitates rapid switching from quiescence to proliferation.

Network Pharmacology of Ginseng (Part III): Antitumor Potential of a Fixed Combination of Red Ginseng and Red Sage as Determined by Transcriptomics

Background: This study aimed to assess the effect of a fixed combination of Red Ginseng and Red Sage (RG–RS) on the gene expression of neuronal cells to evaluate the potential impacts on cellular functions and predict its relevance in the treatment of stress and aging-related diseases and disorders. Methods: Gene expression profiling was conducted by transcriptome-wide mRNA microarray analyses of murine HT22 hippocampal cell culture after treatment with RG–RS preparation. Ingenuity pathway analysis (IPA) was performed with datasets of significantly upregulated or downregulated genes and the expected effects on the physiological and cellular function and the diseases were identified. Results: RG–RS deregulates 1028 genes associated with cancer and 139 with metastasis, suggesting a predicted decrease in tumorigenesis, the proliferation of tumor cells, tumor growth, metastasis, and an increase in apoptosis and autophagy by their effects on the various signaling and metabolic pathways, including the inhibition of Warburg’s aerobic glycolysis, estrogen-mediated S-phase entry signaling, osteoarthritis signaling, and the super-pathway of cholesterol biosynthesis. Conclusion: The results of this study provide evidence of the potential efficacy of the fixed combination of Red Ginseng (Panax ginseng C.A. Mey.) and Red Sage/Danshen (Salvia miltiorrhiza Bunge) in cancer. Further clinical and experimental studies are required to assess the efficacy and safety of RG–RS in preventing the progression of cancer, osteoarthritis, and other aging-related diseases.

ChemPert: mapping between chemical perturbation and transcriptional response for non-cancer cells

Prior knowledge of perturbation data can significantly assist in inferring the relationship between chemical perturbations and their specific transcriptional response. However, current databases mostly contain cancer cell lines, which are unsuitable for the aforementioned inference in non-cancer cells, such as cells related to non-cancer disease, immunology and aging. Here, we present ChemPert (https://chempert.uni.lu/), a database consisting of 82 270 transcriptional signatures in response to 2566 unique perturbagens (drugs, small molecules and protein ligands) across 167 non-cancer cell types, as well as the protein targets of 57 818 perturbagens. In addition, we develop a computational tool that leverages the non-cancer cell datasets, which enables more accurate predictions of perturbation responses and drugs in non-cancer cells compared to those based onto cancer databases. In particular, ChemPert correctly predicted drug effects for treating hepatitis and novel drugs for osteoarthritis. The ChemPert web interface is user-friendly and allows easy access of the entire datasets and the computational tool, providing valuable resources for both experimental researchers who wish to find datasets relevant to their research and computational researchers who need comprehensive non-cancer perturbation transcriptomics datasets for developing novel algorithms. Overall, ChemPert will facilitate future in silico compound screening for non-cancer cells.

Bayesian Inference of Stochastic Dynamic Models Using Early-Rejection Methods Based on Sequential Stochastic Simulations

Stochastic modelling is an important method to investigate the functions of noise in a wide range of biological systems. However, the parameter inference for stochastic models is still a challenging problem partially due to the large computing time required for stochastic simulations. To address this issue, we propose a novel early-rejection method by using sequential stochastic simulations. We first show that a large number of stochastic simulations are required to obtain reliable inference results. Instead of generating a large number of simulations for each parameter sample, we propose to generate these simulations in a number of stages. The simulation process will go to the next stage only if the accuracy of simulations at the current stage satisfies a given error criterion. We propose a formula to determine the error criterion and use a stochastic differential equation model to examine the effects of different criteria. Three biochemical network models are used to evaluate the efficiency and accuracy of the proposed method. Numerical results suggest the proposed early-rejection method achieves substantial improvement in the efficiency for the inference of stochastic models.

Network Pharmacology of Ginseng (Part II): The Differential Effects of Red Ginseng and Ginsenoside Rg5 in Cancer and Heart Diseases as Determined by Transcriptomics

Panax ginseng C.A.Mey. is an adaptogenic plant traditionally used to enhance mental and physical capacities in cases of weakness, exhaustion, tiredness, or loss of concentration, and during recovery. According to ancient records, red ginseng root preparations enhance longevity with long-term intake. Recent pharmacokinetic studies of ginsenosides in humans and our in vitro study in neuronal cells suggest that ginsenosides are effective when their levels in blood is low—at concentrations from 10⁻⁶ to 10⁻¹⁸ M. In the present study, we compared the effects of red ginseng root preparation HRG80^TM(HRG) at concentrations from 0.01 to 10,000 ng/mL with effects of white ginseng (WG) and purified ginsenosides Rb1, Rg3, Rg5 and Rk1 on gene expression in isolated hippocampal neurons. The aim of this study was to predict the effects of differently expressed genes on cellular and physiological functions in organismal disorders and diseases. Gene expression profiling was performed by transcriptome-wide mRNA microarray analyses in murine HT22 cells after treatment with ginseng preparations. Ingenuity pathway downstream/upstream analysis (IPA) was performed with datasets of significantly up- or downregulated genes, and expected effects on cellular function and disease were identified by IPA software. Ginsenosides Rb1, Rg3, Rg5, and Rk1 have substantially varied effects on gene expression profiles (signatures) and are different from signatures of HRG and WG. Furthermore, the signature of HRG is changed significantly with dilution from 10,000 to 0.01 ng/mL. Network pharmacological analyses of gene expression profiles showed that HRG exhibits predictable positive effects in neuroinflammation, senescence, apoptosis, and immune response, suggesting beneficial soft-acting effects in cancer, gastrointestinal, and endocrine systems diseases and disorders in a wide range of low concentrations in blood.

Network Pharmacology of Red Ginseng (Part I): Effects of Ginsenoside Rg5 at Physiological and Sub-Physiological Concentrations

Numerous in vitro studies on isolated cells have been conducted to uncover the molecular mechanisms of action of Panax ginseng Meyer root extracts and purified ginsenosides. However, the concentrations of ginsenosides and the extracts used in these studies were much higher than those detected in pharmacokinetic studies in humans and animals orally administered with ginseng preparations at therapeutic doses. Our study aimed to assess: (a) the effects of ginsenoside Rg5, the major “rare” ginsenoside of Red Ginseng, on gene expression in the murine neuronal cell line HT22 in a wide range of concentrations, from 10⁻⁴ to 10⁻¹⁸ M, and (b) the effects of differentially expressed genes on cellular and physiological functions in organismal disorders and diseases. Gene expression profiling was performed by transcriptome-wide mRNA microarray analyses in HT22 cells after treatment with ginsenoside Rg5. Ginsenoside Rg5 exhibits soft-acting effects on gene expression of neuronal cells in a wide range of physiological concentrations and strong reversal impact at high (toxic) concentration: significant up- or downregulation of expression of about 300 genes at concentrations from 10⁻⁶ M to 10⁻¹⁸ M, and dramatically increased both the number of differentially expressed target genes (up to 1670) and the extent of their expression (fold changes compared to unexposed cells) at a toxic concentration of 10⁻⁴ M. Network pharmacology analyses of genes’ expression profiles using ingenuity pathway analysis (IPA) software showed that at low physiological concentrations, ginsenoside Rg5 has the potential to activate the biosynthesis of cholesterol and to exhibit predictable effects in senescence, neuroinflammation, apoptosis, and immune response, suggesting soft-acting, beneficial effects on organismal death, movement disorders, and cancer.

E7386, a Selective Inhibitor of the Interaction between β-Catenin and CBP, Exerts Antitumor Activity in Tumor Models with Activated Canonical Wnt Signaling

The Wnt/β-catenin signaling pathway plays crucial roles in embryonic development and the development of multiple types of cancer, and its aberrant activation provides cancer cells with escape mechanisms from immune checkpoint inhibitors. E7386, an orally active selective inhibitor of the interaction between β-catenin and CREB binding protein, which is part of the Wnt/β-catenin signaling pathway, disrupts the Wnt/β-catenin signaling pathway in HEK293 and adenomatous polyposis coli (APC)-mutated human gastric cancer ECC10 cells. It also inhibited tumor growth in an ECC10 xenograft model and suppressed polyp formation in the intestinal tract of Apc^{Min /+} mice, in which mutation of Apc activates the Wnt/β-catenin signaling pathway. E7386 demonstrated antitumor activity against mouse mammary tumors developed in mouse mammary tumor virus (MMTV)-Wnt1 transgenic mice. Gene expression profiling using RNA sequencing data of MMTV-Wnt1 tumor tissue from mice treated with E7386 showed that E7386 downregulated genes in the hypoxia signaling pathway and immune responses related to the CCL2, and IHC analysis showed that E7386 induced infiltration of CD8⁺ cells into tumor tissues. Furthermore, E7386 showed synergistic antitumor activity against MMTV-Wnt1 tumor in combination with anti-PD-1 antibody. In conclusion, E7386 demonstrates clear antitumor activity via modulation of the Wnt/β-catenin signaling pathway and alteration of the tumor and immune microenvironments, and its antitumor activity can be enhanced in combination with anti-PD-1 antibody. SIGNIFICANCE: These findings demonstrate that the novel anticancer agent, E7386, modulates Wnt/β-catenin signaling, altering the tumor immune microenvironment and exhibiting synergistic antitumor activity in combination with anti-PD-1 antibody.

Hearing impairment due to Mir183/96/182 mutations suggests both loss and gain of function effects

The microRNA miR-96 is important for hearing, as point mutations in humans and mice result in dominant progressive hearing loss. Mir96 is expressed in sensory cells along with Mir182 and Mir183, but the roles of these closely-linked microRNAs are as yet unknown. Here we analyse mice carrying null alleles of Mir182, and of Mir183 and Mir96 together to investigate their roles in hearing. We found that Mir183/96 heterozygous mice had normal hearing and homozygotes were completely deaf with abnormal hair cell stereocilia bundles and reduced numbers of inner hair cell synapses at four weeks old. Mir182 knockout mice developed normal hearing then exhibited progressive hearing loss. Our transcriptional analyses revealed significant changes in a range of other genes, but surprisingly there were fewer genes with altered expression in the organ of Corti of Mir183/96 null mice compared with our previous findings in Mir96 Dmdo mutants, which have a point mutation in the miR-96 seed region. This suggests the more severe phenotype of Mir96 Dmdo mutants compared with Mir183/96 mutants, including progressive hearing loss in Mir96 Dmdo heterozygotes, is likely to be mediated by the gain of novel target genes in addition to the loss of its normal targets. We propose three mechanisms of action of mutant miRNAs; loss of targets that are normally completely repressed, loss of targets whose transcription is normally buffered by the miRNA, and gain of novel targets. Any of these mechanisms could lead to a partial loss of a robust cellular identity and consequent dysfunction.

Non-Coding RNAs in the Brain-Heart Axis: The Case of Parkinson's Disease

Parkinson's disease (PD) is a complex and heterogeneous disorder involving multiple genetic and environmental influences. Although a wide range of PD risk factors and clinical markers for the symptomatic motor stage of the disease have been identified, there are still no reliable biomarkers available for the early pre-motor phase of PD and for predicting disease progression. High-throughput RNA-based biomarker profiling and modeling may provide a means to exploit the joint information content from a multitude of markers to derive diagnostic and prognostic signatures. In the field of PD biomarker research, currently, no clinically validated RNA-based biomarker models are available, but previous studies reported several significantly disease-associated changes in RNA abundances and activities in multiple human tissues and body fluids. Here, we review the current knowledge of the regulation and function of non-coding RNAs in PD, focusing on microRNAs, long non-coding RNAs, and circular RNAs. Since there is growing evidence for functional interactions between the heart and the brain, we discuss the benefits of studying the role of non-coding RNAs in organ interactions when deciphering the complex regulatory networks involved in PD progression. We finally review important concepts of harmonization and curation of high throughput datasets, and we discuss the potential of systems biomedicine to derive and evaluate RNA biomarker signatures from high-throughput expression data.

Delivering Robust Candidates to the Drug Pipeline through Computational Analysis of Arrayed CRISPR Screens

Genome-wide arrayed CRISPR screening is a powerful method for drug target identification as it enables exploration of the effect of individual gene perturbations using diverse highly multiplexed functional and phenotypic assays. Using high-content imaging, we can measure changes in biomarker expression, intracellular localization, and cell morphology. Here we present the computational pipeline we have developed to support the analysis and interpretation of arrayed CRISPR screens. This includes evaluating the quality of guide RNA libraries, performing image analysis, evaluating assay results quality, data processing, hit identification, ranking, visualization, and biological interpretation.

Adenosine Signaling Is Prognostic for Cancer Outcome and Has Predictive Utility for Immunotherapeutic Response

PURPOSE

There are several agents in early clinical trials targeting components of the adenosine pathway including A2AR and CD73. The identification of cancers with a significant adenosine drive is critical to understand the potential for these molecules. However, it is challenging to measure tumor adenosine levels at scale, thus novel, clinically tractable biomarkers are needed.

EXPERIMENTAL DESIGN

We generated a gene expression signature for the adenosine signaling using regulatory networks derived from the literature and validated this in patients. We applied the signature to large cohorts of disease from The Cancer Genome Atlas (TCGA) and cohorts of immune checkpoint inhibitor-treated patients.

RESULTS

The signature captures baseline adenosine levels in vivo (r ² = 0.92, P = 0.018), is reduced after small-molecule inhibition of A2AR in mice (r ² = -0.62, P = 0.001) and humans (reduction in 5 of 7 patients, 70%), and is abrogated after A2AR knockout. Analysis of TCGA confirms a negative association between adenosine and overall survival (OS, HR = 0.6, P < 2.2e^-16) as well as progression-free survival (PFS, HR = 0.77, P = 0.0000006). Further, adenosine signaling is associated with reduced OS (HR = 0.47, P < 2.2e^-16) and PFS (HR = 0.65, P = 0.0000002) in CD8⁺ T-cell-infiltrated tumors. Mutation of TGFβ superfamily members is associated with enhanced adenosine signaling and worse OS (HR = 0.43, P < 2.2e^-16). Finally, adenosine signaling is associated with reduced efficacy of anti-PD1 therapy in published cohorts (HR = 0.29, P = 0.00012).

CONCLUSIONS

These data support the adenosine pathway as a mediator of a successful antitumor immune response, demonstrate the prognostic potential of the signature for immunotherapy, and inform patient selection strategies for adenosine pathway modulators currently in development.

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.

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

Curcumin downregulates expression of opioid-related nociceptin receptor gene (OPRL1) in isolated neuroglia cells

BACKGROUND

Curcumin (CC) exerts polyvalent pharmacological actions and multi-target effects, including pain relief and anti-nociceptive activity. In combination with Boswellia serrata extract (BS), curcumin shows greater efficacy in knee osteoarthritis management, presumably due to synergistic interaction of the ingredients.

AIM

To elucidate the molecular mechanisms underlying the analgesic activity of curcumin and its synergistic interaction with BS.

METHODS

We performed gene expression profiling by transcriptome-wide mRNA sequencing in human T98G neuroglia cells treated with CC (Curamed), BS, and the combination of CC and BS (CC-BS; Curamin), followed by interactive pathways analysis of the regulated genes.

RESULTS

Treatment with CC and with CC-BS selectively downregulated opioid-related nociceptin receptor 1 gene (OPRL1) expression by 5.9-fold and 7.2-fold, respectively. No changes were detected in the other canonical opioid receptor genes: OPRK1, OPRD1, and OPRM1. Nociceptin reportedly increases the sensation of pain in supra-spinal pain transduction pathways. Thus, CC and CC-BS may downregulate OPRL1, consequently inhibiting production of the nociception receptor NOP, leading to pain relief. In neuroglia cells, CC and CC-BS inhibited signaling pathways related to opioids, neuropathic pain, neuroinflammation, osteoarthritis, and rheumatoid diseases. CC and CC-BS also downregulated ADAM metallopeptidase gene ADAMTS5 expression by 11.2-fold and 13.5-fold, respectively. ADAMTS5 encodes a peptidase that plays a crucial role in osteoarthritis development via inhibition of a corresponding signaling pathway.

CONCLUSION

Here, we report for the first time that CC and CC-BS act as nociceptin receptor antagonists, selectively downregulating opioid-related nociceptin receptor 1 gene (OPRL1) expression, which is associated with pain relief. BS alone did not affect OPRL1 expression, but rather appears to potentiate the effects of CC via multiple mechanisms, including synergistic interactions of molecular networks.

Computational systems biology approaches for Parkinson's disease

Parkinson's disease (PD) is a prime example of a complex and heterogeneous disorder, characterized by multifaceted and varied motor- and non-motor symptoms and different possible interplays of genetic and environmental risk factors. While investigations of individual PD-causing mutations and risk factors in isolation are providing important insights to improve our understanding of the molecular mechanisms behind PD, there is a growing consensus that a more complete understanding of these mechanisms will require an integrative modeling of multifactorial disease-associated perturbations in molecular networks. Identifying and interpreting the combinatorial effects of multiple PD-associated molecular changes may pave the way towards an earlier and reliable diagnosis and more effective therapeutic interventions. This review provides an overview of computational systems biology approaches developed in recent years to study multifactorial molecular alterations in complex disorders, with a focus on PD research applications. Strengths and weaknesses of different cellular pathway and network analyses, and multivariate machine learning techniques for investigating PD-related omics data are discussed, and strategies proposed to exploit the synergies of multiple biological knowledge and data sources. A final outlook provides an overview of specific challenges and possible next steps for translating systems biology findings in PD to new omics-based diagnostic tools and targeted, drug-based therapeutic approaches.

Robust phenotype prediction from gene expression data using differential shrinkage of co-regulated genes

Discovery of robust diagnostic or prognostic biomarkers is a key to optimizing therapeutic benefit for select patient cohorts - an idea commonly referred to as precision medicine. Most discovery studies to derive such markers from high-dimensional transcriptomics datasets are weakly powered with sample sizes in the tens of patients. Therefore, highly regularized statistical approaches are essential to making generalizable predictions. At the same time, prior knowledge-driven approaches have been successfully applied to the manual interpretation of high-dimensional transcriptomics datasets. In this work, we assess the impact of combining two orthogonal approaches for the discovery of biomarker signatures, namely (1) well-known lasso-based regression approaches and its more recent derivative, the group lasso, and (2) the discovery of significant upstream regulators in literature-derived biological networks. Our method integrates both approaches in a weighted group-lasso model and differentially weights gene sets based on inferred active regulatory mechanism. Using nested cross-validation as well as independent clinical datasets, we demonstrate that our approach leads to increased accuracy and generalizable results. We implement our approach in a computationally efficient, user-friendly R package called creNET. The package can be downloaded at https://github.com/kouroshz/creNethttps://github.com/kouroshz/creNet and is accompanied by a parsed version of the STRING DB data base.

A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data

BACKGROUND

Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model.

RESULTS

We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database.

CONCLUSION

In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.