Gene network inference and visualization tools for biologists: application to new human transcriptome datasets

An essential gene signature of breast cancer metastasis reveals targetable pathways

BACKGROUND

The differential gene expression profile of metastatic versus primary breast tumors represents an avenue for discovering new or underappreciated pathways underscoring processes of metastasis. However, as tumor biopsy samples are a mixture of cancer and non-cancer cells, most differentially expressed genes in metastases would represent confounders involving sample biopsy site rather than cancer cell biology.

METHODS

By paired analysis, we defined a top set of differentially expressed genes in breast cancer metastasis versus primary tumors using an RNA-sequencing dataset of 152 patients from The Breast International Group Aiming to Understand the Molecular Aberrations dataset (BIG-AURORA). To filter the genes higher in metastasis for genes essential for breast cancer proliferation, we incorporated CRISPR-based data from breast cancer cell lines.

RESULTS

A significant fraction of genes with higher expression in metastasis versus paired primary were essential by CRISPR. These 264 genes represented an essential signature of breast cancer metastasis. In contrast, nonessential metastasis genes largely involved tumor biopsy site. The essential signature predicted breast cancer patient outcome based on primary tumor expression patterns. Pathways underlying the essential signature included proteasome degradation, the electron transport chain, oxidative phosphorylation, and cancer metabolic reprogramming. Transcription factors MYC, MAX, HDAC3, and HCFC1 each bound significant fractions of essential genes.

CONCLUSIONS

Associations involving the essential gene signature of breast cancer metastasis indicate true biological changes intrinsic to cancer cells, with important implications for applying existing therapies or developing alternate therapeutic approaches.

The unique interplay of mitochondrial oxidative phosphorylation (OXPHOS) and immunity and its potential implication for the sex- and age-related morbidity of severe COVID-19 patients

Aged male patients are more vulnerable to severe or critical symptoms of COVID-19, but the underlying mechanism remains elusive. In this study, we analyzed previously published scRNA-seq data from a large cohort of COVID-19 patients, castrated and regenerated mice, and bulk RNA-seq of a RNAi library of 400 genes, and revealed that both immunity and OXPHOS displayed cell-type-, sex-, and age-related variation in the severe or critical COVID-19 patients during disease progression, with a more prominent increase in immunity and decrease in OXPHOS in myeloid cells in the males relative to the females (60-69 years old). Male severe or critical patients above 70 years old were an exception in that the compromised negative correlation between OXPHOS and immunity in these patients was associated with its disordered transcriptional regulation. Finally, the expression levels of OXPHOS and androgens were revealed to be positively correlated, and the responses of macrophages to android fluctuation were more striking than other types of detected immune cells in the castrated mice model. Therefore, the interplay of OXPHOS and immunity displayed a cell-type-specific, age-related, and sex-biased pattern, and the underlying potential regulatory role of the hormonal milieu should not be neglected.

Pan-cancer molecular subtypes of metastasis reveal distinct and evolving transcriptional programs

Molecular mechanisms underlying cancer metastasis span diverse tissues of origin. Here, we synthesize and collate the transcriptomes of patient-derived xenografts and patient tumor metastases, and these data collectively represent 38 studies and over 3,000 patients and 4,000 tumors. We identify four expression-based subtypes of metastasis transcending tumor lineage. The first subtype has extensive copy alterations, higher expression of MYC transcriptional targets and DNA repair genes, and bromodomain inhibitor response association. The second subtype has higher expression of genes involving metabolism and prostaglandin synthesis and regulation. The third subtype has evidence of neuronal differentiation, higher expression of DNA and histone methylation genes and EZH2 transcriptional targets, and BCL2 inhibitor response association. The fourth subtype has higher expression of immune checkpoint and Notch pathway genes. The metastasis subtypes reflect expression differences from paired primaries, with subtype switching being common. These subtypes facilitate understanding of the molecular underpinnings of metastases beyond tissue-oriented domains, with therapeutic implications.

Co-Expression Analysis of Airway Epithelial Transcriptome in Asthma Patients with Eosinophilic vs. Non-Eosinophilic Airway Infiltration

Asthma heterogeneity complicates the search for targeted treatment against airway inflammation and remodeling. We sought to investigate relations between eosinophilic inflammation, a phenotypic feature frequent in severe asthma, bronchial epithelial transcriptome, and functional and structural measures of airway remodeling. We compared epithelial gene expression, spirometry, airway cross-sectional geometry (computed tomography), reticular basement membrane thickness (histology), and blood and bronchoalveolar lavage (BAL) cytokines of n = 40 moderate to severe eosinophilic (EA) and non-eosinophilic asthma (NEA) patients distinguished by BAL eosinophilia. EA patients showed a similar extent of airway remodeling as NEA but had an increased expression of genes involved in the immune response and inflammation (e.g., KIR3DS1), reactive oxygen species generation (GYS2, ATPIF1), cell activation and proliferation (ANK3), cargo transporting (RAB4B, CPLX2), and tissue remodeling (FBLN1, SOX14, GSN), and a lower expression of genes involved in epithelial integrity (e.g., GJB1) and histone acetylation (SIN3A). Genes co-expressed in EA were involved in antiviral responses (e.g., ATP1B1), cell migration (EPS8L1, STOML3), cell adhesion (RAPH1), epithelial-mesenchymal transition (ASB3), and airway hyperreactivity and remodeling (FBN3, RECK), and several were linked to asthma in genome- (e.g., MRPL14, ASB3) or epigenome-wide association studies (CLC, GPI, SSCRB4, STRN4). Signaling pathways inferred from the co-expression pattern were associated with airway remodeling (e.g., TGF-β/Smad2/3, E2F/Rb, and Wnt/β-catenin).

bulkAnalyseR: an accessible, interactive pipeline for analysing and sharing bulk multi-modal sequencing data

Bulk sequencing experiments (single- and multi-omics) are essential for exploring wide-ranging biological questions. To facilitate interactive, exploratory tasks, coupled with the sharing of easily accessible information, we present bulkAnalyseR, a package integrating state-of-the-art approaches using an expression matrix as the starting point (pre-processing functions are available as part of the package). Static summary images are replaced with interactive panels illustrating quality-checking, differential expression analysis (with noise detection) and biological interpretation (enrichment analyses, identification of expression patterns, followed by inference and comparison of regulatory interactions). bulkAnalyseR can handle different modalities, facilitating robust integration and comparison of cis-, trans- and customised regulatory networks.

FABP4 Controls Fat Mass Expandability (Adipocyte Size and Number) through Inhibition of CD36/SR-B2 Signalling

Adipose tissue hypertrophy during obesity plays pleiotropic effects on health. Adipose tissue expandability depends on adipocyte size and number. In mature adipocytes, lipid accumulation as triglycerides into droplets is imbalanced by lipid uptake and lipolysis. In previous studies, we showed that adipogenesis induced by oleic acid is signed by size increase and reduction of FAT/CD36 (SR-B2) activity. The present study aims to decipher the mechanisms involved in fat mass regulation by fatty acid/FAT-CD36 signalling. Human adipose stem cells, 3T3-L1, and its 3T3-MBX subclone cell lines were used in 2D cell cultures or co-cultures to monitor in real-time experiments proliferation, differentiation, lipolysis, and/or lipid uptake and activation of FAT/CD36 signalling pathways regulated by oleic acid, during adipogenesis and/or regulation of adipocyte size. Both FABP4 uptake and its induction by fatty acid-mediated FAT/CD36-PPARG gene transcription induce accumulation of intracellular FABP4, which in turn reduces FAT/CD36, and consequently exerts a negative feedback loop on FAT/CD36 signalling in both adipocytes and their progenitors. Both adipocyte size and recruitment of new adipocytes are under the control of FABP4 stores. This study suggests that FABP4 controls fat mass homeostasis.

Accurate determination of causalities in gene regulatory networks by dissecting downstream target genes

Accurate determination of causalities between genes is a challenge in the inference of gene regulatory networks (GRNs) from the gene expression profile. Although many methods have been developed for the reconstruction of GRNs, most of them are insufficient in determining causalities or regulatory directions. In this work, we present a novel method, namely, DDTG, to improve the accuracy of causality determination in GRN inference by dissecting downstream target genes. In the proposed method, the topology and hierarchy of GRNs are determined by mutual information and conditional mutual information, and the regulatory directions of GRNs are determined by Taylor formula-based regression. In addition, indirect interactions are removed with the sparseness of the network topology to improve the accuracy of network inference. The method is validated on the benchmark GRNs from DREAM3 and DREAM4 challenges. The results demonstrate the superior performance of the DDTG method on causality determination of GRNs compared to some popular GRN inference methods. This work provides a useful tool to infer the causal gene regulatory network.

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.

Late phase endothelial cell inflammation is characterized by interferon response genes and driven by JAK/STAT, not NFκB

Chronic vascular inflammation underlies many diseases, including atherosclerosis, autoimmune vasculitides and transplant rejection. The resolution of inflammation is critical for proper healing and restoration of homeostasis, but the timing and signaling mechanisms involved in the return to a non-inflamed state are not well understood. Pro-adhesive gene expression, phenotype and secretome of human endothelial cells was measured in primary human aortic endothelium under chronic TNFα stimulation, and after short-term TNFα priming followed by withdrawal. The effects of NFκB, MAPK and JAK1/2 inhibitors on TNFα-induced gene expression were tested. The majority of inducible TNFα effectors, such as E-selectin, VCAM-1 and most chemokines, required continuous exposure for reinforcement of the altered phenotype, and were NFκB dependent. However, 3 h priming with TNFα induced late phase STAT activation and interferon response genes after 18 h, as well as enhanced ICAM-1, BST2 and CXCR3 ligand expression. Chronic activation was autonomous of continuous TNFα, and could be blocked by the JAK1/2 inhibitor ruxolitinib. The results demonstrate that NFκB is not a significant driver of the later phase of endothelial cell activation by TNFα, but that sustained inflammation is JAK1/2-dependent and characterized by adaptive chemokines.

Inference on the structure of gene regulatory networks

In this paper, we conduct theoretical analyses on inferring the structure of gene regulatory networks. Depending on the experimental method and data type, the inference problem is classified into 20 different scenarios. For each scenario, we discuss the problem that with enough data, under what assumptions, what can be inferred about the structure. For scenarios that have been covered in the literature, we provide a brief review. For scenarios that have not been covered in literature, if the structure can be inferred, we propose new mathematical inference methods and evaluate them on simulated data. Otherwise, we prove that the structure cannot be inferred.

EnGRaiN: a supervised ensemble learning method for recovery of large-scale gene regulatory networks

MOTIVATION

Reconstruction of genome-scale networks from gene expression data is an actively studied problem. A wide range of methods that differ between the types of interactions they uncover with varying trade-offs between sensitivity and specificity have been proposed. To leverage benefits of multiple such methods, ensemble network methods that combine predictions from resulting networks have been developed, promising results better than or as good as the individual networks. Perhaps owing to the difficulty in obtaining accurate training examples, these ensemble methods hitherto are unsupervised.

RESULTS

In this article, we introduce EnGRaiN, the first supervised ensemble learning method to construct gene networks. The supervision for training is provided by small training datasets of true edge connections (positives) and edges known to be absent (negatives) among gene pairs. We demonstrate the effectiveness of EnGRaiN using simulated datasets as well as a curated collection of Arabidopsis thaliana datasets we created from microarray datasets available from public repositories. EnGRaiN shows better results not only in terms of receiver operating characteristic and PR characteristics for both real and simulated datasets compared with unsupervised methods for ensemble network construction, but also generates networks that can be mined for elucidating complex biological interactions.

AVAILABILITY AND IMPLEMENTATION

EnGRaiN software and the datasets used in the study are publicly available at the github repository: https://github.com/AluruLab/EnGRaiN.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Trends in biological data integration for the selection of enzymes and transcription factors related to cellulose and hemicellulose degradation in fungi

Fungi are key players in biotechnological applications. Although several studies focusing on fungal diversity and genetics have been performed, many details of fungal biology remain unknown, including how cellulolytic enzymes are modulated within these organisms to allow changes in main plant cell wall compounds, cellulose and hemicellulose, and subsequent biomass conversion. With the advent and consolidation of DNA/RNA sequencing technology, different types of information can be generated at the genomic, structural and functional levels, including the gene expression profiles and regulatory mechanisms of these organisms, during degradation-induced conditions. This increase in data generation made rapid computational development necessary to deal with the large amounts of data generated. In this context, the origination of bioinformatics, a hybrid science integrating biological data with various techniques for information storage, distribution and analysis, was a fundamental step toward the current state-of-the-art in the postgenomic era. The possibility of integrating biological big data has facilitated exciting discoveries, including identifying novel mechanisms and more efficient enzymes, increasing yields, reducing costs and expanding opportunities in the bioprocess field. In this review, we summarize the current status and trends of the integration of different types of biological data through bioinformatics approaches for biological data analysis and enzyme selection.

IFNγ, and to a Lesser Extent TNFα, Provokes a Sustained Endothelial Costimulatory Phenotype

Background

Vascular endothelial cells (EC) are critical for regulation of local immune responses, through coordination of leukocyte recruitment from the blood and egress into the tissue. Growing evidence supports an additional role for endothelium in activation and costimulation of adaptive immune cells. However, this function remains somewhat controversial, and the full repertoire and durability of an enhanced endothelial costimulatory phenotype has not been wholly defined.

Methods

Human endothelium was stimulated with continuous TNFα or IFNγ for 1-48hr; or primed with TNFα or IFNγ for only 3hr, before withdrawal of stimulus for up to 45hr. Gene expression of cytokines, costimulatory molecules and antigen presentation molecules was measured by Nanostring, and publicly available datasets of EC stimulation with TNFα or IFNγ were leveraged to further corroborate the results. Cell surface protein expression was detected by flow cytometry, and secretion of cytokines was assessed by Luminex and ELISA. Key findings were confirmed in primary human endothelial cells from 4-6 different vascular beds.

Results

TNFα triggered mostly positive immune checkpoint molecule expression on endothelium, including CD40, 4-1BB, and ICOSLG but in the context of only HLA class I and immunoproteasome subunits. IFNγ promoted a more tolerogenic phenotype of high PD-L1 and PD-L2 expression with both HLA class I and class II molecules and antigen processing genes. Both cytokines elicited secretion of IL-15 and BAFF/BLyS, with TNFα stimulated EC additionally producing IL-6, TL1A and IL-1β. Moreover, endothelium primed for a short period (3hr) with TNFα mostly failed to alter the costimulatory phenotype 24-48hr later, with only somewhat augmented expression of HLA class I. In contrast, brief exposure to IFNγ was sufficient to cause late expression of antigen presentation, cytokines and costimulatory molecules. In particular HLA class I, PD-1 ligand and cytokine expression was markedly high on endothelium two days after IFNγ was last present.

Conclusions

Endothelia from multiple vascular beds possess a wide range of other immune checkpoint molecules and cytokines that can shape the adaptive immune response. Our results further demonstrate that IFNγ elicits prolonged signaling that persists days after initiation and is sufficient to trigger substantial gene expression changes and immune phenotype in vascular endothelium.

ESDN inhibits melanoma progression by blocking E-selectin expression in endothelial cells via STAT3

An interactive crosstalk between tumor and stroma cells is essential for metastatic melanoma progression. We evidenced that ESDN/DCBLD2/CLCP1 plays a crucial role in endothelial cells during the spread of melanoma. Precisely, increased extravasation and metastasis formation were revealed in ESDN-null mice injected with melanoma cells, even if the primary tumor growth, vessel permeability, and angiogenesis were not enhanced. Interestingly, improved adhesion of melanoma cells to ESDN-depleted endothelial cells was observed, due to the presence of higher levels of E-selectin transcripts/proteins in ESDN-defective cells. In accordance with these results, anticorrelation was observed between ESDN and E-selectin in human endothelial cells. Most importantly, our data revealed that cimetidine, an E-selectin inhibitor, was able to block cell adhesion, extravasation, and metastasis formation in ESDN-null mice, underlying a major role of ESDN in E-selectin transcription upregulation, which according to our data, may presumably be linked to STAT3. Based on our results, we propose a protective role for ESDN during the spread of melanoma and reveal its therapeutic potential.

An order independent algorithm for inferring gene regulatory network using quantile value for conditional independence tests

In recent years, due to the difficulty and inefficiency of experimental methods, numerous computational methods have been introduced for inferring the structure of Gene Regulatory Networks (GRNs). The Path Consistency (PC) algorithm is one of the popular methods to infer the structure of GRNs. However, this group of methods still has limitations and there is a potential for improvements in this field. For example, the PC-based algorithms are still sensitive to the ordering of nodes i.e. different node orders results in different network structures. The second is that the networks inferred by these methods are highly dependent on the threshold used for independence testing. Also, it is still a challenge to select the set of conditional genes in an optimal way, which affects the performance and computation complexity of the PC-based algorithm. We introduce a novel algorithm, namely Order Independent PC-based algorithm using Quantile value (OIPCQ), which improves the accuracy of the learning process of GRNs and solves the order dependency issue. The quantile-based thresholds are considered for different orders of CMI tests. For conditional gene selection, we consider the paths between genes with length equal or greater than 2 while other well-known PC-based methods only consider the paths of length 2. We applied OIPCQ on the various networks of the DREAM3 and DREAM4 in silico challenges. As a real-world case study, we used OIPCQ to reconstruct SOS DNA network obtained from Escherichia coli and GRN for acute myeloid leukemia based on the RNA sequencing data from The Cancer Genome Atlas. The results show that OIPCQ produces the same network structure for all the permutations of the genes and improves the resulted GRN through accurately quantifying the causal regulation strength in comparison with other well-known PC-based methods. According to the GRN constructed by OIPCQ, for acute myeloid leukemia, two regulators BCLAF1 and NRSF reported previously are significantly important. However, the highest degree nodes in this GRN are ZBTB7A and PU1 which play a significant role in cancer, especially in leukemia. OIPCQ is freely accessible at https://github.com/haammim/OIPCQ-and-OIPCQ2 .

Environmental Impact on Male (In)Fertility via Epigenetic Route

In the last 40 years, male reproductive health-which is very sensitive to both environmental exposure and metabolic status-has deteriorated and the poor sperm quality observed has been suggested to affect offspring development and its health in adult life. In this scenario, evidence now suggests that epigenetics shapes endocrine functions, linking genetics and environment. During fertilization, spermatozoa share with the oocyte their epigenome, along with their haploid genome, in order to orchestrate embryo development. The epigenetic signature of spermatozoa is the result of a dynamic modulation of the epigenetic marks occurring, firstly, in the testis-during germ cell progression-then, along the epididymis, where spermatozoa still receive molecules, conveyed by epididymosomes. Paternal lifestyle, including nutrition and exposure to hazardous substances, alters the phenotype of the next generations, through the remodeling of a sperm epigenetic blueprint that dynamically reacts to a wide range of environmental and lifestyle stressors. With that in mind, this review will summarize and discuss insights into germline epigenetic plasticity caused by environmental stimuli and diet and how spermatozoa may be carriers of induced epimutations across generations through a mechanism known as paternal transgenerational epigenetic inheritance.

Advancing from protein interactomes and gene co-expression networks towards multi-omics-based composite networks: approaches for predicting and extracting biological knowledge

Prediction of biological interaction networks from single-omics data has been extensively implemented to understand various aspects of biological systems. However, more recently, there is a growing interest in integrating multi-omics datasets for the prediction of interactomes that provide a global view of biological systems with higher descriptive capability, as compared to single omics. In this review, we have discussed various computational approaches implemented to infer and analyze two of the most important and well studied interactomes: protein-protein interaction networks and gene co-expression networks. We have explicitly focused on recent methods and pipelines implemented to infer and extract biologically important information from these interactomes, starting from utilizing single-omics data and then progressing towards multi-omics data. Accordingly, recent examples and case studies are also briefly discussed. Overall, this review will provide a proper understanding of the latest developments in protein and gene network modelling and will also help in extracting practical knowledge from them.

Non-coding RNA regulatory networks

It is well established that the vast majority of human RNA transcripts do not encode for proteins and that non-coding RNAs regulate cell physiology and shape cellular functions. A subset of them is involved in gene regulation at different levels, from epigenetic gene silencing to post-transcriptional regulation of mRNA stability. Notably, the aberrant expression of many non-coding RNAs has been associated with aggressive pathologies. Rapid advances in network biology indicates that the robustness of cellular processes is the result of specific properties of biological networks such as scale-free degree distribution and hierarchical modularity, suggesting that regulatory network analyses could provide new insights on gene regulation and dysfunction mechanisms. In this study we present an overview of public repositories where non-coding RNA-regulatory interactions are collected and annotated, we discuss unresolved questions for data integration and we recall existing resources to build and analyse networks.

The Spring of Systems Biology-Driven Breeding

Genetics and molecular biology have contributed to the development of rationalized plant breeding programs. Recent developments in both high-throughput experimental analyses of biological systems and in silico data processing offer the possibility to address the whole gene regulatory network (GRN) controlling a given trait. GRN models can be applied to identify topological features helping to shortlist potential candidate genes for breeding purposes. Time-series data sets can be used to support dynamic modelling of the network. This will enable a deeper comprehension of network behaviour and the identification of the few elements to be genetically rewired to push the system towards a modified phenotype of interest. This paves the way to design more efficient, systems biology-based breeding strategies.

Oxidized phospholipids regulate amino acid metabolism through MTHFD2 to facilitate nucleotide release in endothelial cells

Oxidized phospholipids (oxPAPC) induce endothelial dysfunction and atherosclerosis. Here we show that oxPAPC induce a gene network regulating serine-glycine metabolism with the mitochondrial methylenetetrahydrofolate dehydrogenase/cyclohydrolase (MTHFD2) as a causal regulator using integrative network modeling and Bayesian network analysis in human aortic endothelial cells. The cluster is activated in human plaque material and by atherogenic lipoproteins isolated from plasma of patients with coronary artery disease (CAD). Single nucleotide polymorphisms (SNPs) within the MTHFD2-controlled cluster associate with CAD. The MTHFD2-controlled cluster redirects metabolism to glycine synthesis to replenish purine nucleotides. Since endothelial cells secrete purines in response to oxPAPC, the MTHFD2-controlled response maintains endothelial ATP. Accordingly, MTHFD2-dependent glycine synthesis is a prerequisite for angiogenesis. Thus, we propose that endothelial cells undergo MTHFD2-mediated reprogramming toward serine-glycine and mitochondrial one-carbon metabolism to compensate for the loss of ATP in response to oxPAPC during atherosclerosis.

Dual regulation of Stat1 and Stat3 by the tumor suppressor protein PML contributes to interferon α-mediated inhibition of angiogenesis

IFNs are effective in inhibiting angiogenesis in preclinical models and in treating several angioproliferative disorders. However, the detailed mechanisms of IFNα-mediated anti-angiogenesis are not completely understood. Stat1/2/3 and PML are IFNα downstream effectors and are pivotal regulators of angiogenesis. Here, we investigated PML's role in the regulation of Stat1/2/3 activity. In Pml knock-out (KO) mice, ablation of Pml largely reduces IFNα angiostatic ability in Matrigel plug assays. This suggested an essential role for PML in IFNα's anti-angiogenic function. We also demonstrated that PML shared a large cohort of regulatory genes with Stat1 and Stat3, indicating an important role of PML in regulating Stat1 and Stat3 activity. Using molecular tools and primary endothelial cells, we demonstrated that PML positively regulates Stat1 and Stat2 isgylation, a ubiquitination-like protein modification. Accordingly, manipulation of the isgylation system by knocking down USP18 altered IFNα-PML axis-mediated inhibition of endothelial cell migration and network formation. Furthermore, PML promotes turnover of nuclear Stat3, and knockdown of PML mitigates the effect of LLL12, a selective Stat3 inhibitor, on IFNα-mediated anti-angiogenic activity. Taken together, we elucidated an unappreciated mechanism in which PML, an IFNα-inducible effector, possess potent angiostatic activity, doing so in part by forming a positive feedforward loop with Stat1/2 and a negative feedback loop with Stat3. The interplay between PML, Stat1/Stat2, and Stat3 contributes to IFNα-mediated inhibition of angiogenesis, and disruption of this network results in aberrant IFNα signaling and altered angiostatic activity.

Data-driven reverse engineering of signaling pathways using ensembles of dynamic models

Despite significant efforts and remarkable progress, the inference of signaling networks from experimental data remains very challenging. The problem is particularly difficult when the objective is to obtain a dynamic model capable of predicting the effect of novel perturbations not considered during model training. The problem is ill-posed due to the nonlinear nature of these systems, the fact that only a fraction of the involved proteins and their post-translational modifications can be measured, and limitations on the technologies used for growing cells in vitro, perturbing them, and measuring their variations. As a consequence, there is a pervasive lack of identifiability. To overcome these issues, we present a methodology called SELDOM (enSEmbLe of Dynamic lOgic-based Models), which builds an ensemble of logic-based dynamic models, trains them to experimental data, and combines their individual simulations into an ensemble prediction. It also includes a model reduction step to prune spurious interactions and mitigate overfitting. SELDOM is a data-driven method, in the sense that it does not require any prior knowledge of the system: the interaction networks that act as scaffolds for the dynamic models are inferred from data using mutual information. We have tested SELDOM on a number of experimental and in silico signal transduction case-studies, including the recent HPN-DREAM breast cancer challenge. We found that its performance is highly competitive compared to state-of-the-art methods for the purpose of recovering network topology. More importantly, the utility of SELDOM goes beyond basic network inference (i.e. uncovering static interaction networks): it builds dynamic (based on ordinary differential equation) models, which can be used for mechanistic interpretations and reliable dynamic predictions in new experimental conditions (i.e. not used in the training). For this task, SELDOM's ensemble prediction is not only consistently better than predictions from individual models, but also often outperforms the state of the art represented by the methods used in the HPN-DREAM challenge.

Variable neighborhood search for reverse engineering of gene regulatory networks

A new search heuristic, Divided Neighborhood Exploration Search, designed to be used with inference algorithms such as Bayesian networks to improve on the reverse engineering of gene regulatory networks is presented. The approach systematically moves through the search space to find topologies representative of gene regulatory networks that are more likely to explain microarray data. In empirical testing it is demonstrated that the novel method is superior to the widely employed greedy search techniques in both the quality of the inferred networks and computational time.

Past Roadblocks and New Opportunities in Transcription Factor Network Mapping

One of the principal mechanisms by which cells differentiate and respond to changes in external signals or conditions is by changing the activity levels of transcription factors (TFs). This changes the transcription rates of target genes via the cell's TF network, which ultimately contributes to reconfiguring cellular state. Since microarrays provided our first window into global cellular state, computational biologists have eagerly attacked the problem of mapping TF networks, a key part of the cell's control circuitry. In retrospect, however, steady-state mRNA abundance levels were a poor substitute for TF activity levels and gene transcription rates. Likewise, mapping TF binding through chromatin immunoprecipitation proved less predictive of functional regulation and less amenable to systematic elucidation of complete networks than originally hoped. This review explains these roadblocks and the current, unprecedented blossoming of new experimental techniques built on second-generation sequencing, which hold out the promise of rapid progress in TF network mapping.

Information theoretic approaches for inference of biological networks from continuous-valued data

Background

Characterising programs of gene regulation by studying individual protein-DNA and protein-protein interactions would require a large volume of high-resolution proteomics data, and such data are not yet available. Instead, many gene regulatory network (GRN) techniques have been developed, which leverage the wealth of transcriptomic data generated by recent consortia to study indirect, gene-level relationships between transcriptional regulators. Despite the popularity of such methods, previous methods of GRN inference exhibit limitations that we highlight and address through the lens of information theory.

Results

We introduce new model-free and non-linear information theoretic measures for the inference of GRNs and other biological networks from continuous-valued data. Although previous tools have implemented mutual information as a means of inferring pairwise associations, they either introduce statistical bias through discretisation or are limited to modelling undirected relationships. Our approach overcomes both of these limitations, as demonstrated by a substantial improvement in empirical performance for a set of 160 GRNs of varying size and topology.

Conclusions

The information theoretic measures described in this study yield substantial improvements over previous approaches (e.g. ARACNE) and have been implemented in the latest release of NAIL (Network Analysis and Inference Library). However, despite the theoretical and empirical advantages of these new measures, they do not circumvent the fundamental limitation of indeterminacy exhibited across this class of biological networks. These methods have presently found value in computational neurobiology, and will likely gain traction for GRN analysis as the volume and quality of temporal transcriptomics data continues to improve.

Integrating Transcriptomic and Proteomic Data Using Predictive Regulatory Network Models of Host Response to Pathogens

Mammalian host response to pathogenic infections is controlled by a complex regulatory network connecting regulatory proteins such as transcription factors and signaling proteins to target genes. An important challenge in infectious disease research is to understand molecular similarities and differences in mammalian host response to diverse sets of pathogens. Recently, systems biology studies have produced rich collections of omic profiles measuring host response to infectious agents such as influenza viruses at multiple levels. To gain a comprehensive understanding of the regulatory network driving host response to multiple infectious agents, we integrated host transcriptomes and proteomes using a network-based approach. Our approach combines expression-based regulatory network inference, structured-sparsity based regression, and network information flow to infer putative physical regulatory programs for expression modules. We applied our approach to identify regulatory networks, modules and subnetworks that drive host response to multiple influenza infections. The inferred regulatory network and modules are significantly enriched for known pathways of immune response and implicate apoptosis, splicing, and interferon signaling processes in the differential response of viral infections of different pathogenicities. We used the learned network to prioritize regulators and study virus and time-point specific networks. RNAi-based knockdown of predicted regulators had significant impact on viral replication and include several previously unknown regulators. Taken together, our integrated analysis identified novel module level patterns that capture strain and pathogenicity-specific patterns of expression and helped identify important regulators of host response to influenza infection.

An important challenge in infectious disease research is to understand how the human immune system responds to different types of pathogenic infections. An important component of mounting proper response is the transcriptional regulatory network that specifies the context-specific gene expression program in the host cell. However, our understanding of this regulatory network and how it drives context-specific transcriptional programs is incomplete. To address this gap, we performed a network-based analysis of host response to influenza viruses that integrated high-throughput mRNA- and protein measurements and protein-protein interaction networks to identify virus and pathogenicity-specific modules and their upstream physical regulatory programs. We inferred regulatory networks for human cell line and mouse host systems, which recapitulated several known regulators and pathways of the immune response and viral life cycle. We used the networks to study time point and strain-specific subnetworks and to prioritize important regulators of host response. We predicted several novel regulators, both at the mRNA and protein levels, and experimentally verified their role in the virus life cycle based on their ability to significantly impact viral replication.

Effective gene expression data generation framework based on multi-model approach

OBJECTIVE

Overcome the lack of enough samples in gene expression data sets having thousands of genes but a small number of samples challenging the computational methods using them.

METHODS AND MATERIAL

This paper introduces a multi-model artificial gene expression data generation framework where different gene regulatory network (GRN) models contribute to the final set of samples based on the characteristics of their underlying paradigms. In the first stage, we build different GRN models, and sample data from each of them separately. Then, we pool the generated samples into a rich set of gene expression samples, and finally try to select the best of the generated samples based on a multi-objective selection method measuring the quality of the generated samples from three different aspects such as compatibility, diversity and coverage. We use four alternative GRN models, namely, ordinary differential equations, probabilistic Boolean networks, multi-objective genetic algorithm and hierarchical Markov model.

RESULTS

We conducted a comprehensive set of experiments based on both real-life biological and synthetic gene expression data sets. We show that our multi-objective sample selection mechanism effectively combines samples from different models having up to 95% compatibility, 10% diversity and 50% coverage. We show that the samples generated by our framework has up to 1.5x higher compatibility, 2x higher diversity and 2x higher coverage than the samples generated by the individual models that the multi-model framework uses. Moreover, the results show that the GRNs inferred from the samples generated by our framework can have 2.4x higher precision, 12x higher recall, and 5.4x higher f-measure values than the GRNs inferred from the original gene expression samples.

CONCLUSIONS

Therefore, we show that, we can significantly improve the quality of generated gene expression samples by integrating different computational models into one unified framework without dealing with complex internal details of each individual model. Moreover, the rich set of artificial gene expression samples is able to capture some biological relations that can even not be captured by the original gene expression data set.

Transcriptomic analysis reveals how a lack of potassium ions increases Sulfolobus acidocaldarius sensitivity to pH changes

Extremely acidophilic microorganisms (optimum growth pH of ≤3) maintain a near neutral cytoplasmic pH via several homeostatic mechanisms, including an inside positive membrane potential created by potassium ions. Transcriptomic responses to pH stress in the thermoacidophilic archaeon, Sulfolobus acidocaldarius were investigated by growing cells without added sodium and/or potassium ions at both optimal and sub-optimal pH. Culturing the cells in the absence of added sodium or potassium ions resulted in a reduced growth rate compared to full-salt conditions as well as 43 and 75 significantly different RNA transcript ratios, respectively. Differentially expressed RNA transcripts during growth in the absence of added sodium ions included genes coding for permeases, a sodium/proline transporter and electron transport proteins. In contrast, culturing without added potassium ions resulted in higher RNA transcripts for similar genes as a lack of sodium ions plus genes related to spermidine that has a general role in response to stress and a decarboxylase that potentially consumes protons. The greatest RNA transcript response occurred when S. acidocaldarius cells were grown in the absence of potassium and/or sodium at a sub-optimal pH. These adaptations included those listed above plus osmoregulated glucans and mechanosensitive channels that have previously been shown to respond to osmotic stress. In addition, data analyses revealed two co-expressed IclR family transcriptional regulator genes with a previously unknown role in the S. acidocaldarius pH stress response. Our study provides additional evidence towards the importance of potassium in acidophile growth at acidic pH.

Inferring gene targets of drugs and chemical compounds from gene expression profiles

Motivation: Finding genes which are directly perturbed or targeted by drugs is of great interest and importance in drug discovery. Several network filtering methods have been created to predict the gene targets of drugs from gene expression data based on an ordinary differential equation model of the gene regulatory network (GRN). A critical step in these methods involves inferring the GRN from the expression data, which is a very challenging problem on its own. In addition, existing network filtering methods require computationally intensive parameter tuning or expression data from experiments with known genetic perturbations or both.

Results: We developed a method called DeltaNet for the identification of drug targets from gene expression data. Here, the gene target predictions were directly inferred from the data without a separate step of GRN inference. DeltaNet formulation led to solving an underdetermined linear regression problem, for which we employed least angle regression (DeltaNet-LAR) or LASSO regularization (DeltaNet-LASSO). The predictions using DeltaNet for expression data of Escherichia coli, yeast, fruit fly and human were significantly more accurate than those using network filtering methods, namely mode of action by network identification (MNI) and sparse simultaneous equation model (SSEM). Furthermore, DeltaNet using LAR did not require any parameter tuning and could provide computational speed-up over existing methods.

Conclusion: DeltaNet is a robust and numerically efficient tool for identifying gene perturbations from gene expression data. Importantly, the method requires little to no expert supervision, while providing accurate gene target predictions.

Availability and implementation: DeltaNet is available on http://www.cabsel.ethz.ch/tools/DeltaNet.

Contact:rudi.gunawan@chem.ethz.ch

Supplementary information:Supplementary data are available at Bioinformatics online.

Single-cell transcriptome sequencing: recent advances and remaining challenges

Single-cell RNA-sequencing methods are now robust and economically practical and are becoming a powerful tool for high-throughput, high-resolution transcriptomic analysis of cell states and dynamics. Single-cell approaches circumvent the averaging artifacts associated with traditional bulk population data, yielding new insights into the cellular diversity underlying superficially homogeneous populations. Thus far, single-cell RNA-sequencing has already shown great effectiveness in unraveling complex cell populations, reconstructing developmental trajectories, and modeling transcriptional dynamics. Ongoing technical improvements to single-cell RNA-sequencing throughput and sensitivity, the development of more sophisticated analytical frameworks for single-cell data, and an increasing array of complementary single-cell assays all promise to expand the usefulness and potential applications of single-cell transcriptomic profiling.

Enabling network inference methods to handle missing data and outliers

Background

The inference of complex networks from data is a challenging problem in biological sciences, as well as in a wide range of disciplines such as chemistry, technology, economics, or sociology. The quantity and quality of the data greatly affect the results. While many methodologies have been developed for this task, they seldom take into account issues such as missing data or outlier detection and correction, which need to be properly addressed before network inference.

Results

Here we present an approach to (i) handle missing data and (ii) detect and correct outliers based on multivariate projection to latent structures. The method, called trimmed scores regression (TSR), enables network inference methods to analyse incomplete datasets by imputing the missing values coherently with the latent data structure. Furthermore, it substitutes the faulty values in a dataset by proper estimations. We provide an implementation of this approach, and show how it can be integrated with any network inference method as a preliminary data curation step. This functionality is demonstrated with a state of the art network inference method based on mutual information distance and entropy reduction, MIDER.

Conclusion

The methodology presented here enables network inference methods to analyse a large number of incomplete and faulty datasets that could not be reliably analysed so far. Our comparative studies show the superiority of TSR over other missing data approaches used by practitioners. Furthermore, the method allows for outlier detection and correction.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0717-7) contains supplementary material, which is available to authorized users.

Review: High-performance computing to detect epistasis in genome scale data sets

It is becoming clear that most human diseases have a complex etiology that cannot be explained by single nucleotide polymorphisms (SNPs) or simple additive combinations; the general consensus is that they are caused by combinations of multiple genetic variations. The limited success of some genome-wide association studies is partly a result of this focus on single genetic markers. A more promising approach is to take into account epistasis, by considering the association of multiple SNP interactions with disease. However, as genomic data continues to grow in resolution, and genome and exome sequencing become more established, the number of combinations of variants to consider increases rapidly. Two potential solutions should be considered: the use of high-performance computing, which allows us to consider a larger number of variables, and heuristics to make the solution more tractable, essential in the case of genome sequencing. In this review, we look at different computational methods to analyse epistatic interactions within disease-related genetic data sets created by microarray technology. We also review efforts to use epistatic analysis results to produce biomarkers for diagnostic tests and give our views on future directions in this field in light of advances in sequencing technology and variants in non-coding regions.

From blastocyst to gastrula: gene regulatory networks of embryonic stem cells and early mouse embryogenesis

To date, many regulatory genes and signalling events coordinating mammalian development from blastocyst to gastrulation stages have been identified by mutational analyses and reverse-genetic approaches, typically on a gene-by-gene basis. More recent studies have applied bioinformatic approaches to generate regulatory network models of gene interactions on a genome-wide scale. Such models have provided insights into the gene networks regulating pluripotency in embryonic and epiblast stem cells, as well as cell-lineage determination in vivo. Here, we review how regulatory networks constructed for different stem cell types relate to corresponding networks in vivo and provide insights into understanding the molecular regulation of the blastocyst-gastrula transition.

VISIONET: intuitive visualisation of overlapping transcription factor networks, with applications in cardiogenic gene discovery

BACKGROUND

Existing de novo software platforms have largely overlooked a valuable resource, the expertise of the intended biologist users. Typical data representations such as long gene lists, or highly dense and overlapping transcription factor networks often hinder biologists from relating these results to their expertise.

RESULTS

VISIONET, a streamlined visualisation tool built from experimental needs, enables biologists to transform large and dense overlapping transcription factor networks into sparse human-readable graphs via numerically filtering. The VISIONET interface allows users without a computing background to interactively explore and filter their data, and empowers them to apply their specialist knowledge on far more complex and substantial data sets than is currently possible. Applying VISIONET to the Tbx20-Gata4 transcription factor network led to the discovery and validation of Aldh1a2, an essential developmental gene associated with various important cardiac disorders, as a healthy adult cardiac fibroblast gene co-regulated by cardiogenic transcription factors Gata4 and Tbx20.

CONCLUSIONS

We demonstrate with experimental validations the utility of VISIONET for expertise-driven gene discovery that opens new experimental directions that would not otherwise have been identified.

Signaling via PI3K/FOXO1A pathway modulates formation and survival of human embryonic stem cell-derived endothelial cells

Vascular derivatives of human embryonic stem cells (hESC) are being developed as sources of tissue-specific cells for organ regeneration. However, identity of developmental pathways that modulate the specification of endothelial cells is not known yet. We studied phosphatidylinositol 3-kinase (PI3K)-Forkhead box O transcription factor 1A (FOXO1A) pathways during differentiation of hESC toward endothelial lineage and on proliferation, maturation, and cell death of hESC-derived endothelial cells (hESC-EC). During differentiation of hESC, expression of FOXO1A transcription factor was linked to the expression of a cluster of angiogenesis- and vascular remodeling-related genes. PI3K inhibitor LY294002 activated FOXO1A and induced formation of CD31(+) hESC-EC. In contrast, differentiating hESC with silenced FOXO1A by small interfering RNA (siRNA) showed lower mRNA levels of CD31 and angiopoietin2. LY294002 decreased proliferative activity of purified hESC-EC, while FOXO1A siRNA increased their proliferation. LY294002 inhibits migration and tube formation of hESC-EC; in contrast, FOXO1A siRNA increased in vitro tube formation activity of hESC-EC. After in vivo conditioning of cells in athymic nude rats, cells retain their low FOXO1A expression levels. PI3K/FOXO1A pathway is important for function and survival of hESC-EC and in the regulation of endothelial cell fate. Understanding these properties of hESC-EC may help in future applications for treatment of injured organs.

Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks

Mutual information (MI), a quantity describing the nonlinear dependence between two random variables, has been widely used to construct gene regulatory networks (GRNs). Despite its good performance, MI cannot separate the direct regulations from indirect ones among genes. Although the conditional mutual information (CMI) is able to identify the direct regulations, it generally underestimates the regulation strength, i.e. it may result in false negatives when inferring gene regulations. In this work, to overcome the problems, we propose a novel concept, namely conditional mutual inclusive information (CMI2), to describe the regulations between genes. Furthermore, with CMI2, we develop a new approach, namely CMI2NI (CMI2-based network inference), for reverse-engineering GRNs. In CMI2NI, CMI2 is used to quantify the mutual information between two genes given a third one through calculating the Kullback-Leibler divergence between the postulated distributions of including and excluding the edge between the two genes. The benchmark results on the GRNs from DREAM challenge as well as the SOS DNA repair network in Escherichia coli demonstrate the superior performance of CMI2NI. Specifically, even for gene expression data with small sample size, CMI2NI can not only infer the correct topology of the regulation networks but also accurately quantify the regulation strength between genes. As a case study, CMI2NI was also used to reconstruct cancer-specific GRNs using gene expression data from The Cancer Genome Atlas (TCGA). CMI2NI is freely accessible at http://www.comp-sysbio.org/cmi2ni.

Quantitative and logic modelling of molecular and gene networks

Behaviours of complex biomolecular systems are often irreducible to the elementary properties of their individual components. Explanatory and predictive mathematical models are therefore useful for fully understanding and precisely engineering cellular functions. The development and analyses of these models require their adaptation to the problems that need to be solved and the type and amount of available genetic or molecular data. Quantitative and logic modelling are among the main methods currently used to model molecular and gene networks. Each approach comes with inherent advantages and weaknesses. Recent developments show that hybrid approaches will become essential for further progress in synthetic biology and in the development of virtual organisms.

NAIL, a software toolset for inferring, analyzing and visualizing regulatory networks

The wide variety of published approaches for the problem of regulatory network inference makes using multiple inference algorithms complex and time-consuming. Network Analysis and Inference Library (NAIL) is a set of software tools to simplify the range of computational activities involved in regulatory network inference. It uses a modular approach to connect different network inference algorithms to the same visualization and network-based analyses. NAIL is technology-independent and includes an interface layer to allow easy integration of components into other applications.

AVAILABILITY AND IMPLEMENTATION

NAIL is implemented in MATLAB, runs on Windows, Linux and OSX, and is available from SourceForge at https://sourceforge.net/projects/nailsystemsbiology/ for all researchers to use.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Predictive modelling of gene expression from transcriptional regulatory elements

Predictive modelling of gene expression provides a powerful framework for exploring the regulatory logic underpinning transcriptional regulation. Recent studies have demonstrated the utility of such models in identifying dysregulation of gene and miRNA expression associated with abnormal patterns of transcription factor (TF) binding or nucleosomal histone modifications (HMs). Despite the growing popularity of such approaches, a comparative review of the various modelling algorithms and feature extraction methods is lacking. We define and compare three methods of quantifying pairwise gene-TF/HM interactions and discuss their suitability for integrating the heterogeneous chromatin immunoprecipitation (ChIP)-seq binding patterns exhibited by TFs and HMs. We then construct log-linear and ϵ-support vector regression models from various mouse embryonic stem cell (mESC) and human lymphoblastoid (GM12878) data sets, considering both ChIP-seq- and position weight matrix- (PWM)-derived in silico TF-binding. The two algorithms are evaluated both in terms of their modelling prediction accuracy and ability to identify the established regulatory roles of individual TFs and HMs. Our results demonstrate that TF-binding and HMs are highly predictive of gene expression as measured by mRNA transcript abundance, irrespective of algorithm or cell type selection and considering both ChIP-seq and PWM-derived TF-binding. As we encourage other researchers to explore and develop these results, our framework is implemented using open-source software and made available as a preconfigured bootable virtual environment.

Advances in Human Biology: Combining Genetics and Molecular Biophysics to Pave the Way for Personalized Diagnostics and Medicine

Advances in several biology-oriented initiatives such as genome sequencing and structural genomics, along with the progress made through traditional biological and biochemical research, have opened up a unique opportunity to better understand the molecular effects of human diseases. Human DNA can vary significantly from person to person and determines an individual’s physical characteristics and their susceptibility to diseases. Armed with an individual’s DNA sequence, researchers and physicians can check for defects known to be associated with certain diseases by utilizing various databases. However, for unclassified DNA mutations or in order to reveal molecular mechanism behind the effects, the mutations have to be mapped onto the corresponding networks and macromolecular structures and then analyzed to reveal their effect on the wild type properties of biological processes involved. Predicting the effect of DNA mutations on individual’s health is typically referred to as personalized or companion diagnostics. Furthermore, once the molecular mechanism of the mutations is revealed, the patient should be given drugs which are the most appropriate for the individual genome, referred to as pharmacogenomics. Altogether, the shift in focus in medicine towards more genomic-oriented practices is the foundation of personalized medicine. The progress made in these rapidly developing fields is outlined.

Nucleotide excision repair/transcription gene defects in the fetus and impaired TFIIH-mediated function in transcription in placenta leading to preeclampsia

BACKGROUND

Preeclampsia is a significant cause of maternal and fetal mortality and morbidity worldwide. We previously reported associations between trichothiodystrophy (TTD) nucleotide excision repair (NER) and transcription gene mutations in the fetus and the risk of gestational complications including preeclampsia. TTD NER/transcription genes, XPD, XPB and TTD-A, code for subunits of Transcription Factor (TF)IIH. Interpreting XPD mutations in the context of available biochemical data led us to propose adverse effects on CDK-activating kinase (CAK) subunit of TFIIH and TFIIH-mediated functions as a relevant mechanism in preeclampsia. In order to gain deeper insight into the underlying biologic mechanisms involving TFIIH-mediated functions in placenta, we analyzed NER/transcription and global gene expression profiles of normal and preeclamptic placentas and studied gene regulatory networks.

RESULTS

We found high expression of TTD NER/transcription genes in normal human placenta, above the mean of their expression in all organs. XPD and XPB were consistently expressed from 14 to 40 weeks gestation while expression of TTD-A was strongly negatively correlated (r=-0.7, P<0.0001) with gestational age. Analysis of gene expression patterns of placentas from a case-control study of preeclampsia using Algorithm for Reconstruction of Accurate Cellular Networks (ARACNE) revealed GTF2E1, a component of TFIIE which modulates TFIIH, among major regulators of differentially-expressed genes in preeclampsia. The basal transcription pathway was among the largest dysregulated protein-protein interaction networks in this preeclampsia dataset. Within the basal transcription pathway, significantly down-regulated genes besides GTF2E1 included those coding for the CAK complex of TFIIH, namely CDK7, CCNH, and MNAT1. Analysis of other relevant gene expression and gene regulatory network data also underscored the involvement of transcription pathways and identified JUNB and JUND (components of transcription factor AP-1) as transcription regulators of the network involving the TTD genes, GTF2E1, and selected gene regulators implicated in preeclampsia.

CONCLUSIONS

Our results indicate that TTD NER/transcription genes are expressed in placenta during gestational periods critical to preeclampsia development. Our overall findings suggest that impairment of TFIIH-mediated function in transcription in placenta is a likely mechanism leading to preeclampsia and provide etiologic clues which may be translated into therapeutic and preventive measures.

MIDER: network inference with mutual information distance and entropy reduction

The prediction of links among variables from a given dataset is a task referred to as network inference or reverse engineering. It is an open problem in bioinformatics and systems biology, as well as in other areas of science. Information theory, which uses concepts such as mutual information, provides a rigorous framework for addressing it. While a number of information-theoretic methods are already available, most of them focus on a particular type of problem, introducing assumptions that limit their generality. Furthermore, many of these methods lack a publicly available implementation. Here we present MIDER, a method for inferring network structures with information theoretic concepts. It consists of two steps: first, it provides a representation of the network in which the distance among nodes indicates their statistical closeness. Second, it refines the prediction of the existing links to distinguish between direct and indirect interactions and to assign directionality. The method accepts as input time-series data related to some quantitative features of the network nodes (such as e.g. concentrations, if the nodes are chemical species). It takes into account time delays between variables, and allows choosing among several definitions and normalizations of mutual information. It is general purpose: it may be applied to any type of network, cellular or otherwise. A Matlab implementation including source code and data is freely available (http://www.iim.csic.es/~gingproc/mider.html). The performance of MIDER has been evaluated on seven different benchmark problems that cover the main types of cellular networks, including metabolic, gene regulatory, and signaling. Comparisons with state of the art information–theoretic methods have demonstrated the competitive performance of MIDER, as well as its versatility. Its use does not demand any a priori knowledge from the user; the default settings and the adaptive nature of the method provide good results for a wide range of problems without requiring tuning.

Computational identification of potential transcriptional regulators of TGF-ß1 in human atherosclerotic arteries

TGF-ß is protective in atherosclerosis but deleterious in metastatic cancers. Our aim was to determine whether TGF-ß transcriptional regulation is tissue-specific in early atherosclerosis. The computational methods included 5 steps: (i) from microarray data of human atherosclerotic carotid tissue, to identify the 10 best co-expressed genes with TGFB1 (TGFB1 gene cluster), (ii) to choose the 11 proximal promoters, (iii) to predict the TFBS shared by the promoters, (iv) to identify the common TFs co-expressed with the TGFB1 gene cluster, and (v) to compare the common TFs in the early lesions to those identified in advanced atherosclerotic lesions and in various cancers. Our results show that EGR1, SP1 and KLF6 could be responsible for TGFB1 basal expression, KLF6 appearing specific to atherosclerotic lesions. Among the TFs co-expressed with the gene cluster, transcriptional activators (SLC2A4RG, MAZ) and repressors (ZBTB7A, PATZ1, ZNF263) could be involved in the fine-tuning of TGFB1 expression in atherosclerosis.

Inference of the Xenopus tropicalis embryonic regulatory network and spatial gene expression patterns

BACKGROUND

During embryogenesis, signaling molecules produced by one cell population direct gene regulatory changes in neighboring cells and influence their developmental fates and spatial organization. One of the earliest events in the development of the vertebrate embryo is the establishment of three germ layers, consisting of the ectoderm, mesoderm and endoderm. Attempts to measure gene expression in vivo in different germ layers and cell types are typically complicated by the heterogeneity of cell types within biological samples (i.e., embryos), as the responses of individual cell types are intermingled into an aggregate observation of heterogeneous cell types. Here, we propose a novel method to elucidate gene regulatory circuits from these aggregate measurements in embryos of the frog Xenopus tropicalis using gene network inference algorithms and then test the ability of the inferred networks to predict spatial gene expression patterns.

RESULTS

We use two inference models with different underlying assumptions that incorporate existing network information, an ODE model for steady-state data and a Markov model for time series data, and contrast the performance of the two models. We apply our method to both control and knockdown embryos at multiple time points to reconstruct the core mesoderm and endoderm regulatory circuits. Those inferred networks are then used in combination with known dorsal-ventral spatial expression patterns of a subset of genes to predict spatial expression patterns for other genes. Both models are able to predict spatial expression patterns for some of the core mesoderm and endoderm genes, but interestingly of different gene subsets, suggesting that neither model is sufficient to recapitulate all of the spatial patterns, yet they are complementary for the patterns that they do capture.

CONCLUSION

The presented methodology of gene network inference combined with spatial pattern prediction provides an additional layer of validation to elucidate the regulatory circuits controlling the spatial-temporal dynamics in embryonic development.

Systematic repression of transcription factors reveals limited patterns of gene expression changes in ES cells

Networks of transcription factors (TFs) are thought to determine and maintain the identity of cells. Here we systematically repressed each of 100 TFs with shRNA and carried out global gene expression profiling in mouse embryonic stem (ES) cells. Unexpectedly, only the repression of a handful of TFs significantly affected transcriptomes, which changed in two directions/trajectories: one trajectory by the repression of either Pou5f1 or Sox2; the other trajectory by the repression of either Esrrb, Sall4, Nanog, or Tcfap4. The data suggest that the trajectories of gene expression change are already preconfigured by the gene regulatory network and roughly correspond to extraembryonic and embryonic fates of cell differentiation, respectively. These data also indicate the robustness of the pluripotency gene network, as the transient repression of most TFs did not alter the transcriptomes.

Integration of steady-state and temporal gene expression data for the inference of gene regulatory networks

We develop a new regression algorithm, cMIKANA, for inference of gene regulatory networks from combinations of steady-state and time-series gene expression data. Using simulated gene expression datasets to assess the accuracy of reconstructing gene regulatory networks, we show that steady-state and time-series data sets can successfully be combined to identify gene regulatory interactions using the new algorithm. Inferring gene networks from combined data sets was found to be advantageous when using noisy measurements collected with either lower sampling rates or a limited number of experimental replicates. We illustrate our method by applying it to a microarray gene expression dataset from human umbilical vein endothelial cells (HUVECs) which combines time series data from treatment with growth factor TNF and steady state data from siRNA knockdown treatments. Our results suggest that the combination of steady-state and time-series datasets may provide better prediction of RNA-to-RNA interactions, and may also reveal biological features that cannot be identified from dynamic or steady state information alone. Finally, we consider the experimental design of genomics experiments for gene regulatory network inference and show that network inference can be improved by incorporating steady-state measurements with time-series data.

A network integration approach to predict conserved regulators related to pathogenicity of influenza and SARS-CoV respiratory viruses

Respiratory infections stemming from influenza viruses and the Severe Acute Respiratory Syndrome corona virus (SARS-CoV) represent a serious public health threat as emerging pandemics. Despite efforts to identify the critical interactions of these viruses with host machinery, the key regulatory events that lead to disease pathology remain poorly targeted with therapeutics. Here we implement an integrated network interrogation approach, in which proteome and transcriptome datasets from infection of both viruses in human lung epithelial cells are utilized to predict regulatory genes involved in the host response. We take advantage of a novel “crowd-based” approach to identify and combine ranking metrics that isolate genes/proteins likely related to the pathogenicity of SARS-CoV and influenza virus. Subsequently, a multivariate regression model is used to compare predicted lung epithelial regulatory influences with data derived from other respiratory virus infection models. We predicted a small set of regulatory factors with conserved behavior for consideration as important components of viral pathogenesis that might also serve as therapeutic targets for intervention. Our results demonstrate the utility of integrating diverse ‘omic datasets to predict and prioritize regulatory features conserved across multiple pathogen infection models.