Differential gene regulatory networks in development and disease

Network-based analysis of heterogeneous patient-matched brain and extracranial melanoma metastasis pairs reveals three homogeneous subgroups

Melanoma, the deadliest form of skin cancer, can metastasize to different organs. Molecular differences between brain and extracranial melanoma metastases are poorly understood. Here, promoter methylation and gene expression of 11 heterogeneous patient-matched pairs of brain and extracranial metastases were analyzed using melanoma-specific gene regulatory networks learned from public transcriptome and methylome data followed by network-based impact propagation of patient-specific alterations. This innovative data analysis strategy allowed to predict potential impacts of patient-specific driver candidate genes on other genes and pathways. The patient-matched metastasis pairs clustered into three robust subgroups with specific downstream targets with known roles in cancer, including melanoma (SG1: RBM38, BCL11B, SG2: GATA3, FES, SG3: SLAMF6, PYCARD). Patient subgroups and ranking of target gene candidates were confirmed in a validation cohort. Summarizing, computational network-based impact analyses of heterogeneous metastasis pairs predicted individual regulatory differences in melanoma brain metastases, cumulating into three consistent subgroups with specific downstream target genes.

Form, function, mind: What doesn't compute (and what might)

The applicability of computational and dynamical systems models to organisms is scrutinized, using examples from developmental biology and cognition. Developmental morphogenesis is dependent on the inherent material properties of developing animal (metazoan) tissues, a non-computational modality, but cell differentiation, which utilizes chromatin-based revisable memory banks and program-like function-calling, via the developmental gene co-expression system unique to the metazoans, has a quasi-computational basis. Multi-attractor dynamical models are argued to be misapplied to global properties of development, and it is suggested that along with computationalism, classic forms of dynamicism are similarly unsuitable to accounting for cognitive phenomena. Proposals are made for treating brains and other nervous tissues as novel forms of excitable matter with inherent properties which enable the intensification of cell-based basal cognition capabilities present throughout the tree of life. Finally, some connections are drawn between the viewpoint described here and active inference models of cognition, such as the Free Energy Principle.

A new suite of reporter vectors and a novel landing site survey system to study cis-regulatory elements in diverse insect species

Comparative analyses between traditional model organisms, such as the fruit fly Drosophila melanogaster, and more recent model organisms, such as the red flour beetle Tribolium castaneum, have provided a wealth of insight into conserved and diverged aspects of gene regulation. While the study of trans-regulatory components is relatively straightforward, the study of cis-regulatory elements (CREs, or enhancers) remains challenging outside of Drosophila. A central component of this challenge has been finding a core promoter suitable for enhancer-reporter assays in diverse insect species. Previously, we demonstrated that a Drosophila Synthetic Core Promoter (DSCP) functions in a cross-species manner in Drosophila and Tribolium. Given the over 300 million years of divergence between the Diptera and Coleoptera, we reasoned that DSCP-based reporter constructs will be useful when studying cis-regulation in a variety of insect models across the holometabola and possibly beyond. To this end, we sought to create a suite of new DSCP-based reporter vectors, leveraging dual compatibility with piggyBac and PhiC31-integration, the 3xP3 universal eye marker, GATEWAY cloning, different colors of reporters and markers, as well as Gal4-UAS binary expression. While all constructs functioned properly with a Tc-nub enhancer in Drosophila, complications arose with tissue-specific Gal4-UAS binary expression in Tribolium. Nevertheless, the functionality of these constructs across multiple holometabolous orders suggests a high potential compatibility with a variety of other insects. In addition, we present the piggyLANDR (piggyBac-LoxP AttP Neutralizable Destination Reporter) platform for the establishment of proper PhiC31 landing sites free from position effects. As a proof-of-principle, we demonstrated the workflow for piggyLANDR in Drosophila. The potential utility of these tools ranges from molecular biology research to pest and disease-vector management, and will help advance the study of gene regulation beyond traditional insect models.

miR-186 regulates epithelial-mesenchymal transformation to promote nasopharyngeal carcinoma metastasis by targeting ZEB1

OBJECTIVES

Nasopharyngeal carcinoma (NPC) is an aggressive epithelial cancer. The expression of miR-186 is decreased in a variety of malignancies and can promote the invasion and metastasis of cancer cells. This study aimed to explore the role and possible mechanism of miR-186 in the metastasis and epithelial-mesenchymal transformation (EMT) of NPC.

METHODS

The expression of miR-186 in NPC tissues and cells was detected by RT-PCR. Then, miR-186 mimic was used to transfect NPC cell lines C666-1 and CNE-2, and cell activity, invasion and migration were detected by CCK8, transwell and scratch assay, respectively. The expression of EMT-related proteins was analyzed by western blotting analysis. The binding relationship between miR-186 and target gene Zinc Finger E-Box Binding Homeobox 1 (ZEB1) was confirmed by double luciferase assay.

RESULTS

The expression of miR-186 in NPC was significantly decreased, and transfection of miR-186 mimic could significantly inhibit the cell activity, invasion, and migration, and regulate the protein expressions of E-cadherin, N-cadherin and vimentin in C666-1 and CNE-2 cells. Further experiments confirmed that miR-186 could directly target ZEB1 and negatively regulate its expression. In addition, ZEB1 has been confirmed to be highly expressed in NPC, and inhibition of ZEB1 could inhibit the activity, invasion, metastasis and EMT of NPC cells. And co-transfection of miR-186 mimic and si-ZEB1 could further inhibit the proliferation and metastasis of NPC.

CONCLUSION

miR-186 may inhibit the proliferation, metastasis and EMT of NPC by targeting ZEB1, and the miR-186/ZEB1 axis plays an important role in NPC.

k- Strong Inference Algorithm: A Hybrid Information Theory Based Gene Network Inference Algorithm

Gene networks allow researchers to understand the underlying mechanisms between diseases and genes while reducing the need for wet lab experiments. Numerous gene network inference (GNI) algorithms have been presented in the literature to infer accurate gene networks. We proposed a hybrid GNI algorithm, k-Strong Inference Algorithm (ksia), to infer more reliable and robust gene networks from omics datasets. To increase reliability, ksia integrates Pearson correlation coefficient (PCC) and Spearman rank correlation coefficient (SCC) scores to determine mutual information scores between molecules to increase diversity of relation predictions. To infer a more robust gene network, ksia applies three different elimination steps to remove redundant and spurious relations between genes. The performance of ksia was evaluated on microbe microarrays database in the overlap analysis with other GNI algorithms, namely ARACNE, C3NET, CLR, and MRNET. Ksia inferred less number of relations due to its strict elimination steps. However, ksia generally performed better on Escherichia coli (E.coli) and Saccharomyces cerevisiae (yeast) gene expression datasets due to F- measure and precision values. The integration of association estimator scores and three elimination stages slightly increases the performance of ksia based gene networks. Users can access ksia R package and user manual of package via https://github.com/ozgurcingiz/ksia .

scTIGER: A Deep-Learning Method for Inferring Gene Regulatory Networks from Case versus Control scRNA-seq Datasets

Inferring gene regulatory networks (GRNs) from single-cell RNA-seq (scRNA-seq) data is an important computational question to find regulatory mechanisms involved in fundamental cellular processes. Although many computational methods have been designed to predict GRNs from scRNA-seq data, they usually have high false positive rates and none infer GRNs by directly using the paired datasets of case-versus-control experiments. Here we present a novel deep-learning-based method, named scTIGER, for GRN detection by using the co-differential relationships of gene expression profiles in paired scRNA-seq datasets. scTIGER employs cell-type-based pseudotiming, an attention-based convolutional neural network method and permutation-based significance testing for inferring GRNs among gene modules. As state-of-the-art applications, we first applied scTIGER to scRNA-seq datasets of prostate cancer cells, and successfully identified the dynamic regulatory networks of AR, ERG, PTEN and ATF3 for same-cell type between prostatic cancerous and normal conditions, and two-cell types within the prostatic cancerous environment. We then applied scTIGER to scRNA-seq data from neurons with and without fear memory and detected specific regulatory networks for BDNF, CREB1 and MAPK4. Additionally, scTIGER demonstrates robustness against high levels of dropout noise in scRNA-seq data.

From time-series transcriptomics to gene regulatory networks: A review on inference methods

Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the ever increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. This review is intended to give an updated reference of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims, and experimental data.

siVAE: interpretable deep generative models for single-cell transcriptomes

Neural networks such as variational autoencoders (VAE) perform dimensionality reduction for the visualization and analysis of genomic data, but are limited in their interpretability: it is unknown which data features are represented by each embedding dimension. We present siVAE, a VAE that is interpretable by design, thereby enhancing downstream analysis tasks. Through interpretation, siVAE also identifies gene modules and hubs without explicit gene network inference. We use siVAE to identify gene modules whose connectivity is associated with diverse phenotypes such as iPSC neuronal differentiation efficiency and dementia, showcasing the wide applicability of interpretable generative models for genomic data analysis.

Differential co-expression network analysis with DCoNA reveals isomiR targeting aberrations in prostate cancer

MOTIVATION

One of the standard methods of high-throughput RNA sequencing analysis is differential expression. However, it does not detect changes in molecular regulation. In contrast to the standard differential expression analysis, differential co-expression one aims to detect pairs or clusters whose mutual expression changes between two conditions.

RESULTS

We developed Differential Co-expression Network Analysis (DCoNA)-an open-source statistical tool that allows one to identify pair interactions, which correlation significantly changes between two conditions. Comparing DCoNA with the state-of-the-art analog, we showed that DCoNA is a faster, more accurate and less memory-consuming tool. We applied DCoNA to prostate mRNA/miRNA-seq data collected from The Cancer Genome Atlas (TCGA) and compared predicted regulatory interactions of miRNA isoforms (isomiRs) and their target mRNAs between normal and cancer samples. As a result, almost all highly expressed isomiRs lost negative correlation with their targets in prostate cancer samples compared to ones without the pathology. One exception to this trend was the canonical isomiR of hsa-miR-93-5p acquiring cancer-specific targets. Further analysis showed that cancer aggressiveness simultaneously increased with the expression level of this isomiR in both TCGA primary tumor samples and 153 blood plasma samples of P. Hertsen Moscow Oncology Research Institute patients' cohort analyzed by miRNA microarrays.

AVAILABILITY AND IMPLEMENTATION

Source code and documentation of DCoNA are available at https://github.com/zhiyanov/DCoNA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Increased expression of GPX4 promotes the tumorigenesis of thyroid cancer by inhibiting ferroptosis and predicts poor clinical outcomes

BACKGROUND

Ferroptosis plays a critical role in suppressing cancer progression, and its essential regulator is glutathione peroxidase 4 (GPX4). High GPX4 expression can inhibit accumulation of iron, thus suppressing ferroptosis. However, its function in thyroid cancer has not been fully illuminated. Here, we explore the effect of GPX4 on thyroid cancer tumorigenesis and prognosis.

METHODS

Based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, GPX4 expression was investigated in cancer tissues and adjacent tissues. We determined the biological functions of GPX4-associated differentially expressed genes (DEGs) by using the "clusterProfiler" R package. In addition, the predictive value of GPX4 in thyroid cancer was assessed by using Cox regression analysis and nomograms. Finally, we conducted several in vitro experiments to determine the influence of GPX4 expression on proliferation and ferroptosis in thyroid cancer cells.

RESULTS

GPX4 expression was obviously elevated in thyroid cancer tissues compared with normal tissues. Biological function analysis indicated enrichment in muscle contraction, contractile fiber, metal ion transmembrane transporter activity, and complement and coagulation cascades. GPX4 overexpression was associated with stage T3-T4 and pathologic stage III-IV in thyroid cancer patients. Cox regression analysis indicated that GPX4 may be a risk factor for the overall survival of thyroid cancer patients. In vitro research showed that knockdown of GPX4 suppressed proliferation and induced ferroptosis in thyroid cancer cells.

CONCLUSIONS

GPX4 overexpression in thyroid cancer might play an essential role in tumorigenesis and may have prognostic value for thyroid cancer patients.

GPR176 Is a Biomarker for Predicting Prognosis and Immune Infiltration in Stomach Adenocarcinoma

Immunotherapy based on immune checkpoint inhibitors (ICIs) is considered to be a promising treatment for stomach adenocarcinoma (STAD), but only a minority of patients benefit from it. It is believed that the poor therapeutic efficacy is attributed to the complex tumor immune microenvironment (TIM) of STAD. Therefore, elucidating the specific regulatory mechanism of TIM in STAD is critical. Previous study suggests that GRP176 may be involved in regulating the pace of circadian behavior, and its role in tumors has not been reported. In this study, we first found that GPR176 was highly expressed in STAD and negatively correlated with patient prognosis. Next, we investigated the relationship between GPR176 and clinical characteristics, and the results showed that the stage is closely related to the level of GPR176. In addition, our further analysis found that GRP176 expression level was significantly correlated with chemotherapeutic drug sensitivity and ICI response. KEGG and GO analyses showed that GPR176 might be involved in stromal remodeling of STAD. Furthermore, we analyzed the association between GPR176 expression and immune implication, and the results revealed that GPR176 was negatively related to the infiltration of various immune cells. Interestingly, GPR176 induced the conversion of TIM while reducing the tumor immune burden (TMB). The expression of GRP176 is closely related to the level of various immunomodulators. Moreover, we performed univariate and multivariate regression analyses on the immunomodulators and finally obtained 4 genes (CRCR4, TNSF18, PDCD1, and TGFB1). Then, we constructed a GRP176-related immunomodulator prognostic model (GRIM) based on the above 4 genes, which was validated to have good predictive power. Finally, we developed a nomogram based on the risk score of GRIM and verified its accuracy. These results suggested that GPR176 is closely related to the prognosis and TIM of STAD. GPR176 may be a new potential target for immunotherapy in STAD.

Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets

There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead to the discovery of biomarkers and therapeutic targets. In this review, multi-omics data harmonization methods were introduced, and common approaches to molecular network inference were summarized. Our Prediction Logic Boolean Implication Networks (PLBINs) have advantages over other methods in constructing genome-scale multi-omics networks in bulk tumors and single cells in terms of computational efficiency, scalability, and accuracy. Based on the constructed multi-modal regulatory networks, graph theory network centrality metrics can be used in the prioritization of candidates for discovering biomarkers and therapeutic targets. Our approach to integrating multi-omics profiles in a patient cohort with large-scale patient EMRs such as the SEER-Medicare cancer registry combined with extensive external validation can identify potential biomarkers applicable in large patient populations. These methodologies form a conceptually innovative framework to analyze various available information from research laboratories and healthcare systems, accelerating the discovery of biomarkers and therapeutic targets to ultimately improve cancer patient survival outcomes.

Learning in Transcriptional Network Models: Computational Discovery of Pathway-Level Memory and Effective Interventions

Trainability, in any substrate, refers to the ability to change future behavior based on past experiences. An understanding of such capacity within biological cells and tissues would enable a particularly powerful set of methods for prediction and control of their behavior through specific patterns of stimuli. This top-down mode of control (as an alternative to bottom-up modification of hardware) has been extensively exploited by computer science and the behavioral sciences; in biology however, it is usually reserved for organism-level behavior in animals with brains, such as training animals towards a desired response. Exciting work in the field of basal cognition has begun to reveal degrees and forms of unconventional memory in non-neural tissues and even in subcellular biochemical dynamics. Here, we characterize biological gene regulatory circuit models and protein pathways and find them capable of several different kinds of memory. We extend prior results on learning in binary transcriptional networks to continuous models and identify specific interventions (regimes of stimulation, as opposed to network rewiring) that abolish undesirable network behavior such as drug pharmacoresistance and drug sensitization. We also explore the stability of created memories by assessing their long-term behavior and find that most memories do not decay over long time periods. Additionally, we find that the memory properties are quite robust to noise; surprisingly, in many cases noise actually increases memory potential. We examine various network properties associated with these behaviors and find that no one network property is indicative of memory. Random networks do not show similar memory behavior as models of biological processes, indicating that generic network dynamics are not solely responsible for trainability. Rational control of dynamic pathway function using stimuli derived from computational models opens the door to empirical studies of proto-cognitive capacities in unconventional embodiments and suggests numerous possible applications in biomedicine, where behavior shaping of pathway responses stand as a potential alternative to gene therapy.

Network-based approaches for modeling disease regulation and progression

Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid advances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses. Consequently, various modeling techniques, including network enrichment, differential network extraction, and network inference, have proven to be useful for gaining new mechanistic insights. We provide an overview of recent network-based methods and their core ideas to facilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medicine. We further discuss current challenges and provide perspectives in the field, highlighting the need for more integrative and dynamic network approaches to model disease development and progression.

Inherency and agency in the origin and evolution of biological functions

Although discussed by 20th century philosophers in terms drawn from the sciences of non-living systems, in recent decades biological function has been considered in relationship to organismal capability and purpose. Bringing two phenomena generally neglected in evolutionary theory (i.e. inherency and agency) to bear on questions of function leads to a rejection of the adaptationist ‘selected effects’ notion of biological function. I review work showing that organisms such as the placozoans can thrive with almost no functional embellishments beyond those of their constituent cells and physical properties of their simple tissues. I also discuss work showing that individual tissue cells and their artificial aggregates exhibit agential behaviours that are unprecedented in the histories of their respective lineages. I review findings on the unique metazoan mechanism of developmental gene expression that has recruited, during evolution, inherent ancestral cellular functionalities into specialized cell types and organs of the different animal groups. I conclude that most essential functions in animal species are inherent to the cells from which they evolved, not selected effects, and that many of the others are optional ‘add-ons’, a status inimical to fitness-based models of evolution positing that traits emerge from stringent cycles of selection to meet external challenges.

Transcriptome Profile Reveals Genetic and Metabolic Mechanisms Related to Essential Fatty Acid Content of Intramuscular Longissimus thoracis in Nellore Cattle

Beef is a source of essential fatty acids (EFA), linoleic (LA) and alpha-linolenic (ALA) acids, which protect against inflammatory and cardiovascular diseases in humans. However, the intramuscular EFA profile in cattle is a complex and polygenic trait. Thus, this study aimed to identify potential regulatory genes of the essential fatty acid profile in Longissimus thoracis of Nellore cattle finished in feedlot. Forty-four young bulls clustered in four groups of fifteen animals with extreme values for each FA were evaluated through differentially expressed genes (DEG) analysis and two co-expression methodologies (WGCNA and PCIT). We highlight the ECHS1, IVD, ASB5, and ERLIN1 genes and the TF NFIA, indicated in both FA. Moreover, we associate the NFYA, NFYB, PPARG, FASN, and FADS2 genes with LA, and the RORA and ELOVL5 genes with ALA. Furthermore, the functional enrichment analysis points out several terms related to FA metabolism. These findings contribute to our understanding of the genetic mechanisms underlying the beef EFA profile in Nellore cattle finished in feedlot.

NSCGRN: a network structure control method for gene regulatory network inference

Accurate inference of gene regulatory networks (GRNs) is an essential premise for understanding pathogenesis and curing diseases. Various computational methods have been developed for GRN inference, but the identification of redundant regulation remains a challenge faced by researchers. Although combining global and local topology can identify and reduce redundant regulations, the topologies' specific forms and cooperation modes are unclear and real regulations may be sacrificed. Here, we propose a network structure control method [network-structure-controlling-based GRN inference method (NSCGRN)] that stipulates the global and local topology's specific forms and cooperation mode. The method is carried out in a cooperative mode of 'global topology dominates and local topology refines'. Global topology requires layering and sparseness of the network, and local topology requires consistency of the subgraph association pattern with the network motifs (fan-in, fan-out, cascade and feedforward loop). Specifically, an ordered gene list is obtained by network topology centrality sorting. A Bernaola-Galvan mutation detection algorithm applied to the list gives the hierarchy of GRNs to control the upstream and downstream regulations within the global scope. Finally, four network motifs are integrated into the hierarchy to optimize local complex regulations and form a cooperative mode where global and local topologies play the dominant and refined roles, respectively. NSCGRN is compared with state-of-the-art methods on three different datasets (six networks in total), and it achieves the highest F1 and Matthews correlation coefficient. Experimental results show its unique advantages in GRN inference.

Transcriptomic profile of longissimus thoracis associated with fatty acid content in Nellore beef cattle

The beef fatty acid (FA) profile has the potential to impact human health, and displays polygenic and complex features. This study aimed to identify the transcriptomic FA profile in the longissimus thoracis muscle in Nellore beef cattle finished in feedlot. Forty-four young bulls were sampled to assess the beef FA profile by considering 14 phenotypes and including differentially expressed genes (DEG), co-expressed (COE), and differentially co-expressed genes (DCO) analyses. All samples (n = 44) were used for COE analysis, whereas 30 samples with extreme phenotypes for the beef FA profile were used for DEG and DCO. A total of 912 DEG were identified, and the polyunsaturated (n = 563) and unsaturated ω-3 (n = 346) FA sums groups were the most frequently observed. The COE analyses identified three modules, of which the blue module (n = 1776) was correlated with eight of 14 FA phenotypes. Also, 759 DCO genes were listed, and the oleic acid (n = 358) and monounsaturated fatty acids sum (n = 120) were the most frequent. Furthermore, 243 and 13, 319 and seven, and 173 and 12 gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways were enriched respectively for the DEG, COE, and DCO analyses. Combining the results, we highlight the unexplored GIPC2, ASB5, and PPP5C genes in cattle. Besides LIPE and INSIG2 genes in COE modules, the ACSL3, ECI1, DECR2, FITM1, and SDHB genes were signaled in at least two analyses. These findings contribute to understand the genetic mechanisms underlying the beef FA profile in Nellore beef cattle finished in feedlot.

Generation and network analysis of an RNA-seq transcriptional atlas for the rat

The laboratory rat is an important model for biomedical research. To generate a comprehensive rat transcriptomic atlas, we curated and downloaded 7700 rat RNA-seq datasets from public repositories, downsampled them to a common depth and quantified expression. Data from 585 rat tissues and cells, averaged from each BioProject, can be visualized and queried at http://biogps.org/ratatlas. Gene co-expression network (GCN) analysis revealed clusters of transcripts that were tissue or cell type restricted and contained transcription factors implicated in lineage determination. Other clusters were enriched for transcripts associated with biological processes. Many of these clusters overlap with previous data from analysis of other species, while some (e.g. expressed specifically in immune cells, retina/pineal gland, pituitary and germ cells) are unique to these data. GCN analysis on large subsets of the data related specifically to liver, nervous system, kidney, musculoskeletal system and cardiovascular system enabled deconvolution of cell type-specific signatures. The approach is extensible and the dataset can be used as a point of reference from which to analyse the transcriptomes of cell types and tissues that have not yet been sampled. Sets of strictly co-expressed transcripts provide a resource for critical interpretation of single-cell RNA-seq data.

Data analysis methods for defining biomarkers from omics data

Omics mainly includes genomics, epigenomics, transcriptomics, proteomics and metabolomics. The rapid development of omics technology has opened up new ways to study disease diagnosis and prognosis and to define prospective information of complex diseases. Since omics data are usually large and complex, the method used to analyze the data and to define important information is crucial in omics study. In this review, we focus on advances in biomarker discovery methods based on omics data in the last decade, and categorize them as individual feature analysis, combinatorial feature analysis and network analysis. We also discuss the challenges and perspectives in this field.

Novel cancer subtyping method based on patient-specific gene regulatory network

The identification of cancer subtypes is important for the understanding of tumor heterogeneity. In recent years, numerous computational methods have been proposed for this problem based on the multi-omics data of patients. It is widely accepted that different cancer subtypes are induced by different molecular regulatory networks. However, only a few incorporate the differences between their molecular systems into the identification processes. In this study, we present a novel method to identify cancer subtypes based on patient-specific molecular systems. Our method realizes this by quantifying patient-specific gene networks, which are estimated from their transcriptome data, and by clustering their quantified networks. Comprehensive analyses of The Cancer Genome Atlas (TCGA) datasets applied to our method confirmed that they were able to identify more clinically meaningful cancer subtypes than the existing subtypes and found that the identified subtypes comprised different molecular features. Our findings also show that the proposed method can identify the novel cancer subtypes even with single omics data, which cannot otherwise be captured by existing methods using multi-omics data.

Adaptation and compensation in a bacterial gene regulatory network evolving under antibiotic selection

Gene regulatory networks allow organisms to generate coordinated responses to environmental challenges. In bacteria, regulatory networks are re-wired and re-purposed during evolution, though the relationship between selection pressures and evolutionary change is poorly understood. In this study, we discover that the early evolutionary response of Escherichia coli to the antibiotic trimethoprim involves derepression of PhoPQ signaling, an Mg²⁺-sensitive two-component system, by inactivation of the MgrB feedback-regulatory protein. We report that derepression of PhoPQ confers trimethoprim-tolerance to E. coli by hitherto unrecognized transcriptional upregulation of dihydrofolate reductase (DHFR), target of trimethoprim. As a result, mutations in mgrB precede and facilitate the evolution of drug resistance. Using laboratory evolution, genome sequencing, and mutation re-construction, we show that populations of E. coli challenged with trimethoprim are faced with the evolutionary ‘choice’ of transitioning from tolerant to resistant by mutations in DHFR, or compensating for the fitness costs of PhoPQ derepression by inactivating the RpoS sigma factor, itself a PhoPQ-target. Outcomes at this evolutionary branch-point are determined by the strength of antibiotic selection, such that high pressures favor resistance, while low pressures favor cost compensation. Our results relate evolutionary changes in bacterial gene regulatory networks to strength of selection and provide mechanistic evidence to substantiate this link.

Network analysis with biological data of cancer patients: A scoping review

BACKGROUND & OBJECTIVE

Network Analysis (NA) is a mathematical method that allows exploring relations between units and representing them as a graph. Although NA was initially related to social sciences, the past two decades was introduced in Bioinformatics. The recent growth of the networks' use in biological data analysis reveals the need to further investigate this area. In this work, we attempt to identify the use of NA with biological data, and specifically: (a) what types of data are used and whether they are integrated or not, (b) what is the purpose of this analysis, predictive or descriptive, and (c) the outcome of such analyses, specifically in cancer diseases.

METHODS & MATERIALS

The literature review was conducted on two databases, PubMed & IEEE, and was restricted to journal articles of the last decade (January 2010 - December 2019). At a first level, all articles were screened by title and abstract, and at a second level the screening was conducted by reading the full text article, following the predefined inclusion & exclusion criteria leading to 131 articles of interest. A table was created with the information of interest and was used for the classification of the articles. The articles were initially classified to analysis studies and studies that propose a new algorithm or methodology. Each one of these categories was further screened by the following clustering criteria: (a) data used, (b) study purpose, (c) study outcome. Specifically for the studies proposing a new algorithm, the novelty presented in each one was detected.

RESULTS

& Conclusions: In the past five years researchers are focusing on creating new algorithms and methodologies to enhance this field. The articles' classification revealed that only 25% of the analyses are integrating multi-omics data, although 50% of the new algorithms developed follow this integrative direction. Moreover, only 20% of the analyses and 10% of the newly developed methodologies have a predictive purpose. Regarding the result of the works reviewed, 75% of the studies focus on identifying, prognostic or not, gene signatures. Concluding, this review revealed the need for deploying predictive and multi-omics integrative algorithms and methodologies that can be used to enhance cancer diagnosis, prognosis and treatment.

Dynamic changes in gene-to-gene regulatory networks in response to SARS-CoV-2 infection

The current pandemic of SARS-CoV-2 has caused extensive damage to society. The characterization of SARS-CoV-2 profiles has been addressed by researchers globally with the aim of resolving this disruptive crisis. This investigation process is indispensable to understand how SARS-CoV-2 behaves in human host cells. However, little is known about the systematic molecular mechanisms involved in the effects of SARS-CoV-2 infection on human host cells. Here, we present gene-to-gene regulatory networks in response to SARS-CoV-2 using a Bayesian network. We examined the dynamic changes in the SARS-CoV-2-purturbated networks established by our proposed framework for gene network analysis, thus revealing that interferon signaling gradually switched to the subsequent inflammatory cytokine signaling cascades. Furthermore, we succeeded in capturing a COVID-19 patient-specific network in which transduction of these signals was concurrently induced. This enabled us to explore the local regulatory systems influenced by SARS-CoV-2 in host cells more precisely at an individual level. Our panel of network analyses has provided new insights into SARS-CoV-2 research from the perspective of cellular systems.

Comparing Statistical Tests for Differential Network Analysis of Gene Modules

Genes often work together to perform complex biological processes, and "networks" provide a versatile framework for representing the interactions between multiple genes. Differential network analysis (DiNA) quantifies how this network structure differs between two or more groups/phenotypes (e.g., disease subjects and healthy controls), with the goal of determining whether differences in network structure can help explain differences between phenotypes. In this paper, we focus on gene co-expression networks, although in principle, the methods studied can be used for DiNA for other types of features (e.g., metabolome, epigenome, microbiome, proteome, etc.). Three common applications of DiNA involve (1) testing whether the connections to a single gene differ between groups, (2) testing whether the connection between a pair of genes differs between groups, or (3) testing whether the connections within a "module" (a subset of 3 or more genes) differs between groups. This article focuses on the latter, as there is a lack of studies comparing statistical methods for identifying differentially co-expressed modules (DCMs). Through extensive simulations, we compare several previously proposed test statistics and a new p-norm difference test (PND). We demonstrate that the true positive rate of the proposed PND test is competitive with and often higher than the other methods, while controlling the false positive rate. The R package discoMod (differentially co-expressed modules) implements the proposed method and provides a full pipeline for identifying DCMs: clustering tools to derive gene modules, tests to identify DCMs, and methods for visualizing the results.

DysRegSig: an R package for identifying gene dysregulations and building mechanistic signatures in cancer

Summary

Dysfunctional regulations of gene expression programs relevant to fundamental cell processes can drive carcinogenesis. Therefore, systematically identifying dysregulation events is an effective path for understanding carcinogenesis and provides insightful clues to build predictive signatures with mechanistic interpretability for cancer precision medicine. Here, we implemented a machine learning-based gene dysregulation analysis framework in an R package, DysRegSig, which is capable of exploring gene dysregulations from high-dimensional data and building mechanistic signature based on gene dysregulations. DysRegSig can serve as an easy-to-use tool to facilitate gene dysregulation analysis and follow-up analysis.

Availability and implementation

The source code and user’s guide of DysRegSig are freely available at Github: https://github.com/SCBIT-YYLab/DysRegSig.

Supplementary information

Supplementary data are available at Bioinformatics online.

High-Throughput Transcriptomics Platform for Screening Environmental Chemicals

New approach methodologies (NAMs) that efficiently provide information about chemical hazard without using whole animals are needed to accelerate the pace of chemical risk assessments. Technological advancements in gene expression assays have made in vitro high-throughput transcriptomics (HTTr) a feasible option for NAMs-based hazard characterization of environmental chemicals. In this study, we evaluated the Templated Oligo with Sequencing Readout (TempO-Seq) assay for HTTr concentration-response screening of a small set of chemicals in the human-derived MCF7 cell model. Our experimental design included a variety of reference samples and reference chemical treatments in order to objectively evaluate TempO-Seq assay performance. To facilitate analysis of these data, we developed a robust and scalable bioinformatics pipeline using open-source tools. We also developed a novel gene expression signature-based concentration-response modeling approach and compared the results to a previously implemented workflow for concentration-response analysis of transcriptomics data using BMDExpress. Analysis of reference samples and reference chemical treatments demonstrated highly reproducible differential gene expression signatures. In addition, we found that aggregating signals from individual genes into gene signatures prior to concentration-response modeling yielded in vitro transcriptional biological pathway altering concentrations (BPACs) that were closely aligned with previous ToxCast high-throughput screening assays. Often these identified signatures were associated with the known molecular target of the chemicals in our test set as the most sensitive components of the overall transcriptional response. This work has resulted in a novel and scalable in vitro HTTr workflow that is suitable for high-throughput hazard evaluation of environmental chemicals.

Gene regulatory networks exhibit several kinds of memory: quantification of memory in biological and random transcriptional networks

Gene regulatory networks (GRNs) process important information in developmental biology and biomedicine. A key knowledge gap concerns how their responses change over time. Hypothesizing long-term changes of dynamics induced by transient prior events, we created a computational framework for defining and identifying diverse types of memory in candidate GRNs. We show that GRNs from a wide range of model systems are predicted to possess several types of memory, including Pavlovian conditioning. Associative memory offers an alternative strategy for the biomedical use of powerful drugs with undesirable side effects, and a novel approach to understanding the variability and time-dependent changes of drug action. We find evidence of natural selection favoring GRN memory. Vertebrate GRNs overall exhibit more memory than invertebrate GRNs, and memory is most prevalent in differentiated metazoan cell networks compared with undifferentiated cells. Timed stimuli are a powerful alternative for biomedical control of complex in vivo dynamics without genomic editing or transgenes.

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Gene regulatory networks' dynamics are modified by transient stimuli

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GRNs have several different types of memory, including associative conditioning

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Evolution favored GRN memory, and differentiated cells have the most memory capacity

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Training GRNs offers a novel biomedical strategy not dependent on genetic rewiring

Quantitative Proteomics Reveals Association of Neuron Projection Development Genes ARF4, KIF5B, and RAB8A With Hirschsprung Disease

Hirschsprung disease (HSCR) is a heterogeneous group of neurocristopathy characterized by the absence of the enteric ganglia along a variable length of the intestine. Genetic defects play a major role in the pathogenesis of HSCR, whereas family studies of pathogenic variants in all the known genes (loci) only demonstrate incomplete penetrance and variable expressivity for unknown reasons. Here, we applied large-scale, quantitative proteomics of human colon tissues from 21 patients using isobaric tags for relative and absolute quantification. method followed by bioinformatics analysis. Selected findings were confirmed by parallel reaction monitoring verification. At last, the interesting differentially expressed proteins were confirmed by Western blot. A total of 5341 proteins in human colon tissues were identified. Among them, 664 proteins with >1.2-fold difference were identified in six groups: groups A1 and A2 pooled protein from the ganglionic and aganglionic colon of male, long-segment HSCR patients (n = 7); groups B1 and B2 pooled protein from the ganglionic and aganglionic colon of male, short-segment HSCR patients (n = 7); and groups C1 and C2 pooled protein from the ganglionic and aganglionic colon of female, short-segment HSCR patients (n = 7). Based on these analyses, 49 proteins from five pathways were selected for parallel reaction monitoring verification, including ribosome, endocytosis, spliceosome, oxidative phosphorylation, and cell adhesion. The downregulation of three neuron projection development genes ARF4, KIF5B, and RAB8A in the aganglionic part of the colon was verified in 15 paired colon samples using Western blot. The findings of this study will shed new light on the pathogenesis of HSCR and facilitate the development of therapeutic targets.

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Large-scale, quantitative proteomics of human colon tissues from Hirschsprung disease patients.

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Parallel reaction monitoring, Western blotting, and immunohistochemical staining for validation.

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Four pathways related to differentially expressed proteins: ribosome, endocytosis, spliceosome, and axon guidance.

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Downregulation of ARF4, KIF5B, and RAB8A in the aganglionic (stenotic) colon segment.

Automatic Gene Function Prediction in the 2020's

The current rate at which new DNA and protein sequences are being generated is too fast to experimentally discover the functions of those sequences, emphasizing the need for accurate Automatic Function Prediction (AFP) methods. AFP has been an active and growing research field for decades and has made considerable progress in that time. However, it is certainly not solved. In this paper, we describe challenges that the AFP field still has to overcome in the future to increase its applicability. The challenges we consider are how to: (1) include condition-specific functional annotation, (2) predict functions for non-model species, (3) include new informative data sources, (4) deal with the biases of Gene Ontology (GO) annotations, and (5) maximally exploit the GO to obtain performance gains. We also provide recommendations for addressing those challenges, by adapting (1) the way we represent proteins and genes, (2) the way we represent gene functions, and (3) the algorithms that perform the prediction from gene to function. Together, we show that AFP is still a vibrant research area that can benefit from continuing advances in machine learning with which AFP in the 2020s can again take a large step forward reinforcing the power of computational biology.

Expression Quantitative Trait Locus Mapping in Pulmonary Arterial Hypertension

Expression quantitative trait loci (eQTL) can provide a link between disease susceptibility variants discovered by genetic association studies and biology. To date, eQTL mapping studies have been primarily conducted in healthy individuals from population-based cohorts. Genetic effects have been known to be context-specific and vary with changing environmental stimuli. We conducted a transcriptome- and genome-wide eQTL mapping study in a cohort of patients with idiopathic or heritable pulmonary arterial hypertension (PAH) using RNA sequencing (RNAseq) data from whole blood. We sought confirmation from three published population-based eQTL studies, including the GTEx Project, and followed up potentially novel eQTL not observed in the general population. In total, we identified 2314 eQTL of which 90% were cis-acting and 75% were confirmed by at least one of the published studies. While we observed a higher GWAS trait colocalization rate among confirmed eQTL, colocalisation rate of novel eQTL reported for lung-related phenotypes was twice as high as that of confirmed eQTL. Functional enrichment analysis of genes with novel eQTL in PAH highlighted immune-related processes, a suspected contributor to PAH. These potentially novel eQTL specific to or active in PAH could be useful in understanding genetic risk factors for other diseases that share common mechanisms with PAH.

Exploiting transfer learning for the reconstruction of the human gene regulatory network

MOTIVATION

The reconstruction of gene regulatory networks (GRNs) from gene expression data has received increasing attention in recent years, due to its usefulness in the understanding of regulatory mechanisms involved in human diseases. Most of the existing methods reconstruct the network through machine learning approaches, by analyzing known examples of interactions. However, (i) they often produce poor results when the amount of labeled examples is limited, or when no negative example is available and (ii) they are not able to exploit information extracted from GRNs of other (better studied) related organisms, when this information is available.

RESULTS

In this paper, we propose a novel machine learning method that overcomes these limitations, by exploiting the knowledge about the GRN of a source organism for the reconstruction of the GRN of the target organism, by means of a novel transfer learning technique. Moreover, the proposed method is natively able to work in the positive-unlabeled setting, where no negative example is available, by fruitfully exploiting a (possibly large) set of unlabeled examples. In our experiments, we reconstructed the human GRN, by exploiting the knowledge of the GRN of Mus musculus. Results showed that the proposed method outperforms state-of-the-art approaches and identifies previously unknown functional relationships among the analyzed genes.

AVAILABILITY AND IMPLEMENTATION

http://www.di.uniba.it/∼mignone/systems/biosfer/index.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of Prognostic Biomarkers for Multiple Solid Tumors Using a Human Villi Development Model

The processes of embryonic development that rely on epithelial-mesenchymal transition (EMT) for the implantation of trophoblast cells are co-opted by tumors, reflecting their inherent uncontrolled characteristics and leading to invasion and metastasis. Although tumorigenesis and embryogenesis have similar EMT characteristics, trophoblasts have been shown to exhibit "physiological metastasis" or be "pseudo-malignant," resulting in different outcomes. The gene co-expression network is the basis of embryonic development and tumorigenesis. We hypothesize that if the gene co-expression network in tumors is "off-track" from that in villi, it is more likely to develop into malignant tumors and have a worse prognosis, and we proposed the "off-track theory" for the first time. In this study, we examined gene co-expression networks in villi and multiple solid tumors. Through network functional enrichment analyses, we found that most tumors and villi exhibited a significantly enriched EMT, but the genes that performed this function were not identical. Then, we identified the "off-track genes" in the EMT-related gene interaction network using the "off-track theory," and through survival analysis, we discovered that the risk score of "off-track genes" was associated with poor survival of cancer patients. Our study indicated that villi development is a reliable and strictly regulated model that can illuminate the trajectory of human cancer development and that the gene co-expression networks in tumor development are "off-track" from those in villi. These "off-track genes" may have a substantial impact on tumor development and could reveal novel prognostic biomarkers.

Distinctions between sex and time in patterns of DNA methylation across puberty

BACKGROUND

There are significant sex differences in human physiology and disease; the genomic sources of these differences, however, are not well understood. During puberty, a drastic neuroendocrine shift signals physical changes resulting in robust sex differences in human physiology. Here, we explore how shifting patterns of DNA methylation may inform these pathways of biological plasticity during the pubertal transition. In this study we analyzed DNA methylation (DNAm) in saliva at two time points across the pubertal transition within the same individuals. Our purpose was to compare two domains of DNAm patterns that may inform processes of sexual differentiation 1) sex related sites, which demonstrated differences between males from females and 2) time related sites in which DNAm shifted significantly between timepoints. We further explored the correlated network structure sex and time related DNAm networks and linked these patterns to pubertal stage, assays of salivary testosterone, a reliable diagnostic of free, unbound hormone that is available to act on target tissues, and overlap with androgen response elements.

RESULTS

Sites that differed by biological sex were largely independent of sites that underwent change across puberty. Time-related DNAm sites, but not sex-related sites, formed correlated networks that were associated with pubertal stage. Both time and sex DNAm networks reflected salivary testosterone levels that were enriched for androgen response elements, with sex-related DNAm networks being informative of testosterone levels above and beyond biological sex later in the pubertal transition.

CONCLUSIONS

These results inform our understanding of the distinction between sex- and time-related differences in DNAm during the critical period of puberty and highlight a novel linkage between correlated patterns of sex-related DNAm and levels of salivary testosterone.

Functional Annotation of the Transcriptome of the Pig, Sus scrofa, Based Upon Network Analysis of an RNAseq Transcriptional Atlas

The domestic pig (Sus scrofa) is both an economically important livestock species and a model for biomedical research. Two highly contiguous pig reference genomes have recently been released. To support functional annotation of the pig genomes and comparative analysis with large human transcriptomic data sets, we aimed to create a pig gene expression atlas. To achieve this objective, we extended a previous approach developed for the chicken. We downloaded RNAseq data sets from public repositories, down-sampled to a common depth, and quantified expression against a reference transcriptome using the mRNA quantitation tool, Kallisto. We then used the network analysis tool Graphia to identify clusters of transcripts that were coexpressed across the merged data set. Consistent with the principle of guilt-by-association, we identified coexpression clusters that were highly tissue or cell-type restricted and contained transcription factors that have previously been implicated in lineage determination. Other clusters were enriched for transcripts associated with biological processes, such as the cell cycle and oxidative phosphorylation. The same approach was used to identify coexpression clusters within RNAseq data from multiple individual liver and brain samples, highlighting cell type, process, and region-specific gene expression. Evidence of conserved expression can add confidence to assignment of orthology between pig and human genes. Many transcripts currently identified as novel genes with ENSSSCG or LOC IDs were found to be coexpressed with annotated neighbouring transcripts in the same orientation, indicating they may be products of the same transcriptional unit. The meta-analytic approach to utilising public RNAseq data is extendable to include new data sets and new species and provides a framework to support the Functional Annotation of Animals Genomes (FAANG) initiative.

System-Based Differential Gene Network Analysis for Characterizing a Sample-Specific Subnetwork

Gene network estimation is a method key to understanding a fundamental cellular system from high throughput omics data. However, the existing gene network analysis relies on having a sufficient number of samples and is required to handle a huge number of nodes and estimated edges, which remain difficult to interpret, especially in discovering the clinically relevant portions of the network. Here, we propose a novel method to extract a biomedically significant subnetwork using a Bayesian network, a type of unsupervised machine learning method that can be used as an explainable and interpretable artificial intelligence algorithm. Our method quantifies sample specific networks using our proposed Edge Contribution value (ECv) based on the estimated system, which realizes condition-specific subnetwork extraction using a limited number of samples. We applied this method to the Epithelial-Mesenchymal Transition (EMT) data set that is related to the process of metastasis and thus prognosis in cancer biology. We established our method-driven EMT network representing putative gene interactions. Furthermore, we found that the sample-specific ECv patterns of this EMT network can characterize the survival of lung cancer patients. These results show that our method unveils the explainable network differences in biological and clinical features through artificial intelligence technology.

The detection and analysis of differential regulatory communities in lung cancer

The tumorgenesis process of lung cancer involves the regulatory dysfunctions of multiple pathways. Although many signaling pathways have been identified to be associated with lung cancer, there are little quantitative models of how inactions between genes change during the process from normal to cancer. These changes belong to different dynamic co-expressions patterns. We quantitatively analyzed differential co-expression of gene pairs in four datasets. Each dataset included a large number of lung cancer and normal samples. By overlapping their results, we got 14 highly confident gene pairs with consistent co-expression change patterns. Some of they, such as ARHGAP30 and GIMAP4, had been recorded in STRING network database while some of them were novel discoveries, such as C9orf135 and MORN5, TEKT1 and TSPAN1 were positively correlated in both normal and cancer but more correlated in normal than cancer. These gene pairs revealed the underlying mechanisms of lung cancer occurrence.

Gene regulatory network inference resources: A practical overview

Transcriptional regulation is a fundamental molecular mechanism involved in almost every aspect of life, from homeostasis to development, from metabolism to behavior, from reaction to stimuli to disease progression. In recent years, the concept of Gene Regulatory Networks (GRNs) has grown popular as an effective applied biology approach for describing the complex and highly dynamic set of transcriptional interactions, due to its easy-to-interpret features. Since cataloguing, predicting and understanding every GRN connection in all species and cellular contexts remains a great challenge for biology, researchers have developed numerous tools and methods to infer regulatory processes. In this review, we catalogue these methods in six major areas, based on the dominant underlying information leveraged to infer GRNs: Coexpression, Sequence Motifs, Chromatin Immunoprecipitation (ChIP), Orthology, Literature and Protein-Protein Interaction (PPI) specifically focused on transcriptional complexes. The methods described here cover a wide range of user-friendliness: from web tools that require no prior computational expertise to command line programs and algorithms for large scale GRN inferences. Each method for GRN inference described herein effectively illustrates a type of transcriptional relationship, with many methods being complementary to others. While a truly holistic approach for inferring and displaying GRNs remains one of the greatest challenges in the field of systems biology, we believe that the integration of multiple methods described herein provides an effective means with which experimental and computational biologists alike may obtain the most complete pictures of transcriptional relationships. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

The Putative Association of TOB1-AS1 Long Non-coding RNA with Immune Tolerance: A Study on Multiple Sclerosis Patients

The hallmark of multiple sclerosis (MS) pathogenesis is the breakdown of peripheral tolerance in the immune system. However, its molecular mechanism is not completely understood. Since long non-coding RNAs (lncRNAs) has played important roles in regulation of immunological pathways, here, we evaluated the expression of a novel lncRNA, TOB1-AS1, and its putative associated coding genes in the mechanism of maintaining immune tolerance in peripheral blood of MS patients to assess their possible roles in MS pathogenesis. In this study, 39 MS patients and 32 healthy matched controls were recruited. Real-time PCR standard curve method was used to quantify transcript levels of TOB1-AS1, TOB1, SKP2, and TSG. In addition, the potential sex hormone receptor binding sites on target genes promoter were analyzed using JASPR software. This work demonstrates a negative correlation between TOB1-AS1 expression and EDSS of patients. Also, a robust dysregulation of co-expression of TOB1-AS1 lncRNA and the coding genes in MS patients compared to controls was observed. Such dysregulation in this pathway may be related to MS pathogenesis and response to interferon treatment.

A Network of SLC and ABC Transporter and DME Genes Involved in Remote Sensing and Signaling in the Gut-Liver-Kidney Axis

Genes central to drug absorption, distribution, metabolism and elimination (ADME) also regulate numerous endogenous molecules. The Remote Sensing and Signaling Hypothesis argues that an ADME gene-centered network—including SLC and ABC “drug” transporters, “drug” metabolizing enzymes (DMEs), and regulatory genes—is essential for inter-organ communication via metabolites, signaling molecules, antioxidants, gut microbiome products, uremic solutes, and uremic toxins. By cross-tissue co-expression network analysis, the gut, liver, and kidney (GLK) formed highly connected tissue-specific clusters of SLC transporters, ABC transporters, and DMEs. SLC22, SLC25 and SLC35 families were network hubs, having more inter-organ and intra-organ connections than other families. Analysis of the GLK network revealed key physiological pathways (e.g., involving bile acids and uric acid). A search for additional genes interacting with the network identified HNF4α, HNF1α, and PXR. Knockout gene expression data confirmed ~60–70% of predictions of ADME gene regulation by these transcription factors. Using the GLK network and known ADME genes, we built a tentative gut-liver-kidney “remote sensing and signaling network” consisting of SLC and ABC transporters, as well as DMEs and regulatory proteins. Together with protein-protein interactions to prioritize likely functional connections, this network suggests how multi-specificity combines with oligo-specificity and mono-specificity to regulate homeostasis of numerous endogenous small molecules.

Genetic and environmental perturbations lead to regulatory decoherence

Correlation among traits is a fundamental feature of biological systems that remains difficult to study. To address this problem, we developed a flexible approach that allows us to identify factors associated with inter-individual variation in correlation. We use data from three human cohorts to study the effects of genetic and environmental variation on correlations among mRNA transcripts and among NMR metabolites. We first show that environmental exposures (infection and disease) lead to a systematic loss of correlation, which we define as 'decoherence'. Using longitudinal data, we show that decoherent metabolites are better predictors of whether someone will develop metabolic syndrome than metabolites commonly used as biomarkers of this disease. Finally, we demonstrate that correlation itself is under genetic control by mapping hundreds of 'correlation quantitative trait loci (QTLs)'. Together, this work furthers our understanding of how and why coordinated biological processes break down, and points to a potential role for decoherence in disease.

Editorial note

This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

Spontaneous premature birth as a target of genomic research

Spontaneous preterm birth is a serious and common pregnancy complication associated with hormonal dysregulation, infection, inflammation, immunity, rupture of fetal membranes, stress, bleeding, and uterine distention. Heredity is 25-40% and mostly involves the maternal genome, with contribution of the fetal genome. Significant discoveries of candidate genes by genome-wide studies and confirmation in independent replicate populations serve as signposts for further research. The main task is to define the candidate genes, their roles, localization, regulation, and the associated pathways that influence the onset of human labor. Genomic research has identified some candidate genes that involve growth, differentiation, endocrine function, immunity, and other defense functions. For example, selenocysteine-specific elongation factor (EEFSEC) influences synthesis of selenoproteins. WNT4 regulates decidualization, while a heat-shock protein family A (HSP70) member 1 like, HSPAIL, influences expression of glucocorticoid receptor and WNT4. Programming of pregnancy duration starts before pregnancy and during placentation. Future goals are to understand the interactive regulation of the pathways in order to define the clocks that influence the risk of prematurity and the duration of pregnancy. Premature birth has a great impact on the duration and the quality of life. Intensification of focused research on causes, prediction and prevention of prematurity is justified.

Key elements involved in Epstein-Barr virus-associated gastric cancer and their network regulation

Background

The molecular mechanism of Epstein–Barr virus (EBV)-associated gastric cancer (EBVaGC) remains elusive. A collection of molecular regulators including transcription factor and noncoding RNA (ncRNAs) may affect the carcinogenesis of EBVaGC by regulating the expression and function of key genes. In this study, integration of multi-level expression data and bioinformatics approach was used to identify key elements and their interactions involved in mechanism of EBVaGC and their network regulation.

Methods

Data of the gene expression profiling data sets (GSE51575) was downloaded from GEO database. Differentially expressed genes between EBVaGC and normal samples were identified by GEO2R. Gene ontology and pathway enrichment analyses were performed using R packages Cluster profiler. STRING database was used to find interacting proteins between different genes. Transcription factors in differentially expressed genes were obtained from TF Checkpoint database. Using Cytoscape, we built transcription factor regulation network. miRNAs involved in the gene-interacting proteins and the miRNA-targeted lncRNA were predicted through miRWalk. Using ViRBase, EBV related miRNA regulation network was built. Overlapping genes and regulators of the above three networks were further identified, and the cross network was constructed using Cytoscape software. Moreover, the differential expressions of the target genes and transcription factors in the cross network were explored in different molecular subtypes of GC using cBioPortal. By histological verification, the expression of two main target genes in the cross network were further analyzed.

Results

A total of 104 genes showed differential expressions between EBVaGC and normal tissues, which were associated with digestion, G-protein coupled receptor binding, gastric acid secretion, etc. Pathway analysis showed that the differentially expressed genes were mainly enriched in gastric acid secretion and protein digestion and absorption. Using STRING dataset, a total of 54 proteins interacted with each other. Based on the transcription factor network, the hub transcription factors IRX3, NKX6-2, PTGER3 and SMAD5 were identified to regulate their target genes SST and GDF5, etc. After screening and matching in miRwalk datasets, a ceRNA network was established, in which the top five miRNAs were hsa-miR-4446-3p, hsa-miR-5787, hsa-miR-1915-3p, hsa-miR-335-3p and hsa-miR-6877-3p, and the top two lncRNAs were RP5-1039K5.19 and TP73-AS1. According to the EBV related miRNA regulation network, CXCL10 and SMAD5 were found to be regulated by EBV-miR-BART1-3p and EBV-mir-BART22, respectively. By overlapping the three networks, CXCL10, GDF5, PTGER3, SMAD5, miR-6877-3p, RP5-1039K5.19, TP73-AS1, EBV-miR-BART1-3p and EBV-mir-BART22 were found to be key elements of regulation mechanism of EBVaGC. CXCL10, GDF5, PTGER3 and SMAD5 were also differentially expressed among the four molecular subtypes of GC. The histological verification experiment showed differential expressions of the two main target genes GDF5 and CXCL10 between EBVaGC and non-tumor tissues as well as EBVnGC.

Conclusion

In the current study, our results revealed key elements and their interactions involved in EBVaGC. Some hub transcription factors, miRNAs, lncRNAs and EBV related miRNAs were observed to regulate their target genes. Overlapping genes and regulators were observed in diverse regulation networks, such as CXCL10, GDF5, PTGER3, SMAD5, miR-6877-3p, RP5-1039K5.19, TP73-AS1, EBV-miR-BART1-3p and EBV-mir-BART22. Moreover, CXCL10, GDF5, PTGER3 and SMAD5 were also differentially expressed among the four molecular subtypes of GC. The histological verification experiment showed differential expressions of the two main target genes GDF5 and CXCL10 between EBVaGC and non-tumor tissues as well as EBVnGC. Therefore, the identified key elements and their network regulation may be specifically involved in EBVaGC mechanisms.

Electronic supplementary material

The online version of this article (10.1186/s12935-018-0637-5) contains supplementary material, which is available to authorized users.

FACS-Seq analysis of Pax3-derived cells identifies non-myogenic lineages in the embryonic forelimb

Skeletal muscle in the forelimb develops during embryonic and fetal development and perinatally. While much is known regarding the molecules involved in forelimb myogenesis, little is known about the specific mechanisms and interactions. Migrating skeletal muscle precursor cells express Pax3 as they migrate into the forelimb from the dermomyotome. To compare gene expression profiles of the same cell population over time, we isolated lineage-traced Pax3⁺ cells (Pax3^EGFP) from forelimbs at different embryonic days. We performed whole transcriptome profiling via RNA-Seq of Pax3⁺ cells to construct gene networks involved in different stages of embryonic and fetal development. With this, we identified genes involved in the skeletal, muscular, vascular, nervous and immune systems. Expression of genes related to the immune, skeletal and vascular systems showed prominent increases over time, suggesting a non-skeletal myogenic context of Pax3-derived cells. Using co-expression analysis, we observed an immune-related gene subnetwork active during fetal myogenesis, further implying that Pax3-derived cells are not a strictly myogenic lineage, and are involved in patterning and three-dimensional formation of the forelimb through multiple systems.

Ectopic GRHL2 Expression Due to Non-coding Mutations Promotes Cell State Transition and Causes Posterior Polymorphous Corneal Dystrophy 4

In a large family of Czech origin, we mapped a locus for an autosomal-dominant corneal endothelial dystrophy, posterior polymorphous corneal dystrophy 4 (PPCD4), to 8q22.3-q24.12. Whole-genome sequencing identified a unique variant (c.20+544G>T) in this locus, within an intronic regulatory region of GRHL2. Targeted sequencing identified the same variant in three additional previously unsolved PPCD-affected families, including a de novo occurrence that suggests this is a recurrent mutation. Two further unique variants were identified in intron 1 of GRHL2 (c.20+257delT and c.20+133delA) in unrelated PPCD-affected families. GRHL2 is a transcription factor that suppresses epithelial-to-mesenchymal transition (EMT) and is a direct transcriptional repressor of ZEB1. ZEB1 mutations leading to haploinsufficiency cause PPCD3. We previously identified promoter mutations in OVOL2, a gene not normally expressed in the corneal endothelium, as the cause of PPCD1. OVOL2 drives mesenchymal-to-epithelial transition (MET) by directly inhibiting EMT-inducing transcription factors, such as ZEB1. Here, we demonstrate that the GRHL2 regulatory variants identified in PPCD4-affected individuals induce increased transcriptional activity in vitro. Furthermore, although GRHL2 is not expressed in corneal endothelial cells in control tissue, we detected GRHL2 in the corneal "endothelium" in PPCD4 tissue. These cells were also positive for epithelial markers E-Cadherin and Cytokeratin 7, indicating they have transitioned to an epithelial-like cell type. We suggest that mutations inducing MET within the corneal endothelium are a convergent pathogenic mechanism leading to dysfunction of the endothelial barrier and disease.