VisANT: an online visualization and analysis tool for biological interaction data

Transcriptomic analysis revealed potential regulatory biomarkers and repurposable drugs for breast cancer treatment

Breast cancer (BC) is the most widespread cancer worldwide. Over 2 million new cases of BC were identified in 2020 alone. Despite previous studies, the lack of specific biomarkers and signaling pathways implicated in BC impedes the development of potential therapeutic strategies. We employed several RNAseq datasets to extract differentially expressed genes (DEGs) based on the intersection of all datasets, followed by protein-protein interaction network construction. Using the shared DEGs, we also identified significant gene ontology (GO) and KEGG pathways to understand the signaling pathways involved in BC development. A molecular docking simulation was performed to explore potential interactions between proteins and drugs. The intersection of the four datasets resulted in 146 DEGs common, including AURKB, PLK1, TTK, UBE2C, CDCA8, KIF15, and CDC45 that are significant hub-proteins associated with breastcancer development. These genes are crucial in complement activation, mitotic cytokinesis, aging, and cancer development. We identified key microRNAs (i.e., hsa-miR-16-5p, hsa-miR-1-3p, hsa-miR-147a, hsa-miR-195-5p, and hsa-miR-155-5p) that are associated with aggressive tumor behavior and poor clinical outcomes in BC. Notable transcription factors (TFs) were FOXC1, GATA2, FOXL1, ZNF24 and NR2F6. These biomarkers are involved in regulating cancer cell proliferation, invasion, and migration. Finally, molecular docking suggested Hesperidin, 2-amino-isoxazolopyridines, and NMS-P715 as potential lead compounds against BC progression. We believe that these findings will provide important insight into the BC progression as well as potential biomarkers and drug candidates for therapeutic development.

Dissecting gene expression networks in the developing hippocampus through the lens of NEIL3 depletion

Gene regulation in the hippocampus is fundamental for its development, synaptic plasticity, memory formation, and adaptability. Comparisons of gene expression among different developmental stages, distinct cell types, and specific experimental conditions have identified differentially expressed genes contributing to the organization and functionality of hippocampal circuits. The NEIL3 DNA glycosylase, one of the DNA repair enzymes, plays an important role in hippocampal maturation and neuron functionality by shaping transcription. While differential gene expression (DGE) analysis has identified key genes involved, broader gene expression patterns crucial for high-order hippocampal functions remain uncharted. By utilizing the weighted gene co-expression network analysis (WGCNA), we mapped gene expression networks in immature (p8-neonatal) and mature (3 m-adult) hippocampal circuits in wild-type and NEIL3-deficient mice. Our study unveiled intricate gene network structures underlying hippocampal maturation, delineated modules of co-expressed genes, and pinpointed highly interconnected hub genes specific to the maturity of hippocampal subregions. We investigated variations within distinct gene network modules following NEIL3 depletion, uncovering NEIL3-targeted hub genes that influence module connectivity and specificity. By integrating WGCNA with DGE, we delve deeper into the NEIL3-dependent molecular intricacies of hippocampal maturation. This study provides a comprehensive systems-level analysis for assessing the potential correlation between gene connectivity and functional connectivity within the hippocampal network, thus shaping hippocampal function throughout development.

Co-methylation analyses identify CpGs associated with lipid traits in Chinese discordant monozygotic twins

To control genetic background and early life milieu in genome-wide DNA methylation analysis for blood lipids, we recruited Chinese discordant monozygotic twins to explore the relationships between DNA methylations and total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG). 132 monozygotic (MZ) twins were included with discordant lipid levels and completed data. A linear mixed model was conducted in Epigenome-wide association study (EWAS). Generalized estimating equation model was for gene expression analysis. We conducted Weighted correlation network analysis (WGCNA) to build co-methylated interconnected network. Additional Qingdao citizens were recruited for validation. Inference about Causation through Examination of Familial Confounding (ICE FALCON) was used to infer the possible direction of these relationships. A total of 476 top CpGs reached suggestively significant level (P < 10-4), of which, 192 CpGs were significantly associated with TG (FDR < 0.05). They were used to build interconnected network and highlight crucial genes from WGCNA. Finally, four CpGs in GATA4 were validated as risk factors for TC; six CpGs at ITFG2-AS1 were negatively associated with TG; two CpGs in PLXND1 played protective roles in HDL-C. ICE FALCON indicated abnormal TC was regarded as the consequence of DNA methylation in CpGs at GATA4, rather than vice versa. Four CpGs in ITFG2-AS1 were both causes and consequences of modified TG levels. Our results indicated that DNA methylation levels of 12 CpGs in GATA4, ITFG2-AS1, and PLXND1 were relevant to TC, TG, and HDL-C, respectively, which might provide new epigenetic insights into potential clinical treatment of dyslipidemia.

Transcriptomic Analyses Reveal Insights into the Shared Regulatory Network of Phenolic Compounds and Steviol Glycosides in Stevia rebaudiana

Stevia rebaudiana (Bertoni) is a highly valuable crop for the steviol glycoside content in its leaves, which are no-calorie sweeteners hundreds of times more potent than sucrose. The presence of health-promoting phenolic compounds, particularly flavonoids, in the leaf of S. rebaudiana adds further nutritional value to this crop. Although all these secondary metabolites are highly desirable in S. rebaudiana leaves, the genes regulating the biosynthesis of phenolic compounds and the shared gene network between the regulation of biosynthesis of steviol glycosides and phenolic compounds still need to be investigated in this species. To identify putative candidate genes involved in the synergistic regulation of steviol glycosides and phenolic compounds, four genotypes with different contents of these compounds were selected for a pairwise comparison RNA-seq analysis, yielding 1136 differentially expressed genes. Genes that highly correlate with both steviol glycosides and phenolic compound accumulation in the four genotypes of S. rebaudiana were identified using the weighted gene co-expression network analysis. The presence of UDP-glycosyltransferases 76G1, 76H1, 85C1, and 91A1, and several genes associated with the phenylpropanoid pathway, including peroxidase, caffeoyl-CoA O-methyltransferase, and malonyl-coenzyme A:anthocyanin 3-O-glucoside-6″-O-malonyltransferase, along with 21 transcription factors like SCL3, WRK11, and MYB111, implied an extensive and synergistic regulatory network involved in enhancing the production of such compounds in S. rebaudiana leaves. In conclusion, this work identified a variety of putative candidate genes involved in the biosynthesis and regulation of particular steviol glycosides and phenolic compounds that will be useful in gene editing strategies for increasing and steering the production of such compounds in S. rebaudiana as well as in other species.

Hubs and Bottlenecks in Protein-Protein Interaction Networks

Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.

INO80 function is required for mouse mammary gland development, but mutation alone may be insufficient for breast cancer

The aberrant function of ATP-dependent chromatin remodeler INO80 has been implicated in multiple types of cancers by altering chromatin architecture and gene expression; however, the underlying mechanism of the functional involvement of INO80 mutation in cancer etiology, especially in breast cancer, remains unclear. In the present study, we have performed a weighted gene co-expression network analysis (WCGNA) to investigate links between INO80 expression and breast cancer sub-classification and progression. Our analysis revealed that INO80 repression is associated with differential responsiveness of estrogen receptors (ERs) depending upon breast cancer subtype, ER networks, and increased risk of breast carcinogenesis. To determine whether INO80 loss induces breast tumors, a conditional INO80-knockout (INO80 cKO) mouse model was generated using the Cre-loxP system. Phenotypic characterization revealed that INO80 cKO led to reduced branching and length of the mammary ducts at all stages. However, the INO80 cKO mouse model had unaltered lumen morphology and failed to spontaneously induce tumorigenesis in mammary gland tissue. Therefore, our study suggests that the aberrant function of INO80 is potentially associated with breast cancer by modulating gene expression. INO80 mutation alone is insufficient for breast tumorigenesis.

McAN: a novel computational algorithm and platform for constructing and visualizing haplotype networks

Haplotype networks are graphs used to represent evolutionary relationships between a set of taxa and are characterized by intuitiveness in analyzing genealogical relationships of closely related genomes. We here propose a novel algorithm termed McAN that considers mutation spectrum history (mutations in ancestry haplotype should be contained in descendant haplotype), node size (corresponding to sample count for a given node) and sampling time when constructing haplotype network. We show that McAN is two orders of magnitude faster than state-of-the-art algorithms without losing accuracy, making it suitable for analysis of a large number of sequences. Based on our algorithm, we developed an online web server and offline tool for haplotype network construction, community lineage determination, and interactive network visualization. We demonstrate that McAN is highly suitable for analyzing and visualizing massive genomic data and is helpful to enhance the understanding of genome evolution. Availability: Source code is written in C/C++ and available at https://github.com/Theory-Lun/McAN and https://ngdc.cncb.ac.cn/biocode/tools/BT007301 under the MIT license. Web server is available at https://ngdc.cncb.ac.cn/bit/hapnet/. SARS-CoV-2 dataset are available at https://ngdc.cncb.ac.cn/ncov/. Contact: songshh@big.ac.cn (Song S), zhaowm@big.ac.cn (Zhao W), baoym@big.ac.cn (Bao Y), zhangzhang@big.ac.cn (Zhang Z), ybxue@big.ac.cn (Xue Y).

Transcriptomic Analysis on the Peel of UV-B-Exposed Peach Fruit Reveals an Upregulation of Phenolic- and UVR8-Related Pathways

UV-B treatment deeply influences plant physiology and biochemistry, especially by activating the expression of responsive genes involved in UV-B acclimation through a UV-B-specific perception mechanism. Although the UV-B-related molecular responses have been widely studied in Arabidopsis, relatively few research reports deepen the knowledge on the influence of post-harvest UV-B treatment on fruit. In this work, a transcriptomic approach is adopted to investigate the transcriptional modifications occurring in the peel of UV-B-treated peach (Prunus persica L., cv Fairtime) fruit after harvest. Our analysis reveals a higher gene regulation after 1 h from the irradiation (88% of the differentially expressed genes-DEGs), compared to 3 h recovery. The overexpression of genes encoding phenylalanine ammonia-lyase (PAL), chalcone syntase (CHS), chalcone isomerase (CHI), and flavonol synthase (FLS) revealed a strong activation of the phenylpropanoid pathway, resulting in the later increase in the concentration of specific flavonoid classes, e.g., anthocyanins, flavones, dihydroflavonols, and flavanones, 36 h after the treatment. Upregulation of UVR8-related genes (HY5, COP1, and RUP) suggests that UV-B-triggered activation of the UVR8 pathway occurs also in post-harvest peach fruit. In addition, a regulation of genes involved in the cell-wall dismantling process (PME) is observed. In conclusion, post-harvest UV-B exposure deeply affects the transcriptome of the peach peel, promoting the activation of genes implicated in the biosynthesis of phenolics, likely via UVR8. Thus, our results might pave the way to a possible use of post-harvest UV-B treatments to enhance the content of health-promoting compounds in peach fruits and extending the knowledge of the UVR8 gene network.

Molecular subtypes of ALS are associated with differences in patient prognosis

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease with poorly understood clinical heterogeneity, underscored by significant differences in patient age at onset, symptom progression, therapeutic response, disease duration, and comorbidity presentation. We perform a patient stratification analysis to better understand the variability in ALS pathology, utilizing postmortem frontal and motor cortex transcriptomes derived from 208 patients. Building on the emerging role of transposable element (TE) expression in ALS, we consider locus-specific TEs as distinct molecular features during stratification. Here, we identify three unique molecular subtypes in this ALS cohort, with significant differences in patient survival. These results suggest independent disease mechanisms drive some of the clinical heterogeneity in ALS.

The global integrative network: integration of signaling and metabolic pathways

The crosstalk between signaling and metabolic pathways has been known to play key roles in human diseases and plant biological processes. The integration of signaling and metabolic pathways can provide an essential reference framework for crosstalk analysis. However, current databases use distinct structures to present signaling and metabolic pathways, which leads to the chaos in the integrated networks. Moreover, for the metabolic pathways, the metabolic enzymes and the reactions are disconnected by the current widely accepted layout of edges and nodes, which hinders the topological analysis of the integrated networks. Here, we propose a novel "meta-pathway" structure, which uses the uniformed structure to display the signaling and metabolic pathways, and resolves the difficulty in linking the metabolic enzymes to the reactions topologically. We compiled a comprehensive collection of global integrative networks (GINs) by merging the meta-pathways of 7077 species. We demonstrated the assembly of the signaling and metabolic pathways using the GINs of four species-human, mouse, Arabidopsis, and rice. Almost all of the nodes were assembled into one major network for each of the four species, which provided opportunities for robust crosstalk and topological analysis, and knowledge graph construction.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42994-022-00078-1.

Immuno-Modulatory Effects of Intervertebral Disc Cells

Low back pain is a highly prevalent, chronic, and costly medical condition predominantly triggered by intervertebral disc degeneration (IDD). IDD is often caused by structural and biochemical changes in intervertebral discs (IVD) that prompt a pathologic shift from an anabolic to catabolic state, affecting extracellular matrix (ECM) production, enzyme generation, cytokine and chemokine production, neurotrophic and angiogenic factor production. The IVD is an immune-privileged organ. However, during degeneration immune cells and inflammatory factors can infiltrate through defects in the cartilage endplate and annulus fibrosus fissures, further accelerating the catabolic environment. Remarkably, though, catabolic ECM disruption also occurs in the absence of immune cell infiltration, largely due to native disc cell production of catabolic enzymes and cytokines. An unbalanced metabolism could be induced by many different factors, including a harsh microenvironment, biomechanical cues, genetics, and infection. The complex, multifactorial nature of IDD brings the challenge of identifying key factors which initiate the degenerative cascade, eventually leading to back pain. These factors are often investigated through methods including animal models, 3D cell culture, bioreactors, and computational models. However, the crosstalk between the IVD, immune system, and shifted metabolism is frequently misconstrued, often with the assumption that the presence of cytokines and chemokines is synonymous to inflammation or an immune response, which is not true for the intact disc. Therefore, this review will tackle immunomodulatory and IVD cell roles in IDD, clarifying the differences between cellular involvements and implications for therapeutic development and assessing models used to explore inflammatory or catabolic IVD environments.

DJExpress: An Integrated Application for Differential Splicing Analysis and Visualization

RNA-seq analysis of alternative pre-mRNA splicing has facilitated an unprecedented understanding of transcriptome complexity in health and disease. However, despite the availability of countless bioinformatic pipelines for transcriptome-wide splicing analysis, the use of these tools is often limited to expert bioinformaticians. The need for high computational power, combined with computational outputs that are complicated to visualize and interpret present obstacles to the broader research community. Here we introduce DJExpress, an R package for differential expression analysis of transcriptomic features and expression-trait associations. To determine gene-level differential junction usage as well as associations between junction expression and molecular/clinical features, DJExpress uses raw splice junction counts as input data. Importantly, DJExpress runs on an average laptop computer and provides a set of interactive and intuitive visualization formats. In contrast to most existing pipelines, DJExpress can handle both annotated and de novo identified splice junctions, thereby allowing the quantification of novel splice events. Moreover, DJExpress offers a web-compatible graphical interface allowing the analysis of user-provided data as well as the visualization of splice events within our custom database of differential junction expression in cancer (DJEC DB). DJEC DB includes not only healthy and tumor tissue junction expression data from TCGA and GTEx repositories but also cancer cell line data from the DepMap project. The integration of DepMap functional genomics data sets allows association of junction expression with molecular features such as gene dependencies and drug response profiles. This facilitates identification of cancer cell models for specific splicing alterations that can then be used for functional characterization in the lab. Thus, DJExpress represents a powerful and user-friendly tool for exploration of alternative splicing alterations in RNA-seq data, including multi-level data integration of alternative splicing signatures in healthy tissue, tumors and cancer cell lines.

Single-cell RNA-sequencing Reveals Thoracolumbar Vertebra Heterogeneity and Rib-genesis in Pigs

Thoracolumbar vertebra (TLV) and rib primordium (RP) development is a common evolutionary feature across vertebrates, although whole-organism analysis of TLV and RP gene expression dynamics has been lacking. Here we investigated the single-cell transcriptomic landscape of thoracic vertebra (TV), lumbar vertebra (LV), and RP cells from a pig embryo at 27 days post-fertilization (dpf) and identified six cell types with distinct gene-expression signatures. In-depth dissection of the gene-expression dynamics and RNA velocity revealed a coupled process of osteogenesis and angiogenesis during TLV and rib development. Further analysis of cell-type-specific and strand-specific expression uncovered the extremely high levels of Hoxa10 3'-untranslated region (UTR) sequence specific to osteoblast of LV cells, which may function as anti-Hoxa10-antisense by counteracting the Hoxa10-antisense effect to determine TLV transition. Thus, this work provides a valuable resource for understanding embryonic osteogenesis and angiogenesis underlying vertebrate TLV and RP development at the cell-type-specific resolution, which serves as a comprehensive view on the transcriptional profile of animal embryo development.

Integration of transcriptomics and network analysis reveals co-expressed genes in Frankliniella occidentalis larval guts that respond to tomato spotted wilt virus infection

Background

The gut is the first barrier to infection by viruses that are internally borne and transmitted persistently by arthropod vectors to plant and animal hosts. Tomato spotted wilt virus (TSWV), a plant-pathogenic virus, is transmitted exclusively by thrips vectors in a circulative-propagative manner. Frankliniella occidentalis (western flower thrips), the principal thrips vector of TSWV, is transmission-competent only if the virus is acquired by young larvae. To begin to understand the larval gut response to TSWV infection and accumulation, a genome-assisted, transcriptomic analysis of F. occidentalis gut tissues of first (early L1) and second (early L2 and late L2) instar larvae was conducted using RNA-Seq to identify differentially-expressed transcripts (DETs) in response to TSWV compared to non-exposed cohorts.

Results

The larval gut responded in a developmental stage-dependent manner, with the majority of DETs (71%) associated with the early L1 stage at a time when virus infection is limited to the midgut epithelium. Provisional annotations of these DETs inferred roles in digestion and absorption, insect innate immunity, and detoxification. Weighted gene co-expression network analysis using all assembled transcripts of the gut transcriptome revealed eight gene modules that distinguish larval development. Intra-module interaction network analysis of the three most DET-enriched modules revealed ten central hub genes. Droplet digital PCR-expression analyses of select network hub and connecting genes revealed temporal changes in gut expression during and post exposure to TSWV.

Conclusions

These findings expand our understanding of the developmentally-mediated interaction between thrips vectors and orthotospoviruses, and provide opportunities for probing pathways for biomarkers of thrips vector competence.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08100-4.

Drug Discovery of Spinal Muscular Atrophy (SMA) from the Computational Perspective: A Comprehensive Review

Spinal muscular atrophy (SMA), one of the leading inherited causes of child mortality, is a rare neuromuscular disease arising from loss-of-function mutations of the survival motor neuron 1 (SMN1) gene, which encodes the SMN protein. When lacking the SMN protein in neurons, patients suffer from muscle weakness and atrophy, and in the severe cases, respiratory failure and death. Several therapeutic approaches show promise with human testing and three medications have been approved by the U.S. Food and Drug Administration (FDA) to date. Despite the shown promise of these approved therapies, there are some crucial limitations, one of the most important being the cost. The FDA-approved drugs are high-priced and are shortlisted among the most expensive treatments in the world. The price is still far beyond affordable and may serve as a burden for patients. The blooming of the biomedical data and advancement of computational approaches have opened new possibilities for SMA therapeutic development. This article highlights the present status of computationally aided approaches, including in silico drug repurposing, network driven drug discovery as well as artificial intelligence (AI)-assisted drug discovery, and discusses the future prospects.

Description and functional analysis of the transcriptome from malting barley

The present study aimed to establish an early model of the malting barley transcriptome, which describes the expression of genes and their ontologies, identify the period during malting with the largest dynamic shift in gene expression for future investigation, and to determine the expression patterns of all starch degrading enzyme genes relevant to the malting and brewing industry. Large dynamic increases in gene expression occurred early in malting with differential expressed genes enriched for cell wall and starch hydrolases amongst many malting related categories. Twenty-five of forty starch degrading enzyme genes were differentially expressed in the malting barley transcriptome including eleven α-amylase genes, six β-amylase genes, three α-glucosidase genes, and all five starch debranching enzyme genes. Four new or novel α-amylase genes, one β-amylase gene (Bmy3), three α-glucosidase genes, and two isoamylase genes had appreciable expression that requires further exploration into their potential relevance to the malting and brewing industry.

Mapping the multiscale structure of biological systems

Biological systems are by nature multiscale, consisting of subsystems that factor into progressively smaller units in a deeply hierarchical structure. At any level of the hierarchy, an ever-increasing diversity of technologies can be applied to characterize the corresponding biological units and their relations, resulting in large networks of physical or functional proximities-e.g., proximities of amino acids within a protein, of proteins within a complex, or of cell types within a tissue. Here, we review general concepts and progress in using network proximity measures as a basis for creation of multiscale hierarchical maps of biological systems. We discuss the functionalization of these maps to create predictive models, including those useful in translation of genotype to phenotype, along with strategies for model visualization and challenges faced by multiscale modeling in the near future. Collectively, these approaches enable a unified hierarchical approach to biological data, with application from the molecular to the macroscopic.

High-dimensional profiling clusters asthma severity by lymphoid and non-lymphoid status

Clinical definitions of asthma fail to capture the heterogeneity of immune dysfunction in severe, treatment-refractory disease. Applying mass cytometry and machine learning to bronchoalveolar lavage (BAL) cells, we find that corticosteroid-resistant asthma patients cluster largely into two groups: one enriched in interleukin (IL)-4+ innate immune cells and another dominated by interferon (IFN)-γ+ T cells, including tissue-resident memory cells. In contrast, BAL cells of a healthier population are enriched in IL-10+ macrophages. To better understand cellular mediators of severe asthma, we developed the Immune Cell Linkage through Exploratory Matrices (ICLite) algorithm to perform deconvolution of bulk RNA sequencing of mixed-cell populations. Signatures of mitosis and IL-7 signaling in CD206-FcεRI+CD127+IL-4+ innate cells in one patient group, contrasting with adaptive immune response in T cells in the other, are preserved across technologies. Transcriptional signatures uncovered by ICLite identify T-cell-high and T-cell-poor severe asthma patients in an independent cohort, suggesting broad applicability of our findings.

Search and visualization of gene-drug-disease interactions for pharmacogenomics and precision medicine research using GeneDive

BACKGROUND

Understanding the relationships between genes, drugs, and disease states is at the core of pharmacogenomics. Two leading approaches for identifying these relationships in medical literature are: human expert led manual curation efforts, and modern data mining based automated approaches. The former generates small amounts of high-quality data, and the latter offers large volumes of mixed quality data. The algorithmically extracted relationships are often accompanied by supporting evidence, such as, confidence scores, source articles, and surrounding contexts (excerpts) from the articles, that can be used as data quality indicators. Tools that can leverage these quality indicators to help the user gain access to larger and high-quality data are needed.

APPROACH

We introduce GeneDive, a web application for pharmacogenomics researchers and precision medicine practitioners that makes gene, disease, and drug interactions data easily accessible and usable. GeneDive is designed to meet three key objectives: (1) provide functionality to manage information-overload problem and facilitate easy assimilation of supporting evidence, (2) support longitudinal and exploratory research investigations, and (3) offer integration of user-provided interactions data without requiring data sharing.

RESULTS

GeneDive offers multiple search modalities, visualizations, and other features that guide the user efficiently to the information of their interest. To facilitate exploratory research, GeneDive makes the supporting evidence and context for each interaction readily available and allows the data quality threshold to be controlled by the user as per their risk tolerance level. The interactive search-visualization loop enables relationship discoveries between diseases, genes, and drugs that might not be explicitly described in literature but are emergent from the source medical corpus and deductive reasoning. The ability to utilize user's data either in combination with the GeneDive native datasets or in isolation promotes richer data-driven exploration and discovery. These functionalities along with GeneDive's applicability for precision medicine, bringing the knowledge contained in biomedical literature to bear on particular clinical situations and improving patient care, are illustrated through detailed use cases.

CONCLUSION

GeneDive is a comprehensive, broad-use biological interactions browser. The GeneDive application and information about its underlying system architecture are available at http://www.genedive.net. GeneDive Docker image is also available for download at this URL, allowing users to (1) import their own interaction data securely and privately; and (2) generate and test hypotheses across their own and other datasets.

An Early Stage Researcher's Primer on Systems Medicine Terminology

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.

Gene Expression Network Analysis of Precursor Lesions in Familial Pancreatic Cancer

Purpose: High-grade pancreatic intraepithelial neoplasia (PanIN) are aggressive premalignant lesions, associated with risk of progression to pancreatic ductal adenocarcinoma (PDAC). A depiction of co-dysregulated gene activity in high-grade familial pancreatic cancer (FPC)-related PanIN lesions may characterize the molecular events during the progression from familial PanIN to PDAC. Materials and Methods: We performed weighted gene coexpression network analysis (WGCNA) to identify clusters of coexpressed genes associated with FPC-related PanIN lesions in 13 samples with PanIN-2/3 from FPC predisposed individuals, 6 samples with PDAC from sporadic pancreatic cancer (SPC) patients, and 4 samples of normal donor pancreatic tissue. Results: WGCNA identified seven differentially expressed gene (DEG) modules and two commonly expressed gene (CEG) modules with significant enrichment for Gene Ontology (GO) terms in FPC and SPC, including three upregulated (p < 5e-05) and four downregulated (p < 6e-04) gene modules in FPC compared to SPC. Among the DEG modules, the upregulated modules include 14 significant genes (p < 1e-06): ALOX12-AS1, BCL2L11, EHD4, C4B, BTN3A3, NDUFA11, RBM4B, MYOC, ZBTB47, TTTY15, NAPRT, LOC102606465, LOC100505711, and PTK2. The downregulated modules include 170 genes (p < 1e-06), among them 13 highly significant genes (p < 1e-10): COL10A1, SAMD9, PLPP4, COMP, POSTN, IGHV4-31, THBS2, MMP9, FNDC1, HOPX, TMEM200A, INHBA, and SULF1. The DEG modules are enriched for GO terms related to mitochondrial structure and adenosine triphosphate metabolic processes, extracellular structure and binding properties, humoral and complement mediated immune response, ligand-gated ion channel activity, and transmembrane receptor activity. Among the CEG modules, IL22RA1, DPEP1, and BCAT1 were found as highly connective hub genes associated with both FPC and SPC. Conclusion: FPC-related PanIN lesions exhibit a common molecular basis with SPC as shown by gene network activities and commonly expressed high-connectivity hub genes. The differential molecular pathology of FPC and SPC involves multiple coexpressed gene clusters enriched for GO terms including extracellular activities and mitochondrion function.

Resources and tools for the high-throughput, multi-omic study of intestinal microbiota

The human gut microbiome impacts several aspects of human health and disease, including digestion, drug metabolism and the propensity to develop various inflammatory, autoimmune and metabolic diseases. Many of the molecular processes that play a role in the activity and dynamics of the microbiota go beyond species and genic composition and thus, their understanding requires advanced bioinformatics support. This article aims to provide an up-to-date view of the resources and software tools that are being developed and used in human gut microbiome research, in particular data integration and systems-level analysis efforts. These efforts demonstrate the power of standardized and reproducible computational workflows for integrating and analysing varied omics data and gaining deeper insights into microbe community structure and function as well as host-microbe interactions.

The Integrated Analyses of Driver Genes Identify Key Biomarkers in Thyroid Cancer

AIM

Thyroid cancer is the most common endocrine cancer, the incidence rate has continuously increased worldwide. However, there are still lack of effective molecular biomarkers for the diagnosis and treatment of the disease. The study was conducted to identify driver genes that may serve as potential biomarkers for the disease.

METHODS

The computational tools oncodriveCLUST, oncodriveFM, icages and drgap were used to detect driver genes in thyroid cancer using somatic mutations from The Cancer Genome Atlas database. Integrated analyses were performed on the driver genes using multiomics data from the TCGA database.

RESULTS

A set of 291 driver genes were identified in thyroid cancer. BRAF, NRAS, HRAS, OTUD4, EIF1AX were the top 5 frequently mutated genes in thyroid cancer. The weighted gene co-expression network analysis identified 4 coexpression modules. The modules 1-3 were significantly associated with patients' tumor size, residual tumor, cancer stage, distant metastasis and multifocality. SEC24B, MET and ITGAL were the hub genes in the modules 1-3 respectively. Hierarchical clustering analysis of the 20 driver genes with the most frequent copy number changes revealed 3 clusters of PRAD patients. Cluster 1 tumors exhibited significantly older age, tumor size, cancer stages, and poorer prognosis than cluster 2 and 3 tumors. 16 genes were significantly associated with number of lymph nodes, tumor size and pathologic stage, such as IL7 R, IRS1, PTK2B, MAP3K3 and FGFR2.

CONCLUSIONS

The set of cancer genes and subgroups of patients shed insight on the tumorigenesis of thyroid cancer and open up avenues for developing prognostic biomarkers and driver gene-targeted therapies in thyroid cancer.

Novel microRNAs are associated with population divergence in transcriptional response to thermal stress in an intertidal copepod

The role of gene expression in adaptation to differing thermal environments has been assayed extensively. Yet, in most natural systems, analyses of gene expression reveal only one level of the complexity of regulatory machineries. MicroRNAs (miRNAs) are small noncoding RNAs which are key components of many gene regulatory networks, and they play important roles in a variety of cellular pathways by modulating post-transcriptional quantities of mRNA available for protein synthesis. The characterization of miRNA loci and their regulatory dynamics in nonmodel systems are still largely understudied. In this study, we examine the role of miRNAs in response to high thermal stress in the intertidal copepod Tigriopus californicus. Allopatric populations of this species show varying levels of local adaptation with respect to thermal regimes, and previous studies showed divergence in gene expression between populations from very different thermal environments. We examined the transcriptional response to temperature stress in two populations separated by only 8 km by utilizing RNA-seq to quantify both mRNA and miRNA levels. Using the currently available genome sequence, we first describe the repertoire of miRNAs in T. californicus and assess the degree to which transcriptional response to temperature stress is governed by miRNA activity. The two populations showed large differences in the number of genes involved, the magnitude of change in commonly used genes and in the number of miRNAs involved in transcriptional modulation during stress. Our results suggest that an increased level of regulatory network complexity may underlie improved survivorship under thermal stress in one of the populations.

PanoromiX: a time-course network medicine platform integrating molecular assays and pathophenotypic data

BACKGROUND

Network medicine aims to map molecular perturbations of any given diseases onto complex networks with functional interdependencies that underlie a pathological phenotype. Furthermore, investigating the time dimension of disease progression from a network perspective is key to gaining key insights to the disease process and to identify diagnostic or therapeutic targets. Existing platforms are ineffective to modularize the large complex systems into subgroups and consolidate heterogeneous data to web-based interactive animation.

RESULTS

We have developed PanoromiX platform, a data-agnostic dynamic interactive visualization web application, enables the visualization of outputs from genome based molecular assays onto modular and interactive networks that are correlated with any pathophenotypic data (MRI, Xray, behavioral, etc.) over a time course all in one pane. As a result, PanoromiX reveals the complex organizing principles that orchestrate a disease-pathology from a gene regulatory network (nodes, edges, hubs, etc.) perspective instead of snap shots of assays. Without extensive programming experience, users can design, share, and interpret their dynamic networks through the PanoromiX platform with rich built-in functionalities.

CONCLUSIONS

This emergent tool of network medicine is the first to visualize the interconnectedness of tailored genome assays to pathological networks and phenotypes for cells or organisms in a data-agnostic manner. As an advanced network medicine tool, PanoromiX allows monitoring of panel of biomarker perturbations over the progression of diseases, disease classification based on changing network modules that corresponds to specific patho-phenotype as opposed to clinical symptoms, systematic exploration of complex molecular interactions and distinct disease states via regulatory network changes, and the discovery of novel diagnostic and therapeutic targets.

Baseline metabolic profiles of early rheumatoid arthritis patients achieving sustained drug-free remission after initiating treat-to-target tocilizumab, methotrexate, or the combination: insights from systems biology

Background

We previously identified, in newly diagnosed rheumatoid arthritis (RA) patients, networks of co-expressed genes and proteomic biomarkers associated with achieving sustained drug-free remission (sDFR) after treatment with tocilizumab- or methotrexate-based strategies. The aim of this study was to identify, within the same patients, metabolic pathways important for achieving sDFR and to subsequently study the complex interactions between different components of the biological system and how these interactions might affect the therapeutic response in early RA.

Methods

Serum samples were analyzed of 60 patients who participated in the U-Act-Early trial (ClinicalTrials.gov number NCT01034137) and initiated treatment with methotrexate, tocilizumab, or the combination and who were thereafter able to achieve sDFR (n = 37); as controls, patients were selected who never achieved a drug-free status (n = 23). Metabolomic measurements were performed using mass spectrometry on oxidative stress, amine, and oxylipin platforms covering various compounds. Partial least square discriminant analyses (PLSDA) were performed to identify, per strategy arm, relevant metabolites of which the biological pathways were studied. In addition, integrative analyses were performed correlating the previously identified transcripts and proteins with the relevant metabolites.

Results

In the tocilizumab plus methotrexate, tocilizumab, and methotrexate strategy, respectively, 19, 13, and 12 relevant metabolites were found, which were subsequently used for pathway analyses. The most significant pathway in the tocilizumab plus methotrexate strategy was “histidine metabolism” (p < 0.001); in the tocilizumab strategy it was “arachidonic acid metabolism” (p = 0.018); and in the methotrexate strategy it was “arginine and proline metabolism” (p = 0.022). These pathways have treatment-specific drug interactions with metabolites affecting either the signaling of interleukin-6, which is inhibited by tocilizumab, or affecting protein synthesis from amino acids, which is inhibited by methotrexate.

Conclusion

In early RA patients treated-to-target with a tocilizumab- or methotrexate-based strategy, several metabolites were found to be associated with achieving sDFR. In line with our previous observations, by analyzing relevant transcripts and proteins within the same patients, the metabolic profiles were found to be different between the strategy arms. Our metabolic analysis further supports the hypothesis that achieving sDFR is not only dependent on predisposing biomarkers, but also on the specific treatment that has been initiated.

Trial registration

ClinicalTrials.gov, NCT01034137. Registered on January 2010

Electronic supplementary material

The online version of this article (10.1186/s13075-018-1729-2) contains supplementary material, which is available to authorized users.

Signalling maps in cancer research: construction and data analysis

Generation and usage of high-quality molecular signalling network maps can be augmented by standardizing notations, establishing curation workflows and application of computational biology methods to exploit the knowledge contained in the maps. In this manuscript, we summarize the major aims and challenges of assembling information in the form of comprehensive maps of molecular interactions. Mainly, we share our experience gained while creating the Atlas of Cancer Signalling Network. In the step-by-step procedure, we describe the map construction process and suggest solutions for map complexity management by introducing a hierarchical modular map structure. In addition, we describe the NaviCell platform, a computational technology using Google Maps API to explore comprehensive molecular maps similar to geographical maps and explain the advantages of semantic zooming principles for map navigation. We also provide the outline to prepare signalling network maps for navigation using the NaviCell platform. Finally, several examples of cancer high-throughput data analysis and visualization in the context of comprehensive signalling maps are presented.

Genome-wide identification and differentially expression analysis of lncRNAs in tilapia

Background

Long noncoding RNAs (LncRNAs) play important roles in fundamental biological processes. However, knowledge about the genome-wide distribution and stress-related expression of lncRNAs in tilapia is still limited.

Results

Genome-wide identification of lncRNAs in the tilapia genome was carried out in this study using bioinformatics tools. 103 RNAseq datasets that generated in our laboratory or collected from NCBI database were analyzed. In total, 72,276 high-confidence lncRNAs were identified. The averaged positive correlation coefficient (r_mean = 0.286) between overlapped lncRNA and mRNA pairs showed significant differences with the values for all lncRNA-mRNA pairs (r_mean = 0.176, z statistics = − 2.45, p value = 0.00071) and mRNA-mRNA pairs (r_mean = 0.186, z statistics = − 2.23, p value = 0.0129). Weighted correlation network analysis of the lncRNA and mRNA datasets from 12 tissues identified 21 modules and many interesting mRNA genes that clustered with lncRNAs. Overrepresentation test indicated that these mRNAs enriched in many biological processes, such as meiosis (p = 0.00164), DNA replication (p = 0.00246), metabolic process (p = 0.000838) and in molecular function, e.g., helicase activity (p = 0.000102) and catalytic activity (p = 0.0000612). Differential expression (DE) analysis identified 99 stress-related lncRNA genes and 1955 tissue-specific DE lncRNA genes. MiRNA-lncRNA interaction analysis detected 72,267 lncRNAs containing motifs with sequence complementary to 458 miRNAs.

Conclusions

This study provides an invaluable resource for further studies on molecular bases of lncRNAs in tilapia genomes. Further function analysis of the lncRNAs will help to elucidate their roles in regulating stress-related adaptation in tilapia.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5115-x) contains supplementary material, which is available to authorized users.

A molecular network for functional versatility of HECATE transcription factors

During the plant life cycle, diverse signaling inputs are continuously integrated and engage specific genetic programs depending on the cellular or developmental context. Consistent with an important role in this process, HECATE (HEC) basic helix-loop-helix transcription factors display diverse functions, from photomorphogenesis to the control of shoot meristem dynamics and gynoecium patterning. However, the molecular mechanisms underlying their functional versatility and the deployment of specific HEC subprograms remain elusive. To address this issue, we systematically identified proteins with the capacity to interact with HEC1, the best-characterized member of the family, and integrated this information with our data set of direct HEC1 target genes. The resulting core genetic modules were consistent with specific developmental functions of HEC1, including its described activities in light signaling, gynoecium development and auxin homeostasis. Importantly, we found that HEC genes also play a role in the modulation of flowering time, and uncovered that their role in gynoecium development may involve the direct transcriptional regulation of NGATHA1 (NGA1) and NGA2 genes. NGA factors were previously shown to contribute to fruit development, but our data now show that they also modulate stem cell homeostasis in the shoot apical meristem. Taken together, our results delineate a molecular network underlying the functional versatility of HEC transcription factors. Our analyses have not only allowed us to identify relevant target genes controlling shoot stem cell activity and a so far undescribed biological function of HEC1, but also provide a rich resource for the mechanistic elucidation of further context-dependent HEC activities.

Transcriptome profiling of individual rhesus macaque oocytes and preimplantation embryos

Early mammalian embryonic transcriptomes are dynamic throughout the process of preimplantation development. Cataloging of primate transcriptomics during early development has been accomplished in humans, but global characterization of transcripts is lacking in the rhesus macaque: a key model for human reproductive processes. We report here the systematic classification of individual macaque transcriptomes using RNA-Seq technology from the germinal vesicle stage oocyte through the blastocyst stage embryo. Major differences in gene expression were found between sequential stages, with the 4- to 8-cell stages showing the highest level of differential gene expression. Analysis of putative transcription factor binding sites also revealed a striking increase in key regulatory factors in 8-cell embryos, indicating a strong likelihood of embryonic genome activation occurring at this stage. Furthermore, clustering analyses of gene co-expression throughout this period resulted in distinct groups of transcripts significantly associated to the different embryo stages assayed. The sequence data provided here along with characterizations of major regulatory transcript groups present a comprehensive atlas of polyadenylated transcripts that serves as a useful resource for comparative studies of preimplantation development in humans and other species.

Global isoform-specific transcript alterations and deregulated networks in clear cell renal cell carcinoma

Extensive genome-wide analyses of deregulated gene expression have now been performed for many types of cancer. However, most studies have focused on deregulation at the gene-level, which may overlook the alterations of specific transcripts for a given gene. Clear cell renal cell carcinoma (ccRCC) is one of the best-characterized and most pervasive renal cancers, and ccRCCs are well-documented to have aberrant RNA processing. In the present study, we examine the extent of aberrant isoform-specific RNA expression by reporting a comprehensive transcript-level analysis, using the new kallisto-sleuth-RATs pipeline, investigating coding and non-coding differential transcript expression in ccRCC. We analyzed 50 ccRCC tumors and their matched normal samples from The Cancer Genome Altas datasets. We identified 7,339 differentially expressed transcripts and 94 genes exhibiting differential transcript isoform usage in ccRCC. Additionally, transcript-level coexpression network analyses identified vasculature development and the tricarboxylic acid cycle as the most significantly deregulated networks correlating with ccRCC progression. These analyses uncovered several uncharacterized transcripts, including lncRNAs FGD5-AS1 and AL035661.1, as potential regulators of the tricarboxylic acid cycle associated with ccRCC progression. As ccRCC still presents treatment challenges, our results provide a new resource of potential therapeutics targets and highlight the importance of exploring alternative methodologies in transcriptome-wide studies.

Mycobacterium tuberculosis Transfer RNA Induces IL-12p70 via Synergistic Activation of Pattern Recognition Receptors within a Cell Network

Upon recognition of a microbial pathogen, the innate and adaptive immune systems are linked to generate a cell-mediated immune response against the foreign invader. The culture filtrate of Mycobacterium tuberculosis contains ligands, such as M. tuberculosis tRNA, that activate the innate immune response and secreted Ags recognized by T cells to drive adaptive immune responses. In this study, bioinformatics analysis of gene-expression profiles derived from human PBMCs treated with distinct microbial ligands identified a mycobacterial tRNA-induced innate immune network resulting in the robust production of IL-12p70, a cytokine required to instruct an adaptive Th1 response for host defense against intracellular bacteria. As validated by functional studies, this pathway contained a feed-forward loop, whereby the early production of IL-18, type I IFNs, and IL-12p70 primed NK cells to respond to IL-18 and produce IFN-γ, enhancing further production of IL-12p70. Mechanistically, tRNA activates TLR3 and TLR8, and this synergistic induction of IL-12p70 was recapitulated by the addition of a specific TLR8 agonist with a TLR3 ligand to PBMCs. These data indicate that M. tuberculosis tRNA activates a gene network involving the integration of multiple innate signals, including types I and II IFNs, as well as distinct cell types to induce IL-12p70.

Characterization of a gene regulatory network underlying astringency loss in persimmon fruit

Transcriptome analysis of a persimmon population segregating for an astringency trait in fruit suggested central roles for a limited number of transcriptional regulators in the loss of proanthocyanidin accumulation. Persimmon (Diospyros kaki; 2n = 6x = 90) accumulates a large amount of proanthocyanidins (PAs) in its fruit, resulting in an astringent taste. Persimmon cultivars are classified into four types based on the nature of astringency loss and the amount of PAs at maturity. Pollination constant and non-astringent (PCNA)-type cultivars stop accumulating PAs in the early stages of fruit development and their fruit can be consumed when still firm without the need for artificial deastringency treatments. While the PCNA trait has been shown to be conferred by a recessive allele at a single locus (ASTRINGENCY; AST), the exact genetic determinant remains unidentified. Here, we conducted transcriptome analyses to elucidate the regulatory mechanism underlying this trait using developing fruits of an F₁ population segregating for the PCNA trait. Comparisons of the transcriptomes of PCNA and non-PCNA individuals and hierarchical clustering revealed that genes related to the flavonoid pathway and to abiotic stress responses involving light stimulation were expressed coordinately with PA accumulation. Furthermore, coexpression network analyses suggested that three putative transcription factors were central to the PA regulatory network and that at least DkMYB4 and/or DkMYC1, which have been reported to form a protein complex with each other for PA regulation, may have a central role in the differential expression of PA biosynthetic pathway genes between PCNA and non-PCNA.

Amylin Treatment Reduces Neuroinflammation and Ameliorates Abnormal Patterns of Gene Expression in the Cerebral Cortex of an Alzheimer's Disease Mouse Model

Our recent study has demonstrated that peripheral amylin treatment reduces the amyloid pathology in the brain of Alzheimer's disease (AD) mouse models, and improves their learning and memory. We hypothesized that the beneficial effects of amylin for AD was beyond reducing the amyloids in the brain, and have now directly tested the actions of amylin on other aspects of AD pathogenesis, especially neuroinflammation. A 10-week course of peripheral amylin treatment significantly reduced levels of cerebral inflammation markers, Cd68 and Iba1, in amyloid precursor protein (APP) transgenic mice. Mechanistic studies indicated the protective effect of amylin required interaction with its cognate receptor because silencing the amylin receptor expression blocked the amylin effect on Cd68 in microglia. Using weighted gene co-expression network analysis, we discovered that amylin treatment influenced two gene modules linked with amyloid pathology: 1) a module related to proinflammation and transport/vesicle process that included a hub gene of Cd68, and 2) a module related to mitochondria function that included a hub gene of Atp5b. Amylin treatment restored the expression of most genes in the APP cortex toward levels observed in the wild-type (WT) cortex in these two modules including Cd68 and Atp5b. Using a human dataset, we found that the expression levels of Cd68 and Atp5b were significantly correlated with the neurofibrillary tangle burden in the AD brain and with their cognition. These data suggest that amylin acts on the pathological cascade in animal models of AD, and further supports the therapeutic potential of amylin-type peptides for AD.

Aberrant transcriptional networks in step-wise neurogenesis of paroxysmal kinesigenic dyskinesia-induced pluripotent stem cells

Paroxysmal kinesigenic dyskinesia (PKD) is an episodic movement disorder with autosomal-dominant inheritance and marked variability in clinical manifestations.

Proline-rich transmembrane protein 2 (PRRT2) has been identified as a causative gene of PKD, but the molecular mechanism underlying the pathogenesis of PKD still remains a mystery. The phenotypes and transcriptional patterns of the PKD disease need further clarification. Here, we report the generation and neural differentiation of iPSC lines from two familial PKD patients with c.487C>T (p. Gln163X) and c.573dupT (p. Gly192Trpfs*8) PRRT2 mutations, respectively. Notably, an extremely lower efficiency in neural conversion from PKD-iPSCs than control-iPSCs is observed by a step-wise neural differentiation method of dual inhibition of SMAD signaling. Moreover, we show the high expression level of PRRT2 throughout the human brain and the expression pattern of PRRT2 in other human tissues for the first time. To gain molecular insight into the development of the disease, we conduct global gene expression profiling of PKD cells at four different stages of neural induction and identify altered gene expression patterns, which peculiarly reflect dysregulated neural transcriptome signatures and a differentiation tendency to mesodermal development, in comparison to control-iPSCs. Additionally, functional and signaling pathway analyses indicate significantly different cell fate determination between PKD-iPSCs and control-iPSCs. Together, the establishment of PKD-specific in vitro models and the illustration of transcriptome features in PKD cells would certainly help us with better understanding of the defects in neural conversion as well as further investigations in the pathogenesis of the PKD disease.

GeneDive: A gene interaction search and visualization tool to facilitate precision medicine

Obtaining relevant information about gene interactions is critical for understanding disease processes and treatment. With the rise in text mining approaches, the volume of such biomedical data is rapidly increasing, thereby creating a new problem for the users of this data: information overload. A tool for efficient querying and visualization of biomedical data that helps researchers understand the underlying biological mechanisms for diseases and drug responses, and ultimately helps patients, is sorely needed. To this end we have developed GeneDive, a web-based information retrieval, filtering, and visualization tool for large volumes of gene interaction data. GeneDive offers various features and modalities that guide the user through the search process to efficiently reach the information of their interest. GeneDive currently processes over three million gene-gene interactions with response times within a few seconds. For over half of the curated gene sets sourced from four prominent databases, more than 80% of the gene set members are recovered by GeneDive. In the near future, GeneDive will seamlessly accommodate other interaction types, such as gene-drug and gene-disease interactions, thus enabling full exploration of topics such as precision medicine. The GeneDive application and information about its underlying system architecture are available at http://www.genedive.net.

The IBD interactome: an integrated view of aetiology, pathogenesis and therapy

Crohn's disease and ulcerative colitis are prototypical complex diseases characterized by chronic and heterogeneous manifestations, induced by interacting environmental, genomic, microbial and immunological factors. These interactions result in an overwhelming complexity that cannot be tackled by studying the totality of each pathological component (an '-ome') in isolation without consideration of the interaction among all relevant -omes that yield an overall 'network effect'. The outcome of this effect is the 'IBD interactome', defined as a disease network in which dysregulation of individual -omes causes intestinal inflammation mediated by dysfunctional molecular modules. To define the IBD interactome, new concepts and tools are needed to implement a systems approach; an unbiased data-driven integration strategy that reveals key players of the system, pinpoints the central drivers of inflammation and enables development of targeted therapies. Powerful bioinformatics tools able to query and integrate multiple -omes are available, enabling the integration of genomic, epigenomic, transcriptomic, proteomic, metabolomic and microbiome information to build a comprehensive molecular map of IBD. This approach will enable identification of IBD molecular subtypes, correlations with clinical phenotypes and elucidation of the central hubs of the IBD interactome that will aid discovery of compounds that can specifically target the hubs that control the disease.

The potential role of genetic assimilation during maize domestication

Domestication research has largely focused on identification of morphological and genetic differences between extant populations of crops and their wild relatives. Little attention has been paid to the potential effects of environment despite substantial known changes in climate from the time of domestication to modern day. In recent research, the exposure of teosinte (i.e., wild maize) to environments similar to the time of domestication, resulted in a plastic induction of domesticated phenotypes in teosinte. These results suggest that early agriculturalists may have selected for genetic mechanisms that cemented domestication phenotypes initially induced by a plastic response of teosinte to environment, a process known as genetic assimilation. To better understand this phenomenon and the potential role of environment in maize domestication, we examined differential gene expression in maize (Zea mays ssp. mays) and teosinte (Zea mays ssp. parviglumis) between past and present conditions. We identified a gene set of over 2000 loci showing a change in expression across environmental conditions in teosinte and invariance in maize. In fact, overall we observed both greater plasticity in gene expression and more substantial changes in co-expressionnal networks in teosinte across environments when compared to maize. While these results suggest genetic assimilation played at least some role in domestication, genes showing expression patterns consistent with assimilation are not significantly enriched for previously identified domestication candidates, indicating assimilation did not have a genome-wide effect.

Gene co-expression network analysis for identifying modules and functionally enriched pathways in SCA2

Spinocerebellar ataxia type 2 (SCA2) is an autosomal dominant neurodegenerative disease caused by CAG repeat expansion in the ATXN2 gene. The repeat resides in an encoded region of the gene resulting in polyglutamine (polyQ) expansion which has been assumed to result in gain of function, predominantly, for the ATXN2 protein. We evaluated temporal cerebellar expression profiles by RNA sequencing of ATXN2Q127 mice versus wild-type (WT) littermates. ATXN2Q127 mice are characterized by a progressive motor phenotype onset, and have progressive cerebellar molecular and neurophysiological (Purkinje cell firing frequency) phenotypes. Our analysis revealed previously uncharacterized early and progressive abnormal patterning of cerebellar gene expression. Weighted Gene Coexpression Network Analysis revealed four gene modules that were significantly correlated with disease status, composed primarily of genes associated with GTPase signaling, calcium signaling and cell death. Of these genes, few overlapped with differentially expressed cerebellar genes that we identified in Atxn2-/- knockout mice versus WT littermates, suggesting that loss-of-function is not a significant component of disease pathology. We conclude that SCA2 is a disease characterized by gain of function for ATXN2.

Identification of differential co-expressed gene networks in early rheumatoid arthritis achieving sustained drug-free remission after treatment with a tocilizumab-based or methotrexate-based strategy

BACKGROUND

Methotrexate is endorsed to be used as first-line treatment in rheumatoid arthritis (RA). However, a large proportion of patients need additional treatment with a biological disease-modifying anti-rheumatic drug (DMARD) to adequately suppress their disease activity. A better understanding of genotypes could help to distinguish between patients with different pathogenic mechanisms. The aim of this study was therefore to identify networks of genes within DMARD-naive early RA patients associated with achieving sustained drug-free remission (sDFR) after initiating tocilizumab plus methotrexate, tocilizumab, or methotrexate therapy.

METHODS

Samples were used from 60 patients from the U-Act-Early study who received tocilizumab plus methotrexate, tocilizumab, or methotrexate therapy, and who achieved sDFR (≥3 months in drug-free remission until the end of the study, n = 37) after therapy was tapered and subsequently stopped, or who were not able to discontinue the therapy as controls (n = 23). Whole blood samples were collected and ribonucleic acid (RNA) was isolated from positive cluster of differentiation 4 (CD4⁺) and CD14⁺ cells and analysed using high-throughput sequencing. Weighted gene co-expression network analyses were performed to identify clusters (i.e. modules) of differently expressed genes associated with achieving sDFR and which were subsequently used for pathway analyses.

RESULTS

Network analyses within CD4⁺ cells identified two significant modules in the tocilizumab plus methotrexate arm and four modules in the tocilizumab and methotrexate arms, respectively (p ≤ 0.039). Important pathways in the module best correlating with achieving sDFR were in the tocilizumab plus methotrexate arm related to processes involved with transcription and translation; in the tocilizumab arm, pathways were related to migration of white blood cells and G-protein coupled receptors, and in the methotrexate arm pathways were involved with the response to a bacterial or biotic (i.e. biological material)-related stimulus. No relevant networks could be identified in the sequenced CD14⁺ cells.

CONCLUSIONS

Within networks of co-expressed genes, several pathways were found related to achieving sDFR after initiating therapy with tocilizumab, methotrexate, or the combination. Between the three strategy arms, we identified different networks of predisposing genes which indicates that specific gene expression profiles, depending on the treatment strategy chosen, are associated with a higher chance of achieving sDFR.

TRIAL REGISTRATION

Clinicaltrials.gov, NCT01034137 . Registered on 16 December 2009.

Whole-transcriptome analysis delineates the human placenta gene network and its associations with fetal growth

BACKGROUND

The placenta is the principal organ regulating intrauterine growth and development, performing critical functions on behalf of the developing fetus. The delineation of functional networks and pathways driving placental processes has the potential to provide key insight into intrauterine perturbations that result in adverse birth as well as later life health outcomes.

RESULTS

We generated the transcriptome-wide profile of 200 term human placenta using the Illumina HiSeq 2500 platform and characterized the functional placental gene network using weighted gene coexpression network analysis (WGCNA). We identified 17 placental coexpression network modules that were dominated by functional processes including growth, organ development, gas exchange and immune response. Five network modules, enriched for processes including cellular respiration, amino acid transport, hormone signaling, histone modifications and gene expression, were associated with birth weight; hub genes of all five modules (CREB3, DDX3X, DNAJC14, GRHL1 and C21orf91) were significantly associated with fetal growth restriction, and one hub gene (CREB3) was additionally associated with fetal overgrowth.

CONCLUSIONS

In this largest RNA-Seq based transcriptome-wide profiling study of human term placenta conducted to date, we delineated a placental gene network with functional relevance to fetal growth using a network-based approach with superior scale reduction capacity. Our study findings not only implicate potential molecular mechanisms underlying fetal growth but also provide a reference placenta gene network to inform future studies investigating placental dysfunction as a route to future disease endpoints.

From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data

The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.

A taxonomy of visualization tasks for the analysis of biological pathway data

BACKGROUND

Understanding complicated networks of interactions and chemical components is essential to solving contemporary problems in modern biology, especially in domains such as cancer and systems research. In these domains, biological pathway data is used to represent chains of interactions that occur within a given biological process. Visual representations can help researchers understand, interact with, and reason about these complex pathways in a number of ways. At the same time, these datasets offer unique challenges for visualization, due to their complexity and heterogeneity.

RESULTS

Here, we present taxonomy of tasks that are regularly performed by researchers who work with biological pathway data. The generation of these tasks was done in conjunction with interviews with several domain experts in biology. These tasks require further classification than is provided by existing taxonomies. We also examine existing visualization techniques that support each task, and we discuss gaps in the existing visualization space revealed by our taxonomy.

CONCLUSIONS

Our taxonomy is designed to support the development and design of future biological pathway visualization applications. We conclude by suggesting future research directions based on our taxonomy and motivated by the comments received by our domain experts.

Intra- and interregional coregulation of opioid genes: broken symmetry in spinal circuits

Regulation of the formation and rewiring of neural circuits by neuropeptides may require coordinated production of these signaling molecules and their receptors that may be established at the transcriptional level. Here, we address this hypothesis by comparing absolute expression levels of opioid peptides with their receptors, the largest neuropeptide family, and by characterizing coexpression (transcriptionally coordinated) patterns of these genes. We demonstrated that expression patterns of opioid genes highly correlate within and across functionally and anatomically different areas. Opioid peptide genes, compared with their receptor genes, are transcribed at much greater absolute levels, which suggests formation of a neuropeptide cloud that covers the receptor-expressed circuits. Surprisingly, we found that both expression levels and the proportion of opioid receptors are strongly lateralized in the spinal cord, interregional coexpression patterns are side specific, and intraregional coexpression profiles are affected differently by left- and right-side unilateral body injury. We propose that opioid genes are regulated as interconnected components of the same molecular system distributed between distinct anatomic regions. The striking feature of this system is its asymmetric coexpression patterns, which suggest side-specific regulation of selective neural circuits by opioid neurohormones.-Kononenko, O., Galatenko, V., Andersson, M., Bazov, I., Watanabe, H., Zhou, X. W., Iatsyshyna, A., Mityakina, I., Yakovleva, T., Sarkisyan, D., Ponomarev, I., Krishtal, O., Marklund, N., Tonevitsky, A., Adkins, D. L., Bakalkin, G. Intra- and interregional coregulation of opioid genes: broken symmetry in spinal circuits.

Transcriptome sequencing of the choroid plexus in schizophrenia

The choroid plexus (CP) has a key role in maintaining brain homeostasis by producing cerebrospinal fluid (CSF), by mediating transport of nutrients and removing metabolic products from the central nervous system and by responding to peripheral inflammatory signals. Although abnormal markers of immune response and inflammation are apparent in individuals with schizophrenia, the CP of these individuals has not been characterized. We therefore sequenced mRNA from the CP from two independent collections of individuals with schizophrenia and unaffected controls. Genes related to immune function and inflammation were upregulated in both collections. In addition, a co-expression module related to immune/inflammation response that was generated by combining mRNA-Seq data from both collections was significantly associated with disease status. The immune/inflammation-related co-expression module was positively correlated with levels of C-reactive protein (CRP), cortisol and several immune modulator proteins in the serum of the same individuals and was also positively correlated with CRP, cortisol and pro-inflammatory cytokines in the frontal cortex of the same individuals. In addition, we found a substantial number of nodes (genes) that were common to our schizophrenia-associated immune/inflammation module from the pooled data and a module we generated from lippopolysaccharides-treated mouse model data. These results suggest that the CP of individuals with schizophrenia are responding to signals from the periphery by upregulating immune/inflammation-related genes to protect the brain and maintain the homeostasis but nevertheless fails to completely prevent immune/inflammation related changes in the brain.

Rack1 Mediates the Interaction of P-Glycoprotein with Anxa2 and Regulates Migration and Invasion of Multidrug-Resistant Breast Cancer Cells

The emergence of multidrug resistance is always associated with more rapid tumor recurrence and metastasis. P-glycoprotein (P-gp), which is a well-known multidrug-efflux transporter, confers enhanced invasion ability in drug-resistant cells. Previous studies have shown that P-gp probably exerts its tumor-promoting function via protein-protein interaction. These interactions were implicated in the activation of intracellular signal transduction. We previously showed that P-gp binds to Anxa2 and promotes the invasiveness of multidrug-resistant (MDR) breast cancer cells through regulation of Anxa2 phosphorylation. However, the accurate mechanism remains unclear. In the present study, a co-immunoprecipitation coupled with liquid chromatography tandem mass spectrometry-based interactomic approach was performed to screen P-gp binding proteins. We identified Rack1 as a novel P-gp binding protein. Knockdown of Rack1 significantly inhibited proliferation and invasion of MDR cancer cells. Mechanistic studies demonstrated that Rack1 functioned as a scaffold protein that mediated the binding of P-gp to Anxa2 and Src. We showed that Rack1 regulated P-gp activity, which was necessary for adriamycin-induced P-gp-mediated phosphorylation of Anxa2 and Erk1/2. Overall, the findings in this study augment novel insights to the understanding of the mechanism employed by P-gp for promoting migration and invasion of MDR cancer cells.

Cell-type deconvolution with immune pathways identifies gene networks of host defense and immunopathology in leprosy

Transcriptome profiles derived from the site of human disease have led to the identification of genes that contribute to pathogenesis, yet the complex mixture of cell types in these lesions has been an obstacle for defining specific mechanisms. Leprosy provides an outstanding model to study host defense and pathogenesis in a human infectious disease, given its clinical spectrum, which interrelates with the host immunologic and pathologic responses. Here, we investigated gene expression profiles derived from skin lesions for each clinical subtype of leprosy, analyzing gene coexpression modules by cell-type deconvolution. In lesions from tuberculoid leprosy patients, those with the self-limited form of the disease, dendritic cells were linked with MMP12 as part of a tissue remodeling network that contributes to granuloma formation. In lesions from lepromatous leprosy patients, those with disseminated disease, macrophages were linked with a gene network that programs phagocytosis. In erythema nodosum leprosum, neutrophil and endothelial cell gene networks were identified as part of the vasculitis that results in tissue injury. The present integrated computational approach provides a systems approach toward identifying cell-defined functional networks that contribute to host defense and immunopathology at the site of human infectious disease.