Conservation and evolution of gene coexpression networks in human and chimpanzee brains

Sex-Specific Co-expression Networks and Sex-Biased Gene Expression in the Salmonid Brook Charr Salvelinus fontinalis

Networks of co-expressed genes produce complex phenotypes associated with functional novelty. Sex differences in gene expression levels or in the structure of gene co-expression networks can cause sexual dimorphism and may resolve sexually antagonistic selection. Here we used RNA-sequencing in the salmonid Brook Charr Salvelinus fontinalis to characterize sex-specific co-expression networks in the liver of 47 female and 53 male offspring. In both networks, modules were characterized for functional enrichment, hub gene identification, and associations with 15 growth, reproduction, and stress-related phenotypes. Modules were then evaluated for preservation in the opposite sex, and in the congener Arctic Charr Salvelinus alpinus Overall, more transcripts were assigned to a module in the female network than in the male network, which coincided with higher inter-individual gene expression and phenotype variation in the females. Most modules were preserved between sexes and species, including those involved in conserved cellular processes (e.g., translation, immune pathways). However, two sex-specific male modules were identified, and these may contribute to sexual dimorphism. To compare with the network analysis, differentially expressed transcripts were identified between the sexes, revealing a total of 16% of expressed transcripts as sex-biased. For both sexes, there was no overrepresentation of sex-biased genes or sex-specific modules on the putative sex chromosome. Sex-biased transcripts were also not overrepresented in sex-specific modules, and in fact highly male-biased transcripts were enriched in preserved modules. Comparative network analysis and differential expression analyses identified different aspects of sex differences in gene expression, and both provided new insights on the genes underlying sexual dimorphism in the salmonid Brook Charr.

Proteomic Atlas of the Human Brain in Alzheimer's Disease

The brain represents one of the most divergent and critical organs in the human body. Yet, it can be afflicted by a variety of neurodegenerative diseases specifically linked to aging, about which we lack a full biomolecular understanding of onset and progression, such as Alzheimer's disease (AD). Here we provide a proteomic resource comprising nine anatomically distinct sections from three aged individuals, across a spectrum of disease progression, categorized by quantity of neurofibrillary tangles. Using state-of-the-art mass spectrometry, we identify a core brain proteome that exhibits only small variance in expression, accompanied by a group of proteins that are highly differentially expressed in individual sections and broader regions. AD affected tissue exhibited slightly elevated levels of tau protein with similar relative expression to factors associated with the AD pathology. Substantial differences were identified between previous proteomic studies of mature adult brains and our aged cohort. Our findings suggest considerable value in examining specifically the brain proteome of aged human populations from a multiregional perspective. This resource can serve as a guide, as well as a point of reference for how specific regions of the brain are affected by aging and neurodegeneration.

A modular analysis of microglia gene expression, insights into the aged phenotype

BACKGROUND

Microglia are multifunctional cells that are key players in brain development and homeostasis. Recent years have seen tremendous growth in our understanding of the role microglia play in neurodegeneration, CNS injury, and developmental disorders. Given that microglia show diverse functional phenotypes, there is a need for more precise tools to characterize microglial states. Here, we experimentally define gene modules as the foundation for describing microglial functional states.

RESULTS

In an effort to develop a comprehensive classification scheme, we profiled transcriptomes of mouse microglia in a stimulus panel with 96 different conditions. Using the transcriptomic data, we generated fine-resolution gene modules that are robustly preserved across datasets. These modules served as the basis for a combinatorial code that we then used to characterize microglial activation under various inflammatory stimulus conditions.

CONCLUSIONS

The microglial gene modules described here were robustly preserved, and could be applied to in vivo as well as in vitro conditions to dissociate the signaling pathways that distinguish acutely inflamed microglia from aged microglia. The microglial gene modules presented here are a novel resource for classifying and characterizing microglial states in health and disease.

WRINKLED1 transcription factor orchestrates the regulation of carbon partitioning for C18:1 (oleic acid) accumulation in Siberian apricot kernel

WRINKLED1 (WRI1), an APETALA2 (AP2)-type transcription factor, has been shown to be required for the regulation of carbon partitioning into fatty acid (FA) synthesis in plant seeds. To our knowledge, the regulatory network of WRI1 remains unknown in Prunus sibirica kernel (PSK), a novel woody biodiesel feedstock in China. In this study, based on the transcriptional data from developing oilseeds of multiple plant species, we identified 161 WRI1-coexpressed genes using weighted gene co-expression network analysis (WGCNA). The major portion of WRI1-coexpressed genes was characterized to be involved in carbon partitioning and FA biosynthesis. Additionally, we detected the temporal patterns for oil content and FA compositions in developing PSK from two different germplasms (AS-85 and AS-86). The major differences between the two germplasms are higher contents of oil and C18:1 in AS-85 than in AS-86 at a mature stage. Thus, AS-85 and AS-86 are desirable materials to explore the molecular and metabolic mechanisms of oil accumulation in Siberian apricot. Expression analysis in developing PSK of AS-85 and AS-86 indicated that the expression level of P. sibirica WRI1 (PsWRI1) was closely correlated to accumulative rate of oil. Also, the comparison of expression profiles in developing PSK of AS-85 and AS-86 displayed that the pPK, E1-α, E2, TAL, BC, MCMT, BS, SAD and FAD2 have a high correlation with PsWRI1. Transient expression showed that Pro_SAD- and Pro_BS-driving GUS expression showed no substantial difference between AS-85 and AS-86, while the expression level of Pro_PEPCK-AS-85 driving GUS was significantly higher than that of Pro_PEPCK-AS-86 driving GUS. Additionally, transient co-transformation with PsWRI1 revealed that Pro_SAD, Pro_PEPCK and Pro_BS activity could be specifically up-regulated by PsWRI1. This regulatory mechanism of PsWRI1 may create a steep concentration difference, thereby facilitating carbon flux into C18:1 accumulation in developing PSK. Overall, all our findings imply a versatile mechanism of WRI1 to optimize carbon allocation for oil accumulation, which can provide reference for researching the woody biodiesel plants.

Comparative transcriptome profiling of the human and mouse dorsal root ganglia: an RNA-seq-based resource for pain and sensory neuroscience research

Molecular neurobiological insight into human nervous tissues is needed to generate next-generation therapeutics for neurological disorders such as chronic pain. We obtained human dorsal root ganglia (hDRG) samples from organ donors and performed RNA-sequencing (RNA-seq) to study the hDRG transcriptional landscape, systematically comparing it with publicly available data from a variety of human and orthologous mouse tissues, including mouse DRG (mDRG). We characterized the hDRG transcriptional profile in terms of tissue-restricted gene coexpression patterns and putative transcriptional regulators, and formulated an information-theoretic framework to quantify DRG enrichment. Relevant gene families and pathways were also analyzed, including transcription factors, G-protein-coupled receptors, and ion channels. Our analyses reveal an hDRG-enriched protein-coding gene set (∼140), some of which have not been described in the context of DRG or pain signaling. Most of these show conserved enrichment in mDRG and were mined for known drug-gene product interactions. Conserved enrichment of the vast majority of transcription factors suggests that the mDRG is a faithful model system for studying hDRG, because of evolutionarily conserved regulatory programs. Comparison of hDRG and tibial nerve transcriptomes suggests trafficking of neuronal mRNA to axons in adult hDRG, and are consistent with studies of axonal transport in rodent sensory neurons. We present our work as an online, searchable repository (https://www.utdallas.edu/bbs/painneurosciencelab/sensoryomics/drgtxome), creating a valuable resource for the community. Our analyses provide insight into DRG biology for guiding development of novel therapeutics and a blueprint for cross-species transcriptomic analyses.

Gene expression in the social behavior network of the wire-tailed manakin (Pipra filicauda) brain

The vertebrate basal forebrain and midbrain contain a set of interconnected nuclei that control social behavior. Conserved anatomical structures and functions of these nuclei have now been documented among fish, amphibians, reptiles, birds and mammals, and these brain regions have come to be known as the vertebrate social behavior network (SBN). While it is known that nuclei (nodes) of the SBN are rich in steroid and neuropeptide activity linked to behavior, simultaneous variation in the expression of neuroendocrine genes among several SBN nuclei has not yet been described in detail. In this study, we use RNA-seq to profile gene expression across seven brain regions representing five nodes of the vertebrate SBN in a passerine bird, the wire-tailed manakin Pipra filicauda. Using weighted gene co-expression network analysis, we reconstructed sets of coregulated genes, showing striking patterns of variation in neuroendocrine gene expression across the SBN. We describe regional variation in gene networks comprising a broad set of hormone receptors, neuropeptides, steroidogenic enzymes, catecholamines and other neuroendocrine signaling molecules. Our findings show heterogeneous patterns of brain gene expression across nodes of the avian SBN and provide a foundation for future analyses of how the regulation of gene networks may mediate social behavior. These results highlight the importance of region-specific sampling in studies of the mechanisms of behavior.

The role of systems biology approaches in determining molecular signatures for the development of more effective vaccines

INTRODUCTION

Emerging infectious diseases are a major threat to public health, and while vaccines have proven to be one of the most effective preventive measures for infectious diseases, we still do not have safe and effective vaccines against many human pathogens, and emerging diseases continually pose new threats. The purpose of this review is to discuss how the creation of vaccines for these new threats has been hindered by limitations in the current approach to vaccine development. Recent advances in high-throughput technologies have enabled scientists to apply systems biology approaches to collect and integrate increasingly large datasets that capture comprehensive biological changes induced by vaccines, and then decipher the complex immune response to those vaccines.

AREAS COVERED

This review covers advances in these technologies and recent publications that describe systems biology approaches to understanding vaccine immune responses and to understanding the rational design of new vaccine candidates.

EXPERT OPINION

Systems biology approaches to vaccine development provide novel information regarding both the immune response and the underlying mechanisms and can inform vaccine development.

Integrative Analysis of Transcriptome and GWAS Data to Identify the Hub Genes Associated With Milk Yield Trait in Buffalo

The mammary gland is the production organ in mammals that is of great importance for milk production and quality. However, characterization of the buffalo mammary gland transcriptome and identification of the valuable candidate genes that affect milk production is limited. Here, we performed the differential expressed genes (DEGs) analysis of mammary gland tissue on day 7, 50, 140, and 280 after calving and conducted gene-based genome-wide association studies (GWAS) of milk yield in 935 Mediterranean buffaloes. We then employed weighted gene co-expression network analysis (WGCNA) to identify specific modules and hub genes related to milk yield based on gene expression profiles and GWAS data. The results of the DEGs analysis showed that a total of 1,420 DEGs were detected across different lactation points. In the gene-based analysis, 976 genes were found to have genome-wide association (P ≤ 0.05) that could be defined as the nominally significant GWAS geneset (NSGG), 9 of which were suggestively associated with milk yield (P < 10⁻⁴). Using the WGCNA analysis, 544 and 225 genes associated with milk yield in the turquoise module were identified from DEGs and NSGG datasets, respectively. Several genes (including BNIPL, TUBA1C, C2CD4B, DCP1B, MAP3K5, PDCD11, SRGAP1, GDPD5, BARX2, SCARA3, CTU2, and RPL27A) were identified and considered as the hub genes because they were involved in multiple pathways related to milk production. Our findings provide an insight into the dynamic characterization of the buffalo mammary gland transcriptome, and these potential candidate genes may be valuable for future functional characterization of the buffalo mammary gland.

Assessment of weighted topological overlap (wTO) to improve fidelity of gene co-expression networks

BACKGROUND

For more than a decade, gene expression data sets have been used as basis for the construction of co-expression networks used in systems biology investigations, leading to many important discoveries in a wide range of subjects spanning human disease to evolution and the development of organisms. A commonly encountered challenge in such investigations is first that of detecting, then subsequently removing, spurious correlations (i.e. links) in these networks. While access to a large number of measurements per gene would reduce this problem, often only a small number of measurements are available. The weighted Topological Overlap (wTO) measure, which incorporates information from the shared network-neighborhood of a given gene-pair into a single score, is a metric that is frequently used with the implicit expectation of producing higher-quality networks. However, the actual extent to which wTO improves on the accuracy of a co-expression analysis has not been quantified.

RESULTS

Here, we used a large-sample biological data set containing 338 gene-expression measurements per gene as a reference system. From these data, we generated ensembles consisting of 10, 20 and 50 randomly selected measurements to emulate low-quality data sets, finding that the wTO measure consistently generates more robust scores than what results from simple correlation calculations. Furthermore, for the data sets consisting of only 10 and 20 samples per gene, we find that wTO serves as a better predictor of the correlation scores generated from the full data set. However, we find that using wTO as a score for network building substantially alters several topographical aspects of the resulting networks, with no conclusive evidence that the resulting structure is more accurate. Importantly, we find that the much used approach of applying a soft-threshold modifier to link weights prior to computing the wTO substantially decreases the robustness of the resulting wTO network, but increases the predictive power of wTO networks with regards to the reference correlation (soft threshold) network, particularly as the size of the data sets increases.

CONCLUSION

Our analysis demonstrates that, in agreement with previous assumptions, the wTO approach is capable of significantly improving the fidelity of co-expression networks, and that this effect is especially evident for cases of low-sample number gene-expression data sets.

A Microglial Signature Directing Human Aging and Neurodegeneration-Related Gene Networks

Aging is regarded as a major risk factor for neurodegenerative diseases. Thus, a better understanding of the similarities between the aging process and neurodegenerative diseases at the cellular and molecular level may reveal better understanding of this detrimental relationship. In the present study, we mined publicly available gene expression datasets from healthy individuals and patients affected by neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, and Huntington's disease) across a broad age spectrum and compared those with mouse aging and mouse cell-type specific gene expression profiles. We performed weighted gene co-expression network analysis (WGCNA) and found a gene network strongly related with both aging and neurodegenerative diseases. This network was significantly enriched with a microglial signature as imputed from cell type-specific sequencing data. Since mouse models are extensively used for the study of human diseases, we further compared these human gene regulatory networks with age-specific mouse brain transcriptomes. We discovered significantly preserved networks with both human aging and human disease and identified 17 shared genes in the top-ranked immune/microglia module, among which we found five human hub genes TYROBP, FCER1G, ITGB2, MYO1F, PTPRC, and two mouse hub genes Trem2 and C1qa. Taken together, these results support the hypothesis that microglia are key players involved in human aging and neurodegenerative diseases, and suggest that mouse models should be appropriate for studying these microglial changes in human.

Assessment of complementarity of WGCNA and NERI results for identification of modules associated to schizophrenia spectrum disorders

Psychiatric disorders involve both changes in multiple genes as well different types of variations. As such, gene co-expression networks allowed the comparison of different stages and parts of the brain contributing to an integrated view of genetic variation. Two methods based on co-expression networks presents appealing results: Weighted Gene Correlation Network Analysis (WGCNA) and Network-Medicine Relative Importance (NERI). By selecting two different gene expression databases related to schizophrenia, we evaluated the biological modules selected by both WGCNA and NERI along these databases as well combining both WGCNA and NERI results (WGCNA-NERI). Also we conducted a enrichment analysis for the identification of partial biological function of each result (as well a replication analysis). To appraise the accuracy of whether both algorithms (as well our approach, WGCNA-NERI) were pointing to genes related to schizophrenia and its complex genetic architecture we conducted the MSET analysis, based on a reference gene list of schizophrenia database (SZDB) related to DNA Methylation, Exome, GWAS as well as copy number variation mutation studies. The WGCNA results were more associated with inflammatory pathways and immune system response; NERI obtained genes related with cellular regulation, embryological pathways e cellular growth factors. Only NERI were able to provide a statistical meaningful results to the MSET analysis (for Methylation and de novo mutations data). However, combining WGCNA and NERI provided a much more larger overlap in these two categories and additionally on Transcriptome database. Our study suggests that using both methods in combination is better for establishing a group of modules and pathways related to a complex disease than using each method individually. NERI is available at: https://bitbucket.org/sergionery/neri.

Exploiting locational and topological overlap model to identify modules in protein interaction networks

Background

Clustering molecular network is a typical method in system biology, which is effective in predicting protein complexes or functional modules. However, few studies have realized that biological molecules are spatial-temporally regulated to form a dynamic cellular network and only a subset of interactions take place at the same location in cells.

Results

In this study, considering the subcellular localization of proteins, we first construct a co-localization human protein interaction network (PIN) and systematically investigate the relationship between subcellular localization and biological functions. After that, we propose a Locational and Topological Overlap Model (LTOM) to preprocess the co-localization PIN to identify functional modules. LTOM requires the topological overlaps, the common partners shared by two proteins, to be annotated in the same localization as the two proteins. We observed the model has better correspondence with the reference protein complexes and shows more relevance to cancers based on both human and yeast datasets and two clustering algorithms, ClusterONE and MCL.

Conclusion

Taking into consideration of protein localization and topological overlap can improve the performance of module detection from protein interaction networks.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2598-7) contains supplementary material, which is available to authorized users.

Differential Connectivity in Colorectal Cancer Gene Expression Network

BACKGROUND

Colorectal cancer (CRC) is one of the challenging types of cancers; thus, exploring effective biomarkers related to colorectal could lead to significant progresses toward the treatment of this disease.

METHODS

In the present study, CRC gene expression datasets have been reanalyzed. Mutual differentially expressed genes across 294 normal mucosa and adjacent tumoral samples were then utilized in order to build two independent transcriptional regulatory networks. By analyzing the networks topologically, genes with differential global connectivity related to cancer state were determined for which the potential transcriptional regulators including transcription factors were identified.

RESULTS

The majority of differentially connected genes (DCGs) were up-regulated in colorectal transcriptome experiments. Moreover, a number of these genes have been experimentally validated as cancer or CRC-associated genes. The DCGs, including GART, TGFB1, ITGA2, SLC16A5, SOX9, and MMP7, were investigated across 12 cancer types. Functional enrichment analysis followed by detailed data mining exhibited that these candidate genes could be related to CRC by mediating in metastatic cascade in addition to shared pathways with 12 cancer types by triggering the inflammatory events.

DISCUSSION

Our study uncovered correlated alterations in gene expression related to CRC susceptibility and progression that the potent candidate biomarkers could provide a link to disease.

A network approach to quantifying radiotherapy effect on cancer: Radiosensitive gene group centrality

Radiotherapy plays a vital role in cancer treatment, for which accurate prognosis is important for guiding sequential treatment and improving the curative effect for patients. An issue of great significance in radiotherapy is to assess tumor radiosensitivity for devising the optimal treatment strategy. Previous studies focused on gene expression in cells closely associated with radiosensitivity, but factors such as the response of a cancer patient to irradiation and the patient survival time are largely ignored. For clinical cancer treatment, a specific pre-treatment indicator taking into account cancer cell type and patient radiosensitivity is of great value but it has been missing. Here, we propose an effective indicator for radiosensitivity: radiosensitive gene group centrality (RSGGC), which characterizes the importance of the group of genes that are radiosensitive in the whole gene correlation network. We demonstrate, using both clinical patient data and experimental cancer cell lines, which RSGGC can provide a quantitative estimate of the effect of radiotherapy, with factors such as the patient survival time and the survived fraction of cancer cell lines under radiotherapy fully taken into account. Our main finding is that, for patients with a higher RSGGC score before radiotherapy, cancer treatment tends to be more effective. The RSGGC can have significant applications in clinical prognosis, serving as a key measure to classifying radiosensitive and radioresistant patients.

Large-scale associations between the leukocyte transcriptome and BOLD responses to speech differ in autism early language outcome subtypes

Heterogeneity in early language development in autism spectrum disorders (ASD) is clinically important and may reflect neurobiologically distinct subtypes. Here we identify a large-scale association between multiple coordinated blood leukocyte gene co-expression modules and multivariate functional neuroimaging (fMRI) response to speech. Gene co-expression modules associated with multivariate fMRI response to speech are different for all pairwise comparisons between typically developing toddlers and toddlers with ASD and either poor versus good early language outcome. Associated co-expression modules are enriched in genes that are broadly expressed in the brain and many other tissues. These co-expression modules are also enriched for ASD, prenatal, human-specific and language-relevant genes. This work highlights distinctive neurobiology in ASD subtypes with different early language outcomes that is present well before such outcomes are known. Associations between neuroimaging measures and gene expression levels in blood leukocytes may offer a unique in-vivo window into identifying brain-relevant molecular mechanisms in ASD.

C3HeB/FeJ Mice mimic many aspects of gene expression and pathobiological features of human hepatocellular carcinoma

Hepatocellular carcinoma (HCC) remains a deadly cancer, underscoring the need for relevant preclinical models. Male C3HeB/FeJ mice model spontaneous HCC with some hepatocarcinogenesis susceptibility loci corresponding to syntenic regions of human chromosomes altered in HCC. We tested other properties of C3HeB/FeJ tumors for similarity to human HCC. C3HeB/FeJ tumors were grossly visible at 4 months of age, with prevalence and size increasing until about 11 months of age. Histologic features shared with human HCC include hepatosteatosis, tumor progression from dysplasia to poorly differentiated, vascular invasion, and trabecular, oncocytic, vacuolar, and clear cell variants. More tumor cells displayed cytoplasmic APE1 staining versus normal liver. Ultrasound effectively detected and monitored tumors, with 85.7% sensitivity. Over 5000 genes were differentially expressed based on the GSE62232 and GSE63898 human HCC datasets. Of these, 158 and 198 genes, respectively, were also differentially expressed in C3HeB/FeJ. Common cancer pathways, cell cycle, p53 signaling and other molecular aspects, were shared between human and mouse differentially expressed genes. We established eigengenes that distinguish HCC from normal liver in the C3HeB/FeJ model and a subset of human HCC. These features extend the relevance and improve the utility of the C3HeB/FeJ line for HCC studies.

Transcriptional co-expression regulatory network analysis for Snail and Slug identifies IL1R1, an inflammatory cytokine receptor, to be preferentially expressed in ST-EPN-RELA and PF-EPN-A molecular subgroups of intracranial ependymomas

Recent molecular subgrouping of ependymomas (EPN) by DNA methylation profiling has identified ST-EPN-RELA and PF-EPN-A subgroups to be associated with poor outcome. Snail/Slug are cardinal epithelial-to-mesenchymal transcription factors (EMT-TFs) and are overexpressed in several CNS tumors, including EPNs. A systematic analysis of gene-sets/modules co-expressed with Snail and Slug genes using published expression microarray dataset (GSE27279)identified 634 genes for Snail with enriched TGF-β, PPAR and PI3K signaling pathways, and 757 genes for Slug with enriched focal adhesion, ECM-receptor interaction and regulation of actin cytoskeleton related pathways. Of 37 genes commonly expressed with both Snail and Slug, IL1R1, a cytokine receptor of interleukin-1 receptor family, was positively correlated with Snail (r=0.43) and Slug (r=0.51), preferentially expressed in ST-EPN-RELA and PF-EPN-A molecular groups, and enriched for pathways related to inflammation, angiogenesis and glycolysis. IL1R1 expression was fairly specific to EPNs among various CNS tumors analyzed. It also showed significant positive correlation with EMT, stemness and MDSC (myeloid derived suppressor cell) markers. Our study reports IL1R1 as a poor prognostic marker associated with EMT-like phenotype and stemness in EPNs. Our findings emphasize the need to further examine and validate IL1R1 as a novel therapeutic target in aggressive subsets of intracranial EPNs.

Postmitotic cell longevity-associated genes: a transcriptional signature of postmitotic maintenance in neural tissues

Different cell types have different postmitotic maintenance requirements. Nerve cells, however, are unique in this respect as they need to survive and preserve their functional complexity for the entire lifetime of the organism, and failure at any level of their supporting mechanisms leads to a wide range of neurodegenerative conditions. Whether these differences across tissues arise from the activation of distinct cell type-specific maintenance mechanisms or the differential activation of a common molecular repertoire is not known. To identify the transcriptional signature of postmitotic cellular longevity (PMCL), we compared whole-genome transcriptome data from human tissues ranging in longevity from 120 days to over 70 years and found a set of 81 genes whose expression levels are closely associated with increased cell longevity. Using expression data from 10 independent sources, we found that these genes are more highly coexpressed in longer-living tissues and are enriched in specific biological processes and transcription factor targets compared with randomly selected gene samples. Crucially, we found that PMCL-associated genes are downregulated in the cerebral cortex and substantia nigra of patients with Alzheimer's and Parkinson's disease, respectively, as well as Hutchinson-Gilford progeria-derived fibroblasts, and that this downregulation is specifically linked to their underlying association with cellular longevity. Moreover, we found that sexually dimorphic brain expression of PMCL-associated genes reflects sexual differences in lifespan in humans and macaques. Taken together, our results suggest that PMCL-associated genes are part of a generalized machinery of postmitotic maintenance and functional stability in both neural and non-neural cells and support the notion of a common molecular repertoire differentially engaged in different cell types with different survival requirements.

GLIS2 redundancy causes chemoresistance and poor prognosis of gastric cancer based on co‑expression network analysis

Gastric cancer is currently the fourth most common cancer and the third leading cause of cancer-associated mortality worldwide. Studies have identified that certain biomarkers contribute to the prognosis, diagnosis and treatment of gastric cancer. However, the biomarkers of gastric cancer are rarely used clinically. Therefore, it is imperative to define novel molecular networks and key genes to guide the further study and clinical treatment of gastric cancer. In the present study, raw RNA sequencing data and clinicopathological information on patients with gastric cancer were downloaded from The Cancer Genome Atlas, and a weighted gene co-expression network analysis was conducted. Additionally, functional enrichment and protein-protein interaction analyses were implemented to further examine the significant modules. As a result, 16 modules of highly correlated genes were acquired and colour coded, and the yellow module containing 174 genes associated with chemotherapy resistance and prognosis in gastric cancer was further analysed. The biological processes of the yellow module were primarily associated with cell adhesion, vasculature development and the regulation of cell proliferation. In addition, the Kyoto Encyclopedia of Genes and Genomes pathways primarily involved the transforming growth factor-β signalling pathway, the cellular tumour antigen p53 signalling pathway, extracellular matrix-receptor interactions and focal adhesions. Notably, survival analysis and cell verification confirmed that high expression of GLIS family zinc finger 2 is significantly associated with chemoresistance and a worse prognosis in gastric cancer, and that this high expression is likely to be an important biomarker for the guidance of clinical treatment and prognostic evaluation.

Comparative Hippocampal Synaptic Proteomes of Rodents and Primates: Differences in Neuroplasticity-Related Proteins

Key to the human brain’s unique capacities are a myriad of neural cell types, specialized molecular expression signatures, and complex patterns of neuronal connectivity. Neurons in the human brain communicate via well over a quadrillion synapses. Their specific contribution might be key to the dynamic activity patterns that underlie primate-specific cognitive function. Recently, functional differences were described in transmission capabilities of human and rat synapses. To test whether unique expression signatures of synaptic proteins are at the basis of this, we performed a quantitative analysis of the hippocampal synaptic proteome of four mammalian species, two primates, human and marmoset, and two rodents, rat and mouse. Abundance differences down to 1.15-fold at an FDR-corrected p-value of 0.005 were reliably detected using SWATH mass spectrometry. The high measurement accuracy of SWATH allowed the detection of a large group of differentially expressed proteins between individual species and rodent vs. primate. Differentially expressed proteins between rodent and primate were found highly enriched for plasticity-related proteins.

Transcriptomics and co-expression networks reveal tissue-specific responses and regulatory hubs under mild and severe drought in papaya (Carica papaya L.)

Plants respond to drought stress through the ABA dependent and independent pathways, which in turn modulate transcriptional regulatory hubs. Here, we employed Illumina RNA-Seq to analyze a total of 18 cDNA libraries from leaves, sap, and roots of papaya plants under drought stress. Reference and de novo transcriptomic analyses identified 8,549 and 6,089 drought-responsive genes and unigenes, respectively. Core sets of 6 and 34 genes were simultaneously up- or down-regulated, respectively, in all stressed samples. Moreover, GO enrichment analysis revealed that under moderate drought stress, processes related to cell cycle and DNA repair were up-regulated in leaves and sap; while responses to abiotic stress, hormone signaling, sucrose metabolism, and suberin biosynthesis were up-regulated in roots. Under severe drought stress, biological processes related to abiotic stress, hormone signaling, and oxidation-reduction were up-regulated in all tissues. Moreover, similar biological processes were commonly down-regulated in all stressed samples. Furthermore, co-expression network analysis revealed three and eight transcriptionally regulated modules in leaves and roots, respectively. Seventeen stress-related TFs were identified, potentially serving as main regulatory hubs in leaves and roots. Our findings provide insight into the molecular responses of papaya plant to drought, which could contribute to the improvement of this important tropical crop.

Peroxisome Proliferator Activated Receptor Agonists Modulate Transposable Element Expression in Brain and Liver

Peroxisome proliferator activated receptors (PPARs) are nuclear hormone receptors that act as transcription factors in response to endogenous lipid messengers. The fibrates and thiazolidinediones are synthetic PPAR agonists used clinically to treat dyslipidemia and Type 2 Diabetes Mellitus, respectively, but also improve symptoms of several other diseases. Transposable elements (TEs), repetitive sequences in mammalian genomes, are implicated in many of the same conditions for which PPAR agonists are therapeutic, including neurodegeneration, schizophrenia, and drug addiction. We tested the hypothesis that there is a link between actions of PPAR agonists and TE expression. We developed an innovative application of microarray data by mapping Illumina mouse WG-6 microarray probes to areas of the mouse genome that contain TEs. Using this information, we assessed the effects of systemic administration of three PPAR agonists with different PPAR subtype selectivity: fenofibrate, tesaglitazar, and bezafibrate, on TE probe expression in mouse brain [prefrontal cortex (PFC) and amygdala] and liver. We found that fenofibrate, and bezafibrate to a lesser extent, up-regulated probes mapped to retrotransposons: Short-Interspersed Elements (SINEs) and Long-Interspersed Elements (LINEs), in the PFC. Conversely, all PPAR agonists down-regulated LINEs and tesaglitazar and bezafibrate also down-regulated SINEs in liver. We built gene coexpression networks that partitioned the diverse transcriptional response to PPAR agonists into groups of probes with highly correlated expression patterns (modules). Most of the differentially expressed retrotransposons were within the same module, suggesting coordinated regulation of their expression, possibly by PPAR signaling. One TE module was conserved across tissues and was enriched with genes whose products participate in epigenetic regulation, suggesting that PPAR agonists affect TE expression via epigenetic mechanisms. Other enriched functional categories included phenotypes related to embryonic development and learning and memory, suggesting functional links between these biological processes and TE expression. In summary, these findings suggest mechanistic relationships between retrotransposons and PPAR agonists and provide a basis for future exploration of their functional roles in brain and liver.

Vocal practice regulates singing activity-dependent genes underlying age-independent vocal learning in songbirds

The development of highly complex vocal skill, like human language and bird songs, is underlain by learning. Vocal learning, even when occurring in adulthood, is thought to largely depend on a sensitive/critical period during postnatal development, and learned vocal patterns emerge gradually as the long-term consequence of vocal practice during this critical period. In this scenario, it is presumed that the effect of vocal practice is thus mainly limited by the intrinsic timing of age-dependent maturation factors that close the critical period and reduce neural plasticity. However, an alternative, as-yet untested hypothesis is that vocal practice itself, independently of age, regulates vocal learning plasticity. Here, we explicitly discriminate between the influences of age and vocal practice using a songbird model system. We prevented zebra finches from singing during the critical period of sensorimotor learning by reversible postural manipulation. This enabled to us to separate lifelong vocal experience from the effects of age. The singing-prevented birds produced juvenile-like immature song and retained sufficient ability to acquire a tutored song even at adulthood when allowed to sing freely. Genome-wide gene expression network analysis revealed that this adult vocal plasticity was accompanied by an intense induction of singing activity-dependent genes, similar to that observed in juvenile birds, rather than of age-dependent genes. The transcriptional changes of activity-dependent genes occurred in the vocal motor robust nucleus of the arcopallium (RA) projection neurons that play a critical role in the production of song phonology. These gene expression changes were accompanied by neuroanatomical changes: dendritic spine pruning in RA projection neurons. These results show that self-motivated practice itself changes the expression dynamics of activity-dependent genes associated with vocal learning plasticity and that this process is not tightly linked to age-dependent maturational factors.

How is plasticity associated with vocal learning regulated during a critical period? Although there are abundant studies on the critical period in sensory systems, which are passively regulated by the external environment, few studies have manipulated the sensorimotor experience through the entire critical period. Thus, it is a commonly held belief that age or intrinsic maturation is a crucial factor for the closure of the critical period of vocal learning. Contrary to this idea, our study using songbirds provides a new insight that self-motivated vocal practice, not age, regulates vocal learning plasticity during the critical period. To examine the effects of vocal practice on vocal learning, we prevented juvenile zebra finches from singing during the critical period by postural manipulation, which separated the contribution of lifelong vocal experience from that of age. When these birds were allowed to freely sing as adults, they generated highly plastic songs and maintained the ability to mimic tutored songs, as normal juveniles did. Genome-wide transcriptome analysis revealed that both juveniles and singing-prevented adults, but not normally reared adults, expressed a similar set of singing-dependent genes in a song nucleus in the brain that regulates syllable acoustics. However, age-dependent genes were still similarly expressed in both singing-prevented and normally reared adult birds. These results exhibit that vocal learning plasticity is actively controlled by self-motivated vocal practice.

Identification and replication of RNA-Seq gene network modules associated with depression severity

Genomic variation underlying major depressive disorder (MDD) likely involves the interaction and regulation of multiple genes in a network. Data-driven co-expression network module inference has the potential to account for variation within regulatory networks, reduce the dimensionality of RNA-Seq data, and detect significant gene-expression modules associated with depression severity. We performed an RNA-Seq gene co-expression network analysis of mRNA data obtained from the peripheral blood mononuclear cells of unmedicated MDD (n = 78) and healthy control (n = 79) subjects. Across the combined MDD and HC groups, we assigned genes into modules using hierarchical clustering with a dynamic tree cut method and projected the expression data onto a lower-dimensional module space by computing the single-sample gene set enrichment score of each module. We tested the single-sample scores of each module for association with levels of depression severity measured by the Montgomery-Åsberg Depression Scale (MADRS). Independent of MDD status, we identified 23 gene modules from the co-expression network. Two modules were significantly associated with the MADRS score after multiple comparison adjustment (adjusted p = 0.009, 0.028 at 0.05 FDR threshold), and one of these modules replicated in a previous RNA-Seq study of MDD (p = 0.03). The two MADRS-associated modules contain genes previously implicated in mood disorders and show enrichment of apoptosis and B cell receptor signaling. The genes in these modules show a correlation between network centrality and univariate association with depression, suggesting that intramodular hub genes are more likely to be related to MDD compared to other genes in a module.

Co-regulatory networks of human serum proteins link genetics to disease

Proteins circulating in the blood are critical for age-related disease processes; however, the serum proteome has remained largely unexplored. To this end, 4137 proteins covering most predicted extracellular proteins were measured in the serum of 5457 Icelanders over 65 years of age. Pairwise correlation between proteins as they varied across individuals revealed 27 different network modules of serum proteins, many of which were associated with cardiovascular and metabolic disease states, as well as overall survival. The protein modules were controlled by cis- and trans-acting genetic variants, which in many cases were also associated with complex disease. This revealed co-regulated groups of circulating proteins that incorporated regulatory control between tissues and demonstrated close relationships to past, current, and future disease states.

De novo assembly of a Chinese soybean genome

Soybean was domesticated in China and has become one of the most important oilseed crops. Due to bottlenecks in their introduction and dissemination, soybeans from different geographic areas exhibit extensive genetic diversity. Asia is the largest soybean market; therefore, a high-quality soybean reference genome from this area is critical for soybean research and breeding. Here, we report the de novo assembly and sequence analysis of a Chinese soybean genome for "Zhonghuang 13" by a combination of SMRT, Hi-C and optical mapping data. The assembled genome size is 1.025 Gb with a contig N50 of 3.46 Mb and a scaffold N50 of 51.87 Mb. Comparisons between this genome and the previously reported reference genome (cv. Williams 82) uncovered more than 250,000 structure variations. A total of 52,051 protein coding genes and 36,429 transposable elements were annotated for this genome, and a gene co-expression network including 39,967 genes was also established. This high quality Chinese soybean genome and its sequence analysis will provide valuable information for soybean improvement in the future.

Species-Specific Changes in a Primate Transcription Factor Network Provide Insights into the Molecular Evolution of the Primate Prefrontal Cortex

The human prefrontal cortex (PFC) differs from that of other primates with respect to size, histology, and functional abilities. Here, we analyzed genome-wide expression data of humans, chimpanzees, and rhesus macaques to discover evolutionary changes in transcription factor (TF) networks that may underlie these phenotypic differences. We determined the co-expression networks of all TFs with species-specific expression including their potential target genes and interaction partners in the PFC of all three species. Integrating these networks allowed us inferring an ancestral network for all three species. This ancestral network as well as the networks for each species is enriched for genes involved in forebrain development, axonogenesis, and synaptic transmission. Our analysis allows us to directly compare the networks of each species to determine which links have been gained or lost during evolution. Interestingly, we detected that most links were gained on the human lineage, indicating increase TF cooperativity in humans. By comparing network changes between different tissues, we discovered that in brain tissues, but not in the other tissues, the human networks always had the highest connectivity. To pinpoint molecular changes underlying species-specific phenotypes, we analyzed the sub-networks of TFs derived only from genes with species-specific expression changes in the PFC. These sub-networks differed significantly in structure and function between the human and chimpanzee. For example, the human-specific sub-network is enriched for TFs implicated in cognitive disorders and for genes involved in synaptic plasticity and cognitive functions. Our results suggest evolutionary changes in TF networks that might have shaped morphological and functional differences between primate brains, in particular in the human PFC.

Dissecting Brain Networks Underlying Alcohol Binge Drinking Using a Systems Genomics Approach

Alcohol use disorder (AUD) is a complex psychiatric disorder with strong genetic and environmental risk factors. We studied the molecular perturbations underlying risky drinking behavior by measuring transcriptome changes across the neurocircuitry of addiction in a genetic mouse model of binge drinking. Sixteen generations of selective breeding for high blood alcohol levels after a binge drinking session produced global changes in brain gene expression in alcohol-naïve High Drinking in the Dark (HDID-1) mice. Using gene expression profiles to generate circuit-level hypotheses, we developed a systems approach that integrated regulation of gene coexpression networks across multiple brain regions, neuron-specific transcriptional signatures, and knowledgebase analytics. Whole-cell, voltage-clamp recordings from nucleus accumbens shell neurons projecting to the ventral tegmental area showed differential ethanol-induced plasticity in HDID-1 and control mice and provided support for one of the hypotheses. There were similarities in gene networks between HDID-1 mouse brains and postmortem brains of human alcoholics, suggesting that some gene expression patterns associated with high alcohol consumption are conserved across species. This study demonstrated the value of gene networks for data integration across biological modalities and species to study mechanisms of disease.

Transcriptional analysis of adipose tissue during development reveals depot-specific responsiveness to maternal dietary supplementation

Brown adipose tissue (BAT) undergoes pronounced changes after birth coincident with the loss of the BAT-specific uncoupling protein (UCP)1 and rapid fat growth. The extent to which this adaptation may vary between anatomical locations remains unknown, or whether the process is sensitive to maternal dietary supplementation. We, therefore, conducted a data mining based study on the major fat depots (i.e. epicardial, perirenal, sternal (which possess UCP1 at 7 days), subcutaneous and omental) (that do not possess UCP1) of young sheep during the first month of life. Initially we determined what effect adding 3% canola oil to the maternal diet has on mitochondrial protein abundance in those depots which possessed UCP1. This demonstrated that maternal dietary supplementation delayed the loss of mitochondrial proteins, with the amount of cytochrome C actually being increased. Using machine learning algorithms followed by weighted gene co-expression network analysis, we demonstrated that each depot could be segregated into a unique and concise set of modules containing co-expressed genes involved in adipose function. Finally using lipidomic analysis following the maternal dietary intervention, we confirmed the perirenal depot to be most responsive. These insights point at new research avenues for examining interventions to modulate fat development in early life.

Human-specific features of spatial gene expression and regulation in eight brain regions

Molecular maps of the human brain alone do not inform us of the features unique to humans. Yet, the identification of these features is important for understanding both the evolution and nature of human cognition. Here, we approached this question by analyzing gene expression and H3K27ac chromatin modification data collected in eight brain regions of humans, chimpanzees, gorillas, a gibbon, and macaques. An analysis of spatial transcriptome trajectories across eight brain regions in four primate species revealed 1851 genes showing human-specific transcriptome differences in one or multiple brain regions, in contrast to 240 chimpanzee-specific differences. More than half of these human-specific differences represented elevated expression of genes enriched in neuronal and astrocytic markers in the human hippocampus, whereas the rest were enriched in microglial markers and displayed human-specific expression in several frontal cortical regions and the cerebellum. An analysis of the predicted regulatory interactions driving these differences revealed the role of transcription factors in species-specific transcriptome changes, and epigenetic modifications were linked to spatial expression differences conserved across species.

Integrative Analysis of Hippocampus Gene Expression Profiles Identifies Network Alterations in Aging and Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder contributing to rapid decline in cognitive function and ultimately dementia. Most cases of AD occur in elderly and later years. There is a growing need for understanding the relationship between aging and AD to identify shared and unique hallmarks associated with the disease in a region and cell-type specific manner. Although genomic studies on AD have been performed extensively, the molecular mechanism of disease progression is still not clear. The major objective of our study is to obtain a higher-order network-level understanding of aging and AD, and their relationship using the hippocampal gene expression profiles of young (20-50 years), aging (70-99 years), and AD (70-99 years). The hippocampus is vulnerable to damage at early stages of AD and altered neurogenesis in the hippocampus is linked to the onset of AD. We combined the weighted gene co-expression network and weighted protein-protein interaction network-level approaches to study the transition from young to aging to AD. The network analysis revealed the organization of co-expression network into functional modules that are cell-type specific in aging and AD. We found that modules associated with astrocytes, endothelial cells and microglial cells are upregulated and significantly correlate with both aging and AD. The modules associated with neurons, mitochondria and endoplasmic reticulum are downregulated and significantly correlate with AD than aging. The oligodendrocytes module does not show significant correlation with neither aging nor disease. Further, we identified aging- and AD-specific interactions/subnetworks by integrating the gene expression with a human protein-protein interaction network. We found dysregulation of genes encoding protein kinases (FYN, SYK, SRC, PKC, MAPK1, ephrin receptors) and transcription factors (FOS, STAT3, CEBPB, MYC, NFKβ, and EGR1) in AD. Further, we found genes that encode proteins with neuroprotective function (14-3-3 proteins, PIN1, ATXN1, BDNF, VEGFA) to be part of the downregulated AD subnetwork. Our study highlights that simultaneously analyzing aging and AD will help to understand the pre-clinical and clinical phase of AD and aid in developing the treatment strategies.

Applications of Bayesian network models in predicting types of hematological malignancies

Network analysis is the preferred approach for the detection of subtle but coordinated changes in expression of an interacting and related set of genes. We introduce a novel method based on the analyses of coexpression networks and Bayesian networks, and we use this new method to classify two types of hematological malignancies; namely, acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Our classifier has an accuracy of 93%, a precision of 98%, and a recall of 90% on the training dataset (n = 366); which outperforms the results reported by other scholars on the same dataset. Although our training dataset consists of microarray data, our model has a remarkable performance on the RNA-Seq test dataset (n = 74, accuracy = 89%, precision = 88%, recall = 98%), which confirms that eigengenes are robust with respect to expression profiling technology. These signatures are useful in classification and correctly predicting the diagnosis. They might also provide valuable information about the underlying biology of diseases. Our network analysis approach is generalizable and can be useful for classifying other diseases based on gene expression profiles. Our previously published Pigengene package is publicly available through Bioconductor, which can be used to conveniently fit a Bayesian network to gene expression data.

Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex

Complex network topology is characteristic of many biological systems, including anatomical and functional brain networks (connectomes). Here, we first constructed a structural covariance network from MRI measures of cortical thickness on 296 healthy volunteers, aged 14-24 years. Next, we designed a new algorithm for matching sample locations from the Allen Brain Atlas to the nodes of the SCN. Subsequently we used this to define, transcriptomic brain networks by estimating gene co-expression between pairs of cortical regions. Finally, we explored the hypothesis that transcriptional networks and structural MRI connectomes are coupled. A transcriptional brain network (TBN) and a structural covariance network (SCN) were correlated across connection weights and showed qualitatively similar complex topological properties: assortativity, small-worldness, modularity, and a rich-club. In both networks, the weight of an edge was inversely related to the anatomical (Euclidean) distance between regions. There were differences between networks in degree and distance distributions: the transcriptional network had a less fat-tailed degree distribution and a less positively skewed distance distribution than the SCN. However, cortical areas connected to each other within modules of the SCN had significantly higher levels of whole genome co-expression than expected by chance. Nodes connected in the SCN had especially high levels of expression and co-expression of a human supragranular enriched (HSE) gene set that has been specifically located to supragranular layers of human cerebral cortex and is known to be important for large-scale, long-distance cortico-cortical connectivity. This coupling of brain transcriptome and connectome topologies was largely but not entirely accounted for by the common constraint of physical distance on both networks.

Discovering Perturbation of Modular Structure in HIV Progression by Integrating Multiple Data Sources Through Non-Negative Matrix Factorization

Detecting perturbation in modular structure during HIV-1 disease progression is an important step to understand stage specific infection pattern of HIV-1 virus in human cell. In this article, we proposed a novel methodology on integration of multiple biological information to identify such disruption in human gene module during different stages of HIV-1 infection. We integrate three different biological information: gene expression information, protein-protein interaction information, and gene ontology information in single gene meta-module, through non negative matrix factorization (NMF). As the identified meta-modules inherit those information so, detecting perturbation of these, reflects the changes in expression pattern, in PPI structure and in functional similarity of genes during the infection progression. To integrate modules of different data sources into strong meta-modules, NMF based clustering is utilized here. Perturbation in meta-modular structure is identified by investigating the topological and intramodular properties and putting rank to those meta-modules using a rank aggregation algorithm. We have also analyzed the preservation structure of significant GO terms in which the human proteins of the meta-modules participate. Moreover, we have performed an analysis to show the change of coregulation pattern of identified transcription factors (TFs) over the HIV progression stages.

Network-Based Genomic Analysis of Human Oligodendrocyte Progenitor Differentiation

Impaired human oligodendrocyte progenitor cell (hOPC) differentiation likely contributes to failed remyelination in multiple sclerosis. The characterization of molecular pathways that regulate hOPC differentiation will provide means to induce remyelination. In this study, we determined the gene expression profile of PDGFαR⁺ hOPCs during initial oligodendrocyte commitment. Weighted gene coexpression network analysis was used to define progenitor and differentiation-specific gene expression modules and functionally important hub genes. These modules were compared with rodent OPC and oligodendrocyte data to determine the extent of species conservation. These analyses identified G-protein β₄ (GNB4), which was associated with hOPC commitment. Lentiviral GNB4 overexpression rapidly induced human oligodendrocyte differentiation. Following xenograft in hypomyelinating shiverer/rag2 mice, GNB4 overexpression augmented myelin synthesis and the ability of hOPCs to ensheath host axons, establishing GNB4 as functionally important in human myelination. As such, network analysis of hOPC gene expression accurately predicts genes that influence human oligodendrocyte differentiation in vivo.

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Transcriptional database of differentiating human oligodendrocyte progenitor cells

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WGCNA reveals coordinated gene networks in oligodendrocyte specification

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Dataset comparison identifies unique and shared cross-species gene networks

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G-protein β₄ (GNB4) expression accelerates human oligodendrocyte differentiation

Gene expression signatures of mating system evolution

The diversity of mating systems among animals is astounding. Importantly, similar mating systems have evolved even across distantly related taxa. However, our understanding of the mechanisms underlying these convergently evolved phenotypes is limited. Here, we examine on a genomic scale the neuromolecular basis of social organization in cichlids of the tribe Ectodini from Lake Tanganyika. Using field-collected males and females of four closely related species representing two independent evolutionary transitions from polygyny to monogamy, we take a comparative transcriptomic approach to test the hypothesis that these independent transitions have recruited similar gene sets. Our results demonstrate that while lineage and species exert a strong influence on neural gene expression profiles, social phenotype can also drive gene expression evolution. Specifically, 331 genes (∼6% of those assayed) were associated with monogamous mating systems independent of species or sex. Among these genes, we find a strong bias (4:1 ratio) toward genes with increased expression in monogamous individuals. A highly conserved nonapeptide system known to be involved in the regulation of social behavior across animals was not associated with mating system in our analysis. Overall, our findings suggest deep molecular homologies underlying the convergent or parallel evolution of monogamy in different cichlid lineages of Ectodini.

Discovery of Blood Transcriptional Endotypes in Women with Pelvic Inflammatory Disease

Sexually transmitted infections with Chlamydia trachomatis and/or Neisseria gonorrhoeae and rates of pelvic inflammatory disease (PID) in women continue to rise, with reinfection being common because of poor adaptive immunity. Diagnosis remains imprecise, and pathogenesis data are derived primarily from monoinfection of mice with C. trachomatis or N. gonorrhoeae By comparing blood mRNA responses of women with C. trachomatis- and/or N. gonorrhoeae-induced PID and histologic endometritis with those from women with C. trachomatis and/or N. gonorrhoeae infection limited to their cervix and asymptomatic uninfected women determined via microarray, we discovered important pathogenic mechanisms in PID and response differences that provide a pathway to biomarker discovery. Women with N. gonorrhoeae- and/or C. trachomatis-induced PID exhibit overexpression of myeloid cell genes and suppression of protein synthesis, mitochondrial oxidative phosphorylation, and T cell-specific genes. Coinfected women exhibited the greatest activation of cell death pathways and suppression of responses essential for adaptive immunity. Women solely infected with C. trachomatis expressed elevated levels of type I and type II IFN genes, and enhanced type I IFN-induced chemokines in cervical secretions were associated with ascension of C. trachomatis to the endometrium. Blood microarrays reveal discrete pathobiological endotypes in women with PID that are driven by pathogen invasion of the upper genital tract.

Dissecting the Transcriptional Patterns of Social Dominance across Teleosts

In many species, under varying ecological conditions, social interactions among individuals result in the formation of dominance hierarchies. Despite general similarities, there are robust differences among dominance hierarchies across species, populations, environments, life stages, sexes, and individuals. Understanding the proximate mechanisms underlying the variation is an important step toward understanding the evolution of social behavior. However, physiological changes associated with dominance, such as gonadal maturation and somatic growth, often complicate efforts to identify the specific underlying mechanisms. Traditional gene expression analyses are useful for generating candidate gene lists, but are biased by choice of significance cut-offs and difficult to use for between-study comparisons. In contrast, complementary analysis tools allow one to both test a priori hypotheses and generate new hypotheses. Here we employ a meta-analysis of high-throughput expression profiling experiments to investigate the gene expression patterns that underlie mechanisms and evolution of behavioral social phenotypes. Specifically, we use a collection of datasets on social dominance in fish across social contexts, sex, and species. Using experimental manipulation to produce female dominance hierarchies in the cichlid Astatotilapia burtoni, heralded as a genomic model of social dominance, we generate gene lists, and assess molecular gene modules. In the dominant female gene expression profile, we demonstrate a strong pattern of up-regulation of genes previously identified as having male-biased expression and furthermore, compare expression biases between male and female dominance phenotypes. Using a threshold-free approach to identify correlation throughout ranked gene lists, we query previously published datasets associated with maternal behavior, alternative reproductive tactics, cooperative breeding, and sex-role reversal to describe correlations among these various neural gene expression profiles associated with different instances of social dominance. These complementary approaches capitalize on the high-throughput gene expression profiling from similar behavioral phenotypes in order to address the mechanisms associated with social dominance behavioral phenotypes.

Transcriptome analysis reveals rod/cone photoreceptor specific signatures across mammalian retinas

A defined set of genetic instructions encodes functionality in complex organisms. Delineating these unique genetic signatures is essential to understanding the formation and functionality of specialized tissues. Vision, one of the five central senses of perception, is initiated by the retina and has evolved over time to produce rod and cone photoreceptors that vary in a species-specific manner, and in some cases by geographical region resulting in higher order visual acuity in humans. RNA-sequencing and use of existing and de novo transcriptome assemblies allowed ocular transcriptome mapping from a diverse set of rodent and primate species. Global genomic refinements along with systems-based comparative and co-expression analyses of these transcriptome maps identified gene modules that correlated with specific features of rod versus cone retinal cellular composition. Organization of the ocular transcriptome demonstrated herein defines the molecular basis of photoreceptor architecture and functionality, providing a new paradigm for neurogenetic analyses of the mammalian retina in health and disease.

Improving stability of prediction models based on correlated omics data by using network approaches

Building prediction models based on complex omics datasets such as transcriptomics, proteomics, metabolomics remains a challenge in bioinformatics and biostatistics. Regularized regression techniques are typically used to deal with the high dimensionality of these datasets. However, due to the presence of correlation in the datasets, it is difficult to select the best model and application of these methods yields unstable results. We propose a novel strategy for model selection where the obtained models also perform well in terms of overall predictability. Several three step approaches are considered, where the steps are 1) network construction, 2) clustering to empirically derive modules or pathways, and 3) building a prediction model incorporating the information on the modules. For the first step, we use weighted correlation networks and Gaussian graphical modelling. Identification of groups of features is performed by hierarchical clustering. The grouping information is included in the prediction model by using group-based variable selection or group-specific penalization. We compare the performance of our new approaches with standard regularized regression via simulations. Based on these results we provide recommendations for selecting a strategy for building a prediction model given the specific goal of the analysis and the sizes of the datasets. Finally we illustrate the advantages of our approach by application of the methodology to two problems, namely prediction of body mass index in the DIetary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome study (DILGOM) and prediction of response of each breast cancer cell line to treatment with specific drugs using a breast cancer cell lines pharmacogenomics dataset.

CEMiTool: a Bioconductor package for performing comprehensive modular co-expression analyses

Background

The analysis of modular gene co-expression networks is a well-established method commonly used for discovering the systems-level functionality of genes. In addition, these studies provide a basis for the discovery of clinically relevant molecular pathways underlying different diseases and conditions.

Results

In this paper, we present a fast and easy-to-use Bioconductor package named CEMiTool that unifies the discovery and the analysis of co-expression modules. Using the same real datasets, we demonstrate that CEMiTool outperforms existing tools, and provides unique results in a user-friendly html report with high quality graphs. Among its features, our tool evaluates whether modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group, as well as it integrates transcriptomic data with interactome information, identifying the potential hubs on each network. We successfully applied CEMiTool to over 1000 transcriptome datasets, and to a new RNA-seq dataset of patients infected with Leishmania, revealing novel insights of the disease’s physiopathology.

Conclusion

The CEMiTool R package provides users with an easy-to-use method to automatically implement gene co-expression network analyses, obtain key information about the discovered gene modules using additional downstream analyses and retrieve publication-ready results via a high-quality interactive report.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2053-1) contains supplementary material, which is available to authorized users.

Exploring Potential Biomarkers Underlying Pathogenesis of Alzheimer's Disease by Differential Co-expression Analysis

BACKGROUND

Alzheimer's Disease (AD) is the most common form of dementia in the elderly. Due to the facts that biological causes of AD are complex in addition to increasing rates of AD worldwide, a deeper understanding of AD etiology is required for AD treatment and diagnosis.

METHODS

To identify molecular pathological alterations in AD brains, GSE36980 series containing microarray data samples from temporal cortex, frontal cortex and hippocampus were downloaded from Gene Expression Omnibus (GEO) database and valid gene symbols were subjected to building a gene co-expression network by a bioinformatics tool known as differential regulation from differential co-expression (DCGL) software package. Then, a network-driven integrative analysis was performed to find significant genes and underlying biological terms.

RESULTS

A total of 17088 unique genes were parsed into three independent differential co-expression networks. As a result, a small number of differentially co-regulated genes mostly in frontal and hippocampus lobs were detected as potential biomarkers related to AD brains. Ultimately differentially co-regulated genes were enriched in biological terms including response to lipid and fatty acid and pathways mainly signaling pathway such as G-protein signaling pathway and glutamate receptor groups II and III. By conducting co-expression analysis, our study identified multiple genes that may play an important role in the pathogenesis of AD.

CONCLUSION

The study aimed to provide a systematic understanding of the potential relationships among these genes and it is hoped that it could aid in AD biomarker discovery.

A comprehensive analysis on preservation patterns of gene co-expression networks during Alzheimer's disease progression

BACKGROUND

Alzheimer's disease (AD) is a chronic neuro-degenerative disruption of the brain which involves in large scale transcriptomic variation. The disease does not impact every regions of the brain at the same time, instead it progresses slowly involving somewhat sequential interaction with different regions. Analysis of the expression patterns of the genes in different regions of the brain influenced in AD surely contribute for a enhanced comprehension of AD pathogenesis and shed light on the early characterization of the disease.

RESULTS

Here, we have proposed a framework to identify perturbation and preservation characteristics of gene expression patterns across six distinct regions of the brain ("EC", "HIP", "PC", "MTG", "SFG", and "VCX") affected in AD. Co-expression modules were discovered considering a couple of regions at once. These are then analyzed to know the preservation and perturbation characteristics. Different module preservation statistics and a rank aggregation mechanism have been adopted to detect the changes of expression patterns across brain regions. Gene ontology (GO) and pathway based analysis were also carried out to know the biological meaning of preserved and perturbed modules.

CONCLUSIONS

In this article, we have extensively studied the preservation patterns of co-expressed modules in six distinct brain regions affected in AD. Some modules are emerged as the most preserved while some others are detected as perturbed between a pair of brain regions. Further investigation on the topological properties of preserved and non-preserved modules reveals a substantial association amongst "betweenness centrality" and "degree" of the involved genes. Our findings may render a deeper realization of the preservation characteristics of gene expression patterns in discrete brain regions affected by AD.

Inducible and reversible phenotypes in a novel mouse model of Friedreich's Ataxia

Friedreich's ataxia (FRDA), the most common inherited ataxia, is caused by recessive mutations that reduce the levels of frataxin (FXN), a mitochondrial iron binding protein. We developed an inducible mouse model of Fxn deficiency that enabled us to control the onset and progression of disease phenotypes by the modulation of Fxn levels. Systemic knockdown of Fxn in adult mice led to multiple phenotypes paralleling those observed in human patients across multiple organ systems. By reversing knockdown after clinical features appear, we were able to determine to what extent observed phenotypes represent reversible cellular dysfunction. Remarkably, upon restoration of near wild-type FXN levels, we observed significant recovery of function, associated pathology and transcriptomic dysregulation even after substantial motor dysfunction and pathology were observed. This model will be of broad utility in therapeutic development and in refining our understanding of the relative contribution of reversible cellular dysfunction at different stages in disease.

Weighted gene co-expression network analysis of expression data of monozygotic twins identifies specific modules and hub genes related to BMI

BACKGROUND

The therapeutic management of obesity is challenging, hence further elucidating the underlying mechanisms of obesity development and identifying new diagnostic biomarkers and therapeutic targets are urgent and necessary. Here, we performed differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) to identify significant genes and specific modules related to BMI based on gene expression profile data of 7 discordant monozygotic twins.

RESULTS

In the differential gene expression analysis, it appeared that 32 differentially expressed genes (DEGs) were with a trend of up-regulation in twins with higher BMI when compared to their siblings. Categories of positive regulation of nitric-oxide synthase biosynthetic process, positive regulation of NF-kappa B import into nucleus, and peroxidase activity were significantly enriched within GO database and NF-kappa B signaling pathway within KEGG database. DEGs of NAMPT, TLR9, PTGS2, HBD, and PCSK1N might be associated with obesity. In the WGCNA, among the total 20 distinct co-expression modules identified, coral1 module (68 genes) had the strongest positive correlation with BMI (r = 0.56, P = 0.04) and disease status (r = 0.56, P = 0.04). Categories of positive regulation of phospholipase activity, high-density lipoprotein particle clearance, chylomicron remnant clearance, reverse cholesterol transport, intermediate-density lipoprotein particle, chylomicron, low-density lipoprotein particle, very-low-density lipoprotein particle, voltage-gated potassium channel complex, cholesterol transporter activity, and neuropeptide hormone activity were significantly enriched within GO database for this module. And alcoholism and cell adhesion molecules pathways were significantly enriched within KEGG database. Several hub genes, such as GAL, ASB9, NPPB, TBX2, IL17C, APOE, ABCG4, and APOC2 were also identified. The module eigengene of saddlebrown module (212 genes) was also significantly correlated with BMI (r = 0.56, P = 0.04), and hub genes of KCNN1 and AQP10 were differentially expressed.

CONCLUSION

We identified significant genes and specific modules potentially related to BMI based on the gene expression profile data of monozygotic twins. The findings may help further elucidate the underlying mechanisms of obesity development and provide novel insights to research potential gene biomarkers and signaling pathways for obesity treatment. Further analysis and validation of the findings reported here are important and necessary when more sample size is acquired.

Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment

As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in “learning and memory” related functions and pathways. Subsequent differential network analysis of this “learning and memory” module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they provide a new insight and generate new hypotheses into the molecular mechanisms responsible for age associated learning impairment, including spatial learning.

Differentiated transcriptional signatures in the maize landraces of Chiapas, Mexico

Background

Landrace farmers are the keepers of crops locally adapted to the environments where they are cultivated. Patterns of diversity across the genome can provide signals of past evolution in the face of abiotic and biotic change. Understanding this rich genetic resource is imperative especially since diversity can provide agricultural security as climate continues to shift.

Results

Here we employ RNA sequencing (RNA-seq) to understand the role that conditions that vary across a landscape may have played in shaping genetic diversity in the maize landraces of Chiapas, Mexico. We collected landraces from three distinct elevational zones and planted them in a midland common garden. Early season leaf tissue was collected for RNA-seq and we performed weighted gene co-expression network analysis (WGCNA). We then used association analysis between landrace co-expression module expression values and environmental parameters of landrace origin to elucidate genes and gene networks potentially shaped by environmental factors along our study gradient. Elevation of landrace origin affected the transcriptome profiles. Two co-expression modules were highly correlated with temperature parameters of landrace origin and queries into their ‘hub’ genes suggested that temperature may have led to differentiation among landraces in hormone biosynthesis/signaling and abiotic and biotic stress responses. We identified several ‘hub’ transcription factors and kinases as candidates for the regulation of these responses.

Conclusions

These findings indicate that natural selection may influence the transcriptomes of crop landraces along an elevational gradient in a major diversity center, and provide a foundation for exploring the genetic basis of local adaptation. While we cannot rule out the role of neutral evolutionary forces in the patterns we have identified, combining whole transcriptome sequencing technologies, established bioinformatics techniques, and common garden experimentation can powerfully elucidate structure of adaptive diversity across a varied landscape. Ultimately, gaining such understanding can facilitate the conservation and strategic utilization of crop genetic diversity in a time of climate change.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-4005-y) contains supplementary material, which is available to authorized users.

A composite network of conserved and tissue specific gene interactions reveals possible genetic interactions in glioma

Differential co-expression network analyses have recently become an important step in the investigation of cellular differentiation and dysfunctional gene-regulation in cell and tissue disease-states. The resulting networks have been analyzed to identify and understand pathways associated with disorders, or to infer molecular interactions. However, existing methods for differential co-expression network analysis are unable to distinguish between various forms of differential co-expression. To close this gap, here we define the three different kinds (conserved, specific, and differentiated) of differential co-expression and present a systematic framework, CSD, for differential co-expression network analysis that incorporates these interactions on an equal footing. In addition, our method includes a subsampling strategy to estimate the variance of co-expressions. Our framework is applicable to a wide variety of cases, such as the study of differential co-expression networks between healthy and disease states, before and after treatments, or between species. Applying the CSD approach to a published gene-expression data set of cerebral cortex and basal ganglia samples from healthy individuals, we find that the resulting CSD network is enriched in genes associated with cognitive function, signaling pathways involving compounds with well-known roles in the central nervous system, as well as certain neurological diseases. From the CSD analysis, we identify a set of prominent hubs of differential co-expression, whose neighborhood contains a substantial number of genes associated with glioblastoma. The resulting gene-sets identified by our CSD analysis also contain many genes that so far have not been recognized as having a role in glioblastoma, but are good candidates for further studies. CSD may thus aid in hypothesis-generation for functional disease-associations.