Multiple Transcriptome Datasets Research Articles

The genes controlling normal function of citrate and spermine secretion are lost in aggressive prostate cancer and prostate model systems

SummaryHigh secretion of the metabolites citrate and spermine is a unique hallmark for normal prostate epithelial cells, and is reduced in aggressive prostate cancer. However, the identity of the genes controlling this biological process is mostly unknown. In this study, we have created a gene signature of 150 genes connected to citrate and spermine secretion in the prostate. We have computationally integrated metabolic measurements with multiple transcriptomics datasets from the public domain, including 3826 tissue samples from prostate and prostate cancer. The accuracy of the signature is validated by its unique enrichment in prostate samples and prostate epithelial tissue compartments. The signature highlights genes AZGP1, ANPEP and metallothioneins with zinc-binding properties not previously studied in the prostate, and the expression of these genes are reduced in more aggressive cancer lesions. However, the absence of signature enrichment in common prostate model systems can make it challenging to study these genes mechanistically.

Cancer germline antigen gene MAGEB2 promotes cell invasion and correlates with immune microenvironment and immunotherapeutic efficiency in laryngeal cancer

By multiple transcriptome datasets (TCGA, GSE59102, GSE25727, GSE27020 and GSE65858) and multi-omics (RNA-seq, SNP, CNV, DNA methylation) in-depth analysis, we found that cancer germline antigen (CGA) family/genes MAGEB2 is involved in the imitation, progression and prognosis in LC as well as correlated positively with lymphatic metastasis and negatively with DNA methylation. Then, in vitro experiment verified that MAGEB2 expression renders significant alteration in LC tissues and cells via immunohistochemical (IHC), qRT-PCR and western blot (WB), and up-regulation of MAGEB2 expression could facilitate the proliferation, migration and invasion of LC cells and vice versa. Subsequently, MAGEB2 knockdown suppressed tumor growth and lung metastasis in vivo animal experiment, while MAGEB2 overexpression promoted tumor growth and lung metastasis. Lastly, MAGEB2 is significantly associated with immune cell infiltration (CD8+ T cells particularly, IHC staining confirmed that as the protein expression of MAGEB2 increased, the protein level of CD8 (representing tumor-infiltrating CD8 + T cells) decreased in vitro), immunomodulators (knockdown or overexpression of MAGEB2 on LC cell lines can significantly affect the chemokine/cytokine secretion in vitro), and immunogenicity(TMB) in LC, which hints that MAGEB2 is tightly correlated with immune characteristics and might guide more effective immunotherapy strategies for LC patients.

Identification of Potential Driver Genes and Pathways Based on Transcriptomics Data in Alzheimer's Disease.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. To identify AD-related genes from transcriptomics and help to develop new drugs to treat AD. In this study, firstly, we obtained differentially expressed genes (DEG)-enriched coexpression networks between AD and normal samples in multiple transcriptomics datasets by weighted gene co-expression network analysis (WGCNA). Then, a convergent genomic approach (CFG) integrating multiple AD-related evidence was used to prioritize potential genes from DEG-enriched modules. Subsequently, we identified candidate genes in the potential genes list. Lastly, we combined deepDTnet and SAveRUNNER to predict interaction among candidate genes, drug and AD. Experiments on five datasets show that the CFG score of GJA1 is the highest among all potential driver genes of AD. Moreover, we found GJA1 interacts with AD from target-drugs-diseases network prediction. Therefore, candidate gene GJA1 is the most likely to be target of AD. In summary, identification of AD-related genes contributes to the understanding of AD pathophysiology and the development of new drugs.

Analysis of transcriptomic data sets supports the role of IL-6 in NETosis and immunothrombosis in severe COVID-19

BackgroundThere is an urgent need to understand the key events driving pathogenesis of severe COVID-19 disease, so that precise treatment can be instituted. In this respect NETosis is gaining increased attention in the scientific community, as an important pathological process contributing to mortality. We sought to test if indeed there exists robust evidence of NETosis in multiple transcriptomic data sets from human subjects with severe COVID-19 disease. Gene set enrichment analysis was performed to test for up-regulation of gene set functional in NETosis in the blood of patients with COVID-19 illness.ResultsBlood gene expression functional in NETosis increased with severity of illness, showed negative correlation with blood oxygen saturation, and was validated in the lung of COVID-19 non-survivors. Temporal expression of IL-6 was compared between severe and moderate illness with COVID-19. Unsupervised clustering was performed to reveal co-expression of IL-6 with complement genes. In severe COVID-19 illness, there is transcriptional evidence of activation of NETosis, complement and coagulation cascade, and negative correlation between NETosis and respiratory function (oxygen saturation). An early spike in IL-6 is observed in severe COVID-19 illness that is correlated with complement activation.ConclusionsBased on the transcriptional dynamics of IL-6 expression and its downstream effect on complement activation, we constructed a model that links early spike in IL-6 level with persistent and self-perpetuating complement activation, NETosis, immunothrombosis and respiratory dysfunction. Our model supports the early initiation of anti-IL6 therapy in severe COVID-19 disease before the life-threatening complications of the disease can perpetuate themselves autonomously.

Genetic Exchange of Lung-Derived Exosome to Brain Causing Neuronal Changes on COVID-19 Infection.

The pandemic of novel coronavirus 2 (SARS-CoV-2) has made global chaos for normal human living. Despite common COVID-19 symptoms, variability in clinical phenotypes was reported worldwide. Reports on SARS-CoV-2 suggest causing neurological manifestation. In addition, the susceptibility of SARS-CoV-2 in patients with neurodegenerative diseases and its complexity are largely unclear. Here, we aimed to demonstrate the possible transport of exosome from SARS-CoV-2–infected lungs to the brain regions associated with neurodegenerative diseases using multiple transcriptome datasets of SARS-CoV-2–infected lungs, RNA profiles from lung exosome, and gene expression profiles of the human brain. Upon transport, the transcription factors localized in the exosome regulate genes at lateral substantia nigra, medial substantia nigra, and superior frontal gyrus regions of Parkinson’s disease (PD) and frontal cortex, hippocampus, and temporal cortex of Alzheimer’s disease (AD). On SARS-CoV-2 infection, BCL3, JUND, MXD1, IRF2, IRF9, and STAT1 transcription factors in the exosomes influence the neuronal gene regulatory network and accelerate neurodegeneration. STAT1 transcription factor regulates 64 PD genes at lateral substantia nigra, 65 at superior frontal gyrus, and 19 at medial substantia nigra. Similarly, in AD, STAT1 regulates 74 AD genes at the temporal cortex, 40 genes at the hippocampus, and 16 genes at the frontal cortex. We further demonstrate that dysregulated neuronal genes showed involvement in immune response, signal transduction, apoptosis, and stress response process. In conclusion, SARS-CoV-2 may dysregulate neuronal gene regulatory network through exosomes that attenuate disease severity of neurodegeneration.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12035-021-02485-9.

Arabidopsis Coexpression Tool: a tool for gene coexpression analysis in Arabidopsis thaliana

SummaryGene coexpression analysis refers to the discovery of sets of genes which exhibit similar expression patterns across multiple transcriptomic data sets, such as microarray experiment data of public repositories. Arabidopsis Coexpression Tool (ACT), a gene coexpression analysis web tool for Arabidopsis thaliana, identifies genes which are correlated to a driver gene. Primary microarray data from ATH1 Affymetrix platform were processed with Single-Channel Array Normalization algorithm and combined to produce a coexpression tree which contains ∼21,000 A. thaliana genes. ACT was developed to present subclades of coexpressed genes, as well as to perform gene set enrichment analysis, being unique in revealing enriched transcription factors targeting coexpressed genes. ACT offers a simple and user-friendly interface producing working hypotheses which can be experimentally verified for the discovery of gene partnership, pathway membership, and transcriptional regulation. ACT analyses have been successful in identifying not only genes with coordinated ubiquitous expressions but also genes with tissue-specific expressions.

Model-based assessment of mammalian cell metabolic functionalities using omics data

SummaryOmics experiments are ubiquitous in biological studies, leading to a deluge of data. However, it is still challenging to connect changes in these data to changes in cell functions because of complex interdependencies between genes, proteins, and metabolites. Here, we present a framework allowing researchers to infer how metabolic functions change on the basis of omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. Genome-scale metabolic networks were used to define gene sets associated with each metabolic task. We further developed a framework to overlay omics data on these sets and predict pathway usage for each metabolic task. We demonstrated how this approach can be used to quantify metabolic functions of diverse biological samples from the single cell to whole tissues and organs by using multiple transcriptomic datasets. To facilitate its adoption, we integrated the approach into GenePattern (www.genepattern.org—CellFie).

Gene co-expression network analysis identifies BEH3 as a stabilizer of secondary vascular development in Arabidopsis.

In plants, vascular stem cells located in the cambium continuously undergo self-renewal and differentiation during secondary growth. Recent advancements in cell sorting techniques have enabled access to the transcriptional regulatory framework of cambial cells. However, mechanisms underlying the robust control of vascular stem cells remain unclear. Here, we identified a new cambium-related regulatory module through co-expression network analysis using multiple transcriptome datasets obtained from an ectopic vascular cell transdifferentiation system using Arabidopsis cotyledons, Vascular cell Induction culture System Using Arabidopsis Leaves (VISUAL). The cambium gene list included a gene encoding the transcription factor BES1/BZR1 Homolog 3 (BEH3), whose homolog BES1 negatively affects vascular stem cell maintenance. Interestingly, null beh3 mutant alleles showed a large variation in their vascular size, indicating that BEH3 functions as a stabilizer of vascular stem cells. Genetic analysis revealed that BEH3 and BES1 perform opposite functions in the regulation of vascular stem cells and the differentiation of vascular cells in the context of the VISUAL system. At the biochemical level, BEH3 showed weak transcriptional repressor activity and functioned antagonistically to other BES/BZR members by competing for binding to the brassinosteroid response element. Furthermore, mathematical modeling suggested that the competitive relationship between BES/BZR homologs leads to the robust regulation of vascular stem cells.

GAB2 and GAB3 are expressed in a tumor stage-, grade- and histotype-dependent manner and are associated with shorter progression-free survival in ovarian cancer.

Ovarian cancer is the most lethal gynecological malignancy and molecular mechanisms of its progression and metastasis are not completely understood. Some members of GAB (GRB2-associated binding) protein family have been reported to be involved in tumor cell proliferation and metastasis in various cancer types. In the present study, we analyzed the expression of GAB proteins (GAB1, GAB2 and GAB3) in ovarian cancer compared to normal ovarian tissue, in terms of tumor stage, tumor grade and histological type. Differential expression analyses performed in R programming environment using multiple transcriptome datasets (n = 1449) showed that GAB1 expression is decreased in ovarian cancer independently of tumor stage, grade and histotype. Unlike GAB1, expression of GAB2 and GAB3 are increased from early stage to late stage and from low grade to high grade in epithelial ovarian cancer. GAB2 and GAB3 also showed histotype-dependent expression. GAB3 was computed as a top gene whose expression most significantly changed between tumor cells from primary tumor, metastases and ascites. High expression of GAB2 and GAB3 was shown to be associated with shorter progression-free survival in ovarian cancer. This study shows that GAB2 and GAB3 can be important regulators of tumor progression and metastasis in ovarian cancer.

Are Epithelial Ovarian Cancers of the Mesenchymal Subtype Actually Intraperitoneal Metastases to the Ovary?

Primary ovarian high-grade serous carcinoma (HGSC) has been classified into 4 molecular subtypes: Immunoreactive, Proliferative, Differentiated, and Mesenchymal (Mes), of which the Mes subtype (Mes-HGSC) is associated with the worst clinical outcomes. We propose that Mes-HGSC comprise clusters of cancer and associated stromal cells that detached from tumors in the upper abdomen/omentum and disseminated in the peritoneal cavity, including to the ovary. Using comparative analyses of multiple transcriptomic data sets, we provide the following evidence that the phenotype of Mes-HGSC matches the phenotype of tumors in the upper abdomen/omentum: (1) irrespective of the primary ovarian HGSC molecular subtype, matched upper abdominal/omental metastases were typically of the Mes subtype, (2) the Mes subtype was present at the ovarian site only in patients with concurrent upper abdominal/omental metastases and not in those with HGSC confined to the ovary, and (3) ovarian Mes-HGSC had an expression profile characteristic of stromal cells in the upper abdominal/omental metastases. We suggest that ovarian Mes-HGSC signifies advanced intraperitoneal tumor dissemination to the ovary rather than a subtype of primary ovarian HGSC. This is consistent with the presence of upper abdominal/omental disease, suboptimal debulking, and worst survival previously reported in patients with ovarian Mes-HGSC compared to other molecular subtypes.

Genome-wide identification, characterization analysis and expression profiling of auxin-responsive GH3 family genes in wheat (Triticum aestivum L.).

Auxin affects many aspects of plant growth and development by regulating the expression of auxin-responsive genes. As one of the three major auxin-responsive families the Gretchen Hagen3 (GH3) gene family maintains hormonal homeostasis by conjugating excess indole-3-acetic acid (IAA), salicylic acid (SA), and jasmonic acid (JA) to amino acids during hormone and stress-related signaling. Although some work has been carried out the functions of wheat GH3 (TaGH3) family genes in response to abiotic stresses (including salt stress and osmotic stress) are largely unknown. Access to the complete wheat genome sequence permits genome-wide studies on TaGH3s. We performed a systematic identification of the TaGH3 gene family at the genome level and detected 36 members on 14 wheat chromosomes. Many of the genes were segmentally duplicated and Ka/Ks and inter-species synthetic analyses indicated that polyploidization was the contributor to the increased number of TaGH3 members. Phylogenetic analyses revealed that TaGH3 proteins could divided into three major categories (TaGH3-I, TaGH3-II, and TaGH3-III). Diversified cis-elements in the promoters of TaGH3 genes were predicted as essential players in regulating TaGH3 expression patterns. Gene structure and motif analyses indicated that most TaGH3 genes have relatively conserved exon/intron arrangements and motif compositions. Analysis of multiple transcriptome data sets indicated that many TaGH3 genes are responsive to biological and abiotic stresses and possibly have important functions in stress response. qRT-PCR analysis revealed that TaGH3s were induced by salt and osmotic stresses. Customized annotation results revealed that TaGH3s were widely involved in phytohormone response, defense, growth and development, and metabolism. Overall, our work provides a comprehensive insight into the TaGH3 family members, and a basis for the further study of their biological functions in wheat.

Transcriptome-Guided Drug Repositioning.

Drug repositioning can save considerable time and resources and significantly speed up the drug development process. The increasing availability of drug action and disease-associated transcriptome data makes it an attractive source for repositioning studies. Here, we have developed a transcriptome-guided approach for drug/biologics repositioning based on multi-layer self-organizing maps (ml-SOM). It allows for analyzing multiple transcriptome datasets by segmenting them into layers of drug action- and disease-associated transcriptome data. A comparison of expression changes in clusters of functionally related genes across the layers identifies “drug target” spots in disease layers and evaluates the repositioning possibility of a drug. The repositioning potential for two approved biologics drugs (infliximab and brodalumab) confirmed the drugs’ action for approved diseases (ulcerative colitis and Crohn’s disease for infliximab and psoriasis for brodalumab). We showed the potential efficacy of infliximab for the treatment of sarcoidosis, but not chronic obstructive pulmonary disease (COPD). Brodalumab failed to affect dysregulated functional gene clusters in Crohn’s disease (CD) and systemic juvenile idiopathic arthritis (SJIA), clearly indicating that it may not be effective in the treatment of these diseases. In conclusion, ml-SOM offers a novel approach for transcriptome-guided drug repositioning that could be particularly useful for biologics drugs.

Light Modulates Ethylene Synthesis, Signaling, and Downstream Transcriptional Networks to Control Plant Development.

The inhibition of hypocotyl elongation by ethylene in dark-grown seedlings was the basis of elegant screens that identified ethylene-insensitive Arabidopsis mutants, which remained tall even when treated with high concentrations of ethylene. This simple approach proved invaluable for identification and molecular characterization of major players in the ethylene signaling and response pathway, including receptors and downstream signaling proteins, as well as transcription factors that mediate the extensive transcriptional remodeling observed in response to elevated ethylene. However, the dark-adapted early developmental stage used in these experiments represents only a small segment of a plant’s life cycle. After a seedling’s emergence from the soil, light signaling pathways elicit a switch in developmental programming and the hormonal circuitry that controls it. Accordingly, ethylene levels and responses diverge under these different environmental conditions. In this review, we compare and contrast ethylene synthesis, perception, and response in light and dark contexts, including the molecular mechanisms linking light responses to ethylene biology. One powerful method to identify similarities and differences in these important regulatory processes is through comparison of transcriptomic datasets resulting from manipulation of ethylene levels or signaling under varying light conditions. We performed a meta-analysis of multiple transcriptomic datasets to uncover transcriptional responses to ethylene that are both light-dependent and light-independent. We identified a core set of 139 transcripts with robust and consistent responses to elevated ethylene across three root-specific datasets. This “gold standard” group of ethylene-regulated transcripts includes mRNAs encoding numerous proteins that function in ethylene signaling and synthesis, but also reveals a number of previously uncharacterized gene products that may contribute to ethylene response phenotypes. Understanding these light-dependent differences in ethylene signaling and synthesis will provide greater insight into the roles of ethylene in growth and development across the entire plant life cycle.

Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimer\u2019s disease patients

BackgroundGene co-expression network (GCN) mining is a systematic approach to efficiently identify novel disease pathways, predict novel gene functions and search for potential disease biomarkers. However, few studies have systematically identified GCNs in multiple brain transcriptomic data of Alzheimer’s disease (AD) patients and looked for their specific functions.MethodsIn this study, we first mined GCN modules from AD and normal brain samples in multiple datasets respectively; then identified gene modules that are specific to AD or normal samples; lastly, condition-specific modules with similar functional enrichments were merged and enriched differentially expressed upstream transcription factors were further examined for the AD/normal-specific modules.ResultsWe obtained 30 AD-specific modules which showed gain of correlation in AD samples and 31 normal-specific modules with loss of correlation in AD samples compared to normal ones, using the network mining tool lmQCM. Functional and pathway enrichment analysis not only confirmed known gene functional categories related to AD, but also identified novel regulatory factors and pathways. Remarkably, pathway analysis suggested that a variety of viral, bacteria, and parasitic infection pathways are activated in AD samples. Furthermore, upstream transcription factor analysis identified differentially expressed upstream regulators such as ZFHX3 for several modules, which can be potential driver genes for AD etiology and pathology.ConclusionsThrough our state-of-the-art network-based approach, AD/normal-specific GCN modules were identified using multiple transcriptomic datasets from multiple regions of the brain. Bacterial and viral infectious disease related pathways are the most frequently enriched in modules across datasets. Transcription factor ZFHX3 was identified as a potential driver regulator targeting the infectious diseases pathways in AD-specific modules. Our results provided new direction to the mechanism of AD as well as new candidates for drug targets.

Phylotranscriptomics suggests the jawed vertebrate ancestor could generate diverse helper and regulatory T cell subsets

BackgroundThe cartilaginous fishes diverged from other jawed vertebrates ~ 450 million years ago (mya). Despite this key evolutionary position, the only high-quality cartilaginous fish genome available is for the elephant shark (Callorhinchus milii), a chimaera whose ancestors split from the elasmobranch lineage ~ 420 mya. Initial analysis of this resource led to proposals that key components of the cartilaginous fish adaptive immune system, most notably their array of T cell subsets, was primitive compared to mammals. This proposal is at odds with the robust, antigen-specific antibody responses reported in elasmobranchs following immunization. To explore this discrepancy, we generated a multi-tissue transcriptome for small-spotted catshark (Scyliorhinus canicula), a tractable elasmobranch model for functional studies. We searched this, and other newly available sequence datasets, for CD4+ T cell subset-defining genes, aiming to confirm the presence or absence of each subset in cartilaginous fishes.ResultsWe generated a new transcriptome based on a normalised, multi-tissue RNA pool, aiming to maximise representation of tissue-specific and lowly expressed genes. We utilized multiple transcriptomic datasets and assembly variants in phylogenetic reconstructions to unambiguously identify several T cell subset-specific molecules in cartilaginous fishes for the first time, including interleukins, interleukin receptors, and key transcription factors. Our results reveal the inability of standard phylogenetic reconstruction approaches to capture the site-specific evolutionary processes of fast-evolving immune genes but show that site-heterogeneous mixture models can adequately do so.ConclusionsOur analyses reveal that cartilaginous fishes are capable of producing a range of CD4+ T cell subsets comparable to that of mammals. Further, that the key molecules required for the differentiation and functioning of these subsets existed in the jawed vertebrate ancestor. Additionally, we highlight the importance of considering phylogenetic diversity and, where possible, utilizing multiple datasets for individual species, to accurately infer gene presence or absence at higher taxonomic levels.

Pan-senescence transcriptome analysis identified RRAD as a marker and negative regulator of cellular senescence

Cellular senescence, an irreversible proliferative arrest, functions in tissue remodeling during development and is implicated in multiple aging-associated diseases. While senescent cells often manifest an array of senescence-associated phenotypes, such as cell cycle arrest, altered heterochromatin architecture, reprogrammed metabolism and senescence-associated secretory phenotype (SASP), the identification of senescence cells has been hindered by lack of specific and universal biomarkers. To systematically identify universal biomarkers of cellular senescence, we integrated multiple transcriptome data sets of senescent cells obtained through different in vitro manipulation modes as well as age-related gene expression data of human tissues. Our analysis showed that RRAD (Ras-related associated with diabetes) expression is up-regulated in all the manipulation modes and increases with age in human skin and adipose tissues. The elevated RRAD expression was then confirmed in senescent human fibroblasts that were induced by Ras, H2O2, ionizing radiation, hydroxyurea, etoposide and replicative passage, respectively. Further functional study suggests that RRAD up-regulation acts as a negative feedback mechanism to counter cellular senescence by reducing the level of reactive oxygen species. Finally, we found both p53 and NF-κB bind to RRAD genomic regions and modulate RRAD transcription. This study established RRAD to be a biomarker as well as a novel negative regulator of cellular senescence.

A novel molecular mechanism for a long non-coding RNA PCAT92 implicated in prostate cancer.

The role of many lncRNAs in cancer remains elusive including that for a Prostate Cancer Associated Transcript 92 (PCAT92). PCAT92 shares the locus on chromosome 13 with ABCC4 gene, known to be implicated in prostate cancer. It has been shown that PCAT92 and ABCC4 are up-regulated in prostate cancer samples from multiple transcriptome datasets. Among the prostate cancer cell-lines LNCaP showed maximum overexpression of PCAT92 compared to control cell-line RWPE-1. We have shown that knockdown of PCAT92 in LNCaP cells reduces cell viability and proliferation and down-regulates ABCC4 transcript/protein expression. The shared region between PCAT92 and ABCC4 has a binding site for an oncogenic transcription factor (ZIC2) which is also upregulated in the majority of datasets studied here. ZIC2 binding to the predicted ABCC4 promoter has been confirmed using pull-down assay. Interestingly, under PCAT92 knockdown condition, there is a reduction in the ZIC2 binding to ABCC4 promoter indicating the potential involvement of PCAT92 in the recruitment of ZIC2. We have identified distinct regions on PCAT92 with potential to bind to ZIC2 non-DNA binding Zinc-finger domain and potential for triplex formation near ABCC4 promoter region, which have been experimentally validated. Together, these observations and localization in the nucleus suggests that PCAT92 may play a role in prostate cancer by increasing the local concentration of ZIC2 by forming RNA-DNA triplex near ABCC4 promoter thus helping in recruitment of ZIC2 for ABCC4 regulation.

Integrative transcriptome analysis identifies genes and pathways associated with enzalutamide resistance of prostate cancer

Background: Enzalutamide, a novel androgen receptor (AR) signaling inhibitor, has been widely used to increase survival in patients with castration-resistant prostate cancer. However, resistance to enzalutamide invariably develops.Methods: To understand the underlying mechanisms of resistance to enzalutamide, we performed integrative analysis on multiple transcriptome datasets to identify those genes constantly up- or down-regulated in response to enzalutamide treatment.Results: There were 703 and 581 differentially expressed genes derived from enzalutamide-sensitive and -resistant cell lines, respectively. Functional enrichment analysis on these genes demonstrated that biological processes of cell proliferation and ubiquitin mediated proteolysis pathway are specifically disturbed in sensitive cell lines but not resistant ones. Such divergence explained why enzalutamide ineffective for resistant prostate cancer.Conclusions: Taken together, the present study revealed a set of critical genes, which can provide etiologic clues as to enzalutamide-resistant prostate cancer and guide novel therapeutic approaches.

A regulation probability model-based meta-analysis of multiple transcriptomics data sets for cancer biomarker identification

BackgroundLarge-scale accumulation of omics data poses a pressing challenge of integrative analysis of multiple data sets in bioinformatics. An open question of such integrative analysis is how to pinpoint consistent but subtle gene activity patterns across studies. Study heterogeneity needs to be addressed carefully for this goal.ResultsThis paper proposes a regulation probability model-based meta-analysis, jGRP, for identifying differentially expressed genes (DEGs). The method integrates multiple transcriptomics data sets in a gene regulatory space instead of in a gene expression space, which makes it easy to capture and manage data heterogeneity across studies from different laboratories or platforms. Specifically, we transform gene expression profiles into a united gene regulation profile across studies by mathematically defining two gene regulation events between two conditions and estimating their occurring probabilities in a sample. Finally, a novel differential expression statistic is established based on the gene regulation profiles, realizing accurate and flexible identification of DEGs in gene regulation space. We evaluated the proposed method on simulation data and real-world cancer datasets and showed the effectiveness and efficiency of jGRP in identifying DEGs identification in the context of meta-analysis.ConclusionsData heterogeneity largely influences the performance of meta-analysis of DEGs identification. Existing different meta-analysis methods were revealed to exhibit very different degrees of sensitivity to study heterogeneity. The proposed method, jGRP, can be a standalone tool due to its united framework and controllable way to deal with study heterogeneity.

Placental transcriptome co-expression analysis reveals conserved regulatory programs across gestation

BackgroundMammalian development in utero is absolutely dependent on proper placental development, which is ultimately regulated by the placental genome. The regulation of the placental genome can be directly studied by exploring the underlying organisation of the placental transcriptome through a systematic analysis of gene-wise co-expression relationships.ResultsIn this study, we performed a comprehensive analysis of human placental co-expression using RNA sequencing and intergrated multiple transcriptome datasets spanning human gestation. We identified modules of co-expressed genes that are preserved across human gestation, and also identifed modules conserved in the mouse indicating conserved molecular networks involved in placental development and gene expression patterns more specific to late gestation. Analysis of co-expressed gene flanking sequences indicated that conserved co-expression modules in the placenta are regulated by a core set of transcription factors, including ZNF423 and EBF1. Additionally, we identified a gene co-expression module enriched for genes implicated in the pregnancy pathology preeclampsia. By using an independnet transcriptome dataset, we show that these co-expressed genes are differentially expressed in preeclampsia.ConclusionsThis study represents a comprehensive characterisation of placental co-expression and provides insight into potential transcriptional regulators that govern conserved molecular programs fundamental to placental development.