Sub-network Enrichment Analysis Research Articles

Returning to normal? Assessing transcriptome recovery over time in male rainbow darter (Etheostoma caeruleum) liver in response to wastewater-treatment plant upgrades.

The present study measured hepatic transcriptome responses in male rainbow darter (Etheostoma caeruleum) exposed to 2 municipal wastewater-treatment plants (MWWTPs; Kitchener and Waterloo) over 4 fall seasons (2011-2014) in the Grand River (Ontario, Canada). The overall goal was to determine if upgrades at the Kitchener MWWTP (in 2012) resulted in transcriptome responses indicative of improved effluent quality. The number of differentially expressed probes in fish downstream of the Kitchener outfall (904-1223) remained comparable to that downstream of Waterloo (767-3867). Noteworthy was that year and the interaction of year and site explained variability in more than twice the number of transcripts than site alone, suggesting that year and the interaction of year and site had a greater effect on the transcriptome than site alone. Gene set enrichment analysis revealed a gradual reduction in the number of gene ontologies over time at exposure sites, which corresponded with lower contaminant load. Subnetwork enrichment analysis revealed that there were noticeable shifts in the cell pathways differently expressed in the liver preupgrade and postupgrade. The dominant pathways altered preupgrade were related to genetic modifications and cell division, whereas postupgrade they were associated with the immune system, reproduction, and biochemical responses. Molecular pathways were dynamic over time, and following the upgrades, there was little evidence that gene expression profiles in fish collected from high-impact sites postupgrade were more similar to those in fish collected from reference site. Environ Toxicol Chem 2017;36:2108-2122. © 2017 SETAC.

Integrative Analysis of Genomic And Neuroimaging Data Reveal A Genetic-Brain Region Pathway Regulating Schizophrenia

BackgroundSchizophrenia (SCZ) is one of the most chronically disabling psychiatric illnesses with a global median lifetime morbid risk of 7.2/1000 persons. Both genetic and brain region pathways were identified as causal regulating tunnels towards the pathogenesis of SCZ. MethodWe integrated a functional magnetic resonance imaging (fMRI) data set and a brain-gene ResNet (BGR) data set to study the functional pathways through which genes regulate the brain functions associated with SCZ phenotypes. Automated Anatomical Labeling (AAL) based brain connectivity analysis were conducted for fMRI data and a Gene Set Enrichment Analysis (GSEA) and Sub-Network Enrichment Analysis (SNEA) were performed to study the functional profile of the genes associating with these brain regions. Additionally, pathway and network based metrics analysis were proposed to identify the top genes with pathogenesis significance to SCZ. ResultsWe identified 4 SCZ candidate brain regions (P-value<1e-5) using fMRI data alone, which were replicated with the BGR data set. Furthermore, we identified from the BRG data set that the 4 SCZ candidate brain regions were related to 26 SCZ candidate genes. GSEA and SNEA confirmed the observation and demonstrated that 24/26 genes significantly enriched 62 genetic SCZ candidate pathways (P-value<1e-4), 16 of which were related to brain functional development (P-value: 5.5e-13 ~ 5.1e-5). Additionally, pathway and network metrics analysis suggested that 7 genes were top SCZ candidates in terms of both network centrality and functional diversity, including SNAP25, APP, MAPT, APOE, NTRK2, GRM5, and BDNF. ConclusionOur results suggest that integrating both genomic and neuroimaging data could reveal functional genetic-brain region pathways that help in understanding the pathogenic mechanisms of SCZ.

Progress In Psychiatric Genetics In China

Overall Abstract The purpose of this symposium is to highlight recent progress in psychiatric genetics in Chinese samples, complementing findings largely from Caucasian samples. We will stress commonalities and differences between existing studies and Chinese studies. Weihua Yue from Peking University reports on a multi-site GWAS of schizophrenia with >10,000 samples. 13 SNPs were selected for replication in another 10,000 samples. 4 SNPs in 3 regions out of 13 survived after confirmation in the replication sample, 9/13 SNPs were in the same direction, and 7/13 SNPs were both p Yin Yao from NIMH integrated functional magnetic resonance imaging (fMRI) and brain-gene ResNet (BGR) data to identify functional pathways associated with SCZ phenotypes. Automated Anatomical Labeling based brain connectivity analysis on fMRI data and Gene Set and Subnetwork Enrichment Analyses identified 4 SCZ candidate brain regions (P Gang Chen from Nanjing University of Chinese Medicine will report on a GWAS of alcohol dependence symptom count (ADSC) in sample of 3838 European and African Americans from the “Study of Addiction: Genetics and Environment”. Using a mixed linear approach, 20 SNPs showed significant (permutation testing) association with ADSC after accounting for ethnicity, co-morbidities, and including additive as well as dominance and epistasis in the model. Association with the previously known ADH1C gene was confirmed, as well as several other known and new genes. The detected SNPs explain ~20% of the variance, half of which is accounted for by dominance and epistatic interactions, suggesting such networks are important in the genetic architecture of alcoholism. Margit Burmeister will report on implementing in China Srijan Sen’s “medical intern health study”. Medical graduates are recruited after matching in now 55 hospitals in the US. Baseline surveys include personality, stress level, other psychological tests and the PHQ9 depression score. Depression increases from ~4% of incoming Medical graduates to ~ 26% during internship. Perceived stress, work hour and medical errors are associated with increased PHQ9 scores. Most residents do not seek care for their depression symptoms, out of fear of reprisal. In China, a formal residency program has recently been introduced in several cities. Interviews with residents documented similar concerns as in the US. Our pilot data (N=75, ~75% participation) in 2015 in Peking Union Medical College demonstrate a similar increase of depression scores from ~4% to ~ 22%, suggesting that work-stress induced depression may be modeled similarly in China as in the US, where a GWAS of the first ~7500 interns will soon become available.

The Genetic Network underlying Eating Disorder and its Small Molecular Level Supports

Background:It is believed that the genetic cause of complex diseases, such as Eating Disorder (ED), is linked to a large genetic network. In recent years, there has been an increased number of studies reporting dozens of genes associated with ED, posing an increased need of a systematically evaluation of the genetic markers underlying the disease. Methods:ED-Gene relation data were extracted from the ResNet Mammalian database, containing 69 ED candidate genes. Pathway Enrichment Analysis, Sub-Network Enrichment Analysis, Network Connectivity Analysis and Network Metrics Analysis were conducted to study network attributes and select the top genes for ED. Additionally, ED-Drug and Drug-Gene relation data were employed to study the ED-Gene relation at the small molecule level. Results:66 out of 69 genes enriched 104 ED candidate pathways (p-values < 1e-5), demonstrating strong gene-gene interactions. Metrics analysis suggest 6 genes worthy of further study for ED, including CHR, DRD2, AVP, OPRM1, IL6, and ESR2. Additionally, the majority of the ED candidate genes (64/69) demonstrated strong interaction with 46/55 ED effective drugs, providing support for the ED-gene relationships identified. Conclusion:Our results suggested that the genetic causes of ED were linked to a genetic network composed of a large group of genes. The gene network, together with the literature and enrichment metrics provided in this study, laid the groundwork for further biological/genetic studies in the field

Genetic Biomarker Selection for Obsessive-Compulsive Disorder by Sparse Representation Based Variable Selection Method

Background:Obsessive-compulsive disorder (OCD) is a debilitating neuropsychiatric condition estimated to afflict 1-3 % of the world population. Dozens of OCD candidate genes have been reported by an increased number of articles. Nevertheless, each patient/patient group may demonstrate unique etiologic characteristics that need personalized treatment. Methods:We integrated a sparse representation based variable selection (SRVS) approach with an OCD-gene ResNet relation data analysis to select top genes for a specific group of 118 subjects, including 16 OCD cases and 102 healthy controls. The gene expression profile were acquired from the dorsolateral prefrontal cortex (DLPFC) of postmortem tissue of these subjects. A 77 OCD candidate genes were acquired from ResNet relation data analysis. Pathway enrichment analysis (PEA), sub-network enrichment analysis (SNEA) and gene-gene Interaction analysis (GGI) were conducted to study the functional profile of the top genes selected by SRVS, and compared with previous reported genetic markers. Results:A significantly high classification accuracy (CR) of 79.66 % was acquired (permutation p-value = 0.0046) using the top 9 genes selected by SRVS, including HOXB8, HTR2C, CRHR2, GRIK3, HGF, OXT, TPH2, DRD2 and ADRA1A. These genes were enriched within multiple pathways and sub-networks that were previously implicated with OCD. In contrast, using the same number of most frequently reported, a CR of only 65.5 % is achieved. Moreover, GGI results showed that these genes demonstrated a strong functional correlation with the frequently reported OCD genes. Conclusion:Our study suggests that SRVS is an effective method for data driven variable selection for OCD, and that the genes that were frequently reported to associate with OCD might not be the best biomarkers for a specific OCD patient/ patient group

Molecular initiating events of the intersex phenotype: Low-dose exposure to 17α-ethinylestradiol rapidly regulates molecular networks associated with gonad differentiation in the adult fathead minnow testis

Intersex, or the presence of oocytes in the testes, has been documented in fish following exposure to wastewater effluent and estrogenic compounds. However, the molecular networks underlying the intersex condition are not completely known. To address this, we exposed male fathead minnows to a low, environmentally-relevant concentration of 17alpha-ethinylestradiol (EE2) (15ng/L) and measured the transcriptome response in the testis after 96h to identify early molecular initiating events that may proceed the intersex condition. The short-term exposure to EE2 did not affect gonadosomatic index and proportion of gametes within the testes. However, the production of 11-ketotestosterone and testosterone from the testis in vitro was decreased relative to controls. Expression profiling using a 8×60K fathead minnow microarray identified 10 transcripts that were differentially expressed in the testes, the most dramatic change being that of coagulation factor XIII A chain (20-fold increase). Transcripts that included guanine nucleotide binding protein (Beta Polypeptide 2), peroxisome proliferator-activated receptor delta, and WNK lysine deficient protein kinase 1a, were down-regulated by EE2. Subnetwork enrichment analysis revealed that EE2 suppressed transcriptional networks associated with steroid metabolism, hormone biosynthesis, and sperm mobility. Most interesting was that gene networks associated with doublesex and mab-3 related transcription factor 1 (dmrt1) were suppressed in the adult testis, despite the fact that dmrt1 itself was not different in expression from control males. Transcriptional networks involving forkhead box L2 (foxl2) (transcript involved in ovarian follicle development) were increased in expression in the testis. Noteworthy was that a gene network associated to granulosa cell development was increased over 100%, suggesting that this transcriptome network may be important for monitoring estrogenic exposures. Other cell processes rapidly downregulated by EE2 at the transcript level included glucose homeostasis, response to heavy metal, amino acid catabolism, and the cyclooxygenase pathway. Conversely, lymphocyte chemotaxis, intermediate filament polymerization, glucocorticoid metabolism, carbohydrate utilization, and anterior/posterior axis specification were increased. These data provide new insight into the transcriptional responses that are perturbed prior to gonadal remodeling and intersex following exposure to estrogens. These data demonstrate that low concentrations of EE2 (1) rapidly suppresses male hormone production, (2) down-regulate molecular networks related to male sex differentiation, and (3) induce transcriptional networks related to granulosa cell development in the adult testis. These responses are hypothesized to be key molecular initiating events that occur prior to the development of the intersex phenotype following estrogenic exposures.

Network based genetic marker evaluation for Lewis lung carcinoma

Background: Lung cancer is the most common human cancer. There are numerous studies that have sought to discover genetic impact factors associated with it. The study reported on in this article is a systematic search for the genetic networks underlying the Lewis lung carcinoma (LLC) model.Methods: LLC-Gene relation data were extracted from the ResNet 11 Mammalian database, containing 175 LLC candidate genes (nodes). Pathway Enrichment Analysis, Sub-Network Enrichment Analysis, Network Connectivity Analysis, and Network Metrics Analysis were conducted to study network attributes and select the top nodes (genes). LLC-Drug and Drug-Gene relation data examined LLC-Gene relations at the small molecule level.Results: A total of 166 of 175 genes that were enriched in 134 LLC candidate pathways (p < 1e-07), demonstrated strong gene-gene interactions. Metrics analysis revealed 4 genes, IL6, TNF, VEGFA, and HIF1A, as top LLC candidates as measured by replication frequency, network centrality, and functional diversity. Additionally, 169 of 175 LLC candidate genes demonstrated strong interactions with 213 of 253 LLC effective drugs which supports an LLC-gene relationship.Conclusion: The results suggest that LLC genetic causes link to a genetic network composed of a large number of genes. This network, together with literature and enrichment metrics provide a foundation for further biological/genetic studies in the field.

The genetic network underlying anxiety disorder and its small molecular level supports

Background:It is believed that the genetic causes of complex diseases, such as anxiety, are linked to large genetic networks. In recent years, there has been an increased number of studies reporting hundreds of genes associated with anxiety, posing the necessity for a systematic study of the genetic networks which underlie the disease.Method:Disease (anxiety) - gene relation data was extracted from the Pathway Studio ResNet 11 Mammalian database yielding a total of 592 anxiety related genes. Pathway Enrichment Analysis, Sub-Network Enrichment Analysis, Network Connectivity Analysis and Network Metrics Analysis was conducted to study network attributes and select critical genes. Additionally, disease (anxiety) – drug, and drug - gene relation data was further studied in order to evaluate the known literature regarding the disorder of anxiety at the small molecule level. Results:526 out of the 592 anxiety related genes were enriched in a total of 100 pathways at a p-value < 1E-15, demonstrating strong and comprehensive pathway participation. Literature metrics analysis revealed 6 genes; DRD2, ADORA2A, IL1B, CRH, AVP, CRHR1, as top candidates for anxiety, in terms of replication frequency, network centrality, and pathway enrichment. Additionally, the majority of the anxiety candidate genes (538/592) demonstrated an interaction with 548/618 anxiety effective drugs, providing further support for the literature identified anxiety-gene relationship. conclusion:Our results suggested that the genetic causes of anxiety were linked to a coherent genetic network composed of a large group of genes. This gene network, together with the literature and enrichment metrics provided in this study, lays the groundwork for further biological/genetic studies in the field

Sparse Representation Based Genetic Biomarker Evaluation for Congenital Heart Defects

Background: Congenital heart defects (CHD) are the most common type of birth defect, affecting approximately 8 in 1,000 newborns. Hundreds of genes have been reported as CHD candidate genes. Nevertheless, each patient/patient group may demonstrate unique etiologic characteristics requiring personalized treatment.Methods: We proposed a sparse representation-based variable selection (SRVS) approach to select disease-related genetic markers from a huge disease candidate gene pool acquired from ResNet relation database. The proposed approach was used to evaluate 167 CHD candidate genes and was followed by validation on a microarray expression data set. Pathway enrichment analysis (PEA), sub-network enrichment analysis (SNEA), and network connectivity analysis (NCA) were conducted to study the functional profile of the variables selected by SRVS and compare them with previous reported genetic markers.Results: A significantly high disease prediction accuracy of 81.40 % was obtained (permutation p-value < 0.0002) using the top 24 SRVS-selected genes, which had been enriched within multiple pathways and sub-networks that had been previously implicated with CHD. Using the most frequently reported genes out of the 167 CHD candidate genes, the highest accuracy of 69.77 % was obtained with a permutation p-value = 0.017. Enrichment analysis and NCA showed that the top genes selected by the proposed SRVS approach were strongly related to the frequently reported CHD genes, although functional differences were present.Conclusion: Our study suggests that SRVS is an effective method in data driven variable selection for CHD and that frequently reported CHD candidate genes may not be the best biomarkers for a specific CHD patient/ patient group.

Leukemia Candidate Genetic Markers-An Evaluation

Background: The cause of leukemia, the most common type of cancer, remains unknown. Genetic studies have reported more than a thousand of genes as being linked to the disease.Methods: A total of 1,093 leukemia candidate genes, identified from leukemia-gene relations data extracted from the ResNet 11 Mammalian database and supported by 6,524 references were evaluated. Four network metrics were used to evaluate individual gene potential relevance to leukemia. Gene-set enrichment, sub-network enrichment, and network-connectivity analyses were conducted on gene attributes. An expression dataset of 71 leukemia patients, and 76 healthy controls, was employed for validationResults: A total of 952 out of 1,093 genes were enriched in 100 pathways (p < 3.3e-20), demonstrating strong gene-gene interaction. A network metrics analysis revealed 5 genes (TP53, CTNNB1, AKT1, TNF, and RARA), as measured by both functional diversity and replication frequency, as the top leukemia candidates. Validation, using expression data, showed that the 1,093 genes, as a whole, and the top genes, as identified by the proposed metrics, were efficient in distinguishing leukemia patients from controls (maximum classification ratio = 95.3 % with permutation p-value = 0.0054).Conclusion: The genetic causes of leukemia are linked to a genetic network composed of a large number of genes. This network, together with the network metrics provided in this study, could provide a basis for further molecular studies in the field.

Literature Data Mining and Enrichment Analysis Reveal A Genetic Network of 423 Genes for Renal Cancer

Background: Renal cancer (RC) originates in the cells of the kidneys. Worldwide, approximately 208,500 new cases of renal cancer are diagnosed annually. This accounts for just under 2 % of all cancers. Those with a family history of RC have an increased risk of developing the disease. Recent research has identified hundreds of genes which may relate to its development. No study has systematically summarized these findings or provided an objective view of the genes reportedly associated with RC.Methods: Literature data mining (LDM) was performed on more than 1,100 articles for publications between 1988 and April 2016 in which 423 genes were reported to be RC-associated. A gene set enrichment analysis (GSEA) and a sub-network enrichment analysis (SNEA) were performed to study the functional profile and pathogenic significance of these genes. A network connectivity analysis (NCA) to study the associations between the reported genes was done. Literature, and enrichment metrics, analyses were used to identify genes with specific RC significance.Results: Multiple RC associations for 329 of the 423 genes enriched 100 pathways (p < 1.2e-10) were demonstrated. Ten genes (IL6, VEGFA, HIF1A, EGFR, PTEN, TP53, FGF2, CTNNB1, HMOX1, and BRCA1) were identified as having the most significant association with RC in terms of both functional diversity and replication frequency. Three novel genes, CD274, NOTCH1, and CREB1, were found to play roles within many significant RC-related pathways, warranting their further study. SNEA, and, NCA results indicated that many of these genes work as a functional network that plays roles in the RC-related disorder pathogeneses.Conclusion: The results suggest that the genetic causes of RC are linked to a genetic network composed of a large number of genes. The gene lists, together with the literature, and enrichment, metrics provided by this study, may serve a basis for further biological and genetic studies in the field.

Literature Data Mining and Enrichment Analysis on Top 235 Genes for Attention Deficit Hyperactivity Disorder

Background:Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder of the neuro-developmental type, marked by an ongoing pattern of inattention or hyperactivity/impulsivity, which interferes with functioning or development. The disorder affects approximately 5-7 % children and 2-5 % of adults worldwide. Numerous studies have indicated that genetic factors predominate the causes for ADHD. Nevertheless, no systematic study has summarized these findings and provided an objective and complete list of genes with a reported association to ADHD. Methods:Literature and enrichment metrics analyses were used to discover genes of specific significance associated with ADHD. We conducted a literature data mining (LDM) of over 2,410 articles covering publications from Jan. 1988 to Apr. 2016, where 235 genes were reported to be associated with the disease. Then we performed a gene set enrichment analysis (GSEA) and a sub-network enrichment analysis (SNEA) to study the functional profile and pathogenic significance of these genes associated with ADHD. Lastly, we performed a network connectivity analysis (NCA) to study the associations between the reported genes. Results:181/235 genes enriched 100 pathways (p<1.1e-007), demonstrating multiple associations with ADHD. Twelve genes were discovered to be associated with ADHD, in terms of both functional diversity and replication frequency, including SLC6A3, DRD4, BDNF, DRD2, HTR2A, DBH, HTR1B, DRD5, GRM7, DRD3, TH and GRIN2A. In addition, one novel gene, SHANK2, was suggested worthy of further study. Moreover, SNEA and NCA results indicated that many of these genes form a functional network, playing roles in the pathogenesis of other ADHD related disorders. Conclusion:Our results suggest that the genetic causes of ADHD are linked to a genetic and functional network composed of a large group of genes. The gene lists, together with the literature and enrichment metrics provided in this study, could serve as groundwork for further biological/genetic studies in the field

High contaminant loads in Lake Apopka's riparian wetland disrupt gene networks involved in reproduction and immune function in largemouth bass

Lake Apopka (FL, USA) has elevated levels of some organochlorine pesticides in its sediments and a portion of its watershed has been designated a US Environmental Protection Agency Superfund site. This study assessed reproductive endpoints in Florida largemouth bass (LMB) (Micropterus salmoides floridanus) after placement into experimental ponds adjacent to Lake Apopka. LMB collected from a clean reference site (DeLeon Springs) were stocked at two periods of time into ponds constructed in former farm fields on the north shore of the lake. LMB were stocked during early and late oogenesis to determine if there were different effects of contamination on LMB that may be attributed to their reproductive stage. LMB inhabiting the ponds for ~4months had anywhere from 2 to 800 times higher contaminant load for a number of organochlorine pesticides (e.g. p, p′-DDE, methoxychlor) compared to control animals. Gonadosomatic index and plasma vitellogenin were not different between reproductively-stage matched LMB collected at reference sites compared to those inhabiting the ponds. However, plasma 17β-estradiol was lower in LMB inhabiting the Apopka ponds compared to ovary stage-matched LMB from the St. Johns River, a site used as a reference site. Sub-network enrichment analysis revealed that genes related to reproduction (granulosa function, oocyte development), endocrine function (steroid metabolism, hormone biosynthesis), and immune function (T cell suppression, leukocyte accumulation) were differentially expressed in the ovaries of LMB placed into the ponds. These data suggest that (1) LMB inhabiting the Apopka ponds showed disrupted reproduction and immune responses and that (2) gene expression profiles provided site-specific information by discriminating LMB from different macro-habitats.

Proteomic profiling reveals dopaminergic regulation of progenitor cell functions of goldfish radial glial cells in vitro

Radial glial cells (RGCs) are stem-like cells found in the developing and adult central nervous system. They function as both a scaffold to guide neuron migration and as progenitor cells that support neurogenesis. Our previous study revealed a close anatomical relationship between dopamine neurons and RGCs in the telencephalon of female goldfish. In this study, label-free proteomics was used to identify the proteins in a primary RGC culture and to determine the proteome response to the selective dopamine D1 receptor agonist SKF 38393 (10μM), in order to better understand dopaminergic regulation of RGCs. A total of 689 unique proteins were identified in the RGCs and these were classified into biological and pathological pathways. Proteins such as nucleolin (6.9-fold) and ependymin related protein 1 (4.9-fold) were increased in abundance while proteins triosephosphate isomerase (10-fold) and phosphoglycerate dehydrogenase (5-fold) were decreased in abundance. Pathway analysis revealed that proteins that consistently changed in abundance across biological replicates were related to small molecules such as ATP, lipids and steroids, hormones, glucose, cyclic AMP and Ca2+. Sub-network enrichment analysis suggested that estrogen receptor signaling, among other transcription factors, is regulated by D1 receptor activation. This suggests that these signaling pathways are correlated to dopaminergic regulation of radial glial cell functions. Most proteins down-regulated by SKF 38393 were involved in cell cycle/proliferation, growth, death, and survival, which suggests that dopamine inhibits the progenitor-related processes of radial glial cells. Examples of differently expressed proteins including triosephosphate isomerase, nucleolin, phosphoglycerate dehydrogenase and capping protein (actin filament) muscle Z-line beta were validated by qPCR and western blot, which were consistent with MS/MS data in the direction of change. This is the first study to characterize the RGC proteome on a large scale in a vertebrate species. These data provide novel insight into glial protein networks that are associated with neuroendocrine function and neurogenesis in the teleost brain. Biological significanceWhile the role of radial glial cells in organizing brain structure and neurogenesis has been well studied, protein profiling experiments in this unique cell type has not been conducted. This study is the first to profile the proteome of goldfish radial glial cells in culture and to study the regulation of progenitor functions of radial glial cells by the neurotransmitter dopamine. This study provides the foundation for molecular network analysis in fish radial glial cells, and identifies cellular processes and signaling pathways in these cells with roles in neurogenesis and neuroendocrine function. Lastly, this study begins to characterize signatures and biomarkers for specific neuroendocrine and neurogenesis disruptors.

Functional network composed of 1,219 genes for Schizophrenia-- a literature data mining and enrichment analysis

Background:Over the past decade, numerous studies have focused on identifying genetic factors associated with schizophrenia (SCZ). Sample variations, such as size, population, race, disease status, and data processing methods resulted in selection differences. Nevertheless, no systemic study has been completed to summarize these reports and provide an objective full list of genes with a reported association to SCZ. Methods:We conducted a literature data mining (LDM) of over 13,515 articles covering publications from 1958 to Feb. 2016. These articles reported multiple types of marker-disease associations between 1,219 genes and SCZ. Then we conducted a gene set enrichment analysis (GSEA) and a sub-network enrichment analysis (SNEA) to study the functional profile and validate the pathogenic significance of these genes to SCZ. Finally, we presented additional results from the systemic review, including publication date, quality scores, and author affiliations. Results:All of these genes have been demonstrated to present multiple mutations associating with SCZ, some of which were supported by a large number of high quality articles. Enrichment analyses showed that many psychiatric and neuropathic pathways/groups related to SCZ have been significantly enriched by these genes and that they are functionally associated with each other. Conclusion:Our results indicate that these genes may operate as a functional biomarker network influencing the development of SCZ, and that LDM together with GSEA and SNEA could serve as an effective approach in finding these potential target genes

Literature data mining based enrichment analysis on 1,925 genes for lung cancer

Background: Approximately 8 % of all lung cancer is due to inherited factors. Risk more than doubles for those with relatives who have had lung cancer. To date, numerous genetic studies have found large number of genes that are related to lung cancer. Most of the studies focused on separate activities of genes that influence disease development.Methods: Literature data mining (LDM) of over 17,884 articles for publications from 1978 to February 2016 was conducted. The articles reported multiple types of marker-disease associations between 1,925 genes and lung cancer. A gene set enrichment analysis (GSEA) and a sub-network enrichment analysis (SNEA) was performed to discover functional profiles and validate pathogenic significances of these genes to lung cancer. Finally, a network connectivity analysis (NCA) was performed to study associations between the reported genes.Results: The reported genes demonstrate multiple types of association with lung cancer. Results from the enrichment analysis confirm the reports and suggest that these genes play significant roles in lung cancer pathogenesis, as well as in the pathogenesis of other lung cancer-related disorders. NCA results demonstrate that these genes, especially those with high RScores, present strong functional associations with each other.Conclusion: The results suggest that lung cancer genetic causes are linked to a network composed of a large number of genes. LDM together with enrichment and network analysis could serve as an effective approach in finding these potential target genes.

Differential recruitment of co-regulatory proteins to the human estrogen receptor 1 in response to xenoestrogens

The diverse biological effects of xenoestrogens may be explained by their ability to differentially recruit co-regulatory proteins to the estrogen receptor (ER). We employed high-throughput receptor affinity binding and co-regulatory protein recruitment screening assays based on fluorescence polarization and time resolved florescence resonance energy transfer (TR-FRET), respectively, to assess xenoestrogen-specific binding and co-regulatory protein recruitment to the ER. Then we used a functional proteomic assay based on co-immunoprecipitation of ER-bound proteins to isolate and identify intact co-regulatory proteins recruited to a ligand-activated ER. Through these approaches, we revealed differential binding affinity of bisphenol-A (BPA) and genistein (GEN) to the human ERα (ESR1) and ligand-dependent recruitment of SRC-1 and SRC-3 peptides. Recruitment profiles were variable for each ligand and in some cases were distinct compared to 17β-estradiol (E2). For example, E2 and GEN recruited both SRC-1 and -3 peptides whereas BPA recruited only SRC-1 peptides. Results of the functional proteomic assay showed differential recruitment between ligands where E2 recruited the greatest number of proteins followed by BPA then GEN. A number of proteins share previously identified relationships with ESR1 as determined by STRING analysis. Although there was limited overlap in proteins identified between treatments, all ligands recruited proteins involved in cell growth as determined by subnetwork enrichment analysis (p<0.05). A comparative, in silico analysis revealed that fewer interactions exist between zebrafish (Danio rerio) esr1 and zebrafish orthologs of proteins identified in our functional proteomic analysis. Taken together these results identify recruitment of known and previously unknown co-regulatory proteins to ESR1 and highlight new methods to assay recruitment of low abundant and intact, endogenous co-regulatory proteins to ESR1 or other nuclear receptors, in both human and aquatic species.

The G Protein-Coupled Estrogen Receptor Agonist G-1 Inhibits Nuclear Estrogen Receptor Activity and Stimulates Novel Phosphoproteomic Signatures.

Estrogen exerts cellular effects through both nuclear (ESR1 and ESR2) and membrane-bound estrogen receptors (G-protein coupled estrogen receptor, GPER); however, it is unclear if they act independently or engage in crosstalk to influence hormonal responses. To investigate each receptor's role in proliferation, transcriptional activation, and protein phosphorylation in breast cancer cells (MCF-7), we employed selective agonists for ESR1 propyl-pyrazole-triol (PPT), ESR2 diarylpropionitrile (DPN), and GPER (G-1) and also determined the impact of xenoestrogens bisphenol-A (BPA) and genistein on these effects. As anticipated, 17β-estradiol (E2), PPT, DPN, BPA, and genistein each enhanced proliferation and activation of an ERE-driven reporter gene whereas G-1 had no significant impact. However, G-1 significantly reduced E2-, PPT-, DPN-, BPA-, and genistein-induced proliferation and ERE activation at doses greater than 500 nM indicating that G-1 mediated inhibition is not ESR isotype specific. As membrane receptors initiate cascades of phosphorylation events, we performed a global phosphoproteomic analysis on cells exposed to E2 or G-1 to identify potential targets of receptor crosstalk via downstream protein phosphorylation targets. Of the 211 phosphorylated proteins identified, 40 and 13 phosphoproteins were specifically modified by E2 and G-1, respectively. Subnetwork enrichment analysis revealed several processes related to cell cycle were specifically enriched by G-1 compared with E2. Further there existed a number of newly identified proteins that were specifically phosphorylated by G-1. These phosphorylation networks highlight specific proteins that may modulate the inhibitory effects of G-1 and suggest a novel role for interference with nuclear receptor activity driven by E2 and xenoestrogens.

Global Transcriptomic Analysis of Interactions between Pseudomonas aeruginosa and Bacteriophage PaP3.

The interactions between Bacteriophage (phage) and host bacteria are widespread in nature and influences of phage replication on the host cells are complex and extensive. Here, we investigate genome-wide interactions of Pseudomonas aeruginosa (P. aeruginosa) and its temperate phage PaP3 at five time points during phage infection. Compared to the uninfected host, 38% (2160/5633) genes of phage-infected host were identified as differentially expressed genes (DEGs). Functional analysis of the repressed DEGs revealed infection-stage-dependent pathway communications. Based on gene co-expression analysis, most PaP3 middle genes were predicted to have negative impact on host transcriptional regulators. Sub-network enrichment analysis revealed that adjacent genes of PaP3 interacted with the same host genes and might possess similar functions. Finally, our results suggested that during the whole infection stage, the early genes of PaP3 had stronger regulatory role in host gene expression than middle and late genes, while the host genes involved amino acid metabolism were the most “vulnerable” targets of these phage genes. This work provides the basis for understanding survival mechanisms of parasites and host, and seeking phage gene products that could potentially be used in anti-bacterial infection.

Genome-wide analysis reveals conserved transcriptional responses downstream of resting potential change in Xenopus embryos, axolotl regeneration, and human mesenchymal cell differentiation.

Endogenous bioelectric signaling via changes in cellular resting potential (V mem) is a key regulator of patterning during regeneration and embryogenesis in numerous model systems. Depolarization of V mem has been functionally implicated in dedifferentiation, tumorigenesis, anatomical re‐specification, and appendage regeneration. However, no unbiased analyses have been performed to understand genome‐wide transcriptional responses to V mem change in vivo. Moreover, it is unknown which genes or gene networks represent conserved targets of bioelectrical signaling across different patterning contexts and species. Here, we use microarray analysis to comparatively analyze transcriptional responses to V mem depolarization. We compare the response of the transcriptome during embryogenesis (Xenopus development), regeneration (axolotl regeneration), and stem cell differentiation (human mesenchymal stem cells in culture) to identify common networks across model species that are associated with depolarization. Both subnetwork enrichment and PANTHER analyses identified a number of key genetic modules as targets of V mem change, and also revealed important (well‐conserved) commonalities in bioelectric signal transduction, despite highly diverse experimental contexts and species. Depolarization regulates specific transcriptional networks across all three germ layers (ectoderm, mesoderm, and endoderm) such as cell differentiation and apoptosis, and this information will be used for developing mechanistic models of bioelectric regulation of patterning. Moreover, our analysis reveals that V mem change regulates transcripts related to important disease pathways such as cancer and neurodegeneration, which may represent novel targets for emerging electroceutical therapies.