Pathway-Based Polygenic Risk Scores for Schizophrenia and Associations With Reported Psychotic-like Experiences and Neuroimaging Phenotypes in the UK Biobank

Background

Schizophrenia is a heritable psychiatric disorder with a polygenic architecture. Genome-wide association studies have reported that an increasing number of risk-associated variants and polygenic risk scores (PRSs) explain 17% of the variance in the disorder. Substantial heterogeneity exists in the effect of these variants, and aggregating them based on biologically relevant functions may provide mechanistic insight into the disorder.

Methods

Using the largest schizophrenia genome-wide association study conducted to date, we associated PRSs based on 5 gene sets previously found to contribute to schizophrenia pathophysiology-postsynaptic density of excitatory synapses, postsynaptic membrane, dendritic spine, axon, and histone H3-K4 methylation-along with respective whole-genome PRSs, with neuroimaging (n > 29,000) and reported psychotic-like experiences (n > 119,000) variables in healthy UK Biobank subjects.

Results

Several variables were significantly associated with the axon gene-set (psychotic-like communications, parahippocampal gyrus volume, fractional anisotropy thalamic radiations, and fractional anisotropy posterior thalamic radiations (β range -0.016 to 0.0916, false discovery rate-corrected p [pFDR] ≤ .05), postsynaptic density gene-set (psychotic-like experiences distress, global surface area, and cingulate lobe surface area [β range -0.014 to 0.0588, pFDR ≤ .05]), and histone gene set (entorhinal surface area: β = -0.016, pFDR = .035). From these, whole-genome PRSs were significantly associated with psychotic-like communications (β = 0.2218, pFDR = 1.34 × 10-7), distress (β = 0.1943, pFDR = 7.28 × 10-16), and fractional anisotropy thalamic radiations (β = -0.0143, pFDR = .036). Permutation analysis revealed that these associations were not due to chance.

Conclusions

Our results indicate that genetic variation in 3 gene sets relevant to schizophrenia may confer risk for the disorder through effects on previously implicated neuroimaging variables. Because associations were stronger overall for whole-genome PRSs, findings here highlight that selection of biologically relevant variants is not yet sufficient to address the heterogeneity of the disorder.

The key role of denitrification and dissimilatory nitrate reduction in nitrogen pollution along vertical landfill profiles from metagenomic perspective

Landfill are persistent sources of nitrogen (N) pollution even in the decades after closure. However, the biological pathways of N-pollution, particularly N₂O and NH₄⁺, at different landfill depths have received little attention. In this study, metagenomic analysis was conducted on landfill refuse from vertical reservoir profiles in two closed landfills named XT and MT. NH₄⁺ concentrations were found to be higher in deeper layers of MT, while greater potential for N₂O emissions occurred in XT and the shallow layers of MT. Furthermore, the community structure and function of N-metabolizing microbes were more strongly defined by landfill depth than landfill type. Denitrification, involving abundant nirK and norB genes, was identified as the major pathway for N₂O production in both XT and MT-shallow, while dissimilatory nitrate reduction with abundant nirBD genes was identified as the major pathway for NH₄⁺ accumulation. Microbes of norB-type and nirBD-type were positively affected by NO₃^- in XT, whereas negatively affected by contents of organic material and moisture in MT-shallow. The mechanism by which nitrogen fixation, with abundant nifH genes, contributes to NH₄⁺ accumulation in MT-deep should be further elucidated. These findings can provide a theoretical basis for governing scientific N-pollution control strategies throughout the entire landfill process.

First metabolic profiling of 4-n-nonylphenol in human liver microsomes by integrated approaches to testing and assessment: Metabolites, pathways, and biological effects

4-n-nonylphenol (4-n-NP), a typical endocrine disrupting chemical, has been so far frequently detected in various environmental mediums and editable food. However, the specific metabolic pathways in human and potential adverse effects of metabolites have not been elucidated yet. Here, metabolic profiling of 4-n-NP in human liver microsome (HLM) was comprehensively characterized by integrated approaches of testing and assessment. A total of 21 metabolites were identified using nontarget analysis with high-resolution mass spectrum, including three groups of unique phase I metabolites first determined in HLM. Seven various metabolic pathways of 4-n-NP were identified by both in silico and in vitro, and CYP1A2, 2C19, and 2D6 were the mainly participating enzymes. Two secondary metabolites with carbonyl groups on side chains (M4, M7) presented most abundant in HLM, which were also predicted to have high binding affinities towards HPG-axis-related receptors (AR, ER, and PR). ESRs (estrogen receptors) were shared core protein targets for all metabolites revealed by protein-protein interaction networks. Biological functions enrichment analysis indicated that 4-n-NP metabolites might primarily involve in ESR-mediated signaling, GPCR ligand binding, Class A/1 (Rhodopsin-like receptors) and metabolism-related pathways. These findings of 4-n-NP metabolites, pathways, and biological effects provide insightful information for its environmental exposure and risk assessment.

Revisiting the biological pathway for methanogenesis in landfill from metagenomic perspective-A case study of county-level sanitary landfill of domestic waste in North China plain

Landfill is the third highest contributor to anthropogenic methane (CH₄) emissions, produced primarily by the anaerobic decomposition of organic matter by microbes. However, how various microbial metabolic processes contribute to CH₄ production in domestic waste landfill remains elusive. We addressed this problem by investigating the methanogenic communities, methanogenic functional genes, KEGG modules and KEGG pathways in a county-level MSW sanitary landfill in North China Plain, China. Results showed that Methanomicrobiales, Methanobacteriales, Methanosarcinales, Micrococcales, Corynebacteriales and Bacillales were the dominant methanogens. M00357, M00346, M00567 and M00563 were the four major methane metabolic modules. The most abundant genes were ACSS, ackA and fwd with the relative abundance of 19.26-54.54%, 6.14-25.78% and 6.76-16.51%, respectively. The two essential genes of methanogenesis were detected with the relative abundance of 2.66-9.58% (mtr) and 1.63-9.14% (mcr). These findings indicated that acetotrophic and hydrogenotrophic methanogenesis were the major pathways. Methanomicrobiales, Methanosarcinales and Clostridiales were the key microbes to these pathways identified by co-occurrence network. Analysis of relative contribution of species to function further showed that Micrococcales, Corynebacteriales and Bacillales were special contributors to acetotrophic methanogenesis pathway. Redundancy analysis revealed that above functional genes and microbes were mainly controlled by NH₄⁺ and pH. Our results can help to provide develop the fine management strategies for methane utilization and emission reduction in landfill.

Joint toxicity mechanisms of binary emerging PFAS mixture on algae (Chlorella pyrenoidosa) at environmental concentration

Since traditional Per- and polyfluoroalkyl substances (PFAS) were banned in 2009 due to their bioaccumulation, persistence and biological toxicity, the emerging PFAS have been widely used as their substitutes and entered the aquatic environment in the form of mixtures. However, the joint toxicity mechanisms of these emerging PFAS mixtures to aquatic organisms remain largely unknown. Then, based on the testing of growth inhibition, cytotoxicity, photosynthesis and oxidative stress, and the toxicity mechanism of PFAS mixture (Perfluorobutane sulfonate and Perfluorobutane sulfonamide) to algae was explored using the Gene set enrichment analysis (GSEA). The results revealed that all three emerging PFAS treatments had a certain growth inhibitory effect on Chlorella pyrenoidosa (C. pyrenoidosa), but the toxicity of PFAS mixture was stronger than that of individual PFAS and showed a significant synergistic effect at environmental concentration. The joint toxicity mechanisms of binary PFAS mixture to C. pyrenoidosa were related to the damage of photosynthetic system, obstruction of ROS metabolism, and inhibition of DNA replication. Our findings are conductive to adding knowledge in understanding the joint toxicity mechanisms and provide a basis for assessing the environmental risk of emerging PFAS.

Using BioPAX-Parser (BiP) to enrich lists of genes or proteins with pathway data

BACKGROUND

Pathway enrichment analysis (PEA) is a well-established methodology for interpreting a list of genes and proteins of interest related to a condition under investigation. This paper aims to extend our previous work in which we introduced a preliminary comparative analysis of pathway enrichment analysis tools. We extended the earlier work by providing more case studies, comparing BiP enrichment performance with other well-known PEA software tools.

METHODS

PEA uses pathway information to discover connections between a list of genes and proteins as well as biological mechanisms, helping researchers to overcome the problem of explaining biological entity lists of interest disconnected from the biological context.

RESULTS

We compared the results of BiP with some existing pathway enrichment analysis tools comprising Centrality-based Pathway Enrichment, pathDIP, and Signaling Pathway Impact Analysis, considering three cancer types (colorectal, endometrial, and thyroid), for a total of six datasets (that is, two datasets per cancer type) obtained from the The Cancer Genome Atlas and Gene Expression Omnibus databases. We measured the similarities between the overlap of the enrichment results obtained using each couple of cancer datasets related to the same cancer.

CONCLUSION

As a result, BiP identified some well-known pathways related to the investigated cancer type, validated by the available literature. We also used the Jaccard and meet-min indices to evaluate the stability and the similarity between the enrichment results obtained from each couple of cancer datasets. The obtained results show that BiP provides more stable enrichment results than other tools.

Proteomic correlates of cortical thickness in cognitively normal individuals with normal and abnormal cerebrospinal fluid beta-amyloid1-42

Cortical atrophy is an early feature of Alzheimer´s disease (AD). The biological processes associated with variability in cortical thickness remain largely unknown. We studied 220 cerebrospinal fluid (CSF) proteins to evaluate biological pathways associated with cortical thickness in 34 brain regions in 79 cognitively normal older individuals with normal (>192 ng/L, n = 47), and abnormal (≤192 ng/L, n = 32) CSF beta-amyloid_1-42 (Aβ₄₂). Interactions for Aβ₄₂ status were tested. Panther GeneOntology and Cytoscape ClueGO analyses were used to evaluate biological processes associated with regional cortical thickness. 170 (77.3 %) proteins related with cortical thickness in at least 1 brain region across the total group, and 171 (77.7 %) proteins showed Aβ₄₂ specific associations. Higher levels of proteins related to axonal and synaptic integrity, amyloid accumulation, and inflammation were associated with thinner cortex in lateral temporal regions, the rostral anterior cingulum, the lateral occipital cortex and the pars opercularis only in the abnormal Aβ₄₂ group. Alterations in CSF proteomics are associated with a regional cortical atrophy in the earliest stages of AD.

Identification of hub genes and biological pathways in hepatocellular carcinoma by integrated bioinformatics analysis

Background

Hepatocellular carcinoma (HCC), the main type of liver cancer in human, is one of the most prevalent and deadly malignancies in the world. The present study aimed to identify hub genes and key biological pathways by integrated bioinformatics analysis.

Methods

A bioinformatics pipeline based on gene co-expression network (GCN) analysis was built to analyze the gene expression profile of HCC. Firstly, differentially expressed genes (DEGs) were identified and a GCN was constructed with Pearson correlation analysis. Then, the gene modules were identified with 3 different community detection algorithms, and the correlation analysis between gene modules and clinical indicators was performed. Moreover, we used the Search Tool for the Retrieval of Interacting Genes (STRING) database to construct a protein protein interaction (PPI) network of the key gene module, and we identified the hub genes using nine topology analysis algorithms based on this PPI network. Further, we used the Oncomine analysis, survival analysis, GEO data set and random forest algorithm to verify the important roles of hub genes in HCC. Lastly, we explored the methylation changes of hub genes using another GEO data (GSE73003).

Results

Firstly, among the expression profiles, 4,130 up-regulated genes and 471 down-regulated genes were identified. Next, the multi-level algorithm which had the highest modularity divided the GCN into nine gene modules. Also, a key gene module (m1) was identified. The biological processes of GO enrichment of m1 mainly included the processes of mitosis and meiosis and the functions of catalytic and exodeoxyribonuclease activity. Besides, these genes were enriched in the cell cycle and mitotic pathway. Furthermore, we identified 11 hub genes, MCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 which played key roles in HCC. The results of multiple verification methods indicated that the 11 hub genes had highly diagnostic efficiencies to distinguish tumors from normal tissues. Lastly, the methylation changes of gene CDC20, TOP2A, TK1, FEN1 in HCC samples had statistical significance (P-value < 0.05).

Conclusion

MCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 could be potential biomarkers or therapeutic targets for HCC. Meanwhile, the metabolic pathway, the cell cycle and mitotic pathway might played vital roles in the progression of HCC.

Molecular characteristics of varicocele: integration of whole-exome and transcriptome sequencing

OBJECTIVE

To explore the exome and transcriptome characteristics potentially underlying the pathogenesis of varicocele (VE).

DESIGN

Experimental study and cohort study.

SETTING

Academic research laboratory and university-affiliated hospital.

PATIENT(S)

Eleven VE patients whose fathers also had VE, plus 151 additional patients and 324 healthy men for variants genotyping; for the rat model, eight Sprague-Dawley male rats.

INTERVENTION(S)

Partial ligation of renal vein was conducted to establish VE rat models for whole-transcriptome RNA sequencing (RNA-seq).

MAIN OUTCOME MEASURE(S)

Genes with differential expression and/or harboring potential pathogenic variants detected via RNA-seq and whole-exome sequencing (WES) then subjected to population-based survey to define candidate genes of VE and analyzed via Gene Ontology and Kyoto Encyclopedia of Genes and Genomes to identify VE-involved pathways.

RESULT(S)

Whole-transcriptome RNA sequencing (RNA-seq) was performed using left spermatic veins of five rat VE models and three controls. We identified 9,688 genes and 18 pathways via RNA-seq, and via WES 160 genes harboring 279 potential deleterious variants and 16 pathways. Nine genes (AAMP, KMT2D, IRS2, SPINT1, IFT122, MKI67, DCHS1, LAMA2, and CBL) had variants in more than one patient who underwent WES, and six of these genes (AAMP, SPINT1, MKI67, IFT122, LAMA2, and DCHS1) showed differential expression. The population-based survey showed that AAMP, SPINT1, and MKI67 were strongly associated with VE risk. Together, two omic 67 data sets revealed four pathways potentially related to VE.

CONCLUSION(S)

For the first time, we have described the exome and transcriptome characteristics of VE. The bi-omics identified novel candidate genes and pathways involving the occurrence and development of VE.

Logic-based analysis of gene expression data predicts association between TNF, TGFB1 and EGF pathways in basal-like breast cancer

For breast cancer, clinically important subtypes are well characterized at the molecular level in terms of gene expression profiles. In addition, signaling pathways in breast cancer have been extensively studied as therapeutic targets due to their roles in tumor growth and metastasis. However, it is challenging to put signaling pathways and gene expression profiles together to characterize biological mechanisms of breast cancer subtypes since many signaling events result from post-translational modifications, rather than gene expression differences. We designed a logic-based computational framework to explain the differences in gene expression profiles among breast cancer subtypes using Pathway Logic and transcriptional network information. Pathway Logic is a rewriting-logic-based formal system for modeling biological pathways including post-translational modifications. Our method demonstrated its utility by constructing subtype-specific path from key receptors (TNFR, TGFBR1 and EGFR) to key transcription factor (TF) regulators (RELA, ATF2, SMAD3 and ELK1) and identifying potential association between pathways via TFs in basal-specific paths, which could provide a novel insight on aggressive breast cancer subtypes. Codes and results are available at http://epigenomics.snu.ac.kr/PL/.

Perform Pathway Enrichment Analysis Using ReactomeFIViz

Modern large-scale biological data analysis often generates a set of significant genes, frequently associated with scores. Pathway-based approaches are routinely performed to understand the functional contexts of these genes. Reactome is the most comprehensive open-access biological pathway knowledge base, widely used in the research community, providing a solid foundation for pathway-based data analysis. ReactomeFIViz is a Cytoscape app built upon Reactome pathways to help users perform pathway- and network-based data analysis and visualization. In this chapter we describe procedures on how to perform pathway enrichment analysis using ReactomeFIViz for a gene score file. We describe two types of analysis: pathway enrichment based on a set of significant genes and GSEA analysis using gene scores without cutoff. We also describe a feature to overlay gene scores onto pathway diagrams, enabling users to understand the underlying mechanisms for up- or down- regulated pathways collected from pathway analysis.

An integrative bioinformatics analysis identified miR-375 as a candidate key regulator of malignant breast cancer

MicroRNAs (miRNAs) are key regulators that play important biological roles in carcinogenesis and are promising biomarkers for cancer diagnosis and therapy. hsa-miR-375-3p (miR-375) has been suggested to serve as a tumor suppressor or oncogene in various tumor types; however, its specific expression and potential regulatory role in malignant breast cancer remain unclear. In this study, the results from noncoding RNA microarray analysis indicated that the miR-375 expression level is significantly decreased in malignant basal-like breast cancer compared with luminal-like breast cancer. A total of 1895 co-downregulated and 1645 co-upregulated genes were identified in miR-375 mimic-transfected basal-like breast cancer cell lines. Predicted miR-375 targets were obtained from the online databases TargetScan and DIANA-microT-CDS. Combined KEGG enrichment analysis for coregulated genes and predicted miR-375 targets provided information and revealed differences in potential dynamic signaling pathways regulated by miR-375 and also indicated specific regulatory pathways, such as RNA transport and processing, in basal-like breast cancer. Additionally, gene expression microarray analysis accompanied by UALCAN analysis was performed to screen upregulated genes in the basal-like subtype. Four potential key genes, including LDHB, CPNE8, QKI, and EIF5A2, were identified as candidate target genes of miR-375. Therefore, the present study demonstrated that miR-375 may be a potential key regulator and provide a promising direction for diagnostic and therapeutic developments for malignant breast cancer.

Genome-wide association analysis of dementia and its clinical endophenotypes reveal novel loci associated with Alzheimer's disease and three causality networks: The GR@ACE project

INTRODUCTION

Large variability among Alzheimer's disease (AD) cases might impact genetic discoveries and complicate dissection of underlying biological pathways.

METHODS

Genome Research at Fundacio ACE (GR@ACE) is a genome-wide study of dementia and its clinical endophenotypes, defined based on AD's clinical certainty and vascular burden. We assessed the impact of known AD loci across endophenotypes to generate loci categories. We incorporated gene coexpression data and conducted pathway analysis per category. Finally, to evaluate the effect of heterogeneity in genetic studies, GR@ACE series were meta-analyzed with additional genome-wide association study data sets.

RESULTS

We classified known AD loci into three categories, which might reflect the disease clinical heterogeneity. Vascular processes were only detected as a causal mechanism in probable AD. The meta-analysis strategy revealed the ANKRD31-rs4704171 and NDUFAF6-rs10098778 and confirmed SCIMP-rs7225151 and CD33-rs3865444.

DISCUSSION

The regulation of vasculature is a prominent causal component of probable AD. GR@ACE meta-analysis revealed novel AD genetic signals, strongly driven by the presence of clinical heterogeneity in the AD series.

Assessment of the effect of methyl-triclosan and its mixture with triclosan on developing zebrafish (Danio rerio) embryos using mass spectrometry-based metabolomics

Methyl-triclosan (MTCS), as a biodegradation product from antibacterial triclosan (TCS), has been detected in water catchments, and it has also been verified to accumulate in biota due to its hydrophobicity. There is a lack, however, of toxicity studies on MTCS and its effects on organisms in conjunction with TCS. In this study, exposure experiments were conducted to assess the toxicity to embryonic zebrafish of selected concentrations of MTCS (from 1 ng/L to 400 μg/L) and MTCS/TCS mixtures (from 1 μg/L TCS and 100 ng/L MTCS to 300 μg/L TCS and 30 μg/L MTCS). Specimens were extracted using acetonitrile: isopropanol: water (3:3:2; v/v/v) and then analyzed using Gas chromatography-mass spectrometry (GC-MS) to identify the metabolites based on the Fiehn library database. The results showed that MTCS exposure led to the alterations of the metabolomes of the zebrafish embryos, including level changes of l-valine, d-mannose, d-glucose, and other metabolites. Multivariate analysis (PCA, PLS-DA, sPLS-DA) and univariate analysis (one-way ANOVA) indicated differences between the control and exposure groups of the metabolites, indicating that biological pathways, such as amino acid synthesis, pentose phosphate pathway (PPP), starch and sucrose metabolism were influenced. Moreover, when the embryos were exposed to a mix of TCS and MTCS, TCS dominated the mixture's effect on biological pathways because the concentration ratio within the mixture, which mimics environmental ratio of 10 TCS : 1 MTCS, leads to high bioavailability of TCS.

Differential gene expression for carotenoid biosynthesis in a green alga Ulva prolifera based on transcriptome analysis

BACKGROUND

Carotenoids are widely distributed in plants and algae, and their biosynthesis has attracted widespread interest. Carotenoid-related research has mostly focused on model species, and there is a lack of data on the carotenoid biosynthetic pathway in U. prolifera that is the main species leading to green tide, a harmful plague of floating green algae.

RESULTS

The carotenoid content of U. prolifera samples, that is the main species leading to green tide, a harmful plague of floating green algae at different temperatures revealed that its terpenoid was highest in the samples subjected to high temperature at 28 °C (H), followed by the samples subjected to low temperature at 12 °C (L). Its terpenoid was lowest in the samples subjected to medium temperature at 20 °C (M). We conducted transcriptome sequencing (148.5 million raw reads and 49,676 unigenes in total) of samples that were subjected to different temperatures to study the carotenoid biosynthesis of U. prolifera. There were 1125-3164 significant differentially expressed genes between L, M and H incubation temperatures, of which 11-672 genes were upregulated and 453-3102 genes were downregulated. A total of 3164 genes were significantly differentially expressed between H and M, of which 62 genes were upregulated and 3102 genes were downregulated. A total of 2669 significant differentially expressed genes were observed between L and H, of which 11 genes were upregulated and 2658 genes were downregulated. A total of 13 genes were identified to be involved in carotenoid biosynthesis in U. prolifera, and the expression levels of the majority were highest at H and lowest at M of incubation temperature. Both the carotenoid concentrations and the expression of the analysed genes were lowest in the normal temperature group, while low temperature and high temperature seemed to activate the biosynthesis of carotenoids in U. prolifera.

CONCLUSIONS

In this study, transcriptome sequencing provided critical information for understanding the accumulation of carotenoids and will serve as an important reference for the study of other metabolic pathways in U. prolifera.

The expression and biological information analysis of miR-375-3p in head and neck squamous cell carcinoma based on 1825 samples from GEO, TCGA, and peer-reviewed publications

BACKGROUND

The specific expression level and clinical significance of miR-375-3p in HNSCC had not been fully stated, as well as the overall biological function and molecular mechanisms. Therefore, we purpose to carry out a comprehensive meta-analysis to further explore the clinical significance and potential function mechanism of miR-375-3p in HNSCC.

METHODS

HNSCC-related data was gained from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and peer-reviewed journals. A meta-analysis was carried out to comprehensively explore the relationship between miR-375-3p expression level and clinicopathological features of HNSCC. And summary receiver operating characteristic (SROC) curve analysis was applied for evaluating disease diagnosis value of miR-375-3p. In addition, a biological pathway analysis was also performed to assess the possible molecular mechanism of miR-375-3p in HNSCC.

RESULTS

A total of 24 available records and references were added into analysis. The overall pooled meta-analysis outcome revealed a relatively lower expression level of miR-375-3p in HNSCC specimens than that in non-cancerous controls (P < 0.001). And SROC curve analysis showed that the pooled area under the SROC curve (AUC) was 0.90 (95%CI: 0.88-0.93). In addition, biological pathway analysis indicated that LAMC1, EDIL3, FN1, VEGFA, IGF2BP2, and IGF2BP3 maybe the latent target genes of miR-375-3p, which were greatly enriched in the pathways of beta3 integrin cell surface interactions and the binding of RNA via the insulin-like growth factor-2 mRNA-binding protein (IGF2BPs/IMPs/VICKZs).

CONCLUSION

MiR-375-3p expression clearly decreased in HNSCC samples compared with non-cancerous controls. Meanwhile, miR-375-3p may serve as a tumor suppressor via regulating tumor-related genes LAMC1, EDIL3, FN1, VEGFA, IGF2BP2, and IGF2BP3 in HNSCC.

Multiple model species selection for transcriptomics analysis of non-model organisms

Background

Transcriptomic sequencing (RNA-seq) related applications allow for rapid explorations due to their high-throughput and relatively fast experimental capabilities, providing unprecedented progress in gene functional annotation, gene regulation analysis, and environmental factor verification. However, with increasing amounts of sequenced reads and reference model species, the selection of appropriate reference species for gene annotation has become a new challenge.

Methods

We proposed a novel approach for finding the most effective reference model species through taxonomic associations and ultra-conserved orthologous (UCO) gene comparisons among species. An online system for multiple species selection (MSS) for RNA-seq differential expression analysis was developed, and comprehensive genomic annotations from 291 reference model eukaryotic species were retrieved from the RefSeq, KEGG, and UniProt databases.

Results

Using the proposed MSS pipeline, gene ontology and biological pathway enrichment analysis can be efficiently achieved, especially in the case of transcriptomic analysis of non-model organisms. The results showed that the proposed method solved problems related to limitations in annotation information and provided a roughly twenty-fold reduction in computational time, resulting in more accurate results than those of traditional approaches of using a single model reference species or the large non-redundant reference database.

Conclusions

Selection of appropriate reference model species helps to reduce missing annotation information, allowing for more comprehensive results than those obtained with a single model reference species. In addition, adequate model species selection reduces the computational time significantly while retaining the same order of accuracy. The proposed system indeed provides superior performance by selecting appropriate multiple species for transcriptomic analysis compared to traditional approaches.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2278-z) contains supplementary material, which is available to authorized users.

Association of Whole-Genome and NETRIN1 Signaling Pathway-Derived Polygenic Risk Scores for Major Depressive Disorder and White Matter Microstructure in the UK Biobank

Background

Major depressive disorder is a clinically heterogeneous psychiatric disorder with a polygenic architecture. Genome-wide association studies have identified a number of risk-associated variants across the genome and have reported growing evidence of NETRIN1 pathway involvement. Stratifying disease risk by genetic variation within the NETRIN1 pathway may provide important routes for identification of disease mechanisms by focusing on a specific process, excluding heterogeneous risk-associated variation in other pathways. Here, we sought to investigate whether major depressive disorder polygenic risk scores derived from the NETRIN1 signaling pathway (NETRIN1-PRSs) and the whole genome, excluding NETRIN1 pathway genes (genomic-PRSs), were associated with white matter microstructure.

Methods

We used two diffusion tensor imaging measures, fractional anisotropy (FA) and mean diffusivity (MD), in the most up-to-date UK Biobank neuroimaging data release (FA: n = 6401; MD: n = 6390).

Results

We found significantly lower FA in the superior longitudinal fasciculus (β = −.035, p_corrected = .029) and significantly higher MD in a global measure of thalamic radiations (β = .029, p_corrected = .021), as well as higher MD in the superior (β = .034, p_corrected = .039) and inferior (β = .029, p_corrected = .043) longitudinal fasciculus and in the anterior (β = .025, p_corrected = .046) and superior (β = .027, p_corrected = .043) thalamic radiation associated with NETRIN1-PRS. Genomic-PRS was also associated with lower FA and higher MD in several tracts.

Conclusions

Our findings indicate that variation in the NETRIN1 signaling pathway may confer risk for major depressive disorder through effects on a number of white matter tracts.

Functional Interaction Network Construction and Analysis for Disease Discovery

Network-based approaches project seemingly unrelated genes or proteins onto a large-scale network context, therefore providing a holistic visualization and analysis platform for genomic data generated from high-throughput experiments, reducing the dimensionality of data via using network modules and increasing the statistic analysis power. Based on the Reactome database, the most popular and comprehensive open-source biological pathway knowledgebase, we have developed a highly reliable protein functional interaction network covering around 60 % of total human genes and an app called ReactomeFIViz for Cytoscape, the most popular biological network visualization and analysis platform. In this chapter, we describe the detailed procedures on how this functional interaction network is constructed by integrating multiple external data sources, extracting functional interactions from human curated pathway databases, building a machine learning classifier called a Naïve Bayesian Classifier, predicting interactions based on the trained Naïve Bayesian Classifier, and finally constructing the functional interaction database. We also provide an example on how to use ReactomeFIViz for performing network-based data analysis for a list of genes.

Comparison of grouper infection with two different iridoviruses using transcriptome sequencing and multiple reference species selection

Due to high-density aquafarming in Taiwan, groupers are commonly infected with two different iridoviruses: Megalocytivirus (grouper iridovirus of Taiwan, TGIV) and Ranavirus (grouper iridovirus, GIV). Iridoviral diseases cause mass mortality, and surviving fish retain these pathogens, which can then be horizontally transferred. These viruses have therefore become a major challenge for grouper aquaculture. In this study, comparisons of the biological responses of groupers to infection with these two different iridoviruses were performed. A novel approach for transcriptomic analysis was proposed to enhance the discovery of differentially expressed genes and associated biological pathways. In this method, suitable and available reference species are selected from the NCBI taxonomy tree and the Ensembl and KEGG databases instead of either choosing only one model species or adopting the NCBI non-redundant dataset as references. Our results show that selection of multiple appropriate model species as references increases the efficiency and performance of analyses compared to those of traditional approaches. Using this method, 17 shared pathways and 5 specific pathways were found to be significantly differentially expressed following infection with the two iridoviruses, among which 11 pathways were additionally identified based on the proposed method of multiple reference species selection. Among the pathways responsive to infection with a specific iridovirus, the spliceosomal pathway (ko03040; p-value = 0.0011) was exclusively associated with TGIV infection, while the glycolysis/gluconeogenesis pathway (ko00010; p-value = 0.0032) was associated with GIV infection. These findings and designed corresponding biological experiments may facilitate a deeper understanding of the mechanisms by which both TGIV and GIV cause fatal infections, as well as the ways in which they induce different pathologies and symptoms. We believe that the proposed novel mechanism for de novo transcriptomic analysis provides superior and comprehensive functional annotations and that the resulting shared and specific pathways identified may help immunologists develop specific vaccines against various types of iridovirus in the near future.

Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data

Background

Molecular interaction data at proteomic and genetic levels provide physical and functional insights into a molecular biosystem and are helpful for the construction of pathway structures complementarily. Despite advances in inferring biological pathways using genetic interaction data, there still exists weakness in developed models, such as, activity pathway networks (APN), when integrating the data from proteomic and genetic levels. It is necessary to develop new methods to infer pathway structure by both of interaction data.

Results

We utilized probabilistic graphical model to develop a new method that integrates genetic interaction and protein interaction data and infers exquisitely detailed pathway structure. We modeled the pathway network as Bayesian network and applied this model to infer pathways for the coherent subsets of the global genetic interaction profiles, and the available data set of endoplasmic reticulum genes. The protein interaction data were derived from the BioGRID database. Our method can accurately reconstruct known cellular pathway structures, including SWR complex, ER-Associated Degradation (ERAD) pathway, N-Glycan biosynthesis pathway, Elongator complex, Retromer complex, and Urmylation pathway. By comparing N-Glycan biosynthesis pathway and Urmylation pathway identified from our approach with that from APN, we found that our method is able to overcome its weakness (certain edges are inexplicable). According to underlying protein interaction network, we defined a simple scoring function that only adopts genetic interaction information to avoid the balance difficulty in the APN. Using the effective stochastic simulation algorithm, the performance of our proposed method is significantly high.

Conclusion

We developed a new method based on Bayesian network to infer detailed pathway structures from interaction data at proteomic and genetic levels. The results indicate that the developed method performs better in predicting signaling pathways than previously described models.

Path2enet: generation of human pathway-derived networks in an expression specific context

Background

Biological pathways are subsets of the complex biomolecular wiring that occur in living cells. They are usually rationalized and depicted in cartoon maps or charts to show them in a friendly visible way. Despite these efforts to present biological pathways, the current progress of bioinformatics indicates that translation of pathways in networks can be a very useful approach to achieve a computer-based view of the complex processes and interactions that occurr in a living system.

Results

We have developed a bioinformatic tool called Path2enet that provides a translation of biological pathways in protein networks integrating several layers of information about the biomolecular nodes in a multiplex view. Path2enet is an R package that reads the relations and links between proteins stored in a comprehensive database of biological pathways, KEGG (Kyoto Encyclopedia of Genes and Genomes, http://www.genome.jp/kegg/), and integrates them with expression data from various resources and with data on protein-protein physical interactions. Path2enet tool uses the expression data to determine if a given protein in a network (i.e., a node) is active (ON) or inactive (OFF) in a specific cellular context or sample type. In this way, Path2enet reduces the complexity of the networks and reveals the proteins that are active (expressed) under specific conditions. As a proof of concept, this work presents a practical “case of use” generating the pathway-expression-networks corresponding to the NOTCH Signaling Pathway in human B- and T-lymphocytes. This case is produced by the analysis and integration in Path2enet of an experimental dataset of genome-wide expression microarrays produced with these cell types (i.e., B cells and T cells).

Conclusions

Path2enet is an open source and open access tool that allows the construction of pathway-expression-networks, reading and integrating the information from biological pathways, protein interactions and gene expression cell specific data. The development of this type of tools aims to provide a more integrative and global view of the links and associations that exist between the proteins working in specific cellular systems.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3066-7) contains supplementary material, which is available to authorized users.

NaCl stress-induced transcriptomics analysis of Salix linearistipularis (syn. Salix mongolica)

Background

Salix linearistipularis (syn. S. mongolica) is a woody halophyte, which is distributed naturally in saline-alkali soil of Songnen plain, Heilongjiang, China. It plays an important role in maintaining ecological balance and in improving saline soil. Furthermore, S. linearistipularis is also a genetic resource; however, there is no available information of genomic background for salt tolerance mechanism. We conducted the transcriptome analysis of S. linearistipularis to understand the mechanisms of salt tolerance by using RNA-seq technology.

Results

The transcription profiles of both the salt stress (SLH-treated) and the control (SLH-control) sample for S. linearistipularis were obtained by using RNA-seq in this study. By comparative analysis, only 3034 of 53,362 all-unigenes between two samples were expressed differently at more than 1.5-fold (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left| {fold - change} \right| \ge 1.5$$\end{document}fold-change≥1.5, FDR ≤ 0.05), including 1397 up-regulated genes and 1637 down-regulated genes. In total, 2199 genes were classified into 50 Gene Ontology (GO) terms and 1103 genes were involved in 116 biological pathways. To find salt stress related genes, all-unigenes of S. linearistipularis were classified into three categories according to their degree of the differentially expressed genes (DEGs) at 0–1.5-fold (non differently expressed genes, N-DEGs), at 1.5–4.0-fold and more than 4.0-fold. The pathways of three categorized genes were compared with the DEGs of Arabidopsis thaliana, showing that 22, 10 and 1 pathway of S. linearistipularis were overlapped with A. thaliana. Degree of the overlapping was categorized as 0–1.5-fold, 1.5–4.0-fold and more than 4.0-folds.

Conclusion

Our study revealed that the N-DEGs of 22 pathways in S. linearistipularis were overlapped with the DEGs of A. thaliana. This result suggests that those overlapped genes that contrasted with the up- or down-regulated genes in A. thaliana were possibility evolved into housekeeping genes in S. linearistipularis under salt stress.

Electronic supplementary material

The online version of this article (doi:10.1186/s40709-016-0038-7) contains supplementary material, which is available to authorized users.

Cross-talk between α7 nAchR and NMDAR revealed by protein profiling

Functional regulation of NMDA receptor (NMDAR) by the activation of α7 nicotinic acetylcholine receptor (α7nAChR) has been reported, although the molecular signaling pathway underlying this process remains largely unknown. We employed a label-free quantitative proteomics approach to identify potential intracellular molecules and pathways that might be involved in the functional cross-talk between NMDAR and α7nAChR. 43 proteins showed significantly expression changes after choline treatment in which 35 out of 43 proteins was significantly altered by co-treatment with NMDA. Western blot analysis verified proteins expression determined by LC-MS. Furthermore, protein expression in vivo in neurons from fetal rats were visualized and quantified by Confocal microscopy,which showed consistency of relative changes of AHA-1 expressionmeasured by LC-MS and Western blot. Biological network analysis of differently expressed proteins found 21 kind of biological pathways involved. Of those pathways, 6 pathways were directly involved in regulation of neurotransmitters. Lastly, the levels of neurotransmitters (dopamine, glutamate, GABA and 5-HT) were measured by HPLC-ECD. Co-treatment choline/NMDA significantly enhances the release of dopamine, glutamate and GABA and dramatically decrease 5-HT to only 65% of control level, which is consist with this protein interaction network analysis, providing an additional evidence for the cross-talk between NMDAR and α7nAChR.

Use of comparative genomics approaches to characterize interspecies differences in response to environmental chemicals: challenges, opportunities, and research needs

A critical challenge for environmental chemical risk assessment is the characterization and reduction of uncertainties introduced when extrapolating inferences from one species to another. The purpose of this article is to explore the challenges, opportunities, and research needs surrounding the issue of how genomics data and computational and systems level approaches can be applied to inform differences in response to environmental chemical exposure across species. We propose that the data, tools, and evolutionary framework of comparative genomics be adapted to inform interspecies differences in chemical mechanisms of action. We compare and contrast existing approaches, from disciplines as varied as evolutionary biology, systems biology, mathematics, and computer science, that can be used, modified, and combined in new ways to discover and characterize interspecies differences in chemical mechanism of action which, in turn, can be explored for application to risk assessment. We consider how genetic, protein, pathway, and network information can be interrogated from an evolutionary biology perspective to effectively characterize variations in biological processes of toxicological relevance among organisms. We conclude that comparative genomics approaches show promise for characterizing interspecies differences in mechanisms of action, and further, for improving our understanding of the uncertainties inherent in extrapolating inferences across species in both ecological and human health risk assessment. To achieve long-term relevance and consistent use in environmental chemical risk assessment, improved bioinformatics tools, computational methods robust to data gaps, and quantitative approaches for conducting extrapolations across species are critically needed. Specific areas ripe for research to address these needs are recommended.