A comprehensive network and pathway analysis of candidate genes in major depressive disorder

Network-based analysis on the genes and their interactions reveals link between schizophrenia and Alzheimer's disease

Schizophrenia (SCZ) is a heterogeneous psychiatric disorder marked by impaired thinking, emotions, and behaviors. Studies have suggested a strong connection between SCZ and Alzheimer's disease (AD), however, controversies exist and the underlying mechanisms linking these two disorders remain largely unknown. Therefore, systematic studies of SCZ- and AD-related genes will provide valuable insights into the molecular features of these two diseases and their comorbidities. In this study, we obtained 331 SCZ-related genes, 650 AD-related genes, 65 shared genes between SCZ and AD. Enrichment analysis shown that these 65 shared genes were mainly involved in cognition, neural development, synaptic transmission, drug reactions, metabolic processes and immune related processes, suggesting a complex mechanism for the co-existence of SCZ and AD. In addition, we performed pathway enrichment analysis and found a total of 57 common pathways between SCZ and AD, which could be largely grouped into three modules: immune module, neurodevelopment module and cancer module. We eventually identified the potential disease-related genes whose interactions provide clues to the overlapping symptoms between SCZ and AD.

Network and pathway-based analysis of genes associated with esophageal squamous cell carcinoma

Background

Although diagnostic methods and treatments have improved over the last few years, the 5-year survival rate of esophageal squamous cell carcinoma (ESCC) patients remains generally poor. The development of high-throughput technology has facilitated great achievements in localization of ESCC-related genes. To take a further step toward a thorough understanding of ESCC at a molecular level, the potential pathogenesis of ESCC needs to be deciphered.

Methods

The interaction of ESCC-related genes was explored by collecting genes associated with ESCC and then performing gene enrichment assays, pathway enrichment assays, pathway crosstalk analysis, and extraction of ESCC-specific subnetwork to describe the relevant biochemical processes.

Results

Through Gene Ontology (GO) enrichment analysis, many molecular functions related to response to chemical, cellular response to stimulus, and cell proliferation were found to be significantly enriched in ESCC-related genes. The results of pathway and pathway crosstalk analysis showed that pathways associated with multiple malignant tumors, the immune system, and metabolic processes were significantly enriched in ESCC-related genes. Through the analysis of specific subnetworks, we obtained some novel ESCC-related potential genes, such as MUC13, GSTO1, FIN, GRB2, CDC25C, and others.

Conclusions

The molecular mechanism of ESCC is extremely complex. Some inducing factors change the expression status of many genes. The abnormal expression of genes mediates the biological processes involved in immunity and metabolism, apoptosis, and cell proliferation, leading to the occurrence of tumors. The genes MUC13, RYK, and FIN may be potential prognostic indicators of ESCC; GRB2 and CDC25C may be potential targets of ESCC in proliferation. Our work may provide valuable information for further understanding the molecular mechanisms for the development of ESCC.

Potential gene identification and pathway crosstalk analysis of age-related macular degeneration

Age-related macular degeneration (AMD), the most prevalent visual disorder among the elderly, is confirmed as a multifactorial disease. Studies demonstrated that genetic factors play an essential role in its pathogenesis. Our study aimed to make a relatively comprehensive study about biological functions of AMD related genes and crosstalk of their enriched pathways. 1691 AMD genetic studies were reviewed, GO enrichment and pathway crosstalk analyses were conducted to elucidate the biological features of these genes and to demonstrate the pathways that these genes participate. Moreover, we identified novel AMD-specific genes using shortest path algorithm in the context of human interactome. We retrieved 176 significantly AMD-related genes. GO results showed that the most significant term in each of these three GO categories was: signaling receptor binding (P_BH = 4.835 × 10⁻⁷), response to oxygen-containing compound (P_BH = 2.764 × 10⁻²¹), and extracellular space (P_BH = 2.081 × 10⁻¹⁹). The pathway enrichment analysis showed that complement pathway is the most enriched. The pathway crosstalk study showed that the pathways could be divided into two main modules. These two modules were connected by cytokine-cytokine receptor interaction pathway. 42 unique genes potentially participating AMD development were obtained. The aberrant expression of the mRNA of FASN and LRP1 were validated in AMD cell and mouse models. Collectively, our study carried out a comprehensive analysis based on genetic association study of AMD and put forward several evidence-based genes for future study of AMD.

A Systematic Analysis on the Genes and Their Interaction Underlying the Comorbidity of Alzheimer's Disease and Major Depressive Disorder

Background

During the past years, clinical and epidemiological studies have indicated a close relationship between Alzheimer's disease (AD) and other mental disorders like major depressive disorder (MDD). At the same time, a number of genes genetically associated with AD or MDD have been detected. However, our knowledge on the mechanisms that link the two disorders is still incomplete, and controversies exist. In such a situation, a systematic analysis on these genes could provide clues to understand the molecular features of two disorders and their comorbidity.

Methods

In this study, we compiled the genes reported to be associated with AD or MDD by a comprehensive search of human genetic studies and genes curated in disease-related database. Then, we investigated the features of the shared genes between AD and MDD using the functional enrichment analysis. Furthermore, the major biochemical pathways enriched in the AD- or MDD-associated genes were identified, and the cross talks between the pathways were analyzed. In addition, novel candidate genes related to AD and MDD were predicted in the context of human protein-protein interactome.

Results

We obtained 650 AD-associated genes, 447 MDD-associated genes, and 77 shared genes between AD and MDD. The functional analysis revealed that biological processes involved in cognition, neural development, synaptic transmission, and immune-related processes were enriched in the common genes, indicating a complex mechanism underlying the comorbidity of the two diseases. In addition, we conducted the pathway enrichment analysis and found 102 shared pathways between AD and MDD, which involved in neuronal development, endocrine, cell growth, and immune response. By using the pathway cross-talk analysis, we found that these pathways could be roughly clustered into four modules, i.e., the immune response-related module, the neurodevelopmental module, the cancer or cell growth module, and the endocrine module. Furthermore, we obtained 37 novel candidate genes potentially related to AD and MDD with node degrees > 5.0 by mapping the shared genes to human protein-protein interaction network (PPIN). Finally, we found that 37 novel candidate genes are significantly expressed in the brain.

Conclusion

These results indicated shared biological processes and pathways between AD and MDD and provided hints for the comorbidity of AD and MDD.

Genetic Pathways and Functional Subnetworks for the Complex Nature of Bipolar Disorder in Genome-Wide Association Study

Bipolar disorder is a complex psychiatric trait that is also recognized as a high substantial heritability from a worldwide distribution. The success in identifying susceptibility loci for bipolar disorder (BPD) has been limited due to its complex genetic architecture. Growing evidence from association studies including genome-wide association (GWA) studies points to the need of improved analytic strategies to pinpoint the missing heritability for BPD. More importantly, many studies indicate that BPD has a strong association with dementia. We conducted advanced pathway analytics strategies to investigate synergistic effects of multilocus within biologically functional pathways, and further demonstrated functional effects among proteins in subnetworks to examine mechanisms underlying the complex nature of bipolarity using a GWA dataset for BPD. We allowed bipolar susceptible loci to play a role that takes larger weights in pathway-based analytic approaches. Having significantly informative genes identified from enriched pathways, we further built function-specific subnetworks of protein interactions using MetaCore. The gene-wise scores (i.e., minimum p-value) were corrected for the gene-length, and the results were corrected for multiple tests using Benjamini and Hochberg’s method. We found 87 enriched pathways that are significant for BPD; of which 36 pathways were reported. Most of them are involved with several metabolic processes, neural systems, immune system, molecular transport, cellular communication, and signal transduction. Three significant and function-related subnetworks with multiple hotspots were reported to link with several Gene Ontology processes for BPD. Our comprehensive pathway-network frameworks demonstrated that the use of prior knowledge is promising to facilitate our understanding between complex psychiatric disorders (e.g., BPD) and dementia for the access to the connection and clinical implications, along with the development and progression of dementia.

Association study of six candidate genes with major depressive disorder in the North-Western population of Pakistan

People around the world are currently affected by Major Depressive Disorder (MDD). Despite its many aspects, symptoms, manifestations and impacts, efforts have been made to identify the root causes of the disorder. In particular, genetic studies have concentrated on identifying candidate genes for MDD and exploring associations between these genes and some specific group of individuals. The aim of this research was to find out the association between single nucleotide polymorphisms in 6 candidate genes linked to the neurobiology of major depressive disorder in the North-Western population of Pakistan. We performed a case-control analysis, with 400 MDD and 232 controls. A trained psychiatrist or clinical psychologists evaluated the patients. Six polymorphisms were genotyped and tested for allele and genotype association with MDD. There were no statistical variations between MDD patients and healthy controls for genotypic and allelic distribution of all the polymorphisms observed. Thus, our analysis does not support the major role of these polymorphisms in contributing to MDD susceptibility, although it does not preclude minor impact. The statistically significant correlation between six polymorphisms and major depressive disorder in the studied population was not observed. There are inconsistencies in investigations around the world. Future research, including GWAS and association analysis on larger scale should be addressed for further validation and replication of the present findings.

Network analysis with biological data of cancer patients: A scoping review

BACKGROUND & OBJECTIVE

Network Analysis (NA) is a mathematical method that allows exploring relations between units and representing them as a graph. Although NA was initially related to social sciences, the past two decades was introduced in Bioinformatics. The recent growth of the networks' use in biological data analysis reveals the need to further investigate this area. In this work, we attempt to identify the use of NA with biological data, and specifically: (a) what types of data are used and whether they are integrated or not, (b) what is the purpose of this analysis, predictive or descriptive, and (c) the outcome of such analyses, specifically in cancer diseases.

METHODS & MATERIALS

The literature review was conducted on two databases, PubMed & IEEE, and was restricted to journal articles of the last decade (January 2010 - December 2019). At a first level, all articles were screened by title and abstract, and at a second level the screening was conducted by reading the full text article, following the predefined inclusion & exclusion criteria leading to 131 articles of interest. A table was created with the information of interest and was used for the classification of the articles. The articles were initially classified to analysis studies and studies that propose a new algorithm or methodology. Each one of these categories was further screened by the following clustering criteria: (a) data used, (b) study purpose, (c) study outcome. Specifically for the studies proposing a new algorithm, the novelty presented in each one was detected.

RESULTS

& Conclusions: In the past five years researchers are focusing on creating new algorithms and methodologies to enhance this field. The articles' classification revealed that only 25% of the analyses are integrating multi-omics data, although 50% of the new algorithms developed follow this integrative direction. Moreover, only 20% of the analyses and 10% of the newly developed methodologies have a predictive purpose. Regarding the result of the works reviewed, 75% of the studies focus on identifying, prognostic or not, gene signatures. Concluding, this review revealed the need for deploying predictive and multi-omics integrative algorithms and methodologies that can be used to enhance cancer diagnosis, prognosis and treatment.

Unveiling the Pathogenesis of Psychiatric Disorders Using Network Models

Psychiatric disorders are complex brain disorders with a high degree of genetic heterogeneity, affecting millions of people worldwide. Despite advances in psychiatric genetics, the underlying pathogenic mechanisms of psychiatric disorders are still largely elusive, which impedes the development of novel rational therapies. There has been accumulating evidence suggesting that the genetics of complex disorders can be viewed through an omnigenic lens, which involves contextualizing genes in highly interconnected networks. Thus, applying network-based multi-omics integration methods could cast new light on the pathophysiology of psychiatric disorders. In this review, we first provide an overview of the recent advances in psychiatric genetics and highlight gaps in translating molecular associations into mechanistic insights. We then present an overview of network methodologies and review previous applications of network methods in the study of psychiatric disorders. Lastly, we describe the potential of such methodologies within a multi-tissue, multi-omics approach, and summarize the future directions in adopting diverse network approaches.

Identification of genes associated with sudden cardiac death: a network- and pathway-based approach

Background

Sudden cardiac death (SCD) accounts for a large proportion of the total deaths across different age groups. Although numerous candidate genes related to SCD have been identified by genetic association studies and genome wide association studies (GWAS), the molecular mechanisms underlying SCD are still unclear, and the biological functions and interactions of these genes remain obscure. To clarify this issue, we performed a comprehensive and systematic analysis of SCD-related genes by a network and pathway-based approach.

Methods

By screening the publications deposited in the PubMed and Gene-Cloud Biotechnology Information (GCBI) databases, we collected the genes genetically associated with SCD, which were referred to as the SCD-related gene set (SCDgset). To analyze the biological processes and biochemical pathways of the SCD-related genes, functional analysis was performed. To explore interlinks and interactions of the enriched pathways, pathway crosstalk analysis was implemented. To construct SCD-specific molecular networks, Markov cluster algorithm and Steiner minimal tree algorithm were employed.

Results

We collected 257 genes that were reported to be associated with SCD and summarized them in the SCDgset. Most of the biological processes and biochemical pathways were related to heart diseases, while some of the biological functions may be noncardiac causes of SCD. The enriched pathways could be roughly grouped into two modules. One module was related to calcium signaling pathway and the other was related to MAPK pathway. Moreover, two different SCD-specific molecular networks were inferred, and 23 novel genes potentially associated with SCD were also identified.

Conclusions

In summary, by means of a network and pathway-based methodology, we explored the pathogenetic mechanism underlying SCD. Our results provide valuable information in understanding the pathogenesis of SCD and include novel biomarkers for diagnosing potential patients with heart diseases; these may help in reducing the corresponding risks and even aid in preventing SCD.

Quantitative Proteomic Approach for Discriminating Major Depressive Disorder and Bipolar Disorder by Multiple Reaction Monitoring-Mass Spectrometry

Because major depressive disorder (MDD) and bipolar disorder (BD) manifest with similar symptoms, misdiagnosis is a persistent issue, necessitating their differentiation through objective methods. This study was aimed to differentiate between these disorders using a targeted proteomic approach. Multiple reaction monitoring-mass spectrometry (MRM-MS) analysis was performed to quantify protein targets regarding the two disorders in plasma samples of 270 individuals (90 MDD, 90 BD, and 90 healthy controls (HCs)). In the training set (72 MDD and 72 BD), a generalizable model comprising nine proteins was developed. The model was evaluated in the test set (18 MDD and 18 BD). The model demonstrated a good performance (area under the curve (AUC) >0.8) in discriminating MDD from BD in the training (AUC = 0.84) and test sets (AUC = 0.81) and in distinguishing MDD from BD without current hypomanic/manic/mixed symptoms (90 MDD and 75 BD) (AUC = 0.83). Subsequently, the model demonstrated excellent performance for drug-free MDD versus BD (11 MDD and 10 BD) (AUC = 0.96) and good performance for MDD versus HC (AUC = 0.87) and BD versus HC (AUC = 0.86). Furthermore, the nine proteins were associated with neuro, oxidative/nitrosative stress, and immunity/inflammation-related biological functions. This proof-of-concept study introduces a potential model for distinguishing between the two disorders.

An integrative network analysis framework for identifying molecular functions in complex disorders examining major depressive disorder as a test case

In addition to the psychological depressive phenotype, major depressive disorder (MDD) patients are also associated with underlying immune dysregulation that correlates with metabolic syndrome prevalent in depressive patients. A robust integrative analysis of biological pathways underlying the dysregulated neural connectivity and systemic inflammatory response will provide implications in the development of effective strategies for the diagnosis, management and the alleviation of associated comorbidities. In the current study, focusing on MDD, we explored an integrative network analysis methodology to analyze transcriptomic data combined with the meta-analysis of biomarker data available throughout public databases and published scientific peer-reviewed articles. Detailed gene set enrichment analysis and complex protein–protein, gene regulatory and biochemical pathway analysis has been undertaken to identify the functional significance and potential biomarker utility of differentially regulated genes, proteins and metabolite markers. This integrative analysis method provides insights into the molecular mechanisms along with key glycosylation dysregulation underlying altered neutrophil-platelet activation and dysregulated neuronal survival maintenance and synaptic functioning. Highlighting the significant gap that exists in the current literature, the network analysis framework proposed reduces the impact of data gaps and permits the identification of key molecular signatures underlying complex disorders with multiple etiologies such as within MDD and presents multiple treatment options to address their molecular dysfunction.

Network-based analysis on genetic variants reveals the immunological mechanism underlying Alzheimer's disease

Alzheimer's Disease (AD) is a neurodegenerative disorder characterized by the impairment of cognitive function and loss of memory. Previous studies indicate an essential role of immune response in AD, but the detailed mechanisms remain unclear. In this study, we obtained 1664 credible risk variants (CRVs) based on the most significant SNP detected by International Genomics of Alzheimer's Project, from which 99 genes (CRVs-related genes) were identified. Function analysis revealed that these genes were mainly involved in immune response and amyloid-β and its precursor metabolisms, indicating a potential role of immune response in regulating neurobiological processes in the etiology of neurodegenerative disease. Pathway crosstalk analysis revealed the complicated connections between immune-related pathways. Further, we found that the CRVs-related genes showed temporal-specific expression in the thalamus in adolescence developmental period. Cell type-specific expression analysis found that CRVs-related genes might be specifically expressed in brain cells such as astrocytes and oligodendrocytes. Protein-protein interaction network analysis identified the highly interconnected 'hub' genes, all of which were susceptible loci of AD. These results indicated that the CRVs may exert a potential influence in AD by regulating immune response, thalamus development, astrocytes activities, and amyloid-β binding. Our results provided hints for further experimental verification of AD pathophysiology.

Association of DRD4 exon III and 5-HTTLPR VNTR genetic polymorphisms with psychiatric symptoms in hemodialysis patients

Mental illness is prevalent among hemodialysis (HD) patients. Given that the dopaminergic and serotonergic pathways are involved in the etiology of psychiatric disease, this study evaluated the genetic association of dopamine D4 receptor (DRD4) and serotonin transporter (SLC6A4) genes with psychiatric symptom susceptibility among HD patients. Hospital Anxiety and Depression Scale (HADS) was used to assess anxiety and depressive symptoms among patients (n = 265). Genetic polymorphisms of DRD4 (48 bp VNTR) and SLC6A4 (5-HTTLPR VNTR and rs25531) were examined using a conventional polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) technique, as appropriate. Significant differences were observed in the distribution of 5-HTTLPR genotypes, SLC6A4 tri-allelic-phased genotype, and DRD4-Exon III VNTR genotypes/alleles between patients with anxiety symptoms versus those with normal/borderline conditions (p<0.05). Binary logistic regression analyses showed that the heterozygous 4,5 VNTR genotype of DRD4 was associated with a higher risk of anxiety symptoms after adjusting for other covariates (odds ratio = 4.25, p = 0.028). None of the studied polymorphisms was linked to depression in HD patients. Collectively, the current findings provide genetic clues to psychopathology in HD patients and suggest that the DRD4 exon III VNTR polymorphism is involved in the etiology of anxiety in this patient population.

Identification of Candidate Genes Associated with Charcot-Marie-Tooth Disease by Network and Pathway Analysis

Charcot-Marie-Tooth Disease (CMT) is the most common clinical genetic disease of the peripheral nervous system. Although many studies have focused on elucidating the pathogenesis of CMT, few focuses on achieving a systematic analysis of biology to decode the underlying pathological molecular mechanisms and the mechanism of its disease remains to be elucidated. So our study may provide further useful insights into the molecular mechanisms of CMT based on a systematic bioinformatics analysis. In the current study, by reviewing the literatures deposited in PUBMED, we identified 100 genes genetically related to CMT. Then, the functional features of the CMT-related genes were examined by R software and KOBAS, and the selected biological process crosstalk was visualized with the software Cytoscape. Moreover, CMT specific molecular network analysis was conducted by the Molecular Complex Detection (MCODE) Algorithm. The biological function enrichment analysis suggested that myelin sheath, axon, peripheral nervous system, mitochondrial function, various metabolic processes, and autophagy played important roles in CMT development. Aminoacyl-tRNA biosynthesis, metabolic pathways, and vasopressin-regulated water reabsorption were significantly enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway network, suggesting that these pathways may play key roles in CMT occurrence and development. According to the crosstalk, the biological processes could be roughly divided into a correlative module and two separate modules. MCODE clusters showed that in top 3 clusters, 13 of CMT-related genes were included in the network and 30 candidate genes were discovered which might be potentially related to CMT. The study may help to update the new understanding of the pathogenesis of CMT and expand the potential genes of CMT for further exploration.

Chemopreventive Property of Sencha Tea Extracts towards Sensitive and Multidrug-Resistant Leukemia and Multiple Myeloma Cells

The popular beverage green tea possesses chemopreventive activity against various types of tumors. However, the effects of its chemopreventive effect on hematological malignancies have not been defined. In the present study, we evaluated antitumor efficacies of a specific green tea, sencha tea, on sensitive and multidrug-resistant leukemia and a panel of nine multiple myelomas (MM) cell lines. We found that sencha extracts induced cytotoxicity in leukemic cells and MM cells to different extents, yet its effect on normal cells was limited. Furthermore, sencha extracts caused G2/M and G0/G1 phase arrest during cell cycle progression in CCRF/CEM and KMS-12-BM cells, respectively. Specifically, sencha-MeOH/H₂O extracts induced apoptosis, ROS, and MMP collapse on both CCRF/CEM and KMS-12-BM cells. The analysis with microarray and COMPARE in 53 cell lines of the NCI panel revealed diverse functional groups, including cell morphology, cellular growth and proliferation, cell cycle, cell death, and survival, which were closely associated with anti-tumor effects of sencha tea. It is important to note that PI3K/Akt and NF-κB pathways were the top two dominant networks by ingenuity pathway analysis. We demonstrate here the multifactorial modes of action of sencha tea leading to chemopreventive effects of sencha tea against cancer.

Tubastatin A inhibits HDAC and Sirtuin activity rather than being a HDAC6-specific inhibitor in mouse oocytes

Tubastatin A (TubA) is a highly selective histone deacetylase 6 (HDAC6) inhibitor. As expected, mouse germinal vesicle oocytes fail to extrude the first polar body following TubA treatment. However, a previous study demonstrated that homozygous Hdac6 knockout (KO) mice can be viable and fertile. Therefore, we asked whether TubA is indeed a specific inhibitor of HDAC6 activity. RNA-sequencing and in silico analysis demonstrated that the TubA-treated group presented significant changes in the expression of Hdac subfamily genes such as Hdac6, 10, and 11, and Sirtuin 2, 5, 6, and 7. Additionally, gene expression related to the p53, MAPK, Wnt, and Notch signaling pathways in the TubA-treated group were increased significantly; in contrast, gene expression related to metabolism, DNA replication, and oxidative phosphorylation was decreased significantly. Furthermore, gene expression related to cell cycle, cell structure, pyrimidine metabolism, pentose phosphate pathway, mitochondrial activation, proteasome pathway, RNA polymerase, DNA replication, cyclin-dependent kinase, nucleolar activity, and MI arrest were significantly decreased, indicating that TubA-induced abnormal meiotic maturation and oocyte senescence may be due to the combined effects of HDAC and Sirtuin inhibition, and not HDAC6 inhibition alone. Thus, we believed that this system could provide a model for monitoring the effects of TubA on mouse oocytes.

Dense module searching for gene networks associated with multiple sclerosis

Background

Multiple sclerosis (MS) is a complex disease in which the immune system attacks the central nervous system. The molecular mechanisms contributing to the etiology of MS remain poorly understood. Genome-wide association studies (GWAS) of MS have identified a small number of genetic loci significant at the genome level, but they are mainly non-coding variants. Network-assisted analysis may help better interpret the functional roles of the variants with association signals and potential translational medicine application. The Dense Module Searching of GWAS tool (dmGWAS version 2.4) developed in our team is applied to 2 MS GWAS datasets (GeneMSA and IMSGC GWAS) using the human protein interactome as the reference network. A dual evaluation strategy is used to generate results with reproducibility.

Results

Approximately 7500 significant network modules were identified for each independent GWAS dataset, and 20 significant modules were identified from the dual evaluation. The top modules included GRB2, HDAC1, JAK2, MAPK1, and STAT3 as central genes. Top module genes were enriched with functional terms such as “regulation of glial cell differentiation” (adjusted p-value = 2.58 × 10^− 3), “T-cell costimulation” (adjusted p-value = 2.11 × 10^− 6) and “virus receptor activity” (adjusted p-value = 1.67 × 10^− 3). Interestingly, top gene networks included several MS FDA approved drug target genes HDAC1, IL2RA, KEAP1, and RELA,

Conclusions

Our dmGWAS network analyses highlighted several genes (GRB2, HDAC1, IL2RA, JAK2, KEAP1, MAPK1, RELA and STAT3) in top modules that are promising to interpret GWAS signals and link to MS drug targets. The genes enriched with glial cell differentiation are important for understanding neurodegenerative processes in MS and for remyelination therapy investigation. Importantly, our identified genetic signals enriched in T cell costimulation and viral receptor activity supported the viral infection onset hypothesis for MS.

Computational medicine: quantitative modeling of complex diseases

Biological complex systems are composed of numerous components that interact within and across different scales. The ever-increasing generation of high-throughput biomedical data has given us an opportunity to develop a quantitative model of nonlinear biological systems having implications in health and diseases. Multidimensional molecular data can be modeled using various statistical methods at different scales of biological organization, such as genome, transcriptome and proteome. I will discuss recent advances in the application of computational medicine in complex diseases such as network-based studies, genome-scale metabolic modeling, kinetic modeling and support vector machines with specific examples in the field of cancer, psychiatric disorders and type 2 diabetes. The recent advances in translating these computational models in diagnosis and identification of drug targets of complex diseases are discussed, as well as the challenges researchers and clinicians are facing in taking computational medicine from the bench to bedside.

Network and pathway-based analyses of genes associated with osteoporosis

Osteoporosis (OP) is a disease characterized by bone mass loss, bone microstructure damage, increased bone fragility, and easy fracture. The molecular mechanism underlying OP remains unclear.In this study, we identified 217 genes associated with OP, and formed a gene set [OP-related genes gene set (OPgset)].The highly enriched GOs and pathways showed OPgset genes were significantly involved in multiple biological processes (skeletal system development, ossification, and osteoblast differentiation), and several OP-related pathways (Wnt signaling pathway, osteoclast differentiation, steroid hormone biosynthesis, and adipocytokine signaling pathway). Besides, pathway crosstalk analysis indicated three major modules, with first module consisted of pathways mainly involved in bone development-related signaling pathways, second module in Wnt-related signaling pathway and third module in metabolic pathways. Further, we calculated degree centrality of a node and selected ten key genes/proteins, including TGFB1, IL6, WNT3A, TNF, PTH, TP53, WNT1, IGF1, IL10, and SERPINE1. We analyze the K-core and construct three k-core sub-networks of OPgset genes.In summary, we for the first time explored the molecular mechanism underlying OP via network- and pathway-based methods, results from our study will improve our understanding of the pathogenesis of OP. In addition, these methods performed in this study can be used to explore pathogenesis and genes related to a specific disease.

Analyzing the genes and pathways related to major depressive disorder via a systems biology approach

INTRODUCTION

Major depressive disorder (MDD) is a mental disorder caused by the combination of genetic, environmental, and psychological factors. Over the years, a number of genes potentially associated with MDD have been identified. However, in many cases, the role of these genes and their relationship in the etiology and development of MDD remains unclear. Under such situation, a systems biology approach focusing on the function correlation and interaction of the candidate genes in the context of MDD will provide useful information on exploring the molecular mechanisms underlying the disease.

METHODS

We collected genes potentially related to MDD by screening the human genetic studies deposited in PubMed (https://www.ncbi.nlm.nih.gov/pubmed). The main biological themes within the genes were explored by function and pathway enrichment analysis. Then, the interaction of genes was analyzed in the context of protein-protein interaction network and a MDD-specific network was built by Steiner minimal tree algorithm.

RESULTS

We collected 255 candidate genes reported to be associated with MDD from available publications. Functional analysis revealed that biological processes and biochemical pathways related to neuronal development, endocrine, cell growth and/or survivals, and immunology were enriched in these genes. The pathways could be largely grouped into three modules involved in biological procedures related to nervous system, the immune system, and the endocrine system, respectively. From the MDD-specific network, 35 novel genes potentially associated with the disease were identified.

CONCLUSION

By means of network- and pathway-based methods, we explored the molecular mechanism underlying the pathogenesis of MDD at a systems biology level. Results from our work could provide valuable clues for understanding the molecular features of MDD.

The core genes involved in the promotion of depression in patients with ovarian cancer

The present study aimed to identify the core genes and pathways involved in depression in patients with ovarian cancer (OC) who suffer from high or low-grade depression. The dataset GSE9116 from Gene Expression Omnibus database was analyzed to identify differentially expressed genes (DEGs) in these patients. To elucidate how certain genes could promote depression in patients with OC, pathway crosstalk, protein-protein interaction (PPI) and comprehensive gene-pathway analyses were determined using WebGestalt, ToppGene and Search Tool for the Retrieval of Interacting Genes and gene ontology analysis. Key genes and pathways were extracted from the gene-pathway network, and gene expression and survival analysis were evaluated. A total of 93 DEGs were identified from GSE9116 dataset, including 84 upregulated genes and nine downregulated genes. The PPI, pathway crosstalk and comprehensive gene-pathway analyses highlighted C-C motif chemokine ligand 2 (CCL2), Fos proto-oncogene, AP-1 transcription factor subunit (FOS), serpin family E member 1 (SERPINE1) and serpin family G member 1 (SERPING1) as core genes involved in the promotion of depression in patients with OC. These core genes were involved in the following four pathways 'Ensemble of genes encoding ECM-associated proteins including ECM-affiliated proteins', 'ECM regulators and secreted factors', 'Ensemble of genes encoding extracellular matrix and extracellular matrix-associated proteins' and 'MAPK signaling pathway and IL-17 signaling pathway'. The results from gene expression and survival analysis demonstrated that these four key genes were upregulated in patients with OC and high-grade depression and could worsen patients' survival. These results suggested that CCL2, FOS, SERPINE1 and SERPING1 may serve a crucial role in the promotion of depression in patients with OC. This finding may provide novel markers for predicting and treating depression in patients with OC; however, the underlying mechanisms remain unknown and require further investigation.

Bioinformatics prediction and analysis of hub genes and pathways of three types of gynecological cancer

Cervical, endometrial and vulvar cancer are three common types of gynecological tumor that threaten the health of females worldwide. Since their underlying mechanisms and associations remain unclear, a comprehensive and systematic bioinformatics analysis is required. The present study downloaded GSE63678 from the GEO database and then performed functional enrichment analyses, including gene ontology and pathway analysis. To further investigate the molecular mechanisms underlying the three types of gynecological cancer, protein-protein interaction (PPI) analysis was performed. A biological network was generated with the guidance of the Kyoto Encyclopedia of Genes and Genomes database and was presented in Cytoscape. A total of 1,219 DEGs were identified for the three types of cancer, and 25 hub genes were revealed. Pathway analysis and the PPI network indicated that four main types of pathway participate in the mechanism of gynecological cancer, including viral infections and cancer formation, tumorigenesis and development, signal transduction, and endocrinology and metabolism. A preliminary gynecological cancer biological network was constructed. Notably, following all analysis, the phosphoinositide 3-kinase (PI3K)/Akt pathway was identified as a potential biomarker pathway. Seven pivotal hub genes (CCNA2, CDK1, CCND1, FGF2, IGF1, BCL2 and VEGFA) of the three gynecological cancer types were proposed. The seven hub genes may serve as targets in gynecological cancer for prevention and early intervention. The PI3K/Akt pathway was identified as a critical biomarker of the three types of gynecological cancer, which may serve a role in the pathogenesis. In summary, the present study provided evidence that could support the treatment of gynecologic tumors in the future.

Comparative assessment of the therapeutic drug targets of C. botulinum ATCC 3502 and C. difficile str. 630 using in silico subtractive proteomics approach

Growing antimicrobial resistance of the pathogens against multiple drugs posed a serious threat to the human health worldwide. This fueled the need of identifying the novel therapeutic targets that can be used for developing new class of the drugs. Recently, there is a substantial rise in the rate of Clostridium infections as well as in the emergence of virulent and antibiotic resistant strains. Hence, there is an urgent need for the identification of potential therapeutic targets and the development of new drugs for the treatment and prevention of Clostridium infections. In the present study, a combinatorial approach involving systems biology and comparative genomics strategy was tested against Clostridium botulinum ATCC 3502 and Clostridium difficile str. 630 pathogens, to render potential therapeutic target at qualitative and quantitative level. This resulted in the identification of five common (present in both the pathogens, 34 in C. botulinum ATCC 3502 and 42 in C. difficile str. 630) drug targets followed by virtual screening-based identification of potential inhibitors employing molecular docking and molecular dynamics simulations. The identified targets will provide a solid platform for the designing of novel wide-spectrum lead compounds capable of inhibiting their catalytic activities against multidrug-resistant Clostridium in the near future.

Systems Approach to Identify Common Genes and Pathways Associated with Response to Selective Serotonin Reuptake Inhibitors and Major Depression Risk

Despite numerous studies on major depressive disorder (MDD) susceptibility, the precise underlying molecular mechanism has not been elucidated which restricts the development of etiology-based disease-modifying drug. Major depressive disorder treatment is still symptomatic and is the leading cause of (~30%) failure of the current antidepressant therapy. Here we comprehended the probable genes and pathways commonly associated with antidepressant response and MDD. A systematic review was conducted, and candidate genes/pathways associated with antidepressant response and MDD were identified using an integrative genetics approach. Initially, single nucleotide polymorphisms (SNPs)/genes found to be significantly associated with antidepressant response were systematically reviewed and retrieved from the candidate studies and genome-wide association studies (GWAS). Also, significant variations concerning MDD susceptibility were extracted from GWAS only. We found 245 (Set A) and 800 (Set B) significantly associated genes with antidepressant response and MDD, respectively. Further, gene set enrichment analysis revealed the top five co-occurring molecular pathways (p ≤ 0.05) among the two sets of genes: Cushing syndrome, Axon guidance, cAMP signaling pathway, Insulin secretion, and Glutamatergic synapse, wherein all show a very close relation to synaptic plasticity. Integrative analyses of candidate gene and genome-wide association studies would enable us to investigate the putative targets for the development of disease etiology-based antidepressant that might be more promising than current ones.

Dynamic modeling and network approaches for omics time course data: overview of computational approaches and applications

Inferring networks and dynamics of genes, proteins, cells and other biological entities from high-throughput biological omics data is a central and challenging issue in computational and systems biology. This is essential for understanding the complexity of human health, disease susceptibility and pathogenesis for Predictive, Preventive, Personalized and Participatory (P4) system and precision medicine. The delineation of the possible interactions of all genes/proteins in a genome/proteome is a task for which conventional experimental techniques are ill suited. Urgently needed are rapid and inexpensive computational and statistical methods that can identify interacting candidate disease genes or drug targets out of thousands that can be further investigated or validated by experimentations. Moreover, identifying biological dynamic systems, and simultaneously estimating the important kinetic structural and functional parameters, which may not be experimentally accessible could be important directions for drug-disease-gene network studies. In this article, we present an overview and comparison of recent developments of dynamic modeling and network approaches for time-course omics data, and their applications to various biological systems, health conditions and disease statuses. Moreover, various data reduction and analytical schemes ranging from mathematical to computational to statistical methods are compared including their merits, drawbacks and limitations. The most recent software, associated web resources and other potentials for the compared methods are also presented and discussed in detail.

Prioritization and comprehensive analysis of genes related to major depressive disorder

Background

Major depressive disorder (MDD) is a serious mental health problem in modern society, which is difficult to identify and diagnose in the early stages. Despite strong evidence supporting the heritability of MDD, progresses in large‐scale and individual genetic studies remain preliminary.

Methods

In this study, a multi‐data source‐based prioritization (MDSP) method was proposed, and an appropriate threshold was determined for the optimization of depression‐related genes (DEPgenes). Analyses on Gene Ontology biological processes, KEGG pathway and the specific pathway crosstalk network were further proposed.

Results

A total of 143 DEPgenes were identified and the MDD‐specific network was constructed for the pathogenesis investigation and therapeutic methods development of MDD. Comparing with existing research strategies, the genetic optimization and analysis results were confirmed to be reliable. Finally, the pathway enrichment and crosstalk analyses revealed two unique pathway interaction modules that were significantly enriched with MDD genes. The related core pathways of neuroactive ligand‐receptor interaction and dopaminergic synapse supported the neuropathology hypothesis of MDD. And the pathways of serotonergic synapse and morphine addiction indicated the mechanism of drug addiction caused by serotonin used in the treatment.

Conclusions

This work provided a reference for the study of MDD, although future validation by extensive experimentation is still required.

Network approach identifies Pacer as an autophagy protein involved in ALS pathogenesis

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a multifactorial fatal motoneuron disease without a cure. Ten percent of ALS cases can be pointed to a clear genetic cause, while the remaining 90% is classified as sporadic. Our study was aimed to uncover new connections within the ALS network through a bioinformatic approach, by which we identified C13orf18, recently named Pacer, as a new component of the autophagic machinery and potentially involved in ALS pathogenesis.

METHODS

Initially, we identified Pacer using a network-based bioinformatic analysis. Expression of Pacer was then investigated in vivo using spinal cord tissue from two ALS mouse models (SOD1^G93A and TDP43^A315T) and sporadic ALS patients. Mechanistic studies were performed in cell culture using the mouse motoneuron cell line NSC34. Loss of function of Pacer was achieved by knockdown using short-hairpin constructs. The effect of Pacer repression was investigated in the context of autophagy, SOD1 aggregation, and neuronal death.

RESULTS

Using an unbiased network-based approach, we integrated all available ALS data to identify new functional interactions involved in ALS pathogenesis. We found that Pacer associates to an ALS-specific subnetwork composed of components of the autophagy pathway, one of the main cellular processes affected in the disease. Interestingly, we found that Pacer levels are significantly reduced in spinal cord tissue from sporadic ALS patients and in tissues from two ALS mouse models. In vitro, Pacer deficiency lead to impaired autophagy and accumulation of ALS-associated protein aggregates, which correlated with the induction of cell death.

CONCLUSIONS

This study, therefore, identifies Pacer as a new regulator of proteostasis associated with ALS pathology.

The role of miR-106p-5p in cervical cancer: from expression to molecular mechanism

This study aims to investigate the role of miR-106b-5p in cervical cancer by performing a comprehensive analysis on its expression and identifying its putative molecular targets and pathways based on The Cancer Genome Atlas (TCGA) dataset, Gene Expression Omnibus (GEO) dataset, and literature review. Significant upregulation of miR-106b-5p in cervical cancer is confirmed by meta-analysis with the data from TCGA, GEO, and literature. Moreover, the expression of miR-106b-5p is significantly correlated with the number of metastatic lymph nodes. Our bioinformatics analyses show that miR-106b could promote cervical cancer progression by modulating the expression of GSK3B, VEGFA, and PTK2 genes. Importantly, these three genes play a crucial role in PI3K-Akt signaling, focal adhesion, and cancer. Both the expression of miR-106b-5p and key genes are upregulated in cervical cancer. Several explanations could be implemented for this upregulation. However, the specific mechanism needs to be investigated further.

Cardiovascular and inflammatory mechanisms in healthy humans exposed to air pollution in the vicinity of a steel mill

Background

There is a paucity of mechanistic information that is central to the understanding of the adverse health effects of source emission exposures. To identify source emission-related effects, blood and saliva samples from healthy volunteers who spent five days near a steel plant (Bayview site, with and without a mask that filtered many criteria pollutants) and at a well-removed College site were tested for oxidative stress, inflammation and endothelial dysfunction markers.

Methods

Biomarker analyses were done using multiplexed protein-array, HPLC-Fluorescence, EIA and ELISA methods. Mixed effects models were used to test for associations between exposure, biological markers and physiological outcomes. Heat map with hierarchical clustering and Ingenuity Pathway Analysis (IPA) were used for mechanistic analyses.

Results

Mean CO, SO₂ and ultrafine particles (UFP) levels on the day of biological sampling were higher at the Bayview site compared to College site. Bayview site exposures “without” mask were associated with increased (p < 0.05) pro-inflammatory cytokines (e.g IL-4, IL-6) and endothelins (ETs) compared to College site. Plasma IL-1β, IL-2 were increased (p < 0.05) after Bayview site “without” compared to “with” mask exposures. Interquartile range (IQR) increases in CO, UFP and SO₂ were associated with increased (p < 0.05) plasma pro-inflammatory cytokines (e.g. IL-6, IL-8) and ET-1_(1–21) levels. Plasma/saliva BET-1 levels were positively associated (p < 0.05) with increased systolic BP. C-reactive protein (CRP) was positively associated (p < 0.05) with increased heart rate. Protein network analyses exhibited activation of distinct inflammatory mechanisms after “with” and “without” mask exposures at the Bayview site relative to College site exposures.

Conclusions

These findings suggest that air pollutants in the proximity of steel mill site can influence inflammatory and vascular mechanisms. Use of mask and multiple biomarker data can be valuable in gaining insight into source emission-related health impacts.

Electronic supplementary material

The online version of this article (10.1186/s12989-018-0270-4) contains supplementary material, which is available to authorized users.

Dysregulated genes targeted by microRNAs and metabolic pathways in bladder cancer revealed by bioinformatics methods

The present study aimed to identify bladder cancer-associated microRNAs (miRNAs) and target genes, and further analyze the potential molecular mechanisms involved in bladder cancer. The mRNA and miRNA expression profiling dataset GSE40355 was downloaded from the Gene Expression Omnibus database. The Limma package in R was used to identify differential expression levels. The Human microRNA Disease Database was used to identify bladder cancer-associated miRNAs and Target prediction programs were used to screen for miRNA target genes. Enrichment analysis was performed to identify biological functions. The Database for Annotation, Visualization and Integration Discovery was used to perform OMIM_DISEASE analysis, and then protein-protein interaction (PPI) analysis was performed to identify hubs with biological essentiality. ClusterONE plugins in cytoscape were used to screen modules and the InterPro database was used to perform protein domain enrichment analysis. A group of 573 disease dysregulated genes were identified in the present study. Enrichment analysis indicated that the muscle organ development and vascular smooth muscle contraction pathways were significantly enriched in terms of disease dysregulated genes. miRNAs targets (frizzled class receptor 8, EYA transcriptional coactivator and phosphatase 4, sacsin molecular chaperone, calcium voltage-gated channel auxiliary subunit β2, peptidase inhibitor 15 and catenin α2) were mostly associated with bladder cancer. PPI analysis revealed that calmodulin 1 (CALM1), Jun proto-oncogene, AP-1 transcription factor subunit (JUN) and insulin like growth factor 1 (IGF1) were the important hub nodes. Additionally, protein domain enrichment analysis indicated that the serine/threonine protein kinase active site was enriched in module 1 extracted from the PPI network. Overall, the results suggested that the IGF signaling pathway and RAS/MEK/extracellular signal-regulated kinase transduction signaling may exert vital molecular mechanisms in bladder cancer, and that CALM1, JUN and IGF1 may be used as novel potential therapeutic targets.

Analyzing the pathways enriched in genes associated with nicotine dependence in the context of human protein-protein interaction network

Nicotine dependence is the primary addictive stage of cigarette smoking. Although a lot of studies have been performed to explore the molecular mechanism underlying nicotine dependence, our understanding on this disorder is still far from complete. Over the past decades, an increasing number of candidate genes involved in nicotine dependence have been identified by different technical approaches, including the genetic association analysis. In this study, we performed a comprehensive collection of candidate genes reported to be genetically associated with nicotine dependence. Then, the biochemical pathways enriched in these genes were identified by considering the gene's propensity to be related to nicotine dependence. One of the most widely used pathway enrichment analysis approach, over-representation analysis, ignores the function non-equivalence of genes in candidate gene set and may have low discriminative power in identifying some dysfunctional pathways. To overcome such drawbacks, we constructed a comprehensive human protein-protein interaction network, and then assigned a function weighting score to each candidate gene based on their network topological features. Evaluation indicated the function weighting score scheme was consistent with available evidence. Finally, the function weighting scores of the candidate genes were incorporated into pathway analysis to identify the dysfunctional pathways involved in nicotine dependence, and the interactions between pathways was detected by pathway crosstalk analysis. Compared to conventional over-representation-based pathway analysis tool, the modified method exhibited improved discriminative power and detected some novel pathways potentially underlying nicotine dependence. In summary, we conducted a comprehensive collection of genes associated with nicotine dependence and then detected the biochemical pathways enriched in these genes using a modified pathway enrichment analysis approach with function weighting score of candidate genes integrated. Our results may provide insight into the molecular mechanism underlying nicotine dependence.

Genetic variations in the p11/tPA/BDNF pathway are associated with post stroke depression

BACKGROUND

The effects of BDNF on post stroke depression (PSD) may be influenced by genetic variations in intracellular signal transduction pathways, such as the p11/tPA/BDNF pathway. In this study, we aimed to determine the association of polymorphisms in candidate genes of the gene transduction pathway with PSD, as well as the effects of the interactions between genes in our Chinese sample.

METHODS

Two-hundred-fifty-four Chinese samples with acute ischaemic stroke included 122 PSD patients and 132 nonPSD patients. Sixty-five single nucleotide polymorphisms (SNPs) in six genes (p11, tPA, PAI-1, BDNF, TrkB and p75NTR) of the p11/tPA/BDNF pathway with minor allele frequencies > 5% were successfully genotyped from an initial series of 76 SNPs. The severity of depressive symptoms was assessed by the 17-item Hamilton Depression Rating scale score. Environmental factors were measured with the life events scale and social support rating scale for all patients. SNP and haplotype associations were analysed using gPLINK software. Gene-gene interactions were evaluated with generalized multifactor dimensionality reduction software.

RESULTS

The results showed that TrkB polymorphisms (rs11140793AC genotype, rs7047042CG genotype, rs1221CT genotype, rs2277193TC genotype and rs2277192AG genotype) were significantly associated with PSD. Three haplotypes (AT, GG, and AAT) of TrkB were significantly associated with PSD. Seven haplotypes (GC, AG, ACG, CGC, GCT, ACGC and ACAT) of BDNF were significantly correlated with PSD. We identified significant gene-gene interactions between the p11 (rs11204922 SNP), tPA (rs8178895, rs2020918 SNPs) and BDNF (rs6265, rs2049046, rs16917271, rs727155 SNPs) genes in the PSD group. We also identified significant gene-gene interactions between the BDNF (rs2049046, rs7931247 SNPs) and TrkB (rs7816 SNP) genes with increased occurrence of PSD and sig gene-gene interactions between the BDNF gene (rs6265, rs56164415, rs2049046, rs4923468, rs2883187, rs16917271, rs1491850, rs727155, rs2049048 SNPs) and p75NTR gene (rs2072446, rs11466155) in the PSD group.

CONCLUSION

These findings provides evidence that the TrkB gene, BDNF and TrkB haplotypes, and gene-gene interactions between p11, tPA and BDNF are all associated with PSD, which suggests that genetic variations in the p11/tPA/BDNF pathway may play a central role in regulating the underlying mechanism of PSD.

Discerning molecular interactions: A comprehensive review on biomolecular interaction databases and network analysis tools

Computational analysis of biomolecular interaction networks is now gaining a lot of importance to understand the functions of novel genes/proteins. Gene interaction (GI) network analysis and protein-protein interaction (PPI) network analysis play a major role in predicting the functionality of interacting genes or proteins and gives an insight into the functional relationships and evolutionary conservation of interactions among the genes. An interaction network is a graphical representation of gene/protein interactome, where each gene/protein is a node, and interaction between gene/protein is an edge. In this review, we discuss the popular open source databases that serve as data repositories to search and collect protein/gene interaction data, and also tools available for the generation of interaction network, visualization and network analysis. Also, various network analysis approaches like topological approach and clustering approach to study the network properties and functional enrichment server which illustrates the functions and pathway of the genes and proteins has been discussed. Hence the distinctive attribute mentioned in this review is not only to provide an overview of tools and web servers for gene and protein-protein interaction (PPI) network analysis but also to extract useful and meaningful information from the interaction networks.

Quantitative proteomic study of the plasma reveals acute phase response and LXR/RXR and FXR/RXR activation in the chronic unpredictable mild stress mouse model of depression

Major depressive disorder is a severe neuropsychiatric disease that negatively impacts the quality of life of a large portion of the population. However, the molecular mechanisms underlying depression are still unclear. The pathogenesis of depression involves several brain regions. However, most previous studies have focused only on one specific brain region. Plasma and brain tissues exchange numerous components through the blood-brain barrier. Therefore, in the present study, plasma samples from control (CON) mice and mice subjected to chronic unpredictable mild stress (CUMS) were used to investigate the molecular pathogenesis of depression, and the association between the peripheral circulation and the central nervous system. A total of 47 significant differentially expressed proteins were identified between the CUMS and CON group by an isobaric tag for relative and absolute quantitation (iTRAQ) coupled with tandem mass spectrometry approach. These 47 differentially expressed proteins were analyzed with ingenuity pathway analysis (IPA) software. This revealed that the acute phase response, LXR/RXR and FXR/RXR activation, the complement system and the intrinsic prothrombin activation pathway were significantly changed. Four of the significant differentially expressed proteins (lipopolysaccharide binding protein, fibrinogen β chain, α-1 antitrypsin, and complement factor H) were validated by western blotting. the present findings provide a novel insight into the molecular pathogenesis of depression.

Multiple Testing of Submatrices of a Precision Matrix with Applications to Identification of Between Pathway Interactions

Making accurate inference for gene regulatory networks, including inferring about pathway by pathway interactions, is an important and difficult task. Motivated by such genomic applications, we consider multiple testing for conditional dependence between subgroups of variables. Under a Gaussian graphical model framework, the problem is translated into simultaneous testing for a collection of submatrices of a high-dimensional precision matrix with each submatrix summarizing the dependence structure between two subgroups of variables. A novel multiple testing procedure is proposed and both theoretical and numerical properties of the procedure are investigated. Asymptotic null distribution of the test statistic for an individual hypothesis is established and the proposed multiple testing procedure is shown to asymptotically control the false discovery rate (FDR) and false discovery proportion (FDP) at the pre-specified level under regularity conditions. Simulations show that the procedure works well in controlling the FDR and has good power in detecting the true interactions. The procedure is applied to a breast cancer gene expression study to identify between pathway interactions.

Protein-protein interaction analysis to identify biomarker networks for endometriosis

The identification of biomarkers and their interaction network involved in the processes of endometriosis is a critical step in understanding the underlying mechanisms of the disease. The aim of the present study was to construct biomarker networks of endometriosis that integrated human protein-protein interactions and known disease-causing genes. Endometriosis-associated genes were extracted from Genotator and DisGeNet and biomarker network and pathway analyses were constructed using atBioNet. Of 100 input genes, 96 were strongly mapped to six major modules. The majority of the pathways in the first module were associated with the proliferation of cancer cells, the enriched pathways in module B were associated with the immune system and infectious diseases, module C included pathways related to immune and metastasis, the enriched pathways in module D were associated with inflammatory processes, and the majority of the pathways in module E were related to replication and repair. The present approach identified known and potential biomarkers in endometriosis. The identified biomarker networks are highly enriched in biological pathways associated with endometriosis, which may provide further insight into the molecular mechanisms underlying endometriosis.

A comprehensive regional analysis of genome-wide expression profiles for major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a global health challenge. In recent years, a large number of genome-wide expression studies (GWES) have been carried out to identify the transcriptomic profiles for MDD. The objective of this work was to carry out a comprehensive meta-analysis of available GWES for MDD.

METHODS

GWES for MDD with available raw data were searched in NCBI GEO, Array Express and Stanley databases. Raw GWES data were preprocessed and normalized and meta-analytical procedures were carried out with the Network Analyst program. 743 samples from 24 primary studies were included in our meta-analyses for blood (Blo), amygdala (Amy), cerebellum (Cer), anterior cingulate cortex (ACC) and prefrontal cortex (PFC) regions. A functional enrichment analysis was carried out.

RESULTS

We identified 35, 793, 231, 668 and 252 differentially expressed (DE) genes for Blo, Amy, Cer, ACC and PFC regions. A region-dependent significant enrichment for several functional categories, such as gene ontologies, signaling pathways and topographic parameters, was identified. There was convergence with other available genome-wide studies, such as GWAS, DNA methylation analyses and miRNA expression studies.

LIMITATIONS

Raw data were not available for several primary studies that have been published previously.

CONCLUSIONS

This is the largest meta-analysis for GWES in MDD. The examination of convergence of genome-wide evidence and of the functional enrichment analysis provides a global overview of potential neural signaling mechanisms dysregulated in MDD. Our comprehensive analysis of several brain regions identified lists of DE genes for MDD that are interesting candidates for further studies.

Infrequently expressed miRNAs influence survival after diagnosis with colorectal cancer

Half of miRNAs expressed in colorectal tissue are expressed < 50% of the population. Many infrequently expressed miRNAs have low levels of expression. We hypothesize that less frequently expressed miRNAs, when expressed at higher levels, influence both disease stage and survival after diagnosis with colorectal cancer (CRC); low levels of expression may be background noise.

We examine 304 infrequently expressed miRNAs in 1893 population-based cases of CRC with paired carcinoma and normal mucosa miRNA profiles. We evaluate miRNAs with disease stage and survival after adjusting for age, study center, sex, MSI status, and AJCC stage. These miRNAs were further evaluated with RNA-Seq data to identify miRNA::mRNA associations that may provide insight into the functionality of miRNAs.

Eleven miRNAs were associated with advanced disease stage among colon cancer patients (Q value = 0.10). Eight infrequently expressed miRNAs influenced survival if highly expressed in overall CRC. Of these, five increased likelihood of dying if they were highly expressed, i.e. miR-124-3p, miR-143-5p, miR-145-3p, miR31-5p, and miR-99b-5p, while three were associated with better survival if highly expressed, i.e. miR-362-5p, miR-374a-5p, and miR-590-5p. Thirteen miRNAs infrequently expressed in colon-specific carcinoma tissue were associated with CRC survival if highly expressed. Evaluation of miRNAs::mRNA associations showed that mRNA expression influenced by infrequently expressed miRNA contributed to networks and pathways shown to influence disease progression and prognosis.

Our large study enabled us to examine the implications of infrequently expressed miRNAs after removal of background noise. These results require replication in other studies. Confirmation of our findings in other studies could lead to important markers for prognosis.

Infrequently expressed miRNAs in colorectal cancer tissue and tumor molecular phenotype

We have previously shown that commonly expressed miRNAs influenced tumor molecular phenotype in colorectal cancer. We hypothesize that infrequently expressed miRNAs, when showing higher levels of expression, help to define tumor molecular phenotype. In this study, we examine 304 miRNAs expressed in at least 30 individuals, but in <50% of the population and with a mean level of expression above 1.0 relative florescent unit. We examine associations in 1893 individuals who have the tumor molecular phenotype data as well as miRNA expression levels for both carcinoma and normal colorectal tissue. We compare miRNAs uniquely associated with tumor molecular phenotype to the RNAseq data to identify genes associated with these miRNAs. This information is used to further identify unique pathways associated with tumor molecular phenotypes of TP53-mutated, KRAS-mutated, CpG island methylator phenotype and microsatellite instability tumors. Thirty-seven miRNAs were uniquely associated with TP53-mutated tumors; 30 of these miRNAs had higher level of expression in TP53-mutated tumors, while seven had lower levels of expression. Of the 34 miRNAs associated with CpG island methylator phenotype-high tumors, 16 were more likely to have a CpG island methylator phenotype-high tumor and 19 were less likely to be CpG island methylator phenotype-high. For microsatellite instability, 13 of the 22 infrequently expressed miRNAs were significantly less likely to be expressed in microsatellite unstable tumors. KRAS-mutated tumors were not associated with any miRNAs after adjustment for multiple comparisons. Of the dysregulated miRNAs, 17 were more likely to be TP53-mutated tumors while simultaneously being less likely to be CpG island methylator phenotype-high and/or microsatellite instability tumors. Genes regulated by these miRNAs were involved in numerous functions and pathways that influence cancer risk and progression. In summary, some infrequently expressed miRNAs, when expressed at higher levels, appear to have significant biological meaning in terms of tumor molecular phenotype and gene expression profiles.

Sharing of Genes and Pathways Across Complex Phenotypes: A Multilevel Genome-Wide Analysis

Evidence from genome-wide association studies (GWAS) suggest that pleiotropic effects on human complex phenotypes are very common. Recently, an atlas of genetic correlations among complex phenotypes has broadened our understanding of human diseases and traits. Here, we examine genetic overlap, from a gene-centric perspective, among the same 24 phenotypes previously investigated for genetic correlations. After adopting the multilevel pipeline (freely available at http://grass.cgs.hku.hk/limx/kgg/), which includes intragenic single nucleotide polymorphisms (SNPs), genes, and gene-sets, to estimate genetic similarities across phenotypes, a large amount of sharing of several biologically related phenotypes was confirmed. In addition, significant genetic overlaps were also found among phenotype pairs that were previously unidentified by SNP-level approaches. All these pairs with new genetic links are supported by earlier epidemiological evidence, although only a few of them have pleiotropic genes in the GWAS Catalog. Hence, our gene and gene-set analyses are able to provide new insights into cross-phenotype connections. The investigation on genetic sharing at three different levels presents a complementary picture of how common DNA sequence variations contribute to disease comorbidities and trait manifestations.

Computational dynamic approaches for temporal omics data with applications to systems medicine

Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology. This is key for understanding the complexity of the human health, drug response, disease susceptibility and pathogenesis for systems medicine. Temporal omics data used to measure the dynamic biological systems are essentials to discover complex biological interactions and clinical mechanism and causations. However, the delineation of the possible associations and causalities of genes, proteins, metabolites, cells and other biological entities from high throughput time course omics data is challenging for which conventional experimental techniques are not suited in the big omics era. In this paper, we present various recently developed dynamic trajectory and causal network approaches for temporal omics data, which are extremely useful for those researchers who want to start working in this challenging research area. Moreover, applications to various biological systems, health conditions and disease status, and examples that summarize the state-of-the art performances depending on different specific mining tasks are presented. We critically discuss the merits, drawbacks and limitations of the approaches, and the associated main challenges for the years ahead. The most recent computing tools and software to analyze specific problem type, associated platform resources, and other potentials for the dynamic trajectory and interaction methods are also presented and discussed in detail.

Analyzing the genes related to Alzheimer's disease via a network and pathway-based approach

Background

Our understanding of the molecular mechanisms underlying Alzheimer’s disease (AD) remains incomplete. Previous studies have revealed that genetic factors provide a significant contribution to the pathogenesis and development of AD. In the past years, numerous genes implicated in this disease have been identified via genetic association studies on candidate genes or at the genome-wide level. However, in many cases, the roles of these genes and their interactions in AD are still unclear. A comprehensive and systematic analysis focusing on the biological function and interactions of these genes in the context of AD will therefore provide valuable insights to understand the molecular features of the disease.

Method

In this study, we collected genes potentially associated with AD by screening publications on genetic association studies deposited in PubMed. The major biological themes linked with these genes were then revealed by function and biochemical pathway enrichment analysis, and the relation between the pathways was explored by pathway crosstalk analysis. Furthermore, the network features of these AD-related genes were analyzed in the context of human interactome and an AD-specific network was inferred using the Steiner minimal tree algorithm.

Results

We compiled 430 human genes reported to be associated with AD from 823 publications. Biological theme analysis indicated that the biological processes and biochemical pathways related to neurodevelopment, metabolism, cell growth and/or survival, and immunology were enriched in these genes. Pathway crosstalk analysis then revealed that the significantly enriched pathways could be grouped into three interlinked modules—neuronal and metabolic module, cell growth/survival and neuroendocrine pathway module, and immune response-related module—indicating an AD-specific immune-endocrine-neuronal regulatory network. Furthermore, an AD-specific protein network was inferred and novel genes potentially associated with AD were identified.

Conclusion

By means of network and pathway-based methodology, we explored the pathogenetic mechanism underlying AD at a systems biology level. Results from our work could provide valuable clues for understanding the molecular mechanism underlying AD. In addition, the framework proposed in this study could be used to investigate the pathological molecular network and genes relevant to other complex diseases or phenotypes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13195-017-0252-z) contains supplementary material, which is available to authorized users.

Transcriptome analysis reveals potential mechanisms underlying differential heart development in fast- and slow-growing broilers under heat stress

BACKGROUND

Modern fast-growing broilers are susceptible to heart failure under heat stress because their relatively small hearts cannot meet increased need of blood pumping. To improve the cardiac tolerance to heat stress in modern broilers through breeding, we need to find the important genes and pathways that contribute to imbalanced cardiac development and frequent occurrence of heat-related heart dysfunction. Two broiler lines - Ross 708 and Illinois - were included in this study as a fast-growing model and a slow-growing model respectively. Each broiler line was separated to two groups at 21 days posthatch. One group was subjected to heat stress treatment in the range of 35-37 °C for 8 h per day, and the other was kept in thermoneutral condition. Body and heart weights were measured at 42 days posthatch, and gene expression in left ventricles were compared between treatments and broiler lines through RNA-seq analysis.

RESULTS

Body weight and normalized heart weight were significantly reduced by heat stress only in Ross broilers. RNA-seq results of 44 genes were validated using Biomark assay. A total of 325 differentially expressed (DE) genes were detected between heat stress and thermoneutral in Ross 708 birds, but only 3 in Illinois broilers. Ingenuity pathway analysis (IPA) predicted dramatic changes in multiple cellular activities especially downregulation of cell cycle. Comparison between two lines showed that cell cycle activity is higher in Ross than Illinois in thermoneutral condition but is decreased under heat stress. Among the significant pathways (P < 0.01) listed for different comparisons, "Mitotic Roles of Polo-like Kinases" is always ranked first.

CONCLUSIONS

The increased susceptibility of modern broilers to cardiac dysfunction under heat stress compared to slow-growing broilers could be due to diminished heart capacity related to reduction in relative heart size. The smaller relative heart size in Ross heat stress group than in Ross thermoneutral group is suggested by the transcriptome analysis to be caused by decreased cell cycle activity and increased apoptosis. The DE genes in RNA-seq analysis and significant pathways in IPA provides potential targets for breeding of heat-tolerant broilers with optimized heart function.

Neuropathic pain promotes adaptive changes in gene expression in brain networks involved in stress and depression

Neuropathic pain is a complex chronic condition characterized by various sensory, cognitive, and affective symptoms. A large percentage of patients with neuropathic pain are also afflicted with depression and anxiety disorders, a pattern that is also seen in animal models. Furthermore, clinical and preclinical studies indicate that chronic pain corresponds with adaptations in several brain networks involved in mood, motivation, and reward. Chronic stress is also a major risk factor for depression. We investigated whether chronic pain and stress affect similar molecular mechanisms and whether chronic pain can affect gene expression patterns that are involved in depression. Using two mouse models of neuropathic pain and depression [spared nerve injury (SNI) and chronic unpredictable stress (CUS)], we performed next-generation RNA sequencing and pathway analysis to monitor changes in gene expression in the nucleus accumbens (NAc), the medial prefrontal cortex (mPFC), and the periaqueductal gray (PAG). In addition to finding unique transcriptome profiles across these regions, we identified a substantial number of signaling pathway-associated genes with similar changes in expression in both SNI and CUS mice. Many of these genes have been implicated in depression, anxiety, and chronic pain in patients. Our study provides a resource of the changes in gene expression induced by long-term neuropathic pain in three distinct brain regions and reveals molecular connections between pain and chronic stress.

Dietary intake alters gene expression in colon tissue: possible underlying mechanism for the influence of diet on disease

Supplemental Digital Content is available in the text.

Background

Although the association between diet and disease is well documented, the biologic mechanisms involved have not been entirely elucidated. In this study, we evaluate how dietary intake influences gene expression to better understand the underlying mechanisms through which diet operates.

Methods

We used data from 144 individuals who had comprehensive dietary intake and gene expression data from RNAseq using normal colonic mucosa. Using the DESeq2 statistical package, we identified genes that showed statistically significant differences in expression between individuals in high-intake and low-intake categories for several dietary variables of interest adjusting for age and sex. We examined total calories, total fats, vegetable protein, animal protein, carbohydrates, trans-fatty acids, mutagen index, red meat, processed meat, whole grains, vegetables, fruits, fiber, folate, dairy products, calcium, and prudent and western dietary patterns.

Results

Using a false discovery rate of less than 0.1, meat-related foods were statistically associated with 68 dysregulated genes, calcium with three dysregulated genes, folate with four dysregulated genes, and nonmeat-related foods with 65 dysregulated genes. With a more stringent false discovery rate of less than 0.05, there were nine meat-related dysregulated genes and 23 nonmeat-related genes. Ingenuity pathway analysis identified three major networks among genes identified as dysregulated with respect to meat-related dietary variables and three networks among genes identified as dysregulated with respect to nonmeat-related variables. The top networks (Ingenuity Pathway Analysis network score >30) associated with meat-related genes were (i) cancer, organismal injury, and abnormalities, tumor morphology, and (ii) cellular function and maintenance, cellular movement, cell death, and survival. Among genes related to nonmeat consumption variables, the top networks were (i) hematological system development and function, nervous system development and function, tissue morphology and (ii) connective tissue disorders, organismal injury, and abnormalities.

Conclusion

Several dietary factors were associated with gene expression in our data. These findings provide insight into the possible mechanisms by which diet may influence disease processes.

Characterizing biomarkers in osteosarcoma metastasis based on an ego-network

OBJECTIVES

To characterize biomarkers that underlie osteosarcoma (OS) metastasis based on an ego-network.

RESULTS

From the microarray data, we obtained 13,326 genes. By combining PPI data and microarray data, 10,520 shared genes were found and constructed into ego-networks. 17 significant ego-networks were identified with p < 0.05. In the pathway enrichment analysis, seven ego-networks were identified with the most significant pathway.

CONCLUSIONS

These significant ego-modules were potential biomarkers that reveal the potential mechanisms in OS metastasis, which may contribute to understanding cancer prognoses and providing new perspectives in the treatment of cancer.

Consensus paper of the WFSBP Task Force on Genetics: Genetics, epigenetics and gene expression markers of major depressive disorder and antidepressant response

Major depressive disorder (MDD) is a heritable disease with a heavy personal and socio-economic burden. Antidepressants of different classes are prescribed to treat MDD, but reliable and reproducible markers of efficacy are not available for clinical use. Further complicating treatment, the diagnosis of MDD is not guided by objective criteria, resulting in the risk of under- or overtreatment. A number of markers of MDD and antidepressant response have been investigated at the genetic, epigenetic, gene expression and protein levels. Polymorphisms in genes involved in antidepressant metabolism (cytochrome P450 isoenzymes), antidepressant transport (ABCB1), glucocorticoid signalling (FKBP5) and serotonin neurotransmission (SLC6A4 and HTR2A) were among those included in the first pharmacogenetic assays that have been tested for clinical applicability. The results of these investigations were encouraging when examining patient-outcome improvement. Furthermore, a nine-serum biomarker panel (including BDNF, cortisol and soluble TNF-α receptor type II) showed good sensitivity and specificity in differentiating between MDD and healthy controls. These first diagnostic and response-predictive tests for MDD provided a source of optimism for future clinical applications. However, such findings should be considered very carefully because their benefit/cost ratio and clinical indications were not clearly demonstrated. Future tests may include combinations of different types of biomarkers and be specific for MDD subtypes or pathological dimensions.