Functional features, biological pathways, and protein interaction networks of addiction-related genes

Shared Biological Pathways and Processes in Patients with Intellectual Disability: A Multicenter Study

BACKGROUND

 Although the underlying genetic causes of intellectual disability (ID) continue to be rapidly identified, the biological pathways and processes that could be targets for a potential molecular therapy are not yet known. This study aimed to identify ID-related shared pathways and processes utilizing enrichment analyses.

METHOD

 In this multicenter study, causative genes of patients with ID were used as input for Disease Ontology (DO), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes enrichment analysis.

RESULTS

 Genetic test results of 720 patients from 27 centers were obtained. Patients with chromosomal deletion/duplication, non-ID genes, novel genes, and results with changes in more than one gene were excluded. A total of 558 patients with 341 different causative genes were included in the study. Pathway-based enrichment analysis of the ID-related genes via ClusterProfiler revealed 18 shared pathways, with lysine degradation and nicotine addiction being the most common. The most common of the 25 overrepresented DO terms was ID. The most frequently overrepresented GO biological process, cellular component, and molecular function terms were regulation of membrane potential, ion channel complex, and voltage-gated ion channel activity/voltage-gated channel activity, respectively.

CONCLUSION

 Lysine degradation, nicotine addiction, and thyroid hormone signaling pathways are well-suited to be research areas for the discovery of new targeted therapies in ID patients.

Identification and Expression Analysis of Cold Shock Protein 3 (BcCSP3) in Non-Heading Chinese Cabbage (Brassica rapa ssp. chinensis)

A cold-related protein, cold shock protein 3 (BcCSP3), was isolated from non-heading Chinese cabbage in this study. BcCSP3 can encode 205 amino acids (aa) with an open reading frame (ORF) of 618 base pairs (bp). Multiple sequence alignment and phylogenetic tree analyses showed that BcCSP3 contains an N-terminal cold shock domain and is highly similar to AtCSP2, their kinship is recent. Real-time quantitative polymerase chain reaction (RT-qPCR) showed that the expression level of BcCSP3 in stems and leaves is higher than that in roots. Compared with other stress treatments, the change in BcCSP3 expression level was most pronounced under cold stress. In addition, a BcCSP3–GFP fusion protein was localized to the nucleus and cytoplasm. These results indicated that BcCSP3 may play an important role in response to cold stress in non-heading Chinese cabbage. This work may provide a reference for the identification and expression analysis of other CSP genes in non-heading Chinese cabbage.

Genetic basis of psychopathological dimensions shared between schizophrenia and bipolar disorder

Shared genetic vulnerability between schizophrenia (SCZ) and bipolar disorder (BP) was demonstrated, but the genetic underpinnings of specific symptom domains are unclear. This study investigated which genes and gene sets may modulate specific psychopathological domains and if genome-wide significant loci previously associated with SCZ or BP may play a role. Genome-wide data were available in patients with SCZ (n = 226) or BP (n = 228). Phenotypes under investigation were depressive and positive symptoms severity, suicidal ideation, onset age and substance use disorder comorbidity. Genome-wide analyses were performed at gene and gene set level, while 148 genome-wide significant loci previously associated with SCZ and/or BP were investigated. Each sample was analyzed separately then a meta-analysis was performed. SH3GL2 and CLVS1 genes were associated with suicidal ideation in SCZ (p = 5.62e-08 and 0.01, respectively), the former also in the meta-analysis (p = .01). SHC4 gene was associated with depressive symptoms severity in BP (p = .003). A gene set involved in cellular differentiation (GO:0048661) was associated with substance disorder comorbidity in the meta-analysis (p = .03). Individual loci previously associated with SCZ or BP did not modulate the phenotypes of interest. This study provided confirmatory and new findings. SH3GL2 (endophilin A1) showed a role in suicidal ideation that may be due to its relevance to the glutamate system. SHC4 regulates BDNF-induced MAPK activation and was previously associated with depression. CLVS1 is involved in lysosome maturation and was for the first time associated with a psychiatric trait. GO:0048661 may mediate the risk of substance disorder through an effect on neurodevelopment/neuroplasticity.

Comparison between dopaminergic and non-dopaminergic neurons in the VTA following chronic nicotine exposure during pregnancy

Exposure to nicotine during pregnancy through maternal smoking or nicotine replacement therapy is associated with adverse birth outcomes as well as several cognitive and neurobehavioral deficits. Several studies have shown that nicotine produces long-lasting effects on gene expression within many brain regions, including the ventral tegmental area (VTA), which is the origin of dopaminergic neurons and the dopamine reward pathway. Using a well-established rat model for perinatal nicotine exposure, we sought to investigate altered biological pathways using mRNA and miRNA expression profiles of dopaminergic (DA) and non-dopaminergic (non-DA) neurons in this highly-valuable area. Putative miRNA-gene target interactions were assessed as well as miRNA-pathway interactions. Our results indicate that extracellular matrix (ECM) receptor interactions were significantly altered in DA and non-DA neurons due to chronic nicotine exposure during pregnancy. They also show that the PI3K/AKT signaling pathway was enriched in DA neurons with multiple significant miRNA-gene targets, but the same changes were not seen in non-DA neurons. We speculate that nicotine exposure during pregnancy could differentially affect the gene expression of DA and non-DA neurons in the VTA.

FunGeneNet: a web tool to estimate enrichment of functional interactions in experimental gene sets

Background

Estimation of functional connectivity in gene sets derived from genome-wide or other biological experiments is one of the essential tasks of bioinformatics. A promising approach for solving this problem is to compare gene networks built using experimental gene sets with random networks. One of the resources that make such an analysis possible is CrossTalkZ, which uses the FunCoup database. However, existing methods, including CrossTalkZ, do not take into account individual types of interactions, such as protein/protein interactions, expression regulation, transport regulation, catalytic reactions, etc., but rather work with generalized types characterizing the existence of any connection between network members.

Results

We developed the online tool FunGeneNet, which utilizes the ANDSystem and STRING to reconstruct gene networks using experimental gene sets and to estimate their difference from random networks. To compare the reconstructed networks with random ones, the node permutation algorithm implemented in CrossTalkZ was taken as a basis. To study the FunGeneNet applicability, the functional connectivity analysis of networks constructed for gene sets involved in the Gene Ontology biological processes was conducted. We showed that the method sensitivity exceeds 0.8 at a specificity of 0.95. We found that the significance level of the difference between gene networks of biological processes and random networks is determined by the type of connections considered between objects. At the same time, the highest reliability is achieved for the generalized form of connections that takes into account all the individual types of connections. By taking examples of the thyroid cancer networks and the apoptosis network, it is demonstrated that key participants in these processes are involved in the interactions of those types by which these networks differ from random ones.

Conclusions

FunGeneNet is a web tool aimed at proving the functionality of networks in a wide range of sizes of experimental gene sets, both for different global networks and for different types of interactions. Using examples of thyroid cancer and apoptosis networks, we have shown that the links over-represented in the analyzed network in comparison with the random ones make possible a biological interpretation of the original gene/protein sets. The FunGeneNet web tool for assessment of the functional enrichment of networks is available at http://www-bionet.sscc.ru/fungenenet/.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4474-7) contains supplementary material, which is available to authorized users.

Spatial transcriptome analysis provides insights of key gene(s) involved in steroidal saponin biosynthesis in medicinally important herb Trillium govanianum

Trillium govanianum, an endangered medicinal herb native to the Himalaya, is less studied at the molecular level due to the non-availability of genomic resources. To facilitate the basic understanding of the key genes and regulatory mechanism of pharmaceutically important biosynthesis pathways, first spatial transcriptome sequencing of T. govanianum was performed. 151,622,376 (~11.5 Gb) high quality reads obtained using paired-end Illumina sequencing were de novo assembled into 69,174 transcripts. Functional annotation with multiple public databases identified array of genes involved in steroidal saponin biosynthesis and other secondary metabolite pathways including brassinosteroid, carotenoid, diterpenoid, flavonoid, phenylpropanoid, steroid and terpenoid backbone biosynthesis, and important TF families (bHLH, MYB related, NAC, FAR1, bZIP, B3 and WRKY). Differentially expressed large number of transcripts, together with CYPs and UGTs suggests involvement of these candidates in tissue specific expression. Combined transcriptome and expression analysis revealed that leaf and fruit tissues are the main site of steroidal saponin biosynthesis. In conclusion, comprehensive genomic dataset created in the current study will serve as a resource for identification of potential candidates for genetic manipulation of targeted bioactive metabolites and also contribute for development of functionally relevant molecular marker resource to expedite molecular breeding and conservation efforts in T. govanianum.

A Systematic Analysis of Candidate Genes Associated with Nicotine Addiction

Nicotine, as the major psychoactive component of tobacco, has broad physiological effects within the central nervous system, but our understanding of the molecular mechanism underlying its neuronal effects remains incomplete. In this study, we performed a systematic analysis on a set of nicotine addiction-related genes to explore their characteristics at network levels. We found that NAGenes tended to have a more moderate degree and weaker clustering coefficient and to be less central in the network compared to alcohol addiction-related genes or cancer genes. Further, clustering of these genes resulted in six clusters with themes in synaptic transmission, signal transduction, metabolic process, and apoptosis, which provided an intuitional view on the major molecular functions of the genes. Moreover, functional enrichment analysis revealed that neurodevelopment, neurotransmission activity, and metabolism related biological processes were involved in nicotine addiction. In summary, by analyzing the overall characteristics of the nicotine addiction related genes, this study provided valuable information for understanding the molecular mechanisms underlying nicotine addiction.

Pathways and networks-based analysis of candidate genes associated with nicotine addiction

Nicotine is the addictive substance in tobacco and it has a broad impact on both the central and peripheral nervous systems. Over the past decades, an increasing number of genes potentially involved in nicotine addiction have been identified by different technical approaches. However, the molecular mechanisms underlying nicotine addiction remain largely unclear. Under such situation, a comprehensive analysis focusing on the overall functional characteristics of these genes, as well as how they interact with each other will provide us valuable information to understand nicotine addiction. In this study, we presented a systematic analysis on nicotine addiction-related genes to identify the major underlying biological themes. Functional analysis revealed that biological processes and biochemical pathways related to neurodevelopment, immune system and metabolism were significantly enriched in the nicotine addiction-related genes. By extracting the nicotine addiction-specific subnetwork, a number of novel genes associated with addiction were identified. Moreover, we constructed a schematic molecular network for nicotine addiction via integrating the pathways and network, providing an intuitional view to understand the development of nicotine addiction. Pathway and network analysis indicated that the biological processes related to nicotine addiction were complex. Results from our work may have important implications for understanding the molecular mechanism underlying nicotine addiction.

Prioritizing Genes Related to Nicotine Addiction Via a Multi-source-Based Approach

Nicotine has a broad impact on both the central and peripheral nervous systems. Over the past decades, an increasing number of genes potentially involved in nicotine addiction have been identified by different technical approaches. However, the molecular mechanisms underlying nicotine addiction remain largely unknown. Under such situation, prioritizing the candidate genes for further investigation is becoming increasingly important. In this study, we presented a multi-source-based gene prioritization approach for nicotine addiction by utilizing the vast amounts of information generated from for nicotine addiction study during the past years. In this approach, we first collected and curated genes from studies in four categories, i.e., genetic association analysis, genetic linkage analysis, high-throughput gene/protein expression analysis, and literature search of single gene/protein-based studies. Based on these resources, the genes were scored and a weight value was determined for each category. Finally, the genes were ranked by their combined scores, and 220 genes were selected as the prioritized nicotine addiction-related genes. Evaluation suggested the prioritized genes were promising targets for further analysis and replication study.

Dynamic protein interaction modules in human hepatocellular carcinoma progression

Background

Gene expression profiles have been frequently integrated with the human protein interactome to uncover functional modules under specific conditions like disease state. Beyond traditional differential expression analysis, differential co-expression analysis has emerged as a robust approach to reveal condition-specific network modules, with successful applications in a few human disease studies. Hepatocellular carcinoma (HCC), which is often interrelated with the Hepatitis C virus, typically develops through multiple stages. A comprehensive investigation of HCC progression-specific differential co-expression modules may advance our understanding of HCC's pathophysiological mechanisms.

Results

Compared with differentially expressed genes, differentially co-expressed genes were found more likely enriched with Hepatitis C virus binding proteins and cancer-mutated genes, and they were clustered more densely in the human reference protein interaction network. These observations indicated that a differential co-expression approach could outperform the standard differential expression network analysis in searching for disease-related modules. We then proposed a differential co-expression network approach to uncover network modules involved in HCC development. Specifically, we discovered subnetworks that enriched differentially co-expressed gene pairs in each HCC transition stage, and further resolved modules with coherent co-expression change patterns over all HCC developmental stages. Our identified network modules were enriched with HCC-related genes and implicated in cancer-related biological functions. In particular, APC and YWHAZ were highlighted as two most remarkable genes in the network modules, and their dynamic interaction partnership was resolved in HCC development.

Conclusions

We demonstrated that integration of differential co-expression with the protein interactome could outperform the traditional differential expression approach in discovering network modules of human diseases. In our application of this approach to HCC's gene expression data, we successfully identified subnetworks with marked differential co-expression in individual HCC stage transitions and network modules with coherent co-expression change patterns over all HCC developmental stages. Our results shed light on subtle HCC mechanisms, including temporal activation and dismissal of pivotal functions and dynamic interaction partnerships of key genes.

Exploring drug-target interaction networks of illicit drugs

Background

Drug addiction is a complex and chronic mental disease, which places a large burden on the American healthcare system due to its negative effects on patients and their families. Recently, network pharmacology is emerging as a promising approach to drug discovery by integrating network biology and polypharmacology, allowing for a deeper understanding of molecular mechanisms of drug actions at the systems level. This study seeks to apply this approach for investigation of illicit drugs and their targets in order to elucidate their interaction patterns and potential secondary drugs that can aid future research and clinical care.

Results

In this study, we extracted 188 illicit substances and their related information from the DrugBank database. The data process revealed 86 illicit drugs targeting a total of 73 unique human genes, which forms an illicit drug-target network. Compared to the full drug-target network from DrugBank, illicit drugs and their target genes tend to cluster together and form four subnetworks, corresponding to four major medication categories: depressants, stimulants, analgesics, and steroids. External analysis of Anatomical Therapeutic Chemical (ATC) second sublevel classifications confirmed that the illicit drugs have neurological functions or act via mechanisms of stimulants, opioids, and steroids. To further explore other drugs potentially having associations with illicit drugs, we constructed an illicit-extended drug-target network by adding the drugs that have the same target(s) as illicit drugs to the illicit drug-target network. After analyzing the degree and betweenness of the network, we identified hubs and bridge nodes, which might play important roles in the development and treatment of drug addiction. Among them, 49 non-illicit drugs might have potential to be used to treat addiction or have addictive effects, including some results that are supported by previous studies.

Conclusions

This study presents the first systematic review of the network characteristics of illicit drugs, their targets, and other drugs that share the targets of these illicit drugs. The results, though preliminary, provide some novel insights into the molecular mechanisms of drug addiction. The observation of illicit-related drugs, with partial verification from previous studies, demonstrated that the network-assisted approach is promising for the identification of drug repositioning.

Genome-wide association study of a quantitative disordered gambling trait

Disordered gambling is a moderately heritable trait, but the underlying genetic basis is largely unknown. We performed a genome-wide association study (GWAS) for disordered gambling using a quantitative factor score in 1312 twins from 894 Australian families. Association was conducted for 2 381 914 single-nucleotide polymorphisms (SNPs) using the family-based association test in Merlin followed by gene and pathway enrichment analyses. Although no SNP reached genome-wide significance, six achieved P-values < 1 × 10(-5) with variants in three genes (MT1X, ATXN1 and VLDLR) implicated in disordered gambling. Secondary case-control analyses found two SNPs on chromosome 9 (rs1106076 and rs12305135 near VLDLR) and rs10812227 near FZD10 on chromosome 12 to be significantly associated with lifetime Diagnostic and Statistical Manual of Mental Disorders, fourth edition pathological gambling and South Oaks Gambling Screen classified probable pathological gambling status. Furthermore, several addiction-related pathways were enriched for SNPs associated with disordered gambling. Finally, gene-based analysis of 24 candidate genes for dopamine agonist-induced gambling in individuals with Parkinson's disease suggested an enrichment of SNPs associated with disordered gambling. We report the first GWAS of disordered gambling. While further replication is required, the identification of susceptibility loci and biological pathways will be important in characterizing the biological mechanisms that underpin disordered gambling.

Integrative analysis of common neurodegenerative diseases using gene association, interaction networks and mRNA expression data

Alzheimer's and Parkinson's diseases (AD and PD) are two common neurodegenerative diseases primarily affecting memory and motor functions, respectively. In this study, we integrated data from various sources, and took a systems-biology approach to compare and contrast the molecular and network based dysregulation associated with AD and PD and we integrated these data with known pathways of drug treatment. First, we identified genes that exhibit consistent prior evidence of association with each disease. Then, we extracted disease-specific sub-networks from a human interactome database using associated genes as seeds. To rank the sub-networks we used existing gene expression data from cases and controls. Comparison of resulting disease-associated genes and networks revealed significant overlap between AD and PD. In addition, the identified sub-networks correlated with known drug interdiction pathways, and suggested new potential targets for intervention.