Genetic and Protein Network Underlying the Convergence of Rett-Syndrome-like (RTT-L) Phenotype in Neurodevelopmental Disorders.

Mutations of the X-linked gene encoding methyl-CpG-binding protein 2 (MECP2) cause classical forms of Rett syndrome (RTT) in girls. A subset of patients who are recognized to have an overlapping neurological phenotype with RTT but are lacking a mutation in a gene that causes classical or atypical RTT can be described as having a 'Rett-syndrome-like phenotype (RTT-L). Here, we report eight patients from our cohort diagnosed as having RTT-L who carry mutations in genes unrelated to RTT. We annotated the list of genes associated with RTT-L from our patient cohort, considered them in the light of peer-reviewed articles on the genetics of RTT-L, and constructed an integrated protein-protein interaction network (PPIN) consisting of 2871 interactions connecting 2192 neighboring proteins among RTT- and RTT-L-associated genes. Functional enrichment analysis of RTT and RTT-L genes identified a number of intuitive biological processes. We also identified transcription factors (TFs) whose binding sites are common across the set of RTT and RTT-L genes and appear as important regulatory motifs for them. Investigation of the most significant over-represented pathway analysis suggests that HDAC1 and CHD4 likely play a central role in the interactome between RTT and RTT-L genes.

Interrogating the Human Diplome: Computational Methods, Emerging Applications, and Challenges.

Human DNA sequencing protocols have revolutionized human biology, biomedical science, and clinical practice, but still have very important limitations. One limitation is that most protocols do not separate or assemble (i.e., "phase") the nucleotide content of each of the maternally and paternally derived chromosomal homologs making up the 22 autosomal pairs and the chromosomal pair making up the pseudo-autosomal region of the sex chromosomes. This has led to a dearth of studies and a consequent underappreciation of many phenomena of fundamental importance to basic and clinical genomic science. We discuss a few protocols for obtaining phase information as well as their limitations, including those that could be used in tumor phasing settings. We then describe a number of biological and clinical phenomena that require phase information. These include phenomena that require precise knowledge of the nucleotide sequence in a chromosomal segment from germline or somatic cells, such as DNA binding events, and insight into unique cis vs. trans-acting functionally impactful variant combinations-for example, variants implicated in a phenotype governed by compound heterozygosity. In addition, we also comment on the need for reliable and consensus-based diploid-context computational workflows for variant identification as well as the need for laboratory-based functional verification strategies for validating cis vs. trans effects of variant combinations. We also briefly describe available resources, example studies, as well as areas of further research, and ultimately argue that the science behind the study of human diploidy, referred to as "diplomics," which will be enabled by nucleotide-level resolution of phased genomes, is a logical next step in the analysis of human genome biology.

Cross-Species and Human Inter-Tissue Network Analysis of Genes Implicated in Longevity and Aging Reveal Strong Support for Nutrient Sensing.

Intensive research efforts have been undertaken to slow human aging and therefore potentially delay the onset of age-related diseases. These efforts have generated an enormous amount of high-throughput data covering different levels in the physiologic hierarchy, e.g., genetic, epigenetic, transcriptomic, proteomic, and metabolomic, etc. We gathered 15 independent sources of information about genes potentially involved in human longevity and lifespan (N = 5836) and subjected them to various integrated analyses. Many of these genes were initially identified in non-human species, and we investigated their orthologs in three non-human species [i.e., mice (N = 967), fruit fly (N = 449), and worm (N = 411)] for further analysis. We characterized experimentally determined protein-protein interaction networks (PPIN) involving each species' genes from 9 known protein databases and studied the enriched biological pathways among the individually constructed PPINs. We observed three important signaling pathways: FoxO signaling, mTOR signaling, and autophagy to be common and highly enriched in all four species (p-value ≤ 0.001). Our study implies that the interaction of proteins involved in the mechanistic target of rapamycin (mTOR) signaling pathway is somewhat limited to each species or that a "rewiring" of specific networks has taken place over time. To corroborate our findings, we repeated our analysis in 43 different human tissues. We investigated conserved modules in various tissue-specific PPINs of the longevity-associated genes based upon their protein expression. This analysis also revealed mTOR signaling as shared biological processes across four different human tissue-specific PPINs for liver, heart, skeletal muscle, and adipose tissue. Further, we explored our results' translational potential by assessing the protein interactions with all the reported drugs and compounds that have been experimentally verified to promote longevity in the three-comparator species. We observed that the target proteins of the FDA-approved drug rapamycin (a known inhibitor of mTOR) were conserved across all four species. Drugs like melatonin and metformin exhibited shared targets with rapamycin in the human PPIN. The detailed information about the curated gene list, cross-species orthologs, PPIN, and pathways was assembled in an interactive data visualization portal using RStudio's Shiny framework (https://agingnetwork.shinyapps.io/frontiers/).

Drug Target Prioritization for Alzheimer's Disease Using Protein Interaction Network Analysis.

Alzheimer's disease (AD) is an age-related neurodegenerative disorder that accounts for numerous deaths worldwide. AD is the most common cause of dementia, characterized by accumulation of fibrous amyloid beta protein in the brain with clinical symptoms, such as loss of intellectual and social skills, gradually leading to the death of brain cells. The genetic complexity of AD during disease progression requires a systems-level understanding to design viable therapeutics. We present an integrative computational analysis to prioritize AD-associated genes outlined through a protein-protein interaction network. Multiple topological parameters of the network were considered to target proteins which are accountable for disease susceptibility. Furthermore, in silico protein structure modeling and molecular dynamics simulation approaches were implemented to characterize presenilin 2 (PSEN2) protein as one of the leading targets in the network. The findings are constructive to aid future drug discovery endeavors in the treatment of AD.

Data on overlapping brain disorders and emerging drug targets in human Dopamine Receptors Interaction Network.

Intercommunication of Dopamine Receptors (DRs) with their associate protein partners is crucial to maintain regular brain function in human. Majority of the brain disorders arise due to malfunctioning of such communication process. Hence, contributions of genetic factors, as well as phenotypic indications for various neurological and psychiatric disorders are often attributed as sharing in nature. In our earlier research article entitled "Human Dopamine Receptors Interaction Network (DRIN): a systems biology perspective on topology, stability and functionality of the network" (Podder et al., 2014) [1], we had depicted a holistic interaction map of human Dopamine Receptors. Given emphasis on the topological parameters, we had characterized the functionality along with the vulnerable properties of the network. In support of this, we hereby provide an additional data highlighting the genetic overlapping of various brain disorders in the network. The data indicates the sharing nature of disease genes for various neurological and psychiatric disorders in dopamine receptors connecting protein-protein interactions network. The data also indicates toward an alternative approach to prioritize proteins for overlapping brain disorders as valuable drug targets in the network.

CD4-gp120 interaction interface - a gateway for HIV-1 infection in human: molecular network, modeling and docking studies.

The major causative agent for Acquired Immune Deficiency Syndrome (AIDS) is Human Immunodeficiency Virus-1 (HIV-1). HIV-1 is a predominant subtype of HIV which counts on human cellular mechanism virtually in every aspect of its life cycle. Binding of viral envelope glycoprotein-gp120 with human cell surface CD4 receptor triggers the early infection stage of HIV-1. This study focuses on the interaction interface between these two proteins that play a crucial role for viral infectivity. The CD4-gp120 interaction interface has been studied through a comprehensive protein-protein interaction network (PPIN) analysis and highlighted as a useful step towards identifying potential therapeutic drug targets against HIV-1 infection. We prioritized gp41, Nef and Tat proteins of HIV-1 as valuable drug targets at early stage of viral infection. Lack of crystal structure has made it difficult to understand the biological implication of these proteins during disease progression. Here, computational protein modeling techniques and molecular dynamics simulations were performed to generate three-dimensional models of these targets. Besides, molecular docking was initiated to determine the desirability of these target proteins for already available HIV-1 specific drugs which indicates the usefulness of these protein structures to identify an effective drug combination therapy against AIDS.

Investigating the structural impact of S311C mutation in DRD2 receptor by molecular dynamics & docking studies.

Dopamine receptors (DR) are neuronal cell surface proteins that mediate the action of neurotransmitter dopamine in brain. Dopamine receptor D2 (DRD2) that belongs to G-protein coupled receptors (GPCR) family is a major therapeutic target for of various neurological and psychiatric disorders in human. The third inter cellular loop (ICL3) in DRD2 is essential for coupling G proteins and several signaling scaffold proteins. A mutation in ICL3 can interfere with this binding interface, thereby altering the DRD2 signaling. In this study we have examined the deleterious effect of serine to cysteine mutation at position 311 (S311C) in the ICL3 region that is implicated in diseases like schizophrenia and alcoholism. An in silico structure modeling approach was employed to determine the wild type (WT) and mutant S311C structures of DRD2, scaffold proteins - Gαi/o and NEB2. Protein-ligand docking protocol was exercised to predict the interactions of natural agonist dopamine with both the WT and mutant structures of DRD2. Besides, atomistic molecular dynamics (MD) simulations were performed to provide insights into essential dynamics of the systems-unbound and dopamine bound DRD2 (WT and mutant) and three independent simulations for Gαi, Gαo and NEB2 systems. To provide information on intra-molecular arrangement of the structures, a comprehensive residue interactions network of both dopamine bound WT and mutant DRD2 protein were studied. We also employed a protein-protein docking strategy to find the interactions of scaffold proteins - Gαi/o and NEB2 with both dopamine bound WT and mutant structures of DRD2. We observed a marginal effect of the mutation in dopamine binding mechanism on the trajectories analyzed. However, we noticed a significant structural alteration of the mutant receptor which affects Gαi/o and NEB2 binding that can be causal for malfunctioning in cAMP-dependent signaling and Ca(+) homeostasis in the brain dopaminergic system leading to neuropsychiatric disorders.

New insights into schizophrenia disease genes interactome in the human brain: emerging targets and therapeutic implications in the postgenomics era.

Schizophrenia, a complex neurological disorder, is comprised of interactions between multiple genetic and environmental factors wherein each of the factors individually exhibits a small effect. In this regard a network-based strategy is best suited to capture the combined effect of multiple genes with their definite pattern of interactions. Given that schizophrenia affects multiple regions of the brain, we postulated that instead of any single specific tissue, a mutual set of interactions occurs between different regions of brain in a well-defined pattern responsible for the disease phenotype. To validate, we constructed and compared tissue specific co-expression networks of schizophrenia candidate genes in twenty diverse brain tissues. As predicted, we observed a common interaction network of certain genes in all the studied brain tissues. We examined fundamental network topologies of the common network to sequester essential common candidates for schizophrenia. We also performed a gene set analysis to identify the essential biological pathways enriched by the common candidates in the network. Finally, the candidate drug targets were prioritized and scored against known available schizophrenic drugs by molecular docking studies. We distinctively identified protein kinases as the top candidates in the network that can serve as probable drug targets for the disease. Conclusively, we propose that a comprehensive study of the connectivity amongst the disease genes themselves may turn out to be more informative to understand schizophrenia disease etiology and the underlying complexity.

Human Dopamine Receptors Interaction Network (DRIN): a systems biology perspective on topology, stability and functionality of the network.

Dopamine receptors (DR) are one of the major neurotransmitter receptors present in human brain. Malfunctioning of these receptors is well established to trigger many neurological and psychiatric disorders. Taking into consideration that proteins function collectively in a network for most of the biological processes, the present study is aimed to depict the interactions between all dopamine receptors following a systems biology approach. To capture comprehensive interactions of candidate proteins associated with human dopamine receptors, we performed a protein-protein interaction network (PPIN) analysis of all five receptors and their protein partners by mapping them into human interactome and constructed a human Dopamine Receptors Interaction Network (DRIN). We explored the topology of dopamine receptors as molecular network, revealing their characteristics and the role of central network elements. More to the point, a sub-network analysis was done to determine major functional clusters in human DRIN that govern key neurological pathways. Besides, interacting proteins in a pathway were characterized and prioritized based on their affinity for utmost drug molecules. The vulnerability of different networks to the dysfunction of diverse combination of components was estimated under random and direct attack scenarios. To the best of our knowledge, the current study is unique to put all five dopamine receptors together in a common interaction network and to understand the functionality of interacting proteins collectively. Our study pinpointed distinctive topological and functional properties of human dopamine receptors that have helped in identifying potential therapeutic drug targets in the dopamine interaction network.

Elevated level of C-reactive protein is associated with risk of prediabetes in Indians.

BACKGROUND: Relationship of high sensitivity C-reactive protein (hsCRP) with prediabetes has not been explored extensively in Indians. Here we sought to investigate the association of hsCRP levels with prediabetes, as represented by impaired fasting glucose (IFG) and impaired glucose tolerance (IGT), and the influence of risk factors like obesity, decreased HDL cholesterol, hypertension, family history of diabetes and current smoking habit on the relationship. METHODS: A cross-sectional study on 1726 Indians, comprising of 1276 individuals with normal glucose tolerance (NGT), 250 IFG and 200 IGT individuals. Subjects were defined according to WHO criteria based on fasting plasma and 2 h glucose levels. RESULTS: Median levels of hsCRP were significantly higher in IFG (2.20 mg/l) and IGT (2.32 mg/l) compared to NGT (1.64 mg/l) subjects. Individuals with high risk hsCRP levels (>3 mg/l) had an odds ratio (OR) (95% confidence interval (CI)) of 2.60 (1.56-5.34) [P=1.3×10(-4)] for IGT after adjusting the effect of age, sex, medication, body mass index (BMI), waist circumference (WC) and risk factors like decreased high-density lipoprotein cholesterol (HDL-cholesterol), hypertension, family history of diabetes and current smoking. Significant increase in risk of IGT was found with a unit increase in natural log transformed hsCRP levels after adjustment for covariates [OR (95%CI)=1.57 (1.27-1.94), P=3.0×10(-5)]. When subjects were stratified on the basis of risk factors, we found stronger association of elevated hsCRP levels with risk of IFG and IGT in subjects having HDL-cholesterol ≤50 mg/dl and with hypertension. CONCLUSIONS: Our study demonstrates that elevated hsCRP levels are independently associated with risk of IFG and IGT in Indians.

Elevated levels of C-reactive protein as a risk factor for metabolic syndrome in Indians.

OBJECTIVE: Relationship of high sensitivity C-reactive protein (hsCRP) with metabolic syndrome (MetS) is well documented in many populations, but comprehensive data is lacking in Indian population. Thus, we set out to investigate the association of hsCRP levels with MetS and its features and the effect of obesity and insulin resistance on this association in urban Indians. METHODS: This is a cross-sectional study that included 9517 subjects comprising 4066 subjects with MetS. MetS was defined according to the modified National Cholesterol Education Program (NCEP) Adult Treatment Panel (ATP III) criteria for Asians. RESULTS: Median levels of hsCRP were considerably higher in individuals with MetS with higher levels in women compared to men. Among the features of MetS, waist circumference was most strongly correlated with hsCRP levels (r=0.28) and contributed maximally (ß=0.025mg/l lnhsCRP, P=7.4×10(-147)). Subjects with high risk hsCRP levels (>3mg/l) were at high risk of MetS (OR (95% CI)=1.65(1.41-1.92), P=1.7×10(-10)). Risk of MetS increased in a dose dependent manner from low risk to high risk hsCRP category with increase in BMI and HOMA-IR. CONCLUSIONS: Our findings suggest that hsCRP predicts the risk of MetS, independent of obesity and insulin resistance, and therefore, can be a valuable tool to aid the identification of individuals at risk of MetS. The study provides a lead for future investigation for effects of hsCRP, obesity, and insulin resistance on MetS in this population.