Parkinson's disease and schizophrenia interactomes contain temporally distinct gene clusters underlying comorbid mechanisms and unique disease processes.

Genome-wide association studies suggest significant overlaps in Parkinson's disease (PD) and schizophrenia (SZ) risks, but the underlying mechanisms remain elusive. The protein-protein interaction network ('interactome') plays a crucial role in PD and SZ and can incorporate their spatiotemporal specificities. Therefore, to study the linked biology of PD and SZ, we compiled PD- and SZ-associated genes from the DisGeNET database, and constructed their interactomes using BioGRID and HPRD. We examined the interactomes using clustering and enrichment analyses, in conjunction with the transcriptomic data of 26 brain regions spanning foetal stages to adulthood available in the BrainSpan Atlas. PD and SZ interactomes formed four gene clusters with distinct temporal identities (Disease Gene Networks or 'DGNs'1-4). DGN1 had unique SZ interactome genes highly expressed across developmental stages, corresponding to a neurodevelopmental SZ subtype. DGN2, containing unique SZ interactome genes expressed from early infancy to adulthood, correlated with an inflammation-driven SZ subtype and adult SZ risk. DGN3 contained unique PD interactome genes expressed in late infancy, early and late childhood, and adulthood, and involved in mitochondrial pathways. DGN4, containing prenatally-expressed genes common to both the interactomes, involved in stem cell pluripotency and overlapping with the interactome of 22q11 deletion syndrome (comorbid psychosis and Parkinsonism), potentially regulates neurodevelopmental mechanisms in PD-SZ comorbidity. Our findings suggest that disrupted neurodevelopment (regulated by DGN4) could expose risk windows in PD and SZ, later elevating disease risk through inflammation (DGN2). Alternatively, variant clustering in DGNs may produce disease subtypes, e.g., PD-SZ comorbidity with DGN4, and early/late-onset SZ with DGN1/DGN2.

Meta-analysis of genomic variants and gene expression data in schizophrenia suggests the potential need for adjunctive therapeutic interventions for neuropsychiatric disorders.

Schizophrenia (SZ) is a debilitating mental illness with a multigenic aetiology and significant heritability. Despite extensive genetic studies, the molecular aetiology has remained enigmatic. A recent systems biology study suggested a protein-protein interaction network for SZ with 504 novel interactions. The onset of psychiatric disorders is predominant during adolescence, often accompanied by subtle structural abnormalities in multiple regions of the brain. The availability of BrainSpan Atlas data allowed us to re-examine the genes present in the SZ interactome as a function of space and time. The availability of genomes of healthy centenarians and nonpsychiatric Exome Aggregation Consortium database allowed us to identify the variants of criticality. The expression of the SZ candidate genes responsible for cognition and disease onset was studied in different brain regions during particular developmental stages. A subset of novel interactors detected in the network was further validated using gene expression data of post-mortem brains of patients with psychiatric illness. We have narrowed down the list of drug targets proposed by theprevious interactome study to 10 proteins. These proteins belonging to 81 biological pathways are targeted by 34 known Food and Drug Administration-approved drugs that have distinct potential for the treatment of neuropsychiatric disorders. We also report the possibility of targeting key genes belonging to celecoxib pharmacodynamics, Gα signalling and cGMP-PKG signalling pathwaysthat are not known to be specific to SZ aetiology.

Intronic miRNA mediated gene expression regulation controls protein crowding inside the cell.

Gene regulatory effects of microRNAs at a posttranscriptional level have been established over the last decade. In this study, we analyze the interaction networks of mRNA translation regulation through intronic miRNA, under various tissue-specific cellular contexts, taking into account the thermodynamic affinity, chemical kinetics, co-localization, concentration levels, network parameters and the presence of competitive interactors. This database, and analysis has been made available through an open-access web-server, miRiam, to promote further exploration. Here we report that expression of genes involved in Apoptosis Processes, Immune System Processes, Translation Regulator Activities, and Molecular Transport Activities within the cell are predominately regulated by miRNA mediation. Our findings further indicate that this regulatory effect has a profound effect in controlling protein crowding inside the cell. A miRNA mediated gene expression regulation serves as a temporal regulator, allowing the cellular machinery to temporarily 'pause' the translation of mRNA, indicating that the miRNA-mRNA interactions may be important for governing the optimal usage of cell volume.

Paradigm for disease deconvolution in rare neurodegenerative disorders in Indian population: insights from studies in cerebellar ataxias.

Cerebellar ataxias are a group of rare progressive neurodegenerative disorders with an average prevalence ranges from 4.8 to 13.8 in 100,000 individuals. The inherited disorders affect multiple members of the families, or a community that is endogamous or consanguineous. Presence of more than 3000 mutations in different genes with overlapping clinical symptoms, genetic anticipation and pleiotropy, as well as incomplete penetrance and variable expressivity due to modifiers pose challenges in genotype-phenotype correlation. Development of a diagnostic algorithm could reduce the time as well as cost in clinicogenetic diagnostics and also help in reducing the economic and social burden of the disease. In a unique research collaboration spanning over 20 years, we have been able to develop a paradigm for studying cerebellar ataxias in the Indian population which would also be relevant in other rare diseases. This has involved clinical and genetic analysis of thousands of families from diverse Indian populations. The extensive resource on ataxia has led to the development of a clinicogenetic algorithm for cost-effective screening of ataxia and a unique ataxia clinic in the tertiary referral centre in All India Institute of Medical Sciences. Utilizing a population polymorphism scanning approach, we have been able to dissect the mechanisms of repeat instability and expansion in many ataxias, and also identify founders, and trace the mutational histories in the Indian population. This provides information for genetic testing of at-risk as well as protected individuals and populations. To dissect uncharacterized cases which comprises more than 50% of the cases, we have explored the potential of next-generation sequencing technologies coupled with the extensive resource of baseline data generated in-house and other public domains. We have also developed a repository of patient-derived peripheral blood mononuclear cells, lymphoblastoid cell lines and neuronal lineages (derived from iPSCs) for ascribing functionality to novel genes/mutations. Through integrating these technologies, novel genes have been identified that has broadened the diagnostic panel, increased the diagnostic yield to over 75%, helped in ascribing pathogenicity to novel mutations and enabled understanding of disease mechanisms. It has also provided a platform for testing novel molecules for amelioration of pathophysiological phenotypes. This review through a perspective on CAs suggests a generic paradigm fromdiagnostics to therapeutic interventions for rare disorders in the context of heterogeneous Indian populations.

Structure based drug discovery for designing leads for the non-toxic metabolic targets in multi drug resistant Mycobacterium tuberculosis.

BACKGROUND: The problem of drug resistance and bacterial persistence in tuberculosis is a cause of global alarm. Although, the UN's Sustainable Development Goals for 2030 has targeted a Tb free world, the treatment gap exists and only a few new drug candidates are in the pipeline. In spite of large information from medicinal chemistry to 'omics' data, there has been a little effort from pharmaceutical companies to generate pipelines for the development of novel drug candidates against the multi drug resistant Mycobacterium tuberculosis. METHODS: In the present study, we describe an integrated methodology; utilizing systems level information to optimize ligand selection to lower the failure rates at the pre-clinical and clinical levels. In the present study, metabolic targets (Rv2763c, Rv3247c, Rv1094, Rv3607c, Rv3048c, Rv2965c, Rv2361c, Rv0865, Rv0321, Rv0098, Rv0390, Rv3588c, Rv2244, Rv2465c and Rv2607) in M. tuberculosis, identified using our previous Systems Biology and data-intensive genome level analysis, have been used to design potential lead molecules, which are likely to be non-toxic. Various in silico drug discovery tools have been utilized to generate small molecular leads for each of the 15 targets with available crystal structures. RESULTS: The present study resulted in identification of 20 novel lead molecules including 4 FDA approved drugs (droxidropa, tetroxoprim, domperidone and nemonapride) which can be further taken for drug repurposing. This comprehensive integrated methodology, with both experimental and in silico approaches, has the potential to not only tackle the MDR form of Mtb but also the most important persister population of the bacterium, with a potential to reduce the failures in the Tb drug discovery. CONCLUSION: We propose an integrated approach of systems and structural biology for identifying targets that address the high attrition rate issue in lead identification and drug development We expect that this system level analysis will be applicable for identification of drug candidates to other pathogenic organisms as well.

iCHRCloud: Web & Mobile based Child Health Imprints for Smart Healthcare.

Reducing child mortality with quality care is the prime-most concern of all nations. Thus in current IT era, our healthcare industry needs to focus on adapting information technology in healthcare services. Barring few preliminary attempts to digitalize basic hospital administrative and clinical functions, even today in India, child health and vaccination records are still maintained as paper-based records. Also, error in manually plotting the parameters in growth charts results in missed opportunities for early detection of growth disorders in children. To address these concerns, we present India's first hospital linked, affordable automated vaccination and real-time child's growth monitoring cloud based application- Integrated Child Health Record cloud (iCHRcloud). This application is based on HL7 protocol enabling integration with hospital's HIS/EMR system. It provides Java (Enterprise Service Bus and Hibernate) based web portal for doctors and mobile application for parents, enhancing doctor-parent engagement. It leverages highchart to automate chart preparation and provides access of data via Push Notification (GCM and APNS) to parents on iOS and Android mobile platforms. iCHRcloud has also been recognized as one of the best innovative solution in three nationwide challenges, 2016 in India. iCHRcloud offers a seamless, secure (256 bit HTTPS) and sustainable solution to reduce child mortality. Detail analysis on preliminary data of 16,490 child health records highlight the diversified need of various demographic regions. Thus, primary lesson would be to implement better validation strategies to fulfill the customize requisites of entire population. This paper presents first glimpse of data and power of the analytics in policy framework.

Erratum: Data Intensive Genome Level Analysis for Identifying Novel, Non-Toxic Drug Targets for Multi Drug Resistant Mycobacterium tuberculosis.

This corrects the article DOI: 10.1038/srep46595.

iNICU - Integrated Neonatal Care Unit: Capturing Neonatal Journey in an Intelligent Data Way.

Neonatal period represents first 28 days of life, which is the most vulnerable time for a child's survival especially for the preterm babies. High neonatal mortality is a prominent and persistent problem across the globe. Non-availability of trained staff and infrastructure are the major recognized hurdles in the quality care of these neonates. Hourly progress growth charts and reports are still maintained manually by nurses along with continuous calculation of drug dosage and nutrition as per the changing weight of the baby. iNICU (integrated Neonatology Intensive Care Unit) leverages Beaglebone and Intel Edison based IoT integration with biomedical devices in NICU i.e. monitor, ventilator and blood gas machine. iNICU is hosted on IBM Softlayer based cloud computing infrastructure and map NICU workflow in Java based responsive web application to provide translational research informatics support to the clinicians. iNICU captures real time vital parameters i.e. respiration rate, heart rate, lab data and PACS amounting for millions of data points per day per child. Stream of data is sent to Apache Kafka layer which stores the same in Apache Cassandra NoSQL. iNICU also captures clinical data like feed intake, urine output, and daily assessment of child in PostgreSQL database. It acts as first Big Data hub (of both structured and unstructured data) of neonates across India offering temporal (longitudinal) data of their stay in NICU and allow clinicians in evaluating efficacy of their interventions. iNICU leverages drools based clinical rule based engine and deep learning based big data analytical model coded in R and PMML. iNICU solution aims to improve care time, fills skill gap, enable remote monitoring of neonates in rural regions, assists in identifying the early onset of disease, and reduction in neonatal mortality.

Data Intensive Genome Level Analysis for Identifying Novel, Non-Toxic Drug Targets for Multi Drug Resistant Mycobacterium tuberculosis.

We report the construction of a novel Systems Biology based virtual drug discovery model for the prediction of non-toxic metabolic targets in Mycobacterium tuberculosis (Mtb). This is based on a data-intensive genome level analysis and the principle of conservation of the evolutionarily important genes. In the 1623 sequenced Mtb strains, 890 metabolic genes identified through a systems approach in Mtb were evaluated for non-synonymous mutations. The 33 genes showed none or one variation in the entire 1623 strains, including 1084 Russian MDR strains. These invariant targets were further evaluated for their experimental and in silico essentiality as well as availability of their crystal structure in Protein Data Bank (PDB). Along with this, targets for the common existing antibiotics and the new Tb drug candidates were also screened for their variation across 1623 strains of Mtb for understanding the drug resistance. We propose that the reduced set of these reported targets could be a more effective starting point for medicinal chemists in generating new chemical leads. This approach has the potential of fueling the dried up Tuberculosis (Tb) drug discovery pipeline.

The P4 Health Spectrum - A Predictive, Preventive, Personalized and Participatory Continuum for Promoting Healthspan.

Chronic diseases (i.e., noncommunicable diseases), mainly cardiovascular disease, cancer, respiratory diseases and type-2-diabetes, are now the leading cause of death, disability and diminished quality of life on the planet. Moreover, these diseases are also a major financial burden worldwide, significantly impacting the economy of many countries. Healthcare systems and medicine have progressively improved upon the ability to address infectious diseases and react to adverse health events through both surgical interventions and pharmacology; we have become efficient in delivering reactive care (i.e., initiating interventions once an individual is on the verge of or has actually suffered a negative health event). However, with slowly progressing and often 'silent' chronic diseases now being the main cause of illness, healthcare and medicine must evolve into a proactive system, moving away from a merely reactive approach to care. Minimal interactions among the specialists and limited information to the general practitioner and to the individual receiving care lead to a fragmented health approach, non-concerted prescriptions, a scattered follow-up and a suboptimal cost-effectiveness ratio. A new approach in medicine that is predictive, preventive, personalized and participatory, which we label here as "P4" holds great promise to reduce the burden of chronic diseases by harnessing technology and an increasingly better understanding of environment-biology interactions, evidence-based interventions and the underlying mechanisms of chronic diseases. In this concept paper, we propose a 'P4 Health Continuum' model as a framework to promote and facilitate multi-stakeholder collaboration with an orchestrated common language and an integrated care model to increase the healthspan.

Anti-HIV microRNA expression in a novel Indian cohort.

HIV-1 replication inside host cells is known to be regulated by various host factors. Host miRNAs, by virtue of its normal functioning, also regulate HIV-1 RNA expression by either directly targeting virus mRNAs or indirectly by regulating host proteins that HIV-1 uses for own replication. Therefore, it is highly possible that with differential miRNA expression, rate of disease progression will vary in HIV-1 infected individuals. In this study we have compared expression of a panel of 13 reported anti-HIV miRNAs in human PBMCs from long term non progressors (LTNPs), regular progressors and rapid progressors. We found that LTNPs have substantial lower expression of miR-382-5p that positively correlates with viral loads. Combinatorial regulation is highly probable in dictating differential disease progression as average expression of miR-382-5p and miR-155-5p can substantially distinguish LTNP individuals from regular progressors.

Prioritization of anticancer drugs against a cancer using genomic features of cancer cells: A step towards personalized medicine.

In this study, we investigated drug profile of 24 anticancer drugs tested against a large number of cell lines in order to understand the relation between drug resistance and altered genomic features of a cancer cell line. We detected frequent mutations, high expression and high copy number variations of certain genes in both drug resistant cell lines and sensitive cell lines. It was observed that a few drugs, like Panobinostat, are effective against almost all types of cell lines, whereas certain drugs are effective against only a limited type of cell lines. Tissue-specific preference of drugs was also seen where a drug is more effective against cell lines belonging to a specific tissue. Genomic features based models have been developed for each anticancer drug and achieved average correlation between predicted and actual growth inhibition of cell lines in the range of 0.43 to 0.78. We hope, our study will throw light in the field of personalized medicine, particularly in designing patient-specific anticancer drugs. In order to serve the scientific community, a webserver, CancerDP, has been developed for predicting priority/potency of an anticancer drug against a cancer cell line using its genomic features (http://crdd.osdd.net/raghava/cancerdp/).

A web-based resource for designing therapeutics against Ebola Virus.

In this study, we describe a web-based resource, developed for assisting the scientific community in designing an effective therapeutics against the Ebola virus. Firstly, we predicted and identified experimentally validated epitopes in each of the antigens/proteins of the five known ebolaviruses. Secondly, we generated all the possible overlapping 9mer peptides from the proteins of ebolaviruses. Thirdly, conserved peptides across all the five ebolaviruses (four human pathogenic species) with no identical sequence in the human proteome, based on 1000 Genomes project, were identified. Finally, we identified peptide or epitope-based vaccine candidates that could activate both the B- and T-cell arms of the immune system. In addition, we also identified efficacious siRNAs against the mRNA transcriptome (absent in human transcriptome) of all the five ebolaviruses. It was observed that three species can potentially be targeted by a single siRNA (19mer) and 75 siRNAs can potentially target at least two species. A web server, EbolaVCR, has been developed that incorporates all the above information and useful computational tools (http://crdd.osdd.net/oscadd/ebola/).

Metformin as a potential combination therapy with existing front-line antibiotics for Tuberculosis.

Tuberculosis (TB), the disease caused by Mycobacterium tuberculosis (Mtb) remains a global health concern. The evolution of various multi-drug resistant strains through genetic mutations or drug tolerant strains through bacterial persistence renders existing antibiotics ineffective. Hence there is need for the development of either new antibiotics or rationalizing approved drugs that can be utilized in combination with existing antibiotics as a therapeutic strategy. A comprehensive systems level mapping of metabolic complexity in Mtb revels a putative role of NDH-I in the formation of bacterial persistence under the influence of front-line antibiotics. Possibilities of targeting bacterial NDH-I with existing FDA approved drug for type-II diabetes, Metformin, along with existing front-line antibiotics is discussed and proposed as a potential combination therapy for TB.

Systems level mapping of metabolic complexity in Mycobacterium tuberculosis to identify high-value drug targets.

BACKGROUND: The effectiveness of current therapeutic regimens for Mycobacterium tuberculosis (Mtb) is diminished by the need for prolonged therapy and the rise of drug resistant/tolerant strains. This global health threat, despite decades of basic research and a wealth of legacy knowledge, is due to a lack of systems level understanding that can innovate the process of fast acting and high efficacy drug discovery. METHODS: The enhanced functional annotations of the Mtb genome, which were previously obtained through a crowd sourcing approach was used to reconstruct the metabolic network of Mtb in a bottom up manner. We represent this information by developing a novel Systems Biology Spindle Map of Metabolism (SBSM) and comprehend its static and dynamic structure using various computational approaches based on simulation and design. RESULTS: The reconstructed metabolism of Mtb encompasses 961 metabolites, involved in 1152 reactions catalyzed by 890 protein coding genes, organized into 50 pathways. By accounting for static and dynamic analysis of SBSM in Mtb we identified various critical proteins required for the growth and survival of bacteria. Further, we assessed the potential of these proteins as putative drug targets that are fast acting and less toxic. Further, we formulate a novel concept of metabolic persister genes (MPGs) and compared our predictions with published in vitro and in vivo experimental evidence. Through such analyses, we report for the first time that de novo biosynthesis of NAD may give rise to bacterial persistence in Mtb under conditions of metabolic stress induced by conventional anti-tuberculosis therapy. We propose such MPG's as potential combination of drug targets for existing antibiotics that can improve their efficacy and efficiency for drug tolerant bacteria. CONCLUSION: The systems level framework formulated by us to identify potential non-toxic drug targets and strategies to circumvent the issue of bacterial persistence can substantially aid in the process of TB drug discovery and translational research.

Genetic epidemiology of pharmacogenetic variations in CYP2C9, CYP4F2 and VKORC1 genes associated with warfarin dosage in the Indian population.

AIM: Warfarin, a widely used anticoagulant, exhibits large interindividual variability in dose requirements. CYP2C9 and VKORC1 polymorphisms in various ethnic groups have been extensively studied as genetic markers associated with variable drug response. However, allele frequencies of these variants have not been assessed in major ethnic groups in the Indian population. MATERIALS & METHODS: To study the functional variants known to affect warfarin dosing, we reanalyzed genotype microarray datasets generated as a part of genome-wide association studies as well as data from the Indian Genome Variation database. We examined data from 2680 individuals across 24 ethnically diverse Indian subpopulations. RESULTS: Allelic distribution of VKORC1 (-1639G>A) showed a greater degree of variation across Indian subpopulations, with frequencies as low as 6.5% in an out-group subpopulation to >70% in Tibeto-Burmans. Risk allele frequency of CYP4F2*3 (V433M) was higher in north Indians (0.30-0.44), as compared with other world populations, such as African-American (0.12), Caucasian (0.34) and Hispanic (0.23). TheVKORC1 variant (-1639A) was shown to be prevalent amongst Tibeto-Burmans, whereas CYP2C9 (R144C, I359L) and CYP4F2 (V433M) variants were observed in considerable variability amongst Indo-Europeans. The frequency of CYP2C9*3 (I359L) in north Indians was found to be higher than in most Asian populations. Furthermore, geographical distribution patterns of these variants in north India showed an increased trend of warfarin extensive metabolizers from the Himalayan to Gangetic region. Combined allele frequency (CYP2C9*3 and CYP4F2*3) data suggest that poor metabolizers varied in the range of 0.38-1.85% in Indo-Europeans. CONCLUSION: Based on genotypic distribution, the majority of the Indian subpopulation might require higher doses for stable anticoagulation, whereas careful assessment is required for Tibeto-Burmans who are expected to have intermediate dose requirement. This is the largest global genetic epidemiological study examining variants associated with warfarin that could potentially be valuable to clinicians in optimizing dosage strategies.

Open source software and web services for designing therapeutic molecules.

Despite the tremendous progress in the field of drug designing, discovering a new drug molecule is still a challenging task. Drug discovery and development is a costly, time consuming and complex process that requires millions of dollar and 10-15 years to bring new drug molecules in the market. This huge investment and long-term process are attributed to high failure rate, complexity of the problem and strict regulatory rules, in addition to other factors. Given the availability of 'big' data with ever improving computing power, it is now possible to model systems which is expected to provide time and cost effectiveness to drug discovery process. Computer Aided Drug Designing (CADD) has emerged as a fast alternative method to bring down the cost involved in discovering a new drug. In past, numerous computer programs have been developed across the globe to assist the researchers working in the field of drug discovery. Broadly, these programs can be classified in three categories, freeware, shareware and commercial software. In this review, we have described freeware or open-source software that are commonly used for designing therapeutic molecules. Major emphasis will be on software and web services in the field of chemo- or pharmaco-informatics that includes in silico tools used for computing molecular descriptors, inhibitors designing against drug targets, building QSAR models, and ADMET properties.

Social networks to biological networks: systems biology of Mycobacterium tuberculosis.

Contextualizing relevant information to construct a network that represents a given biological process presents a fundamental challenge in the network science of biology. The quality of network for the organism of interest is critically dependent on the extent of functional annotation of its genome. Mostly the automated annotation pipelines do not account for unstructured information present in volumes of literature and hence large fraction of genome remains poorly annotated. However, if used, this information could substantially enhance the functional annotation of a genome, aiding the development of a more comprehensive network. Mining unstructured information buried in volumes of literature often requires manual intervention to a great extent and thus becomes a bottleneck for most of the automated pipelines. In this review, we discuss the potential of scientific social networking as a solution for systematic manual mining of data. Focusing on Mycobacterium tuberculosis, as a case study, we discuss our open innovative approach for the functional annotation of its genome. Furthermore, we highlight the strength of such collated structured data in the context of drug target prediction based on systems level analysis of pathogen.