Biology subjects

Pathak, A. K.

Publications and source records attributed to Pathak, A. K..

17 recordsLinked to original sources

bioRxiv2025

Non-Invasive Diagnostic Evaluation of Urinary Exosomal Let-7c Cluster Expression in Bladder Cancer Using Machine Learning Approaches

BackgroundBladder cancer (BCa) diagnosis typically relies on invasive cystoscopy, which is effective but costly and uncomfortable. Urinary microRNAs (miRNAs), especially exosomal ones, are promising non-invasive biomarkers due to their stability in biological fluids and disease specificity. However, challenges such as population variability, methodological inconsistencies and normalization issues hinder their clinical translation, emphasizing the need for innovative approaches to enhance diagnostic performance. ObjectiveTo evaluate the diagnostic potential of urinary exosomal let-7c cluster (let-7c-5p, miR-99a-5p and miR-125b-5p) in BCa patients by integrating miRNA expression data with Machine Learning (ML) models. MethodsUrine samples were collected from 66 participants, including 50 BCa patients and 16 healthy controls (HC). Exosomal miRNAs were isolated and quantified using Quantitative Real-Time-Polymerase-Chain-Reaction (qRT-PCR). Statistical analysis and hypothesis tests were conducted to explore the nature and diagnostic relevance of individual biomarkers. A logistic regression classifier was applied to evaluate both the combined and differential diagnostic capabilities of the selected biomarkers. Accuracy, precision, recall and AU-ROC scores were used to assess model performance. Bioinformatics analysis was performed to identify pathways associated with the features prioritized by the ML models, ensuring their relevance to BCa. ResultsThe result revealed significant differentiation between BCa patients and HC, with miR-99a-5p (p=0.013, AU-ROC=0.71) and miR-125b-5p (p=0.047, AU-ROC=0.64) demonstrating reliable diagnostic performance (let-7c-5p showed weaker discrimination, AU-ROC=0.65, p>0.1). The logistic regression ML model achieved an accuracy of 80.0% (AU-ROC=0.86, recall=100%) in distinguishing cancer from HC and 53.3% (AU-ROC=0.63) when applied to miRNA-only grade classification. When clinical variables were integrated with miRNA expression, performance improved to 73.3% accuracy (AU-ROC=0.61) for high-versus low-grade differentiation. Across Ta-T2, miR-99a-5p displayed relatively better separation, whereas let-7c-5p and miR-125b-5p showed weak stage-related differences. The integration of bioinformatics analysis confirmed the biological relevance of these miRNAs in BCa-related pathways, including PI3K-Akt, p53, NF-{kappa}B and RAS/MAPK signaling, with hub genes such as TP53, MYC, EGFR, and CCND1 identified, further validating the diagnostic utility of the selected biomarkers. ConclusionUrinary let-7c cluster miRNAs demonstrate promising diagnostic potential when analyzed with ML models, offering a non-invasive alternative to conventional methods. These findings highlight the promise of ML-based approaches alongside molecular markers for advancing clinical diagnostics in BCa.

molecular biology↗

bioRxiv2024

miRNA Biomarkers in Prostate Cancer: Leveraging Machine Learning for Improved Diagnostic Accuracy

Prostate cancer (PCa) diagnosis often relies on prostate-specific antigen (PSA) testing, but its high false-positive rates often lead to unnecessary biopsies. MicroRNAs (miRNAs) have emerged as promising non-invasive biomarkers for cancer detection due to their stability in biological fluid and disease specificity. Despite their potential, the clinical translation of miRNAs as non-invasive cancer biomarkers is hindered by several challenges - population-based variability, environmental Factors, methodological Inconsistencies, lack of standardization, normalization Issues, and complexity of the biological System. These factors significantly impact the consistency of miRNA expression readouts, particularly in terms of Ct-values, across different studies, which in turn affects the determination of cutoff values that are crucial in a diagnostic setup. This preliminary study offers a pilot demonstration for integrating miRNA biomarker expression with machine learning (ML), which can help identify patterns and improve classification, potentially reducing the reliance on fixed cutoff values in certain contexts and pave the path to wider clinical translation. We analyzed the expression of key miRNAs (miR-21-5p, miR-221-3p, and miR-141-3p) in blood samples from patients with PCa and benign prostatic hyperplasia (BPH). Utilizing a Random Forest classifier, we achieved an accuracy of 77.42%, a precision of 86.21%, a recall of 71.43%, and an AUC-ROC score of 0.78. The application of ML enabled us to leverage complex features, such as combinations and ratios of miRNA expression data, which enhanced the robustness and reliability of the diagnostic model. Additionally, bioinformatics analysis of the preferential features identified by the ML model confirmed the biological relevance of these miRNAs in PCa-related pathways, further supporting their potential as clinical biomarkers. In the future, ML is poised to significantly enhance diagnostic performance compared to traditional linear analyses of a limited set of biomarkers. While our study did not explore multiple populations or the effects of methodological variables, it highlights the potential of ML by demonstrating improved accuracy and eliminating the need for cutoff values. This capability could broaden the applicability of miRNA-based diagnostics, making them more reliable and actionable in clinical settings. However, to fully realize this potential, further validation with larger and more diverse cohorts is essential. Overall, this study lays the groundwork for utilizing ML-enhanced miRNA panels as powerful tools for the early and non-invasive diagnosis of PCa in future clinical practice. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/618146v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@2eddd9org.highwire.dtl.DTLVardef@e6d6c8org.highwire.dtl.DTLVardef@11eea0forg.highwire.dtl.DTLVardef@989c15_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2024

Resolving out of Africa event for Papua New Guinean population using neural network

The demographic history of the Papua New Guinean population is a subject of significant interest due to its early settlement in New Guinea, at least 50 thousand years ago, and its relative isolation compared to other out of Africa populations. This isolation, combined with substantial Denisovan ancestry, contributes to the unique genetic makeup of the Papua New Guinean population. Previous research suggested the possibility of admixture with an early diverged modern human population, but the extent of this contribution remains debated. This study re-examines the demographic history of the Papua New Guinean population using newly published samples and advanced analytical methods. Our findings demonstrate that the observed shifts in relative cross coalescent rate curves are unlikely to result from technical artefacts or contributions from an earlier out of Africa population. Instead, they are likely due to a significant bottleneck and slower population growth rate within the Papua New Guinean population. Our analysis positions the Papua New Guinean population as a sister group to other Asian populations, challenging the notion of Papua New Guinean as an outgroup to both European and Asian populations. This study provides new insights into the complex demographic history of the Papua New Guinean population and underscores the importance of considering population-specific demographic events in interpreting relative cross coalescent rate curves.

evolutionary biology↗

bioRxiv2024

Thermal variation influences the transcriptome of the major malaria vector Anopheles stephensi.

The abundance and distribution of ectotherms is being shaped at unprecedented scales by human-induced climate change. Unraveling molecular adaptations to thermal variation is prescient. Vector-borne diseases are of particular concern and although mosquitoes are responsible for most of the 621 million cases annually, their response to temperature is not well characterized. We describe the transcriptome of the major vector for human malaria, Anopheles stephensi maintained under a diurnal temperature regime (DTR) totaling 9{degrees}C and daily means of 20{degrees}C, 24{degrees}C and 28{degrees}C respectively. Gene expression profiles were examined in the midguts and carcasses of adult mosquitoes from one to 19 days post-blood meal. Differences in temporal expression profiles at each temperature revealed a total of 3,106 and 3,590 genes in the carcasses and midguts respectively and analyzed further to make three inferences. First, we identified genes with shared temporal expression profiles across all three temperatures, suggesting their indispensability to mosquito life history. Second, tolerance to 20 DTR 9{degrees}C and 28 DTR 9{degrees}C was associated with a larger and more diverse repertoire of gene products compared to 24 DTR 9{degrees}C; assuming physiological costs scale accordingly, this finding could explain the fitness trade-offs underlying the unimodal effect of temperature. Third, the long duration of our study revealed two general trends in gene expression, representative of a coordinated, tissue- and temperature-specific response to blood meal digestion, managing oxidative stress, and reproduction. Our results suggest how temperature could regulate the mosquitos capacity to transmit Plasmodium parasites and offer a reference point for understanding ectotherm adaptation.

molecular biology↗

bioRxiv2024

Pervasive ancestry bias in variant effect predictors

Variant effect predictors (VEPs) - computational tools that assess the potential impact of genetic variants - have become increasingly vital for clinical variant interpretation. Currently, most VEPs used in variant prioritisation have been trained on datasets of clinically curated or population-derived variants. These datasets, however, disproportionately represent individuals of European descent. We hypothesised that this bias may lead to unequal VEP performance across different populations. To test this, we evaluated the scoring patterns of 52 VEPs for missense variants across 14 ancestry groups. We observe striking disparities: some VEPs predict a markedly higher proportion of damaging variants in underrepresented populations, such as those of Malay descent, compared to individuals of European ancestry. In contrast, VEPs that do not rely on clinical or population data predict more consistent pathogenicity burdens across ancestry groups. Moreover, we could closely link these discrepancies across methods to biases in training data. Our findings underscore the urgent need to adopt tools that minimise ancestry bias to ensure fairer and more accurate variant effect prediction and genetic diagnoses for all populations.

bioinformatics↗

bioRxiv2023

gQuant: A Robust and Generalizable Algorithm for Identifying Normalizer Genes in qRT-PCR Data: A Case Study on Urinary Exosomal miRNAs

The emergent role of nucleic acid-based biomarkers, such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and messenger RNAs (mRNAs), is becoming increasingly prominent in the realms of disease diagnostics and risk assessment. Quantitative reverse transcription PCR (qRT-PCR) is the primary analytical method for quantitative measurement of biomarkers. Yet, the relative infancy of non-coding RNAs (ncRNAs) recognition as biomarkers poses a challenge due to the absence of a consensus on a universally accepted normalizer gene, which is pivotal for accurate quantification. Current tools for selecting normalizer genes in qRT-PCR are fraught with limitations, including inadequate handling of null values, reliance on elementary statistical tools, use of a biased integrated approach, outlier sensitivity, and suboptimal graphical user interface for data visualization. These deficiencies underscore the necessity for a more nuanced and algorithmically balanced tool tailored to handle qRT-PCR datasets and facilitate the discernment of the most appropriate normalizer gene for specific datasets. Addressing the identified challenges, we have developed gQuant, a tool crafted to address the limitations present in existing methods. In gQuant we employed voting classifiers as an ensemble technique that combines predictions from multiple statistical methods to make more accurate rankings than any individual statistical measures. The tools efficacy was substantiated through rigorous validation against datasets from the Gene Expression Omnibus (GEO) database and corroborated with experimental data derived from urinary exosomal miRNAs. Comparative analysis with existing tools revealed that their integrated methodologies could skew the ranking of normalizer genes, whereas gQuant consistently yielded rankings characterized by lower standard deviation, reduced covariance, and enhanced kernel density estimation (KDE) values. Given gQuants promising performance, normalizer gene identification will be greatly improved, improving the precision of gene expression quantification in a variety of research scenarios.

molecular biology↗

bioRxiv2022

Blood meals from 'dead-end' vertebrate hosts enhance transmission potential of malaria-infected mosquitoes

Ingestion of an additional blood meal(s) by mosquitoes can accelerate parasite migration to the salivary glands in infected mosquitoes. Most studies, however, offer blood from the same vertebrate host species as the original challenge (for e.g., human for primary and additional blood meals). Here, we show a second blood meal from bovine and canine hosts can also enhance sporozoite migration in Anopheles stephensi mosquitoes infected with the human- and rodent-restricted Plasmodium falciparum and P. berghei respectively. The extrinsic incubation period (time to sporozoite appearance in salivary glands) showed more consistent reductions with blood from human and bovine donors than canine blood, although the latters effect may be confounded by the toxicity, albeit non-specific, associated with the anticoagulant used to collect whole blood from donors. The complex patterns of enhancement highlight the limitations of a laboratory system but are nonetheless reminiscent of parasite host-specificity and mosquito adaptations, and the genetic predisposition of An. stephensi for bovine blood. We suggest that in natural settings, a blood meal from any vertebrate host could accentuate the risk of human infections by P. falciparum, although our observations should also be applicable to other species of Plasmodium and indeed other vector-borne diseases such as arboviruses for instance. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=185 HEIGHT=200 SRC="FIGDIR/small/521872v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@10ec4d7org.highwire.dtl.DTLVardef@9d0d21org.highwire.dtl.DTLVardef@c92628org.highwire.dtl.DTLVardef@1e83587_HPS_FORMAT_FIGEXP M_FIG C_FIG HIGHLIGHTSO_LIPlasmodium falciparum and P. berghei cause malaria in humans and rodents, respectively. C_LIO_LIThey are vectored by Anopheles species that can also imbibe blood from other vertebrate species. C_LIO_LIParasite development is enhanced after another blood meal, usually from the same host (e.g., human). C_LIO_LIHere, bovine, and canine blood also enhanced development of Plasmodium species in An. stephensi. C_LIO_LIIn natural settings, blood from any host could enhance development of vector transmitted parasites. C_LI

ecology↗

bioRxiv2021

Gastrointestinal Helminths Increase Bordetella bronchiseptica Shedding and Host Variation in Supershedding

Multi-species infections have been suggested to facilitate pathogen transmission and the emergence of supershedding events. However, how the interactions between co-infecting pathogens affect their dynamics of shedding, and how this is related to the host immune response, remains largely unclear. We used laboratory experiments and a modeling approach to examine temporal variations in the shedding of the respiratory bacterium Bordetella bronchiseptica in rabbits challenged with one or two gastrointestinal helminth species. Experimental data showed that rabbits co-infected with one or both helminths shed significantly more B. bronchiseptica by direct contact with an agar petri dish than rabbits with bacteria alone. There was also evidence of synergistic effects when both helminth species were present (triple infection). Co-infected hosts generated supershedding events of higher intensity and more frequently than hosts with no helminths. Model simulations revealed that the two helminths affected the relative contribution of neutrophils and specific IgA and IgG to B. bronchiseptica neutralization in the respiratory tract. In turn, these changes led to differences in the magnitude and duration of shedding among the various types of infection. However, the rapid variation in individual shedding, including supershedding, could not be explained by the interactions between infection and immune response at the scale of analysis that we used. We suggest that local rapid changes at the level of respiratory tissue probably played a more important role. This study provides novel insight into the role of helminths to the dynamics of respiratory infections and offers a quantitative explanation for the differences generated by two helminth species. Author summaryThe dynamics of bacterial infections can be facilitated by the presence of gastrointestinal helminths. Understanding the immunological processes that underline the pathogen-parasite interactions, and how they affect the dynamics of shedding, is important particularly for infections where control of the parasite maybe more effective than trying to reduce the bacterial infection. In this study, we examined the role of two gastrointestinal helminth species on the shedding of the respiratory Bordetella bronchiseptica using laboratory experiments of rabbits together with mathematical modeling. Hosts infected with helminths shed significantly more bacteria with evidence of supershedding, than hosts with only B. bronchiseptica. Simulations showed that by altering the relative contribution of neutrophils, specific IgA and IgG, helminths affected the control of bacterial infection in the respiratory tract. These interactions altered the intensity and duration of bacterial shedding, including the frequency and intensity of supershedding events. However, at the host level our model did not explain the rapid variation in shedding observed, suggesting that local processes in the respiratory tissue are critical for the prediction of the daily shed in the environment. This study advances our understanding of the dynamics of shedding in bacteria-helminth co-infections and provides insight that can be used to control disease spread.

ecology↗

bioRxiv2020

Intra-host variability in global SARS-CoV-2 genomes as signatures of RNA editing: implications in viral and host response outcomes

During the course of the COVID-19 pandemic, large-scale genome sequencing of SARS-CoV-2 has been useful in tracking its spread and in identifying Variants Of Concern (VOC). Besides, viral and host factors could contribute to variability within a host that can be captured in next-generation sequencing reads as intra-host Single Nucleotide Variations (iSNVs). Analysing 1, 347 samples collected till June 2020, we recorded 18, 146 iSNV sites throughout the SARS-CoV-2 genome. Both, mutations in RdRp as well as APOBEC and ADAR mediated RNA editing seem to contribute to the differential prevalence of iSNVs in hosts. Noteworthy, 41% of all unique iSNVs were reported as SNVs by 30th September 2020 in samples submitted to GISAID, which increased to [~]80% by 30th June 2021. Following this, analysis of another set of 1, 798 samples sequenced in India between November 2020 and May 2021 revealed that majority of the Delta (B.1.617.2) and Kappa (B.1.617.1) variations appeared as iSNVs before getting fixed in the population. We also observe hyper-editing events at functionally critical residues in Spike protein that could alter the antigenicity and may contribute to immune escape. Thus, tracking and functional annotation of iSNVs in ongoing genome surveillance programs could be important for early identification of potential variants of concern and actionable interventions. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=177 SRC="FIGDIR/small/417519v3_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@12b6ac2org.highwire.dtl.DTLVardef@16df897org.highwire.dtl.DTLVardef@dbbec2org.highwire.dtl.DTLVardef@c8de14_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2020

Fine-scale population structure reveals high genetic heterogeneity of the Kuwaiti population in the Arabian Peninsula

Recent studies have showed the diverse genetic architecture of the highly consanguineous populations inhabiting the Arabian Peninsula. Consanguinity coupled with heterogeneity is complex and makes it difficult to understand the bases of population-specific genetic diseases in the region. Therefore, comprehensive genetic characterization of the populations at the finest scale is warranted. Here, we revisit the genetic structure of the Kuwait population by analyzing genome-wide single nucleotide polymorphisms data from 583 Kuwaiti individuals sorted into three subgroups. We envisage a diverse demographic genetic history among the three subgroups based on drift and allelic sharing with modern and ancient individuals. Furthermore, our comprehensive haplotype-based analyses disclose a high genetic heterogeneity among the Kuwaiti populations. We infer the major sources of ancestry within the newly defined groups; one with an obvious predominance of sub-Saharan/Western Africa mostly comprising Kuwait-B individuals, and other with West Eurasia including Kuwait-P and Kuwait-S individuals. Overall, our results recapitulate the historical population movements and reaffirm the genetic imprints of the legacy of continental trading in the region. Such deciphering of fine-scale population structure and their regional genetic heterogeneity would provide clues to the uncharted areas of disease-gene discovery and related associations in populations inhabiting the Arabian Peninsula.

genomics↗

bioRxiv2020

Human PRE-PIK3C2B exhibits long-range intra- and inter-chromosomal interactions with genomic regions enriched in repressive marks.

Human PRE-PIK3C2B is a dual nature polycomb response element that interacts with both polycomb as well as trithorax members. In the current study, using 4C-Seq (Capturing Circular Chromosomal Conformation-Sequencing), we identified long-range chromatin interactions associated with PRE-PIK3C2B and validated them with 3C-PCR. We identified both intra-as well as inter-chromosomal interactions, a large proportion of which were found to be closely distributed around transcriptional start sites (TSS). A significant number of interactions were also found to be associated with heterochromatic regions. Meta-analysis of ENCODE ChIP-Seq data identified an overall enrichment of YY1, CTCF as well as histone modification such as H3K4me3 and H3K27me marks in different cell lines. Almost 90% interactions were derived from either intronic or intergenic regions. among which large proportions of intronic interactors were either unique sequences or LINE/SINE derived. In case of intergenic interactions, majority of the interaction were associated with LINE/SINE repeats. We further found that genes proximal to the interactor sequences were co-expressed, they showed reduced expression. To the best of our knowledge this is one of the early demonstrations of long-range interaction of PRE sequences in human genome.

genetics↗

bioRxiv2020

Shortlisting genes important in seed maturation by principal component analysis of gene expression data

Transcriptome data are widely used for functional analysis of genes. De-novo assembly of transcriptome gives a large number of unigenes. A large proportion of them remain unannotated. Efficient computational methods are required for identifying genes and modeling those for regulatory and functional roles. Principal component analysis (PCA) was used in a novel approach to shortlist genes, independently of annotation in genome expression data, taking seed development in Arabidopsis thaliana as a representative case. PCA was applied to published genome expression data from four lines of Arabidopsis, mutated in seed development. The PC separating all the developmental stages between a mutant and its respective wild type was selected for shortlisting genes as functionally more important. The shortlisted genes identified by PCA belong to a number of biological functions. The genes reported to give sensitivity to desiccation were identified in PCA analysis also in desiccation intolerant lines only. With respect to the network of 98 genes targeted by ABI3, a higher number of genes was identified as important in the mutants abi 3-5, fus 3-3 andlec 1-1 in comparison to abi 3-1. Ontological analysis and comparison with earlier studies suggest that PCA of genome expression data is useful for shortlisting functionally important genes.

bioinformatics↗

bioRxiv2019

Invariant Genes in Human Genomes

With large-scale human genome and exome sequencing, a lot of focus has gone in studying variations present in genomes and their associations to various diseases. Since major emphasis has been put on their variations, less focus has been given to invariant genes in the population. Here we present 60,706 genomes from the ExAC database to identify population specific invariant genes. Out of 1,336 total genes drawn from various population specific invariant genes, 423 were identified to be mostly (allele frequency less than 0.001) invariant across different populations. 46 of these invariant genes showed absolute invariance in all populations. Most of these common invariant genes have homologs in primates, rodents and placental mammals while 8 of them were unique to human genome and 3 genes still had unknown functions. Surprisingly, a majority were found to be X-linked and around 50% of these genes were not expressed in any tissues. The functional analysis showed that the invariant genes are not only involved in fundamental functions like transcription and translation but also in various developmental processes. The variations in many of these invariant genes were found to be associated with cancer, developmental diseases and dominant genetic disorders.

genomics↗

bioRxiv2019

Field relevant variation in ambient temperature modifies the density-dependent establishment of Plasmodium falciparum in mosquitoes: implications for the infectious reservoir and beyond?

The relationship between Plasmodium falciparum gametocyte density and infections in mosquitoes is central to understanding the rates of transmission with important implications for control. Here, we determined whether field relevant variation in environmental temperature could also modulate this relationship. Anopheles stephensi were challenged with three densities of P. falciparum gametocytes spanning a [~]10-fold gradient, and housed under diurnal/daily temperature range (\"DTR\") of 9{degrees}C around means of 20{degrees}C, 24{degrees}C and 28{degrees}C. At the peak stages of infection for each temperature, the proportion of mosquitoes infected with oocysts in the midguts or infectious with sporozoites in the salivary glands were measured (referred to collectively as vector competence hereon), in addition to oocyst intensities from infected midguts. While vector competence was similar at 20 DTR 9{degrees}C and 24 DTR 9{degrees}C, the proportion of mosquitoes infected and subsequently infectious were also comparable, with evidence, surprisingly, for higher vector competence in mosquitoes challenged with intermediate gametocyte densities. For the same gametocyte densities however, severe reduction in the proportion of infectious mosquitoes was accompanied by a significant decline in vector competence at 28 DTR 9{degrees}C, although density per se showed a positive and linear effect at this temperature. Unlike vector competence, oocyst intensities decreased with increasing temperatures with a predominantly positive and linear association with gametocyte density, especially at 28 DTR 9{degrees}C. Oocyst intensities across individual infected midguts suggested temperature-specific differences in mosquito susceptibility/resistance: at 20 DTR 9{degrees}C and 24 DTR 9{degrees}C, dispersion (aggregation) increased in a density-dependent manner but not at 28 DTR 9{degrees}C where the distributions were consistently random. Limitations notwithstanding, our results have manifold implications in, for instance, how variation in temperature could modify seasonal dynamics of infectious reservoirs and transmission and the contribution of high-/patent- and low-density/sub-patent carriers, to suggestions for design and deployment of transmission-blocking vaccines/drugs, but with a cautionary note suggesting how low efficacy could lead to transmission enhancement in certain environments.

ecology↗

bioRxiv2018

The genetic legacy of continental scale admixture in Indian Austroasiatic speakers

Surrounded by speakers of Indo-European, Dravidian and Tibeto-Burman languages, around 11 million Munda (a branch of Austroasiatic language family) speakers live in the densely populated and genetically diverse South Asia. Their genetic makeup holds components characteristic of South Asians as well as Southeast Asians. The admixture time between these components has been previously estimated on the basis of archaeology, linguistics and uniparental markers. Using genome-wide genotype data of 102 Munda speakers and contextual data from South and Southeast Asia, we retrieved admixture dates between 2000 - 3800 years ago for different populations of Munda. The best modern proxies for the source populations for the admixture with proportions 0.78/0.22 are Lao people from Laos and Dravidian speakers from Kerala in India, while the South Asian population(s), with whom the incoming Southeast Asians intermixed, had a smaller proportion of West Eurasian component than contemporary proxies. Somewhat surprisingly Malaysian Peninsular tribes rather than the geographically closer Austroasiatic languages speakers like Vietnamese and Cambodians show highest sharing of IBD segments with the Munda. In addition, we affirmed that the grouping of the Munda speakers into North and South Munda based on linguistics is in concordance with genome-wide data.

genetics↗

bioRxiv2018

Spatio-Temporal Network Dynamics of Genes Underlying Schizophrenia

Schizophrenia (SZ) is a debilitating mental illness with multigenic etiology and high heritability. Despite extensive genetic studies the molecular etiology stays enigmatic. A systems biology study had suggested a protein-protein interaction (PPI) network for SZ with 504 novel PPIs amongst which several genes happen to be drug targets of existing FDA approved drugs. Although the PPI network presented all possible pairs of interactions (known and novel), it lacks a spatio-temporal information. The onset of psychiatric disorders is predominantly in adolescent and young adult stages, often accompanied by subtle structural abnormalities in multiple regions of the brain. Hence, there is a need to redefine the generic PPI network as a function of time (developmental stages) and space (brain regions). The availability of BrainSpan atlas data allowed us to redefine the SZ interactome as a function of space and time. The absence of non-synonymous variants in centenarians and non-psychiatric ExAC database allowed us to identify the variants of criticality. The expression of candidate genes in different brain regions and during developmental stages, responsible for cognitive processes as well as the onset of disease were studied. A subset of novel interactors detected in the network was further validated using gene-expression data of psychiatric postmortem brains. From the long list of drug targets proposed from the interactome study and based on the microarray gene-expression results, we have shortlisted a probable subset of 10 drug targets (targeted by 34 FDA approved drugs) coalescing into 81 biological pathways, that could be potentially repurposed for neuropsychiatric disorders.

bioinformatics↗

bioRxiv2017

Like Sugar in Milk: Reconstructing the genetic history of the Parsi population

BackgroundThe Parsis, one of the smallest religious community in the world, reside in South Asia. Previous genetic studies on them, although based on low resolution markers, reported both Iranian and Indian ancestries. To understand the population structure and demographic history of this group in more detail, we analyzed Indian and Pakistani Parsi populations using high-resolution autosomal and uniparental (Y-chromosomal and mitochondrial DNA) markers. Additionally, we also assayed 108 mitochondrial DNA markers among 21 ancient Parsi DNA samples excavated from Sanjan, in present day Gujarat, the place of their original settlement in India.\n\nResultsOur extensive analyses indicated that among present-day populations, the Parsis are genetically closest to Middle Eastern (Iranian and the Caucasus) populations rather than their South Asian neighbors. They also share the highest number of haplotypes with present-day Iranians and we estimate that the admixture of the Parsis with Indian populations occurred [~]1,200 years ago. Enriched homozygosity in the Parsi reflects their recent isolation and inbreeding. We also observed 48% South-Asian-specific mitochondrial lineages among the ancient samples, which might have resulted from the assimilation of local females during the initial settlement.\n\nConclusionsWe show that the Parsis are genetically closest to the Neolithic Iranians, followed by present-day Middle Eastern populations rather than those in South Asia and provide evidence of sex-specific admixture from South Asians to the Parsis. Our results are consistent with the historically-recorded migration of the Parsi populations to South Asia in the 7thcentury and in agreement with their assimilation into the Indian sub-continents population and cultural milieu \"like sugar in milk\". Moreover, in a wider context our results suggest a major demographic transition in West Asia due to Islamic-conquest.

evolutionary biology↗