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Kumar, L.

Publications and source records attributed to Kumar, L..

At least 19 recordsLinked to original sources
bioRxiv2026

A new set of DNA methylation variants in the human genome show predominant tissue specificity and sensitivity to reprogramming with a potential for disease susceptibility.

Analyses of 3,370 normal human tissues of ectodermal, endodermal and mesodermal origins identified 12,587 regions averaging [~]585 bp with significant differences in DNA methylation levels within identical tissues. These methylation variants (MeVars) occurred in 8,037 genes enriched in neurological disorders and cancers of which, majority were tissue-specific rather than being systemic. This somatic variation was reduced by reprogramming in vitro into iPSCs and in vivo during spermatogenesis. Analysis of prefrontal cortices showed a higher incidence of MeVars in the candidate genes in controls than schizophrenia patients wherein a subset showed significantly altered transcript levels. Similar effects were observed for oral tissues and skin fibroblast cells. MeVars showed significant association with SINE1, simple and low complexity repeats, H3K27me3, H3k9me3 and H3K4me1 modifications and EZH2, SUZ12 and REST binding sites. Collectively, MeVars have postzygotic origins with an ability to reset during reprogramming, adding a new dimension in the form of epigenetic diversity and its relevance to disease susceptibility in humans.

bioinformatics↗
bioRxiv2026

CDK1 facilitates RAD51-mediated DNA repair to protect dictyate stage arrested oocytes from genotoxic stress

Oocytes arrested at the dictyate stage of meiosis I must maintain genomic integrity for prolonged periods to preserve female fertility. During this extended arrest, DNA lesions arising from endogenous and exogenous sources threaten oocyte survival, yet the molecular mechanisms coordinating DNA repair in dormant oocytes remain poorly understood. Here, we identify cyclin-dependent kinase 1 (CDK1) as a critical regulator of the oocyte DNA damage response and homologous recombination (HR) repair under genotoxic stress. Using cisplatin-induced DNA damage models in fetal goat ovaries and neonatal mouse ovaries, we investigated repair mechanisms operating within the ovarian reserve. Label-free proteomic profiling revealed significant enrichment of DNA damage response pathways following cisplatin exposure, with CDK1 emerging as one of the most prominently upregulated kinases. Pharmacological inhibition of CDK1 had little effect on follicle survival under physiological conditions but aggravated oocyte and follicle loss following DNA damage, indicating a stress-dependent role for CDK1 in preserving ovarian follicle pool integrity. Mechanistically, DNA damage activated a Chk2-dependent signaling pathway that promoted p63 phosphorylation and altered the WEE1-CDK1 regulatory axis, resulting in reduced inhibitory CDK1 phosphorylation (Thr14/Tyr15) and increased activating phosphorylation (Thr161). Activated CDK1 was associated with enhanced RAD51 phosphorylation and accumulation at DNA damage foci, supporting homologous recombination (HR)-mediated repair in dictyate-arrested oocytes. In contrast, CDK1 inhibition reduced phospho-RAD51 levels, impaired RAD51 localization, increased persistent {gamma}H2AX accumulation, and elevated oocyte apoptosis. Notably, suppression of CDK1 was accompanied by increased expression of the non-homologous end joining (NHEJ) marker Ku80 and the nucleotide excision repair (NER) factor XPA, suggesting increased engagement of alternative DNA repair pathways. Furthermore, inhibition of Chk2 abolished the DNA damage-associated CDK1 activation signature and restored WEE1 expression, supporting a model in which CDK1 functions downstream of Chk2 signaling during the oocyte DNA damage response. Collectively, our findings identify a previously unrecognized Chk2-CDK1-RAD51 signaling axis that coordinates homologous recombination repair in dormant oocytes and safeguards ovarian follicular pool integrity under genotoxic stress. These findings provide new mechanistic insight into how dictyate-arrested oocytes maintain genome stability during prolonged meiotic arrest.

Cell Biology↗
bioRxiv2026

Gut microbiota-derived indole-3 acetic acid mitigates key hallmarks of metabolic dysfunction-associated steatotic liver disease (MASLD) in an NLRP3-dependent manner in in vitro and diet-induced mouse models

BackgroundMetabolic-dysfunction associated steatotic liver disease(MASLD) is a global epidemic affecting >30 % global population with no approved therapies till date. Recent reports suggest that activation of NLRP3 inflammasome may be involved in the pathogenesis of MASLD. We, therefore, aimed to identify small molecule inhibitor(s) of NLRP3 inflammasome as a potential therapeutic strategy to manage MASLD and validate their efficacy using in vitro and in vivo models of MASLD. MethodologyWe screened an in-house library of natural products using an in vitro phenotypic assay and identified a gut microbiota derived metabolite of dietary Tryptophan; indole-3 acetic acid (I3A) for its ability to inhibit the levels of IL-1{beta} and IL-18, which are elevated as a result of activation of NLRP3 inflammasome in differentiated THP1 cells. Subsequently, we carried out several in vitro and in vivo studies to confirm the mechanism of action of I3A and its ability to mitigate the key hallmarks of MASLD ResultsOur in vitro data suggest that I3A is an inhibitor of NLRP3 inflammasome. I3A was also found to improve the blood glucose level, plasma lipid profile, hepatic fat, and liver function in high-fat-high-fructose diet induced model of MASLD using C57BL/6 mice. ConclusionOur results show that I3A, which has been previously reported to be a gut microbiota-derived metabolite of dietary tryptophan, mitigates the key hallmarks of MASLD in an NLRP3 dependent manner. A dedicated structure-activity-relationship (SAR) study around the I3A chemotype may be carried out in future to identify novel NLRP3 inhibitors with desirable pharmacological profile.

pharmacology and toxicology↗
bioRxiv2025

Urinary Exosomal miRNA Profiling Reveals Sensitive Non-Invasive Diagnosis of Bladder Cancer

UnstructuredmiRNAs represent a transformative advancement in both research and clinical management of urinary bladder cancer (UBC), emerging as clinically significant molecular targets with the potential to revolutionize existing diagnostic standards. This study exploited the robust stability of miRNAs and profiled urinary miRNAs in UBC patients and controls through miRNA sequencing, revealing an expanded and altered miRNA repertoire in cancer samples. Validation in an independent cohort revealed fold changes for miR-6724-5p, miR-1273h-5p, miR-7704, miR-200-5p, and miR-10400-5p ranged from [~]46 to [~]2,777 (p < 0.01). Stage-specific analyses highlighted dynamic miRNA expression linked to tumor progression. Diagnostic evaluation identified an optimal three-miRNA panel comprising miR-6724-5p, miR-10400-5p, and miR-7704, achieving diagnostic accuracies (AUC > 70%) and high sensitivity (>90%). These data demonstrate the potential of urinary mature miRNAs as robust biomarkers for non-invasive detection and monitoring of UBC in early stages, supporting their translation into clinical diagnostic assays pending larger prospective studies. StructuredO_ST_ABSBackgroundC_ST_ABSUrinary microRNAs (miRNAs) are promising candidates due to their molecular stability and disease-specific expression. miRNAs represent a transformative advancement in both research and clinical management of urinary bladder cancer (UBC), emerging as actionable molecular targets with the potential to revolutionize existing diagnostic standards. MethodsSmall RNA sequencing was performed on urine samples from UBC patients and healthy controls to profile miRNA expression. Mature miRNAs were prioritized by exclusion of precursor forms using computational filtering. Validation by quantitative RT-PCR was conducted on an independent, age-matched cohort (n = 45). Diagnostic potential of candidate miRNAs was assessed by receiver operating characteristic (ROC) curves and area under the curve (AUC) analyses. ResultsSequencing identified 865 known and 11 novel miRNAs, with UBC samples showing greater miRNA diversity (708 known miRNAs) compared to controls (540). Ten miRNAs were significantly dysregulated in UBC (p < 0.05). Validation revealed fold changes for miR-6724-5p, miR-1273h-5p, miR-7704, miR-200-5p, and miR-10400-5p ranged from [~]46 to [~]2,777 (p < 0.01). Stage-specific analyses highlighted dynamic miRNA expression linked to tumor progression. A three-miRNA panel (miR-6724-5p, miR-10400-5p, miR-7704) demonstrated superior diagnostic performance (AUC > 70%, sensitivity > 90%). ConclusionMature urinary miRNAs exhibit reproducible, stage-specific dysregulation in UBC and a combinatorial panel offers sensitive, specific non-invasive detection. These findings warrant further multicenter validation for clinical application.

cancer biology↗
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↗
bioRxiv2025

Strain-level differences in Gardnerella urinary tract persistence and pathogenesis are consistent with comparative phylogenomic analyses.

BackgroundGardnerella is a genus of gram variable anaerobic bacteria that is commonly present in the female urogenital tract, especially during bacterial vaginosis (BV). BV is linked with increased risk of urinary tract infections (UTI) and Gardnerella has been frequently detected in urine collected directly from the bladder. Understanding the contribution of Gardnerella to urogenital pathogenesis has been complicated by its genetic heterogeneity and a shortage of data from in vivo models. Recently, a clinical isolate of Gardnerella displayed covert pathogenesis in a mouse urinary tract inoculation model, triggering urothelial exfoliation and promoting UTI by uropathogenic E. coli. Data from clinical studies suggests differential association of Gardnerella phylogenetic clades with BV or urogenital infections. In vitro data has demonstrated heterogeneity in the presence and expression of putative virulence determinants between Gardnerella strains. This study was designed to compare diverse Gardnerella strains in vivo to identify genomic variation associated with urinary tract persistence and pathogenesis. MethodsEighteen Gardnerella clinical isolates from each of the four main phylogenetic clades were individually inoculated transurethrally into female C57BL/6 mice. Bacteriuria was monitored by quantitative culturing of Gardnerella in urine. Pathologic features were assessed by immunofluorescent and histological staining of bladder tissues. Pan-genome phylogenetic analyses were performed on the 18 Gardnerella isolates used for mouse infections to identify accessory genes that were associated with observable in vivo phenotypes, including long and short-term persistence, urothelial exfoliation and bladder edema. Genes that were significantly associated to phenotype were then matched against a pangenome analysis of 291 publicly available Gardnerella genomes to determine the conservation of these putative colonization and virulence factors across the genus. ResultsGardnerella strains displayed clear differences in persistence and pathogenesis in the mouse bladder that were congruent with phylogeny. Clade 2 strains were more persistent in the urinary tract whereas strains from the other three clades either caused transient bacteriuria or were undetectable. Strains from clade 2 and 4 induced urothelial exfoliation while edema was triggered by strains from clades 2, 3 and 4. Pangenome analyses revealed 45 genes that were associated with in vivo persistence and pathogenicity. Among the wider 291 publicly available genomes, clade 2 strains encoded more of the genes associated with bacteriuria phenotypes compared to strains in the other three clades. Exfoliation-associated genes were present in most clade 4 strains. Clade 3 strains lack most of the in vivo-associated genes, whereas clade 1 strains were more heterogenous. ConclusionsThis study provides in vivo evidence for differential urinary tract colonization and pathogenesis by strains from different clades/species within the genus Gardnerella and identifies new putative persistence and virulence factors. Utilizing the in vivo data from tested strains, pangenome analyses predicts that clade 2 Gardnerella are the most likely to persist in the urinary tract and that clades 2 and 4 have the highest uropathogenic potential. These findings inform future targeted screening and treatment approaches aimed at limiting harmful Gardnerella urinary tract exposures

microbiology↗
bioRxiv2025

Tri-Functional CRISPR Screen Reveals Overexpression of QDR2 and QDR3 Transporters Increase Fumaric Acid Production in Kluyveromyces marxianus

Organic acids such as fumaric acid are widely used in the food and beverage industry as acidulants and preservatives, while also serving as versatile precursors for industrially relevant compounds. Fumaric acid is still predominantly produced through petroleum-derived processes. To enhance production efficiency and diversify supply, we are engineering Kluyveromyces marxianus as a biosynthetic platform from renewable feedstocks. In previous work, we have established K. marxianus Y-1190 as a host for lactose valorization based on its high growth rate on lactose and its tolerance for acid conditions. Here, we establish a trifunctional genome-wide library for K. marxianus using CRISPR activation, interference, and deletion to allow identification of gene expression perturbations that enhance tolerance to fumaric acid. We determined that deletion of ATP7, encoding a subunit of the mitochondrial F1F0 ATP synthase, and overexpression of QDR2 and QDR3, two previously uncharacterized members of the 12-spanner H antiporter (DHA1) family in K. marxianus, can enhance fumaric acid tolerance. We also found that integrated overexpression of both QDR2 and QDR3 in a {Delta}FUM1 background strain improved titers of fumaric acid production from 0.26 g L-1 to 2.16 g L-1. Together, these results highlight roles for membrane transport and mitochondrial function in enabling fumaric acid tolerance and production in K. marxianus. HIGHLIGHTS- Trifunctional CRISPR-AID enables gene activation, interference & deletion in K. marxianus. - Genome-wide CRISPR-AID screen identifies guides conferring fumaric acid tolerance. - verexpressing of QDR2 and QDR3 increases fumaric acid tolerance and production. - Deletion of mitochondrial gene ATP7 significantly improves fumaric acid tolerance. - Fermentation of QDR2 and QDR3 overexpression strain yields 2.16 g L-{superscript 1} fumaric acid.

synthetic biology↗
bioRxiv2025

Investigating the demographic history of Sindhi population inhabited in West coast India

BackgroundSouth Asian populations are genetically well stratified due to multiple waves of migration, admixture events, and endogamy. India remains a rich resource for population genomics studies with many small and socioculturally homogeneous communities whose origins and demographic histories are largely unknown. In this study, we analysed such a small Sindhi settlement in the Thane district in Maharashtra of West coast India using genome-wide autosomal SNP data using both frequency- and haplotype-based approaches. ResultsOur analyses suggest that the West coast Indian Sindhi community is very unique and has significant population affinity with a group more closely related to the Pakistani Burusho than to the Pakistani Sindhi, as it has an additional East/Southeast Asian component. Furthermore, the sharing of haplotype and IBD suggests recent gene flow from the local Konkani population on the west coast of India into Indian Sindhi. Admixture modelling suggested that Indian Sindhi admixture with the East/Southeast Asian source group could be 40-50 GBP, explaining their current unique demographics. However, apart from this additional admixture, they share the basic genetic composition of the Pakistan/NWI groups, as reflected in PCA, outgroup F3 and IBD sharing. ConclusionThese new findings suggest that Indian Sindhi settlement from the Thane in Maharashtra in West coast of India derive their genetic ancestry not directly from Pakistani Sindhis but from other groups related to Burusho in Pakistan. The study therefore encourages further research to identify the heterogeneous nature of migrations to the Indian subcontinent and thus further decipher its unique demographics.

genetics↗
bioRxiv2024

The first comorbidity networks in companion dogs in the Dog Aging Project

Comorbidity and its association with age are of great interest in geroscience. However, there are few model organisms that are well-suited to study comorbidities that will have high relevance to humans. In this light, we turn our attention to the companion dog. The companion dog shares many morbidities with humans. Thus, a better understanding of canine comorbidity relationships could benefit both humans and dogs. We present an analysis of canine comorbidity networks from the Dog Aging Project, a large epidemiological cohort study of companion dogs in the United States. We included owner-reported health conditions that occurred in at least 60 dogs (n=160) and included only dogs that had at least one of those health conditions (n=26,614). We constructed an undirected comorbidity network using a Poisson binomial test, adjusting for age, sex, sterilization status, breed background (i.e., purebred vs. mixed-breed), and weight. The comorbidity network reveals well-documented comorbidities, such as diabetes with cataracts and blindness, and hypertension with chronic kidney disease (CKD). In addition, this network also supports less well-studied comorbidity relationships, such as proteinuria with anemia. A directed comorbidity network accounting for time of reported condition onset suggests that diabetes precedes cataracts, elbow/hip dysplasia before osteoarthritis, and keratoconjunctivitis sicca before corneal ulcer, which are consistent with the canine literature. Analysis of age-stratified networks reveals that global centrality measures increase with age and are the highest in the Senior group compared to the Young Adult and Mature Adult groups. Only the Senior group identified the association between hypertension and CKD. Our results suggest that comorbidity network analysis is a promising method to enhance clinical knowledge and canine healthcare management. Author SummaryCompanion dogs age alongside humans and suffer many of the same diseases, making them an ideal "real-world" model for human health. Using owner-reported data from 26,614 dogs enrolled in the nationwide Dog Aging Project, we built the first large-scale maps--called comorbidity networks--that show which canine diseases tend to appear together and in what order. The networks correctly highlighted well-known pairings such as diabetes with cataracts and blindness, and hypertension with chronic kidney disease. They also revealed under-appreciated links--for example, protein loss in urine associated with anaemia--suggesting new avenues for veterinary research and care. By adding the reported date of diagnosis, we could infer likely sequences of the diseases: diabetes generally preceded cataracts, hip dysplasia came before osteoarthritis, and dry-eye disease often led to corneal ulcers. When we split the data by life stage, we saw disease webs become denser and more centred on a few key conditions as dogs grew older, echoing patterns seen in people. Together, these findings show that network analysis of large pet-health datasets can guide clinicians, inform breeding and prevention strategies, and ultimately improve the wellbeing of both dogs and humans.

physiology↗
bioRxiv2024

Decoding the Functional Interactome of Non-Model Organisms with PHILHARMONIC

Despite the widespread availability of genome sequencing pipelines, many genes remain part of the genome's "dark matter," where existing inference tools cannot even begin to guess the biological function of their proteins from sequence alone. This challenge is especially pronounced in organisms that are highly evolutionarily distant from well-studied models, where homology-based methods break down. Here, we describe PHILHARMONIC, a computational method that combines deep learning-based de novo protein interaction network inference with robust unsupervised spectral clustering and remote homology to illuminate functional organization in any non-model organism. From only a sequenced proteome, we show PHILHARMONIC generates meaningful functional hypothesis for protein functions, functional communities, and higher-order network structure. We validate its performance using experimental gene expression and pathway data in D. melanogaster, and we demonstrate its broad utility by analyzing temperature sensing and stress response pathways in the reef-building coral P. damicornis and its algal symbiont C. goreaui. PHILHARMONIC provides a general-purpose engine for functional discovery and biological hypothesis generation in non-model organisms, enabling systems-level insights across the full diversity of life.

bioinformatics↗
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

Maternal genetic affinities of Konkani population in the southwest coast of India

Ko[n]kan region on the west coast of India is a hotspot of culture, folklore and ethnolinguistic diversity. The genetic landscape of this region remains understudied. The present study features Ko[n]kan[i] population residing along the Ko[n]kan Malabar coast. We have sequenced complete mitogenomes of 85 and the hypervariable region of 210 Ko[n]kan[i] individuals to understand the maternal gene pool of this region. Comparative analysis of the over 5000 mitogenomes revealed that the Ko[n]kan[i] population clustered at a convergence point on the PCA plot, presumably due to a diverse maternal gene pool with both autochthonous and West Eurasian components. A distinct clustering pattern was observed within the subgroups of S[a]rasvata and non-S[a]rasvata Ko[n]kan[i] groups, indicating unique ancestral maternal lineages in them. This distinction is majorly due to the N macrohaplogroup lineages found in this population. We observe low haplotype and nucleotide diversity in Citrapur S[a]rasvata Brahmins (CSB), R[a]j[a]pur S[a]rasvata Brahmins (RSB), Kh[a]rvi and Kudubi compared to Gauda S[a]rasvata Brahmins (GSB) and Roman Catholics. The assimilation of both pre and post Last Glacial Maximum (LGM) haplogroups like M57, M36, M37, M3, M30, R8 and U2 in the Ko[n]kan[i] population suggests active movement and settlement along the Ko[n]kan region on the west coast of India since the Late Pleistocene through the Holocene.

genetics↗
bioRxiv2024

Synergistic Hypoxia and Apoptosis Conditioning Unleashes Superior Mesenchymal Stem Cells Efficacy in Acute Graft-versus-Host-Disease

Mesenchymal stem cells (MSCs) have emerged as promising candidates for immune modulation in various diseases that are associated with dysregulated immune responses like Graft-versus-Host-Disease (GVHD). MSCs are pleiotropic and the fate of MSCs following administration is a major determinant of their therapeutic efficacy. In this context, we here demonstrate that hypoxia preconditioned apoptotic MSCs [bone marrow (BM), Whartons Jelly (WJ)] bear more immune programming ability in a cellular model of acute Graft-versus-Host-Disease (aGVHD). To this purpose, we programmed MSCs by exposing them to hypoxia and inducing apoptosis both sequentially as well as simultaneously. Our findings demonstrated that WJ MSCs that were conditioned with indicated approaches simultaneously induced the differentiation of CD4+T-cell towards Tregs, enhanced Th2 effector, and concomitantly mitigated Th1 and Th17, with polarization of M1 effector macrophages towards their M2 phenotype, and more interestingly enhanced efferocytosis by macrophages indicated Th2 programming ability of MSCs programmed by conjunctional approaches Overall, our study highlights the potential of WJ-MSCs conditioned with hypoxia and apoptosis concurrently, as a promising therapeutic strategy for aGVHD and underscores the importance of considering MSC apoptosis in optimizing MSCs-based cellular therapy protocols for enhanced therapeutic efficacy in aGvHD. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=123 HEIGHT=200 SRC="FIGDIR/small/588248v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@92c102org.highwire.dtl.DTLVardef@bd895forg.highwire.dtl.DTLVardef@185e041org.highwire.dtl.DTLVardef@45ef0f_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗
bioRxiv2024

The genetic landscape, gene flow and adaptation in Ladakh highlanders of trans-Himalayas

Trans-Himalayan Ladakh has witnessed complex cultural movements and demographic changes since the Neolithic period, which is still continue despite the harsh, inhospitable and cold climate. Although geographically isolated from mainland South Asia, Ladakh has historic trade routes and is well connected and accessible to travelers from Tibet and Central Asia. Despite its rich cultural heritage, a detailed description of the genetic landscape of the Ladakh region is completely lacking, particularly with regard to genome-wide analysis and larger sample sizes. Therefore, in the current study, we genotyped 80 individuals from Kargil and Leh districts of the Union Territory of Ladakh, India. Here, we performed a comprehensive genetic analysis based on allele frequency and haplotype sharing. Our analysis revealed the presence of two distinct genetic lineages in the region with quite distinct genetic composition. The population of Leh Region is more similar to East Asian and Southeast Asian populations. In contrast, the population of the Kargil Region (LDKLA) is more similar to Indo-European populations. Demographic modeling suggests that the Leh group shares a genetic history with Tibetans, while the Kargil group showed great affinities with Kashmiri Muslims, Gujjars and Nepalese Brahmins, and both showed recent admixture. Both groups have experienced a founder event around during 11th to 22nd generations ago, the duration of which coincided with the Mughal invasion. The genome-wide scan for a signal of positive selection revealed genetic signatures of high-altitude adaptation (EPAS1 and ELMO2) in the Leh population, while in the Kargil population the key gene signatures were associated with immunity and female fertility.

genetics↗
bioRxiv2024

Human promoter CpG islands contain primate-specific repeats, cluster and resist reprogramming

A subset of imprinting control regions (ICRs) in the human and mouse possess CpG islands associated with imperfect tandem repeats (TRs) that were shown to be essential for genomic imprinting through genetic studies. To identify whether this feature is also present in non-imprinted CpG island promoters, we performed extensive dot plot analyses and identified 365 CpG island gene promoters associated with imperfect TRs of [&ge;] 400nt. These TRs are absent in the orthologous mouse CGI promoters, and most occur as clusters at the human chromosome ends, distinct from the clusters of imprinted genes. These genes showed an enrichment in neurodevelopmental/behavioral disorders and show interindividual variation in methylation levels. A subset of TR-CGIs is highly methylated and remains so during reprogramming to primed iPSCs, but become unmethylated in naive iPSCs, as has been shown for the ICRs. Transcript levels correlate with methylation levels for some TR-CGI genes suggesting their gene regulatory potential. Orthologs of the subset of methylated TR-CGIs are unmethylated in mouse, suggesting the role of TRs in imparting methylation in humans. The human TR-CGIs accompanied primate evolution after divergence from the rodent lineage with evidence of recent additions in human evolution. In summary, the incorporation of TRs in certain CGI promoters in primate evolution results in the unique ability to acquire methylation during embryonic development and resist reprogramming to a pluripotent stem cell state with a possible effect on gene expression.

developmental biology↗
bioRxiv2023

Unveiling Vaginal Fibrosis: A Novel Murine Model Using Bleomycin and Epithelial Disruption

ObjectiveDevelop, validate, and characterize a fibrotic murine vaginal wound healing model using bleomycin instillations and epithelial disruption. ApproachWe tested the effect of repeated bleomycin instillations with mucosal layer disruption on induction of vaginal fibrosis. Tissue samples collected at various time points were analyzed for fibrosis-related gene expression changes and collagen content. ResultsLow (1.5U/kg) and high-dose (2.5U/kg) bleomycin instillations alone did not induce fibrosis, but when high-dose bleomycin was combined with epithelial disruption, increased pro-fibrotic gene expression and trichrome staining were observed. To evaluate spatial and temporal changes in the ECM structure and gene expression, tissue samples were collected at 1 day, 3 weeks, and 6 weeks after bleomycin and epithelial disruption. Data analyses revealed a significant decrease in matrix metabolizing genes and an increase in pro-fibrotic genes and inhibitors of matrix metabolizing genes in the bleomycin plus epithelial disruption group at 3 weeks. Elevated levels of the profibrotic genes Acta2, Col1a1, and Col3a were exclusively detected in this group at 3 weeks, and trichrome staining confirmed increased collagen content after 3 weeks. Hydroxyproline levels showed a tendency towards elevation at 3 weeks (p=0.12) and 6 weeks (p=0.14), indicating fibrosis manifestation at 3 weeks and resolution by 6 weeks post-instillation and epithelial disruption. InnovationWe combined bleomycin instillations with epithelial disruption to induce fibrosis and understand the mechanisms of the vaginal repair process. ConclusionsEpithelial disruption combined with bleomycin induces murine vaginal fibrosis within three weeks, characterized by increased collagen synthesis. Remarkably, the vaginal tissue fully recovers within six weeks, elucidating the regenerative capacity of the vagina.

physiology↗
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↗