Pashupati Prasad Mishra

Studies based on harvest records indicate that grandparental nutrition in youth is associated with the health traits of grandchildren in a sex-specific lineage. We investigated whether male early adulthood anthropometrics associate with epigenetic age, a measure of general well-being, and DNA methylation, a potential molecular inheritance mechanism, in three generations of descendants. Data on weight, height, BMI and chest circumference measured at military draft examinations (at age 18–24 yrs.) were retrieved from national archives for 1,013 men, herein referred to as Generation Archives (G _A ). The genome-wide DNA methylation of their descendants participating in the Young Finns Study (YFS) was measured from the blood and sperm from the children (59–80 yrs., n _blood = 362, n _sperm = 51), grandchildren (34–49 yrs., n _blood = 421, n _sperm = 98) and great-grandchildren (7–30 yrs., n _blood = 200). The men’s (G _A ) BMI had an inverse association with the epigenetic pace of ageing (DunedinPACE) of their daughters (β = −0.01, p = 0.002, n = 169) and the daughters’ daughters (β = −0.01, p = 0.049, n = 126) but not with that of their male descendants. From within the YFS cohort, maternal, but not paternal, BMI at ages 13–34 also had an inverse association with the DunedinPACE of the immediate offspring. However, robust evidence for the inheritance mechanism was not found in the analyses between the anthropometric measurements of the G _A men and the DNA methylation profiles in the blood and sperm of their descendants.

Background: This study characterizes microbial relationships and bacterial transfer across the rectal, vaginal and endometrial microbiomes upon embryo transfer, and evaluates the influence of obesity on site-specific profiles. The Tampere Infertility Microbiome Project (TIMP) included eighty-nine women with male-factor infertility undergoing IVF or ICSI. Participants were classified into normal-weight ( n = 45, BMI < 25 kg/m ² ) and obese ( n = 44, BMI 30–35 kg/m ² ) groups. A total of 267 rectal, vaginal and endometrial samples were collected upon fresh embryo transfer and analysed using next-generation sequencing. Results: The anatomical site was the dominant determinant of microbial structure. Rectal samples showed the highest diversity and differed from Lactobacillus -dominated vaginal and endometrial communities ( p < 0.0001), which partially overlapped but exhibited distinct genera. Lactobacillus accounted for 61.3% of vaginal, 18.2% of endometrial and 1.7% of rectal communities. Higher vaginal abundance of Lactobacillus was associated with lower occurrences of Gardnerella , Prevotella and other non- Lactobacillus genera ( p = 0.001), suggesting a vaginal-endometrial association. Increased rectal abundances of Gardnerella and Prevotella corresponded to higher vaginal abundances of the same genera, suggesting a rectal-vaginal microbial relationship. Obesity was associated with a trend towards increased vaginal diversity and a higher prevalence of Gardnerella in obese versus normal-weight individuals (19% vs. 9%, p = 0.047). Conclusions: Rectal microbiomes were diverse and distinct from vaginal and endometrial communities. Vaginal Lactobacillus dominance was associated with lower abundance of potentially pathogenic genera. Microbial continuity was most pronounced for Gardnerella and Prevotella . BMI-related variation appeared most pronounced in the vaginal microbiome, followed by the endometrium, whereas community-level differences between BMI groups were not statistically significant.

Background: DNA methylation (DNAm) may capture cumulative genetic, environmental, and lifestyle influences on cardiovascular health. Composite DNAm score based on the American Heart Association Life’s Essential 8 (LE8) framework have been linked to clinical events, but their association with early vascular changes and intergenerational effects is unclear. Methods: We studied up to 1432 participants from the multigenerational Young Finns Study (YFS-3G), including parents (G0) and adult offspring (G1). DNAm was measured using Illumina EPIC arrays in 2011 and/or 2018, and carotid intima–media thickness (cIMT) was assessed in 2018. The LE8 DNAm score was calculated as a weighted sum of methylation levels. Associations with cIMT were evaluated in intergenerational, prospective, and cross-sectional settings, adjusting for demographic, technical, and biological covariates and conventional cardiovascular risk factors. Results: Higher parental LE8 DNAm score was associated with lower offspring cIMT (β = −0.022 mm/SD; p-value = 0.02), although the association was attenuated after adjustment for parental cardiovascular risk factors. In G1, a higher baseline DNAm score was associated with lower cIMT measured seven years later (β = −0.030 mm/SD; p-value = 1.1 × 10⁻⁵). This association remained significant after adjustment for follow-up cardiovascular risk factors (p-value=0.009) but not after additional adjustment for prior cIMT. Cross-sectionally, higher DNAm score was associated with lower cIMT in both generations, with attenuation after risk factor adjustment in G1 but not G0. Associations with carotid plaque were not significant. Genes associated with the DNAm score were enriched for immune and inflammatory pathways. Conclusions: An LE8-derived DNAm score was associated with lower cIMT across the life course and, to a lesser extent, across generations. These findings suggest that blood DNAm reflects cumulative cardiovascular health and vascular burden and may complement conventional cardiovascular risk assessment.

Metabolic dysfunction-associated fatty liver disease (MAFLD) and carotid artery plaque (CAP) are both linked to circulatory lipid and lipoprotein metabolism. However, the shared lipidome-wide mechanisms underlying these diseases remain unexplored. To identify plasma lipid species associated with both MAFLD and CAP to uncover their shared metabolic pathways. We analyzed data from the Young Finns Study cohort from the 2007 and 2018 follow-ups (n = 1496, aged 41–56 years, 56.3% females). Ultrasound was used to determine the prevalence of both CAP and MAFLD during the 2018 follow-up. The participants were categorized into three mutually exclusive groups: participants with CAP without MAFLD (n = 257), participants with MAFLD without CAP (n = 150), and a control group free from both diseases (n = 436). Lipidomic profiling of 437 lipid species from plasma was performed during the 2007 follow-up (aged 30–45 years) via liquid chromatography‒tandem mass spectrometry. Logistic regression models, both unadjusted and adjusted for age, sex, physical activity, alcohol consumption, and smoking, were used to assess lipid associations with both disease outcomes separately. Odds ratios (ORs) and confidence intervals (95% CIs) were calculated for each lipid species, and multiple testing corrections were performed via the false discovery rate (FDR) method (< 0.05). Additionally, we performed a hypergeometric enrichment analysis to determine whether certain lipid classes appear more often than expected among the lipids associated with disease. In the unadjusted models, there were a total of 51 significant (FDR < 0.05) overlapping lipids between the CAP and MAFLD groups. In the adjusted models, four lipids were significantly associated with CAP, and 202 lipids were significantly associated with MAFLD. Notably, only one lipid—phosphatidylcholine (PC) 40:4—was significantly associated with both diseases. PC 40:4 was associated with an increased risk of CAP (OR 2.59; 95% CI, 1.57–4.32) and MAFLD (OR 5.26; 95% CI, 2.81–9.85). Our findings highlight PC 40:4 as a novel shared lipid signature for both MAFLD and CAP. This dual association suggests that overlapping metabolic disturbances and potentially common lipid-based pathogenic mechanisms link liver and vascular health. PC 40:4 may serve as a promising early biomarker or therapeutic target for metabolic-vascular comorbidities.

Many non-coding variants influence complex traits and diseases through gene regulation, yet the mechanisms linking these variants to downstream biology remain poorly understood. Here, we present eQTLGen Phase 2 , a comprehensive genome-wide analysis of gene expression quantitative trait loci (eQTLs) in 43,301 blood samples from 52 datasets. Beyond local cis -effects, this sample size enabled the first systematic mapping of trans -eQTLs at scale. We identify cis -eQTLs for nearly all expressed genes (94.7%) and trans -eQTLs for over half (56.2%). Second, by colocalizing cis -eQTLs with trans -eQTLs, we infer a directed gene regulatory network comprising 47,554 directed gene regulatory relationships. These networks reveal how genetic perturbations in upstream regulators produce dose-dependent downstream effects, supported by Perturb-seq and ChIP-seq data. Third, integrating this network with 87 genome-wide association studies allows us to systematically prioritize trait-relevant pathways and candidate genes. Variants exerting both cis – and trans -effects are markedly more likely to colocalize with trait associations than cis -only variants, delineating a subset of functionally active cis -eQTLs from a large group with limited downstream impact. This distinction provides a conceptual framework for identifying regulatory variants that truly mediate complex trait biology. Together, these results provide a publicly available resource of cis – and trans -eQTLs and an in vivo scaffold for human gene-regulatory networks, elucidating how propagation of cis -effects modulates complex disease.

Motivation: The increasing availability of multi-omic datasets from the same individuals presents the opportunity to uncover distinct molecular profiles across subgroups within a study population. These profiles may be linked to specific biological traits, such as disease status, and have distinct disease trajectories. Importantly, they could reveal robust, multi-layered molecular signatures with potential applications in early diagnosis, prognosis, and treatment advancing precision medicine. Although several integrative multi-omic methods have been developed in recent years, most are tailored to population-level analyses and are not well-suited for identifying signals specific to subpopulations. Results: We developed MOBAA (Multi-Omic Bicluster Association Analysis), a novel data-driven integrative machine-learning framework for identifying subgroups within a study population that exhibit distinct multi-omic molecular profiles. MOBAA is scalable and capable of handling multiple omics simultaneously without relying on parametric distributional assumptions. It combines biclustering algorithms with hierarchical clustering-based module identification and uses permutation-derived empirical P-values. This approach provides a comprehensive and intuitive view of underlying biological variation and population heterogeneity facilitating discovery of complex, multi-layered molecular signatures. Availability and implementation: The code is available as MOBAA R package. All source code as well as comprehensive documentation and examples are provided at https://github.com/pmishra912/MOBAA.

Background: While roles for canonical autophagy in the pathophysiology of cardiovascular disease have been established, we have limited understanding of the non-canonical functions of autophagy proteins in this context. LC3-asssociated endocytosis (LANDO) is a novel non-canonical function of autophagy proteins, in which LC3 (microtubule-associated protein light chain 3) is conjugated to early endosome membranes using a portion of the canonical autophagy machinery, and functions in the endocytic recycling of several plasma membrane proteins. Here we ask whether perturbation of LANDO can promote cardiovascular pathogenesis. Methods: Cardiac and endothelial functions were assessed by echocardiography and flow-mediated dilatation in mice lacking Rubicon ( Rubcn ^-/- ) or the WD domain of ATG16L1 ( Atg16l1 ^ΔWDki ), two known effectors of LANDO. Mice with conditional depletion of Rubicon in the endothelial, myeloid and cardiomyocyte compartments were used as well. Three-dimensional murine cardiac vasculature leakiness was investigated by light sheet fluorescence microcopy. Endothelial activation induced by shear stress was characterized in vitro in primary endothelial cells isolated from murine lungs and human aortic endothelial cells. Associations between genetically predicted expression of candidate genes involved in LANDO and human cardiovascular parameters were studied in the Young Finns Study and the UK Biobank. Results: Compared to littermate controls, young Rubcn ^-/- and Atg16l1 ^ΔWDki mice showed a decrease in cardiac and endothelial functions, as did mice with endothelium-specific deficiency. VEGFR2 recycling to the plasma membrane and nitric oxide pathway during shear stress were disrupted in LANDO-deficient primary murine and human endothelial cells. Proteomic analysis in primary human aortic endothelial cells revealed an upregulation of intracellular hemoglobin subunit alpha (Hb-⍺) upon shear stress, which was blunted when RUBCN was ablated. Genetic expression studies uncovered several candidate genes related to LANDO that correlated with cardiovascular parameters. These included the retromer complex subunit VPS29 , disruption of which decreased Hb-⍺ expression levels in human endothelial cells. Conclusions: Our data support a pivotal role of non-canonical functions of autophagy proteins in recycling VEGFR2 upon shear stress activation in endothelial cells together with Hb-⍺ expression that may contribute to the etiology of cardiovascular diseases.

Background: With increasing life expectancy, the prevalence of cardiovascular disease (CVD) accompanied by comorbidities is rising, presenting a growing challenge for healthcare systems. Understanding shared genetic factors underlying CVD and its comorbid conditions may facilitate the development of more effective strategies for prevention and treatment. Methods: In this study, we investigated genetic correlations between CVD and common comorbidities using genome-wide association study (GWAS) summary statistics from the FinnGen R12 release, comprising data from 500,000 Finnish individuals. Following standard quality control procedures, we examined 19 disease endpoints using linkage disequilibrium score regression (LDSC) to estimate heritability and pairwise genetic correlations. Disease traits with significant heritability (z-score ≥ 4) and Bonferroni-corrected significant correlations (adjusted p < 0.05) were selected for genomic structural equation modeling (Genomic SEM) to construct a latent genomic factor (LGF), representing shared genetic liability. Results: Out of the 19 diseases, four CVDs (transient ischemic attack, atrial fibrillation, myocardial infarction and heart failure) and seven comorbidities (type 2 diabetes, asthma, obesity, depression, chronic obstructive pulmonary disease, gingivitis and hypertension) showed statistically significant genetic correlations. A multivariate GWAS of the LGF identified 141 novel associated loci across 29 independent SNPs. These loci overlapped with 16 protein-coding genes, including NPC1 , TMEM106B , PTPN22 , MAP2K5 and MSRA , implicating them in the shared pathogenesis of CVD and its comorbidities. Conclusions: These findings underscore a shared genetic architecture between CVD and its comorbidities. We provide genetic evidence supporting the re-evaluation of these gene targets in the context of integrated, holistic and multi-disease treatment strategies.

Background Inflammatory processes are a key cause of atherosclerosis and cardiovascular diseases. Complement system has been implicated but evidence is less clear. We tested whether atherosclerosis severity, plaque types, and vascular region are associated with mRNA expression of genes encoding proteins of the complement system and investigated the cell-specific expression of the most differentially expressed transcripts in plaque macrophages, fibroblasts, and endothelial, smooth muscle, Schwann, mast, plasma, T-, and B-cells. Methods Total mRNA was isolated, and gene expression analyzed from 29 carotid, 15 abdominal aortic, and 24 femoral plaque samples, and 28 atherosclerosis-free control left internal thoracic artery samples of 95 patients, as well as from 97 whole blood and 97 peripheral mononuclear cell samples of 97 patients. Genome-wide transcriptomic analyses were done using RNA bead microarray platforms. Differential expression was compared between plaques with normal arteries, unstable with stable plaques, as well as CAD patients with CAD-free patients. Results A total of 33 out of 90 (37%) transcripts of the complement system were differentially expressed in atherosclerotic plaques as compared to histologically normal arteries. In aortic, carotid, and femoral plaques 38, 36 and 29 transcripts, respectively, were differentially expressed, of which 25 were shared by the plaques of all arterial beds. Among the most interesting gene-level findings, we observed that transcripts of the integrin gene ITGB2 are highly upregulated mostly in macrophages and T cells while having top centrality in the network and top enriched genesets related to cell junctions. Conclusions This is the first study exploring the association of local complement expression across multiple arterial beds and histologically diverse plaque samples. The local effect of complement-mediated inflammation is indicated in inflammatory plaque tissue but not in smooth muscle cell-dominated plaques. Taken together, gene expression of the complement system components in artery cells is associated with advanced atherosclerosis.

With increasing life expectancy, the prevalence of cardiovascular disease (CVD) accompanied by comorbidities is rising, presenting a growing challenge for healthcare systems. Understanding shared genetic factors underlying CVD and its comorbidities can help develop more effective prevention and treatment strategies. In this study, we investigated genetic correlations between CVD and common comorbidities using genome-wide association study (GWAS) summary statistics from the FinnGen R12 release. Following standard quality control procedures, we examined 19 disease endpoints using linkage disequilibrium score regression (LDSC) to estimate heritability and pairwise genetic correlations. Disease traits with significant heritability (z-score ≥ 4) and Bonferroni-corrected significant correlations (adjusted p < 0.05) were selected for genomic structural equation modeling (Genomic SEM) to construct a latent genomic factor (LGF), representing shared genetic liability. Out of the 19 diseases, four CVDs (transient ischemic attack, atrial fibrillation, myocardial infarction and heart failure) and seven comorbidities (type 2 diabetes, asthma, obesity, depression, chronic obstructive pulmonary disease, gingivitis and hypertension) showed statistically significant genetic correlations. A multivariate GWAS of the LGF identified 141 novel associated loci across 29 independent SNPs. These loci overlapped with 16 protein-coding genes, including NPC1, TMEM106B, PTPN22, MAP2K5 and MSRA , implicating them in the shared pathogenesis of CVD and its comorbidities. These findings highlight a shared genetic architecture underlying CVD and its comorbidities, revealing cross-disease genetic risk factors that may enhance joint risk prediction, inform precision medicine strategies, and offer insights into common biological mechanisms and potential targets for integrated therapeutic approaches.