Karthik Bharadwaj Tallapaka

Oxford Nanopore (ONT) sequencing offers direct detection of DNA base modifications. Numerous tools have been developed to leverage this advantage. However, their performance remains unclear. Here, using diverse bacterial, plant, and mammalian datasets, we systematically evaluate the current landscape of nanopore methylation tools. We demonstrate that although most recent tools perform well, older models remain the reliable choice for studying CpG methylation. Conversely, newer models show substantial improvement in identifying 5-methylcytosine in non-CpG contexts, 6-methyladenine, and 4-methylcytosine. Further, we highlight the sensitivity of tools to confounding methylation nearby, assess their computational performance, and evaluate the effects of sequencing depth, methylation abundance, read quality, and basecalling mode. We provide reusable pipelines and open access datasets to empower future benchmarking efforts. Our work thus details the strengths and limitations of the state-of-the-art methylation models and outlines practical guidelines for researchers using nanopore sequencing to study DNA methylation.

Pathogenic variations in DNA checkpoint kinases, cell cycle regulation, and DNA-damage repair genes are responsible for hereditary cancer predisposition. In a woman with a history of multiple primary cancers (carcinoma rectum and papillary carcinoma of thyroid) at a young age, whole exome sequencing (WES) of the blood revealed a likely pathogenic germline variation NM_001161346.2(CHFR):c.1231C[T (ClinVar variation ID: 1802254) in the CHFR gene. The CHFR gene encodes an E3-ubiquitin ligase and is required for antephase checkpoint maintenance and cell cycle entry into mitosis. It has previously been shown to be silenced in many human tumour types, including colon, lung, and esophageal tumours. Herein, we describe what is possibly the first case of CHFR-related hereditary cancer predisposition syndrome and, using RNA sequencing, elucidated the pathways associated with CHFR inactivation.

Ocular infections have a substantial impact on global visual health. Despite their association with severe vision impairment, very few studies have systematically monitored antimicrobial resistance (AMR) over time using whole genome sequencing approaches. In the current study, we assembled 291 high-fidelity bacterial genomes isolated from patients attending a tertiary eye care centre in India, using long-read sequencing. Pseudomonas aeruginosa (n = 62) and Staphylococcus aureus (n = 60) were the most common pathogens in the cohort, with more than 45% of isolates exhibiting multidrug resistance and more than 15% classified as extensively drug-resistant. Genotype–phenotype analyses and multilocus sequence typing revealed a previously unreported mecA-negative methicillin-resistant Staphylococcus aureus strain, ST9578, resistant to vancomycin and teicoplanin. Potential AMR mechanisms, like a mutation in the 23S rRNA gene associated with linezolid resistance and a KorB-like protein-encoding gene associated with fluoroquinolone resistance, were identified using comparative genomics. This is the first large-scale genomic surveillance effort focused on ocular pathogens in the Indian subcontinent and the potential AMR mechanisms and sequence types identified underscore the need for larger such studies in low-and-middle-income countries.

Wastewater-based surveillance has emerged as a powerful tool for monitoring microbial diversity, antimicrobial resistance genes (ARGs) and mobile genetic elements (MGEs). In this study, wastewater samples collected from March 2022 to March 2024 from 19 locations in four metropolitan cities of India were profiled using shotgun metagenomics. Taxonomic abundance and beta diversity analyses revealed significant differences in microbial community compositions, with city-specific clustering; suggesting distinct local environmental influences. However, such distinct clusters were not evident with the ARGs. A high proportion of potentially novel metagenome-assembled genomes (MAGs) (53-70%) were identified on reconstructing the microbial genomes from the metagenomic data. ARGs conferring resistance to antibiotics such as tetracyclines and beta-lactams showed higher association with MGEs in contrast to macrolide resistance genes. Microbial co-occurrence network analysis revealed a city-specific structure and higher contribution of ARGs from specific communities of microbes. These findings underscore the complex interplay between microbial diversity, ARG dissemination, and MGEs in wastewater environments, emphasizing the need for continued surveillance, for designing appropriate mitigation strategies towards curbing the spread of antimicrobial resistance.

Background: Metabolically–dysfunction–associated steatotic liver disease (MASLD) is rising globally, including in India, yet community-based data remain scarce. We addressed this critical knowledge gap by assessing the prevalence, distribution, and characteristics of MASLD subgroups and fibrosis, leveraging the Phenome India–CSIR Health Cohort Knowledgebase (PI-CHeCK) study. Methods: In this prospective, nationwide study, we recruited 10267 adults across 37 laboratories of the Council of Industrial Research (CSIR) from 27 Indian cities. Steatosis and fibrosis were assessed by Transient Elastography using Fibroscan, with associated clinical, biochemical, cytokine, anthropometric, and lifestyle data collected. Overall, crude and age-adjusted prevalence rates were estimated in the study population and various subgroups. Findings: Of the 10267 individuals screened, 7764 were eligible for analysis after exclusions. Among these, 3,712 (47.8%) fulfilled MASLD criteria, corresponding to an age-adjusted prevalence of 36.3%. Significant fibrosis (≥F2) was more frequent in MASLD (6.3% [234 of 3688]) than in cases of cryptogenic fibrosis without MASLD (1.7% [69 of 4027]), corresponding to an age-adjusted prevalence of 4.1% in MASLD. Overall age-adjusted prevalence of significant fibrosis was 2.3% in the entire cohort, which clustered in older adults (>60 years) and in those with diabetes or obesity II, with evidence of regional variation, peaking in Jorhat, Assam. Importantly, fibrosis in participants without MASLD emerged as a distinct subgroup with disproportionately elevated cytokine levels, exceeding those in MASLD with fibrosis, suggesting a cytokine-rich, high-risk phenotype. Conclusion: In this nationwide Indian cohort, MASLD affected over one-third of participants with a substantial burden of fibrosis. Notably, fibrosis without-MASLD emerged as a cytokine-rich, high-risk phenotype, underscoring an underrecognized dimension of liver disease with major public health implications.

Reference genomes serve as a baseline criterion for comparison of personal genomes to deduce clinical variants. The widely used reference genome, GRCh38, contains stretches of gaps and unresolved bases particularly in complex regions which could obscure variant discovery. In contrast, the gapless telomere-to-telomere CHM13 (T2T-CHM13) reference genome can be used to assess difficult regions of the genome. Optical genome mapping (OGM), an imaging technique for structural variation identification has improved resolution compared to traditional cytogenetic methods. Our study showcases the utility of the T2T-CHM13 reference genome for enhanced structural variant (SV) detection in complex regions. We illustrate this through two clinical cases, where improved alignment with T2T-CHM13 led to significantly higher confidence scores for critical SVs. We demonstrate improved clinical diagnostic outcomes with the updated T2T-CHM13 reference and advocate its adoption.

Long read sequencing technologies such as Oxford Nanopore (ONT) offer direct, simultaneous detection of DNA base modifications. The recent migration of ONT to the upgraded R10 chemistry has spurred the development of diverse methylation detection models. However, their performance and accuracy remain unclear. Here, leveraging diverse bacterial, plant, and mammalian datasets, we systematically evaluate the current landscape of tools and models for studying DNA methylation using nanopore sequencing. Our results demonstrate that the older models remain the best choice for studying CpG methylation. We note substantial improvement of newer tools in identifying 5-methylcytosine in non-CG contexts, 6-methyladenine, and 4-methylcytosine. We highlight the sensitivity of various tools to confounding methylation nearby. We also assess the computational performance of various tools, and effects of sequencing depth, methylation abundance, read quality, and basecalling mode. Our reusable pipelines and fully open access datasets provide a framework of resources to empower future benchmarking efforts. Our work thus details the strengths and limitations of the state-of-the-art methylation models and outlines practical guidelines for researchers using nanopore sequencing to study DNA modifications.

Neuromuscular diseases (NMDs) affect ∼15 million people globally. In high income settings DNA-based diagnosis has transformed care pathways and led to gene-specific therapies. However, most affected families are in low-to-middle income countries (LMICs) with limited access to DNA-based diagnosis. Most (86%) published genetic data is derived from European ancestry. This marked genetic data inequality hampers understanding of genetic diversity and hinders accurate genetic diagnosis in all income settings. We developed a cloud-based transcontinental partnership to build diverse, deeply-phenotyped and genetically characterized cohorts to improve genetic architecture knowledge, and potentially advance diagnosis and clinical management. We connected 18 centres in Brazil, India, South Africa, Turkey, Zambia, Netherlands and the UK. We co-developed a cloud-based data solution and trained 17 international neurology fellows in clinical genomic data interpretation. Single gene and whole exome data were analysed via a bespoke bioinformatics pipeline and reviewed alongside clinical and phenotypic data in global webinars to inform genetic outcome decisions. We recruited 6001 participants in the first 43 months. Initial genetic analyses ‘solved’ or ‘possibly solved’ ∼56% probands overall. In-depth genetic data review of the four commonest clinical categories (limb girdle muscular dystrophy, inherited peripheral neuropathies, congenital myopathy/muscular dystrophies and Duchenne/Becker muscular dystrophy) delivered a ∼59% ‘solved’ and ∼13% ‘possibly solved’ outcome. Almost 29% of disease causing variants were novel, increasing diverse pathogenic variant knowledge. Unsolved participants represent a new discovery cohort. The dataset provides a large resource from under-represented populations for genetic and translational research. In conclusion, we established a remote transcontinental partnership to assess genetic architecture of NMDs across diverse populations. It supported DNA-based diagnosis, potentially enabling genetic counselling, care pathways and eligibility for gene-specific trials. Similar virtual partnerships could be adopted by other areas of global genomic neurological practice to reduce genetic data inequality and benefit patients globally.