Biology subjects

Chambers, J. C.

Publications and source records attributed to Chambers, J. C..

8 recordsLinked to original sources

bioRxiv2025

High-throughput plasma proteomic platforms: Insights from a multi-ethnic Asian cohort

We compared three leading affinity-based and mass-spectrometry-based proteomic platforms (SomaScan11K, Olink Explore HT, Orbitrap Astral with Seer Proteograph [MS-Seer]) in a multi-ethnic Asian cohort, to inform biomarker discovery and functional genomic studies of global populations. We found limited overlap (1,740 proteins) across the three platforms, with modest correlations (0.34-0.10). SomaScan had lower missingness (<1%) and CV (<10%), compared to Olink (51%, 23%) and MS-Seer (12%, 19%). The new assays in Olink Explore HT (absent in Explore3072) primarily drove its higher missingness and CV. The number of phenotypic associations varied by trait, while the number of genetic associations (cis-pQTLs at P<5e-08) were similar for Olink and SomaScan. Protein levels differed between ethnicities, with SomaScan identifying more ethnicity-differentiated proteins than Olink (FDR<0.05). Finally, SomaScan ANML normalization attenuated biologically relevant associations in our study. These findings underscore the importance of platform evaluation and data normalization strategies for application in large-scale, diverse population cohorts.

bioinformatics↗

bioRxiv2025

A Complete Telomere-to-Telomere Diploid Reference Genome for Indian Population

Human reference genomes have been instrumental in advancing genomic and biomedical research, but South and Southeast Asian populations are underrepresented, despite accounting for a large proportion of world population. As a part of effort on generating reference genomes for these populations, we present the first gapless, telomere-to-telomere (T2T) diploid genome assembly created by using a trio sample set of Indian ancestry (I002C), with NG50 of 154.89 Mb and 146.27 Mb for the maternal and paternal haplotypes, including the fully assembled rDNA array for the maternal chromosome 21 and Y chromosome. With the Merqury QVs of 82.05, 83.08 and 82.64 for the maternal, paternal and haploid assemblies respectively, I002C represents the highest-quality human genome assembled in both diploid and haploid forms to date. Compared to CHM13, the I002C genome displays substantial sequence diversity, resulting in 14,943 structural variants, including 3,236 novel variants absent from public databases. Analysis of trio-phased haplotypes further revealed elevated inter-haplotype divergence within centromeric and subtelomeric regions, along with identification of differentially methylated regions (DMRs) as candidates for novel imprinting loci. As a result of substantial SVs between them, I002C is a more suitable reference than CHM13 for the genomic analysis of South Asian samples with less reference bias and better performance in mapping and variant calling, particularly for long read sequencing data. As the first high-quality T2T diploid reference genome for Indian, the largest worlds population, I002C contributes to the growing set of population-specific reference genomes and helps to overcome a significant gap in human genome diversity.

genomics↗

bioRxiv2025

Malassezia and the Asian menopausal skin

BackgroundPost-menopausal women undergo significant dermatological changes, including thinning skin and reduced sebaceous gland activity, alongside increased incidence of dermatological diseases and hair loss. These changes reshape the skins ecological niche, influencing the skin mycobiome composition and behavior. Malassezia, a lipid-dependent human pathobiont and dominant fungal resident of skin, has been implicated in several dermatological disorders. We hypothesize that shifts in Malassezia populations contribute to post-menopausal skin disorders through altered host-microbe interactions. ResultsShotgun metagenomics of facial and scalp skin from 345 Asian women were stratified by menopausal stage (pre- [N=171], peri- [N=36], and post-menopausal [N=138]) and revealed the presence of seven out of the seventeen recognized Malassezia species: M. globosa, M. restricta, M. arunalokei, M. furfur, M. dermatis, M. japonica, and M. sympodialis. Detection frequencies of several species varied markedly across menopausal groups. Notably, M. globosa was detected 20% more frequently on the scalp of post- versus pre-menopausal women. Reduced sebum concentration on post-menopausal womens skin correlated with increased M. globosa abundance. In vitro co-culture of keratinocytes with Malassezia spp. showed cells tolerated fungal loads up to 104.5 CFU/cm{superscript 2}, but severe cytotoxicity was observed at [≥]105.5 CFU/cm{superscript 2}. M. globosa elicited the highest cytotoxicity towards keratinocytes. All Malassezia spp. tested invaded keratinocytes and triggered strong pro-inflammatory responses. Notably, IL-1, IL-1{beta}, IL-6, IL-8, IL-21, TNF-, GM-CSF, G-CSF, and MMP1 were significantly overproduced. Transcriptomics of keratinocytes exposed to toxic fungal loads revealed a gene expression profile characteristic of hyperproliferative and undifferentiated cells, alongside elevated expression of NLRP3, a key inflammasome sensor involved in pyroptosis. ConclusionsMenopause is associated with distinct shifts in Malassezia spp. prevalence and abundance. Reduced skin lipids and thickness may increase fungal burden relative to host cells, promoting inflammation and barrier dysfunction. Malassezias ability to invade keratinocytes suggests a mechanism for immune evasion and induction of chronic inflammation. Furthermore, keratinocytes exposed to high fungal loads exhibited a transcriptomic profile indicative of hyperproliferation and impaired differentiation, resembling patterns observed in psoriasis, seborrheic dermatitis, and other inflammatory skin conditions. Our co-culture model provides mechanistic insight into Malassezia-driven skin inflammation and offers a platform to develop targeted therapies for post-menopausal skin disorders.

microbiology↗

bioRxiv2025

National Scale Genomic Engine for Precision Medicine: Singapore PRECISE-SG100K Experience

The whole-of-nation National Precision Medicine (NPM) programme in Singapore aims to integrate a genomics backbone into standard healthcare. Phase II recently completed the sequencing of 102,202 genomes from the diverse Asian population of Singapore, generating the PRECISE-SG100K dataset. The scaling from the pilot SG10K_Health phase to national production scale demanded a central shift: from earlier generation workflows to an integrated, industrial grade "Genomic Engine". The initiative was co-developed under a public-private partnership model. Our Genomic Engine was designed with an automation-first approach, as a modular platform linking a purpose-built, centralized biospecimen processing and DNA extraction facility (EXTRACT Lab), a high-throughput sequencing operation (Illumina Data Generation Infrastructure, IDGI), with the cloud-native analytics (GRIDS) running on a fully integrated PRECISE Sample Tracking System (STS). Here we describe the key design features of our Genomic Engine. Highlighting the Stress Test phase where the automated DNA extraction yields failed to meet expectations. We share the operational adjustments which were implemented to stabilize production and resulted in a three-fold reduction in sample triage due to improved DNA recovery. Without establishing the operational guardrails during the Stress Test, the Engine would not have reached a steady-state production evidenced by >3,000 genomes per month, median turnaround time 11 days, 99.6% operational success, within in six months. This framework offers a practical blueprint for other population-scale initiatives seeking to bridge the gap between scientific piloting and industrial delivery.

genomics↗

bioRxiv2022

RAPTOR: A Five-Safes approach to a secure, cloud native and serverless genomics data repository

Genomic researchers are increasingly utilizing commercial cloud platforms (CCPs) to manage their data and analytics needs. Commercial clouds allow researchers to grow their storage and analytics capacity on demand, keeping pace with expanding project data footprints and enabling researchers to avoid large capital expenditures while paying only for IT capacity consumed by their project. Cloud computing also allows researchers to overcome common network and storage bottlenecks encountered when combining or re-analysing large datasets. However, cloud computing presents a new set of challenges. Without adequate security controls, the risk of unauthorised access may be higher for data stored on the cloud. In addition, regulators are increasingly mandating data access patterns and specific security protocols on the storage and use of genomic data to safeguard rights of the study participants. While CCPs provide tools for security and regulatory compliance, utilising these tools to build the necessary controls required for cloud solutions is not trivial as such skill sets are not commonly found in a genomics lab. The Research Assets Provisioning and Tracking Online Repository (RAPTOR) by the Genome Institute of Singapore is a cloud native genomics data repository and analytics platform focusing on security and regulatory compliance. Using a "five-safes" framework (Safe Purpose, Safe People, Safe Settings, Safe Data and Safe Output), RAPTOR provides security and governance controls to data contributors and users leveraging cloud computing for sharing and analysis of large genomic datasets without the risk of security breaches or running afoul of regulations. RAPTOR can also enable data federation with other genomic data repositories using GA4GH community-defined standards, allowing researchers to boost the statistical power of their work and overcome geographic and ancestry limitations of data sets

genomics↗

bioRxiv2019

Identification of type 2 diabetes loci in 433,540 East Asian individuals

Meta-analyses of genome-wide association studies (GWAS) have identified >240 loci associated with type 2 diabetes (T2D), however most loci have been identified in analyses of European-ancestry individuals. To examine T2D risk in East Asian individuals, we meta-analyzed GWAS data in 77,418 cases and 356,122 controls. In the main analysis, we identified 298 distinct association signals at 178 loci, and across T2D association models with and without consideration of body mass index and sex, we identified 56 loci newly implicated in T2D predisposition. Common variants associated with T2D in both East Asian and European populations exhibited strongly correlated effect sizes. New associations include signals in/near GDAP1, PTF1A, SIX3, ALDH2, a microRNA cluster, and genes that affect muscle and adipose differentiation. At another locus, eQTLs at two overlapping T2D signals act through two genes, NKX6-3 and ANK1, in different tissues. Association studies in diverse populations identify additional loci and elucidate disease genes, biology, and pathways.\n\nType 2 diabetes (T2D) is a common metabolic disease primarily caused by insufficient insulin production and/or secretion by the pancreatic {beta} cells and insulin resistance in peripheral tissues1. Most genetic loci associated with T2D have been identified in populations of European (EUR) ancestry, including a recent meta-analysis of genome-wide association studies (GWAS) of nearly 900,000 individuals of European ancestry that identified >240 loci influencing the risk of T2D2. Differences in allele frequency between ancestries affect the power to detect associations within a population, particularly among variants rare or monomorphic in one population but more frequent in another3,4. Although smaller than studies in European populations, a recent T2D meta-analysis in almost 200,000 Japanese individuals identified 28 additional loci4. The relative contributions of different pathways to the pathophysiology of T2D may also differ between ancestry groups. For example, in East Asian (EAS) populations, T2D prevalence is greater than in European populations among people of similar body mass index (BMI) or waist circumference5. We performed the largest meta-analysis of East Asian individuals to identify new genetic associations and provide insight into T2D pathogenesis.

genetics↗

bioRxiv2018

Alcohol consumption is associated with widespread changes in blood DNA methylation: analysis of cross-sectional and longitudinal data

BackgroundDNA methylation may be one of the mechanisms by which alcohol consumption is associated with the risk of disease. We conducted a large-scale, cross-sectional, genome-wide DNA methylation association study of alcohol consumption and a longitudinal analysis of repeated measurements taken several years apart.\n\nMethodsUsing the Illumina Infinium HumanMethylation450 BeadChip, DNA methylation measures were determined using baseline peripheral blood samples from 5,606 adult Melbourne Collaborative Cohort Study (MCCS) participants. For a subset of 1,088 of them, these measures were repeated using blood samples collected at follow-up, a median of 11 years later. Associations between alcohol intake and blood DNA methylation were assessed using linear mixed-effects regression models adjusted for batch effects and potential confounders. Independent data from the LOLIPOP (N=4,042) and KORA (N=1,662) cohorts were used to replicate associations discovered in the MCCS.\n\nResultsCross-sectional analyses identified 1,414 CpGs associated with alcohol intake at P<10-7, 1,243 of which had not been reported previously. Of these 1,243 novel associations, 1,078 were replicated (P<0.05) using LOLIPOP and KORA data. Using the MCCS data, we also replicated (P<0.05) 403 of 518 associations that had been reported previously. Interaction analyses suggested that associations were stronger for women, non-smokers, and participants genetically predisposed to consume less alcohol. Of the 1,414 CpGs, 530 were differentially methylated (P<0.05) in former compared with current drinkers. Longitudinal associations between the change in alcohol intake and the change in methylation were observed for 513 of the 1,414 cross-sectional associations.\n\nConclusionOur study indicates that, for middle-aged and older adults, alcohol intake is associated with widespread changes in DNA methylation across the genome. Longitudinal analyses showed that the methylation status of alcohol-associated CpGs may change with changes in alcohol consumption.

epidemiology↗

bioRxiv2018

Proof of concept for quantitative urine NMR metabolomics pipeline for large-scale epidemiology and genetics

BackgroundQuantitative molecular data from urine are rare in epidemiology and genetics. NMR spectroscopy could provide these data in high-throughput, and it has already been applied in epidemiological settings to analyse urine samples. However, quantitative protocols for large-scale applications are not available.\n\nMethodsWe describe in detail how to prepare urine samples and perform NMR experiments to obtain quantitative metabolic information. Semi-automated quantitative lineshape fitting analyses were set up for 43 metabolites and applied to data from various analytical test samples and from 1,004 individuals from a population-based epidemiological cohort. Novel analyses on how urine metabolites associate with quantitative serum NMR metabolomics data (61 metabolic measures; n=995) were performed. In addition, confirmatory genome-wide analyses of urine metabolites were conducted (n=578). The fully automated quantitative regression-based spectral analysis is demonstrated for creatinine and glucose (n= 4,548).\n\nResultsIntra-assay metabolite variations were mostly <5% indicating high robustness and accuracy of the urine NMR spectroscopy methodology per se. Intra-individual metabolite variations were large, ranging from 6% to 194%. However, population-based inter-individual metabolite variations were even larger (from 14% to 1655%), providing a sound base for epidemiological applications. Metabolic associations between urine and serum were found clearly weaker than those within serum and within urine, indicating that urinary metabolomics data provide independent metabolic information. Two previous genome-wide hits for formate and 2-hydroxyisobutyrate were replicated at genome-wide significance.\n\nConclusionsQuantitative urine metabolomics data suggest broad novelty for systems epidemiology. A roadmap for an open access methodology is provided.

epidemiology↗