Biology subjects

Lambert, S. A.

Publications and source records attributed to Lambert, S. A..

4 recordsLinked to original sources

bioRxiv2023

SUMO protease and proteasome recruitment at the nuclear periphery differently affect replication dynamics at arrested forks.

Nuclear pores complexes (NPCs) are genome organizers, defining a particular nuclear compartment enriched for SUMO protease and proteasome activities, and acting as docking sites for DNA repair. In fission yeast, the anchorage of perturbed replication forks to NPCs is an integral part of the recombination-dependent replication restart mechanism (RDR) that resumes DNA synthesis at terminally dysfunctional forks. By mapping DNA polymerase usage, we report that SUMO protease Ulp1-associated NPCs ensure efficient initiation of restarted DNA synthesis, whereas proteasome-associated NPCs sustain the progression of restarted DNA polymerase. In contrast to Ulp1-dependent events, this last function occurs independently of SUMO chains formation. By analyzing the role of the nuclear basket, the nucleoplasmic extension of the NPC, we reveal that the activities of Ulp1 and the proteasome cannot compensate for each other and affect RDR dynamics in distinct ways. Our work probes the mechanisms by which the NPC environment ensures optimal RDR. Highlights[bullet] Ulp1-associated NPCs ensure efficient initiation of restarted DNA synthesis, in a SUMO chain-dependent manner [bullet]Proteasome-associated NPCs foster the progression of restarted DNA synthesis, in a SUMO chain-independent manner [bullet]The nucleoporin Nup60 promotes the spatial sequestration of Ulp1 at the nuclear periphery [bullet]Ulp1 and proteasome activities are differently required for optimal recombination-mediated fork restart.

molecular biology↗

bioRxiv2022

A community driven GWAS summary statistics standard

Summary statistics from genome-wide association studies (GWAS) represent a huge potential for research. A challenge for researchers in this field is the access and sharing of summary statistics data due to a lack of standards for the data content and file format. For this reason, the GWAS Catalog hosted a series of meetings in 2021 with summary statistics stakeholders to guide the development of a standard format. The key requirements from the stakeholders were for a standard that contained key data elements to be able to support a wide range of data analyses, required low bioinformatics skills for file access and generation, to have easily accessible metadata, and unambiguous and interoperable data. Here, we define the specifications for the first version of the GWAS-SSF format, which was developed to meet the requirements discussed with the community. GWAS-SSF consists of a tab-separated data file with well-defined fields and an accompanying metadata file.

bioinformatics↗

bioRxiv2022

An atlas of genetic scores to predict multi-omic traits

Genetically predicted levels of multi-omic traits can uncover the molecular underpinnings of common phenotypes in a highly efficient manner. Here, we utilised a large cohort (INTERVAL; N=50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, N=3,175; Olink, N=4,822), plasma metabolomics (Metabolon HD4, N=8,153), serum metabolomics (Nightingale, N=37,359), and whole blood Illumina RNA sequencing (N=4,136). We used machine learning to train genetic scores for 17,227 molecular traits, including 10,521 which reached Bonferroni-adjusted significance. We evaluated genetic score performances in external validation across European, Asian and African American ancestries, and assessed their longitudinal stability within diverse individuals. We demonstrated the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of UK Biobank to identify disease associations using a phenome-wide scan. Finally, we developed a portal (OmicsPred.org) to facilitate public access to all genetic scores and validation results as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.

genomics↗

bioRxiv2019

Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases

Summary ParagraphCommon human diseases are frequently polygenic in architecture, comprising a large number of risk alleles with small effects spread across the genome1-3. Polygenic scores (PGSs) aggregate these alleles into a metric which represents an individuals genetic predisposition to a specific disease. PGSs have shown promise for early risk prediction4-7, and there is potential to use PGSs to understand disease biology in parallel8. Here, we investigate the role plasma protein levels play in cardiometabolic disease risk in a cohort of 3,087 healthy individuals using PGSs. We found PGSs for coronary artery disease (CAD), type 2 diabetes (T2D), chronic kidney disease (CKD), and ischaemic stroke (IS) were associated with levels of 49 plasma proteins. These associations were polygenic in architecture, largely independent of cis protein QTLs, and robust to environmental variation. Over a median 7.7 years follow-up, 28 of these plasma proteins were associated with future myocardial infarction (MI) or T2D events, 16 of which were causal mediators between polygenic risk and incident disease. These protein mediators of polygenic disease risk included targets of approved therapies which may have repurposing potential. Our results demonstrate that PGSs can identify proteins with causal roles in disease, and may have utility in drug development.

genetics↗