| 1. |
- Bravo, L, et al.
(författare)
-
- 2021
-
swepub:Mat__t
|
|
| 2. |
- Tabiri, S, et al.
(författare)
-
- 2021
-
swepub:Mat__t
|
|
| 3. |
- Abbafati, Cristiana, et al.
(författare)
-
- 2020
-
Tidskriftsartikel (refereegranskat)
|
|
| 4. |
- Ngo, Debby, et al.
(författare)
-
Proteomic profiling reveals novel biomarkers and pathways in yype 2 diabetes risk
- 2021
-
Ingår i: JCI Insight. - : American Society for Clinical Investigation. - 2379-3708. ; 6:5
-
Tidskriftsartikel (refereegranskat)abstract
- Recent advances in proteomic technologies have made high throughput profiling of low abundance proteins in large epidemiological cohorts increasingly feasible. We investigated whether aptamer-based proteomic profiling could identify biomarkers associated with future development of type 2 diabetes (T2DM) beyond known risk factors. We identified dozens of markers with highly significant associations with future T2DM across two large longitudinal cohorts (n=2,839) followed for up to 16 years. We leveraged proteomic, metabolomic, genetic and clinical data from humans to nominate one specific candidate to test for potential causal relationships in model systems. Our studies identified functional effects of aminoacylase 1 (ACY1), a top protein association with future T2DM risk, on amino acid metabolism and insulin homeostasis in vitro and in vivo. Further, a loss-of-function variant associated with circulating levels of the biomarker WAP, Kazal, immunoglobulin, Kunitz and NTR domain-containing protein 2 (WFIKKN2) was in turn associated with fasting glucose, hemoglobin A1c and HOMA-IR measurements in humans. In addition to identifying novel disease markers and potential pathways in T2DM, we provide publicly available data to be leveraged for new insights about gene function and disease pathogenesis in the context of human metabolism. .
|
|
| 5. |
- Robbins, Jeremy M, et al.
(författare)
-
Plasma proteomic changes in response to exercise training are associated with cardiorespiratory fitness adaptations.
- 2023
-
Ingår i: JCI insight. - 2379-3708. ; 8:7
-
Tidskriftsartikel (refereegranskat)abstract
- Regular exercise leads to widespread salutary effects, and there is increasing recognition that exercise-stimulated circulating proteins can impart health benefits. Despite this, limited data exist regarding the plasma proteomic changes that occur in response to regular exercise. Here, we perform large-scale plasma proteomic profiling in 654 healthy human study participants before and after a supervised, 20-week endurance exercise training intervention. We identify hundreds of circulating proteins that are modulated, many of which are known to be secreted. We highlight proteins involved in angiogenesis, iron homeostasis, and the extracellular matrix, many of which are novel, including training-induced increases in fibroblast activation protein (FAP), a membrane-bound and circulating protein relevant in body-composition homeostasis. We relate protein changes to training-induced maximal oxygen uptake adaptations and validate our top findings in an external exercise cohort. Furthermore, we show that FAP is positively associated with survival in 3 separate, population-based cohorts.
|
|
| 6. |
- Xu, Yu, et al.
(författare)
-
An atlas of genetic scores to predict multi-omic traits
- 2023
-
Ingår i: Nature. - : Springer Nature. - 0028-0836 .- 1476-4687. ; 616:7955, s. 123-131
-
Tidskriftsartikel (refereegranskat)abstract
- The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; 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), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show 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 the UK Biobank3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.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.
|
|
