| 1. |
- Ali, Mina, et al.
(författare)
-
The multiple myeloma risk allele at 5q15 lowers ELL2 expression and increases ribosomal gene expression
- 2018
-
Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 9:1, s. 1649-
-
Tidskriftsartikel (refereegranskat)abstract
- Recently, we identified ELL2 as a susceptibility gene for multiple myeloma (MM). To understand its mechanism of action, we performed expression quantitative trait locus analysis in CD138+ plasma cells from 1630 MM patients from four populations. We show that the MM risk allele lowers ELL2 expression in these cells (Pcombined = 2.5 × 10−27; βcombined = −0.24 SD), but not in peripheral blood or other tissues. Consistent with this, several variants representing the MM risk allele map to regulatory genomic regions, and three yield reduced transcriptional activity in plasmocytoma cell lines. One of these (rs3777189-C) co-locates with the best-supported lead variants for ELL2 expression and MM risk, and reduces binding of MAFF/G/K family transcription factors. Moreover, further analysis reveals that the MM risk allele associates with upregulation of gene sets related to ribosome biogenesis, and knockout/knockdown and rescue experiments in plasmocytoma cell lines support a cause–effect relationship. Our results provide mechanistic insight into MM predisposition.
|
|
| 2. |
- Yang, Yang, et al.
(författare)
-
Pon-tstab : Protein variant stability predictor. importance of training data quality
- 2018
-
Ingår i: International Journal of Molecular Sciences. - : MDPI AG. - 1661-6596 .- 1422-0067. ; 19:4
-
Tidskriftsartikel (refereegranskat)abstract
- Several methods have been developed to predict effects of amino acid substitutions on protein stability. Benchmark datasets are essential for method training and testing and have numerous requirements including that the data is representative for the investigated phenomenon. Available machine learning algorithms for variant stability have all been trained with ProTherm data. We noticed a number of issues with the contents, quality and relevance of the database. There were errors, but also features that had not been clearly communicated. Consequently, all machine learning variant stability predictors have been trained on biased and incorrect data. We obtained a corrected dataset and trained a random forests-based tool, PON-tstab, applicable to variants in any organism. Our results highlight the importance of the benchmark quality, suitability and appropriateness. Predictions are provided for three categories: stability decreasing, increasing and those not affecting stability.
|
|
