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Genetic Determinants of Fiber-Associated Traits in Flax Identified by Omics Data Integration.

ABSTRACT: In this paper, we explore potential genetic factors in control of flax phenotypes associated with fiber by mining a collection of 306 flax accessions from the Federal Research Centre of the Bast Fiber Crops, Torzhok, Russia. In total, 11 traits were assessed in the course of 3 successive years. A genome-wide association study was performed for each phenotype independently using six different single-locus models implemented in the GAPIT3 R package. Moreover, we applied a multivariate linear mixed model implemented in the GEMMA package to account for trait correlations and potential pleiotropic effects of polymorphisms. The analyses revealed a number of genomic variants associated with different fiber traits, implying the complex and polygenic control. All stable variants demonstrate a statistically significant allelic effect across all 3 years of the experiment. We tested the validity of the predicted variants using gene expression data available for the flax fiber studies. The results shed new light on the processes and pathways associated with the complex fiber traits, while the pinpointed candidate genes may be further used for marker-assisted selection.

SUBMITTER: Kanapin A

PROVIDER: S-EPMC9738745 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Genetic Determinants of Fiber-Associated Traits in Flax Identified by Omics Data Integration.

Kanapin Alexander A Rozhmina Tatyana T Bankin Mikhail M Surkova Svetlana S Duk Maria M Osyagina Ekaterina E Samsonova Maria M

International journal of molecular sciences 20221122 23

In this paper, we explore potential genetic factors in control of flax phenotypes associated with fiber by mining a collection of 306 flax accessions from the Federal Research Centre of the Bast Fiber Crops, Torzhok, Russia. In total, 11 traits were assessed in the course of 3 successive years. A genome-wide association study was performed for each phenotype independently using six different single-locus models implemented in the GAPIT3 R package. Moreover, we applied a multivariate linear mixed ...[more]

PMID: 36498863

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Project description:BackgroundIn the last two decades, many studies based on omics technologies have contributed to defining the clinical, immunological, and histological fingerprints of chronic antibody-mediated rejection (CAMR), the leading cause of long-term kidney allograft failure. However, the full biological machinery underlying CAMR has only been partially defined, likely due to the fact thatsingle-omics technologies capture only specific aspects of the biological system and fail to provide a comprehensive understanding of this clinical complication.MethodsThis study integrated mass spectrometry-based proteomic profiling of serum samples from 19 patients with clinical and histological evidence of CAMR and 26 kidney transplant recipients with normal graft function and histology (CTR) with transcriptomic analysis of peripheral blood mononuclear cells (PBMCs) from an independent cohort of 10 CAMR and 8 CTR patients. Data analysis was conducted using unsupervised hierarchical clustering (multidimensional scaling with k-means) and Spearman's correlation test. Partial least squares discriminant analysis (PLS-DA) with the importance in projection (VIP) score identified key proteins differentiating CAMR from CTR. ELISA was used to validate the omics results.ResultsProteomic analysis identified 18 proteins that significantly differentiated CAMR from CTR (p < 0.01): five were more abundant (CHI3L1, LYZ, PRSS2, CPQ, IGLV3-32), while 13 were less abundant (SERPINA5, SERPING1, KNG1, CAMP, VNN1, BTD, WDR1, PON3, AHNAK2, MELTF, CA1, CD44, CUL1). Transcriptomic profiling revealed 6 downregulated and 33 upregulated genes in CAMR versus CTR (p < 0.01). Notably, only 2 biological elements were significantly deregulated in both omics analyses: chitinase-3-like protein 1 (CHI3L1) and plasma protease inhibitor C1 (SERPING1). CHI3L1, previously associated with the severity of tissue damage in kidney diseases, was up-regulated in CAMR in both transcriptomics and proteomics, while SERPING1, a serine esterase inhibitor that blocks the classical and lectin pathway of complement, was up-regulated in CAMR in transcriptomics but down-regulated in proteomics. ELISA validated the omics results, and the ROC curve showed that CHI3L1 has good discrimination power between CAMR and CTR (AUC of ROC curve of 0.81).ConclusionsOur multi-omics data, although performed in a relatively small cohort of patients, revealed new systemic biological elements involved in the pathogenesis of CAMR and identified CHI3L1 as a new potential biomarker and/or therapeutic target for this important clinical complication. Future validation of these findings in larger patient cohorts should be conducted to better evaluate their clinical utility.

Dataset Information

Genetic Determinants of Fiber-Associated Traits in Flax Identified by Omics Data Integration.

Publications

Genetic Determinants of Fiber-Associated Traits in Flax Identified by Omics Data Integration.

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