Publications by authors named "Michał T Seweryn"

4 Publications

  • Page 1 of 1

The transcriptome-wide association search for genes and genetic variants which associate with BMI and gestational weight gain in women with type 1 diabetes.

Mol Med 2021 01 20;27(1). Epub 2021 Jan 20.

Center for Medical Genomics OMICRON, Jagiellonian University Medical College, Kraków, Poland.

Background: Clinical data suggest that BMI and gestational weight gain (GWG) are strongly interconnected phenotypes; however, the genetic basis of the latter is rather unclear. Here we aim to find genes and genetic variants which influence BMI and/or GWG.

Methods: We have genotyped 316 type 1 diabetics using Illumina Infinium Omni Express Exome-8 v1.4 arrays. The GIANT, ARIC and T2D-GENES summary statistics were used for TWAS (performed with PrediXcan) in adipose tissue. Next, the analysis of association of imputed expression with BMI in the general and diabetic cohorts (Analysis 1 and 2) or GWG (Analysis 3 and 4) was performed, followed by variant association analysis (1 Mb around identified loci) with the mentioned phenotypes.

Results: In Analysis 1 we have found 175 BMI associated genes and 19 variants (p < 10) which influenced GWG, with the strongest association for rs11465293 in CCL24 (p = 3.18E-06). Analysis 2, with diabetes included in the model, led to discovery of 1812 BMI associated loci and 207 variants (p < 10) influencing GWG, with the strongest association for rs9690213 in PODXL (p = 9.86E-07). In Analysis 3, among 648 GWG associated loci, 2091 variants were associated with BMI (FDR < 0.05). In Analysis 4, 7 variants in GWG associated loci influenced BMI in the ARIC cohort.

Conclusions: Here, we have shown that loci influencing BMI might have an impact on GWG and GWG associated loci might influence BMI, both in the general and T1DM cohorts. The results suggest that both phenotypes are related to insulin signaling, glucose homeostasis, mitochondrial metabolism, ubiquitinoylation and inflammatory responses.
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http://dx.doi.org/10.1186/s10020-020-00266-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7818927PMC
January 2021

Dysregulation of Transcription Factor Activity During Formation of Cancer-Associated Fibroblasts.

Int J Mol Sci 2020 Nov 19;21(22). Epub 2020 Nov 19.

Center for Medical Genomics OMICRON, Jagiellonian University Medical College, 31-034 Kraków, Poland.

The reciprocal interactions between cancer cells and the quiescent fibroblasts leading to the activation of cancer-associated fibroblasts (CAFs) serve an important role in cancer progression. Here, we investigated the activation of transcription factors (TFs) in prostate fibroblasts (WPMY cell line) co-cultured with normal prostate or tumorous cells (RWPE1 and RWPE2 cell lines, respectively). After indirect co-cultures, we performed mRNA-seq and predicted TF activity using mRNA expression profiles with the Systems EPigenomics Inference of Regulatory Activity (SEPIRA) package and the GTEx and mRNA-seq data of 483 cultured fibroblasts. The initial differential expression analysis between time points and experimental conditions showed that co-culture with normal epithelial cells mainly promotes an inflammatory response in fibroblasts, whereas with the cancerous epithelial, it stimulates transformation by changing the expression of the genes associated with microfilaments. TF activity analysis revealed only one positively regulated TF in the RWPE1 co-culture alone, while we observed dysregulation of 45 TFs (7 decreased activity and 38 increased activity) uniquely in co-culture with RWPE2. Pathway analysis showed that these 45 dysregulated TFs in fibroblasts co-cultured with RWPE2 cells may be associated with the RUNX1 and PTEN pathways. Moreover, we showed that observed dysregulation could be associated with expression. We conclude that phenotypic changes in fibroblast responses to co-culturing with cancer epithelium result from orchestrated dysregulation of signaling pathways that favor their transformation and motility rather than proinflammatory status. This dysregulation can be observed both at the TF and transcriptome levels.
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http://dx.doi.org/10.3390/ijms21228749DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7699520PMC
November 2020

Application of information theoretical approaches to assess diversity and similarity in single-cell transcriptomics.

Comput Struct Biotechnol J 2020 21;18:1830-1837. Epub 2020 May 21.

Department of Biomedical Informatics, The Ohio State University, Columbus OH, United States.

Single-cell transcriptomics offers a powerful way to reveal the heterogeneity of individual cells. To date, many information theoretical approaches have been proposed to assess diversity and similarity, and characterize the latent heterogeneity in transcriptome data. Diversity implies gene expression variations and can facilitate the identification of signature genes; while, similarity unravels co-expression patterns for cell type clustering. In this review, we summarized 16 measures of information theory used for evaluating diversity and similarity in single-cell transcriptomic data, provide references and shed light on selected theoretical properties when there is a need to select proper measurements in general cases. We further provide an R package assembling discussed approaches to improve the researchers own single-cell transcriptome study. At last, we prospected further applications of diversity and similarity measures in support of depicting heterogeneity in single-cell multi-omics data.
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http://dx.doi.org/10.1016/j.csbj.2020.05.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7371753PMC
May 2020

Mitochondrial GWAS and association of nuclear - mitochondrial epistasis with BMI in T1DM patients.

BMC Med Genomics 2020 07 7;13(1):97. Epub 2020 Jul 7.

Center for Medical Genomics OMICRON, Jagiellonian University Medical College, Kraków, Poland.

Background: BMI is a strong indicator of complications from type I diabetes, especially under intensive treatment.

Methods: We have genotyped 435 type 1 diabetics using Illumina Infinium Omni Express Exome-8 v1.4 arrays and performed mitoGWAS on BMI. We identified additive interactions between mitochondrial and nuclear variants in genes associated with mitochondrial functioning MitoCarta2.0 and confirmed and refined the results on external cohorts: the Framingham Heart Study (FHS) and GTEx data. Linear mixed model analysis was performed using the GENESIS package in R/Bioconductor.

Results: We find a borderline significant association between the mitochondrial variant rs28357980, localized to MT-ND2, and BMI (β = - 0.69, p = 0.056). This BMI association was confirmed on 1889 patients from FHS cohort (β = - 0.312, p = 0.047). Next, we searched for additive interactions between mitochondrial and nuclear variants. MT-ND2 variants interacted with variants in the genes SIRT3, ATP5B, CYCS, TFB2M and POLRMT. TFB2M is a mitochondrial transcription factor and together with TFAM creates a transcription promoter complex for the mitochondrial polymerase POLRMT. We have found an interaction between rs3021088 in MT-ND2 and rs6701836 in TFB2M leading to BMI decrease (inter_pval = 0.0241), while interaction of rs3021088 in MT-ND2 and rs41542013 in POLRMT led to BMI increase (inter_pval = 0.0004). The influence of these interactions on BMI was confirmed in external cohorts.

Conclusions: Here, we have shown that variants in the mitochondrial genome as well as additive interactions between mitochondrial and nuclear SNPs influence BMI in T1DM and general cohorts.
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http://dx.doi.org/10.1186/s12920-020-00752-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7341625PMC
July 2020