Biology subjects

Kim, M. S.

Publications and source records attributed to Kim, M. S..

3 recordsLinked to original sources

bioRxiv2018

Cancer cell exosomes can initiate malignant cell transformation

Cancer evolves through a multistep process that occurs by the temporal accumulation of genetic mutations mediated by intracellular and extracellular cues. We observe that exosomes isolated from pancreatic cancer cells, but not normal pancreatic cells, can initiate the first step of malignant cell transformation. Injection of exosome-initiated transformed cells into mice results in aggressive tumor growth. Using proteomic profiling and DNA sequencing of exosome-treated and transformed cells, we show that cancer cell exosomes act as a classic initiator by causing random genetic changes in recipient cells. Our studies provide new insight into a function of cancer cell exosomes and how they might specifically contribute to orchestrated local cell transformation.\n\nOne Sentence SummaryExosomes function as an initiator of tumor formation.

cancer biology↗

bioRxiv2017

Characterizing co-expression networks underpinning maize stalk rot virulence in Fusarium verticillioides through computational subnetwork module analyses

Fusarium verticillioides is recognized as an important stalk rot pathogen of maize worldwide, but our knowledge of genetic mechanisms underpinning this pathosystem is limited. Previously, we identified a striatin-like protein Fsr1 that plays an important role in stalk rot. To further characterize transcriptome networks downstream of Fsr1, we performed next-generation sequencing (NGS) to investigate relative read abundance and also to infer co-expression networks utilizing the preprocessed expression data through partial correlation. We used a probabilistic pathway activity inference strategy to identify functional subnetwork modules likely involved in virulence. Each subnetwork modules consisted of multiple correlated genes with coordinated expression patterns, but the collective activation levels were significantly different in F. verticillioides wild type versus the mutant. We also identified putative hub genes from predicted subnetworks for functional validation and network robustness studies through mutagenesis, virulence and qPCR studies. Our results suggest that these genes are important virulence genes that regulate the expression of closely correlated genes, demonstrating that these are important hubs of their respective subnetworks. Lastly, we used key F. verticillioides virulence genes to computationally predict a subnetwork of maize genes that potentially respond to fungal genes by applying cointegration-correlation-expression strategy.

microbiology↗

bioRxiv2017

Ascertainment bias can create the illusion of genetic health disparities

BackgroundAccurate assessment of health disparities requires unbiased knowledge of genetic risks in different populations. Unfortunately, most genome-wide association studies use genotyping arrays and European samples. Here, we integrate whole genome sequence data from global populations, results from thousands of GWAS, and extensive computer simulations to identify how genetic disease risks can be misestimated.\n\nResultsIn contrast to null expectations, we find that risk allele frequencies at known disease loci are significantly different for African populations compared to other continents. Strikingly, ancestral risk alleles are found at 9.51% higher frequency in Africa and derived risk alleles are found at 5.40% lower frequency in Africa. By simulating GWAS with different study populations, we find that non-African cohorts yield disease associations that have biased allele frequencies and that African cohorts yield disease associations that are relatively free of bias. We also find empirical evidence that genotyping arrays and SNP ascertainment bias contribute to continental differences in risk allele frequencies. Because of these causes, polygenic risk scores can be grossly misestimated for individuals of African descent. Importantly, continental differences in risk allele frequencies are only moderately reduced if GWAS use whole genome sequences and hundreds of thousands of cases and controls. Finally, comparisons between uncorrected and corrected genetic risk scores reveal the benefits of considering whether risk alleles are ancestral or derived.\n\nConclusionsOur results imply that caution must be taken when extrapolating GWAS results from one population to predict disease risks in another population.

genomics↗