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Browsing by Author "Akter, Shamima"

Now showing 1 - 4 of 4
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    Identification of common and unique stress responsive genes of Arabidopsis thaliana under different abiotic stress through RNA-Seq meta-analysis
    Akter, Shamima (Virginia Tech, 2018-02-06)
    Abiotic stress is a major constraint for crop productivity worldwide. To better understand the common biological mechanisms of abiotic stress responses in plants, we performed meta-analysis of 652 samples of RNA sequencing (RNA-Seq) data from 43 published abiotic stress experiments in Arabidopsis thaliana. These samples were categorized into eight different abiotic stresses including drought, heat, cold, salt, light and wounding. We developed a multi-step computational pipeline, which performs data downloading, preprocessing, read mapping, read counting and differential expression analyses for RNA-Seq data. We found that 5729 and 5062 genes are induced or repressed by only one type of abiotic stresses. There are only 18 and 12 genes that are induced or repressed by all stresses. The commonly induced genes are related to gene expression regulation by stress hormone abscisic acid. The commonly repressed genes are related to reduced growth and chloroplast activities. We compared stress responsive genes between any two types of stresses and found that heat and cold regulate similar set of genes. We also found that high light affects different set of genes than blue light and red light. Interestingly, ABA regulated genes are different from those regulated by other stresses. Finally, we found that membrane related genes are repressed by ABA, heat, cold and wounding but are up regulated by blue light and red light. The results from this work will be used to further characterize the gene regulatory networks underlying stress responsive genes in plants.
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    Nutritional status impacts dengue virus infection in mice
    Chuong, Christina; Bates, Tyler A.; Akter, Shamima; Werre, Stephen R.; LeRoith, Tanya; Weger-Lucarelli, James (2020-08-27)
    Background Dengue virus (DENV) is estimated to infect 390 million people annually. However, few host factors that alter disease severity are known. Malnutrition, defined as both over- and undernutrition, is a growing problem worldwide and has long been linked to dengue disease severity by epidemiological and anecdotal observations. Accordingly, we sought to establish a mouse model to assess the impact of nutritional status on DENV disease severity. Results Using transiently immunocompromised mice, we established a model of mild dengue disease with measurable viremia. We then applied it to study the effects of healthy weight, obese, and low-protein diets representing normal, over-, and undernutrition, respectively. Upon infection with DENV serotype 2, obese mice experienced more severe morbidity in the form of weight loss and thrombocytopenia compared to healthy weight groups. Additionally, obesity altered cytokine expression following DENV infection. Although low protein-fed mice did not lose significant weight after DENV2 infection, they also experienced a reduction in platelets as well as increased spleen pathology and viral titers. Conclusions Our results indicate that obese or undernourished mice incur greater disease severity after DENV infection. These studies establish a role for nutritional status in DENV disease severity.
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    Peeling back the many layers of competitive exclusion
    Maurer, John J.; Cheng, Ying; Pedroso, Adriana; Thompson, Kasey K.; Akter, Shamima; Kwan, Tiffany; Morota, Gota; Kinstler, Sydney; Porwollik, Steffen; McClelland, Michael; Escalante-Semerena, Jorge C.; Lee, Margie D. (Frontiers, 2024-03-21)
    Baby chicks administered a fecal transplant from adult chickens are resistant to Salmonella colonization by competitive exclusion. A two-pronged approach was used to investigate the mechanism of this process. First, Salmonella response to an exclusive (Salmonella competitive exclusion product, Aviguard®) or permissive microbial community (chicken cecal contents from colonized birds containing 7.85 Log₁ₒ Salmonella genomes/gram) was assessed ex vivo using a S. typhimurium reporter strain with fluorescent YFP and CFP gene fusions to rrn and hilA operon, respectively. Second, cecal transcriptome analysis was used to assess the cecal communities’ response to Salmonella in chickens with low (≤5.85 Log₁ₒ genomes/g) or high (≥6.00 Log₁ₒ genomes/g) Salmonella colonization. The ex vivo experiment revealed a reduction in Salmonella growth and hilA expression following co-culture with the exclusive community. The exclusive community also repressed Salmonella’s SPI-1 virulence genes and LPS modification, while the anti-virulence/inflammatory gene avrA was upregulated. Salmonella transcriptome analysis revealed significant metabolic disparities in Salmonella grown with the two different communities. Propanediol utilization and vitamin B12 synthesis were central to Salmonella metabolism co-cultured with either community, and mutations in propanediol and vitamin B12 metabolism altered Salmonella growth in the exclusive community. There were significant differences in the cecal community’s stress response to Salmonella colonization. Cecal community transcripts indicated that antimicrobials were central to the type of stress response detected in the low Salmonella abundance community, suggesting antagonism involved in Salmonella exclusion. This study indicates complex community interactions that modulate Salmonella metabolism and pathogenic behavior and reduce growth through antagonism may be key to exclusion.
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    Prediction of condition-specific regulatory genes using machine learning
    Song, Qi; Lee, Jiyoung; Akter, Shamima; Rogers, Matthew; Grene, Ruth; Li, Song (Oxford University Press, 2020-06-19)
    Recent advances in genomic technologies have generated data on large-scale protein–DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has become a major challenge in genomic research. To solve this problem, we have developed a method called ConSReg, which provides a novel approach to integrate regulatory genomic data into predictive machine learning models of key regulatory genes. Using Arabidopsis as a model system, we tested our approach to identify regulatory genes in data sets from single cell gene expression and from abiotic stress treatments. Our results showed that ConSReg accurately predicted transcription factors that regulate differentially expressed genes with an average auROC of 0.84, which is 23.5–25% better than enrichment-based approaches. To further validate the performance of ConSReg, we analyzed an independent data set related to plant nitrogen responses. ConSReg provided better rankings of the correct transcription factors in 61.7% of cases, which is three times better than other plant tools. We applied ConSReg to Arabidopsis single cell RNA-seq data, successfully identifying candidate regulatory genes that control cell wall formation. Our methods provide a new approach to define candidate regulatory genes using integrated genomic data in plants.