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Das, Avash

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Das

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Avash

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Das, Avash
Author's Publications: search.filters.isAuthorOfPublication.74184991-a977-4b01-ae0e-e815522f261e

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    Plasma Circulating Extracellular RNAs in Left Ventricular Remodeling Post-Myocardial Infarction
    (Elsevier, 2018) Danielson, Kirsty M.; Shah, Ravi; Yeri, Ashish; Liu, Xiaojun; Camacho Garcia, Fernando; Silverman, Michael; Tanriverdi, Kahraman; Das, Avash; Xiao, Chunyang; Jerosch-Herold, Michael; Heydari, Bobak; Abbasi, Siddique; Van Keuren-Jensen, Kendall; Freedman, Jane E.; Wang, Yaoyu E.; Rosenzweig, Anthony; Kwong, Raymond; Das, Saumya
    Despite substantial declines in mortality following myocardial infarction (MI), subsequent left ventricular remodeling (LVRm) remains a significant long-term complication. Extracellular small non-coding RNAs (exRNAs) have been associated with cardiac inflammation and fibrosis and we hypothesized that they are associated with post-MI LVRm phenotypes. RNA sequencing of exRNAs was performed on plasma samples from patients with “beneficial” (decrease LVESVI ≥ 20%, n = 11) and “adverse” (increase LVESVI ≥ 15%, n = 11) LVRm. Selected differentially expressed exRNAs were validated by RT-qPCR (n = 331) and analyzed for their association with LVRm determined by cardiac MRI. Principal components of exRNAs were associated with LVRm phenotypes post-MI; specifically, LV mass, LV ejection fraction, LV end systolic volume index, and fibrosis. We then investigated the temporal regulation and cellular origin of exRNAs in murine and cell models and found that: 1) plasma and tissue miRNA expression was temporally regulated; 2) the majority of the miRNAs were increased acutely in tissue and at sub-acute or chronic time-points in plasma; 3) miRNA expression was cell-specific; and 4) cardiomyocytes release a subset of the identified miRNAs packaged in exosomes into culture media in response to hypoxia/reoxygenation. In conclusion, we find that plasma exRNAs are temporally regulated and are associated with measures of post-MI LVRm.
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    Evaluation of commercially available small RNASeq library preparation kits using low input RNA
    (BioMed Central, 2018) Yeri, Ashish; Courtright, Amanda; Danielson, Kirsty; Hutchins, Elizabeth; Alsop, Eric; Carlson, Elizabeth; Hsieh, Michael; Ziegler, Olivia; Das, Avash; Shah, Ravi; Rozowsky, Joel; Das, Saumya; Van Keuren-Jensen, Kendall
    Background: Evolving interest in comprehensively profiling the full range of small RNAs present in small tissue biopsies and in circulating biofluids, and how the profile differs with disease, has launched small RNA sequencing (RNASeq) into more frequent use. However, known biases associated with small RNASeq, compounded by low RNA inputs, have been both a significant concern and a hurdle to widespread adoption. As RNASeq is becoming a viable choice for the discovery of small RNAs in low input samples and more labs are employing it, there should be benchmark datasets to test and evaluate the performance of new sequencing protocols and operators. In a recent publication from the National Institute of Standards and Technology, Pine et al., 2018, the investigators used a commercially available set of three tissues and tested performance across labs and platforms. Results: In this paper, we further tested the performance of low RNA input in three commonly used and commercially available RNASeq library preparation kits; NEB Next, NEXTFlex, and TruSeq small RNA library preparation. We evaluated the performance of the kits at two different sites, using three different tissues (brain, liver, and placenta) with high (1 μg) and low RNA (10 ng) input from tissue samples, or 5.0, 3.0, 2.0, 1.0, 0.5, and 0.2 ml starting volumes of plasma. As there has been a lack of robust validation platforms for differentially expressed miRNAs, we also compared low input RNASeq data with their expression profiles on three different platforms (Abcam Fireplex, HTG EdgeSeq, and Qiagen miRNome). Conclusions: The concordance of RNASeq results on these three platforms was dependent on the RNA expression level; the higher the expression, the better the reproducibility. The results provide an extensive analysis of small RNASeq kit performance using low RNA input, and replication of these data on three downstream technologies. Electronic supplementary material The online version of this article (10.1186/s12864-018-4726-6) contains supplementary material, which is available to authorized users.