scholarly journals Large-scale identification of extracellular plant miRNAs in mammals implicates their dietary intake

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257878
Author(s):  
Xi Chen ◽  
Lu Liu ◽  
Qinjie Chu ◽  
Shuo Sun ◽  
Yixuan Wu ◽  
...  
Keyword(s):  
Dietary Intake ◽  
Large Scale ◽  
Conclusive Evidence ◽  
Regulatory Function ◽  
Small Rna Sequencing ◽  
Selective Absorption ◽  
Sequencing Data ◽  
Bioinformatics Pipeline ◽  
Human Blood Sample ◽  
Dietary Origin

Extracellular microRNAs (miRNAs) have been proposed to function in cross-kingdom gene regulation. Among these, plant-derived miRNAs of dietary origin have been reported to survive the harsh conditions of the human digestive system, enter the circulatory system, and regulate gene expression and metabolic function. However, definitive evidence supporting the presence of plant-derived miRNAs of dietary origin in mammals has been difficult to obtain due to limited sample sizes. We have developed a bioinformatics pipeline (ePmiRNA_finder) that provides strident miRNA classification and applied it to analyze 421 small RNA sequencing data sets from 10 types of human body fluids and tissues and comparative samples from carnivores and herbivores. A total of 35 miRNAs were identified that map to plants typically found in the human diet and these miRNAs were found in at least one human blood sample and their abundance was significantly different when compared to samples from human microbiome or cow. The plant-derived miRNA profiles were body fluid/tissue-specific and highly abundant in the brain and the breast milk samples, indicating selective absorption and/or the ability to be transported across tissue/organ barriers. Our data provide conclusive evidence for the presence of plant-derived miRNAs as a consequence of dietary intake and their cross-kingdom regulatory function within human circulating system.

scholarly journals Correction: Evidence for plant-derived xenomiRs based on a large-scale analysis of public small RNA sequencing data from human samples

PLoS ONE ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. e0230146
Author(s):  
Qi Zhao ◽  
Yuanning Liu ◽  
Ning Zhang ◽  
Menghan Hu ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Small Rna ◽  
Large Scale ◽  
Small Rna Sequencing ◽  
Scale Analysis ◽  
Sequencing Data ◽  
Human Samples ◽  
Large Scale Analysis

scholarly journals Large scale automated phylogenomical analysis of bacterial whole-genome isolates and the Evergreen platform

2019 ◽  
Author(s):  
Judit Szarvas ◽  
Johanne Ahrenfeldt ◽  
Jose Luis Bellod Cisneros ◽  
Martin Christen Frølund Thomsen ◽  
Frank M. Aarestrup ◽  
...  
Keyword(s):  
Phylogenetic Trees ◽  
Pathogenic Bacteria ◽  
Large Scale ◽  
Consensus Sequence ◽  
Daily Basis ◽  
Whole Genome Sequence ◽  
Phylogenomic Analysis ◽  
Whole Genome ◽  
Sequencing Data ◽  
Bioinformatics Pipeline

AbstractPublic health authorities whole-genome sequence thousands of pathogenic isolates each month for microbial diagnostics and surveillance of pathogenic bacteria. The computational methods have not kept up with the deluge of data and need for real-time results.We have therefore created a bioinformatics pipeline for rapid subtyping and continuous phylogenomic analysis of bacterial samples, suited for large-scale surveillance. To decrease the computational burden, a two level clustering strategy is employed. The data is first divided into sets by matching each isolate to a closely related reference genome. The reads then are aligned to the reference to gain a consensus sequence and SNP based genetic distance is calculated between the sequences in each set. Isolates are clustered together with a threshold of 10 SNPs. Finally, phylogenetic trees are inferred from the non-redundant sequences and the clustered isolates are placed on a clade with the cluster representative sequence. The method was benchmarked and found to be accurate in grouping outbreak strains together, while discriminating from non-outbreak strains.The pipeline was applied in Evergreen Online, which processes publicly available sequencing data from foodborne bacterial pathogens on a daily basis, updating the phylogenetic trees as needed. It has so far placed more than 100,000 isolates into phylogenies, and has been able to keep up with the daily release of data. The trees are continuously published on https://cge.cbs.dtu.dk/services/Evergreen

scholarly journals Evidence for plant-derived xenomiRs based on a large-scale analysis of public small RNA sequencing data from human samples

PLoS ONE ◽  
2018 ◽  
Vol 13 (6) ◽  
pp. e0187519 ◽  
Author(s):  
Qi Zhao ◽  
Yuanning Liu ◽  
Ning Zhang ◽  
Menghan Hu ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Small Rna ◽  
Large Scale ◽  
Small Rna Sequencing ◽  
Scale Analysis ◽  
Sequencing Data ◽  
Human Samples ◽  
Large Scale Analysis

scholarly journals Correction: Evidence for plant-derived xenomiRs based on a large-scale analysis of public small RNA sequencing data from human samples

PLoS ONE ◽  
2019 ◽  
Vol 14 (10) ◽  
pp. e0224537 ◽  
Author(s):  
Qi Zhao ◽  
Yuanning Liu ◽  
Ning Zhang ◽  
Menghan Hu ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Small Rna ◽  
Large Scale ◽  
Small Rna Sequencing ◽  
Scale Analysis ◽  
Sequencing Data ◽  
Human Samples ◽  
Large Scale Analysis

scholarly journals Expanding the miRNA Transcriptome of Human Kidney and Renal Cell Carcinoma

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Adam P. Sage ◽  
Brenda C. Minatel ◽  
Erin A. Marshall ◽  
Victor D. Martinez ◽  
Greg L. Stewart ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Kidney Cancer ◽  
Renal Cell ◽  
Large Scale ◽  
Target Genes ◽  
Driver Mutations ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Cell Renal Cell Carcinoma

Despite advancements in therapeutic strategies, diagnostic and prognostic molecular markers of kidney cancer remain scarce, particularly in patients who do not harbour well-defined driver mutations. Recent evidence suggests that a large proportion of the human noncoding transcriptome has escaped detection in early genomic explorations. Here, we undertake a large-scale analysis of small RNA-sequencing data from both clear cell renal cell carcinoma (ccRCC) and nonmalignant samples to generate a robust set of miRNAs that remain unannotated in kidney tissues. We find that these novel kidney miRNAs are also expressed in renal cancer cell lines. Moreover, these sequences are differentially expressed between ccRCC and matched nonmalignant tissues, implicating their involvement in ccRCC biology and potential utility as tumour-specific markers of disease. Indeed, we find some of these miRNAs to be significantly associated with patient survival. Finally, target prediction and subsequent pathway analysis reveals that miRNAs previously unannotated in kidney tissues may target genes involved in ccRCC tumourigenesis and disease biology. Taken together, our results represent a new resource for the study of kidney cancer and underscore the need to characterize the unexplored areas of the transcriptome.

scholarly journals Upgrading the Repertoire of miRNAs in Gastric Adenocarcinoma to Provide a New Resource for Biomarker Discovery

2019 ◽  
Vol 20 (22) ◽  
pp. 5697 ◽  
Author(s):  
Michelle E. Pewarchuk ◽  
Mateus C. Barros-Filho ◽  
Brenda C. Minatel ◽  
David E. Cohn ◽  
Florian Guisier ◽  
...  
Keyword(s):  
Gastric Adenocarcinoma ◽  
Biomarker Discovery ◽  
The Cancer Genome Atlas ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Specific Expression ◽  
Novel Mirna ◽  
Cancer Genome Atlas ◽  
Context Specific ◽  
Independent Cohort

Recent studies have uncovered microRNAs (miRNAs) that have been overlooked in early genomic explorations, which show remarkable tissue- and context-specific expression. Here, we aim to identify and characterize previously unannotated miRNAs expressed in gastric adenocarcinoma (GA). Raw small RNA-sequencing data were analyzed using the miRMaster platform to predict and quantify previously unannotated miRNAs. A discovery cohort of 475 gastric samples (434 GA and 41 adjacent nonmalignant samples), collected by The Cancer Genome Atlas (TCGA), were evaluated. Candidate miRNAs were similarly assessed in an independent cohort of 25 gastric samples. We discovered 170 previously unannotated miRNA candidates expressed in gastric tissues. The expression of these novel miRNAs was highly specific to the gastric samples, 143 of which were significantly deregulated between tumor and nonmalignant contexts (p-adjusted < 0.05; fold change > 1.5). Multivariate survival analyses showed that the combined expression of one previously annotated miRNA and two novel miRNA candidates was significantly predictive of patient outcome. Further, the expression of these three miRNAs was able to stratify patients into three distinct prognostic groups (p = 0.00003). These novel miRNAs were also present in the independent cohort (43 sequences detected in both cohorts). Our findings uncover novel miRNA transcripts in gastric tissues that may have implications in the biology and management of gastric adenocarcinoma.

scholarly journals Increased yields of duplex sequencing data by a series of quality control tools

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Gundula Povysil ◽  
Monika Heinzl ◽  
Renato Salazar ◽  
Nicholas Stoler ◽  
Anton Nekrutenko ◽  
...  
Keyword(s):  
Low Frequency ◽  
Variant Calling ◽  
Data Loss ◽  
Sequencing Data ◽  
Bioinformatics Pipeline ◽  
Consensus Sequences ◽  
Sequencing Errors ◽  
Data Output ◽  
Reverse Strand ◽  
Duplex Sequencing

Abstract Duplex sequencing is currently the most reliable method to identify ultra-low frequency DNA variants by grouping sequence reads derived from the same DNA molecule into families with information on the forward and reverse strand. However, only a small proportion of reads are assembled into duplex consensus sequences (DCS), and reads with potentially valuable information are discarded at different steps of the bioinformatics pipeline, especially reads without a family. We developed a bioinformatics toolset that analyses the tag and family composition with the purpose to understand data loss and implement modifications to maximize the data output for the variant calling. Specifically, our tools show that tags contain polymerase chain reaction and sequencing errors that contribute to data loss and lower DCS yields. Our tools also identified chimeras, which likely reflect barcode collisions. Finally, we also developed a tool that re-examines variant calls from raw reads and provides different summary data that categorizes the confidence level of a variant call by a tier-based system. With this tool, we can include reads without a family and check the reliability of the call, that increases substantially the sequencing depth for variant calling, a particular important advantage for low-input samples or low-coverage regions.

scholarly journals Extensive Analysis of miRNA Trimming and Tailing Indicates that AGO1 Has a Complex Role in miRNA Turnover

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 267
Author(s):  
Axel J. Giudicatti ◽  
Ariel H. Tomassi ◽  
Pablo A. Manavella ◽  
Agustin L. Arce
Keyword(s):  
Small Rna ◽  
Stress Responses ◽  
Cellular Localization ◽  
Mirna Biogenesis ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Extensive Analysis ◽  
Small Regulatory Rnas ◽  
Regulatory Rnas ◽  
Argonaute 1

MicroRNAs are small regulatory RNAs involved in several processes in plants ranging from development and stress responses to defense against pathogens. In order to accomplish their molecular functions, miRNAs are methylated and loaded into one ARGONAUTE (AGO) protein, commonly known as AGO1, to stabilize and protect the molecule and to assemble a functional RNA-induced silencing complex (RISC). A specific machinery controls miRNA turnover to ensure the silencing release of targeted-genes in given circumstances. The trimming and tailing of miRNAs are fundamental modifications related to their turnover and, hence, to their action. In order to gain a better understanding of these modifications, we analyzed Arabidopsis thaliana small RNA sequencing data from a diversity of mutants, related to miRNA biogenesis, action, and turnover, and from different cellular fractions and immunoprecipitations. Besides confirming the effects of known players in these pathways, we found increased trimming and tailing in miRNA biogenesis mutants. More importantly, our analysis allowed us to reveal the importance of ARGONAUTE 1 (AGO1) loading, slicing activity, and cellular localization in trimming and tailing of miRNAs.

scholarly journals LncGSEA: a versatile tool to infer lncRNA associated pathways from large-scale cancer transcriptome sequencing data

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yanan Ren ◽  
Ting-You Wang ◽  
Leah C. Anderton ◽  
Qi Cao ◽  
Rendong Yang
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Clinical Samples ◽  
Sequencing Data ◽  
Multiple Cancer ◽  
Regulatory Pathways ◽  
Cancer Transcriptome ◽  
Versatile Tool

Abstract Background Long non-coding RNAs (lncRNAs) are a growing focus in cancer research. Deciphering pathways influenced by lncRNAs is important to understand their role in cancer. Although knock-down or overexpression of lncRNAs followed by gene expression profiling in cancer cell lines are established approaches to address this problem, these experimental data are not available for a majority of the annotated lncRNAs. Results As a surrogate, we present lncGSEA, a convenient tool to predict the lncRNA associated pathways through Gene Set Enrichment Analysis of gene expression profiles from large-scale cancer patient samples. We demonstrate that lncGSEA is able to recapitulate lncRNA associated pathways supported by literature and experimental validations in multiple cancer types. Conclusions LncGSEA allows researchers to infer lncRNA regulatory pathways directly from clinical samples in oncology. LncGSEA is written in R, and is freely accessible at https://github.com/ylab-hi/lncGSEA.

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