RESUMO
Single-cell RNA sequencing (RNA-seq) has revolutionized our understanding of cell biology, developmental and pathophysiological molecular processes, paving the way toward novel diagnostic and therapeutic approaches. However, most of the gene regulatory processes on the single-cell level are still unknown, including post-transcriptional control conferred by microRNAs (miRNAs). Like the established single-cell gene expression analysis, advanced computational expertise is required to comprehensively process newly emerging single-cell miRNA-seq datasets. A web server providing a workflow tailored for single-cell miRNA-seq data with a self-explanatory interface is currently not available. Here, we present SingmiR, enabling the rapid (pre-)processing and quantification of human miRNAs from noncoding single-cell samples. It performs read trimming for different library preparation protocols, generates automated quality control reports and provides feature-normalized count files. Numerous standard and advanced analyses such as dimension reduction, clustered feature heatmaps, sample correlation heatmaps and differential expression statistics are implemented. We aim to speed up the prototyping pipeline for biologists developing single-cell miRNA-seq protocols on small to medium-sized datasets. SingmiR is freely available to all users without the need for a login at https://www.ccb.uni-saarland.de/singmir.
Assuntos
MicroRNAs , Análise de Sequência de RNA , Análise de Célula Única , Software , MicroRNAs/genética , MicroRNAs/metabolismo , Análise de Célula Única/métodos , Humanos , Análise de Sequência de RNA/métodos , Perfilação da Expressão Gênica/métodos , Alinhamento de SequênciaRESUMO
Species respond dynamically to climate change and exhibit time lags. Consequently, species may not occupy their full climatic niche during range shifting. Here, we assessed climate niche tracking during recent range shifts of European and United States (US) birds. Using data from two European bird atlases and from the North American Breeding Bird Survey between the 1980s and 2010s, we analysed range overlap and climate niche overlap based on kernel density estimation. Phylogenetic multiple regression was used to assess the effect of species morphological, ecological and biogeographic traits on range and niche metrics. European birds shifted their ranges north and north-eastwards, US birds westwards. Range unfilling was lower than expected by null models, and niche expansion was more common than niche unfilling. Also, climate niche tracking was generally lower in US birds and poorly explained by species traits. Overall, our results suggest that dispersal limitations were minor in range shifting birds in Europe and the USA while delayed extinctions from unfavourable areas seem more important. Regional differences could be related to differences in land use history and monitoring schemes. Comparative analyses of range and niche shifts provide a useful screening approach for identifying the importance of transient dynamics and time-lagged responses to climate change. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.
Assuntos
Biodiversidade , Aves , Animais , Estados Unidos , Filogenia , Aves/fisiologia , Mudança Climática , América do Norte , EcossistemaRESUMO
BACKGROUND: Different production systems of livestock animals influence various factors, including the gut microbiota. METHODS: We investigated whether changing the conditions from barns to free-range chicken farming impacts the microbiome over the course of three weeks. We compared the stool microbiota of chicken from industrial barns after introducing them either in community or separately to a free-range environment. RESULTS: Over the six time points, 12 taxa-mostly lactobacilli-changed significantly. As expected, the former barn chicken cohort carries more resistances to common antibiotics. These, however, remained positive over the observed period. At the end of the study, we collected eggs and compared metabolomic profiles of the egg white and yolk to profiles of eggs from commercial suppliers. Here, we observed significant differences between commercial and fresh collected eggs as well as differences between the former barn chicken and free-range chicken. CONCLUSION: Our data indicate that the gut microbiota can undergo alterations over time in response to changes in production systems. These changes subsequently exert an influence on the metabolites found in the eggs. The preliminary results of our proof-of-concept study motivate larger scale observations with more individual chicken and longer observation periods.
RESUMO
The identification of targetomes remains a challenge given the pleiotropic effect of miRNAs, the limited effects of miRNAs on individual targets, and the sheer number of estimated miRNA-target gene interactions (MTIs), which is around 44,571,700. Currently, targetome identification for single miRNAs relies on computational evidence and functional studies covering smaller numbers of targets. To ensure that the targetome analysis could be experimentally verified by functional assays, we employed a systematic approach and explored the targetomes of four miRNAs (miR-129-5p, miR-129-1-3p, miR-133b, and miR-873-5p) by analyzing 410 predicted target genes, both of which were previously associated with Parkinson's disease (PD). After performing 13,536 transfections, we validated 442 of the 705 putative MTIs (62,7%) through dual luciferase reporter assays. These analyses increased the number of validated MTIs by at least 2.1-fold for miR-133b and by a maximum of 24.3-fold for miR-873-5p. Our study contributes to the experimental capture of miRNA targetomes by addressing i) the ratio of experimentally verified MTIs to predicted MTIs, ii) the sizes of disease-related miRNA targetomes, and iii) the density of MTI networks. A web service to support the analyses on the MTI level is available online ( https://ccb-web.cs.uni-saarland.de/utr-seremato ), and all the data have been added to the miRATBase database ( https://ccb-web.cs.uni-saarland.de/miratbase ).