Biology subjects

Michael J Axtell

Publications and source records attributed to Michael J Axtell.

4 recordsLinked to original sources

bioRxiv2016

AGO4 is specifically required for heterochromatic siRNA accumulation at Pol V-dependent loci in Arabidopsis thaliana

Significance statementGenome-wide characterization of AGO4-dependent siRNAs revealed that AGO4 is required for the accumulation of a small subset of heterochromatic siRNAs in Arabidopsis thaliana. These AGO4-depdenent siRNAs are likely secondary het-siRNAs produced by a self-reinforcing loop of RdDM. Slicing-defective AGO4 is unable to fully complement het-siRNA accumulation from an ago4 mutant, demonstrating the critical role of AGO4 catalytic ability in het-siRNA accumulation.\n\nSummary: 152; Introduction: 618; Results: 1291; Discussion: 1013; Experimental procedures: 881; Acknowledgements: 24; Figure legends: 568; Author contribution: 25; Conflict of interest: 13; Funding: 34; References: 1289\n\nSummaryIn plants, 24 nucleotide long heterochromatic siRNAs (het-siRNAs) transcriptionally regulate gene expression by RNA-directed DNA methylation (RdDM). The biogenesis of most het-siRNAs depends on the plant-specific RNA polymerase IV (Pol IV), and ARGONAUTE4 (AGO4) is a major het-siRNA effector protein. Through genome-wide analysis of sRNA-seq data sets, we found that AGO4 is required for the accumulation of a small subset of het-siRNAs. The accumulation of AGO4-dependent het-siRNAs also requires several factors known to participate in the effector portion of the RdDM pathway, including RNA POLYMERASE V (POL V), DOMAINS REARRANGED METHYLTRANSFERASE 2 (DRM2) and SAWADEE HOMEODOMAIN HOMOLOG 1 (SHH1). Like many AGO proteins, AGO4 is an endonuclease that can slice RNAs. We found that a slicing-defective AGO4 was unable to fully recover AGO4-dependent het-siRNA accumulation from ago4 mutant plants. Collectively, our data suggest that AGO4-dependent siRNAs are secondary siRNAs dependent on the prior activity of the RdDM pathway at certain loci.

Plant Biology↗

bioRxiv2016

Genome-Wide Analysis of Single Non-Templated Nucleotides in Plant Endogenous siRNAs and miRNAs

Plant small RNAs are subject to various modifications. Previous reports revealed widespread 3' modifications (truncations and non-templated tailing) of plant miRNAs when the 2'-C-methyltransferase HEN1 is absent. However, non-templated nucleotides in plant heterochromatic siRNAs have not been deeply studied, especially in wild-type plants. We systematically studied non-templated nucleotide patterns in plant small RNAs by analyzing small RNA sequencing libraries from Arabidopsis. tomato, Medicago, rice, maize, and Physcomitrella. Elevated rates of non-templated nucleotides were observed at the 3' ends of both miRNAs and endogenous siRNAs from wild-type specimens of all species. 'Off-sized' small RNAs, such as 25 and 23 nt siRNAs arising from loci dominated by 24 nt siRNAs, often had very high rates of 3'-non-templated nucleotides. The same pattern was observed in all species that we studied. Further analysis of 24 nt siRNA clusters in Arabidopsis revealed distinct patterns of 3'-non-templated nucleotides of 23 nt siRNAs arising from heterochromatic siRNA loci. This pattern of non-templated 3' nucleotides on 23 nt siRNAs is not affected by loss of known small RNA 3'-end modifying enzymes, and may result from modifications added to longer heterochromatic siRNA precursors.

Plant Biology↗

bioRxiv2016

Improved Placement of Multi-Mapping Small RNAs

High-throughput sequencing of small RNAs (sRNA-seq) is a popular method used to discover and annotate microRNAs (miRNAs), endogenous short interfering RNAs (siRNAs) and Piwi-associated RNAs (piRNAs). One of the key steps in sRNA-seq data analysis is alignment to a reference genome. sRNA-seq libraries often have a high proportion of reads which align to multiple genomic locations, which makes determining their true loci of origin difficult. Commonly used sRNA-seq alignment methods result in either very low precision (choosing an alignment at random) or sensitivity (ignoring multi-mapping reads). Here, we describe and test an sRNA-seq alignment strategy that uses local genomic context to guide decisions on proper placements of multi-mapped sRNA-seq reads. Tests using simulated sRNA-seq data demonstrated that this local-weighting method outperforms other alignment strategies using three different plant genomes. Experimental analyses with real sRNA-seq data also indicate superior performance of local-weighting methods for both plant miRNAs and heterochromatic siRNAs. The local-weighting methods we have developed are implemented as part of the sRNA-seq analysis program ShortStack, which is freely available under a general public license. Improved genome alignments of sRNA-seq data should increase the quality of downstream analyses and genome annotation efforts.\n\nArticle SummaryHigh-throughput sequencing of small RNAs (sRNA-seq) is a frequently used technique in the study of small RNAs. Alignment to a reference genome is a key step in processing sRNA-seq libraries, but suffers from enormous rates of multi-mapping reads. Current methods for sRNA-seq alignment either place these reads randomly or ignore them, both of which distort downstream analyses. Here, we describe a locality-based weighting approach to make better decisions of placement of multi-mapped sRNA-seq data, and test our implementation of this method. We find that our method gives superior performance in terms of placing multi-mapped sRNA-seq data. An implementation of our method is freely available within the ShortStack small RNA analysis program. Use of this method may dramatically improve genome-wide analyses of small RNAs.

Bioinformatics↗

bioRxiv2014

Butter: High-precision genomic alignment of small RNA-seq data

Eukaryotes produce large numbers of small non-coding RNAs that act as specificity determinants for various gene-regulatory complexes. These include microRNAs (miRNAs), endogenous short interfering RNAs (siRNAs), and Piwi-associated RNAs (piRNAs). These RNAs can be discovered, annotated, and quantified using small RNA-seq, a variant RNA-seq method based on highly parallel sequencing. Alignment to a reference genome is a critical step in analysis of small RNA-seq data. Because of their small size (20-30 nts depending on the organism and sub-type) and tendency to originate from multi-gene families or repetitive regions, reads that align equally well to more than one genomic location are very common. Typical methods to deal with multi-mapped small RNA-seq reads sacrifice either precision or sensitivity. The tool butter balances precision and sensitivity by placing multi-mapped reads using an iterative approach, where the decision between possible locations is dictated by the local densities of more confidently aligned reads. Butter displays superior performance relative to other small RNA-seq aligners. Treatment of multi-mapped small RNA-seq reads has substantial impacts on downstream analyses, including quantification of MIRNA paralogs, and discovery of endogenous siRNA loci. Butter is freely available under a GNU general public license.

Bioinformatics↗