Biology subjects

Moriyama, E. N.

Publications and source records attributed to Moriyama, E. N..

3 recordsLinked to original sources

bioRxiv2024

The Primate-Specific Presence of Interferon Regulatory Factor-5 Pseudogene 1

Interferon regulatory factor 5 (IRF5) is a critical transcription factor, regulating the production of inflammatory cytokines and interferons. Dysregulation of IRF5 has been linked to various autoimmune and inflammatory diseases. Pseudogenes may exhibit gene regulatory functions in various mechanisms. In this study, we find the human IRF5 pseudogene 1 (IRF5P1) is a chimeric processed pseudogene containing sequences derived from multiple sources. Remarkably, IRF5P1 is specific to higher primates such as apes and humans. We propose that IRF5P1 may have originated through the ancient integration of a retroviral sequence containing IRF5 mRNA from other animals. Interestingly, IRF5P1 overlaps with another gene, triple QxxK/R motif containing (TRIQK), and the antisense strand of IRF5P1 is predominantly expressed, likely as part of the TRIQK pre-mRNA. The antisense IRF5P1 RNA may regulate IRF5 expression through complementary binding to the IRF5 mRNA, and variants in the IRF5 gene may modulate this interaction. Our analysis suggests that IRF5P1 likely had positive effects on primate evolution, making IRF5P1 a favored component to maintain in the primate lineage, and rendering the pseudogene primate specific. Further studies are essential to elucidate the potential for IRF5P1 to participate in regulatory networks governing IRF5 activity and inflammation and innate immunity.

immunology↗

bioRxiv2022

A novel class of long small RNAs associates with Argonaute1 and is up-regulated by nutrient deprivation in the alga Chlamydomonas

Small RNAs (sRNAs) associate with Argonaute (AGO) proteins forming effector complexes with key roles in gene regulation and defense responses against molecular parasites. In multicellular eukaryotes, extensive duplication and diversification of RNA interference (RNAi) components have resulted in intricate pathways for epigenetic control of gene expression. The unicellular alga Chlamydomonas reinhardtii also has a complex RNAi machinery, including three AGOs and three Dicer-like (DCL) proteins. However, little is known about the biogenesis and function of most endogenous sRNAs. We demonstrate here that Chlamydomonas contains uncommonly long sRNAs (>26 nt), which associate preferentially with AGO1. Somewhat reminiscent of animal PIWI-interacting RNAs, these long sRNAs are derived from moderately repetitive genomic clusters and their biogenesis appears to be Dicer-independent. Interestingly, long sRNA encoding sequences have been conserved and amplified in phylogenetically related Chlamydomonas species. Additionally, expression of several long sRNAs increases substantially under nutrient deprivation, correlating with the downregulation of predicted target transcripts. We hypothesize that the transposon-like sequences encoding long sRNAs might have been ancestrally targeted for silencing by the RNAi machinery but, during evolution, some long sRNAs might have fortuitously acquired endogenous target genes and become integrated into gene regulatory networks.

genetics↗

bioRxiv2020

A consensus-based ensemble approach to improve de novo transcriptome assembly

BackgroundSystems-level analyses, such as differential gene expression analysis, co-expression analysis, and metabolic pathway reconstruction, depend on the accuracy of the transcriptome. Multiple tools exist to perform transcriptome assembly from RNAseq data. However, assembling high quality transcriptomes is still not a trivial problem. This is especially the case for non-model organisms where adequate reference genomes are often not available. Different methods produce different transcriptome models and there is no easy way to determine which are more accurate. Furthermore, having alternative-splicing events exacerbates such difficult assembly problems. While benchmarking transcriptome assemblies is critical, this is also not trivial due to the general lack of true reference transcriptomes. ResultsIn this study, we first provide a pipeline to generate a set of the benchmark transcriptome and corresponding RNAseq data. Using the simulated benchmarking datasets, we compared the performance of various transcriptome assembly approaches including both de novo and genome-guided methods. The results showed that the assembly performance deteriorates significantly when alternative transcripts (isoforms) exist or for genome-guided methods when the reference is not available from the same genome. To improve the transcriptome assembly performance, leveraging the overlapping predictions between different assemblies, we present a new consensus-based ensemble transcriptome assembly approach, ConSemble. ConclusionsWithout using a reference genome, ConSemble using four de novo assemblers achieved an accuracy up to twice as high as any de novo assemblers we compared. When a reference genome is available, ConSemble using four genome-guided assemblies removed many incorrectly assembled contigs with minimal impact on correctly assembled contigs, achieving higher precision and accuracy than individual genome-guided methods. Furthermore, ConSemble using de novo assemblers matched or exceeded the best performing genome-guided assemblers even when the transcriptomes included isoforms. We thus demonstrated that the ConSemble consensus strategy both for de novo and genome-guided assemblers can improve transcriptome assembly. The RNAseq simulation pipeline, the benchmark transcriptome datasets, and the script to perform the ConSemble assembly are all freely available from: http://bioinfolab.unl.edu/emlab/consemble/.

bioinformatics↗