Biology subjects

Arendsee, Z.

Publications and source records attributed to Arendsee, Z..

6 recordsLinked to original sources

bioRxiv2023

Viral protease NIa-Pro increases MEDIATOR SUBUNIT16 cleavage and plant susceptibility to turnip mosaic virus and its aphid vector Myzus persicae.

Plant viruses, such turnip mosaic virus (TuMV), both trigger and inhibit host plant defense responses, including defenses that target their insect vectors, such as aphids. TuMV infection and its protein, NIa-Pro (nuclear inclusion protease a), suppress aphid-induced plant defenses, however the mechanisms of this suppression are still largely unknown. In this study, we determined that NIa-Pros protease activity is required to increase aphid performance on host plants and that 40 transcripts with predicted NIa-Pro cleavage sequences are regulated in Arabidopsis plants challenged with aphids and/or virus compared to healthy controls. One of the candidates, MEDIATOR 16 (MED16), regulates the transcription of ethylene (ET)/jasmonic acid (JA)-dependent defense responses against necrotrophic pathogens. Using immunoblots, mutants, bioassays, and qRT-PCR, we show that a nuclear localization signal is cleaved from MED16 in virus-infected plants and in the presence of NIa-Pro along with the presence of aphids, suggesting MED16 functions in the nucleus may be impacted. Consistent with this, aphid induction of the MED16-dependent transcript of PLANT DEFENSIN 1.2 (PDF1.2), was reduced in virus-infected plants and in plants expressing NIa-Pro compared to controls, and NIa-Pros protease activity was required for this reduction. Finally, we show performance of both the virus and the aphid vector was enhanced on med16 mutant Arabidopsis compared to controls. Overall, this study demonstrates MED16 regulates defense responses against both the virus and the aphid and provides insights into the mechanism by which TuMV suppresses anti-virus and anti-herbivore defenses.

plant biology↗

bioRxiv2022

Characterizing a century of genetic diversity and contemporary antigenic diversity of N1 neuraminidase in IAV from North American swine

Influenza A viruses (IAV) of the H1N1 classical swine lineage became endemic in North American swine following the 1918 pandemic. Additional human-to-swine transmission events after 1918, and a spillover of H1 viruses from wild birds in Europe, potentiated a rapid increase in genomic diversity via reassortment between introductions and the endemic classical swine lineage. To determine mechanisms affecting reassortment and evolution, we conducted a phylogenetic analysis of N1 and paired HA swine IAV genes in North America between 1930 and 2020. We described fourteen N1 clades within the N1 Eurasian avian lineage (including the N1 pandemic clade) and the N1 classical swine lineage. Seven N1 genetic clades had evidence for contemporary circulation. To assess antigenic drift associated with N1 genetic diversity, we generated a panel of representative swine N1 antisera and quantified the antigenic distance between wild-type viruses using enzyme-linked lectin assays and antigenic cartography. Within the N1 lineage, antigenic similarity was variable and reflected shared evolutionary history. Sustained circulation and evolution of N1 genes in swine had resulted in significant antigenic distance between the N1 pandemic clade and classical swine lineage. We also observed a significant increase in the rate of evolution in the N1 pandemic clade relative to the classical lineage. Between 2010 and 2020, N1 clades and N1-HA pairings fluctuated in detection frequency across North America, with hotspots of diversity generally appearing and disappearing within two years. We also identified frequent N1-HA reassortment events (n = 36), which were rarely sustained (n = 6) and sometimes also concomitant with the emergence of new N1 genetic clades (n = 3). These data form a baseline from which we can identify N1 clades that expand in range or genetic diversity that may impact viral phenotypes or vaccine immunity and subsequently the health of North American swine.

evolutionary biology↗

bioRxiv2019

Maximizing prediction of orphan genes in assembled genomes

Proteins encoded by newly-emerged genes ("orphan genes") share no sequence similarity with proteins in any other species. They provide organisms with a reservoir of genetic elements to quickly respond to changing selection pressures. Here, we systematically assess the ability of five gene annotation pipelines to accurately predict genes in genomes according to phylostratal origin. BRAKER and MAKER are existing, popular ab initio tools that infer gene structures by machine learning. Direct Inference is an evidence-based pipeline we developed to predict gene structures from alignments of RNA-Seq data. The BIND pipeline integrates ab initio predictions of BRAKER and Direct inference; MIND combines Direct Inference and MAKER predictions. We use highly-curated Arabidopsis and yeast annotations as gold-standard benchmarks, and cross-validate in rice. Each pipeline under-predicts orphan genes (as few as 11 percent, under one prediction scenario). Increasing RNA-Seq diversity greatly improves prediction efficacy. The combined methods (BIND and MIND) yield best predictions overall, BIND identifying 68% of annotated orphan genes and 99% of ancient genes in Arabidopsis. We provide a light weight, flexible, reproducible solution to improve gene prediction.

bioinformatics↗

bioRxiv2019

Recycling RNA-Seq Data to Identify Candidate Orphan Genes for experimental analysis

The "dark transcriptome" can be considered the multitude of sequences that are transcribed but not annotated as genes. We evaluated expression of 6,692 annotated genes and 29,354 unannotated ORFs in the Saccharomyces cerevisiae genome across diverse environmental, genetic and developmental conditions (3,457 RNA-Seq samples). Over 48% of the transcribed ORFs have translation evidence. Phylostratigraphic analysis infers most of these transcribed ORFs would encode species-specific proteins ("orphan-ORFs"); hundreds have mean expression comparable to annotated genes. These data reveal unannotated ORFs most likely to be protein-coding genes. We partitioned a co-expression matrix by Markov Chain Clustering; the resultant clusters contain 2,468 orphan-ORFs. We provide the aggregated RNA-Seq yeast data with extensive metadata as a project in MetaOmGraph, a tool designed for interactive analysis and visualization. This approach enables reuse of public RNA-Seq data for exploratory discovery, providing a rich context for experimentalists to make novel, experimentally-testable hypotheses about candidate genes.

bioinformatics↗

bioRxiv2019

synder: inferring genomic orthologs from synteny maps

Ortholog inference is a key step in understanding the evolution and function of a gene or other genomic feature. Yet often no similar sequence can be identified, or the true ortholog is hidden among false positives. A solution is to consider the sequences genomic context. We present the generic program, synder, for tracing features of interest between genomes based on a synteny map. This approach narrows genomic search-space independently of the sequence of the feature of interest. We illustrate the utility of synder by finding orthologs for the Arabidopsis thaliana 13-member gene family of Nuclear Factor YC transcription factor across the Brassicaceae clade.

bioinformatics↗

bioRxiv2018

phylostratr: A framework for phylostratigraphy

MotivationThe goal of phylostratigraphy is to infer the evolutionary origin of each gene in an organism. Currently, there are no general pipelines for this task. We present an R package, phylostratr, to fill this gap, making high-quality phylostratigraphic analysis accessible to non-specialists.\n\nResultsPhylostratigraphic analysis entails searching for homologs within increasingly broad clades. The highest clade that contains all homologs of a gene is that genes phylostratum. We have created a general R-based framework, phylostratr, for estimating the phylostratum of every gene in a species. The program can fully automate an analysis: select species for a balanced representation of each strata, retrieve the sequences from UniProt, build BLAST databases, run BLAST, infer homologs for each gene against each subject species, determine phylostrata, and return summaries and diagnostics. phylostratr allows extensive customization. A user may: modify the automatically-generated clade tree or use their own tree; provide custom sequences in place of those automatically retrieved from UniProt; replace BLAST with an alternative algorithm; or tailor the method and sensitivity of the homology inference classifier. phylostratr also offers proteome quality assessments, false-positive diagnostics, and checks for missing organelle genomes. We show the utility of phylostratr through case studies in Arabidopsis thaliana and Saccharomyces cerevisiae.\n\nAvailabilityphylostratr source code and vignettes are available on GitHub at https://github.com/arendsee/phylostratr\n\nContactevewurtele@gmail.com

bioinformatics↗