Biology subjects

Owoyemi, A.

Publications and source records attributed to Owoyemi, A..

3 recordsLinked to original sources

bioRxiv2024

Genome assembly of the southern pine beetle (Dendroctonus frontalis Zimmerman) reveal the origins of gene content reduction in Dendroctonus

Dendroctonus frontalis, also known as southern pine beetle (SPB), represents the most damaging forest pest in the southeastern United States. Strategies to predict, monitor and suppress SPB outbreaks have had limited success. Genomic data are critical to inform on pest biology and to identify molecular targets to develop improved management approaches. Here, we produced a chromosome-level genome assembly of SPB using long-read sequencing data. Synteny analyses confirmed the conservation of the core coleopteran Stevens elements and validated the bona fide SPB X chromosome. Transcriptomic data were used to obtain 39,588 transcripts corresponding to 13,354 putative protein-coding loci. Comparative analyses of gene content across 14 beetle and 3 other insects revealed several losses of conserved genes in the Dendroctonus clade and gene gains in SPB and Dendroctonus that were enriched for loci encoding membrane proteins and extracellular matrix proteins. While lineage-specific gene losses contributed to the gene content reduction observed in Dendroctonus, we also showed that widespread misannotation of transposable elements represents a major cause of the apparent gene expansion in several non-Dendroctonus species. Our findings uncovered distinctive features of the SPB gene complement and disentangled the role of biological and annotation-related factors contributing to gene content variation across beetles.

genomics↗

bioRxiv2024

Degradation determinants are abundant in human noncanonical proteins

The comprehensive characterization of human proteins, a key objective in contemporary biology, has been revolutionized by the identification of thousands of potential novel proteins through ribosome profiling and proteomics. Determining the physiological activity of these noncanonical proteins has proven difficult, because they are encoded by different types of coding regions and tend to share no sequence similarity with canonical polypeptides. Evidence from immunopeptidomic assays combined with a better understanding of the quality control of protein synthesis suggest that many noncanonical proteins may possess low stability in the cellular environment. Here, we tested this hypothesis by analyzing the frequency of multiple sequence features eliciting either proteasomal degradation or autophagy across 91,003 canonical (annotated) protein isoforms and 232,460 noncanonical proteins. Overall, noncanonical proteins were enriched for degradation-related features compared to all canonical proteins. Notably, degradation determinants were also enriched in canonical protein isoforms starting with a non-methionine amino acid. Analyses of original and shuffled sequences showed evidence of selective pressure either against or towards the accumulation of specific degradation signatures only in major isoforms of canonical proteins. However, stability was significantly higher in noncanonical proteins with evidence of phenotypic effects when knocked-out in cell lines. Notably, we found that the C-terminal tail hydrophobicity represents a reliable proxy for degradation propensity with potential applications in identifying functional noncanonical proteins. These findings underscore the critical role of degradation processes in regulating the half-life of noncanonical proteins and demonstrate the power of degradation-associated signatures in discriminating noncanonical genes likely to encode for biologically functional molecules.

genomics↗

bioRxiv2022

Accurate identification of de novo genes in plant genomes using machine learning algorithms

De novo gene birth--the evolution of new protein-coding genes from ancestrally noncoding DNA--is increasingly appreciated as an important source of genetic and phenotypic innovation. However, the frequency and overall biological impact of de novo genes (DNGs) remain controversial. Large-scale surveys of de novo genes are critical to address these issues, but DNG identification represents a persistent challenge due to the lack of standardized protocols and the laborious analyses traditionally used to detect DNGs. Here, we introduced novel approaches to identify de novo genes that rely on Machine Learning Algorithms (MLAs) and are poised to accelerate DNG discovery. We specifically investigated if MLAs developed in one species using known DNGs can accurately predict de novo genes in other genomes. To maximize the applicability of these methods across species, we relied only on DNA and protein sequence features that can be easily obtained from annotation data. Using hundreds of published and newly annotated DNGs from three angiosperms, we trained and tested both Decision Tree (DT) and Neural Network (NN) algorithms. Both MLAs showed high levels of accuracy and recall within-genomes. Although accuracies and recall decreased in cross-species analyses, they remained elevated between evolutionary closely related species. A few training features, including presence of a protein domain and coding probability, held most of the MLAs predictive power. In analyses of all genes from a genome, recall was still elevated. Although false positive rates were relatively high, MLA screenings of whole-genome datasets reduced by up to ten-fold the number of genes to be examined by conventional comparative genomic methods. Thus, a combination of MLAs and traditional strategies can significantly accelerate the accurate discovery of DNG and the annotation in angiosperm genomes.

genomics↗