Oat is one of the major cereals, especially important as a functional food. However, it is still an understudied crop. Genome repeat elements, known as simple sequence repeats (SSRs), can reveal genome dynamics and be used as markers for selection. SSRs across ancestral (Avena longiglumis and Avena insularis), wild hexaploid (Avena sterilis), and cultivated (Avena sativa) oat genomes were investigated to decouple the evolutionary impacts of polyploidization and domestication on genome architecture. The results revealed a two-step evolutionary trajectory: a "genomic shock" of allohexaploidization triggered a widespread expansion of repetitive elements in the wild A. sterilis, followed by artificial selection imposing a genetic bottleneck, resulting in a global contraction of SSRs in the cultivated A. sativa. In the AA subgenome, an asymmetric decrease of over 72,000 SSRs relative to its diploid ancestor was observed. Elimination of unstable mononucleotides in intergenic regions was observed, whereas conservation of GC-rich trinucleotides in some regions and localized expansion of SSR density within genic sequences on chromosome 3C were found. Genes exhibiting targeted SSR expansions were enriched for transcription factors and receptor-like kinases. Ultimately, these findings suggest that oat evolution is a dynamic structural reorganization driven by natural and artificial selection.
South Africa’s avian biodiversity faces challenges from both illegal activities and legal trade (e.g., breeding and parentage), requiring accurate species identification to inform conservation efforts, enforce regulations, and monitor wildlife trade. Morphological identification presents challenges due to variation within and similarities between species. DNA barcoding offers a reliable approach if supported by comprehensive reference libraries. This study established a DNA barcode reference library using Cytochrome c oxidase 1 (COI) sequences from 14 protected bird species targeted by illegal trade and at risk due to their conservation status, validated by strict Chain-of-Custody (CoC) protocols to ensure the reliability and admissibility of DNA barcoding in wildlife crime prosecutions. The study generated 69 COI sequences, covering 17.30% of South African bird species. Phylogenetic analysis distinguished 97.47% and 87.34% of species using Neighbour-Joining and Bayesian Inference trees, respectively, effectively differentiating protected species from each other and from species morphologically and phylogenetically similar. The limitations detected in this approach included high intraspecific variation, low interspecific distances, single barcode records, database gaps, and outdated taxonomic nomenclature. This study confirms DNA barcoding’s effectiveness and highlights the need for expanded reference databases for improved accuracy in forensic and research applications.
High-throughput multi-omics technologies generate high-dimensional, heterogeneous and noisy datasets, making robust biomarker discovery and disease modelling challenging. This review critically examines dimensionality reduction and representation strategies for multi-omics analysis while distinguishing three related but different goals: feature selection, feature extraction/representation learning, and multi-omics integration. We use sparse canonical correlation analysis (sCCA), variational autoencoders (VAEs), and graph convolutional networks (GCNs) as representative case studies spanning sparse linear, probabilistic deep-learning, and graph-based approaches. Rather than treating these methods as interchangeable, we compare their data requirements, assumptions, interpretability, ability to accommodate non-linearity and missing modalities, computational demands, and validation needs. Applications across cancer, cardiovascular and neurodegenerative disease, and across bulk, single-cell, spatial and imaging multi-omics are summarized in comparative tables. We conclude with practical guidance for matching method families to biological questions and emphasize that robust biomarker claims require leakage-free model development, stability assessment and independent validation.
Lipids are required for brain development and function. Impaired lipid metabolism is associated with various neurodevelopmental conditions. Recent work in Drosophila has begun to reveal the cellular and molecular basis of the role of lipids in this process. Here, we focus on the requirement of lipids in the neuroblasts, neurons, and glia of the Drosophila brain. Specifically, we discuss their role in neuroblast proliferation and fate determination, cellular morphology, neuronal fueling, and phagocytosis. We conclude by reviewing lipid profiling techniques and discuss the implications of these roles on neurodevelopment and disease.
The expansion of soybean ( Glycine max (L.) Merr.) cultivation into Western Canada is constrained by distinct abiotic stressors, including shorter growing seasons and drought. Heat shock proteins (HSPs) are critical regulators of stress adaptation; however, characterizing their expression in field conditions is often confounded by significant annual environmental variability. In this study, we integrated differential expression analysis, consensus Weighted Gene Co-expression Network Analysis (WGCNA), and rigorous protein–protein interactomic modeling across 4 years of field data (2018–2021) involving 10 genotypes grown in Eastern and Western Canada. By filtering temporal noise, we identified a stable, region-specific module that is computationally predicted to resolve into a highly coordinated physical chaperone complex. Subcellular characterization of this network revealed a robust core of chaperones targeting mitochondria and chloroplasts. Crucially, high-confidence predictions identified a physical interaction between a mitochondrial HSP60 hub and a nuclear domains rearranged methyltransferase, suggesting this chaperone complex potentially bridges localized organelle defense with systemic epigenetic regulation to safeguard metabolic capacity. These findings delineate a computationally modeled, geography-dependent stress response machine and provide ideal genetic targets for breeding programs aiming to improve soybean resilience in northern latitudes.
