Origin of $α$-satellite repeat arrays from mitochondrial molecular fossils -- sequential insertion, expansion, and evolution in the nuclear genome

Yihang Zhou

Home/Archive/arXiv

arXiv · 2501.02284

Origin of $α$-satellite repeat arrays from mitochondrial molecular fossils -- sequential insertion, expansion, and evolution in the nuclear genome

Yihang Zhou

View original source ↗Read source document ↗Export metadata ↓

Abstract

Alpha satellite DNA is large tandem arrays of 150-400 bp units, and its origin remains an evolutionary mystery. In this research, we identified 1,545 alpha-satellite-like (SatL) repeat units in the nuclear genome of jewel wasp Nasonia vitripennis. Among them, thirty-nine copies of SatL were organized in two palindromic arrays in mitochondria, resulting in a 50% increase in the genome size. Strikingly, genomic neighborhood analyses of 1,516 nuclear SatL repeats revealed that they are located in NuMT (nuclear mitochondrial DNA) regions, and SatL phylogeny matched perfectly with mitochondrial genes and NuMT pseudogenes. These results support that SatL arrays originated from ten independent mitochondria insertion events into the nuclear genome within the last 500,000 years, after divergence from its sister species N. giraulti. Dramatic repeat GC-percent elevation (from 33.9% to 50.4%) is a hallmark of rapid SatL sequence evolution in mitochondria due to GC-biased gene conversion facilitated by the palindromic sequence pairing of the two mitochondrial SatL arrays. The nuclear SatL repeat arrays underwent substantial copy number expansion, from 12-15 (SatL1) to over 400 copies (SatL4). The oldest SatL4B array consists of four types of repeat units derived from deletions in the AT-rich region of ancestral repeats, and complex high-order structures have evolved through duplications. We also discovered similar repeat insertions into the nuclear genome of Muscidifurax, suggesting this mechanism can be common in insects. This is the first report of the mitochondrial origin of nuclear satellite sequences, and our findings shed new light on the origin and evolution of satellite DNA.

Explore related subjects

q-bio.GN

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Yihang Zhou. 2025-02-12. Origin of $α$-satellite repeat arrays from mitochondrial molecular fossils -- sequential insertion, expansion, and evolution in the nuclear genome. https://arxiv.org/abs/2501.02284

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Transcriptomic Models for Immunotherapy Response Prediction Show Limited Cross-cohort Generalisability

Immune checkpoint inhibitors (ICIs) have transformed cancer therapy; yet substantial proportion of patients exhibit intrinsic or acquired resistance, making accurate pre-treatment response prediction a critical unmet need. Transcriptomics-based biomarkers derived from bulk and single-cell RNA sequencing (scRNA-seq) offer a promising avenue for capturing tumour-immune interactions, yet the cross-cohort generalisability of existing prediction models remains unclear.We systematically benchmark nine state-of-the-art transcriptomic ICI response predictors, five bulk RNA-seq-based models (COMPASS, IRNet, NetBio, IKCScore, and TNBC-ICI) and four scRNA-seq-based models (PRECISE, DeepGeneX, Tres and scCURE), using publicly available independent datasets unseen during model development. Overall, predictive performance was modest: bulk RNA-seq models performed at or near chance level across most cohorts, while scRNA-seq models showed only marginal improvements. Pathway-level analyses revealed sparse and inconsistent biomarker signals across models. Although scRNA-seq-based predictors converged on immune-related programs such as allograft rejection, bulk RNA-seq-based models exhibited little reproducible overlap. PRECISE and NetBio identified the most coherent immune-related themes, whereas IRNet predominantly captured metabolic pathways weakly aligned with ICI biology. Together, these findings demonstrate the limited cross-cohort robustness and biological consistency of current transcriptomic ICI prediction models, underscoring the need for improved domain adaptation, standardised preprocessing, and biologically grounded model design.

q-bio.GN↗

arXiv2026

Large Language Model Agents for Evidence Based Genetic Disease Severity Classification

Disease severity classification for genetic conditions is subjective and labor-intensive, creating bottlenecks in genomic screening, where commercial panels vary widely in size and overlap. We developed an autonomous AI agent integrating Reasoning and Acting (ReAct) with Retrieval-Augmented Generation (RAG) to classify 10,211 Human Phenotype Ontology terms. It uses American College of Medical Genetics (ACMG)-endorsed severity guidelines and American College of Obstetricians and Gynecologists (ACOG) quality-of-life criteria to retrieve PubMed literature, generate interpretable reasoning chains, and independently verify claims. At the phenotype level, using expert-curated cohorts, the agent achieved 93.55% accuracy (MCC 0.9237) with 82.6% to 91.4% of claims supported by direct evidence or valid inferences. Gene-level severity was aggregated across 8,738 pairs, identifying 3,283 autosomal recessive pairs with severe or profound presentations. External validation showed 95.2% concordance with Mackenzie's Mission gene list. This system enables standardized panel design by providing reliable, automated classification supported by direct evidence.

q-bio.GN↗

arXiv2026

Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation

Single-cell and spatial foundation models promise transferable biological representations, yet their generality remains largely untested across modalities, biological domains and analytical tasks. We benchmarked six representative models, Nicheformer, CellPLM, scGPT-spatial, GenePT, scELMo and Novae, using a harmonised framework spanning scRNA-seq, spatial transcriptomics and Perturb-seq. We evaluated zero-shot and continually pretrained clustering, supervised annotation, marker-gene concordance and perturbation prediction. Model performance was strongly conditional: expression-trained cell-level transformers best resolved many cell-identity tasks, spatial and graph-aware models better preserved tissue architecture, and language-derived gene embeddings were competitive for selected perturbation-response metrics. No model dominated across tasks, and rankings shifted with modality, preprocessing, tokenisation, biological prior, domain shift and metric choice. This benchmark provides practical guidance for model selection and argues that future models should be judged by biological generalisation, interpretability and perturbation-grounded validity, not by scale or leaderboard performance alone.

q-bio.GN↗