Biology subjects

Yin, M.

Publications and source records attributed to Yin, M..

At least 19 recordsLinked to original sources

bioRxiv2026

AutoScreen: AI Co-Scientist System for Target Discovery in Functional Genomics

Target discovery in functional genomics remains largely manual and time-consuming, lacking systematic tools for efficient and reproducible gene-level hypothesis generation. We introduce AutoScreen, an AI co-scientist system supporting target discovery through both Pre-screen Design, which constructs perturbation libraries de novo from free-text research descriptions, and Post-screen Analysis, which re-ranks experimental screen hits by integrating statistical scores with biological context. AutoScreen leverages the complementary strengths of multiple specialist agents that perform deep research into multi-modal evidence, information restructuring, parallel searches across >26 biomedical databases, evidence synthesis, and target review to provide transparent, explainable gene prioritization with pipeline provenance. Across 320 genome-scale CRISPR screens as expert-curated benchmarks, AutoScreen achieved a ~19% increase in validated-hit recovery among its top 100 predictions, relative to the strongest agent baseline, and required a 1.2-fold smaller library to recover the same number of hits at the top-500 reference point. AutoScreen reached mean average precision more than two orders-of-magnitude above random baseline. Further, we validated AutoScreen in cancer immune-evasion case studies focusing on natural killer (NK) and T-cell therapeutics. AutoScreen recovered NK-resistance genes that were initially lower-ranked in a leukemia screen, moving MUC1, PDPN, and LRRC15 from raw ranks of 118, 81, 1384 to 5, 44, 659, respectively. In follow-up tumor killing assay with primary human NK cells, individual perturbation validated all three hits successfully. Next, in prospective benchmarking across two cytotoxic T-cell-killing screens, AutoScreen recovered 77.1% of ground-truth hits called by the consensus of two gold-standard analysis pipelines (FDR<0.1), achieving 8% improvement over the strongest general-purpose LLM agent baseline. Finally, we re-analyzed >500 public datasets to construct the AutoScreen Resource Hub, a growing knowledge base of pre-computed, annotated reports for CRISPR screens, RNA-seq differential expression, and gene-level UK Biobank genome-wide association studies. This agentic AI approach enables real-time genomics benchmark construction that are continuously updated, expanded, for evaluating frontier AI co-scientists. Overall, AutoScreen enables AI-powered target discovery to be more auditable, scalable, extensible, and reproducible.

bioengineering↗

bioRxiv2026

Towards Continuous Home Monitoring for Dementia: A Real-Time mmWave Radar Framework for Activity Classification and Tracking

Millimeter-wave radar can quietly monitor health and behavior at home, which is vital for supporting people living with dementia. Most studies, however, remain limited to short-term testing in controlled spaces. Real-world deployment requires robust activity classification as a prerequisite: vital-sign and behavioral sensing require fundamentally different processing pipelines, and absent periods need to be reliably distinguished from stationary states. Bridging the critical gap between controlled laboratory demonstrations and continuous home monitoring, this paper introduces a self-adapting radar framework that extracts meaningful behavioral segments from massive, unconstrained real-world data. The system performs continuous real-time activity classification (stationary, walking, and absent) and target localization, selectively directing downstream processing to the most informative segments. It addresses key real-world deployment challenges including adaptive thresholding across subjects and environments, and walking detection under naturalistic activity conditions. Prior to integration with the Minder platform, the system was validated in a fully instrumented studio apartment against ground truth. Across 12 subjects, the system achieved an overall classification accuracy of 0.98, with F1 scores of 0.99 for absence and stationary states, and 0.95 for walking. Event-based evaluation yielded a per-subject walking sensitivity of 0.916{+/-} 0.058 and F1 score of 0.935 {+/-}0.030. Localization root mean square error during movement was 0.40 m. The results demonstrate reliable performance suitable for transitioning to long-term real-world home deployment.

bioengineering↗

bioRxiv2026

Chd8 haploinsufficiency leads to molecular layer heterotopias and age-dependent cortical expansion

Mutations in the chromatin remodeler CHD8 are associated with autism and macrocephaly. While mouse models of Chd8 haploinsufficiency recapitulate brain overgrowth, the specific cellular mechanisms and developmental timing that lead to these anatomical abnormalities remain poorly understood. Here, we conducted 3D imaging of Chd8V986*/+ mouse brains using magnetic resonance imaging followed by tissue clearing and cellular resolution light-sheet microscopy across embryonic and postnatal developmental stages. We found that brain overgrowth occurs postnatally, driven by an increase in non-neuronal cells prior to volumetric expansion. Unexpectedly, we identified prevalent molecular layer heterotopias (MLH) within the frontal cortex of Chd8V986*/+ mice composed of neurons breaking through the pial surface during embryonic development and persisting throughout life. Increased incidence of MLH, previously identified in individuals with idiopathic autism, was replicated across three independent Chd8+/- mouse models and present across multiple genomic backgrounds, establishing aberrant neuronal migration as a core feature of Chd8 haploinsufficiency.

neuroscience↗

bioRxiv2026

ProtFlow: Flow Matching-based Protein Sequence Design with Comprehensive Protein Semantic Distribution Learning and High-quality Generation

Designing protein sequences with desired properties is a fundamental task in protein engineering. Recent advances in deep generative models have greatly accelerated this design process. However, most existing models face the issue of distribution centralization and focus on local compositional statistics of natural sequences instead of the global semantic organization of protein space, which confines their generation to specific regions of the distribution. These problems are amplified for functional proteins, whose sequence patterns strongly correlate with semantic representations and exhibit a long-tailed functional distribution, causing existing models to miss semantic regions associated with rare but essential functions. Here, we propose ProtFlow, a generative model designed for comprehensive semantic distribution learning of protein sequences, enabling high-quality sequence generation. ProtFlow employs a rectified flow matching algorithm to efficiently capture the underlying semantic distribution of the protein design manifold, and introduces a reflow technique enabling one-step sequence generation. We construct a semantic integration network to reorganize the representation space of large protein language models, facilitating stable and compact incorporation of global protein semantics. We pretrain ProtFlow on 2.6M peptide sequences and fine-tune it on antimicrobial peptides (AMPs), a representative class of therapeutic proteins exhibiting unevenly distributed activities across pathogen targets. Experiments show that ProtFlow outperforms state-of-the-art methods in generating high-quality peptides, and AMPs with desirable activity profiles across a range of pathogens, particularly against underrepresented bacterial species. These results demonstrate ProtFlows effectiveness in capturing the full training distribution and its potential as a general framework for computational protein design.

bioinformatics↗

bioRxiv2026

Ophiuchus-Ab: A Versatile Generative Foundation Model for Advanced Antibody-Based Immunotherapy

Antibodies exhibit extraordinary specificity and diversity in antigen recognition and have become a central class of therapeutics across a wide range of diseases. Despite this clinical success, antibody design remains fundamentally challenging. Antibody function emerges from intricate and highly coupled interactions between heavy and light chains, which complicate sequence-function relationships and limit the rational design of developable antibodies. Here, we reveal that modeling antibody sequence space at the level of paired heavy and light chains is essential to faithfully capture inter-chain dependencies, enabling a deeper understanding of antibody function and facilitating antibody discovery. We present Ophiuchus-Ab, a generative foundation model pre-trained on largescale paired antibody repertoires within a diffusion language modeling framework, unifying antibody generation and representation learning in a single probabilistic formulation. This framework excels diverse antibody design tasks, including CDR infilling, antibody humanization, and light-chain pairing. Beyond generation, diffusion-based pre-training yields transferable representations that enable accurate prediction of antibody properties, including developability, binding affinity, and specificity, even in low-data regimes. Together, these results establish Ophiuchus-Ab as a versatile foundation model for modeling antibodies, providing a foundation for next-generation antibody-based immunotherapy.

bioinformatics↗

bioRxiv2025

Spatially resolved translational dysregulation in Grin2a+/- mouse model of schizophrenia

Loss-of-function (LoF) mutations of GRIN2A, encoding the GluN2A subunit of N-methyl-D-aspartate receptor (NMDAR), confer a high risk for schizophrenia (SCZ)1-3, yet how they affect diverse brain cell types remains poorly understood. Here, we combined subcellular-resolution spatial omics technologies, STARmap4 and RIBOmap5, to jointly resolve single-cell transcriptomes and translatomes for 3,447 genes in the brains of Grin2a+/- mice and their wild-type littermates across 538,188 cells. Translational dysregulation was markedly more prominent than transcriptional changes in neurons. Across neuronal subtypes, a set of genes including Camk2a, Arc, Egr1, Egr3, Chmp2b, and Pja2 exhibited translational reduction in a Grin2a gene dose-dependent fashion, suggesting a connection between NMDAR hypofunction and reduced protein synthesis of downstream synaptic plasticity effectors. In interneurons (particularly parvalbumin interneurons), a strong reduction of Gad2 translation implies loss of inhibitory function in cortical microcircuits, which has long been hypothesized for SCZ pathophysiology. Non-neuronal cell types including astrocytes, oligodendrocytes, and vascular cells also exhibited region-specific translational changes in neurotransmitter transport, lipid synthesis, myelination, and stress response pathways, some of which co-varied with regional neuron state. Together, our study reveals brain-wide translation dysregulation as a critical mechanism underlying SCZ pathophysiology.

neuroscience↗

bioRxiv2025

Trade-offs between phage resistance and conjugative ability shape the ecological and evolutionary response of a multidrug resistance plasmid to plasmid-dependent phage

Phage therapy is a promising alternative to antibiotics to treat multidrug resistant infections. Plasmid dependent phages (PDPs) are particularly attractive as therapeutics because they can both kill targeted pathogen cells, whilst also potentially preventing the further spread of antibiotic resistance genes encoded by plasmids. However, we lack experimental studies of the ecological and evolutionary response of multidrug resistance plasmids against plasmid dependent phage treatment under ecologically relevant scenarios allowing plasmid conjugation. We experimentally evolved populations of E. coli carrying the multidrug resistance RP4 plasmid with the PRD1 PDP under conditions where conjugation was associated with either strong or weak benefits. When opportunities for conjugation were rare, PRD1 only transiently suppressed the conjugative plasmid population due to the rapid evolution of PRD1 resistant plasmids that lacked conjugative ability. Increasing the ecological opportunity for conjugation enhanced plasmid suppression and delayed the evolution of PRD1 resistant plasmids. PRD1 resistance was associated with reduced conjugative ability, but this trade-off was complex due to the heterogeneous impacts of resistance mutations on pilus production and conjugative ability. Mutations and IS element insertions in conjugation genes caused a wide range of PRD1 resistance phenotypes, ranging from complete resistance (virB4) to partial resistance (trbB, trbL). Bioinformatic analysis of publicly available IncP plasmid sequences showed that truncated variants of VirB4 protein are common in natural populations, suggesting that plasmid-dependent phages are an important selective pressure in microbial communities. Our results demonstrate an evolutionary trade-off between conjugative ability and phage resistance that cannot be easily circumvented by plasmids. Targeting multidrug resistance plasmids with PDPs is likely to drive loss of conjugation limiting the transfer of antibiotic resistance genes in bacterial communities.

evolutionary biology↗

bioRxiv2025

Historical Contingency Limits Adaptive Diversification in a Spatially Structured Environment

Understanding how genotype-by-environment (GxE) interactions influence evolutionary trajectories and contribute to historical contingency is key to predicting evolution. In spatially structured, heterogeneous environments, populations often diversify into ecotypes, resulting in niche specialization. However, the ability to specialize depends not only on ecological opportunity but also on whether genetic variation permits access to novel niches, as genetic disruptions may inhibit adaptation unless alternative trajectories or compensatory mutations are subsequently accessible. Previously, we demonstrated that Escherichia coli populations rapidly diversify into two co-existing ecotypes in a nutrient-rich, spatially structured environment. The adaptation of both ecotypes results in benefits only perceived in the spatially structured culture tube environment. Here, diversification is initiated by first-step mutations associated with the overexpression of genes encoding the type 1 fimbria, the major attachment pilus involved in biofilm development, enabling range expansion and allowing E. coli to occupy the surface-air interface of the culture tube. To investigate how first-step mutations shape evolutionary trajectories, we experimentally evolved wild-type and fimbrial-deficient ({Delta}fimA) E. coli for 91 days in both structured (tube) and unstructured (flask) environments. While a fimA deletion initially confers a fitness benefit by avoiding the cost of insufficient biofilm formation, it ultimately prevents range expansion in structured environments and is not compensated by expression of cryptic fimbriae by the end of experimental evolution. As a result, {Delta}fimA populations show constrained adaptation in tubes compared to wild-type. Alternatively, both genotypes perform similarly in flasks, where biofilm formation is not advantageous and whole population sequencing reveals that flask-evolved populations similar early mutational trajectories. Our results highlight the ruggedness of the adaptive landscape in structured environments and show how an initially beneficial mutation can trap a lineage on a local fitness peak, underscoring the importance of GxE interactions and early mutational events in shaping the predictability and contingency of evolutionary outcomes.

evolutionary biology↗

bioRxiv2025

Genome-Bench: A Scientific Reasoning Benchmark from Real-World Expert Discussions

In this short report, we present an automated pipeline tailored for the genomics domain and introduce Genome-Bench, a new benchmark constructed from over a decade of scientific forum discussions on genome engineering. Our pipeline transforms raw interactions into a reinforcement learningfriendly multiple-choice questions format, supported by 3000+ high-quality questionanswer pairs spanning foundational biology, experimental troubleshooting, tool usage, and beyond. To our knowledge, this is the first end-to-end pipeline for teaching LLMs to reason from scientific discussions, with promising potential for generalization across scientific domains beyond biology. The dataset is available at https://huggingface.co/datasets/Mingyin0312/Genome-Bench.

genomics↗

bioRxiv2025

A biGWAS strategy reveals the genetic architecture of the interaction between wheat and Blumeria graminis f. sp. tritici

BackgroundWheat powdery mildew, caused by Blumeria graminis f. sp. tritici (Bgt) is one of the most significant diseases affecting global wheat production. Breeding for disease resistance (R) genes against Bgt often follows a boom-bust cycles, where cultivars with effective resistance are widely deployed on an expanding area until virulence emerges in the avirulence (Avr) pathogen population. While extensive effort has been devoted to identifying and cloning R genes, Avr genes remain relatively understudied, limiting our understanding of R-Avr interactions. That said, understanding R-Avr interactions is crucial for developing durable resistance strategies. ResultsWe conducted whole genome sequencing on 245 Bgt isolates collected from major wheat-growing regions in China, identifying 120 genetically unique isolates. Each of these unique isolates was inoculated and phenotyped on a diverse panel of wheat germplasm. Through Genome-Wide Association Analysis (GWAS) in both Bgt and wheat, we identified 65 Avr loci and 251 R loci overlapping with nine and eight cloned Avr or R genes, respectively. On average, each isolate carries eight Avr alleles, ranging from one to 17. Little geographical preference for Avr alleles or their combinations was observed, suggesting that disease resistance breeding against this pathogen should be coordinated at the national level. The level of resistance level is positively correlated with the number of R alleles carried by a wheat line, and the average frequency of an R allele is 2% among the wheat panel, indicating the potential for accumulating R alleles in breeding programs. We mapped 212 R-Avr interactions based on joint GWAS using both plant and pathogen genomes and cross-species epistasis, where a network of interactions was formed between wheat and Bgt. These interactions indicate that pyramiding five major R loci has the potential to confer resistance to half of the Avr loci. As a proof of concept, we successfully verified two previously described R-Avr pairs using tobacco experiments. Furthermore, we provided molecular validation evidence for three new Avr genes, including Bgt-50651, BgtE-5826 and BgtE-20009, and two new R-Avr interactions. Among them, Bgt-50651 interacts with Pm1a, Pm2a, and another unidentified R gene located near the Pm6/Pm52 interval. ConclusionOur study provides a framework for understanding the genetic interaction between plants and pathogens. The discovery of novel R/Avr loci and their complex interaction network underscores the need to integrate crop and pathogen genetic background, particularly the R/Avr allele composition, into breeding program design. These findings have significant implications for developing durable resistance strategies in wheat alongside offering valuable insights into the broader dynamics of plant-pathogen interactions.

genomics↗

bioRxiv2025

Reshaping epistatic network modules enhances wheat yield potential

Increasing wheat yield is a key approach to ensuring global food security and an enduring focus in crop breeding. Here, we assembled a panel of 3,030 wheat lines to dissect the genetic mechanisms of yield improvement. We conducted large-scale field trials across five ecological environments over two consecutive years to evaluate yield performance and constructed a haplotype atlas using the Wheat 660K genotype array. 234 quantitative trait loci (QTLs) and 522 haplotype blocks (HBs) were identified for 14 traits through genome-wide association studies (GWAS), including 10 QTLs and 35 HBs associated with grain yield. Genomic analysis revealed that wheat yield improvement is primarily driven by changes in the epistatic network rather than the introduction of new haplotype segments. SNPs or HBs explain 53.3%-62.9% of the phenotypic variation in yield, whereas epistasis explains 70.4%, highlighting the role of epistasis in yield improvement. Moreover, yield improvement is significantly correlated with the accumulation of favorable epistatic modules. Pedigree-based analysis revealed that the modules remained relatively stable under the pressure of breeding selection, while new favorable modules were also created during hybrid breeding, highlighting that crop design breeding should be based on genetic modules. This study provides insights into genetic mechanism of wheat yield improvement and guidance for designing future wheat in the era of artificial intelligence.

genetics↗

bioRxiv2025

Elevated rates and biased spectra of mutations in anaerobically cultured lactic acid bacteria

The rate, spectrum, and biases of mutations represent a fundamental force shaping biological evolution. Convention often attributes oxidative DNA damage as a major driver of spontaneous mutations. Yet, despite the contribution of oxygen to mutagenesis and the ecological, industrial, and biomedical importance of anaerobic organisms, relatively little is known about the mutation rates and spectra of anaerobic species. Here, we present the rates and spectra of spontaneous mutations assessed anaerobically over 1000 generations for three fermentative lactic acid bacteria species with varying levels of aerotolerance: Lactobacillus acidophilus, Lactobacillus crispatus, and Lactococcus lactis. Our findings reveal highly elevated mutation rates compared to the average rates observed in aerobically respiring bacteria with mutations strongly biased towards transitions, emphasizing the prevalence of spontaneous deamination in these anaerobic species and highlighting the inherent fragility of purines even under conditions that minimize oxidative stress. Beyond these overarching patterns, we identify several novel mutation dynamics: positional mutation bias around the origin of replication in Lb. acidophilus, a significant disparity between observed and equilibrium GC content in Lc. lactis, and repeated independent deletions of spacer sequences from within the CRISPR locus in Lb. crispatus providing mechanistic insights into the evolution of bacterial adaptive immunity. Overall, our study provides new insights into the mutational landscape of anaerobes, revealing how non-oxygenic factors shape mutation rates and influence genome evolution.

genetics↗

bioRxiv2025

Population-scale gene expression analysis reveals the contribution of expression diversity to the modern wheat improvement

Changes in gene expression are crucial for crop breeding, yet population genomics has primarily focused on sequence polymorphisms rather than gene expression diversity. The strategy of using single genome reference for RNA-seq analysis could not handle introgression bias, especially for hexaploidy wheat. Here, we conducted RNA-seq for 328 wheat lines, including representative diverse landraces and elite cultivars from China and the United States, to investigate the role of gene expression variation in shaping agronomic traits. Using pan-genome resources, we identified 20,615 more transcripts than using the Chinese Spring reference genome alone. We constructed a pan-gene atlas regulatory map through eQTL analysis, demonstrating that genes introgressed from wild relatives were under tight genetic control. Genes responding to environmental stress show higher activity after introgressed into the wheat genome, demonstrating how long-term breeding selection impacted the gene expression regulation of targeted introgression. Multi-omics modeling identified 231 high-confidence candidate genes for 34 field agronomic traits and the seedling resistance phenotypes of 8 powdery mildew isolates. More than one fifth of those candidates have no homolog in Chinese Spring reference genome. By utilizing the indexed KN9204 EMS library, 80% candidates showed significant trait difference between wild type and mutant lines. Furthermore, directional shifts in genes of which expression were changed by breeding improvement demonstrated distinct adaptations to local environments. Our study constructed a pan-gene atlas to correct the reference bias of reads mapping in RNA-seq studies and revealed the expression patterns of introgressed genes within the wheat genome and their regulatory mechanisms, which highlighted the impact of breeding selection on gene expression of the worlds most important crop.

plant biology↗

bioRxiv2025

Peroxisome proliferator-activated receptor gamma (PPARG)-mediated myocardial salvage in acute myocardial infarction managed with left ventricular unloading and coronary reperfusion

Ischemic heart disease and acute myocardial infarction (AMI) is a leading cause of morbidity and mortality. Improvements have been made in coronary interventions to restore blood flow, but ischemia/reperfusion (I/R) injury significantly impacts clinical outcomes. We previously reported that activation of percutaneous mechanical unloading of the left ventricle (LV) with a transvalvular axial-flow device simultaneously with reperfusion improves myocardial salvage. However, the underlying mechanisms, potential adjuvant pharmacological interventions and the timing of the use of LV unloading as a cardioprotective approach in AMI are not well understood. This study investigated a) the mechanisms associated with improved myocardial salvage, b) a pharmacological intervention, and c) the timing of LV unloading. Following 90 minutes of ischemia, adult swine were subjected to reperfusion alone, simultaneous unloading with reperfusion, upfront unloading with delayed reperfusion, upfront reperfusion with delayed unloading, or reperfusion with concurrent use of esmolol and milrinone. Compared to controls, the simultaneous group had a 47% increase in myocardial salvage following AMI. This was associated with increased expression of neutrophil degranulation, macrophage activation, iNOS signaling, wound healing, and PPAR signaling. From these pathways, PPARG (peroxisome proliferator-activated receptor gamma) emerged as a potential cardioprotective gene that was uniquely overexpressed in the simultaneously unloaded and reperfused myocardium. Next, we showed PPARG agonism with rosiglitazone reduces mitochondrial oxygen demand in cardiomyocytes and in vivo, improves myocardial salvage following I/R injury in C57BL6/J mice. Thiazolidinediones (TZDs), such as rosiglitazone could be investigated as therapies combined with simultaneous LV unloading and coronary interventions to mitigate reperfusion injury. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=129 SRC="FIGDIR/small/637726v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1c4d6e6org.highwire.dtl.DTLVardef@1f8d064org.highwire.dtl.DTLVardef@396eddorg.highwire.dtl.DTLVardef@9d932f_HPS_FORMAT_FIGEXP M_FIG C_FIG

physiology↗

bioRxiv2024

CLCNet: a contrastive learning and chromosome-aware network for genomic prediction in plants

Genomic selection (GS) leverages genome-wide markers and phenotypes to predict breeding values, with its effectiveness largely dependent on the accuracy of genomic prediction (GP) models. However, GP methods often struggle to capture inter-individual variability and are limited by the curse of dimensionality, where the number of SNPs far exceeds the sample size. To address these challenges, we present CLCNet (Contrastive Learning and Chromosome-aware Network), a novel deep learning framework that integrates contrastive learning and chromosome-aware feature modeling. CLCNet comprises two key components: (i) a contrastive learning module that enhances the models ability to capture fine-grained, genotype-dependent phenotypic differences among individuals, and (ii) a chromosome-aware module that captures structured feature selection at both chromosome and genome levels, thereby distilling the most informative SNPs. We evaluated CLCNet across four crop species, covering ten agronomically important traits, and compared it with a diverse set of classical linear, machine learning, and deep learning models. CLCNet achieved superior prediction performance, with statistically significant improvements in Pearson correlation coefficient (PCC), ranging from 0.34% to 12.19% over baseline, together with reduced mean squared error (MSE). Performance gains were more pronounced for traits with moderate linkage disequilibrium (LD; r2 = 0.21-0.36) and high heritability (h2 > 0.66), such as those in maize, rapeseed, and soybean. For cotton traits characterized by high LD (r2 = 0.74) and lower heritability (h2 < 0.50), CLCNet maintained robust performance without degradation. Overall, these results demonstrate that CLCNet is an effective framework for improving genomic prediction accuracy and holds strong potential for practical applications in plant breeding. Short abstractCLCNet is a novel deep learning framework for genomic prediction that integrates contrastive learning with chromosome-aware feature selection. By jointly modeling inter-individual genotype-phenotype variation and chromosomal genomic structure, CLCNet improves prediction accuracy under high-dimensional, low-sample-size conditions. Across four crop species and ten agronomic traits, CLCNet consistently outperformed classical statistical, machine learning, and existing deep learning models. The framework also identified biologically relevant SNPs and candidate genes, demonstrating its potential for practical applications in genomic selection and computational plant breeding. Key pointsO_LIWe propose CLCNet, a multi-task deep learning framework that integrates contrastive learning with chromosome-aware feature selection for genomic prediction, under high-dimensional, low-sample-size conditions. C_LIO_LIThe chromosome-aware module explicitly exploits genomic structural information to select representative and informative SNPs across chromosomes. C_LIO_LIContrastive learning improves model robustness by stabilizing representation learning and reducing the influence of random effects across samples. C_LIO_LIBy complementing GWAS analyses, CLCNet provides additional insights into genotype-phenotype relationships with potential relevance for gene discovery. C_LI Biographical NoteJiangwei Huang is a PhD candidate at the Institute of Genetics and Developmental Biology, Chinese Academy of Sciences. His research focuses on genomic prediction, deep learning, and computational plant breeding. Zhihan Yang is a PhD candidate at the Institute of Genetics and Developmental Biology, Chinese Academy of Sciences. Her research interests include genomic prediction and bioinformatics. Rongcheng Han is an associate professor at the Institute of Genetics and Developmental Biology, Chinese Academy of Sciences. His research focuses on bioinformatics and plant phenomics. Yuqiang Jiang is a professor at the Institute of Genetics and Developmental Biology, Chinese Academy of Sciences. His research interests include plant genomics, plant phenomics and genetic improvement. Organization descriptionThe Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, is a leading research institute focusing on genetics, genomics, molecular breeding, bioinformatics, and systems biology in plants and animals.

genomics↗

bioRxiv2024

Two divergent haploid nuclei shaped the landscape of population diversity in wheat stripe rust

Heterozygosity is a measure of allelic diversity within individuals. Puccinia striiformis f. sp. tritici (Pst) is a highly heterozygous dikaryotic crop pathogen. Its source of heterozygosity variation and the contribution to adaptability are still unknown. By analyzing resequencing data of 266 worldwide Pst isolates, we found that Pst may have undergone an important historical hybridization event, introducing substantial diversity and leading to the divergence of its haploid genomes into two distinct haplotypes. Strains with both haplotypes exhibit higher individual allele diversity and wider geographical distribution. Between the two haploid genomes, 16% of the genome had diverged, scattered as mosaic blocks within the genome. These regions are enriched with genes displaying critical roles during infection of the plant host, and exhibit higher expression levels. In these regions, 8.0 Mb shows recombination fingerprints associated with virulence, while 4.9 Mb displays linkage across the entire genome. We demonstrated that sexual recombination in Pst is extensive and significant. Pst can gain genetic diversity and adaptability due to intra- and inter-species hybridization. Our study resolved the debate over the sources of individual allele diversity in Pst and expands the understanding of pathogen virulence evolution. These findings also suggest that interrupting the sexual reproduction of pathogens can be an effective strategy for controlling wheat stripe rust.

genetics↗

bioRxiv2024

Ophiostomatoid fungi associated with bark beetles infesting Pinus armandii, with characterizations of seven new species from western China

Ophiostomatoid fungi mainly refer to the order Ophiostomatales (Ascomycota), which are known to be symbiotic with bark beetles infesting pine trees. These fungi are primarily associated with Dendroctonus armandi and other bark beetles, significantly impacting the local pines Pinus armandii in western China. However, this fungal diversity must be further explored. This study aims to identify Ophiostomatoid fungi associated with P. armandii in Ningshaan, Shaanxi Province. A total of 225 Ophiostomatalean isolates were obtained from 46 gallery samples, accounting for 77.6 % of the total number of isolated strains. Twenty species were identified based on the phylogenetic analyses of ITS, LSU, {beta}-tubulin, and EF-1, as well as morphological characteristics. These include seven new taxa, Ceratocystiopsis rugosa, Graphilbum prolificum, G. ramosus, Masuyamyces huoditangensis, Ophiostoma flavescens, O. lenta, O. mediotumida, and thirteen known species. This study laid a theoretical foundation for understanding the relationship between the beetles and the ophiostomatalean fungi in the Ningshan region, China.

microbiology↗

bioRxiv2024

Ophiostomatalean fungi associated with Cyrtogenius luteus infested Pinus massoniana, including characterization of Masuyamyces qingyuanensis sp. nov. in China

Ophiostomatalean fungi refers to the species in the order Ophiostomatales of Ascomycota. This group of fungi comprises wood blue-stain agents and some species that are important pathogens of trees, nematodes, or humans. Most species in the Ophiostomatales have beneficial relationships with wood-boring insects such as bark beetles. Recent surveys were conducted to investigate the diversity of Ophiostomatoid fungi associated with Cyrtogenius luteus infesting Pinus massoniana in some areas of Guangdong, China. This study, 382 fungal strains were obtained, and 224 were identified as belonging to the Ophiostomatales order. Identification of these strains revealed that they spanned seven distinct genera. A new species, Masuyamyces qingyuanensis sp. nov., was discovered and identified through a combination of morphological and phylogenetic analyses across four gene regions: beta-tubulin (TUBB), internal transcribed spacer (ITS), large subunit ribosomal RNA (LSU), and translation elongation factor 1 alpha (EF1A).

microbiology↗