Biology subjects

Zhao, H.

Publications and source records attributed to Zhao, H..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Whole-genome resequencing-based comparative variant analysis identifies candidate genes associated with cross-beak phenotype in Huiyang Bearded chickens

Cross-beaks are deemed a threat to poultry health, productivity, and animal welfare. Nevertheless, due to sporadic cases, heterogeneity of gene loci and incomplete dominance, the molecular mechanism of cross-beak formation, especially the degree of cross, is not yet clear. Thus, we screen key genes and reveal the possible phenotypic formation mechanism of cross-beak by comparison with different degrees of deformity in Huiyang Bearded chickens by compare whole-genome resequencing-based variant analysis. Comparative analysis between cross-beak and normal-beaked chickens identified differential variants in several candidate genes, including CDH11, CTNNAL1, NRXN3, NRXN1, CDH5, SDC3, and DHFR. Genes harboring these variants were enriched in pathways related to cell adhesion molecules and metabolic processes, with functional annotations involving cell-cell adhesion and neural crest cell migration. Comparative analysis between chickens with severe and slight cross-beak deformities identified additional candidate genes, including MRPL21, NSUN2, DDX55, GNB3, and NFKB2. These genes were associated with enriched terms and pathways related to focal adhesion, amyotrophic lateral sclerosis, steroid 7 -hydroxylase activity, and skin-barrier establishment. These findings provide a preliminary catalogue of genetic variants and candidate genes for future functional studies of cross-beak development and severity in chickens.

genomics↗

bioRxiv2026

LiverDCP: A Disease-Cell-Protein Framework for Multi-scale Modeling of Disease Biology

Understanding how molecular interactions give rise to disease phenotypes across cellular contexts remains a central challenge in biomedical research. Here, we introduce a Disease-Cell-Protein (DCP) paradigm for modeling multi-scale disease biology, which jointly represents disease states, cellular composition, and protein interaction networks within a unified graph architecture. We instantiate this paradigm in the liver as LiverDCP by integrating LiverHomo, a harmonized single-cell atlas of liver diseases, with proteome-wide predicted protein-protein interactions to construct over 280 context-specific interactomes across diverse liver disease and cellular conditions. LiverDCP employs a multi-context representation learning strategy that enables joint training across hundreds of disease-cell environments, capturing shared interaction principles while preserving context-specific variation. LiverDCP incorporates pretrained protein sequence-derived features through a geometry-aware two-phase training scheme that preserves embedding structure while improving predictive performance. The resulting DCP protein embeddings reveal extensive rewiring of protein functional states across diseases, providing a transferable representation for downstream biomedical applications. Without GWAS supervision during representation learning, LiverDCP enables disease-risk gene classification and identifies cell types through which genetic risk may act. For therapeutic target discovery, LiverDCP recovers established Phase II+ MASH targets and prioritizes previously unrecognized candidates from the unannotated proteome, with 26 of the top 50 predictions showing independent PubMed evidence related to MASH biology. Context-specific interaction analysis further provides mechanistic hypotheses for less-characterized candidates. Together, these results establish DCP as a generalizable framework for connecting molecular interactions, cellular context, genetic risk, and therapeutic opportunities across complex diseases.

systems biology↗

bioRxiv2026

Mechanisms of resilience to autosomal dominant Alzheimer's disease via oligogenic modulation of rare variants in the entorhinal cortex

Two PSEN1 E280A carriers have presented extreme protection against autosomal dominant Alzheimers disease (ADAD), with over two decades of delay for dementia onset. One of them, a male heterozygous for the RELN-COLBOS protective variant showed increased neuronal density in the entorhinal cortex 1. We conducted a deep phenotyping and genotyping study of the entorhinal cortex in protected and unprotected PSEN1 E280A cases, sporadic AD, and non-demented controls. We used single nuclei and spatial transcriptomics, whole genome sequencing, and candidate genotype-associated expression changes (GAEC) analysis. Our results showed unique neuronal and oligodendrocytic populations in the male RELN-COLBOS patient. Unique RELN positive inhibitory interneurons were enriched in cortical Layer I, while unique abundant ADAMTSL1 positive excitatory neurons were distributed in Layers II/III and Layer Va. These neurons and mature myelinated oligodendrocytes benefitted from increased expression of LRP6 receptor, functioning as a non-canonical receptor for the mutated Reelin protein encoded by RELN-COLBOS. Finally, GAEC and pathway enrichment analyses suggested that other mutations enhanced RELN-COLBOS effects in oligodendrocytes in the male RELN-COLBOS patient, explaining the phenotypic differences with his sister, a RELN-COLBOS carrier with no evident protection from ADAD. Our findings suggest that extreme deviations of the PSEN1 E280A phenotype are more likely attributed to oligogenic effects, including simultaneous mutations occurring in genes including ITGA2, involved in single molecular pathways, such as the Integrins / Focal Adhesion pathway, as potential disease modifiers for Alzheimers disease (AD).

neuroscience↗

bioRxiv2026

IL-1β/IL-6 signaling circuit in the tumor microenvironment drives prostate cancer development

Despite considerable progress in elucidating mechanisms leading to castration-resistant prostate cancer (CRPC), insight into the early stages of prostate cancer initiation and progression remains limited. Genomic drivers of prostate cancer initiation have been defined through patient tumor sequencing, but the subsequent events responsible for local tissue invasion are poorly understood. Here we leverage a well-studied genetically engineered mouse prostate cancer model (Hi-Myc) that, based on robust and reproducible kinetics for transitioning from pre-invasive prostatic intraepithelial neoplasia (PIN) to invasive prostate adenocarcinoma (PCa), provides an ideal system to systematically address this question using single-cell analysis. Surprisingly, the transcriptomic profiles of early PIN lesions are indistinguishable from those of late-stage, highly invasive tumor cells, suggesting that MYC activation at the PIN stage establishes a transcriptional program that is fully capable of driving invasion but is restrained by the local tumor microenvironment (TME). Indeed, we find that progression to PCa is associated with progressive infiltration of IL-1{beta}+ tumor-infiltrating macrophages at the PIN stage that, based on immunodepletion and cytokine neutralization experiments, are required for the PIN-to-PCa transition. Mechanistically, IL-1{beta} from macrophages acts directly on prostate fibroblasts, leading to the release of IL-6, which drives invasion by activating IL-6R in tumor cells. Collectively, these findings identify a pro-tumorigenic IL-1{beta}/IL-6 signaling circuit mediated through local macrophages and fibroblasts that unleashes the full oncogenic potential of a cancer driver (MYC) activated at the PIN stage. We also find evidence of this circuit in other (non-MYC-driven) prostate cancer models as well as human prostate and lung adenocarcinoma, with implications for TME-specific targeted therapeutics in early-stage disease.

cancer biology↗

bioRxiv2026

Skiaviridae: a large DNA virus family that persistently infects thraustochytrid protists

Genomics and metagenomics have transformed our understanding of the diversity of large DNA viruses infecting eukaryotic microorganisms. Using long-read sequencing, we discovered ubiquitous, co-culturing nucleocytoviruses in several thraustochytrid protists. These viruses are closely related to SmDNAV, an 'orphan' virus of the thraustochytrid Sicyoidochytrium minutum. Transcriptomics, proteomics and electron microscopy demonstrate viral gene expression and particle formation in these cultures without obvious defects in culture growth. We also found SmDNAV-type viruses associated with previously sequenced thraustochytrids. Phylogenetic analysis reveals that the SmDNAV and SmDNAV-like genomes belong to a hitherto unrecognized order of Nucleocytoviricota, here named "Skiavirales". All fifteen identified skiavirus genomes lack numerous viral hallmark genes including DNA-dependent RNA polymerase; all but three also lack DNA polymerase family B (PolB). PolB phylogeny suggests a specific relationship between Skiavirales and the recently discovered mirusviruses, which belong to a different viral realm. Gene exchange between skiaviruses, co-occurring mirusviruses, and host nuclear genomes shows that persistent infection by diverse large DNA viruses provides an opportunity for virus-virus and virus-host co-evolution in thraustochytrids and, perhaps, other microbial eukaryotes.

evolutionary biology↗

bioRxiv2026

Tracing the 'Panda of the Sea': Species-Specific qPCR Assays for eDNA Monitoring of Critically Endangered Bahaba taipingensis in the Pearl River Estuary

Chinese Bahaba (Bahaba taipingensis, Sciaenidae) is a Critically Endangered, Grade I state-protected marine fish in China. Reliable, non-invasive monitoring tools are urgently needed to inform conservation and restocking efforts. We developed and validated three TaqMan probe-based qPCR assays targeting mitochondrial 12S rRNA, ND5, and control region (D-loop) loci for species-specific detection of B. taipingensis. Primer-probe sets were designed from complete mitochondrial genomes and evaluated in silico against GenBank and in vitro against tissue DNA from B. taipingensis and closely related sciaenids, plus positive eDNA samples; sensitivity was quantified using serial dilutions of synthetic target DNA. All three assays showed high specificity and consistent amplification; 12S rRNA assay (Bhb_12S) was selected for field screening based on superior low-copy sensitivity and minimal by-products. We applied the Bhb_12S assay to 414 seawater samples collected from 23 sites in western Hong Kong during 2025 surveys, detecting B. taipingensis eDNA in the September samples (0.71-2.85 copies per L; mean 2.05), with positive detections confirmed by Sanger sequencing. Overall, these validated qPCR assays provide a robust molecular toolkit for non-invasive monitoring of B. taipingensis and will aid conservation planning, restocking evaluation, and long-term biodiversity assessments in the Pearl River Estuary and adjacent coastal waters.

ecology↗

bioRxiv2026

Broad-spectrum polerovirus resistance conferred by a potato TIR-NLR immune receptor

Potato leafroll virus (PLRV) is an economically important viral disease of potato (S. tuberosum). Genetic resistance to this phloem-limited virus is rare, and no cloned resistance (R) genes have been reported. Rladg confers resistance to PLRV in an Andean potato landrace, LOP-868 (Velasquez et al. 2007). We identified the functional Rladg gene as a homolog of the tomato TIR-NLR-encoding Bs4. Rladg interacts with the serine protease domain of the PLRV protein P1, which is essential for virus replication. This recognition is independent of the proteases enzymatic activity, and the Rladg immune receptor oligomerizes upon direct association with the protease. Like PLRV, many poleroviruses contain a serine protease. Despite their diverse amino acid sequences, these proteases are predicted to share similar structures. Rladg recognizes all ten tested polerovirus proteases, suggesting a conserved structural recognition mechanism. We propose that Rladgs broad recognition capacity could enable resistance to poleroviruses in many crop species. Rladg is the first R-gene reported to confer resistance to a phloem-limited pathogen and could provide enhanced resistance to many economically important poleroviruses.

plant biology↗

bioRxiv2026

NLR from soybean Rsv1 locus confers broad-spectrum resistance to soybean mosaic virus G1-G7 strains by recognizing viral P3 protein

Nucleotide-binding leucine-rich repeat (NLR) immune receptor genes are of significant value in disease resistance breeding and the control of viral diseases. Soybean mosaic virus (SMV) poses a serious threat to soybean production and the Rsv1 locus in soybean cultivar Suweon 97 confers broad-spectrum resistance against SMV strains G1 to G7; however, this locus harbors no fewer than 18 NLR genes, and thus the broad-spectrum antiviral mechanisms underlying the Rsv1 locus remain poorly understood to date. Here, we established a rapid and highly efficient screening system for cloning NLR genes from soybean Rsv1 locus and identified a broad-spectrum antiviral NLR gene 13g184900 from this highly complicated locus. The NLR encoded by 13g184900 can recognize viral P3 protein from all SMV strains (G1-G7) and another potyvirus Bean common mosaic virus (BCMV). The coiled-coil (CC) domain of this NLR directly interacts with viral P3 protein. Additionally, we showed that this NLR originated from wild soybean accession in East China and has been introduced into several soybean cultivars during domestication. Collectively, we developed a high-throughput screening system for identifying NLR genes in soybean and our study provides new mechanistic perspective on how the Rsv1 locus mediates the broad-spectrum resistance to all SMV G1-G7 strains.

plant biology↗

bioRxiv2026

IgG4⁺ plasma cell enrichment and {lambda}-chain-biased BCR remodeling drive low-grade autoimmunity in chronic obstructive pulmonary disease

BackgroundThis study aimed to elucidate B cell subset pathology in COPD, a poorly characterized area, with a focus on its similarities to and differences from classical autoimmune disorders. MethodsThe single-cell RNA-sequencing (scRNA-seq) data from COPD and autoimmune diseases were obtained from Gene Expression Omnibus (GEO) for comparative analyses of B cell subsets and functions via differentially expressed genes (DEGs), KEGG, protein-protein interaction (PPI), and cell-cell communication analyses. Serum IgG4 was measured by ELISA and correlated with clinical parameters. The peripheral blood B cells were sorted by flow cytometry for single-cell B cell receptor (BCR) sequencing. A v-Abl/Bcl2 pro-B cell line was stimulated with cigarette smoke extract (CSE) to assess abnormal development in vitro. ResultsIn lung tissue, IgG4+ plasma cells were enriched and expressed BCR activation/inflammatory genes and TNF/NF-{kappa}B/MAPK pathways. Serum IgG4 concentrations correlated negatively with pre-and post-bronchodilator FEV1/FVC. B cell interacted with monocytes, macrophages, fibroblasts and endothelial cells via IL-1{beta}/IL-6, integrin and chemokine signalling, contributing to chronic inflammation and remodelling. In peripheral blood, transitional T1 B cells were increased, accompanied by {lambda}-chain enrichment and increased IGLV1-47 usage, as well as enrichment of autoimmune pathways. In the bone marrow, the numbers of pre-B I cells were increased while those of small pre-B III cells were reduced, with altered expression of BCR development genes. CSE stimulation of the pro-B cell line reduced {lambda}5 expression in a concentration-dependent manner. ConclusionsThe autoimmune abnormalities in COPD appear more restricted, although IgG4 antibody generation may contribute to immune-mediated lung damage.

immunology↗

bioRxiv2026

Spatial co-expression and cell-cell communication inference from spatially resolved transcriptomics with CONCISE

Cell-cell communication is fundamental to tissue organization, homeostasis, and disease progression. Recent advances in spatial transcriptomics provide unprecedented opportunities to systematically characterize ligand-receptor interactions directly within intact tissues. However, robust inference of spatial ligand-receptor interactions remains challenging because intrinsic features of spatial transcriptomics data, including spatial autocorrelation, variation in total molecular counts, and measurement errors, can induce spurious spatial co-expression and lead to inflated false-positive results. Most existing methods do not adequately account for these confounding factors, limiting the reliability of inferred cellular communication. Here, we present CONCISE, a statistical method for spatially constrained co-expression and ligand-receptor interaction inference that jointly models spatial autocorrelation, variation in total molecular counts, measurement errors, and spatial proximity constraints. CONCISE combines efficient moment-based parameter estimation with analytical hypothesis testing, enabling fast and statistically rigorous inference without restrictive distributional assumptions. Through extensive simulations, real-data permutation experiments, and biologically motivated negative-control analyses across different spatial transcriptomics platforms, we show that most existing methods presented inflated false-positive rates, whereas CONCISE achieved well-calibrated inference, robust false-positive control, and improved detection power. Application of CONCISE to high-resolution MERFISH and CosMx datasets from intestinal inflammation and non-small cell lung cancer further highlights its biological utility in disease contexts. CONCISE uncovered inflammation-associated fibroblast-specific interactions during intestinal inflammation and delineated complex tumor-immune and tumor-stromal signaling networks within the tumor microenvironment.

bioinformatics↗

bioRxiv2026

Senataxin loss induces cGAS–STING-mediated mitochondrial dysfunction

Ataxia with oculomotor apraxia type 2 (AOA2) is a rare neurodegenerative disease caused by loss-of-function mutations in Senataxin, which encodes an RNA:DNA helicase. Many studies on Senataxin loss focus on its putative roles in regulating transcription and RNA transcript localization. However, several phenotypes remain underexplored, including metabolic dysregulation associated with ataxias. Using Senataxin-deficient mouse cells, we observed increased nuclear and genomic instability, as well as innate immune activation via the cGAS-STING axis. We also observed elevated ROS levels, decreased mitochondrial function, and hyperfused mitochondria. Importantly, mitochondrial dysfunction depends on cGAS/STING activity, indicating that the two phenotypes are functionally connected. Senataxin-deficient mice and AOA2 patient cells similarly exhibit spontaneous innate immune activation, and AOA2 patient cells also show decreased mitochondrial function. Our work identifies a previously unseen phenotype for AOA2 in which loss of Senataxin results in mitochondrial dysfunction promoted by cGAS and STING. SummaryThis study demonstrates a previously unidentified phenotype in AOA2 and Senataxin research in which cGAS-STING promotes mitochondrial dysregulation in Setx-/- MEFs. Further, these innate immune activation and mitochondrial dysregulated phenotypes are recapitulated in AOA2 patient cells.

Cell Biology↗

bioRxiv2026

Learning Perturbation Effects Through Contrastive Alignment of Multimodal Biological Embeddings

Multimodal single-cell perturbation screens offer a scalable approach for characterizing the effects of genetic and chemical interventions on cellular state. However, most existing representation-learning methods are tailored to a single perturbation modality and fail to explicitly incorporate external semantic knowledge, which limits their ability to generalize across datasets and perturbation types. Here, we introduce PertOmni, a CLIP-style multimodal representation-learning framework that aligns transcriptomic perturbation signatures with text-derived embeddings of curated genes and compound descriptions, as well as image-derived embeddings from cell paintings. PertOmni jointly trains a shared transcriptomic encoder and dataset-specific text encoders using a masked contrastive objective that emphasizes within-cell-type discrimination while mitigating confounding effects arising from cell-type heterogeneity. We evaluate the produced joint embedding space on bi-directional retrieval, drug-gene interaction inference, and perturbation prediction across both small-molecule and CRISPRi perturbation datasets, and demonstrate consistent improvements over strong baseline methods.

bioinformatics↗

bioRxiv2026

CWF19L2 couples pre-mRNA alternative splicing with the maternal-to-zygotic transition to safeguard female fertility

The maternal-to-zygotic transition (MZT) requires precise spatiotemporal execution of pre-mRNA alternative splicing (AS), yet the core splicing machinery driving this developmental reprogramming remains incompletely understood. Here, we identify the CWF19-like protein 2 (CWF19L2) as an indispensable pre-mRNA AS regulator that safeguards against oocyte and early embryo competence defects (OECD). While murine germline-specific depletion of Cwf19l2 spares morphological folliculogenesis, oocyte maturation, or fertilization, it induces complete female sterility characterized by profound developmental arrest at the 2-cell stage. Mechanistically, maternal deficiency of CWF19L2 localized to nuclear speckles disrupts transcription-splicing-translation coupling, collapsing AS homeostasis during maternal reserves and zygotic genome activation. We further demonstrate that CWF19L2 orchestrates the pre-mRNA splicing network through directly binding to target transcripts and indirectly modulating via interacting with the core spliceosomal factor PRPF8. Importantly, exogenous Cwf19l2 mRNA partially rescues the embryonic arrest. Together, our findings establish CWF19L2 as an indispensable AS engine during the MZT, providing a mechanistic foundation for OECD and a novel molecular etiology for female infertility.

Cell Biology↗

bioRxiv2026

Rapid Determination of Drug-to-Antibody Ratios in Antibody Drug Conjugates Using Ultrafast Microdroplet Digestion Technology

Accurate determination of drug-to-antibody ratios (DARs) is essential for the development, quality control, and performance evaluation of antibody-drug conjugates (ADCs); yet conventional analytical approaches often require extensive sample preparation, long analysis time, and substantial sample consumption. The peak distribution of intact ADCs is highly complex due to inherent glycosylation heterogeneity and variable drug conjugation. By applying enzymatic digestion, ADC can be converted into smaller subunits or deglycosylated species, thereby significantly simplifying the mass spectral profile. This reduction in structural heterogeneity facilitates clearer peak assignment and enables more accurate and reliable DAR quantification. Herein, we report an ultrafast microdroplet digestion-mass spectrometry strategy for rapid DAR characterization of ADCs. Microdroplet enzymatic digestion of antibodies and ADCs occurs within microsecond-time scales during spray ionization, enabling direct online subunit analysis with minimal sample preparation. The method was validated using NIST monoclonal antibody (mAb) conjugated to ADC mimics spanning low to high DAR (0-14) ranges, Cetuximab-derived ADC mimics (DAR[~]5) with complex glycosylation, and the commercial ADC Kadcyla (DAR[~]3.5). Consistent DAR values were obtained across multiple enzymatic workflows (IdeS, EndoS2, and EndoF3) with good reproducibility (%CV typically <5%). This approach substantially reduces analysis time while maintaining analytical accuracy and structural specificity, providing a rapid, sensitive platform for high-throughput ADC characterization and process monitoring. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/729562v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@23145corg.highwire.dtl.DTLVardef@10ddd88org.highwire.dtl.DTLVardef@14b1370org.highwire.dtl.DTLVardef@1e94ad6_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2026

CodeCytos: AI-assisted spatial molecular imaging analysis via code-augmented agent action space

Conventional tissue image analysis software provides foundational capabilities for cellular analysis, including segmentation, basic morphological feature extraction, and spatial organization analysis. However, these tools often require manual intervention and are not well integrated with code-driven automation, limiting efficiency and scalability for complex spatial tissue studies. In addition, they offer limited flexibility for custom analyses, as they typically support only a fixed set of pre-implemented spatial cellular features. To address these limitations, we propose CodeCytos, a coding-based reasoning agent framework that enables dynamic, programmable interaction with spatial molecular imaging data, to improve automation and customization. CodeCytos is designed to streamline the exploration of custom spatial cellular features and adapt to diverse research needs. We demonstrate its utility through case studies on four expert-curated datasets from distinct tissue types: frontal cortex, non-small-cell lung cancer, pancreas, and tonsil. We evaluate CodeCytos under a realistic minimal prompt setting, where bioscientists pose simple questions without task-specific instructions or contextual information about spatial cellular analysis, and benchmark multiple LLM backbones with strong coding capabilities. We further show that incorporating tailored, domain-agnostic few-shot in-context coding-reasoning examples (randomly sampled demonstrations outside the spatial analysis domain) can substantially improve performance without requiring costly, expert-crafted in-domain demonstrations. Overall, CodeCytos outperforms baseline approaches, highlighting the potential of code-action agents to assist with custom feature exploration in spatial molecular imaging and to accelerate biomarker discovery.

bioinformatics↗

bioRxiv2026

An SE(3)-equivariant Crystal Structure Prediction Framework for Prospective Identification and Development of Bioactive Nucleoside Self-Assembling Materials

Flexible organic small molecules can assemble into supramolecular biomaterials whose properties are intrinsically governed by their crystal structures, yet experimental structure determination remains difficult to scale during molecular modification and materials optimization. Crystal structure prediction (CSP) provides a potential solution, but its prospective power is limited for flexible molecules owing to incomplete conformational sampling and the difficulty of identifying experimentally realized structures from large candidate sets. Here, an SE(3)-equivariant deep-learning workflow, SE3CSP, is developed for organic crystal structure prediction. By learning molecular conformations, unit-cell parameters and packing patterns from experimentally resolved crystal structures and integrating these predictions with MACE-based structure optimization, SE3CSP establishes an end-to-end pipeline from two-dimensional molecular representations to three-dimensional crystal structures. Using nucleosides as representative flexible self-assembling building blocks, SE3CSP achieves an overall prediction accuracy of [~]57%, substantially outperforming the benchmark MACE-based Genarris 3.0 workflow ([~]14%). Furthermore, a prospective prediction strategy is developed in which an SE3CSP-predicted density window ({+/-} 0.15 g/cm3) is applied prior to energy ranking and structural deduplication, enabling all experimentally realized structure to be consistently ranked within the top 1-2% of all generated candidates. Beyond structure prediction, SE3CSP-derived energy landscapes provide insight into potential single-crystal-to-single-crystal transformations and enable the identification of a nucleoside supramolecular material with dynamic breathing porosity, which is further developed as an adsorptive platform for inflammatory mediator removal with excellent anti-inflammatory performance and biocompatibility. These results establish SE3CSP as a practical framework for prospective CSP and highlight its utility in guiding the discovery and design of bioactive self-assembled materials.

bioengineering↗

bioRxiv2026

Dual histone methylation reader ZCWPW2 links histone methylation to initiation of meiotic recombination

Meiotic homologous recombination initiates with the formation of programmed DNA double-strand breaks (DSBs) by complexes comprising SPO11 and accessory proteins at discrete sites called recombination hotspots. In mammals, PRDM9-dependent H3K4me3 and H3K36me3 define recombination hotspots, but how these epigenetic characteristics determine the physiological DSB formation remains unknown. Here we show that dual histone methylation reader ZCWPW2 can recognize H3K4me3 and H3K36me3 marks in testis. The binding activity of ZCWPW2 to histone methylation is dependent on PRDM9 function. Moreover, we find the epigenetic writer-reader axis PRDM9-ZCWPW2 is essential for DSB formation at meiotic recombination hotspots, which may be partly explained by the finding that ZCWPW2 can physically interact with HORMAD1, IHO1, MEI4, and REC114. Finally, the absence of ZCWPW2 leads to disrupted chromosomal synapsis and recombination, thereby obstructing meiotic progression. Taken together, our findings provide new insights into how histone modifications and their associated regulatory proteins collectively regulate meiotic homologous recombination initiation.

cell biology↗

bioRxiv2026

Proteomic Signatures of Neuropathological Alterations in Alzheimer's Disease: Insights from the Bio-Hermes Study

Large-scale plasma proteomics can capture molecular changes across the Alzheimers disease (AD) continuum and provide insight into biological mechanisms associated with AD pathology. We analysed the Bio-Hermes cohort (n = 961), with participants enrolled across 17 sites in the United States from April 2021 to November 2022. Participants were stratified by clinical status and amyloid PET scan-based Core1 biomarker status (CN Core1-, CN Core1+, MCI Core1+, and AD dementia Core1+). We performed differential abundance analyses across biologically defined contrasts, clustered proteins into co-expression networks, and evaluated protein panels to distinguish participants with biologically defined AD from amyloid-negative cognitively normal controls. We also used Mendelian randomization (MR) to assess genetic evidence for potential causal relationships with AD risk. The biologically defined contrast, Core1+ vs. CN Core1-, identified 69 differentially abundant proteins. Across AD stages, eight core proteins were consistently dysregulated from preclinical through prodromal and dementia phases, and three additional proteins emerged at MCI Core1+ and remained altered in AD dementia Core1+. We identified 29 co-expression modules, six of which varied significantly across the AD continuum. Among differential abundance proteins, ACHE ranked highest for distinguishing biologically defined AD from CN Core1-. Stage-specific protein panels improved the discriminatory performance for MCI Core1+ (AUC = 0.850) and AD dementia Core1+ (AUC = 0.856). MR provided genetic evidence consistent with an association between plasma ACHE abundance and AD risk. Plasma proteomics delineated a stage-spanning core signature across the AD continuum. These findings nominate co-expression modules and candidate proteins for further validation in early detection and AD screening.

neuroscience↗