Biology subjects

Fu, S.

Publications and source records attributed to Fu, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Algorithmic Geometry Shapes the Biological Meaning of Thalamocortical Structural Covariance

Neuroimaging studies often derive and discuss structural covariance (SC) as a representation of underlying brain circuitry that reflects coordinated maturation, anatomical connectivity, transcriptional similarity, and shared genetic influences. These associations motivate its use to study brain organization across development, aging, and disease. However, the algorithms used to derive SC networks impose constraints that may shape their biological interpretation. To understand the impact of algorithm choice, we used the thalamocortical system as an example and compared five common representations of voxelwise SC, namely, principal component analysis (PCA), independent component analysis (ICA), non-negative matrix factorization (NMF), orthogonal projective NMF (OPNMF), and \textit{k}-means clustering, in 755 participants aged 8--22 years from the Philadelphia Neurodevelopmental Cohort (PNC). SC networks derived from deformation-based morphometry (DBM) were evaluated against three complementary biological benchmarks: age-related individual differences, fractional-anisotropy (FA)-based microstructural connectivity of regionally-specific thalamocortical connections, and Allen Human Brain Atlas gene expression. Each algorithm demonstrated underlying sensitivity to differing aspects of neurobiology: PCA and OPNMF showed the strongest associations between participant-level component weights and age. NMF, OPNMF, and \textit{k}-means showed greater spatial overlap between positive cortical SC and tractography-derived projections. ICA and PCA showed stronger correspondence between thalamic and cortical gene-expression profiles across the transcriptome, whereas NMF, OPNMF, and \textit{k}-means more often identified thalamic components with relatively high core-matrix gene expression. This study emphasizes that algorithmic constraints effectively act as biological priors and decomposition methods do not provide neutral compression of the data. We conclude that it is critical to consider whether the algorithmic objective matches the representational geometry of the neurobiology being studied. Overall, our findings suggest that SC studies should select and justify algorithms according to the biological question, explicitly considering whether their constraints favour distributed gradients, overlapping regional parts, or discrete clusters. Meanwhile, interpretations of SC findings could also be informed by the specific algorithmic constraints used to derive them.

neuroscience↗

bioRxiv2026

NeoOmni Atlas: an integrated database for pan-cancer neoantigen derived from proteogenomics analysis

Tumor neoantigens derived from diverse molecular aberrations are important targets for personalized cancer immunotherapy, but existing resources incompletely integrate mass-spectrometry-supported canonical and non-canonical antigen evidence across cancer types. NeoOmni Atlas is an interactive pan-cancer T-cell epitope resource, built via proteogenomic reanalysis of 7,561 immunopeptidomics datasets comprising 511 million raw spectra from 2,341 samples across 26 cancer types. The analysis yielded 872,414 eluted canonical peptides and 198,961 non-canonical peptides spanning 18 molecular alteration classes, along with 759,386 unique ligands presented by 322 human leukocyte antigen (HLA) alleles. Relative to existing repositories, NeoOmni Atlas covers a broader range of cancer types, includes 60 additional HLA alleles, and catalogs 369,781 novel ligands. A computational tumor-specific filtering framework identified 316,126 tumor-associated epitopes and 61,231 tumor-specific epitopes. Tumor-specific epitopes showed stronger predicted binding affinity than tumor-associated epitopes for HLA class I A and B alleles, and 65 conserved tumor-specific epitopes recurrent across more than 10 tumor types represent potential shared antigen candidates. NeoOmni Atlas also prioritizes candidate tumor-associated antigens including PMEL (gp100:209-217) and CSPG4, and supports browsing, searching, and downloading by cancer type, human leukocyte antigen allele, peptide sequence, and source event. NeoOmni Atlas is freely accessible at http://modinfor.com/NeoOmni-Atlas/.

bioinformatics↗

bioRxiv2026

Functional characterization of terpene synthases from Dendrobium nobile provides insights into the biosynthesis of copaborneol, a proposed dendrobine precursor

Abstract: Picrotoxane-type sesquiterpene alkaloids, including dendrobine, are characteristic constituents of Dendrobium nobile, but the enzymes that produce their proposed skeletal precursors remain in completely characterized. Here, we combined tissue metabolite profiling with genome-guided screening and biochemical characterization of terpene synthases (TPSs). GC-MS analysis revealed variation in sesquiterpenoid profiles and dendrobine signals among tissues and developmental stages. Manual curation of the chromosome-level genome yielded 16 full-length TPS-a candidates. Functional screening in an FPP-supplying Escherichia coli system showed that three closely related genes on chromosome 1, DnTPS1~DnTPS3, produced copaborneol as their dominant product. The principal product of DnTPS1 was purified and identified by NMR spectroscopy, and a purified-enzyme assay confirmed its direct conversion of FPP to copaborneol. Fluorescence imaging in rice protoplasts and Nicotiana benthamiana leaves indicated predominantly cytosolic localization of DnTPS1. Guided by sequence comparisons and structural modeling, we tested 12 DnTPS1 variants and found that substitutions at W377 and I295 altered the relative distribution of sesquiterpene products. Phylogenetic and regional gene-order analyses placed DnTPS1~DnTPS3 in the same clade and identified related TPS candidates within corresponding genomic regions of six Dendrobium species. These findings establish that three D. nobile TPSs can form copaborneol and identify DnTPS1 residues that influence product selectivity, providing a basis for testing the proposed connection to dendrobine biosynthesis.

biochemistry↗

bioRxiv2026

MCT6 is an intestinal Lac-Phe exporter required for metformin-associated weight loss

Metabolites are increasingly recognized as circulating molecules that regulate physiology, yet the mechanisms that couple intracellular production to organism-wide action remain poorly defined. Using the anorexigenic metabolite Lac-Phe as a tractable system, we identify the orphan transporter MCT6 (SLC16A5) as a physiologic intestinal Lac-Phe exporter. This mechanism controls the extent to which intracellularly synthesized Lac-Phe acquires systemic activity. MCT6 transports Lac-Phe, mediates its cellular efflux, and is required for maintaining its blood levels in mice following strong glycolytic stimuli. Both global and intestinal epithelial-specific deletion of MCT6 confers resistance to metformin-associated weight loss on a high-fat diet. Bypassing the transport defect with exogenous Lac-Phe normalizes the body weight phenotype of MCT6-KO mice. Together, these data connect MCT6 to metformin pharmacology and intestinal lactate metabolism, and more generally underscore the importance of transporter-mediated release in the conversion of an intracellular metabolic state into a circulating metabolite effector.

physiology↗

bioRxiv2026

PalmLab: A Comprehensive Computational Platform for Systematic Annotation and Functional Interrogation of Protein Palmitoylation

Protein palmitoylation is a dynamic and reversible post-translational lipid modification with progressively recognized pathophysiological relevance in cancer and a range of human diseases. Current databases, however, predominantly catalog palmitoylation sites derived from isolated literature reports, and suffer from a conspicuous lack of systematic harmonization across publicly available mass spectrometry (MS)-based palmitoylome datasets, which severely impedes standardized annotation and cross-cohort comparative analyses. Here, we introduce PalmLab, an integrative computational platform that synergistically couples MS-based palmitoylome data curation with interactive, multi-dimensional analytical functionalities for both human and mouse proteomes. Through a unified and rigorously standardized bioinformatics pipeline, PalmLab enables systematic reanalysis of all published palmitoylome datasets, yielding a curated compendium of 15,968 human and 9,924 mouse MS-validated palmitoylated proteins, a quantitative increase that more than doubles the total entries available in existing public repositories. The platform further provides user-friendly, code-free analytical modules that support differential palmitoylation profiling, context-specific pattern visualization, protein-protein correlation network inference, mutation-palmitoylation association mapping, and sequence-based motif discovery. Collectively, PalmLab constitutes a comprehensive and openly accessible resource for systematic palmitoylome mining and functional interrogation of palmitoylation events, thereby facilitating the elucidation of palmitoylation-mediated regulatory circuits and aiding the prioritization of novel therapeutic targets in oncology. The platform is freely available at https://palmlab.intelligent-oncology.com.

bioinformatics↗

bioRxiv2026

Mammalian TMC Family Proteins are Mechanically Gated Ion Channels

Every known life form senses and reacts to mechanical forces. These mechanical stimuli can be converted into electrical signals by mechanically gated ion channels, a transduction cascade pivotal to numerous physiological functions including touch, hearing, mechanical pain, circulation, gastrointestinal function, and mechanical loading in various tissues. Despite continuous efforts, numerous mechanically gated ion channels with the mechanotransduction process underlying these physiological functions remain unidentified. Here, we focused on the transmembrane channel-like (TMC) protein family expressed in the cultured cells to identify those with potential mechanosensitive activity. Remarkably, in contrast to human TMC1/2 (HsTMC1/2), human TMC3-8 (HsTMC3-8) proteins are localized to the plasma membrane when heterologously expressed in the cultured cells. Further experiments revealed that mechanical poking stimuli can effectively activate HsTMC3-8. In addition, HsTMC3-8 induced stretch-activated currents and elicited well-resolved single-channel activities in response to negative pressure stimulation. The mutants near the putative pore region altered reversal potentials (Erev) of HsTMC3-8, suggesting that TMC3-8 are likely pore-forming subunits of ion channels. In summary, we proposed that TMC proteins are the largest mammalian mechanically gated ion channel family.

neuroscience↗

bioRxiv2026

Different Sources of Expectations Differentially Modulate Communication Between Auditory and Prefrontal Cortices During Auditory Decision-Making

Perceptual decisions can be influenced by expectations that arise from different sources. However, relatively little is known about how and where in the brain these different sources affect decision formation, particularly in the auditory system. To begin to address this question, we recorded local field potentials (LFPs) simultaneously from the auditory cortex (AC) and ventrolateral prefrontal cortex (vlPFC) while monkeys performed an auditory frequency-discrimination task. We manipulated their expectations by presenting either informative cues (LEDs that predicted the target frequency) or uninformative cues (tone sequences that did not predict the target frequency) prior to target onset. Both cues had similar effects on the monkeys accuracy and response times but induced distinct neurophysiological signatures. Coherence, a measure of undirected coupling, between AC and vlPFC increased from cue to target presentation on informative trials but decreased from cue to target presentation on uninformative trials. Phase-slope index (PSI), a measure of directed coupling, between AC and vlPFC was strongest when the target tone contradicted expectations, particularly for ambiguous stimulus conditions when expectations were most valuable. These expectation-dependent changes in the magnitude of functional connectivity also included changes in its spatial organization. Together, these findings provide new insights into how different expectations can dynamically reconfigure functional connectivity between cortical brain areas to support different task demands and behavioral outcomes.

neuroscience↗

bioRxiv2026

Phylogenetic Mosaic of an Arms Race with Asymmetrical Sexual Conflict and Its Macroevolutionary Consequences in a Lineage of Small Water Striders

Sexual conflict has been hypothesized as a driver of speciation, though its effects are likely heterogeneous across phylogenies and between sexes. The Gerromorpha (semi-aquatic bugs), which inhabits water surfaces across diverse aquatic environments, long served as a model for studying sexual conflict. While previous studies have focused on rapid antagonistic coevolution and the genetic basis of sexually antagonistic traits, the macroevolutionary consequences of asymmetrical sexual conflict-particularly male-dominated grasping traits versus female resistance-remain largely unexplored. Within the subgenus Pseudovelia, males exhibit pronounced phenotypic diversification in grasping structures, whereas females show modest, clade-specific resistance traits, suggesting male-biased asymmetric conflict. This system presents a valuable opportunity to examine how sexual conflict influences diversification and asymmetrical trait evolution across lineages. Using 204 individuals, representing over half of the subgenus's species diversity, we reconstructed a time-calibrated phylogeny, quantified diversification rates, assessed sexual conflict intensity across clades, and analyzed correlations between sexual trait evolution and diversification. Our results reveal extensive phylogenetic conflict, particularly within the East Asian clade, driven by introgression and incomplete lineage sorting (ILS). Furthermore, we observe significant phylogenetic heterogeneity in both phenotypic evolution and diversification rates. Notably, a male "trait package" enhancing grasping ability likely drives rapid diversification in the recently radiated "South China" lineage. In contrast, grasping traits involving abdominal segment VIII are associated with lower conflict intensity, facilitating greater evolutionary flexibility in female resistance and resulting in lineage-specific counter-adaptations. These findings highlight the heterogeneous dynamics of asymmetrical sexual conflict in shaping diversification and speciation.

evolutionary biology↗

bioRxiv2026

Killer-cell dominance dichotomy governs tumor immune networks and stratifies inflamed cancers

Cancer immunotherapy benefits remain limited, even among "hot" tumors with high killer lymphocyte infiltration. Here, we investigated the population-level architecture of killer cells based on nearly 5,000 pan-cancer scRNA-seq samples, together with orthogonal validation by spectral cytometry and spatial transcriptomics. Unlike the prevailing "hot-cold" paradigm, which assumes coordinated infiltration of multiple cytotoxic lineages, we uncovered a conserved framework wherein terminal cytotoxic immunity in individuals or malignancies diverges into states dominated by either exhausted CD8+ T cells (Tex) or CD56dimCD16hi NK (NK1) cells. Despite the complexity of the tumor microenvironment, Tex-NK1 divergence governs the primary axis of tumor-intrinsic and tumor-extrinsic variance. Distinct from the conventional view that NK cells positively contribute to immunotherapy efficacy, NK1-skewed tumors, although highly cytolytic, are refractory to current immune checkpoint blockade regimens. This killer divergence defines a foundational axis of cancer immunity and provides a resource for prioritizing next-generation targets for NK-directed immunotherapy.

immunology↗

bioRxiv2026

Buffered sweat microfluidics with AI-enabled translation for clinically actionable blood urea estimation and renal risk stratification

Kidney-function assessment relies on blood urea as a clinically informative metabolic marker; however, its dependence on venipuncture and centralised laboratory testing limits high-frequency monitoring and delays timely clinical intervention. Here, we report an integrated platform combining a wearable buffered microfluidic patch with a physiology-informed, data-driven calibration framework for real-time, non-invasive estimation of blood urea from microlitre-scale sweat volumes (4.79 L). By precisely regulating the release kinetics of internal buffer salts, the device stabilises the local reaction microenvironment, mitigating variability in sweat pH and flow to ensure reproducible measurement. The resulting signals are processed through an artificial intelligence (AI)-enabled analysis pipeline that integrates sweat urea with patient-specific physiological information to generate clinically interpretable outputs. In multicentre studies, sweat urea shows a strong association with blood urea across diverse cohorts, but with nonlinear and time-lagged relationships that limit direct use. The AI-enabled calibration model compensates for these effects, enabling high-fidelity estimation of blood urea (r = 0.945 versus gold-standard measurements) at clinically relevant concordance levels. The platform further identifies kidney injury with 89.1% accuracy and stratifies disease severity with 83.2% accuracy. Notably, these results demonstrate that the integration of physicochemical stabilisation and AI-enabled data-driven translation establishes sweat as a clinically actionable surrogate for renal monitoring, supporting population-level estimation and highlighting the potential for personalised longitudinal assessment, and enabling a scalable, non-invasive strategy for high-frequency kidney disease management.

biophysics↗

bioRxiv2026

Sequence-encoded H2A.Z nucleosome dynamics control DNA unwrapping and SUV420H1 recognition

H2A.Z and canonical H2A adopt nearly identical nucleosomal folds, yet their distinct chromatin functions are not captured by static structural analysis. Using fast magic-angle spinning 1H-detected solid-state NMR, we show that H2A.Z possesses enhanced backbone flexibility in the L1 loop and the 2-L2 region (M2) relative to H2A. Chimeric segment-swapping demonstrates that these dynamic signatures are locally sequence-encoded and functionally transplantable. The inherent mobility of the M2 region promotes nucleosomal DNA-end unwrapping and persists when DNA ends are stabilized by linker histone H1 or opened by SUV420H1, indicating that this mobility is intrinsic rather than a passive consequence of DNA detachment. Chemical shift perturbation mapping and catalytic assays further show that SUV420H1 reads this H2A.Z-specific conformational landscape: the M2 region, together with the H2A.Z DS motif, supports variant-selective methyltransferase activity. These findings establish an axis of sequence-dynamics-accessibility-recognition along which local backbone fluctuations serve as physical determinants of epigenetic enzyme specificity. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/731474v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@a50ceforg.highwire.dtl.DTLVardef@1f43af0org.highwire.dtl.DTLVardef@14969b8org.highwire.dtl.DTLVardef@bbbe0d_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2026

Endogenous network modeling reveals mechanisms of repair Schwann cell decline and potential recovery targets

Schwann cells, the principal glial cells of the peripheral nervous system, play a central role in nerve repair following injury. Upon injury, mature Schwann cells dedifferentiate into repair Schwann cells. These processes are governed by complex gene regulatory networks, yet the quantitative dynamics of these processes remain unclear. Here, using a bottom-up systems biology approach, we constructed an endogenous regulatory network model based on experimentally validated interactions, without relying on high-throughput data as input. The model captures Schwann cell dedifferentiation dynamics and reveals a potential landscape composed of stable states and intermediate transition states. Simulations recapitulate post-injury trajectories and confirm the role of c-Jun upregulation in maintaining repair capacity. Furthermore, the model predicts multiple potential therapeutic targets, including P53, JNK, and PTEN, for sustaining repair competence. We also identify intrinsic heterogeneity within repair Schwann cells and uncover key transition states that simultaneously connect repair-competent cells to both repair-deficient and apoptotic phenotypes, indicating that these intermediate states may represent critical regulatory bottlenecks and key cellular targets for improving the success of peripheral nerve regeneration. Overall, this work provides new insights into the precise regulation of Schwann cell fate and establishes a theoretical framework for regenerative medicine and clinical strategies in peripheral nerve repair.

neuroscience↗

bioRxiv2026

OneGenomeRice (OGR): A Genomic Foundation Model for Rice

The transition of genomics to a predictive intelligence discipline is driven by the advent of genomic foundation models. While substantial progress has been observed in all-life and human-centric models, plant species, particularly for the staple crops, remains hindered by a lack of models. Here we introduce OneGenome-Rice (OGR), a rice (Oryza sativa) genomic foundation model pre-trained on a genomic dataset comprising 422 high-quality genomes of cultivated and wild rice. OGR is engineered upon a Mixture of Experts (MoE) transformer architecture with 1.25-billion parameters and supports an ultra-long context window of up to 1 million base (Mb) pairs at single-nucleotide resolution. A comprehensive benchmark demonstrated that OGR significantly outperforms existing state-of-the-art multi-plant or all-life genome models in 11 categories (e.g. motif identification, sweep detection, etc). We further demonstrated the utility of OGR in several downstream applications, such as indica-japonica subspecies introgression analysis, identification of agronomy trait-associated functional loci and prediction of gene expression from DNA sequences. These results establish OGR as a promising foundational computational infrastructure for rice functional genomics and precision breeding. The OGR and its fine-tuned models, including pretrained weights, training code and the rice genomic benchmark suit, have been fully opened.

genomics↗

bioRxiv2026

Deep-Palm：an integrated deep learning framework for structure-aware prediction of protein S-Palmitoylation

Protein S-palmitoylation is a reversible lipid modification that regulates protein localization, trafficking, and signaling. Its dysregulation has been implicated in cancer and therapeutic resistance, making accurate site annotation important for understanding disease-related regulatory mechanisms. However, experimental identification of S-palmitoylation sites remains labor-intensive, highlighting the need for computational tools that can support large-scale candidate-site prioritization. S-palmitoylation-site recognition requires the integration of multiple levels of information surrounding candidate cysteine residues, including sequence context, local protein properties and structural features. Here, we present Deep-Palm, a deep learning framework that integrates four complementary branches: amino acid sequence, physicochemical properties, ESM embedding and spatial structure. On the testing set, Deep-Palm achieved an AUC of 0.950 and outperformed the compared predictors, including pCysMod, MusiteDeep and GPS-Palm. Deep-Palm also showed stable performance across diverse cysteine sequence contexts and Gene Ontology functional groups. Feature-level analyses revealed distinct spatial structural and protein property patterns between palmitoylated and non-palmitoylated peptide windows. Independent mass spectrometry datasets further demonstrated the ability of Deep-Palm to sensitively identify previously unannotated S-palmitoylation sites. Together, Deep-Palm provides an accurate and biologically informative framework for S-palmitoylation-site prediction, facilitating the discovery of novel candidate S-palmitoylation sites.

bioinformatics↗

bioRxiv2026

Towards building a World Model to simulate perturbation-induced cellular dynamics by AlphaCell

Predicting cellular responses to perturbations is crucial for therapeutic discovery, yet experimental screening is severely constrained by the combinatorial vastness of biological space. While computational simulations offer a scalable alternative, current models are limited by the incomplete latent representation----mainly relying on highly variable genes in feature representation; the poor genome-wide reconstruction fidelity; and the ungeneralizable dynamic laws across diverse contexts. Consequently, they fail to mechanistically transfer learned dynamics to unseen cellular contexts. To address these systemic flaws, we introduce AlphaCell, a generative Virtual Cell World Model that unifies genome-wise representation with continuous state transition modeling. AlphaCell achieves three synergistic innovations: (1) Latent Manifold Rectification, processing the full protein-coding transcriptome to construct a differentiable Virtual Cell Space, effectively filtering noise while preserving intrinsic cellular topology; (2) Biological Reality Reconstruction, utilizing a massive, knowledge-rich decoder to translate abstract latent states back into high-fidelity, genome-wide expression profiles; and (3) Universal State Transition, applying Optimal Transport Conditional Flow Matching to model perturbations as continuous, deterministic vector fields. By abstracting perturbation mechanisms into generalized dynamic laws, AlphaCell makes robust prediction of perturbation responses in a compositional generalization scenario and enables zero-shot prediction of cellular dynamics in entirely unseen cellular contexts, providing a foundational engine for cellular-context-generalizable perturbation prediction and perturbation-induced cellular dynamics simulation.

bioinformatics↗

bioRxiv2026

A Multimodal Single-Cell Epigenomic and 3D Genome Atlas of the Human Basal Ganglia

The basal ganglia (BG) underlie motor control, reward processing, and many neurological and psychiatric disorders, but a comprehensive epigenomic and 3D-genome atlas of the human BG is lacking. Here we present a multimodal single-cell atlas profiling DNA methylation and 3D chromatin conformation in 261,331 nuclei (snm3C-seq) across eight subregions, resolving 12 classes, 31 subclasses, and 59 groups. Harmonized under the HMBA basal-ganglia consensus taxonomy, this atlas integrates with matched RNA, ATAC-seq, and histone-modification data across five regulatory layers. We identify millions of cell-type- and region-specific differentially methylated regions enriched for distinct transcription factor motifs and link them to disease-associated heritability. Neuron-specific loops dominate cell-type-specific 3D contact remodeling, while most non-neuron-specific loops are constitutive. Among spiny projection neurons (SPNs), chromatin loops, rather than TAD boundaries, distinguish D1, D2, and eccentric SPN subclasses, with eccentric SPNs showing the most loop-level reorganization among the three. We characterize STR D2 SMYD2-HTR7 SPN, a newly recognized POU6F2 D2-SPN subtype, and reveal region-specific methylation and contact gradients of disease-associated genes, including CADM1 and PDE8B. Integrative gene-regulatory networks reconstruct cell-type-resolved enhancer-promoter links to interpret Parkinsons disease risk variants at SNCA. Finally, MERFISH spatial profiling combined with cross-species Patch-seq identifies non-SPN neuronal subtypes, including a MOXD1 striosomal STR FS PTHLH-PVALB GABA subtype with distinct electrophysiology, partitioning across the striatal matrix-striosome boundary. HighlightsO_LIA multimodal single-cell atlas maps DNA methylation and 3D genome architecture across human basal ganglia cell types and subregions. C_LIO_LINeuron-specific loops dominate cell-type-specific 3D contact remodeling in the human BG, whereas most non-neuron-specific loops are constitutive. C_LIO_LISpiny Projection Neuron (SPN) subtypes exhibit regionally organized epigenomic and 3D genome signatures that align with dorsal-ventral identities. C_LIO_LIChromatin loops are the primary distinguishing feature among D1, D2, and eccentric SPN subclasses, with eccentric SPNs being the most 3D-reorganized. C_LIO_LIIntegrated regulatory maps link cell-type-specific enhancers to disease-associated genetic risk in the human basal ganglia. C_LIO_LINon-SPN interneurons differ in distribution and in transcriptional and epigenetic identity across the matrix-striosome boundary. C_LI

neuroscience↗

bioRxiv2026

Development of KASP Molecular Markers and Construction of Fingerprinting for Cowpea (Vigna unguiculata (L.) Walp) unguiculata Based on ddRAD-Seq

Cowpea (Vigna unguiculata (L.) Walp.) is a globally important legume crop. However, the scarcity of efficient molecular markers has hindered molecular breeding efforts and the protection of plant breeders rights. In this study, we employed double-digest restriction-site associated DNA sequencing (ddRAD-seq) to characterize the genetic diversity of 19 core cowpea germplasm accessions. We generated 791,621 SNPs, from which 13,469 high-quality SNPs were filtered. Population structure and phylogenetic analyses revealed that these accessions cluster into three distinct groups. To facilitate cost-effective and rapid genotyping, we developed a panel of KASP (Kompetitive Allele Specific PCR) markers. Through rigorous screening for polymorphism and stability, we identified six core KASP markers located in exonic regions. These six markers alone were sufficient to discriminate all 19 accessions. Based on these core markers, we constructed a unique DNA fingerprinting profile and assigned specific QR codes for each accession. This study demonstrates that extracting core KASP markers from ddRAD-seq data is a powerful strategy for germplasm identification. The developed fingerprinting system provides a robust, low-cost tool for seed purity testing, variety authentication, and marker-assisted selection in cowpea breeding programs.

plant biology↗

bioRxiv2026

A small molecule PTER-selective inhibitor reduces food intake and body weight

PTER (phosphotriesterase-related) is an amidohydrolase that mediates catabolism of the anorexigenic taurine metabolite N-acetyltaurine. However, the structural basis of PTER ligand binding and catalysis remain unknown, limiting our ability to harness this pathway therapeutically. Here we solve crystal structures of a eukaryotic PTER in apo and product-bound forms. These structures uncover an unexpected pocket homology between PTER and histone deacetylase (HDAC) enzymes. We exploit this similarity to engineer a first-in-class substrate-competitive PTER inhibitor called PTERi with nanomolar potency and >100-fold selectivity for PTER over HDACs in vitro. Administration of PTERi to diet-induced obese mice reduces feeding, enhances GLP1-RA (glucagon like peptide 1 receptor agonist)-induced weight loss, and prevents weight regain after GLP1-RA discontinuation. The structure of PTER connects histone and metabolite deacetylation into a parallel conceptual framework and enables proof-of-concept data for pharmacological inhibition of PTER in obesity.

biochemistry↗