Biology subjects

Xiao, W.

Publications and source records attributed to Xiao, W..

At least 19 recordsLinked to original sources

bioRxiv2026

Decoding the oxytocinergic and behavioral signatures of milk ejection

Oxytocin-mediated milk ejection (ME) is pivotal to effective breastfeeding and productive health, yet behaviorally decoding and revealing neural mechanisms of ME remains challenging. Here, we combined in vivo calcium imaging and intramammary pressure recording to uncover the temporal connections between episodic activity of oxytocin neurons and ME in conscious lactating rats. Leveraging the association and behavioral responses in dam and pup, we developed a supervised machine learning framework (ME Decoder) to enable automated analyses of ME. Inspired by its interpretable features, we defined the activity-coupled dam-pup interactions (ADPI), manifested by high kyphosis of the dam followed by pup treading and stretch, as the behavioral signatures of ME. By ME Decoder and ADPI analyses, we detected reduced ME but unaffected activity of oxytocinergic neurons after systemic blockade of oxytocin receptor. Our study uncovers the oxytocinergic and behavioral signatures of ME and provides a generalizable approach for further investigation.

neuroscience↗

bioRxiv2026

A Raman Spectroscopy-Based Method for Label-Free Discrimination of Human Inhibin α, Inhibin B, and Activin A

Members of the transforming growth factor-{beta} (TGF-{beta}) superfamily, including inhibins and activins, are structurally related glycoprotein dimers that regulate reproductive and endocrine signaling. Their high degree of molecular similarity presents challenges for label-free analytical discrimination. To evaluate the ability of Raman spectroscopy to distinguish closely related TGF-{beta} superfamily proteins based on intrinsic vibrational fingerprints. Raman spectra of recombinant human Inhibin -subunit, Inhibin B {beta} subunit, and Activin A ({beta}A-{beta}A) were acquired using confocal Raman microscopy with 532 nm excitation. Spectra were baseline-corrected, area-normalized, and analysed using principal component analysis (PCA). Distinct spectral signatures were observed across the 500-1800 cm-1 region. Differences within the S-S stretching region (500-550 cm-1) were consistent with variations in disulfide-bond environments, with the Inhibin -subunit exhibiting the highest relative intensity in this region. Variations in the amide I band (1600-1700 cm-1) suggested differences in protein secondary structure, while aromatic amino acid vibrations provided additional discriminatory features. PCA revealed clear clustering and separation of all three protein classes based on their Raman fingerprints. Raman spectroscopy enables label-free differentiation of structurally related endocrine glycoproteins and demonstrates potential for the structural characterization and classification of inhibin and activin proteins within the TGF-{beta} superfamily.

biochemistry↗

bioRxiv2026

Protenix-v2: Broadening the Reach of Structure Prediction and Biomolecular Design

Advances in biomolecular modeling have broadened the range of problems addressable by structure prediction and design models. Here, we present results from Protenix-v2, a system spanning high-accuracy structure prediction and biomolecular design. On the structure prediction side, Protenix-v2 achieves antibody-antigen success rates with up to 13-point gains over Protenix-v1, while 5-seed performance surpasses previous 1000-seed results. On the design side, Protenix-v2 demonstrates a 100% target-level success rate in novelty-controlled VHH-Fc campaigns, reaching hit rates up to 48%. Crucially, the model enables hit discovery on difficult GPCR targets with hit rates of 16%-88% (VHH-Fc) and up to 50% (mAb) under 16-30 testing budgets per target. Resulting hits show high developability and diversity. Beyond antibody tasks, we report improved ligand-related plausibility and successful cross-variant SARS-CoV-2 spike RBD mini-binder design. These results establish Protenix-v2 as a robust and powerful model for accelerated drug discovery.

molecular biology↗

bioRxiv2026

InSTaPath: Integrating Spatial Transcriptomics and histoPathology Images via Multimodal Topic Learning

Spatial transcriptomic (ST) technologies enable the measurement of gene expression directly within tissue sections while preserving spatial context. Many ST platforms additionally generate paired histological images alongside spatially resolved transcriptomic profiles. However, most existing computational approaches only incorporate histology images as auxiliary features in representation learning models and typically produce latent embeddings that are difficult to interpret. We present InSTaPath (Integrating Spatial Transcriptomics and histoPathology images), a multimodal topic modeling framework that links transcriptional programs with tissue morphology. InSTaPath converts token-level embeddings extracted from pretrained histology foundation models into discrete image words through vector quantization, enabling histological morphology to be represented in a count-based form analogous to gene expression. InSTaPath then jointly analyzes image-word and gene expression counts to infer shared latent topics that are interpretable through both topic-gene and topic-image-word associations. Across multiple ST datasets, InSTaPath improves spatial domain identification and uncovers biologically meaningful relationships between gene programs and tissue morphology through pathway enrichment and in silico perturbation analyses.

bioinformatics↗

bioRxiv2026

GCN-Mamba: Graph Convolutional Network with Mamba for Antibacterial Synergy Prediction

The escalating crisis of antimicrobial resistance necessitates novel therapeutic strategies, among which drug combination therapy shows great promise by enhancing efficacy and reducing toxicity. However, identifying effective synergistic pairs from the vast combinatorial space remains experimentally challenging and resource-intensive. To address this, we introduce GCN-Mamba, a deep learning framework that integrates Graph Convolutional Networks (GCN) with the Mamba State Space Model. This architecture captures both local molecular topological structures and global implicit interactions by leveraging Extended 3-Dimensional Fingerprints (E3FP) and bacterial gene expression profiles. Evaluation on a comprehensive dataset demonstrated that GCN-Mamba significantly outperforms classical machine learning models in predictive accuracy. In a targeted case study against Methicillin-resistant Staphylococcus aureus (MRSA), the model successfully rediscovered known synergistic pairs, such as Quercetin and Curcumin, consistent with recent literature. Furthermore, prospective in vitro validation confirmed a novel synergistic combination of Shikimic acid and Oxacillin, validating the models practical utility. By efficiently prioritizing potential candidates, GCN-Mamba serves as a powerful and reliable tool for accelerating the discovery of synergistic antimicrobial combinations, effectively bridging the gap between computational prediction and experimental validation.

bioinformatics↗

bioRxiv2026

The long noncoding RNA Malat1 contains an internal ribosome entry site mediating micropeptide translation

In most cells, Malat1 long noncoding RNA localizes to the nucleus where it affects splicing and chromatin function. In neurons Malat1 is exported to the cytoplasm where it is translated to generate the M1 micropeptide. Here we characterize an internal ribosome entry site (IRES) required for Malat1 translation. Although preceded by a long Malat1 5' RNA segment this element induces translation at the M1 AUG. In vivo chemical probing and structural modeling identified a 135 nt RNA secondary structure consisting of three stem loops that is sufficient for IRES activity. Using this minimal element for affinity purification from cell extracts, the IRES RNA selectively binds ribosomal subunits and translation factors. Depletion of the binding proteins Rack1 and hnRNP A2/B1 inhibits downstream IRES-dependent translation without affecting translation of an upstream ORF. Our study identifies an unexpected functional unit hidden within a widely studied long noncoding RNA.

molecular biology↗

bioRxiv2026

Protenix-v1: Toward High-Accuracy Open-Source Biomolecular Structure Prediction

We introduce Protenix-v1 (PX-v1), the first fully open-source structure prediction model to attain superior performance to AlphaFold3 while strictly adhering to the same training data cutoff, model size, and inference budget. Beyond standard evaluations, we highlight the effectiveness of inference-time scaling behavior of Protenix-v1, demonstrating that increasing the sampling budget yields consistent improvements in prediction quality--a behavior previously observed in AlphaFold3 and largely absent from prior open-source models. In addition to improved accuracy, Protenix-v1 incorporates key capabilities including protein template integration and RNA MSA support. Furthermore, to better support real-world applications such as drug discovery, we additionally release Protenix-v1-20250630, a variant trained on a larger dataset (cutoff: June 30, 2025), delivering further improved prediction accuracy. Finally, we identify limitations in existing benchmarking practices and provide updated evaluation tools and year-stratified benchmarks to support more reliable and transparent assessment. Collectively, these contributions provide a robust foundation for the Protenix series and the broader field.

bioinformatics↗

bioRxiv2026

Programmable artificial RNA condensates in mammalian cells

Artificial biomolecular condensates have emerged as powerful tools to control cellular behaviors. Here we introduce a method to build artificial condensates within living mammalian cells through the design of modular RNA motifs formed by a single, short strand of RNA. These condensates emerge spontaneously, creating RNA-rich compartments that remain separated from their surrounding environment. The RNA sequences include stem-loop domains that fold as the RNA is transcribed, and then condense in the nucleus and cytoplasm through loop-loop interactions. These sequences can be optimized and diversified, enabling the generation of distinct, non-mixing condensate populations and the programmable control of their subcellular localization. The RNA motifs can also be modified to recruit small molecules, proteins, and RNA molecules in a sequence-specific manner to the RNA-rich phase. By introducing additional RNAs that link two distinct types of condensates, we can create droplets with multiple subcompartments, whose organization can be controlled by tuning the stoichiometry of different RNA sequences. These artificial condensates provide a versatile platform for studying and manipulating molecular functions inside living cells.

synthetic biology↗

bioRxiv2026

Dopamine signatures of excessive and compulsive cocaine and fentanyl use

Excessive and compulsive drug use despite adverse consequences is a hallmark of substance use disorder, yet individuals differ markedly in their vulnerability to develop these behaviors. Drugs of abuse are long known to alter endogenous dopamine (DA) signaling, but shared principles for how DA dynamics impact compulsive use among individuals and across drug classes are lacking. Here, we monitored DA release in the medial shell of nucleus accumbens (NAc) during cocaine and fentanyl self-administration, with or without coincident punishment, in large cohorts of mice. Contingent cocaine and fentanyl self-administration evoked complex and individually distinct DA dynamics; nevertheless, a robust negative correlation held across both drugs, such that high takers exhibited lower drug-evoked DA signals. During punished drug taking, cocaine and fentanyl cases were associated with distinct DA signatures of compulsivity. For cocaine, punishment-resistant mice showed lower sustained DA responses during the post-shock, drug-associated cue period, whereas for fentanyl, punishment-resistant mice displayed larger phasic DA at the co-occurrence of footshock and drug infusion. To identify common principles underlying these observations, we developed a computational model grounded in an Actor-Critic temporal-difference (TD) learning framework that incorporates internal states, agents uncertainty, and drug-specific effects. Remarkably, this model captures the observed diversity in DA dynamics across drug classes and among mice with variable drug taking propensities, hereby providing a unified interpretation of NAc DA signals as encoding TD reward prediction errors.

neuroscience↗

bioRxiv2025

Peri-Head Distance Coding in the Mouse Brainstem

Perceiving object distance in peri-personal space is essential for guiding movement and avoiding danger. During active sensation, distance information is often anchored to the body via touch; yet how early somatosensory circuits extract distance information from tactile inputs remains unclear. Here, we investigate how second-order neurons in the mouse whisker brainstem encode peri-head distance. Using in vivo extracellular recordings in awake mice in a naturalistic wall-passing paradigm, we find brainstem neurons employ two distance-coding schemes: a "proximity" code, where firing increases monotonically as objects approach the face; and a "map" code, where neurons exhibit peak tuning at specific distances to collectively tile peri-head space. The map code outperforms proximity code in population decoding of distance. Perturbation experiments reveal multi-whisker integration and internuclear inhibition contribute to the generation of map-like tuning. These findings highlight a previously underappreciated computational role for brainstem circuits, where inhibition acts as a neural comparator to transform proximity-based sensory inputs into a map-like representation of peri-personal space.

neuroscience↗

bioRxiv2025

Response dynamics in macaque ventral stream recapitulate the visual hierarchy

Chronic recording in macaque inferotemporal cortex (IT) revealed a progression of feature selectivity over the duration of the response, with different images evoking strongest responses in different time bins. Neurons responded briskly and transiently to early best images and gave slower and more sustained responses to later best images. Correlations between IT selectivity and deep neural network (DNN) activations in early layers were faster and more transient than correlations with deeper DNN layers, suggesting a hierarchical representation unfolding. Analysis of best images in different time bins showed that early best images were simpler, or made up of fewer parts, than later best images. This simple-to-complex selectivity dynamic is consistent with, but more universal than, previously reported dynamics of global-to-local or category-to-exemplar selectivity. In parallel with the simple-to-complex selectivity progression, the image selectivity and the population activity became sparser during the response, consistent with the hypothesis of a canonical cortical microcircuit that pools inputs and incorporates neighboring-neuron inhibition. We propose that this temporal cascade in IT recapitulates the multiple antecedent stages in the visual hierarchy, which also show a simple-to-complex progression of feature selectivity.

neuroscience↗

bioRxiv2025

Mineral guided molecular enrichment: An interfacial driving force for protocell emergence on early Earth

The emergence of protocells from a dilute prebiotic environment is a fundamental challenge in origins-of-life research. which requires the simultaneous overcoming of molecular dilution, the establishment of metabolic cycles and the formation of selectively permeable compartments. But where and how such critical conditions are satisfied is very obscure and debated. We demonstrate, using contemporary model proteins and enzymatic systems, that mineral surfaces can facilitate the co-adsorption and spatial arrangement of nucleotides, proteins, and lipids into locally enriched, protocell-like assemblies under the examined laboratory conditions. We showed that geochemically relevant minerals efficiently co-adsorb and colocalize diverse biomolecules, creating crowded interfacial microenvironments that support enzyme cascades with substrate channeling-like behavior. Subsequently, these mineral-protein complexes template the assembly of lipid membranes, leading to the formation of discrete compartments that maintain metabolic activity while permitting molecular exchange. This mineral-guided surface enrichment (MSE) mechanism integrates key aspects of "metabolism-first" and "membrane-first" scenarios into a unified pathway, reconciling long-standing conceptual divides in prebiotic chemistry. Our findings establish mineral interfaces as active organizers of protocell emergence, offering a geochemically plausible framework for the origin of cellular life.

evolutionary biology↗

bioRxiv2025

Cortex-Wide Substrates for Body Schema and Action Awareness

The concept of the body schema originated from studies of patients with cortical lesions who were unable to localize their body parts in space or perceive passive movements (Head & Holmes, 1911). Current consensus defines the body schema as the brains internal representation of the body configuration in space used for both embodied perception and voluntary action (de Vignemont, 2010; Berlucchi & Aglioti, 2010; Vallar et al., 2025). Despite extensive research, how the cortex efficiently encodes the vast repertoire of body postures to support natural behavior remains poorly understood. Here, we reveal the representational format underlying the body schema by combining large-scale electrophysiological recordings with full-body 3D tracking and joint angle computation in freely behaving mice. Across cortical regions, neurons encoded diverse postural features in distinct reference frames: posterior parietal cortex (PPC) populations preferentially encoded body midline-referenced features, while sensorimotor cortices utilized a gravity-referenced frame. Strikingly, during performance of different stereotyped actions, distinct cortical ensembles were selectively activated. These action ensembles fired in phase-locked sequences that tiled the full action cycle, providing a neural substrate for action awareness. A network model recapitulated these dynamics and identified low-dimensional action subspaces within population activity. Together, these findings reveal that the cortex organizes the body schema around actions, compressing high-dimensional kinematics into an efficient, body-midline-and gravity-anchored, action-aware neural code.

neuroscience↗

bioRxiv2025

PXDesign: Fast, Modular, and Accurate De Novo Design of Protein Binders

PXDesign achieves nanomolar binder hit rates of 17-82% across six of seven diverse protein targets, surpassing prior methods such as AlphaProteo. This experimental success rate is enabled by advances in both binder generation and filtering. We develop both a diffusion-based generative model (PXDesign-d) and a hallucination-based approach (PXDesign-h), each showing strong in silico performance that outperforms existing models. Beyond generation, we systematically analyze confidence-based filtering and ranking strategies from multiple structure predictors, comparing their accuracy, efficiency, and complementarity on datasets spanning de novo binders and mutagenesis. Finally, we validate the full design process experimentally, achieving high hit rates and multiple nanomolar binders. To support future research and broaden community adoption, we release the full PXDesign pipeline (https://github.com/bytedance/PXDesign), provide public access to PXDesign through a dedicated web server (https://protenix-server.com), and make all designed binder sequences available at the project page (https://protenix.github.io/pxdesign). O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/670450v3_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@2a414aorg.highwire.dtl.DTLVardef@248f85org.highwire.dtl.DTLVardef@4a6afdorg.highwire.dtl.DTLVardef@1b622dd_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2025

Tracking the Evolutionary Patterns of RNA Modifications from Bryophyte to Flowering Plants by Mass Spectrometry

RNA modifications are critical for the regulation of gene expression. Comprehensive profiling of RNA modifications is a prerequisite for unravelling their identities and functions. The plant hormone auxin plays pivotal roles in regulating plant growth, development, and environmental responses. In contrast to the extensive studies in animals, there is a paucity of knowledge regarding the existence and functions of RNA modifications in plants. This is due to the low abundance of modified ribonucleosides and the lack of accurate analytical methods. This study employs a state-of-the-art hydrophilic interaction liquid chromatography-tandem-mass spectrometry (HILIC-MS/MS) platform to achieve the sensitive detection of 12 nucleosides at the pM to nM level. Utilizing this platform, we were able to identify N4,2-O-dimethylcytidine (m4Cm) in plants by LC-MS/MS for the first time, which provided direct evidence for the widespread of this modification in plants. Moreover, a comprehensive investigation was undertaken to examine the 12 types of RNA modifications and their response patterns to auxin in a range of prototype plants, spanning from bryophyte (Marchantia polymorpha) to flowering plants (Arabidopsis thaliana, Oryza sativa, and Zea mays). The evolutionary patterns of the 12 RNA modifications were depicted from bryophyte to flowering plants, and their potential functions in plant growth, development, and auxin responses were revealed.

biochemistry↗

bioRxiv2025

LIPTER, a cardiomyocyte-enriched long noncoding RNA, controls cardiac cytoskeletal maturation and is regulated by a cardiomyocyte-specific enhancer.

Cardiac development is characterized by a complex series of molecular, cytoskeletal and electrophysiological changes that guarantee the proper functioning of adult cardiomyocytes (CMs). These changes are defined by cell-type-specific transcriptional rewiring of progenitor cells to form CMs, and are regulated by various epigenetic elements, such as long noncoding RNAs (lncRNAs). LncRNAs are versatile epigenetic regulators as they may act in cis or in trans to orchestrate important gene programs during cardiac development and may concurrently encode micropeptides. LIPTER is one such lncRNA, previously shown to regulate lipid droplet transport in cardiomyocytes and thus an important regulator of cardiomyocyte metabolism. Here we show that LIPTER also plays a role in the cytoskeletal maturation of CMs, as loss of LIPTER leads to persistent expression of fetal genes, changes in chromatin accessibility, disorganized sarcomeres and impaired calcium homeostasis in CMs. Furthermore, we have identified a cardiomyocyte-specific regulatory enhancer that regulates the expression of LIPTER in CMs. CRISPR-mediated inhibition of this enhancer led to reduced LIPTER expression in CMs and increased expression of fetal genes. This CM-specific enhancer could therefore be manipulated to control the expression of LIPTER for therapeutic benefit. In summary, we have unravelled a novel role of LIPTER in CMs cytoskeletal maturation and have identified a CM-specific enhancer for LIPTER.

genomics↗

bioRxiv2025

An absolute quantification atlas of small non-coding RNAs across diverse mammalian tissues and cell lines

The low quantitative accuracy of conventional small noncoding RNA sequencing (sncRNA-seq) methods due to extensive ligation bias commonly limits functional investigation of microRNAs (miRNAs) and PIWI-interacting RNAs (piRNAs). Here, we develop 4NBoost, a single-tube sncRNA-seq protocol designed to minimize bias in the estimated absolute quantification of miRNA and piRNA transcripts through the incorporation of quantitative exogenous RNA spike-ins. With 4NBoost, we profile sncRNA expression across 20 murine tissues, 18 macaque tissues, and 24 widely used cell lines, as well as 4 Arabidopsis tissues, to establish a comprehensive quantitative reference atlas. Compared with existing small RNA databases, our data reveal substantial biases in miRNA abundance, strand selection, and tissue-specific expression at both individual and family levels. To further extend its utility, we employ machine learning to model and correct biases in conventional datasets, effectively recovering ground truth transcript abundances. All 4NBoost data and the accompanying bias-correction model are freely available via SmRNAQuant (http://wulg-lab.sibcb.ac.cn/SmRNAQuant/), a web-based repository for exploring sncRNA expression. Together, the 4NBoost, bias-correction model, and SmRNAQuant provide powerful resources to advance sncRNA research.

genomics↗

bioRxiv2025

From Dataset Curation to Unified Evaluation: Revisiting Structure Prediction Benchmarks with PXMeter

Recent advances in deep learning have significantly improved the accuracy of structure prediction for biomolecular complexes; however, robust evaluation of these models remains a major challenge. We introduce PXMeter, an open-source toolkit that support consistent and reproducible evaluation of diverse predictive models across a broad spectrum of biological complex structures. PXMeter provides a unified and reproducible benchmarking framework, offering valuable insights to support the ongoing improvement of structure prediction methods. We also present a high-quality benchmark dataset curated from recently deposited structures in the Protein Data Bank (PDB). These entries are manually reviewed to exclude non-biological interactions, ensuring reliable evaluation. Using these resources, we conducted a comprehensive benchmark of several structure prediction models, namely Chai-1, Boltz-1, and Protenix. Our benchmarking results demonstrate the advancements achieved by deep learning models, while also identifying ongoing challenges--especially in modeling protein-protein and protein-RNA interactions. Project Pagehttps://github.com/bytedance/PXMeter

bioinformatics↗