Biology subjects

Kong, Z.

Publications and source records attributed to Kong, Z..

14 recordsLinked to original sources

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

Medea: An omics AI agent for therapeutic discovery

Therapeutic hypotheses can transfer across diseases but their relevance depends on biological context. The same target, perturbation, or treatment can produce different effects across cell types, disease states, genetic backgrounds, and patients. Therapeutic reasoning therefore requires methods that preserve context, test when evidence supports transfer, and identify where context-specific effects limit it. Although AI agents can perform therapeutic analyses, existing systems often fail to preserve biological context over long workflows, verify intermediate computational steps, or reconcile conflicting evidence across datasets and literature. Here, we present Medea, an AI agent for therapeutic reasoning across biological contexts. Medea executes multi-step analyses using biological tools, machine learning models, and literature retrieval while enforcing verification during planning, execution, and evidence synthesis. We evaluate Medea across 5,673 open-ended analyses in three domains: cell type specific therapeutic target nomination in five diseases and 29 cell types, synthetic lethality prediction in 7 cancer cell lines, and immunotherapy response prediction from multimodal patient profiles. Using a previously unpublished epistatic miniarray profiling screen performed under two DNA-damaging treatments, we evaluate Medea on predicting synthetic lethality among 238,046 gene-gene pairs in yeast. Medea predicts these experimentally measured synthetic lethal interactions, indicating that its performance reflects biological relevance rather than information leakage from benchmark datasets. Across these evaluations, Medea improves performance over large language models, reasoning models, biomedical agents, and specialized machine learning models while maintaining low failure rates and calibrated abstention. These results show that verifiable AI agents can perform therapeutic analyses across biological contexts.

bioinformatics↗

bioRxiv2025

CONCERT predicts niche-aware perturbation responses in spatial transcriptomics

Spatial perturbation transcriptomics measures how genetic or chemical edits alter gene expression while preserving tissue context. Perturbation outcomes depend on a cells intrinsic state and also on how effects propagate across cellular microenvironments. We present CONCERT, a niche-aware generative model that embeds perturbation context and learns spatial kernels with a Gaussian process variational autoencoder to predict perturbation effects across tissue. We formalize three tasks: patch, border, and niche, predicting responses in nearby unperturbed regions, at tissue interfaces, and as a function of surrounding microenvironments. We evaluate CONCERT on Perturb-map lung datasets. CONCERT outperforms state-of-the-art models (dissociated counterfactuals, spatialized perturbation models, and kNN), reducing E-distance by up to 33.77% (patch), 26.05% (border), and 33.74% (niche) versus the next best, with mean absolute error down by up to 23.28% and Pearson correlation up by up to 9.10%. Two case studies go beyond benchmarking. In dextran sodium sulfate-induced colitis, CONCERT reconstructs spatial gene expression at unmeasured time points, produces longitudinal comparisons across unpaired mice, resolves intermouse heterogeneity, and recovers consistent temporal declines of inflammation-associated genes across regions. In ischemic stroke, CONCERT predicts responses under variable lesion sizes and in a 3D formulation across brain sections, capturing lesion-core and peri-lesion patterns. CONCERT performs niche-aware counterfactual prediction, reconstructs missing spatial data, and models perturbation responses across tissues.

bioinformatics↗

bioRxiv2025

Cancer cell-selective ecotopic expression of CD20 as an antigen enables rituximab repurposing for solid tumor immunotherapy

Despite the clinical success of cancer immunotherapies, their efficacy is often compromised by antigen-related problems, including heterogeneity, downregulation, loss, and off-tumor toxicity. To overcome these limitations that challenge the current immunotherapies dependent on native antigens, we here describe a new cancer immunotherapy strategy, which artificially and specifically expresses a clinical validated antigen on variant tumors and thus repurposes clinical antibody drugs to treat cancers not belonging to their indications. To authenticate the strategy, we delivered a CD20 gene under a control of NF-{kappa}B-specific promoter to tumors by adeno-associated virus and then treated them with a CD20 antibody, rituximab. We found that CD20 was selectively expressed in tumors and the followed rituximab treatment activated natural killer (NK) cell to kill cancer cells by antibody-dependent cellular cytotoxicity. We demonstrated that this strategy is effective not only in variant cultivated cancer cells, HCT116 spheroids, and patient-derived organoids of human colorectal cancer, but also in humanized mouse with HCT116 xenograft and immunocompetent mouse with CT26 transplant. The strategy showed high cancer cell specificity in both in vitro and in vivo treatments, leading to high security in animal treatments. This strategy thus creates a new modality of cancer immune-redirection therapy by repurposing the clinical validated both antigen and antibody.

cancer biology↗

bioRxiv2025

Neural dynamics encoding risky choices during deliberation reveal separate choice subspaces

Human decision-making involves the coordinated activity of multiple brain areas, acting in concert, to enable humans to make choices. Most decisions are carried out under conditions of uncertainty, where the desired outcome may not be achieved if the wrong decision is made. In these cases, humans deliberate before making a choice. The neural dynamics underlying deliberation are unknown and intracranial recordings in clinical settings present a unique opportunity to record high temporal resolution electrophysiological data from many (hundreds) brain locations during behavior. Combined with dynamic systems modeling, these allow identification of latent brain states that describe the neural dynamics during decision-making, providing insight into these neural dynamics and computations. Results show that the neural dynamics underlying risky decision, but not decisions without risk, converge to separate subspaces depending on the subjects preferred choice and that the degree of overlap between these subspaces declines as choice approaches, suggesting a network level representation of evidence accumulation. These results bridge the gap between regression analyses and data driven models of latent states and suggest that during risky decisions, deliberation and evidence accumulation toward a final decision are represented by the same neural dynamics, providing novel insights into the neural computations underlying human choice. HighlightsO_LIHighly accurate decoding can be accomplished with dynamical systems modeling that revealed distinct attractor-like subspaces, one for each of the two options (gamble or safe bet). C_LIO_LIEarly during deliberation, single trial neural trajectories show rapid transitioning between these subspaces, but as the time of choice selection approached, the single trial trajectories converged towards one of the subspaces, whose identity was consistent with the subjects choice. C_LIO_LIThese dynamics are specific to risky decisions as we found classification accuracy dropped to chance for trials where the gamble option was guaranteed to be successful (100% win probability) or unsuccessful (0% win probability). C_LIO_LIThese results suggest that deliberation and evidence accumulation toward a final decision in the presence of any risk can be represented by the same neural dynamics, providing novel insights into the neural computations underlying human choice. C_LI

neuroscience↗

bioRxiv2025

Gap-free genomes and transcriptomes uncover race-specific effectors in watermelon wilt pathogen Fusarium oxysporum f. sp. niveum

Watermelon (Citrullus lanatus L.) is one of the worlds most economically important fruit crops, yet it is susceptible to devastating diseases such as vascular wilt caused by Fusarium oxysporum f. sp. niveum (Fon), with limited effective control measures. Fon evolves rapidly in the field to overcome host resistance and global watermelon production is constantly threatened by new pathogenic races. High-quality genomic resources are key to understanding the molecular mechanisms underlying Fon virulence evolution for disease management. Here, we de novo assembled and annotated gapless genomes of three physiological races of Fon (race 1, 2, and 3), and dissected the mechanisms behind their distinctive virulence through comparative genomics and transcriptomics. Whole-genome alignments identified core and accessory chromosomes in Fon where each race carried a unique set of accessory chromosomes or regions. Comparative transcriptomics of Fon infection revealed distinctive temporal patterns of gene expression even among core gene families, particularly those related to cell wall degradation enzymes. Effectoromic prediction and comparative analysis in three gap-free genomes identified 13 race-specific effectors (RSEs) in FonR3, one (FonRSE1) of which was a critical virulence factor of FonR3 on watermelon as demonstrated via functional experiments. The gap-free genome assemblies and annotations, and the RSEs are valuable resources for studying Fon pathobiology and genome evolution, adding in the design of improved disease control strategies.

genomics↗

bioRxiv2025

Active and predictive adjustment of pupil size for accommodating visual regularity

The visual system efficiently encodes information by summarizing visual regularities that could be quantified by summary statistics. While summary statistics are thought to be processed in several cortical and subcortical areas, we investigated whether the earliest stage of visual processing, the pupil, is involved in this process. In three experiments, participants either performed ensemble orientation estimation with or passively viewed bar arrays with different bar orientation distributions while their pupil size was recorded. We found that pupil size increased when the orientation distribution became more dispersed and was closely linked to participants estimation performance. Furthermore, pupillary responses occurred automatically during passive viewing and could even predict ensemble orientation estimation performance. Moreover, when anticipating a dispersed distribution, the pupil dilated in advance. Our findings reveal a new cognitive role of the pupil - it actively and predictively adjusts its size to facilitate the extraction of visual regularity.

neuroscience↗

bioRxiv2025

A synthetic chlorophagy receptor promotes plant fitness by mediating chloroplast microautophagy

Chloroplasts are major photosynthetic and protein-containing organelles in green plants and algae. Unwanted chloroplast proteins and entire chloroplasts are cleared through various degradation pathways including autophagy. Nevertheless, canonical chlorophagy receptors remain unidentified, and whether and to what extent chlorophagy can be enhanced to benefect the plants remain unknown. Here we designed and validated a synthetic chloroplast autophagy receptor using biochemical, genetical, and imaging approaches. This synthetic receptor, LIR-SNT-BFP, was constructed by fusing a fragment containing the LC3-interacting region (LIR) of the selective autophagy receptor NBR1 and the N-terminal amphipathic -helix of the chloroplast outer envelope protein SFR2. The fusion protein LIR-SNT-BFP coated the chloroplast and attracted ATG8a in planta. Upon induction, the synthetic receptor elicited vacuole-mediated microautophagy of entire chloroplasts independent of the ATG8 conjugation machinery proteins ATG5 or ATG7. Meanwhile, it induced chloroplast division; however the induced chlorophagy was independent of PDV2. Notably, moderate chlorophagy improves rosette growth, but excessive levels are detrimental. Furthermore, induced chlorophagy partially protects against herbicide-induced leaf chlorosis. This study demonstrates controlled chloroplast degradation using a synthetic chlorophagy receptor.

plant biology↗

bioRxiv2025

Exploring the Suitability of Piecewise-Linear Dynamical System Models for Cognitive Neural Dynamics

Dynamical system models have proven useful for decoding the current brain state from neural activity. So far, neuroscience has largely relied on either linear models or nonlinear models based on artificial neural networks. Piecewise linear approximations of nonlinear dynamics have proven useful in other technical applications, providing a clear advantage over network-based models, when the dynamical system is not only supposed to be observed, but also controlled. Here we explore whether piecewise-linear dynamical system models (recurrent Switching Linear Dynamical System or rSLDS models) could be useful for modeling brain dynamics, in particular in the context of cognitive tasks. We first generate artificial neural data based on a nonlinear computational model of perceptual decision-making and demonstrate that piecewise-linear dynamics can be successfully recovered from these observations. We then demonstrate that the piecewise-linear model outperforms a linear model in terms of predicting future states of the system and associated neural activity. Finally, we apply our approach to a publicly available dataset recorded from monkeys performing perceptual decisions. Much to our surprise, the piecewise-linear model did not provide a significant advantage over a linear model for these particular data, although linear models that were estimated from different trial epochs showed qualitatively different dynamics. In summary, we present a dynamical system modeling approach that could prove useful in situations, where the brain state needs to be controlled in a closed-loop fashion, for example, in new neuromodulation applications for treating cognitive deficits. Future work will have to show under what conditions the brain dynamics are sufficiently nonlinear to warrant the use of a piecewise-linear model over a linear one.

neuroscience↗

bioRxiv2022

Cerebral oxygenation and cardiac output responses during short repeated sprints exercise and modulatory effect of glucose ingestion

In this study we investigated changes in concentrations of cerebral oxyhaemoglobin (O2Hb), deoxyhaemoglobin (HHb), and total haemoglobin (tHb) during repeated sprints and their relationship with cardiac output. We also examined the effect of glucose ingestion and acute hyperglycaemia on cerebral haemoglobin responses. Ten young male participants ingested either a glucose drink (70 g) or a placebo before performing a set of 10 repeated 6-second sprints on a cycle ergometer. Heart rate, stroke volume, and cardiac output were measured continuously using impedance cardiography, while changes in O2Hb, HHb, and tHb in the frontal region of the cerebral cortex were measured using near-infrared spectroscopy (NIRS). The results showed that each sprint elicited a transient increase in O2Hb and to lesser extent HHb concentrations, which was enhanced with the number of sprint repetitions and correlated with cardiac output. After each sprint, O2Hb and HHb quickly returned to baseline, while cardiac output remained elevated. At the end of the repeated sprints, O2Hb was decreased to below pre-exercise levels, while HHb and tHb were elevated. After a recovery of 10-15 min, O2Hb returned to pre-exercise values in the placebo trial, but increased to above pre-exercise values (reactive hyperaemia) in the glucose trial. Our findings suggest that short sprint exercise increases O2Hb, HHb, and tHb levels during exertion in parallel with cardiac output. However, in addition to the transient increase in cerebral haemoglobin, a progressive decline in cerebral oxygen saturation occurs during repeated sprints. Glucose ingestion does not alter cerebral haemoglobin responses to sprint exercise but appears to be associated with faster recovery of O2Hb.

physiology↗

bioRxiv2022

Ratio-based quantitative multiomics profiling using universal reference materials empowers data integration

Multiomics profiling is a powerful tool to characterize the same samples with complementary features orchestrating the genome, epigenome, transcriptome, proteome, and metabolome. However, the lack of ground truth hampers the objective assessment of and subsequent choice from a plethora of measurement and computational methods aiming to integrate diverse and often enigmatically incomparable omics datasets. Here we establish and characterize the first suites of publicly available multiomics reference materials of matched DNA, RNA, proteins, and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters, providing built-in truth defined by family relationship and the central dogma. We demonstrate that the "ratio"-based omics profiling data, i.e., by scaling the absolute feature values of a study sample relative to those of a concurrently measured universal reference sample, were inherently much more reproducible and comparable across batches, labs, platforms, and omics types, thus empower the horizontal (within-omics) and vertical (cross-omics) data integration in multiomics studies. Our study identifies "absolute" feature quantitation as the root cause of irreproducibility in multiomics measurement and data integration, and urges a paradigm shift from "absolute" to "ratio"-based multiomics profiling with universal reference materials.

systems biology↗

bioRxiv2021

DeepVASP-E: A Flexible Analysis of Electrostatic Isopotentials for Finding and Explaining Mechanisms that Control Binding Specificity

Amino acids that play a role in binding specificity can be identified with many methods, but few techniques identify the biochemical mechanisms by which they act. To address a part of this problem, we present DeepVASP-E, an algorithm that can suggest electrostatic mechanisms that influence specificity. DeepVASP-E uses convolutional neural networks to classify an electrostatic representation of ligand binding sites into specificity categories. It also uses class activation mapping to identify regions of electrostatic potential that are salient for classification. We hypothesize that electrostatic regions that are salient for classification are also likely to play a biochemical role in achieving specificity. Our findings, on two families of proteins with electrostatic influences on specificity, demonstrate that large salient regions can identify amino acids that have an electrostatic role in binding, and that DeepVASP-E is an effective classifier of ligand binding sites.

bioinformatics↗

bioRxiv2020

Regular Tai Chi Practice Is Associated with Improved Memory as well as Structural and Functional Integrity of the Hippocampal Formation in the Elderly

ObjectiveThe current study aimed at determining effects of Tai Chi as an example of a combined motor-cognitive exercise relative to regular walking as an example of an exercise without cognitive demands on cognitive functioning and the functional and structural integrity of the brain in the elderly. MethodsHealthy elderly women with at least 6 years of regular Tai Chi or brisk walking exercise were recruited and underwent cognitive assessment via the Montreal Cognitive Assessment and brain structural and resting state functional MRI assessments. ResultsEpisodic memory in Tai Chi group was superior to that of the walking group; (2) higher gray matter density in inferior and medial temporal regions, including the hippocampal formation; (3) higher ReHo in temporal regions, specifically the fusiform gyrus and hippocampal formation (4) significant partial correlations were found between the gray matter density of the left hippocampus and episodic memory in the whole sample (5) significant partial correlations were observed between the ReHo in left hippocampus, left parahippocampal, left fusiform and delayed memory task was observed among all subjects. ConclusionThe present study suggest that long-term Tai Chi practice may improve memory performance via remodeling structure and the function of the hippocampal formation.

neuroscience↗

bioRxiv2018

Effect of soybean molasses-adsorbents on in vitro ruminal fermentation characteristics, milk production performance in lactating dairy cows

This study aimed to evaluate the in vitro fermentation characteristics of corncob powder (CRP), wheat bran (WB), rice husk (RH), defatted bran (DB) and soybean hulls (SH) when mixed with soybean molasses at a ratio of 30:100 (dry matter basis), using a batch culture incubation. During in vitro study, SH showed better fermentation characteristics (including greater maximum gas production, shorter time to achieve half of Vf, greater concentrations of acetate, propionate and total VFA, and less initial fractional rate of degradation) than other four substrates, while WB had the greatest values of DM and NDF disappearance, NH3-N and butyrate concentrations among substrates. A randomized complete block designed in vivo experiment was conducted with 24 Holstein cows (534 {+/-} 58 kg BW, 2.8 {+/-} 0.7 parity, 129 {+/-} 23 d in milk) randomly assigned to three experimental diets: Control, WB (WB adsorbed to soybean molasses replaced 150 g of corn meal per 1000 g of diet dry matter [DM]) or SH (SH adsorbed to soybean molasses replaced 100 g of wheat bran and 50 g corn meal per 1000 g of diet DM). The results indicated that cows received WB diet had greater (P<0.01) milk fat and total milk solid content than cows fed control and SH diets, and cows received WB and SH diets tended to have greater (P<0.01) milk protein content and blood glutamic-pyruvic transaminase concentration than cows fed control diet. Furtherly, cows received WB diet had greater (P<0.01) blood amylase and lactate dehydrogenase concentration than that of cows fed control diet during middle lactation.\n\nIn conclusion, dietary supplementation of molasses adsorbed by-products like WB and SH have positive effect on promoting rumen fermentation, milk quality and blood metabolism in early- and middle-lactating dairy cows. The results offered a new products and feeding way in dairy farming

animal behavior and cognition↗