Biology subjects

Zhang, H.

Publications and source records attributed to Zhang, H..

At least 253 records · Page 14Linked to original sources

bioRxiv2025

Metabolic dysfunction related to specific genetic variant sites of methylmalonic acidemia

Methylmalonic acidemia (MMA) is a genetic metabolic disease, with mut (mut-MMA, caused by MMUT gene variation) and cblC (cblC-MMA, caused by MMACHC gene variation) being the predominant subtypes. MMA patients carrying c.729_730insTT and c.609G>A exhibit severe clinical manifestations and have poorer outcomes. Little information is known on the potential mechanism. The metabotypes of MMA patients carrying c.729_730insTT and c.609G>A were profiled. The potential effect of genetic variation sites on the metabolism were explored. Leveraging the gene variation spectrum and the targeted metabolomics, propionic acid, indolelactic acid, myristoleic acid, tetradecenoic acid, tridecanoic acid, and oxoadipic acid showed strong enrichment in c.729_730insTT carrying mut-MMA patients. Whereas mutation c.609G>A caused adipic acid, pimelic acid, fructose, AMP, hexanylcarnitine, and methionine significnatly altered in cblC-MMA patients. Furthermore, the metabolomic profiles were obviously different in c.729_730insTT and non-c.729_730insTT carrying mut-MMA patients after treatment. Different metabolic alterations in cblC-MMA patients carrying c.609G > A and non-c.609G > A under treatment were also observed. Our findings provide unique insights into the metabolic basis of MMA specific subtypes and its implications across different variants.

molecular biology↗

bioRxiv2025

Caveolin-1 controls lineage fidelity of adult neural stem cells via lysosomal degradation of PDGFRα

Caveolae are cell-surface signaling hubs involved in stem cell proliferation and differentiation. However, the molecular details of how they influence stem cell quiescence remain poorly understood. In this study, we show that most caveolae components, particularly caveolin-1 (Cav-1), are specifically upregulated in quiescent neural stem cells (NSCs). Furthermore, we demonstrate that Cav-1 restricts platelet-derived growth factor receptor (PDGFR) signaling, a key regulator for NSC proliferation and oligodendrocyte differentiation in the adult brain, through lysosomal degradation. Genetic ablation of Cav-1 leads to aberrant NSC activation, reduced neurogenesis, and enhanced ectopic oligodendrocyte differentiation on the ventricular surface in the ventral side of the ventricular-subventricular zone (V-SVZ), a key NSC niche responsible for neuroblast differentiation. Mechanistically, we show that Cav-1 deficiency suppresses the endo-lysosomal degradation of PDGFR, leading to PDGFR accumulation and excessive signaling for oligodendrocyte differentiation. Taken together, our results uncover a crucial homeostatic mechanism wherein caveolae function as a safeguard to maintain adult NSC quiescence and lineage fidelity by controlling lysosomal turnover of PDGFR in a niche-specific manner.

neuroscience↗

bioRxiv2025

Genomic Structural Variation Underlies Cell-Type-Specific Betacyanin Variegation in Chenopodium quinoa

The allotetraploid crop quinoa (Chenopodium quinoa) accumulates red/violet betacyanins, which function as vital stress-mitigating antioxidants. We investigated the genetic basis of red/green variegation observed in the aerial organs of the P0429 accession. We demonstrated that this color mosaic is primarily localized to the epidermal bladder cells (EBCs), with red EBCs accumulating [~]50-fold higher betacyanin levels than colorless EBCs. Cell-type-specific RNA-sequencing of EBCs identified the cytochrome P450 gene Cqu0091301 (CYP76AD) as the dominant and rate-limiting factor, exhibiting strong upregulation in red EBCs. This high pigmentation requires a specific structural variation in the P0429 accession: a [~]4-kb genomic insertion that restores the full functionality of Cqu0091301, which is otherwise truncated and non-functional in common reference genomes. Genomic analysis reveals that Cqu0091301 is part of a CYP76AD-DODA gene cluster. Notably, expression analysis revealed functional divergence between the quinoa subgenomes, with B-subgenome CYP76AD genes highly dominant in EBCs, while A-subgenome homologs were preferentially expressed in other tissues. Our results establish a clear link between structural genomic variation and cell-type-specific betalain biosynthesis, providing molecular insight into pigment regulation and subgenome specialization in allotetraploid quinoa.

plant biology↗

bioRxiv2025

It's All In The Journey: Putative Strategies Extracted from Navigation Paths Predict Spatial Memory and Hippocampal Recruitment

Navigation in the real world may rely on a multitude of different strategies with distinct advantages and disadvantages. However, the characterization of spontaneous strategy use during wayfinding and underlying neural correlates are scarce. We explore data across three groups of participants who performed a virtual navigation task: (I) epilepsy patients undergoing invasive electrophysiological recordings of the hippocampus, and two groups who underwent fMRI scanning: (II) young adults at genetic risk for Alzheimers disease (APOE-e4 carriers) and (III) healthy controls. We establish three metrics by which to quantitatively characterize each taken path, with the aim of simultaneously reflecting the putative strategies used by humans to solve the task: 1. straightness of paths; 2. deviation towards the edge of the circular arena; and 3. overlap of individual paths leading to a specific object location, which putatively relate to reliance on allocentric (1) and egocentric reference frames (2 & 3). Extracted metrics were related to performance in the task and to hippocampal activity. We find that strategy use is variable within participant, changes across the experiment, and is adaptive in a group-dependent manner. While hippocampal BOLD activity and theta (gamma) power were generally inversely (positively) related, we found that the recruitment of the hippocampus was both group- and strategy- specific. Our results further our understanding of the heterogeneous and idiosyncratic nature of spatial navigation by revealing, how these behaviors are altered by disease and the subsequent compensatory neural dynamics, as well as the link between mesoscopic and macroscopic neural signals.

neuroscience↗

bioRxiv2025

SynPROTAC: synthesizable PROTACs design through synthesis constrained generative model and reinforcement learning

Protein hydrolysis targeting chimeric (PROTAC) has emerged as a promising technology in degrading disease-related proteins for drug design. Recent deep generative models can accelerate PROTAC design, but the generated molecules are often difficult to synthesize. Here we develop SynPROTAC model, which employs Graphormer encoded warhead or E3 ligand as input, and autoregressively samples reaction templates and building blocks through transformer based decoder for PROTAC construction. The model is also fine-tuned via reinforcement learning for generating PROTACs with favorable binding properties. The comprehensive evaluations indicated that SynPROTAC is capable of generating novel PROTACs with feasible synthetic routes, reasonable physico-chemical and binding related properties.

bioinformatics↗

bioRxiv2025

Super-CUT&Tag: A Sensitive, Spatially Resolved Approach for Epigenomic Profiling in Tissue Sections

Spatial profiling of protein-DNA interactions is essential for understanding gene regulatory processes, but current methods suffer from low sensitivity, limited resolution, and complex workflows. Here we introduce Super-CUT&Tag (Solid-phase interface embedded with an Ultra-dense DNA barcode array to Profile Epigenomic landscapes for spatially Resolved CUT&Tag), a robust spatial epigenomic method that directly transfers in situ protein-DNA interactions from tissue sections onto a pre-fabricated spatially barcoded array. Incorporating 3D polyamidoamine (PAMAM) dendrimers markedly improves chromatin capture efficiency, achieving several-fold higher sensitivity compared to current spatial CUT&Tag approaches. Using Super-CUT&Tag, we profile H3K27ac spatial landscape and uncover previously hidden active-chromatin heterogeneity across mouse embryonic tissues and stages. Integration with spatial transcriptomics further reveals spatiotemporal enhancer regulation dynamics of Neurod2 during corticogenesis. By overcoming key bottlenecks in sensitivity, resolution and usability, Super-CUT&Tag establishes a powerful platform for spatial epigenomic profiling in complex tissues.

genetics↗

bioRxiv2025

PDLIM2 in Lung Adenocarcinoma Metastasis

Human and mouse studies have established the unique PDZ-LIM domain-containing protein PDLIM2 as a common tumor suppressor that is especially vital for suppressing the tumorigenesis and therapeutic resistance of lung cancer, the leading cause of cancer-related deaths among both men and women. However, the role of PDLIM2 in tumor metastasis, the predominant cause of cancer morbidity and mortality, is yet to be determined. Here, we report that PDLIM2 repression was positively associated with the metastasis of human lung adenocarcinoma, the major type of non-small cell lung cancer that accounts for more than 40% of all cases of human lung cancer. Interestingly, PDLIM2 repression was also correlated with oncogenic KRAS and/or TP53 mutations, two common drivers of human lung adenocarcinoma that often co-occur. In mice, in comparison to concurrently inducing mutant KRAS expression and TP53 deletion, additional co-ablation of PDLIM2 significantly increased the number and size of lung adenocarcinomas in the lung, and more importantly, the distant metastasis of lung tumor cells. The increased metastasis was accompanied by decreased anti-tumor immunity and increased pro-tumor inflammation. These data demonstrate the role of PDLIM2 in suppressing lung adenocarcinoma metastasis, thereby improving our understanding of this crucial tumor suppressor and lung cancer. They also provide a useful model for studying metastasis and testing new lung cancer treatments in vivo.

cancer biology↗

bioRxiv2025

Dynamic alterations of DNA methylation and transcriptome in adaptation to and recovery from the space environment

Space exploration presents tremendous health challenges. Here, we report time series of multi-omic and phenotypic profiles of seventeen astronauts from six China Manned Space missions with continuous spaceflight durations ranging from 13 to 180 days. We revealed a key role of DNA methylation regulation in reshaping gene expression patterns to adapt to the space environment. Long-duration spaceflight showed more alterations in epigenetic modifications correlated with alternative splicing and protein acetylation. During recovery, an "overrange rebound" phenomenon was observed, furthermore, a mathematical model was established to describe this implying important phenomenon. Moreover, we revealed the correlations between molecular alterations and phenotypic changes such as coagulation activation and bone intensity loss. Additionally, we performed ground-based simulation experiments to estimate the impacts of individual stressors in the space environment on DNA methylation. In summary, our study highlights the importance and complexity of epigenetic regulation in adaptation to and recovery from the space environment.

genomics↗

bioRxiv2025

Information Network Flux interprets the importance of regulatory circuits in the protein and gene spaces

Cells receive information from environment, make decisions, and execute biological functions. This sensor-actuator controlling mode is common, and has benefits for system stability especially when there exist feedback-loops for action correction. In cells, transcription factors (TFs) play the role of "brain" since they control the expression levels of about 20,000 protein-coding genes. However, the detailed topological structure of the regulating relationships among them is far from clear. Circuitlike pathways, which usually contain protein-protein interactions (PPIs), TF-gene regulations and gene expressions, are pervasive in cells, however, their importance is yet to be systematically elucidated by unifying information at both protein and gene levels. Here we developed Information Network Flux (INF), an algorithm that could simulate information transmit on multi-layer networks, and integrate protein and gene level information for cell type specific pathways that respond to given perturbation. We used topological analysis to identify regulatory circuits and found that the TFs participating in these circuits, especially those acted as "sensor" or "actuator" are highly relevant to cellular response to external signals. At the gene level, circuit genes serve as efficient cell type indicators, which showed better performance than commonly used gene expression levels. At the circuit level, shorter circuits enrich housekeeping functions and longer circuits exhibit increased tissue-specificity. These findings suggest that these circuit pathways may contribute to the identity and stability of cell states.

biophysics↗

bioRxiv2025

Lineage-specific evolution of regulatory landscapes in polyploid plant and its diploid progenitors

Cis-regulatory elements are specific DNA sequences that control gene expression in a spatiotemporal manner, and variation within these elements represents a major source of phenotypic diversity and evolutionary innovation. Nevertheless, how regulatory elements evolve and shape gene expression remains poorly understood, particularly in plants. The well-resolved phylogeny of allopolyploid peanut (Arachis hypogaea) and its diploid progenitors, A. duranensis and A. ipaensis, provides an ideal system to investigate the regulatory evolution at a lineage-specific level. By integrating comparative analyses of sequence similarity, chromatin accessibility, histone modifications, conserved noncoding sequences (CNSs), and gene expression, we reconstructed the evolutionary trajectories of Accessible Chromatin Regions (ACRs), where regulatory elements typically reside, and revealed their distinct contributions to homeolog expression bias, unequal expressions between homeologs. Most ACRs exhibited high sequence similarity, comparable chromatin accessibility, and conserved states for H3K4me3, H3K56ac, and H3K36me3, indicating regulatory stability after hybridization and polyploidization. However, a subset of novel ACRs emerged de novo from previously non-regulatory regions or through sequence mutations in preexisting ACRs, arising at different rates and evolutionary stages. Notably, even highly sequence-conserved ACRs exhibited substantial variation in chromatin accessibility, consistent with CNS composition differences and minor sequence variation, although causal relationships remain to be demonstrated. Our analyses further revealed a complex spectrum of CNS dynamics across the diploid-polyploid framework. Overall, our study provides empirical insights into the fine-scale evolution of plant regulatory landscapes and complements previous large-scale comparisons across distant lineages.

evolutionary biology↗

bioRxiv2025

Computational design of CYP102A1 variants for the biosynthesis of a next-generation antiplatelet drug DT-678

Clopidogrel is a widely used antiplatelet prodrug to treat acute coronary syndromes. However, its clinical efficacy is hampered by ineffective bioactivation to produce the pharmacologically active metabolite (AM), leading to variability in antiplatelet response among different ethnic groups. To overcome the shortcomings of clopidogrel, DT-678 was developed by conjugating the AM to 3-nitropyridine-2-thiol via a mixed disulfide bond. It has been challenging to produce the conjugate in high yield by chemical synthesis. Here, we report the first de novo biosynthesis of DT-678 using engineered CYP102A1 variants. We applied structure-based computational design using UniDesign to generate three variants (UD4, UD5, and UD6) that enhanced the catalytic activity and selectivity toward DT-678 synthesis. Among them, UD6 demonstrated the highest total turnover number and DT-678-specific productivity under optimized conditions. Mechanistic analysis revealed that rapid enzyme inactivation, driven by reactive oxygen species (ROS) such as superoxide and hydrogen peroxide, limited overall yield. Remarkably, we found that ascorbic acid significantly protected CYP102A1 variants from inactivation and hence increased production yield. This work establishes a scalable enzymatic strategy for DT-678 biosynthesis and highlights the importance of combining protein engineering with redox control to overcome limitations in CYP-catalyzed reactions.

biochemistry↗

bioRxiv2025

Single-cell multi-omic characterization of the soybean root response to cyst nematode infection

Soybean cyst nematodes parasitize soybean roots by inducing the formation of multinucleate syncytia to feed and complete their life cycle. However, the molecular basis of syncytia initiation and development remains limited. Here, we generated an integrated single-nucleus RNA and chromatin accessibility profile of soybean roots from infected and uninfected plants. We profiled 56,448 high-quality nuclei and identified all major root cell types, including distinct subpopulations enriched with syncytial nuclei. Transcriptomic and chromatin accessibility analyses support their procambium cell signature that progresses through distinct stages associated with immune suppression, cell fusion, and endoreduplication. Integrative gene expression and transcription factor motif chromatin accessibility analyses identified CAMTA1, as a potential transcriptional repressor of defense-related genes including receptor-like kinases, whereas MYB, MYB-related, and E2F were identified as potential transcriptional activators that coordinate cell wall remodeling, chromatin modification and DNA replication, respectively, across developmental trajectories. These findings provide mechanistic insights into host reprogramming during nematode parasitism and serve as a foundational resource for engineering nematode-resistant soybean.

plant biology↗

bioRxiv2025

Representation, Alignment, and Generation: A Comprehensive Survey of Foundation Models for Non-Invasive Brain Decoding

The ability to decode human thoughts, intentions, and perceptions directly from non-invasive brain recordings holds transformative potential for healthcare, communication, and human-computer interaction. However, translating the safety and scalability of methods like Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), and Magnetoencephalography (MEG) into real-world utility has traditionally been hindered by low signal-to-noise ratios, limited spatial-temporal resolution, and the difficulty to collect large-scale high-quality data from an individual user. Recently, the emergence of Foundation Models (FMs)--large-scale, pre-trained architectures--has expanded the feasible operating region of non-invasive brain decoding under controlled protocols, primarily through representation learning, neuro-semantic alignment, and strong generative priors. However, evidence for robust cross-subject and real-world deployment remains uneven, as many results are still demonstrated on limited cohorts or highly controlled settings. This survey provides a comprehensive overview of how FMs are redefining the boundaries of non-invasive brain decoding. We propose a unified methodological framework that synthesizes recent advancements into a coherent process: extracting robust, transferable representations from noisy neural signals; aligning these signals with the rich semantic spaces of pre-trained vision and language models; and leveraging powerful conditional generative priors to reconstruct high-fidelity outputs. We systematically review state-of-the-art applications across three key domains: visual reconstruction, language and speech decoding, and auditory processing. Furthermore, we critically examine the persisting challenges of computational efficiency, cross-subject generalization, and privacy governance. By mapping the current landscape and identifying key gaps, this work outlines a strategic research agenda aimed at transitioning FM-driven neurotechnology from laboratory proofs-of-concept to reliable, real-world applications.

neuroscience↗

bioRxiv2025

Dorsomedial hypothalamus cold-sensitive neurons drive shivering thermogenesis

Hypothermia, a decline in core body temperature below 35{degrees}C in homeotherms, elicits rapid, asynchronous involuntary shivering thermogenesis; however, the hypothalamic neurons responsible for initiating this response remain uncharacterized. Here, retrograde transsynaptic tracing combined with RNA-seq identified a distinct population of VGLUT2-positive neurons enriched with Calbindin D28k (CALB1) within the dorsomedial hypothalamic nucleus (DMHCalb1/Vglut2). Convergent evidence from chemogenetic, optogenetic, fiber photometry calcium imaging, and electromyography (EMG) experiments demonstrated that these neurons selectively mediate hypothermia-triggered shivering thermogenesis. Bidirectional perturbation of DMHCalb1/Vglut2 activity modulated shivering intensity, while CALB1-negative VGLUT2-expressing DMH neurons were responsible for non-shivering thermogenesis (NST). Genetic ablation of afferent inputs from LSI/POA/VLPAG projecting to DMHCalb1/Vglut2, as well as ex vivo pharmacological synaptic blockade, failed to eliminate shivering responses during hypothermia, indicating that their activation is independent of canonical cold-responsive circuits. Hypothermia significantly suppressed transient A-type K (IA) currents, without affecting delayed rectifier K (IK) currents, accompanied by a pronounced reduction in the IA inactivation time constant relative to normothermic 36 {degrees}C. Collectively, hypothermia elevates DMHCalb1/Vglut2 excitability through IA-dependent reshaping of action potential dynamics. Shortened action potential repolarization and afterhyperpolarization duration cooperatively boost neuronal firing, constituting an intrinsic mechanism for DMHCalb1/Vglut2 neuron-driven shivering initiation.

neuroscience↗

bioRxiv2025

Why PAR is not RNA: ion atmospheres, bridging interactions, and ion-induced structural collapse

Poly(ADP-ribose) (PAR) is a highly charged, intrinsically flexible nucleic-acid-like homopolymer composed of ADP-ribose units. It serves as a critical post-translational modification that rapidly synthesized in response to DNA damage, and participates in diverse important cellular processes, including DNA repair, chromatin remodeling, and RNA biogenesis, often by promoting biomolecular phase separation. However, PARs conformational heterogeneity coupled with limited experimental characterization has hindered a mechanistic understanding of these processes. To address this challenge, we develop a five-bead coarse-grained model of PAR and perform molecular dynamics simulations to compare its conformational ensemble and ion atmosphere with those of RNA across diverse ion environments. Our simulations reveal that PAR undergoes a markedly stronger structural transition than RNA in response to divalent ions, driven by its preferential ion binding and effective ion-mediated bridging between phosphate groups. These interactions facilitate cooperative conformational changes and produce a compact, ion-rich atmosphere that is fundamentally distinct from RNA. Together, our work provides a physically grounded model for PAR, uncovers molecular features that differentiate PAR from RNA, and offers mechanistic insight into how PAR nucleates phase separation and organizes protein interactions at sites of DNA damage.

biophysics↗

bioRxiv2025

Deep-learning model guided discovery and characterization of bacterial unspecific peroxygenases

Unspecific peroxygenases (UPOs) are capable of catalyzing the selective oxidation of organic substrates under mild conditions, using hydrogen peroxide (H2O2) as the sole oxidant. This makes them one of the most promising biocatalysts for chemical synthesis. However, the major limitation restricting the application of UPOs to date is their difficulty in heterologous expression. Although more than 4,000 putative UPO enzymes have been recorded in databases, only about 50 of them can currently be heterologously expressed. All UPOs discovered so far originate from eukaryotes (mainly basidiomycetes and some ascomycetes), and they rely on the complex expression systems and post-translational modifications of their native hosts. This further exacerbates the challenges associated with heterologous expression of eukaryotic UPOs. In this work, we developed a deep-learning-based enzyme mining strategy and, for the first time, discovered novel UPO enzymes from bacteria, achieving successful heterologous expression in Escherichia coli. Bacterial UPOs differ greatly from fungal UPOs in sequence similarity, displaying completely distinct evolutionary trajectories. The discovery of bacterial UPOs advances our understanding of the catalytic mechanisms and expression characteristics within the UPO family, breaking the long-held assumption that UPOs can only originate from eukaryotes. Their excellent heterologous expression performance and broad catalytic versatility will further expand the application potential of UPOs.

bioinformatics↗

bioRxiv2025

Accurate Probabilistic Reconstruction of Cell Lineage Trees from SNVs and CNAs with ScisTreeCNA

Cell lineage tree is a fundamental evolutionary model for single-cell evolution. Inference of cell lineage tree from noisy single-cell DNA data has been studied actively in recent years. Existing methods for cell lineage tree inference can be classified into two categories based on the type of genetic variations they work with: single-nucleotide variants (SNVs) or copy-number aberrations (CNAs). Due to various noises and uncertainties in the data, the existing methods are not fully satisfactory, in part because they only used one type of genetic variant. Single-cell DNA sequencing data with both SNVs and CNAs are becoming available. In principle, joint inference of cell lineage trees from both SNVs and CNAs may lead to more accurate results. However, there is a lack of rigorous models and efficient algorithms for such inference. In this paper, we present a new cell lineage tree inference method, called ScisTreeCNA, that jointly infers cell lineage trees from SNVs and CNAs. A key contribution of ScisTreeCNA is a novel probabilistic model for the joint evolution of SNVs and CNAs in single cell data. Based on this model, ScisTreeCNA implemented several efficient algorithms for accelerating probabilistic inference of cell lineage tree. Experiments on both simulated and real biological data show that ScisTreeCNA consistently outperforms existing methods in the accuracy of the inferred cell lineage trees. ScisTreeCNA is available at https://github.com/haotianzh/ScisTreeCNA.

bioinformatics↗

bioRxiv2025

Minute-scale control of ubiquitin-mediated degradation reveals dynamics of bacterial secreted effector-functions

Precise temporal control of protein abundance is essential for dissecting dynamic cellular processes. While degron-based systems enable rapid protein depletion in eukaryotic cells, comparable tools are lacking for bacterial effectors delivered into host cells during infection. Here, we establish AIDE (Auxin-Inducible Degradation of Effectors), a host-directed degradation platform that harnesses the ubiquitin-proteasome system to selectively eliminate secreted bacterial proteins, including membrane-integrated effectors. By integrating a minimal auxin-inducible degron (AID) tag into effector genes, AIDE enables rapid, reversible, and spatially confined degradation while preserving native expression and secretion. Applied to Chlamydia trachomatis, AIDE revealed that the membrane-integrated deubiquitinase Cdu1 suppresses autophagy early and later promotes developmental transitions, whereas the integral membrane fusogen IncA is continuously required for inclusion integrity. This AIDE platform provides minute-scale, spatiotemporal control over bacterial effector activity, offering a broadly applicable framework for dissecting virulence mechanisms and host-pathogen interactions across diverse secretion-dependent pathogens.

microbiology↗