Biology subjects

Cheng, W.

Publications and source records attributed to Cheng, W..

At least 19 recordsLinked to original sources

bioRxiv2026

Molecule-resolved inference reveals the spatial organization of transcriptional programs

Transcriptional programs encode cellular identity and physiological activity, linking gene regulation to cellular phenotypes. Although spatial transcriptomics measures individual RNA molecules across intact tissues, most analyses aggregate them into cells or pixels, obscuring molecular level functional dynamics. We introduce FICTURE2, a statistical model that infers the transcriptional program from which each individual molecule is generated, using gene identity and spatial context alone without segmentation or binning. FICTURE2 learns programs with distinct spatial distributions, ranging from millimeter-scale continuous transition to micron-scale subcellular localization, the coexisting patterns of which then reveal both cellular identity and physiological activity with unified interpretations. FICTURE2 supports both unsupervised and reference-guided inference and is orders of magnitude more efficient than alternative approaches, enabling systematic analysis of large-scale spatial datasets as we demonstrate in data across diverse tissue types profiled by various spatial technologies.

genomics↗

bioRxiv2026

The functional neuronal lipotype is specified by activity and PS synthesis using dietary and lysosomal inputs

Neurons maintain specialized membranes necessary for lifelong circuit function. While neuronal membranes thus contain unique lipidomes, how and why specific lipids become synaptically enriched remains mysterious. Here, we identify a conserved hallmark of mature neurons, phosphatidylserines containing omega-3 fatty acids (n-3 PS), which dynamically accumulate during synaptogenesis from Drosophila to humans. This neuronal lipotype depends on dietary n-3 and lysosomal catabolism mediated by Saposin, the phospholipase PLA2G15, and the lysophospholipid transporter spin/SPNS1. Reducing neuronal PS biosynthesis by targeting the PS synthase (Pss) caused lipidomic, synaptic, and behavioral deficits exacerbated by impaired glycerophospholipid catabolism. While dietary, lysosomal, and lipoprotein manipulations broadly altered n-3 levels, silencing developmental activity-templating neurons selectively reduced n-3 PS. Remarkably, pathogenic Pss variants lacking feedback inhibition similarly reduced n-3 PS and impaired circuit function. As n-3 PS is reduced during neurodegeneration but absent from iPSC-derived neurons, important relationships between lipotype and physiology may not be recapitulated in vitro.

neuroscience↗

bioRxiv2026

A meta-interaction basis for cell-cell communication in tissues

Tissue function depends on signals exchanged between cells and the responses they elicit. Yet whether diverse cell-cell interactions in situ form recurrent sender-receiver programs remains unclear. We present SpiderNet, an interpretable representation-learning framework that discovers such directed programs as a compact basis of cell-cell meta-interactions (MIs) from spatial transcriptomics. SpiderNet jointly learns which sender regulators, ligand-receptor pairs, and receiver targets define each MI and where each program is active across neighboring cell pairs. The resulting representation traces multicellular relays and links communication to cell states, perturbation responses, and phenotypes. SpiderNet recovers ground-truth MIs and their molecular components in simulations and, in real tissues, shows stronger direction-specific agreement with independently curated regulatory programs in senders and receivers than alternative methods. Across more than 5.8 million spatially profiled cells, SpiderNet resolves an SPP1-THBS relay linking monocytes, fibroblasts, and tumor cells within an immune-suppressive ovarian cancer niche, predicts T-cell responses to held-out melanoma-cell perturbations, and identifies a T-cell-associated brain-aging program and age-predictive signals that transfer across regions and platforms. It reveals a recurrent pan-cancer COLLAGEN-linked fibroblast-tumor program whose projected abundance in independent cohorts is associated with poorer survival and non-response to immunotherapy. SpiderNet thus establishes MIs as a reusable organizational layer between molecular interactions and tissue phenotypes, providing a framework to resolve, compare, trace, and perturb multicellular regulation in situ.

bioinformatics↗

bioRxiv2026

dsRADAR: Imaging and Quantifying Cellular dsRNA by Repurposing RNA Binding Proteins

Double-stranded RNA (dsRNA) is recognized by cellular receptors as a sign of viral infection, triggering the innate immune response. Increasing evidence shows that cellular dysregulation, for example in immune disorders and neurodegenerative diseases, can also lead to accumulation of endogenously produced dsRNA that stimulates a viral-like immune response. Additionally, dsRNA contamination in RNA therapeutics can lead to harmful side effects via a similar pathway. Despite the clinical relevance of dsRNA, reliable tools for its detection remain limited. At present, dsRNA detection relies almost exclusively on the monoclonal antibodies J2 and K1, which suffer from sequence bias and low sensitivity, limiting their reliability. To address this challenge, we aimed to repurpose naturally occurring dsRNA-binding domains (dsRBDs) to produce reliable, pan-specific affinity reagents for dsRNA. We first systematically screened the dsRBDs of the three human adenosine deaminases acting on RNA (ADARs). This analysis identified ADAR3 dsRBDs as promising candidates due to their reduced sequence dependence compared to the dsRBDs of ADAR1 and ADAR2. We then engineered ADAR3-derived dsRBD constructs having varying linker lengths and domain combinations, allowing us to specifically vary the length cutoff of dsRNA detected, thus creating dsRNA accumulation detected by ADAR3 RBDs (dsRADAR) affinity reagents. Finally, we demonstrate the superior performance of dsRADAR over currently available dsRNA antibodies in a cell model of viral infection and a tissue model of gastric inflammation. Together, dsRADAR provides a sensitive and reliable approach for imaging and quantifying diverse dsRNA structures in a variety of biological contexts. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=124 SRC="FIGDIR/small/724404v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@1d89c30org.highwire.dtl.DTLVardef@1f64fc1org.highwire.dtl.DTLVardef@1ee391forg.highwire.dtl.DTLVardef@e834a6_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2026

Virus-like antigen display delivers a stand-alone danger signal through the BCR that circumvents tolerance

How B cells discriminate self from foreign antigens remains a central question, given inherent autoreactivity of the mature B cell receptor (BCR) repertoire. Soluble antigen (sAg) induces tolerance, whereas patterned antigen display on virus-like particles (pAg) triggers robust B cell responses that can proceed without T cell help. Here, we show how this divergence arises early in BCR signaling. Unlike sAg, pAg can bypass a Lyn-dependent negative feedback loop to trigger digital signaling, such that ultra-low concentrations of pAg produce strong and sustained Ca2+ responses. Surprisingly, pAg drives maximal nuclear NF-{kappa}B but limited NFAT, whereas sAg does the opposite, reflecting differential production of diacylglycerol. Consequently, sAg induced an NFAT-dependent anergy program, whereas pAg evaded this state and instead engaged a cMyc-driven program that partially resembles a TLR-dependent danger response. Our findings reveal how proximal signaling directs distinct transcriptional fate to enable immunogenic B cell responses to virus-like antigen display.

immunology↗

bioRxiv2026

Rhizo-PET: A Dedicated PET System for 4D Imaging of Carbon Dynamics in the Rhizosphere

Imaging carbon movements in the rhizosphere is fundamentally limited by high soil heterogeneity, low signal levels, and lack of methodology. We present Rhizo-PET, a dedicated positron emission tomography (PET) imaging and analysis framework designed to characterize the 4D spatiotemporal patterns of tracer distribution in intact plant-soil systems. The system achieved a global energy resolution of 11.93 {+/-} 0.02% FWHM at 511 keV and maintained stable performance over 8 h of continuous acquisition, with a coincidence rate variation of only 0.7%. Spatial resolution reached 1.06 mm near the center of the field of view, establishing a high-fidelity region for root-scale analysis. Dynamic datasets were acquired from live Phaseolus vulgaris plants (N= 3) over 180 min following 11CO2 pulse labeling and reconstructed into 3 min temporal frames. Quantitative analysis across 243 independent regions of interest (ROI) revealed that cumulative tracer accumulation decreases monotonically with radial distance from the root axis, while axial transport delays increase systematically in lower root segments (p <0.001). Hierarchical variability analysis showed that within-plant spatial organization (CVTTP = 0.03) is significantly more stable than inter-plant variation (CVTTP = 0.14), proving that the observed heterogeneity reflects biological spatial organization rather than experimental instability. These results establish Rhizo-PET as a robust, reproducible platform for the non-invasive, time-resolved analysis of carbon dynamics in the rhizosphere under realistic soil conditions.

plant biology↗

bioRxiv2026

Comparison of Galleria mellonella, Epithelial Cell Cytotoxicity, and Mouse Model of Bacteremia to Measure Pseudomonas aeruginosa Virulence

Considerable effort has focused on identifying alternatives to mouse models in research studies. In the field of bacterial pathogenesis, Galleria mellonella and epithelial cell lines have been widely used for this purpose, but the concordance of these models with mice remains unclear. To begin to address this knowledge gap, we used 105 clinical isolates of Pseudomonas aeruginosa for which the virulence had been previously determined in a mouse bacteremia model. A semistrong correlation was observed between G. mellonella median time to 50% mortality and mouse 50% pre-lethal dose (LD50) values (Spearmans rank correlation coefficient [{rho}] = 0.75), whereas percent A549 epithelial-like cell lysis during co-culture showed a weak correlation to mouse LD50 values ({rho} = -0.47). Given the stronger correlation between G. mellonella and mouse virulence, we next examined whether G. mellonella could substitute for mice when asking questions about the virulence of large numbers of P. aeruginosa isolates. Results from mice indicated that isolates with resistance to more antibiotics were significantly less virulent, and the use of G. mellonella identified the same inverse correlation. Furthermore, both models found no evidence for the existence of hypervirulent clonal lineages. In particular, isolates belonging to sequence types defined as high-risk clones were not consistently more virulent than other isolates, despite the known association of high-risk clones with poor clinical outcomes. These findings suggest that G. mellonella can serve as an adequate substitute for mice when addressing specific population-based virulence questions, although conclusions should be confirmed in mice. Author SummaryWe found that virulence measurements in a G. mellonella infection model showed a semistrong correlation with those from a mouse bacteremia model and that this insect larval model adequately detected population-level trends similarly to mice. In contrast, A549 epithelial-like cell lysis during bacterial co-culture correlated less well with mouse virulence. Together, these results support the use of G. mellonella as a scalable, low-cost, and humane first-line model for assessing P. aeruginosa virulence but also indicate that conclusions should be validated in mice.

microbiology↗

bioRxiv2026

Iron Metabolism as a Therapeutic Vulnerability in Stem Cell-Like Castration-Resistant Prostate Cancer

Prostate cancer is the second most common malignancy among men, with androgen deprivation therapy (ADT) serving as the standard treatment due to the hormone sensitivity of prostate tumors. However, therapeutic resistance frequently develops, leading to castration-resistant prostate cancer (CRPC), an aggressive and lethal disease. A recently defined subtype, stem cell-like CRPC (CRPC-SCL), accounts for approximately 25% of CRPC cases and demonstrates poor responsiveness to ADT. CRPC-SCL is characterized by the expression of Cluster of Differentiation 44 (CD44), a glycoprotein that promotes hyaluronic acid binding and uptake. Within CRPC-SCL patient-derived xenograft (PDX) model, CD44 high (CD44hi) cells exhibit enhanced tumorigenicity and proliferative capacity. Importantly, iron metabolism emerges as a critical regulator of this population: CD44hi cells maintain elevated intracellular iron, which sustains CD44 expression and stem cell-like properties by modulating H3K9me2 modification. Leveraging this vulnerability, inhibition of the iron-regulatory factor NRF2 was shown to increase intracellular free iron and selectively induce ferroptosis in CD44hi cells. These findings highlight the therapeutic potential of targeting iron metabolism to induce ferroptosis as a novel treatment strategy for CRPC-SCL.

cancer biology↗

bioRxiv2026

Phase response curve and RNA-sequencing demonstrate spiders' sensitivity to light and pinpoint candidate light-responsive genes

Spiders can maintain a wide range of free-running periods while still being entrained to a 24-hour day. To investigate the underlying mechanism of this entrainment, we constructed the phase-response curve (PRC) for the orb weaver, Metazygia wittfeldae, by subjecting the spiders to one-hour light pulses at various times throughout the circadian day. The resulting type 0 PRC showed high amplitude (> 6 hour) phase advance and delays when the light pulse was applied during circadian time (CT) 16-18, with a break point of advances to delays at CT 17. We then investigated the genetic mechanism of the phase response to light by splitting M. wittfeldae adult females entrained to 12 hours light:12 hours dark (LD 12:12) into two groups. One group received a 1-hour light pulse 5 hours after lights off (CT17), and one group did not. We then sacrificed spiders for RNA isolations 1 and 10 hours after the light pulse. We identified numerous genes that were downregulated by the light pulse 1 hour after the pulse relative to no pulse group. Intriguingly, many of these genes had a flipped pattern of expression 9 hours later - the pulse group had higher expression than the no pulse group. This pattern is consistent with the shifted phase of locomotor activity expected after the light pulse application. We also identified clock gene homologs in M. wittfeldae that had distinct expression patterns from other arthropods.

animal behavior and cognition↗

bioRxiv2026

Plasma proteomics reveal heterogeneous subtypes of depression linked to inflammation and aging

The clinical heterogeneity of depression has defied biological classification, limiting personalized treatment. Previous neuroimaging- or symptom-based subtyping of depression failed to clarify the underlying pathoetiology, while plasma proteins which integrates signals from multiple organ systems, offers a promising way to define biologically grounded subtypes. Using plasma proteomics from 2,127 incident depression cases in a cohort of 53000 individuals, we identified three biologically distinct subtypes differing in inflammation, aging, and metabolic profiles. The most prevalent subtype ( inflammation/ageing) was characterized by aging-related inflammation, poorest prognosis with hippocampal atrophy and highest suicide risk, mediated by age-related amygdala atrophy; this subtype had highest anhedonia burden. A distinct inflammation/energy dysregulation group had metabolic pathway enrichment with high inflammation and lifestyle risk factors (smoking) but no ageing trend, predominantly physical/psychomotor symptoms and decreased thalamic volume. In contrast, the inflammation-resilient group had the lowest inflammatory proteomic loading, lowest depression severity, more resilient lifestyle and increased hippocampal volume. These proteomic signatures, detectable years before symptom onset, enable risk stratification and suggest subtype-specific targeted physical and lifestyle interventions.

neuroscience↗

bioRxiv2025

Histopathology and spatial transcriptomics jointly map myofiber-specific pathological programs in mTORC1-driven myopathy

Skeletal muscle is a structurally organized and functionally diverse organ composed of heterogeneous myofiber types and supporting non-myocyte populations that act in concert to generate force, regulate metabolism, and maintain systemic homeostasis. Myopathies occur in many different diseases, but the mechanisms that drive these muscle pathologies are still largely unknown, partly because conventional approaches cannot link histopathological features to molecular states at single-fiber resolution. To address this challenge, we brought histopathology and spatial transcriptomics together by applying high-resolution Seq-Scope technology to a rodent model of mTORC1 hyperactivation, which produces diverse pathological alterations within individual myofibers. Cross-sections from extensor digitorum longus (EDL) and soleus (SOL), two muscles with distinct fiber-type compositions, were profiled to determine how transcriptome changes are linked to histopathological outcomes. Our analyses reveal that mTORC1 hyperactivation elicits distinct, fiber-type-dependent pathological programs. Type I and IIa fibers, abundant in SOL but scarce in EDL, were largely resistant to mTORC1-induced pathology, exhibiting only minimal morphological alterations and no fiber type-specific responses beyond those commonly observed throughout the tissue. In contrast, type IIx fibers, shared between both muscles, diverged into opposing fates: in SOL, they underwent abnormal enlargement driven by sustained growth signaling, cytoskeletal remodeling, and impaired proteostasis with defective autophagy; whereas in EDL, they developed basophilia characterized by lipid-supported respiration fueling excessive ribonucleotide synthesis and RNA accumulation. Within the same muscle, type IIb fibers displayed striking heterogeneity with discrete transcriptional states encompassing canonical stress responses, oxidative metabolic activation, and developmental reprogramming. In parallel, non-myocytic populations, including activated macrophages and fibroblasts, accumulated preferentially in SOL, forming a fibrotic microenvironment supporting inflammation, tissue remodeling and hypertrophy. Taken together, these findings reveal that sustained mTORC1 signaling disrupts muscle homeostasis through distinct metabolic and structural routes, directly linking histopathological phenotypes to their molecular states at single-fiber resolution.

pathology↗

bioRxiv2025

Integrating physiology and transcriptomics reveals the mechanisms underlying the differences in cotton (Gossypium hirsutum L.) growth and development under different photoperiods

Photoperiod is a crucial environmental factor that regulates plant growth and flowering. While the photoperiodic flowering pathway has been extensively studied, the mechanisms underlying photoperiodic regulation of cotton (Gossypium hirsutum L.) growth and development remain unclear. In this study, we selected different genotypes of cotton varieties, XJ12 and XJ21-11, to investigate the effects of long-day (LD) and short-day (SD) conditions on cotton photosynthesis, dry matter accumulation, flowering time, and related gene expression, utilizing physiological index measurements, transcriptomics, and WGCNA analysis. The results indicated that LD treatment significantly enhanced photosynthetic parameters and dry matter weight in both cotton varieties, with the net photosynthetic rate (Pn) being 154.79% and 111.86% higher under LD compared to SD treatment. Additionally, the flowering time of the two varieties was advanced by 12 and 13 days, respectively, under LD treatment relative to SD treatment. LD conditions promoted gene expression in sucrose and starch metabolism, facilitating cotton growth. Furthermore, LD inhibited the expression of genes associated with flavonoid biosynthesis, thereby altering the secondary metabolism of cotton, and activated transcription factors through signaling pathways (including phytohormone signaling and MAPK signaling pathways) and biological clock rhythms, which induced earlier flowering in cotton. This study provides new insights into the molecular mechanism of the effect of photoperiod on cotton growth and development.

physiology↗

bioRxiv2025

Comparative analysis of the chloroplast genome of Paeonia suffruticosa 'Cun Song Ying reveals plastid characteristics and phylogenetic relationships

Chloroplast genomes provide valuable insights into plant evolution, phylogeny, and molecular breeding. Here, we present the first complete chloroplast genome of Paeonia suffruticosa Cun Song Ying. Using high-throughput sequencing combined with multiple bioinformatics approaches, we comprehensively characterized its chloroplast genome and compared it with those of closely related taxa. The chloroplast genome of Cun Song Ying is 152,704 bp in length and exhibits the typical quadripartite structure, comprising a large single-copy region (LSC), a small single-copy region (SSC), and two inverted repeat regions (IRa and IRb). A total of 138 genes were annotated, including 4 pseudogenes and 134 functional genes, which consist of 87 protein-coding genes, 39 tRNAs, and 8 rRNAs. Phylogenetic analysis revealed that Cun Song Ying shares the closest relationship with P. suffruticosa Luo Yang Hong. These findings provide new insights into the evolutionary history of peony plastids, and the identified highly variable regions may serve as potential DNA barcodes for cultivar identification.

genomics↗

bioRxiv2025

Changes in body size with age do not follow the temperature-size rule

The temperature size-rule is often described as a reduction in ectothermic body size with warming. This coincides with the expectation that faster growth under warming would lead to larger sizes early in life but smaller sizes later in life. Here, we use > 99,000 observations of weight-at-age of four commercially-important fishes over 25 years to ask whether changes in size and growth can be explained by temperature. We also examine whether oxygen, a key part of a proposed mechanism behind the temperature size-rule, explains patterns of weight-at-age better than temperature. Changes in weight-at-age over time were more related to temperature than oxygen but the effect of temperature on weight was small and did not vary by age, counter to the temperature size-rule. Importantly, these results were sensitive to how the relationship between weight-at-age and temperature was modeled. While the functional form (linear or polynomial) mattered little, how space was included in the model led to different conclusions regarding how temperature affects size and growth. Assuming that spatial and spatiotemporal random effects - those that account for the higher degree of similarity between observations collected closer in space and time - are shared across ages led to different results compared to allowing these effects to vary by age. Models with shared effects suggested weight for younger ages had positive relationships with temperature and negative relationships for older ages. However, these models provided spurious support for the temperature size-rule as they had less statistical support than models that allowed spatial effects to vary by age. Overall, our work highlights that relationships among size, growth, temperature, and oxygen may not be as straightforward as theory suggests and illustrates that modeling decisions can have a large effect on tests of ecological theory, and more broadly, our ability to understand biological responses to climate change.

ecology↗

bioRxiv2025

Merits and challenges of plasma proteomics on association replicability

Developments in proteomic platforms have enabled the generation of large-scale high-throughput plasma proteomics data [1-3]. With recent breakthroughs in AI modelling, these data have significantly enhanced our understanding of molecular mechanisms underlying human behaviors and diseases [4-6]. However, the replicability of associations between plasma proteomics and phenotypes remains underexplored. Here, we systematically assessed the replicability of associations with recent plasma proteomics data in the UK biobank. Over 75% of cognitive function and mental health traits demonstrated high overall (proteomics-wide) replicability when brain-related traits were considered as phenotypes. Although mean cortical thickness (CT) as phenotype exhibited clearly reduced replicability, total cortical surface area (CSA) and cortical volume (CV) showed high overall replicability across hemispheres and over twenty brain regions. In comparative multi-omics analyses based on the same cohort of participants, proteomics outperformed genomics across all brain-related traits, and exceeded metabolomics for over half of traits where metabolomics also exhibited high overall replicability. Furthermore, we developed a predictive framework to estimate the replicability for potential future proteomics panels based on the crucial influential factors including dilution level, proportion of samples below the limit of detection (LOD), and sample size. Moreover, we constructed an individual replicability index for proteins and identified eleven proteins with highly replicable associations across cognitive function and mental health traits, which was in line with the recent identifications of pleiotropic proteins in large-scale population studies. Collectively, our results revealed the challenges in the association replicability of plasma proteomics under reduced data quality (from "Explore" to "Expansion" assay panels), and we further explored how to sustain high replicability in potential future panels. Fundamentally, our findings affirm the merits of plasma proteomics: this molecular omics platform enables highly replicable associations for mapping biomedical signatures.

bioinformatics↗

bioRxiv2025

Off-target effects of PLX5622 revealed: mixing microglial function in anesthesia and addiction withdrawal

Microglia have gained increasing attention as important regulators in the brain. Acute microglial depletion using the colony-stimulating factor 1 receptor (CSF1R) inhibitor, PLX5622, has become a popular approach to uncover microglial function. PLX5622 treatment has been recently shown to significantly promote anesthetic emergence. In characterizing underlying mechanisms, we found that PLX5622 treatment did not change the global neuronal activity during anesthesia, but robustly enhanced anesthetic metabolism. Moreover, PLX5622 dramatically induced the expression of hepatic enzymes that extensively modify xenobiotic and endobiotic metabolism. Blocking increased enzymatic activity significantly reduced the arousal-promoting effects of PLX5622 in anesthesia, irrespective of microglial elimination. In addition, we demonstrate that enhanced drug metabolism also contributed to PLX5622-induced attenuation in nicotine-withdrawal anxiety. By revealing previously unrecognized effects of PLX5622, our findings raise caution in interpreting data generated from PLX5622 treatment and bring forward the need for designing more specific CSF1R inhibitors.

neuroscience↗

bioRxiv2025

Highly accurate protein structure prediction-based virtual docking pipeline accelerating the identification of anti-schistosomal compounds

Schistosomiasis is a major neglected tropical disease that lacks an effective vaccine and faces increasing challenges from praziquantel resistance, underscoring the urgent need for novel therapeutics. Target-based drug discovery (TBDD) is a powerful strategy for drug development. In this study, we utilized AlphaFold to predict the structures of target proteins from Schistosoma mansoni and Schistosoma japonicum, followed by virtual molecular screening to identify potential inhibitors. Among 202 potential therapeutic targets, we identified 37 proteins with high-accuracy structural predictions suitable for molecular docking with 14,600 compounds. This screening yielded 268 candidate compounds, which were further evaluated in vitro for activity against both adult and juvenile S. mansoni and S. japonicum. Seven compounds exhibited strong anti-schistosomal activity, with HY-B2171A (Carubicin hydrochloride, CH) emerging as the most potent. CH was predicted to target the splicing factor U2AF65, and knockdown of its coding gene Smp_019690 resulted in a phenotype similar to CH treatment. RNA sequencing revealed that both CH treatment and Smp_019690 RNAi disrupted splicing events in the parasites. Further studies demonstrated that CH impairs parasite viability by inhibiting U2AF65 function in mRNA splicing regulation. By integrating RNAi-based target identification with structure-based virtual screening, alongside in vitro phenotypic and molecular analyses of compound-treated schistosomes, our study provides a comprehensive framework for anti-schistosomal drug discovery and identifies promising candidates for further preclinical development. Author SummarySchistosomiasis is a debilitating disease caused by parasitic worms and affects over 230 million people worldwide. For decades, control of the disease has relied on a single drug, praziquantel, which is less effective against juvenile parasites and increasingly threatened by the risk of drug resistance. To accelerate the discovery of new treatments, we integrated advanced computational tools with molecular validation. Using AlphaFold-predicted structures of 37 key schistosome proteins, we performed large-scale virtual screening to identify promising drug candidates. Over 200 compounds were prioritized for phenotypic screening on two major human-infective schistosome species, leading to the discovery of seven with potent anti-parasitic activity. Among them, Carubicin hydrochloride showed strong efficacy by targeting a critical splicing factor, U2AF65. Inhibiting this protein--either chemically or through gene silencing-- disrupted parasite movement, suppressed cell proliferation, and altered splicing events. Our work highlights the power of integrating AI-based protein modeling with phenotypic and mechanistic validation to accelerate drug discovery for neglected tropical diseases like schistosomiasis.

pathology↗

bioRxiv2025

Metabolic and ribosomal dysregulation drive developmental arrest of Schistosoma japonicum in non-permissive rats

Parasitic flatworms of the genus Schistosoma infect a wide range of definitive hosts, which are categorized as permissive or non-permissive based on their capacity to support parasite development. Unlike permissive hosts such as mice, Schistosoma japonicum undergoes pronounced developmental arrest in non-permissive hosts like rats; however, the molecular mechanisms underlying this phenomenon remain largely unclear. In this study, we conducted a comprehensive morphological and transcriptomic comparison of S. japonicum derived from mouse and rat hosts at multiple time points post-infection, identifying the period between 12 and 24 days post-infection (dpi) as a critical window for developmental arrest in parasites from rats. Utilizing single-cell RNA sequencing, we constructed a high-resolution cellular atlas of S. japonicum at 14 and 24 dpi from both host species, revealing host-dependent alterations in cell composition and transcriptional programs during parasite development. Functional analyses further demonstrated that impairments in mitochondrial function and anaerobic glycolysis contribute to the developmental arrest observed in parasites from rats. Additionally, downregulation of ribosomal genes was associated with reduced protein synthesis and impaired cell proliferation. Together, these findings suggest that disruptions in energy metabolism and ribosomal function are key drivers of developmental arrest in non-permissive hosts. This study provides novel, parasite-centered insights into host permissiveness and identifies potential molecular targets for schistosomiasis control strategies.

developmental biology↗