Biology subjects

Hu, J.

Publications and source records attributed to Hu, J..

At least 73 records · Page 4Linked to original sources

bioRxiv2026

Lamins and lineage-relevant transcription factors coordinate gene expression in lineage development

HighlightsO_LILamin-A and lamin-B1 are essential for midgestational embryogenesis. C_LIO_LILamin-A/B1 are required for proper yolk sac endoderm (YSE) gene regulation. C_LIO_LILamin-A/B1 maintain LADs organization and chromatin interactions in YSE. C_LIO_LILamin-A/B1 and YSE transcription factors support proper YSE gene expression. C_LI Lamins are intermediate filament proteins functioning as ubiquitous structural components of the nuclear lamina that interact with and organize the Lamina-Associated chromatin Domains (LADs). LADs remodel during development and lamins maintain LADs and gene expression profile specific to a given cell type. How ubiquitous lamins achieve cell-type-specific functions during development remains unknown. We show lamin-A and -B1 are required for mouse midgestational embryogenesis and maintain LADs, 3D chromatin interactions, and gene expression in the yolk sac endoderm (YSE). Both lamin-regulated genes and remodeled LADs in YSE cells contain binding motifs of YSE-relevant transcription factors. By analyzing changes in chromatin interactions upon lamin-A and -B1 knockout, we reveal that chromatin neighborhoods maintained by these lamins can influence gene expression orchestrated by YSE-relevant transcription factors. Our findings explain how the ubiquitously expressed lamins can collaborate with lineage-relevant transcription factors to maintain LADs and gene expression programs in specific cell types.

developmental biology↗

bioRxiv2026

Task-space dimensions guide human exploration in complex environments

Efficient exploration is essential for rapid learning in high-dimensional environments, where rewards depend on a sparse and unknown subset of task dimensions. Here, we developed a multidimensional value-learning task in which participants explored without explicit knowledge of the underlying structure. Across multiple task designs, participants consistently demonstrated successful learning under structural uncertainty. Behavioral analyses revealed that over half of participants adopted a structured exploration strategy--Full Dimension Scan (FDS)--with three key properties. First, participants systematically isolated and sampled one candidate dimension at a time, effectively reducing search complexity. Second, FDS was temporally persistent within dimensions while maintaining diverse sampling of feature values, enabling efficient local inference. Third, participants sequentially deployed FDS across dimensions, achieving broad coverage of the task space. Importantly, this strategy transferred across tasks: prior experience in lower-dimensional environments promoted stronger FDS use and improved performance in higher-dimensional settings. Consistent with these findings, a dimension-guided exploration model incorporating dimension-level attentional control best captured human behavior. Together, these results identify dimension-guided exploration as a core principle that transforms high-dimensional search into tractable, structured learning, providing new insight into adaptive behavior in complex environments.

animal behavior and cognition↗

bioRxiv2026

On the predictability of progression-free survival in ovarian cancer from NanoString gene expression data

In the treatment of high grade serous ovarian cancer (HGSC), patients initially diagnosed with unresectable tumors are first treated with neoadjuvant chemotherapy (NACT) to reduce tumor burden prior to surgery. Analysis of matched pre- and post-NACT samples from the same patients enables the investigation of chemotherapy impacts and the biomarkers of progression. Although the tumor immune microenvironment (TIME) has increasingly been recognized as critical in shaping the development and progression of HGSC, we lack a comprehensive understanding of how chemotherapy remodels the TIME. Previous studies have found evidence for a general inflammatory response post-NACT, despite inconsistencies regarding which differentially expressed genes and pathways are implicated. We combine matched NanoString gene expression data from multiple sources to create a large dataset of matched pre- and post- NACT samples (N=83, with 29 novel to this study) and investigate reproducibility. Further, we use machine learning methods to investigate whether patient progression-free survival (PFS) can be predicted from the observed impact of chemotherapy on the TIME as represented by the comprehensive set of NanoString features. We find overall low predictability of PFS from all NanoString features, suggesting that previous results may have been limited by small sample size effects and that larger datasets are needed to identify more generalizable and translatable findings. We identify a set of differential expression features that are the most important for predicting patient outcomes that can be validated in future computational and biological studies. Author summaryA subset of patients with high grade serous ovarian cancer are treated with chemotherapy before surgery to reduce tumor burden. We investigate a large dataset of samples taken before and after chemotherapy. These matched samples enable an investigation of how the environment around tumors, for example immune cell infiltration, reacts to chemotherapy, providing insights into biomarkers for treatment response and treatments that could complement chemotherapy. This larger dataset only partially replicates results from previous studies, while also providing new insights. Machine learning models designed to predict the time to patient recurrence from available biomarkers indicate that they are not strongly predictive of patient outcomes, in contrast to past studies. These results suggest that larger datasets are needed. We identify a set of genes that change with chemotherapy and are indicative of and potentially useful for predicting time to disease recurrence and can be further investigated.

cancer biology↗

bioRxiv2026

Structure-guided, physics-aware reconstruction expands scan-limited multiphoton imaging

Multiphoton microscopy is increasingly pushed toward deep, large-scale and fast functional imaging, where acquisition speed has become a bottleneck. Here we present SPARC, a structure-guided, physics-aware reconstruction framework for scan-limited multiphoton imaging. Rather than treating undersampled recovery as generic super-resolution, SPARC formulates it as a constrained reconstruction problem tailored to point-scanning microscopy by incorporating a sample-matched structural reference from the same field of view and the anisotropic acquisition physics of scan-limited imaging. SPARC jointly integrates denoising and deep upsampling, enabling stable recovery from noisy sparse measurements. In simulated calcium imaging with ground truth, SPARC improved spatial reconstruction fidelity and temporal signal recovery relative to existing methods. In vivo, SPARC improved temporal readout in three-photon and mesoscale two-photon calcium imaging, and enabled a more temporally informative 400-Hz voltage readout on a standard resonant-galvo two-photon microscope. These results suggest SPARC expands the functional operating range of existing multiphoton microscopes under scan-limited conditions.

neuroscience↗

bioRxiv2026

scTrends: automated classification and strength quantification of gene expression trends along pseudotime in single-cell RNA-seq

BackgroundPseudotime inference has become a standard approach for reconstructing dynamic biological processes from single-cell transcriptomic data. However, after a pseudotemporal ordering has been established, systematically identifying and interpreting gene expression trends along pseudotime remains challenging. Existing approaches often rely on clustering-based heuristics or subjective parameter choices, which can compromise interpretability, reproducibility, and scalability in large datasets. ResultsWe present scTrends, an automated and interpretable framework for gene-level trend classification and strength quantification along a given pseudotime trajectory. Importantly, scTrends does not perform pseudotime inference; instead, it operates downstream of established pseudotime methods to characterize expression dynamics once a temporal ordering is available. scTrends models pseudotime-binned gene expression profiles using generalized additive models and assigns genes to predefined temporal trend categories through a hierarchical, rule-based procedure combined with empirical significance testing, data-adaptive parameter selection, and quantitative assessment of trend strength. This enables simultaneous identification of the direction, shape, and magnitude of gene expression changes along pseudotime. We applied scTrends tothree distinct datasets: human PBMC, human brain, and mouse pancreas, using three different pseudotime inference methods (CytoTRACE v2, Monocle3, and scVelo, respectively). scTrends systematically characterized gene expression dynamics during T cell differentiation, oligodendrocyte precursor differentiation, and pancreatic endocrine cell maturation. The analysis revealed diverse monotonic, non-monotonic, and complex expression patterns, with varying strengths, consistent with known biological processes. Benchmarking analyses further demonstrate that scTrends is computationally efficient and scalable to large single-cell datasets, with modest memory requirements, making it suitable for diverse applications across a range of biological systems. ConclusionsscTrends provides a systematic, automated, and resource-efficient solution for gene-level trend analysis in single-cell pseudotime studies, enabling reproducible characterization of dynamic expression patterns across diverse biological systems.

bioinformatics↗

bioRxiv2026

Single-crypt evolutionary trajectories of human sporadic and hereditary colorectal precancers

Our understanding of the earliest genomic processes and evolutionary trajectories that distinguish sporadic from hereditary human cancers remains limited, impeding early diagnosis and risk stratification. Here, we performed whole-genome sequencing of 319 single crypts dissected from normal colorectal mucosa, premalignant polyps and malignant colorectal cancers (CRCs) from five sporadic patients and three familial adenomatous polyposis (FAP) patients. We found 66% of premalignant crypts from sporadic polyps did not harbor APC mutations, but frequently accumulated non-canonical but positively selected driver mutations including KMT2C, CACNA1A and FBLN2. In contrast, most premalignant crypts (71%) in FAP indeed acquired one or more somatic APC mutations or 5q loss of heterogeneity (LOH). Sporadic polyps were dominated by clock-like mutational processes at single-crypt level. In contrast, FAP crypts evolved more diverse genomics processes, including reactive oxygen species-associated damages, APOBEC-associated mutagenesis and pks+ Escherichia coli-associated mutagenesis. Premalignant crypts in FAP also exhibited a 2-fold higher burden of LINE-1 retrotranspositions than sporadic ones, despite arising at a substantially younger age. Phylogenetic timing estimations showed that the first somatic APC mutation in FAP often occurs prenatally. Phylogeographic analysis further revealed spatial lineage segregation in early polyclonal polyps, followed by Big-Bang lineage intermixing in later monoclonal ones. Together, these data demonstrate that early colorectal tumorigenesis does not obey a universal APC-first model, where sporadic and hereditary precancers follow distinct evolutionary trajectories. Our study also highlights that polyclonal-to-monoclonal transition in premalignant stages represents the Big-Bang growth of colorectal tumors, offering crucial insight into early diagnosis and risk stratification.

cancer biology↗

bioRxiv2026

Non-canonical histone H3.3 and its chaperones HIRA and DAXX participate in the regulation of KSHV latency

Kaposis sarcoma-associated herpesvirus (KSHV), also named HHV-8, is the etiological agent of Kaposi sarcoma (KS), Primary effusion lymphoma (PEL), and Multicentric Castlemans disease. After de novo infection, KSHV genomes rapidly circularize and acquire a chromatin state that favors latency. During latency, the KSHV episome is decorated with distinct epigenetic marks that segregate the viral genome into transcriptionally active and repressed domains, enabling persistent silencing of lytic genes while retaining the capacity for reactivation. Transcription activity of chromatin is regulated at multiple levels, including the incorporation of histone variants such as H3.3, by a specific set of histone chaperones such as HIRA and DAXX. The interaction between LANA and these interphase active chaperones suggests that H3.3 deposition is a critical driver of early chromatinization and the long-term stability of KSHV latency. We detected rapid H3.3 deposition on KSHV episomes and on episomes within long-term infected cells. Moreover, we demonstrated that genetically disrupting the host H3.3 chaperone HIRA pathway by CRISPR/Cas9-mediated knockout impacted the regulation of LANA and maintenance of viral latency that was not altered in DAXX knockout cells. Collectively, these results support a role for HIRA-mediated H3.3 deposition in the regulation of KSHV latency.

molecular biology↗

bioRxiv2026

Condition-matched in silico prediction of drug transcriptional responses enables mechanism-guided screening and combination discovery

Perturbational transcriptomics links therapeutic compounds to cellular mechanisms and provides a powerful framework for drug discovery, but experimentally profiling transcriptional responses across diverse cell states, doses and durations is costly and often infeasible. Here we present DEPICT (Drug rEsponse Prediction in transCriptomics with Transformers), a deep learning framework that predicts condition-matched drug-induced transcriptional responses from baseline gene expression, perturbation settings and complementary drug representations. Using the LINCS L1000 dataset, DEPICT generalized to unseen drugs and cell types and outperformed five baseline strategies and two recent deep learning models. In the most challenging unseen-cell evaluation, DEPICT was the only model to surpass all baselines, improving differential-expression prediction accuracy and reducing perturbed-expression prediction error by 30.3% and 36.8%, respectively, relative to the next-best deep model. In a non-small cell lung cancer (NSCLC) case study, DEPICT-enabled virtual screening prioritized compounds predicted to reverse disease-associated transcriptional signatures. Notably, 13 of the top 20 prioritized compounds had either previously entered NSCLC-related clinical trials or been validated in NSCLC studies, supporting the translational relevance of the predicted perturbational profiles. DEPICT further enabled condition-matched drug synergy prediction and mechanistic exploration when experimentally matched profiles were unavailable. Together, these results show that accurate, condition-matched in silico perturbation profiling can scale transcriptomics-driven hypothesis generation for drug repurposing and combination discovery.

bioinformatics↗

bioRxiv2026

A synonymous mutation in MSMEG_4729 occurs at a high frequency in spontaneous D29-resistant mutants of Mycobacterium smegmatis

Compassionate use of mycobacteriophage therapy highlights the promising potential of phage therapy as an alternative treatment option for antibiotic-resistant infections when conventional treatments fail. However, realizing the full potential of phage therapy requires addressing key challenges, including host immune responses, the limited arsenal of therapeutically-useful mycobacteriophages, and the emergence of phage resistance. Dissecting the mechanisms of phage resistance is critical for ensuring the effectiveness and sustainability of phage therapy. In this study, we demonstrate that exposure to the lytic mycobacteriophage D29 triggers diverse genetic changes in Mycobacterium smegmatis. A synonymous mutation in MSMEG_4729 arises frequently but is insufficient to confer D29 resistance on its own. Instead, we identified possible Lsr2-independent activation of the lipooligosaccharide (LOS) biosynthesis cluster in a D29-resistant mutant harboring this mutation. We have also detected the possible activity of MSMEG_3213, a type II methyltransferase associated with m6A modifications in M. smegmatis. Finally, we isolated defense escape mutants (DEMs) of D29 capable of overcoming resistance in a strain with the MSMEG_4729 synonymous mutation. This profiling of M. smegmatiss likely defensive arsenal against the therapeutically-useful mycobacteriophage D29 provides a roadmap for further investigations and rational engineering of next-generation mycobacteriophages to combat drug-resistant mycobacterial infections. Impact statementInterest in phage therapy has been gaining traction recently, which is largely due to the serious threat of antimicrobial resistance. However, the efficacy and sustainability of phage therapy is threatened by certain challenges, which includes the ever existent threat of phage resistance. In this study, we identified several likely factors involved in D29 interaction with the model mycobacterium M. smegmatis. These findings set a roadmap for future investigations that would guide rational phage engineering to expand the currently limited arsenal of therapeutically useful mycobacteriophages as well as improve the efficiency of existing ones.

microbiology↗

bioRxiv2026

WTR: A Toolkit for Functional Anterograde Transsynaptic Circuit Mapping

The brain coordinates animal physiology and behavior via neuronal circuits. To understand and simulate brain functions, it is essential to delineate the synaptic connectivity between neurons. Transsynaptic tracers serve as powerful tools for such purposes. In response to the demand for anterograde tracers for circuit mapping and functional interrogation, we developed WTR, a fusion protein of mammalian codon-optimized WGA, TEV-protease cleavage sequence, and Recombinase. WTR expressed via AAV vectors in cell-type-specific starter neurons reaches their postsynaptic neurons and releases Cre/Flpo upon exposure to TEV-protease expressed in downstream neurons. Accompanied by Cre/Flpo-dependent expression of EGFP, GCaMP7s, or ChR2, the toolkit enables labeling, recording, or manipulation of downstream neurons. We utilized WTR to characterize downstream neurons of either glutamatergic or GABAergic neurons in the preoptic area of anterior hypothalamus for their differential actions in thermoregulation or stress responses, respectively. These results establish WTR as a versatile platform for functional anterograde circuit mapping.

neuroscience↗

bioRxiv2026

A circRNA-based uricase replacement therapy for sustained treatment of hyperuricemia

Hyperuricemia, a major risk factor for gout and kidney disease, arises from the evolutionary loss of human uricase and remains a significant medical challenge due to its high prevalence. However, limited therapeutic options are available for refractory hyperuricemia that typically require long-term treatment. Here we developed a circRNA-based uricase replacement strategy and evaluated its efficacy in uricase-knockout mice as a model for severe hyperuricemia. Lipid nanoparticle-mediated delivery of circRNA enabled efficient in vivo expression of an engineered human-like uricase, which rapidly reduced serum urate levels after a single injection and maintained the urate-lowering effect for up to 10 days. Repeated administration led to sustained urate reduction for 10 weeks, mitigated renal injury, and exhibited favorable biosafety. These findings highlight the therapeutic potential of circRNA-based uricase replacement for the long-term treatment of hyperuricemia and its associated complications.

molecular biology↗

bioRxiv2026

Dual targeting of PDPK1 and BRAF V600E is synthetically lethal

PDPK1 functions downstream of PI3K and is essential for activating AKT and other AGC kinases. Although PDPK1 has a central role in the PI3K/AKT/mTOR signaling pathway, there has been limited evaluation of it as a target for cancer therapy. Anaplastic thyroid cancer (ATC) has one of the highest mortality rates of all human malignancies. Although combined BRAF and MEK inhibition in BRAF V600E mutant ATC (present in 45% of cases) results in response, resistance is common, and there is no curative treatment for ATC. The majority (up to 95.8%) of ATC cases have activation in the PI3K/AKT/mTOR and RAS/RAF/MEK/MAPK pathways due to genetic alterations involved in these pathways. In this study, we investigated PDPK1 as a therapeutic target for ATC. We used in vitro, ex vivo, and in vivo ATC models to evaluate the effect of targeting PDPK1 (BX795) alone and in combination with BRAF V600E inhibition (dabrafenib), and the mechanism of action that resulted in ATC cell death. BX795 monotherapy significantly reduced ATC cell proliferation, invasion, colony formation, and spheroid size. Combination BX795 and dabrafenib treatment had strong synergistic anticancer activity in BRAF V600E-mutant ATC models and led to simultaneous and sustained suppression of PDPK1/AKT and MAPK signaling, preventing the compensatory pathway reactivation observed with single-agent treatment. Mechanistically, combined inhibition induced pronounced oxidative stress, DNA damage, and G2-phase cell-cycle arrest, accompanied by mitochondrial dysfunction and robust activation of apoptosis in ATC cells. These effects resulted in marked tumor regression in in vitro, ex vivo, and in vivo ATC models. Our findings identify PDPK1 as a critical therapeutic vulnerability in ATC. Co-targeting PDPK1 and BRAF V600E produces potent synergistic anticancer activity by shutting down convergent oncogenic signaling pathways and amplifying apoptotic stress responses. These data support PDPK1 inhibition alone and in combination with BRAF blockade as a promising therapeutic strategy in BRAF V600E-mutant cancers.

cancer biology↗

bioRxiv2026

Identifying genetic regulations on immune cell type proportions and their impacts on autoimmune diseases

Genetic regulation of immune cell composition plays a crucial role in the etiology of complex diseases, yet remains poorly understood. We propose a unified analytical framework that integrates genome-wide association studies (GWAS) of cell type proportions with cell-type-wide association studies (cWAS) to systematically characterize both the genetic regulation of immune cell composition and its downstream effects on disease risk. Using single-cell RNA sequencing data from the OneK1K cohort, we conducted a GWAS of immune cell-type proportions with a depth-weighted quasi-binomial model designed for bounded, overdispersed traits. We identified 47 genome-wide significant loci influencing eight fine-labeled immune cell subtypes. Leveraging these identified genetic effects, we further imputed genetically regulated proportions (GRPs) using polygenic risk score (PRS)-based imputation and assessed their associations with complex diseases through cWAS. We identified five significant cell type-disease associations, including two with type 1 diabetes, two with Crohns disease, and one with ulcerative colitis. Together, our results demonstrate that cell type proportions observed in scRNA-seq can reveal regulatory loci and offer insights into how genetic variations regulate immune cell type proportions to affect disease risk. Although we focused on immune single-cell data, our framework is applicable to other tissues or cellular compositions as scRNA-seq datasets expand. Author SummaryGenome-wide association studies (GWASs) have uncovered many disease-associated signals, yet most lie in noncoding regions and are difficult to interpret. Mapping GWAS signals to the relevant cell types is therefore important for better understanding the biological mechanisms that drive disease. A major challenge is that observed gene expression and measured cell-type proportions can be influenced by environmental factors and disease status. In contrast, genotypes are less affected by these factors, making them more reliable for interpreting factors of diseases. Moreover, the cell-type proportions are bounded and often skewed, so standard GWAS models that rely on Gaussian assumptions may lose power. To address this, we developed a quasi-binomial approach that better matches the data and improves discovery while controlling false positives. In real data, our method identified more genetic loci associated with cell-type proportions than a traditional linear model. To further investigate how genetic variation regulates immune cell composition to influence disease risk, we integrated our results with disease GWAS summary statistics to identify immune cell types that may contribute to disease susceptibility. Together, our results link disease-associated GWAS signals to specific immune cell types and provide insights into the cellular mechanisms that may underlie these diseases.

genetics↗

bioRxiv2026

Kindlin-2-Moesin interaction orchestrates sprouting angiogenesis via modulating endothelial membrane mechanics and VEGF signaling

Membrane mechanics play a crucial role in cellular signaling and fate determination, yet their impact on angiogenesis remains poorly understood. Here, we identify Kindlin-2 as a key regulator of sprouting angiogenesis via regulating endothelial membrane tension through its interaction with Moesin, a crucial linker protein between the cell membrane and the actin cortex. Mechanistically, Kindlin-2 binds to the N62 residue of Moesin, limiting its overactivation and maintaining proper membrane tension to facilitate VEGFR2 endocytosis and downstream signaling. Using both developmental and pathological models, we demonstrate that the interaction of Kindlin-2 and Moesin is enhanced in high angiogenic conditions, and endothelial Kindlin-2 deletion reduces angiogenesis. Furthermore, mutation of Moesin at N62 phenocopies the effects of Kindlin-2 loss. Together, these findings uncover a previously unrecognized mechanism linking membrane tension regulation to angiogenesis and provide new insights into targeting the Kindlin-2-Moesin axis for therapeutic intervention in neovascular diseases.

developmental biology↗

bioRxiv2026

Unleashed condensation by recurrent mutations of an epigenetic regulator promotes cancer

The pathological mechanisms and significance for the prevalent hotspot mutations in disordered protein regions are poorly understood. ASXL1 is an obligate co-factor for BAP1 in H2AK119 deubiquitination. ASXL1 mutations are very frequent in myeloid malignancies, and are mostly C-terminal truncating mutations concentrated in a specific disordered region of ASXL1. ASXL1 truncations are gain-of-function mutations that promote myeloid malignancies, but the underlying mechanisms remain poorly understood. Here we show that the frequently truncated mutants of ASXL1 possess an intrinsic property of forming phase-separated biomolecular condensates, and this property is normally suppressed by the frequently deleted regions. A disease-mutant of the endogenous ASXL1 in leukemia cells forms dynamic nuclear co-condensates with other endogenous factors important for gene activation. The ASXL1 disease-mutants can greatly enhance H2A deubiquitination activity of BAP1 in cells and in vitro reconstituted system, enhance myeloid leukemia cell growth, and promote leukemogenesis in a mouse transplant model by turning on myeloid leukemogenic transcriptional programs. However, substitution of residues important for condensation disrupted or impaired all these abilities, suggesting that the condensation property is crucially important for the ASXL1 mutants in promoting cancer. Moreover, we discover that the conserved negative charges in the highly disordered and frequently deleted region on ASXL1 suppress the condensation of the wild type ASXL1. Charge-neutralizing mutations in this region restores condensation of the full-length ASXL1, and are sufficient to turn ASXL1 into a leukemogenic protein. Biochemical, biophysical, and simulation analyses suggest the intramolecular interactions normally mask the N-terminal region in engaging intermolecular interactions required for phase separation, and disease truncations escape from the regulatory interactions and unleash the phase separation property to form nuclear hubs to promote expression of tumorigenic gene programs. Finally, by showing a striking correlation of the mutation frequencies with the condensation properties and leukemogenesis activity for a series of human patient mutations, we suggest that dysregulation of condensation is a central mechanism for ASXL1 mutations in promoting myeloid malignancies. This suggests that dysregulation of condensation may be a key mechanism for some of the prevalent hotspot disease mutations in the disordered proteomes.

biochemistry↗

bioRxiv2026

Loop Extrusion Accelerates Long-Range Enhancer-Promoter Searches in Living Embryos

Long-range gene regulation underlies a variety of human developmental disorders including Cornelia de Lange syndrome1 and polydactyly2. Cohesin-mediated loop extrusion and tether-like elements are two major mechanisms implicated in fostering long-range enhancer-promoter (E-P) contacts3. However, our understanding of the contributions of these mechanisms to the kinetics of E-P interactions is limited. Here we employ a combination of quantitative single-cell imaging, genetic manipulations and polymer simulations to examine the interplay of cohesin-mediated loop extrusion and tethering elements4,5 in the timely activation of gene expression in living Drosophila embryos. Depletion of NIPBL or deletion of an enhancer-proximal CTCF anchor element reduced expression without changing the duration of individual transcriptional bursts. Genetic epistasis experiments recapitulated polymer simulations predicting complementation of tether deletions by augmenting the stability of cohesin via reduced levels of WAPL6. We propose a "scan and snag" model whereby directional cohesin-driven enhancer scanning promotes diffusion-mediated tethering interactions to produce successful E-P contacts and transcriptional activation. We discuss how modulating cohesin stability and the "stickiness" of looping factors7-10 can fine-tune the levels and timing of gene expression in mammalian developmental and disease processes.

molecular biology↗

bioRxiv2026

Epigenome-informed prioritization of bivalent chromatin SNPs enhances genomic prediction robustness: a proof-of-concept study in Pacific white shrimp (Litopenaeus vannamei)

BackgroundGenomic selection (GS) has revolutionized animal breeding, spanning livestock sectors such as pigs and cattle to aquatic species like fish and shrimp. However, its broader application across these industries is often constrained by high genotyping costs and reduced predictive reliability across divergent populations or generations. Developing cost-effective, biologically informed genotyping strategies to overcome these limitations remains a critical goal in animal agriculture. Epigenetic annotations, particularly histone modifications, provide direct functional insights into regulatory elements underlying complex trait variation and represent a promising but underexplored resource for marker prioritization. ResultsHere, using the Pacific white shrimp (Litopenaeus vannamei) as a model organism, we conducted a proof-of-concept study integrating resequencing and phenotypic data from 972 individuals. We generated high-resolution epigenomic maps by profiling four histone marks (H3K4me1, H3K4me3, H3K27me3, and H3K27ac) across multiple embryonic stages and adult muscle tissue using CUT&Tag. These functional annotations were then leveraged to prioritize single nucleotide polymorphism (SNP) subsets for genomic prediction. Among the tested strategies, SNPs located in the muscle-specific bivalent promoter/enhancer (E6) state--characterized by the co-occurrence of active and repressive marks--consistently maximized prediction accuracy under the BayesA model. Notably, even at a moderate density (15k), E6-derived SNPs achieved prediction accuracies exceeding those obtained using substantially larger genome-wide SNP sets. Most importantly, in a challenging cross-population validation using an independent strain, the E6-derived SNP subset significantly improved prediction accuracy by 47.6% (increasing from 0.21 {+/-} 0.05 to 0.31 {+/-} 0.04, p < 0.05) compared to random subsets at equivalent density. ConclusionsThese results demonstrate that epigenetic annotation-guided SNP prioritization provides a biologically informed and cost-effective strategy to enhance genomic prediction accuracy and stability. This framework is broadly transferable across species and offers a practical strategy for designing low-density genotyping panels that reduce costs while maintaining reliable selection outcomes in large-scale breeding programs.

genetics↗

bioRxiv2026

Multi-agent reasoning enables predictive design of living materials

Artificial intelligence is increasingly used to accelerate scientific discovery, but most successful frameworks operate within well-defined molecular, protein or materials spaces. Living materials present a more formidable computational problem because functions emerge from context dependent coupling among cells, matrices, fabrication processes and evaluation conditions. Here we introduce LiveMat, a multi-agent reasoning framework that transforms unstructured literature into a computable design space for living materials. LiveMat standardizes 34,215 living material records, integrating 16,769 microorganism and 17,446 polymer entries into a knowledge graph linking living components, abiotic matrices, functional outputs, evaluation contexts and performance metrics. Benchmarking across five large language models shows that living material reasoning is limited mainly by cross-domain feature integration rather than coarse classification. LiveMat overcomes this limitation through constraint decomposition, provenance-aware extraction, consistency checking and expert-anchored ranking. In a prospective wound-healing task, it prioritizes a four-component design with state-of-the-art in vivo performance, establishing a scalable infrastructure for interpretable, evidence-grounded living material discovery.

biochemistry↗