Biology subjects

Zhang, J.

Publications and source records attributed to Zhang, J..

At least 91 records · Page 5Linked to original sources

bioRxiv2026

Targeting Macrophage Connexin 43 Hemichannels Suppresses Synovial Inflammation and Osteoarthritis

Osteoarthritis (OA) is the most common chronic joint disease and a leading cause of disability worldwide, yet no disease-modifying therapies are available. Although OA has traditionally been viewed as a cartilage-centered degenerative disease, growing evidence suggests that synovial inflammation is a critical driver of disease progression. However, the upstream mechanisms and actionable targets that govern this inflammatory response remain poorly defined. Connexin 43 (Cx43), a key regulator of musculoskeletal homeostasis that forms both gap junctions and hemichannels (HCs), is upregulated in osteoarthritic cartilage and synovium, but the specific contribution of Cx43 channels remains unclear. Here, we show that selective inhibition of Cx43 HCs attenuates OA progression in a murine post-traumatic OA model. Using adeno-associated virus (AAV)-mediated gene deletion, we found that Cx43 knockdown in joint tissues reduced cartilage erosion, synovial hyperplasia, subchondral bone sclerosis, and OA-associated pain. To dissect HC-specific functions, we developed a monoclonal antibody, Cx43(M1), that blocks Cx43 HCs without affecting junctional communication. Cx43(M1) preserved cartilage integrity, suppressed synovial inflammation, and alleviated pain behaviors, even when treatment was initiated after established cartilage damage. Mechanistically, the antibody preferentially localized to synovial macrophages, where it inhibited HC activity, reduced ATP and PGE2 release, and suppressed pro-inflammatory gene expression. Macrophage-specific Cx43 deletion recapitulated these protective effects, and Cx43 HC blockade in primary human OA synovial macrophages similarly inhibited inflammatory and catabolic responses. These findings identify synovial macrophage Cx43 HCs as upstream drivers of OA and support a paradigm shift from a predominantly chondrocyte-centered view toward synovial macrophage directed intervention.

immunology↗

bioRxiv2026

Diffusion MR Microscopy of the Human Hippocampus at 50 μm Resolution at 14.1 T for 3D Virtual Histology

Bridging the gap between macroscopic neuroimaging and microscopic histology remains a challenge in neuroscience. Here, we present a technical feasibility study of diffusion MR microscopy (dMRM) in the ex vivo human hippocampus using a 14.1 T ultrahigh-field MRI system. Mid-hippocampal blocks from control and Alzheimers disease (AD) patients were acquired at 50 x 50 x 100 {micro}m3, representing, to our knowledge, the highest resolution reported for human brain diffusion MRI. At a spatial scale approaching cellular dimensions, dMRM maps and tractography showed close correspondence with immunohistological markers of axons and dendrites. The acquired data delineated six hippocampal laminae, distinguished the inner and outer molecular layers, and preserved short, lamina-specific trajectories that became attenuated or blurred at 100 {micro}m resolution. Tractography further depicted the major components of the hippocampal tri-synaptic circuit--the perforant path, mossy fibers, and Schaffer collaterals in 3D. In the AD specimen, dMRM revealed reduced laminar contrast, local discontinuities, and altered radial and tangential fibers that were paralleled by pathological staining. With the 3D nondestructive characterization of human brain microstructure and circuitry, ex vivo dMRM showed its potential toward the goal of 3D virtual histology.

neuroscience↗

bioRxiv2026

EukaUTR: a foundation model for functional modelling and design of eukaryotic 3' UTRs

Eukaryotic mRNA 3' UTRs encode regulatory information that shapes post-transcriptional control, RNA fate and gene expression. However, a 3' UTR-specific foundation model that spans broad eukaryotic sequence diversity while supporting both functional prediction and sequence design is lacking. Here we present EukaUTR, a 3' UTR-specific foundation model trained on a large-scale eukaryotic 3' UTR sequence corpus spanning diverse evolutionary lineages. Across 13 prediction tasks spanning post-transcriptional regulation, RNA fate and expression output, EukaUTR models matched or exceeded the strongest external baselines on nearly all tasks, with relative improvements of up to 27.45%. EukaUTR also generated de novo 3' UTRs with natural-like sequence and regulatory properties. EukaUTR-Guide further derived stability-associated signals from small sequence sets to guide editing towards enhanced predicted stability. Together, EukaUTR provides a sequence-to-function-to-design framework for transferable 3' UTR modelling and design.

bioinformatics↗

bioRxiv2026

A physics-informed hybrid deep learning model for spatiotemporal rice disease prediction using multi-source data

Accurate, reliable, large-scale disease predictions are essential to ensure rice production. Existing disease prediction models often face a trade-off between interpretability and predictive capability, necessitating the integration of mechanistic knowledge and data-driven learning within a modelling framework. Accordingly, we propose a physics-informed hybrid gated recurrent unit (PI-HGRU) model for spatiotemporal dynamic prediction of rice sheath blight disease, caused by a fungus. Our model embeds differential equations describing disease transmission dynamics into a hybrid gated recurrent unit (HGRU) framework as mechanistic constraints, thereby enabling collaborative modelling between epidemiological processes and data-driven learning. We conducted model training and evaluation using a long-term, multi-source dataset spanning 17 years (2000-2016) and covering 16 major rice-producing provinces in southern China. These rich data include spatiotemporally aligned field disease observations, remote sensing data, meteorological data, and soil property data. In addition, to address the challenges of irregular sampling intervals and inconsistent sequence lengths in disease survey data, we adopted a sliding time-window-based prediction framework. We further conducted a time-window sensitivity analysis to determine appropriate configurations of the input time window and lag time, enabling the model to represent the cumulative and delayed effects of environmental factors. Our PI-HGRU framework substantially outperforms the purely data-driven HGRU baseline model, improving the squared Pearson correlation coefficient (r2) by 22.8% while reducing the root mean square error (RMSE) and mean absolute error (MAE) by 10.2% and 18.0%, respectively. Furthermore, analysis of the models intermediate variables showed that the transmission rate {beta}(t) exhibited interpretable relationships with environmental conditions within the input time window, providing a process-related link between environmental drivers and modeled disease transmission dynamics. Overall, our work demonstrates that integrating epidemiological mechanisms into deep learning models in a physics-informed manner can improve predictive accuracy and stability while enhancing model interpretability, highlighting its potential for large-scale disease forecasting and precision disease management.

bioinformatics↗

bioRxiv2026

DLL3 expression predicts response and long-term outcome in small cell lung cancer patients treated with tarlatamab

Background: Small cell lung cancer (SCLC) is an aggressive, high-grade neuroendocrine carcinoma (hgNEC) with poor prognosis. Tarlatamab, a DLL3-targeting T cell engager was approved in 2024 for relapsed SCLC, with almost doubled overall survival benefit compared to chemotherapy. However, over half of patients do not respond to tarlatamab and others develop resistance within months. With multiple DLL3-targeting therapies in clinical trials, there are currently no validated predictive biomarkers to identify patients most likely to benefit from tarlatamab. In this study, we evaluate baseline DLL3 IHC level in patients as a predictive biomarker to tarlatamab clinical outcome. Methods: We assembled a cohort of 138 patients within IRB-approved MD Anderson GEMINI database (PA13-0589) with DLL3 expression by CLIA-validated immunohistochemistry (IHC), and those treated with tarlatamab from 7/1/2024 to 3/30/2026. Only those with DLL3 IHC results and treated with tarlatamab were included in this predictive biomarker study. All DLL3 levels were reported as percentage cells with cytoplasmic or surface DLL3 expression, including 24 with additional intensity score and H-score calculation. demographic, clinical, and outcome data were collected. Systemic and intracranial response data were assessed by RECIST and mRANO BM criteria respectively. Longitudinal liquid biopsies were taken from a subset of patients for circulating tumor DNA (ctDNA) and (Cytometry by Time-of-Flight) CyTOF analyses. Results: In the predictive biomarker cohort of 54 patients with SCLC treated with tarlatamab, Median DLL3% level in tarlatamab treated cohort was 75%. Median follow-up time was 9.9 months, median time on tarlatamab treatment (ToT) was 5.8 months. At the data cut-off, 61% (33 out of 54) patients were alive. Kaplan-Meier curve stratified by DLL3 % of <=75% vs. >75 showed significant longer ToT and a trend toward longer median OS (mOS). In DLL3>75% compared to DLL3 <=75%: mToT was 10.1 months vs. 2.9 months (p=0.036), mOS: not reached vs. 9.5 months (p= 0.066). Among 41 patients who completed one cycle of tarlatamab treatment and were eligible for systemic (extra-cranial) response per RECIST criteria; we observed partial response (PR) in 51% (21) patients, stable disease (SD) in 22% (9) patients; and progressive disease (PD) in 27% (11) patients. The median DLL3 % in those with PR vs. PD were 80% vs. 40% (p=0.006); median DLL3% comparing PR and SD were 80% vs. 60% (p=0.051). Among 35 patients evaluable for intracranial response, best objective response prior to any brain radiation were 5 (14%) CR,11(31%) PR, 8 (23%) SD and 11 (31%) PD. 9 received brain radiation (8 SRS, 1 WBRT) during tarlatamab to achieve better intracranial control while systemic control is maintained. DLL3% does not appear to be correlated with intracranial response. Longitudinal ctDNA and CyTOF analyses showed persistent ctDNA positivity, as well as increasing NEUROD1 subtype cells in circulating tumor cells in post-tarlatamab progression samples. We additionally profiled DLL3 expression by IHC in 138 patients with SCLC and found heterogenous DLL3 expression, with Median DLL3% of 70% across different biopsy sites. Conclusions: Our data suggests DLL3 expression in patients with SCLC is heterogeneous and not ubiquitous, with median DLL3 IHC of 70%. DLL3 IHC predicts objective response and long-term outcome with tarlatamab. In addition, dynamic monitoring of ctDNA level and CTCs by CyTOF are suggestive of development of tarlatamab resistance, however further studies with larger cohort of paired longitudinal samples are needed to validate the novel blood-based biomarkers. To our knowledge, this study is the first to validate DLL3 IHC as a predictive biomarker for tarlatamab in SCLC, which leads the way for optimizing treatment selection and combinatorial therapies for the subset of patients less likely to respond to tarlatamab.

cancer biology↗

bioRxiv2026

Neurokinin 1 receptor blockade is associated with increased survival of patients with C. difficile infection

Clostridioides difficile is a major cause of healthcare associated infections. C. difficile infection (CDI) is a toxin-mediated disease driven by the secretion of two large protein toxins, Toxin A (TcdA) and Toxin B (TcdB). TcdA and TcdB cause neurogenic inflammation that can contribute to intestinal inflammation in rat and mouse models. Substance P (SP), a potent vasoactive neuropeptide, is a major contributor to C. difficile toxin induced inflammation in animal models. SP acts on multiple receptors including the high affinity receptor NK1-R, and lower affinity receptors (Mrgprb2 and NK2-R). Through in vivo mouse infection models, we confirmed the effects of SP in hypervirulent C. difficile are largely mediated by the neurokinin-1 receptor (NK1-R), the high affinity receptor for SP. Through genetic mice that lack mast cells (cKitW-sh) we determined that mast cells which respond to SP in certain kinds of neurogenic inflammation appear dispensable for inflammation caused by C. difficile infection. We explored the role of NK1-R antagonists in a retrospective study of a large clinical cohort. We determined that, after propensity matching, NK1-R antagonists were associated with a significant survival benefit and that patients treated with NK1-R antagonists showed evidence of reduced vascular inflammation (increased serum albumin). Together these data suggest blocking SP activity through antagonism of NK1-R with small molecule inhibitors may be a useful strategy for adjunctive treatment during C. difficile infection.

microbiology↗

bioRxiv2026

Low-Dose Interleukin-2 Resolves Stress-Induced Chronic Sensitization Independent of Opioid Receptor Signaling

Stress is a common trigger of headache and widespread somatic pain. Repeated exposure to stress establishes latent sensitization that is tonically suppressed by endogenous inhibitory systems. Using brain-penetrating and peripherally restricted receptor antagonists in a mouse model of repetitive restraint stress, we systematically identified these protective pathways. Peripheral kappa opioid receptors as well as GABA type A and type B (GABAA and GABAB) receptors, alongside central opioid and cannabinoid signaling, actively suppress headache-related facial mechanical hypersensitivity in stressed mice. Blocking any pathway quickly reinstated cephalic allodynia. Peripheral opioid and GABAA receptor signaling also inhibited stress-induced latent sensitization on hindpaw. In contrast, depletion of anti-inflammatory regulatory T (Treg) cells prolonged facial but not hindpaw sensitization, suggesting that Tregs preferentially limit stress-induced chronic headache. Next, we administered low-dose interleukin-2 (LD-IL-2) to stressed mice to preferentially expand and activate Treg cells. Following LD-IL-2 treatment, neither subthreshold pain triggers nor blockade of endogenous inhibitory pathways reinstated cephalic or hindpaw allodynia in either sex, indicating elimination of stress-induced sensitization. Mechanistically, LD-IL-2 acted through Treg cells to recruit multiple peripheral cytokine pathways without engaging endogenous opioid, GABA, or cannabinoid receptor signaling. Notably, LD-IL-2 was more effective than anti-CGRP (calcitonin gene-related peptide) therapy in preventing headache-related chronic sensitization in stressed mice. Collectively, these findings reveal multiple central and peripheral pathways that act concertedly to mask stress-induced latent sensitization and strongly support further evaluation of LD-IL-2 as a novel treatment for stress-induced headache and widespread pain with a distinct mechanism of action.

neuroscience↗

bioRxiv2026

A Compendium of 49 Experimental SBS Signatures for Decoding Human Cancer Mutational Processes

Human cancer genomes harbor distinct mutational patterns that reflect past processes of DNA damage and repair. However, the precise attribution of these signatures to specific chemical carcinogens lacks a standardized experimental reference framework. To address this gap, we curated 4,282 genome-wide sequencing datasets from 42 model systems across five species exposed to 146 cancer-risk agents. This platform yielded 49 robust experimental single-base substitution signatures (eSS), with 28 matching 19 established COSMIC signatures and 21 defining novel mutational processes. We reconstructed 24 COSMIC signatures, assigning candidate etiologies to five signatures of unknown origin and revising two contested assignments. Pan-cancer decomposition detected four eSS-like mutational processes enriched in smokers across 4,951 tumors. Lastly, independent single-molecule sequencing of primary human organoids reproduced these profiles with high fidelity, confirming true platform-independent biological reproducibility across complex human models. This eSS repertoire provides a reference that links human mutational processes to mechanistic classes of DNA damage.

genomics↗

bioRxiv2026

Transthyretin Contributes to Hippocampal Blood-Brain Barrier Recovery Following Intestinal Inflammation, with Reduced Expression in Alzheimer's Disease

Background: The blood-brain barrier (BBB) is increasingly recognized as an active immunoregulatory interface that responds dynamically to systemic inflammation. As intestinal inflammation can influence brain homeostasis through the gut-brain axis, the endogenous mechanisms that preserve BBB integrity during gut-derived inflammatory stress remain poorly understood. Methods and Results: We combined dextran sulfate sodium-induced colitis, fecal microbiota transplantation, aged mice, APP/PS1 mice, human Alzheimer's disease (AD) brain tissue, single-cell RNA sequencing, in vivo BBB permeability assays, and gain- and loss-of-function approaches to investigate adaptive neurovascular responses to gut inflammation and their mechanism in maintaining BBB integrity. Intestinal inflammation induced a region-specific adaptive response characterized by increased hippocampal vascular remodeling and partial restoration of BBB integrity following the initial inflammatory insult. Single-cell transcriptomic analysis identified a transthyretin (TTR)-enriched vascular-associated microglial state accompanying these neurovascular changes. Functional studies demonstrated that TTR contributes to maintaining BBB integrity by promoting endothelial homeostasis and limiting endothelial endocytosis, consistent with modulation of FcRn-associated transport pathways. This adaptive neurovascular response was progressively attenuated in aged mice and APP/PS1 mice and was accompanied by reduced vascular TTR expression in human AD brains. Conclusion: These findings identify TTR as a contributor of adaptive neurovascular homeostasis during gut-derived neuroinflammation. Impairment of this homeostatic response with aging and AD may contribute to persistent BBB dysfunction and chronic neuroinflammation, highlighting neurovascular resilience as a potential therapeutic target.

neuroscience↗

bioRxiv2026

Ancient tandem duplication of CYCLOIDEA is associated with the evolution of capitulum development in Asteraceae

Asteraceae is one of the largest and most diverse families of flowering plants. Much of their evolutionary success is attributed to their unique inflorescence structure-the capitulum. Despite their profound adaptive and taxonomic significance, the evolutionary genetic mechanisms underlying Asteraceae capitulum diversification remains poorly understood. We integrated comparative genomic analyses of 35 Asteraceae genomes with developmental transcriptome profiling of ray and disc florets. Our results uncovered a deeply conserved CYC2e-CYC2c-CYC2d-CYC2g-CYC2b tandem gene cluster that is prevalent across the Asteraceae family. We demonstrated that this tandem cluster arose during the early evolutionary diversification of Asteraceae. Subsequent lineage-specific gene retention and loss events have reshaped the composition of this cluster across different Asteraceae lineages. Notably, key members of this cluster, particularly CYC2c, CYC2d, and CYC2g, are preferentially retained in lineages with ray florets, which is consistent with their roles in regulating the ray floret development. Collectively, our findings establish a mechanistic link between the evolutionary divergence, spatiotemporal expression differentiation of the CYC2 tandem cluster as a key genetic driver underlying the morphological diversification and evolutionary success of Asteraceae.

evolutionary biology↗

bioRxiv2026

Snapshots from the Catalytic Landscape of Chalcone Isomerase

Chalcone isomerase (CHI) catalyzes the cyclization of 3-ring scaffolds of flavonoids, a class of plant-based natural products important for nutrition and disease prevention. A persistent question has been whether the enzyme uses dynamics to facilitate conformational rearrangements of substrates within the active site. To help resolve this question, CHI was crystallized with phloretin, a flexible substrate analogue that cannot undergo cyclization. The crystal structure possesses eight protein molecules per asymmetric unit, revealing different active site conformations that accommodate different bound conformers of phloretin. Together, the structural snapshots depict a series of coordinated, dynamic chemical interactions that lower barriers to substrate rearrangements approaching bond formation. Differential scanning fluorimetry combined with mutational analysis and enzyme kinetics further confirm that phloretin binds to the enzyme active site and that it acts as a competitive inhibitor of CHI. Together these findings answer outstanding questions about the flexibility and dynamics of CHI catalysis, information that may be useful for future biosynthetic design and enzyme engineering goals. Overall, this work supports a catalytic model in which the CHI enzyme operates as a dynamic ensemble of structures necessary to facilitate catalytic substrate rearrangements.

biochemistry↗

bioRxiv2026

Bacterial vitamin sharing emerges from a balance between release and uptake

Vitamin availability often shapes microbial communities, as many microbes use vitamins they cannot synthesize. Yet how vitamins become available to users remains poorly understood. To explore this process, we quantified vitamin B12 synthesis, uptake, and extracellular accumulation across hundreds of diverse soil, freshwater, and marine bacterial isolates. These measurements revealed distinct source-sink phenotypes and showed that producers vary substantially in the amount of B12 they provide extracellularly. B12 synthesis was predictable across divergent bacterial lineages from genome content, whereas uptake and extracellular accumulation were not. Controlled cell-death experiments and independently parameterized models showed that extracellular B12 could be quantitatively predicted from release by dead cells and reuptake by surviving cells. Thus, extracellular B12 availability is governed not by synthesis alone, but by the balance between release and uptake, with producer reuptake acting as a previously overlooked sink.

microbiology↗

bioRxiv2026

Automatic bioinformatic software named entity recognition from literature

Bioinformatics software and databases are essential components of modern life science research, yet their mentions in the scientific literature are often inconsistent and difficult to systematically identify at scale. The lack of a comprehensive and up-to-date catalog of bioinformatics resources hinders efforts toward automated biomedical knowledge extraction and streamlined data analysis. Here we present SNAIL, a hybrid named entity recognition framework designed to automatically identify bioinformatics software and database (SW/DB) names from biomedical texts. SNAIL integrates complementary lexical and semantic modeling strategies. The lexical component captures orthographic patterns and contextual cues characteristic of SW/DB names, while the semantic component leverages contextual embeddings generated by transformer-based language models such as SciBERT, combined with an explicit token-masking strategy to enhance entity-focused representations. A large training corpus was constructed automatically through a hybrid pipeline that integrates citation-hinted extraction with large language model-assisted distillation. Evaluation on two independent benchmark datasets and real-world research articles demonstrates that SNAIL substantially outperforms existing approaches, including domain-specific methods such as bioNerDS2 and general-purpose large language models such as ChatGPT, Gemini, Grok and Claude. Applying SNAIL to large-scale literature analysis further reveals distinct journal-level preferences across bioinformatics subfields. These results demonstrate that SNAIL provides an accurate and scalable solution for identifying bioinformatics resources in scientific texts and enables systematic meta-analysis of tool usage and research trends.

bioinformatics↗

bioRxiv2026

Recurrent non-canonical proteoforms in acute myeloid leukemia identified by integrative proteogenomics

Reference proteomes incompletely represent proteins translated in cancer, leaving tumor-specific proteoforms outside the search space of conventional mass spectrometry (MS). Such "dark proteome" products may arise from genomic variation, aberrant transcription or splicing, and non-canonical translation, including microproteins encoded by small open reading frames (ORFs). To define this landscape in acute myeloid leukemia (AML), we developed a cohort-informed proteogenomic strategy using paired RNA-sequencing and MS analysis of 123 human patient AML specimens and 13 healthy CD34+ controls. ProteomeGenerator2 was used for de novo transcriptome assembly and ORF prediction, and candidate cancer-specific unannotated sequences were prioritized by unique high-quality mass spectral support, absence from CD34+ controls, recurrence across individual AML patients, and lack of close homology to annotated proteins. We identified 5,849 Swiss-Prot-unannotated proteoforms, including 1,987 without homology to annotated human proteins. Thirty-nine candidates, most encoding microproteins, were recurrently detected in more than 10% of patients, and 14 were independently validated by deep, fractionated, multi-protease data-independent acquisition (DIA) proteomics of human AML cell lines. Structural modeling predicted several functional classes, including intrinsically disordered, alpha-helical microproteins, and membrane- or secretory-pathway-associated proteoforms. These findings define a recurrent AML dark proteome and establish a framework for the discovery of tumor-specific non-canonical proteins for mechanistic and therapeutic studies.

cancer biology↗

bioRxiv2026

Context-dependent regulatory networks connect Alzheimer's disease genetics to microglial inflammatory responses

Inflammation is central to Alzheimer's disease (AD) pathogenesis. Microglia, the resident innate immune cells of the brain, exhibit diverse inflammatory states and are enriched for AD-associated genetic variants within active cis-regulatory elements (CREs). However, the interplay among genetic variants, transcription factor (TF)-CRE-gene programs, and microglial responses across inflammatory and disease contexts remain poorly understood. Here, we develop context-dependent epigenomic networks (cEpiNets), integrating bulk and single-nucleus assay for transposase-accessible chromatin using sequencing (ATAC-seq) to reconstruct regulatory programs across inflammatory, genetic perturbation, and disease contexts. Leveraging TF footprinting and graph embedding, cEpiNets identifies shared and context-specific programs and predicts regulatory circuits in unseen biological contexts. In a SORL1-marked inflammatory microglial state that expands during AD progression, cEpiNets annotates AD risk variants at the SORL1 locus and identifies variants associated with cellular state abundance across donors. Cross-context analysis further identifies ZBTB14, whose inflammation-associated program connects AD risk variant-harboring CREs to target genes and widespread TF remodeling in AD. Donor-level ZBTB14 footprint activity is negatively associated with AD pathology, while combined IFN{gamma}/TNF stimulation represses ZBTB14 and activates a subset of inferred targets. Collectively, cEpiNets bridges genetic variation, regulatory programs, and disease-associated cellular phenotypes to facilitate mechanistic interpretation of complex disease genetics.

bioinformatics↗

bioRxiv2026

CodonMamba: a foundation model for programmable mRNA coding sequence design

Although mRNA codon language models provide a generalizable framework for biological sequence design, effective CDS design requires both a learned sequence design space that captures biological constraints and context-configurable design preferences. Here we present CodonMamba, a codon language model framework for mRNA prediction and programmable CDS design. Pretrained on large-scale coding-sequence corpora, CodonMamba achieved state-of-the-art performance across a comprehensive benchmark of 12 mRNA prediction tasks, ranking first on 10 of 12 tasks. Furthermore, CodonMamba establishes a programmable CDS design framework, transforming codon optimization from a process dependent on preferences embedded during model training into an inference-time steerable generation framework. By introducing user-specified codon usage priors during generation, CodonMamba enables inference-time steering toward host- or application-specific codon preferences without retraining. In design experiments, CodonMamba enabled coordinated optimization over multiple design-relevant sequence properties and demonstrated programmable cross-host CDS retargeting through inference-time prior switching, while preserving most model-derived codon choices. Together, these results establish CodonMamba as a programmable foundation model framework for CDS design, enabling context-specific mRNA sequence engineering and highlighting a promising direction for precise mRNA design using foundation models.

bioinformatics↗

bioRxiv2026

sc-pcQTL: hurdle-based co-expression modeling for multi-gene QTL mapping in single-cell RNA-seq data

Motivation: Single-cell expression quantitative trait locus (eQTL) studies can resolve cell-type-specific genetic effects, but conventional gene-by-gene analyses do not directly capture coordinated genetic regulation of neighboring genes. Principal-component QTL (pcQTL) mapping can summarize such multi-gene effects, but existing approaches were developed for bulk expression and are not designed for sparse single-cell counts. Results: We developed sc-pcQTL, a framework that applies two-component hurdle modeling and sliding-window clustering to identify local co-expression clusters, summarizes each cluster using principal components, and maps cis-pcQTLs. In simulations, the individual hurdle components controlled type I error, while the component-union screening rule was substantially more powerful than donor-level pseudobulk correlation tests. Applied to 1.24 million peripheral blood mononuclear cells from 982 OneK1K donors across 10 cell types, sc-pcQTL identified 2,485 local co-expression clusters and conducted QTL mapping for 4,353 cluster-PC phenotypes at single-cell resolution, of which 2,040 had at least one significant cis-pcQTL association. Fine-mapping and colocalization with genome-wide association study loci across 1,163 phenotypes in the FinnGen study identified 394 colocalized QTL-GWAS signal groups. Each group comprised fine-mapped QTL and GWAS signals connected through one or more colocalization links within the same cell type and local gene cluster. Of these groups, 46 were pcQTL-specific and contained no colocalized single-gene eQTL from a constituent gene. Locus-level analyses further revealed cell-type-specific multi-gene regulatory effects. Thus, sc-pcQTL complements conventional single-gene eQTL analysis by identifying trait-relevant regulatory signals shared across neighboring genes.

bioinformatics↗

bioRxiv2026

Fetal MRI Reveals Altered Subplate Development in Congenital Heart Disease

Background: The subplate is a transient fetal brain compartment that provides an early foundation for downstream cerebral development. Congenital heart disease (CHD) alters fetal circulation and cerebral substrate delivery, but its impact on subplate development and whether resulting alterations relate to later neurodevelopmental outcomes remain unclear. Methods: In this retrospective observational cohort study, We used fetal MRI to quantify whole-brain, lobar, and regional (17 bilateral cortical regions) subplate volume and thickness to evaluate group differences between 76 fetuses with CHD and 62 typically developing (TD) fetuses scanned between 21-32 weeks of gestation, and estimated individualized deviations from TD developmental trajectories. Associations with fetal hemodynamic indices (substrate delivery score, cerebroplacental ratio [CPR]) and two-year neurodevelopmental outcomes (Bayley Scales of Infant and Toddler Development, N=46 CHD, N=37 TD) were explored. Results: Whole-brain subplate volume was lower in CHD, corresponding to a 5.7% reduction relative to age- and sex-expected values (p=0.003), but this difference was substantially attenuated after accounting for global brain volume (p=0.090). In contrast, regional analyses identified persistent spatially structured deviations beyond global scaling, most consistently involving posterior parietal, occipital and temporal regions, with a left-hemisphere bias in subplate thickness. Normative modelling demonstrated bidirectional regional deviations and increased inter-individual variability in CHD, with extreme subplate volume deviations enriched across 73% of cortical regions (p=0.009). Higher CPR was associated with lower SP thickness deviations, with the association strengthening after accounting for cerebral substrate delivery (p=0.010), although these analyses were exploratory. In CHD fetuses with postnatal follow-up, prenatal subplate deviations showed modest associations with neurodevelopmental outcomes, with right precuneus subplate thickness associated with receptive ({beta}=-11.87, q=0.017) and expressive ({beta}=-14.50, q=0.039) communication on the Bayley, after correction for multiple comparisons. Conclusions: Fetal subplate alterations in CHD are dominated by global reductions in brain growth but also include spatially heterogeneous and individually variable regional deviations beyond global scaling. Exploratory associations with fetal hemodynamics and postnatal neurodevelopment provide hypotheses for future studies investigating the developmental significance of these prenatal alterations.

neuroscience↗