Biology subjects

Zheng, X.

Publications and source records attributed to Zheng, X..

At least 19 recordsLinked to original sources

bioRxiv2026

Pediatric brain charts reveal ADHD biotypes with distinct developmental, molecular, and therapeutic profiles

Attention-deficit/hyperactivity disorder (ADHD) exhibits profound clinical heterogeneity; however, its underlying neurobiological substrates remain unclear. Here, leveraging large - scale structural magnetic resonance imaging data from 12,663 typically developing children and 1,699 children with ADHD across Chinese and United States cohorts, we construct population specific pediatric brain charts via hierarchical Bayesian normative modeling to quantify individual deviations in cortical morphology. Data-driven clustering identifies two robust, cross - ethnically replicable ADHD biotypes: an infra - normal biotype characterized by widespread cortical thinning and a supra - normal biotype marked by cortical thickening. Multivariate analyses reveal distinct brain-behavior architectures: cortical deviations correlate preferentially with symptom severity in the infra-normal biotype but with both symptom severity and cognitive performance in the supranormal biotype. Clinically, the infra-normal biotype demonstrates a superior response to methylphenidate over atomoxetine, whereas the supra-normal biotype responds comparably to both. Transcriptomic analyses identify biotype-specific molecular signatures converging with ADHD genome-wide association study (GWAS) risk loci but exhibiting distinct expression patterns in the brain. These findings classify ADHD heterogeneity into neurobiologically distinct, clinically informed biotypes, providing a mechanistic framework for precision psychiatry in pediatric ADHD.

neuroscience↗

bioRxiv2026

IsoMatch enables population-scale transcript integration and reveals reference-dependent isoform novelty

Transcriptome analyses increasingly generate large collections of transcript models that must be integrated, compared and interpreted across samples and reference annotations. Transcript novelty depends on structural equivalence criteria and reference annotation. Here we present IsoMatch, a scalable framework that harmonizes transcript merging and reference-based classification through explicit, configurable structural criteria. IsoMatch preserves source-GTF and original-transcript traceability throughout the workflow, allowing every merged and classified model to be traced back to its contributing inputs. Benchmarking showed that IsoMatch preserves distinct transcript models during integration through configurable matching parameters for both splice junction chains and transcript ends. With its index-based architecture, IsoMatch merged 163 ENCODE transcript catalogs and classified 7.9 million transcript models with peak memory usage of 0.49 GB and 1.1 GB, respectively. Integrating GENCODE, RefSeq and CHESS revealed that 71.2% of transcript groups remained annotation-specific even without terminal constraints. For 4.84 million merged transcript models from 119 long-read transcriptomes from the 1000 Genomes Project, the integrated reference increased the full-splice match proportion from 12.0-13.6% with individual annotations to 18.0%. IsoMatch enables scalable transcript integration and classification with explicit structural criteria and preserved traceability, demonstrating how reference choice shapes estimates of transcript novelty.

bioinformatics↗

bioRxiv2026

Transcription factors read a second regulatory code in chromatin

Transcription factors (TFs) decode gene regulatory information written in DNA, yet how this vocabulary is interpreted within chromatin remains largely unexplored. Here, using an upgraded NCAP-SELEX platform, we systematically map the nucleosomal DNA recognition landscapes of 269 human TFs. We uncover a widespread, chromatin-dependent mode of sequence recognition: many TFs recognize motifs on nucleosomal DNA that are distinct from their canonical naked-DNA binding sites, revealing that nucleosome architecture encodes a second gene regulatory code in chromatin. Cryo-electron microscopy structures of TF-nucleosome complexes demonstrate that this second code is read through a combination of nucleosome-induced DNA deformation and direct protein-histone contacts. Functional analyses show that these chromatin-encoded motifs actively promote chromatin accessibility and drive cell-type-specific cis-regulatory activity in vivo. Strikingly, a single TF can deploy its canonical and nucleosome-derived motif repertoires to partition and govern entirely distinct physiological programs. Together, our findings establish that TFs interpret two complementary layers of genomic information: the primary DNA sequence and a second code written into the nucleosome architecture. This chromatin-encoded layer of regulatory information fundamentally expands our understanding of how TF specificity is achieved and how gene regulatory networks are wired in multicellular organisms.

systems biology↗

bioRxiv2026

A lifespan-scale single-cell atlas defines an early-childhood immunometabolic transition linked to age-referenced immune states

Childhood represents a critical period with rapid metabolic and immune remodeling, during which perturbations may have lasting consequences for health. We integrated single-cell RNA sequencing (scRNA-seq) profiles of peripheral blood mononuclear cells (PBMCs) from 2,008 healthy individuals aged 0-100 years. Age-resolved modeling highlighted an early-childhood transition centered at age 6, characterized by coordinated remodeling of metabolism-related transcriptional programs, particularly in monocyte and B-lineage cells. Matched serum metabolomics and lipidomics in a pediatric subcohort detected a temporally concordant wave of metabolite and lipid changes around ages 6-8 and extensive metabolite/lipid-transcript associations, prominently involving steroid and phospholipid species. Building on these findings, we developed an immunometabolic transition score and age-specific reference chart, which were evaluated in an independent healthy cohort and applied to quantify deviations in pediatric immune disorders. These findings highlight a coordinated immunometabolic transition in early childhood and provide an age-referenced framework for distinguishing atypical immune-state variation from healthy age-associated variation.

immunology↗

bioRxiv2026

Isocall enables scalable transcript identification from long-read RNA-sequencing data

Long-read RNA sequencing directly resolves the full structures of RNA transcripts. Advances in throughput now enable the generation of deeply sequenced cohorts of hundreds of samples, making joint transcript discovery across large datasets possible. However, existing transcript identification methods were designed for small datasets, which limits their applicability at this scale. Here, we present Isocall, a scalable and deterministic computational method for jointly calling transcripts from multiple PacBio long-read RNA sequencing samples. Isocall converts aligned full-length non-concatemer reads into compact per-sample transcript profiles, merges these profiles across samples, and jointly identifies known and novel transcripts supported by reads in the analyzed dataset. Filtering is tunable: presets provide coarse control and individual parameters, including relative abundance and internal priming thresholds, provide fine control. Isocall demonstrated high precision in our accuracy benchmarks, including the WTC11 SIRV spike-in controls, for which Isocall reported 0-2 false-positive transcripts per sample across the three SIRV mixes at default settings. To demonstrate scalability, we applied Isocall to 206 samples, totalling 3.5 billion raw reads, from the Human Pangenome Reference Consortium. After parallelized pbmm2 alignment and Isocall profile, the call step performed joint calling across the entire dataset in 25 minutes, using 1.3 GB of peak memory and 8 threads. Finally, in Genome in a Bottle samples with matched SNP genotypes, splice-site polymorphisms provide an additional measure of call accuracy: Isocall recovered 337 polymorphic splice sites, including a de novo donor site in BTN3A1 that corresponds to a complete isoform switch on the mutant allele.

bioinformatics↗

bioRxiv2026

Deep visual proteomics reveals distinct proximal tubular and glomerular injury programs in experimental diabetic kidney disease

Background: Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease (CKD). However, most proteomic studies of DKD rely on bulk kidney tissue, which cannot distinguish the contribution or response of individual nephron compartments to injury. Methods: Diabetes was induced in male mice by streptozotocin (STZ) injections. After 16-weeks, the mice and vehicle-treated controls were characterized physiologically, biochemically, and histologically. A deep learning-powered Deep Visual Proteomics (DVP) pipeline, validated against manual annotation, was adapted to isolate proximal tubule (PT) and glomeruli from Megalin stained kidney sections by automated laser microdissection. Bulk kidney, PT, and glomerular proteomes were generated by data independent acquisition mass spectrometry. PT-enriched candidates were prioritized using a composite scoring approach and compared with human tubulointerstitial proteomic data from the Kidney Precision Medicine Project. Results: STZ mice developed sustained hyperglycaemia and albuminuria, alongside elevated markers of tubular injury and interstitial fibrosis. Segmentation models isolated PT and glomeruli with high fidelity (Dice coefficients 0.878 and 0.914; area correlations r=0.993 and r=0.996). Compartment-resolved proteomics determined that PT and glomeruli underwent largely distinct, non-overlapping remodelling: PT exhibited loss of proteostatic, cell cycle, and structural programs with compensatory mitochondrial and lipid metabolic upregulation, whereas glomeruli showed broad loss of oxidative metabolic capacity without any compensatory metabolic program. Fourteen of the top twenty prioritized PT candidates, including LARS2 and ANXA2, changed in the same direction in human CKD tubulointerstitial proteomic data. The STZ PT proteome correlated significantly with this human dataset, while the glomerular comparison did not. Conclusions: Compartment-resolved and deep learning-guided visual proteomics can uncover divergent, biologically coherent PT and glomerular injury programs in DKD that are masked in bulk tissue analysis. A PT injury signature was uncovered that is partially conserved in human CKD, identifying novel candidate mechanisms and biomarkers for future exploration.

pathology↗

bioRxiv2026

Mycoplasmal endosymbionts of Trichomonas vaginalis are associated with reduced risk for Chlamydia trachomatis endometrial infection in asymptomatic, coinfected, women.

Trichomonas vaginalis is a protozoan parasite that causes trichomoniasis, the most common curable non-viral sexually transmitted infection, and Chlamydia trachomatis is a bacterial pathogen that can ascend to the upper genital tract and cause pelvic inflammatory disease, infertility, and ectopic pregnancy. T. vaginalis harbors bacterial endosymbionts, including Candidatus Malacoplasma girerdii, an obligate symbiont, and Metamycoplasma hominis, which can live freely or symbiotically. In a 16S rRNA sequencing study of the cervicovaginal microbiome of women at high risk for chlamydial infection, Ca. M. girerdii abundance was one of 13 features predicting lack of chlamydial spread to the endometrium, despite no direct association between T. vaginalis infection and reduced chlamydial ascension. Investigating the relationship between these microorganisms further, we found that T. vaginalis vaginal abundance correlated positively with chlamydial burden in women whose infection was confined to the cervix, while a nonsignificant inverse relationship was seen in women with endometrial spread. Among participants with high chlamydial burden, Ca. M. girerdii was detected exclusively in women without endometrial infection. Both endosymbionts trended toward more frequent detection, and higher abundance, in coinfected women without endometrial spread, while M. hominis abundance correlated strongly with T. vaginalis burden in this group. These findings suggest that mycoplasmal endosymbionts of T. vaginalis, rather than T. vaginalis itself, are microbial factors limiting chlamydial ascension, and point to a three-way interaction between parasite, endosymbiont, and bacterial pathogen that shapes upper genital tract C. trachomatis infection risk.

microbiology↗

bioRxiv2026

SALRR: Scalable Analysis of Long-Read RNA-Seq Enables Comprehensive Transcriptome Profiling in Human Brain

Isoform-resolved transcriptomics is fundamental to decoding the molecular complexity of the human brain, yet population-scale long-read RNA sequencing has remained inaccessible due to labor-intensive library preparation, sensitivity to RNA degradation in postmortem tissue, and the absence of integrated, reproducible analysis pipelines. Here we present SALRR (Scalable Analysis of Long-Read RNA-seq), an integrated wet-lab and computational platform designed to overcome these barriers. Automated ONT long-read cDNA library preparation on the Hamilton Microlab NGS STAR platform reduces hands-on time by 67% and enables 24 libraries per operator per day while maintaining performance across RNA integrity values. A modular, Snakemake-based pipeline performs end-to-end processing from ONT signal data to isoform-level quantification, incorporating SIRV spike-in calibration, multi-stage quality control, and stringent isoform validation. Applied to 10 postmortem frontal cortex samples from the North American Brain Expression Consortium, SALRR identified 31,607 high-confidence isoforms from 10,075 genes, including 8,532 novel splice variants absent from GENCODE v49, and complex splicing events systematically missed by short-read sequencing at neurodegeneration-relevant loci, including GBA1, CCNF, CHCHD10, and TREM2. All protocols and code are openly available, providing a scalable, community-ready framework for isoform-resolved transcriptomics in neurodegeneration, aging, and complex brain disease.

genomics↗

bioRxiv2026

NuclearIDTracker resolves intestinal cell identity and lineage dynamics through nuclear phenotypic signatures

Organoid models have transformed our understanding of intestinal renewal. Fluorescent imaging has been extensively used to identify key cell types and their differentiation pathways, but immunofluorescence provides only static readouts, whereas live imaging requires fluorescent-reporter engineering and is constrained by limited multiplexing and spectral overlap. Here, we introduce NuclearIDTracker, an explainable machine-learning framework that infers cell identity directly from 3D nuclear segmentations. Using a single nuclear marker, NuclearIDTracker accurately classifies intestinal cell types and integrates with single-cell tracking to resolve lineages and reconstruct dynamic state transitions during organoid development. We show that TA-like cells, rather than stem cells, drive early crypt formation and generate enterocyte and Paneth lineages, as well as the stem-cell population, which emerges only later and subsequently replenishes the TA-like compartment. Following stem-cell ablation, crypt regeneration was not driven by a single discrete cell type. Instead, multiple epithelial populations converged on a proliferative regenerative state with a nuclear phenotypic signature that resembled, but remained distinct from, that of homeostatic TA-like cells, and a YAP/TAZ-associated fetal-like transcriptional signature. Thus, nuclear phenotypic signatures resolve cell identity and reveal coordinated epithelial plasticity during crypt regeneration. NuclearIDTracker establishes a non-perturbative tool to quantify cell identity and state dynamics at single-cell resolution, revealing previously inaccessible biological dynamics and expanding the toolkit for studying epithelial homeostasis, regeneration, and disease.

cell biology↗

bioRxiv2026

Specification of embryonic shoot stem cells via a small RNA-driven morphogenic circuit

The specification of embryonic stem cells capable of self-renewing and differentiation into virtually any cell type, is one of the most consequential events in the development of a multicellular organism. Yet the mechanisms establishing embryonic stem cell fate remain poorly understood, particularly in monocotyledonous cereals. Using the classic mutant leafbladeless1-raggedseedling1, we show that the small RNA tasiARF acts as a primary epidermis-derived morphogenic signal that organizes shoot stem cell specification in the maize embryo. tasiARF restricts expression of the AUXIN RESPONSE FACTOR 3 (ARF3) transcription factor, which modulates cell wall mechanics and guides the differential localization of PIN-FORMED (PIN) auxin efflux carriers, establishing a localized auxin minimum permissive for stem cell fate. Interestingly, loss of this auxin minimum and the associated shoot stem cell defects in tasiARF-deficient embryos are buffered by natural variation at a quantitative trait locus (QTL) controlling expression of MICROTUBULE-ASSOCIATED PROTEIN 65-3 (MAP65-3), a critical factor determining cell division orientation, which reshapes auxin dynamics and restores stem cell specification, and is itself under tasiARF-ARF3 control. Thus, embryonic shoot stem cell specification in maize is governed by an intricate morphogenic circuit that couples small RNA-mediated positional information to a self-stabilizing network interdependently linking cell wall mechanics, auxin signaling, and cell division patterning. This mechanistic framework reveals the redeployment of an ancient small RNA pathway as a lineage-specific innovation to establish a conserved, stem cell-permissive low auxin environment within the divergent embryonic architecture of monocotyledonous cereals. More broadly, it identifies molecular entry points for the engineering of embryogenic competence and regeneration capacity for the improvement of cereal crops.

plant biology↗

bioRxiv2026

Lamins promote trophoblast lineage-associated transcription and trophoblast giant cell development in placental organogenesis

Lamins are the major structural components of the nuclear lamina with a variety of roles in development and organogenesis. However, the function of lamins during trophoblast development, the first lineage to differentiate during mouse embryogenesis, remains unexplored. By utilizing an in vitro trophoblast stem cell differentiation model in a lamin null setting, we uncover that lamins maintain expression of genes related to trophoblast differentiation, while repressing genes involved in maintaining trophoblast stem cell stemness and off-lineage development. By deleting different combinations of lamins in mice, we show that both lamin triple-knockout and lamin-A and -B1 (lamin-A/B1) double-knockout result in placental defects, including reduced placenta size and disrupted placental organogenesis at embryonic day (E)9.5. At this stage, lamin-A/B1 are expressed in trophoblast giant cells of the placenta, and lamin-A/B1 loss leads to their impaired maturation in vivo. Lamin-A/B1 double knockout trophoblast giant cells exhibit reduced nuclear size along with a reduction of DNA damage signaling foci, suggesting a role for lamins in supporting trophoblast giant cell polyploidization. Similar to the transcriptional dysregulation observed during differentiation of lamin triple knockout trophoblast stem cells in vitro, lamin-A/B1 knockout in vivo results in downregulation of genes related to trophoblast giant cell function and upregulation of off-lineage genes. Our results suggest lamins are required for placental organogenesis by maintaining polyploidization and lineage-associated transcriptional programs in trophoblast giant cells.

developmental biology↗

bioRxiv2026

IsoAtlas: Visual interpretation of known and novel transcript isoforms using population-scale long-read evidence

Long-read RNA sequencing has revealed extensive transcript diversity, but newly observed isoforms remain difficult to interpret beyond their classification as known or novel. Here, we present IsoAtlas, an interactive multispecies database for visual exploration and population-scale interpretation of transcript isoforms using 1, 035 human and 414 mouse uniformly processed long-read RNA-sequencing samples. Users can search annotated genes and transcripts or submit novel transcript models in GTF format, visualize their structures, and examine sample-level support, prevalence, expression, tissue and disease context, and sequencing-platform evidence. IsoAtlas integrates structurally equivalent transcripts across GENCODE, RefSeq and CHESS, consolidating evidence that would otherwise be distributed across annotation-specific identifiers. It further links corresponding human and mouse transcript models, enabling users to assess cross-species conservation and enabling users to assess cross-species conservation and inform the suitability of mouse models for isoform-specific studies. IsoAtlas can also evaluate arbitrary user-supplied transcript structures directly against accumulated long-read evidence. IsoAtlas therefore complements established reference annotations with an extensible evidence layer that connects transcript structure to population prevalence, biological context and cross-species support. IsoAtlas is freely available at https://www.isoatlas.org/. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC="FIGDIR/small/744438v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@89bd72org.highwire.dtl.DTLVardef@f48bbforg.highwire.dtl.DTLVardef@102ae37org.highwire.dtl.DTLVardef@fbbc2b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

Detection of Spatially Aberrant Cells in Spatial Transcriptomics Data by Conformal Prediction

The hexagonal organization of epithelial cells represents a fundamental feature of normal tissue architecture, reflecting the precise spatial coordination that underlies healthy biological structure. Disruptions to this organization--manifesting as spatially aberrant spots with abnormal gene expression and misplaced positioning--are closely associated with disease initiation and progression. Here, we introduce SPADE, a computational framework that integrates single-cell RNA sequencing and spatial transcriptomics data to quantitatively characterize and detect spatial aberrancy. SPADE leverages a variational autoencoder coupled with Gaussian mixture modeling for cell-type embedding and spatial deconvolution, and incorporates conformal prediction to enable uncertainty-calibrated identification of aberrant spots. Through extensive validation, SPADE demonstrates superior performance in identifying biologically meaningful aberrant spots.

bioinformatics↗

bioRxiv2026

Benchmarking the translational potential of AI-based drug-resistance prediction from Mycobacterium tuberculosis whole-genome sequencing data

BackgroundTuberculosis, especially drug-resistant tuberculosis (DR-TB) including multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains, remains a leading cause of infectious death worldwide. The rapid accumulation of whole-genome sequencing (WGS) data had spurred numerous computational methods for predicting antimicrobial resistance in Mycobacterium tuberculosis. However, heterogeneous datasets, preprocessing pipelines, and evaluation protocols have made fair comparisons impossible and have hindered clinical translation. A critical yet missing resource is a large-scale, unified benchmark to systematically assess and compare existing methods. MethodsWe curated an integrated MTB WGS-phenotypic drug susceptibility testing (pDST) dataset from three sources: the CRyPTIC dataset (Comprehensive Resistance Prediction for Tuberculosis: an International Consortium), a published multi-study compilation, and newly curated literature-derived datasets. The final benchmark contains 54,364 paired WGS-pDST records with broad geographic, lineage, and drug coverage. After harmonizing phenotypes and generating standardized variant features, we evaluated seven models (including classical machine learning and deep learning architectures) across 18 drug-level and six clinical resistance category prediction tasks. ResultsXGBoost achieved the highest mean drug-level AUPRC (0.674) and F1-score (0.620) and ranked first in AUPRC for 11 of 18 drugs, whereas WDNN achieved the highest mean AUROC. Random forest yielded the highest mean specificity (0.956) and accuracy (0.933), whereas logistic regression achieved the highest mean recall (0.774), highlighting distinct clinical trade-offs. Drug-level difficulty was highly heterogeneous: rifampicin and isoniazid were predicted robustly, whereas bedaquiline, delamanid, linezolid, and clofazimine remained persistently difficult. In clinical resistance category evaluation, RR-TB, MDR-TB, and pan-susceptibility were well predicted, but XDR-TB and other resistance categories constituted major bottlenecks. ConclusionsUnder the largest unified benchmark to date, classical machine-learning methods, particularly XGBoost, provided the strongest precision-recall and F1 performance overall, while neural models remained competitive by AUROC. Emerging drugs (bedaquiline, delamanid, linezolid, clofazimine) and XDR cases remain persistently difficult to predict, identifying key bottlenecks for future method development. This benchmark can serve as a community standard for evaluating MTB resistance prediction and the provided evaluation pipeline offers an actionable baseline for regulatory qualification and clinical decision support system validation, accelerating the translation of WGS-based resistance prediction into practice.

bioinformatics↗

bioRxiv2026

Chromosome-level genome assembly of the Northeast China Brown Frog (Rana dybowskii)

Rana dybowskii is distributed across Northeast Asian and represents a valuable medical resource. A high-quality assembly of the genome has not yet been reproted. This species has 2n=24 chromosomes, but a huge genome size that estimated at 3.5 ~4.6 Gb in the previous studies. The relatively large chromosome size, exceeding hundreds of megabases, may result in difficulties of obtaining a complete chromosome level genome. Here, we constructed a chromosome-level genome assembly of R. dybowskii by integrating PacBio HiFi long-read sequencing for de novo assembly and CiFi (3C coupled with HiFi sequencing) for scaffolding. The final assembly consists of 12 chromosomes with a total of 3.95 Gb and a scaffold N50 length of 455 Mb. BUSCO assessment using the tetrapoda_odb12 database identified 94.2% complete and 0.5% fragmented orthologs, suggesting a high level of completeness of the assembly. Genomic annotation revealed that repetitive sequences comprise over 53% of the assembly, with retroelements and DNA transposons accounting for 22% and 25%, respectively. A total of 43,999 protein-coding genes were predicted with the assistance of RNA-seq reads from four tissues (muscle, eye, testis and skin). This high-quality chromosome-level reference genome provides a valuable genomic resource for advancing genetic studies of the species.

genomics↗

bioRxiv2026

A metabolic checkpoint coordinates bacterial cell envelope biosynthesis and c-di-GMP signaling

The bacterial second messenger cyclic dimeric guanosine monophosphate (c-di-GMP) drives the transition from motile to biofilm lifestyles, yet the mechanisms by which bacteria couple cell envelope stress to c-di-GMP signaling remain poorly understood. Here, we show that sub-inhibitory concentrations of antibiotics targeting early cytoplasmic steps of peptidoglycan (PG) biosynthesis, but not inhibitors of PG polymerization, membrane integrity, or other intracellular processes, specifically elevate intracellular c-di-GMP levels in Pseudomonas aeruginosa. Using live-cell imaging and in vitro enzymatic assays, we demonstrate that this elevation results from reduced phosphodiesterase (PDE) activity rather than increased diguanylate cyclase (DGC) activity, with multiple PDEs contributing to the response. A screen of the complete P. aeruginosa DGC/PDE mutant library identified DipA, BifA, RocR, and RmcA as the primary PDEs mediating this effect. Strikingly, we find that acetyl-CoA, a central metabolite consumed during cell envelope precursor biosynthesis, directly inhibits PDE activity by competing for the conserved EAL domain active site, as supported by biochemical assays with purified RocR and molecular docking analysis. Because EAL domain residues that contact acetyl-CoA are broadly conserved across bacterial species, this mechanism may represent a widespread strategy for sensing metabolic perturbations of cell envelope synthesis. Together, these findings reveal that P. aeruginosa monitors the metabolic status of cell envelope biogenesis through acetyl-CoA-mediated allosteric inhibition of c-di-GMP PDEs, linking envelope biosynthetic flux to adaptive biofilm formation.

microbiology↗

bioRxiv2026

Lamin dosage in cardiomyocytes supports nuclear organization and chromatin gating for transcription

The nuclear lamina contains intermediate filaments, called lamins, which function to maintain nuclear integrity and organize the Lamina-Associated chromatin Domains (LADs). Despite near ubiquitous expression, lamins show cell-type-specific functions. In the heart, they support epicardial cell migration, cardiomyocyte nuclear integrity and maturation. How these functions are integrated with different gene expression programs in these cells during heart development is unknown. We show that cardiomyocytes require lamin-A and -B1 for perinatal mouse survival. Importantly, lamin-B1 facilitates timely cardiomyocyte maturation by maintaining LADs and chromosome territories. By examining changes in cardiomyocyte gene expression and 3D genome organization upon lamin-B1 deletion, we show lamin-B1 maintains chromatin neighborhoods, which can in turn support transcription factors that regulate genes involved in cardiomyocyte structural maturation and gradual cessation of cell division. We propose a genomic logic by which the widely expressed lamins collaborate with transcription factors to specifically promote cardiomyocyte maturation and cell cycle exit during development. HighlightsO_LILamin-A/B1 dose-dependently set cardiomyocyte nuclear order and postnatal survival. C_LIO_LILamin-B1 loss disrupts transcription programs for cardiomyocyte maturation. C_LIO_LILamin-B1 shapes chromatin neighborhoods to support cardiomyocyte maturation programs. C_LI

molecular biology↗

bioRxiv2026

A graph-based pangenome reveals the genetic basis of climate-resilient and horticultural traits in pear

Climate change poses an increasing threat to the cultivation of deciduous fruit trees, placing greater demands on modern pear breeding. Using pear germplasm adapted to diverse environments, we assembled 11 chromosome-level genomes. In combination with 13 publicly accessible pear genomes, we analyzed presence-absence variations (PAVs) and constructed a graph-based pangenome for pear. By performing a PAV-eQTL analysis of the fruit of 123 pear accessions, we identified PAVs significantly associated with expression levels of genes that may be involved in regulating agronomic traits. Population analysis of 268 pear accessions revealed two stop-gained variants in DAM1 of independent origin, which may function in advancing the blooming date and reducing the chilling requirement. We detected complex PAVs at the NOR1 locus, including two copy-number variations and one deletion. These PAVs contributed to the rapid diversification of the NOR1 locus and the fruit development period through regulating ARF5 and other ripening-related genes. We revealed the selection history of the NOR1 locus and developed novel pear individuals that accumulated alleles for low chilling requirement, early blooming date, and short fruit development period. The results provide valuable resources for pear genomics research and offer a guideline for breeding modern pears with climate resilience.

plant biology↗