Biology subjects

Ma, Z.

Publications and source records attributed to Ma, Z..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

A Pangenome Centralized NLRome Drives Lineage Specific Diversification and Functional Differentiation in Solanoideae

The Solanoideae subfamily comprises numerous economically important crops that deploy a diverse repertoire of nucleotide-binding leucine-rich repeat receptors (NLRs) to defend against pathogens. However, the evolutionary trajectory of these NLR immune receptors remains poorly understood. Here, we dissect NLR family evolution across 23 Solanoideae species by integrating genomic and transcriptomic data. We uncover a distinctive pangenome-centralized architecture wherein a mere 11.56% of soft-core orthogroups contribute 67.81% of the total NLR repertoire, with copy-number variation driven by lineage-specific expansions rather than stochastic distribution or genome size. This finding indicates that Solanoideae plants counteract biotic stress through targeted amplification of specific key families rather than undergoing broad diversification. Phylogenomic analysis resolves NLRs into four subclasses, revealing CC-NLR dominance across all species alongside extreme TIR-NLR degeneration in certain lineages, suggesting profound immune adaptive shifts. Mechanistically, recent NLR expansions are primarily fueled by proximal and tandem duplications, whereas whole-genome duplications contribute in a ploidy- and time-dependent manner, with WGD-derived genes undergoing extensive loss during diploidization. Transcriptional profiling further demonstrates duplication-type-dependent expression patterns, enabling a robust, multilayered defense network upon pathogen infection. Collectively, our study delineates the asymmetric evolutionary trajectory of the Solanoideae NLRome and establishes a framework model that provides new insights into the adaptive evolution of plant immune systems and resistance gene discovery.

genomics↗

bioRxiv2026

Neurogenin-2 Reprograms Human Microglial Lineage Cells into Neurons In Vitro and in Chimeric Brains

Progressive neuronal loss is a hallmark of many neurological disorders, yet the adult human brain has a limited capacity for endogenous neuronal replacement. Direct neuronal reprogramming represents an alternative strategy for generating new neurons. Microglia, the brains resident immune cells, are uniquely positioned as candidate cellular substrates due to their abundance, self-renewal capacity, high motility, and rapid recruitment to sites of injury. Here, using live-cell imaging and electrophysiological recordings, we show that human pluripotent stem cell (hPSC)-derived primitive macrophage progenitors (PMPs) and their microglial derivatives exhibit neuronal reprogramming competence. Inducible expression of NEUROG2 in hPSC-derived PMPs drives acquisition of neuronal morphology, sequential expression of early and mature neuronal markers, organization of synaptic proteins, and functional excitability characterized by action potential firing. Single-nucleus RNA sequencing reveals a continuous, directionally ordered reprogramming trajectory marked by suppression of myeloid transcriptional programs, progression through intermediate remodeling states, and progressive activation of neuronal gene regulatory networks, consistent with a regulated lineage conversion rather than partial identity switching. Using a xenotransplantation-based human microglia chimeric brain model, we further demonstrate that inducible NEUROG2 expression reprograms donor-derived human microglia toward a neuronal identity in vivo. Together, these findings establish human microglial lineage cells as a previously unexplored substrate for neuronal reprogramming, providing a conceptual framework for microglia-based strategies aimed at neuronal replacement and neural repair.

neuroscience↗

bioRxiv2026

A bacterial effector blocks SUMOylation by steric occlusion of UBC9 via arginine-GlcNAcylation

The post-translational modifier SUMO is crucial for host antimicrobial defense. Here, we discover that Salmonella employs a unique strategy to dismantle the host SUMOylation machinery. Salmonella infection robustly inhibits global SUMOylation, particularly SUMO2/3 conjugation, without altering the expression of SUMO-cycle enzymes. Genetic screening identified the T3SS-2 effector SseK1 as both necessary and sufficient for this suppression. SseK1, an arginine-GlcNAcyltransferase, directly binds and specifically modifies the SUMO E2-conjugating enzyme UBC9 at arginine 17 (R17). Structural and biophysical analyses revealed that GlcNAcylation at R17 sterically hinders UBC9s interaction with SUMO, thereby inactivating the entire SUMOylation cascade. A unique C-terminal lid domain (ARHVQ motif) in SseK1 confers substrate specificity for UBC9, representing an evolutionary innovation within Salmonella genus. Quantitative SUMOylome profiling demonstrated that SseK1-mediated UBC9 inactivation reprograms the host SUMOylation landscape, impairing the modification of key immune regulators such as MyD88, Hspa8 and destabilizing proteins like PDCD4. Consequently, this mechanism is critical for Salmonella intracellular survival and systemic virulence in mice. Our study unveils a unique bacterial strategy to dismantle the host SUMOylation machinery via precise enzymatic inactivation of its central hub.

biochemistry↗

bioRxiv2026

Focal Transcranial Magnetic Stimulation of the Rat Anterior Cingulate Cortex Inhibits Incubation of Opioid Craving after Voluntary Abstinence

Relapse remains a major challenge in opioid addiction treatment, underscoring the need for innovative therapies. Progress in neuromodulation therapies has been limited by insufficient mechanistic understanding of stimulation engagement and disease-related changes in the brain. We used a novel, focal transcranial magnetic stimulation (TMS) system to deliver high-density theta burst stimulation (hdTBS) combined with resting-state fMRI to test whether anterior cingulate cortex (ACC) stimulation reduces relapse-like behavior and alters functional circuitry in a rodent model of opiate dependence. The coil focality and stimulation parameters approximate human TMS protocols, and the targeted region represents a functional homolog of the human ACC. We trained rats to self-administer oxycodone intravenously for 14 days. We then introduced an electric barrier for 13 days, which caused cessation of drug self-administration. We assessed relapse to oxycodone seeking immediately after training (early abstinence) and after electric-barrier exposure (late abstinence). We administered daily hdTBS or sham stimulation for 7 days before the late-abstinence test. Sham-treated rats showed a time-dependent increase in oxycodone seeking during abstinence (incubation of oxycodone craving) and reduced ACC functional connectivity. In contrast, hdTBS prevented the incubation of oxycodone craving and restored ACC connectivity with the dorsal and ventral striatum. Tracer-based axonal-projection data further showed that stimulation-induced effects aligned with regions receiving dense projections from the stimulation site, suggesting that the projection architecture is critical to the propagation of focal stimulation across distributed networks. These findings identify ACC-centered circuits as mechanistically informed targets for TMS-based interventions that aim to reduce opioid relapse during abstinence. One sentence SummaryPrefrontal TMS stimulation reduced relapse-like behavior and restored corticostriatal circuits, highlighting translational targets for addiction treatment.

neuroscience↗

bioRxiv2026

Differential analysis of image-based chromatin tracing data with Dory

Spatial organization of the genome plays a vital role in defining cell identity and regulating gene expression. The three-dimensional (3D) genome structure can be measured by sequencing-based techniques such as Hi-C usually on the cell population level or by imaging-based techniques such as chromatin tracing at the single-cell level. Chromatin tracing is a multiplexed DNA fluorescence in situ hybridization (FISH)-based method that can directly map the 3D positions of genomic loci along individual chromosomes at single-molecule resolution. However, few computational tools are available for statistical differential analysis of chromatin tracing data, which are inherently high-dimensional, highly variable and contain many missing values. Here, we present Dory, a statistical method for identifying differential spatial patterns between two groups of chromatin traces. Dory quantifies pairwise spatial distances among genomic regions in a chromatin trace and applies multi-level statistical tests to detect significant structural differences between the two groups of traces. It produces a differential score matrix highlighting region pairs with significant distance difference. Applying Dory to multiple chromatin tracing datasets, we found that the detected chromatin structural changes were associated with alterations in A/B compartments and promoter-enhancer interactions correlated with differential gene expression. Dory is a robust and user-friendly computational tool for quantitative analysis of imaging-based 3D genome data that enables systematic exploration of chromatin architecture and its roles in gene regulation.

bioinformatics↗

bioRxiv2026

Salmonella exploits metal-responsive UMPylation to enable intracellular biphasic growth and antibiotic tolerance

Salmonella develops profound antibiotic tolerance during in vivo infection, directly contributing to antimicrobial treatment failure. Although phenotypic heterogeneity in bacterial growth rates underlies this tolerance, the exact mechanisms remain elusive. Here, we reveal that Salmonella exhibits a biphasic growth pattern within macrophages, with initial antibiotic levels modulating lag phase duration. Crucially, this growth plasticity is orchestrated by metal ion homeostasis and a reversible PTM-mediated protein phase transitions. Mechanistically, under stress conditions, enhanced Mn2+ uptake activates the UMPylator YdiU, which modifies critical translational machinery components such as elongation factor TufA. These modifications induce protein aggregation, leading to translational arrest and growth stasis. Conversely, upon stress alleviation, Salmonella accumulates Mg2+ and effluxes Mn2+, then the de-UMPylase YdiV is activated, which resolves protein aggregates to enable rapid bacterial resuscitation and proliferation. This metal-responsive proteostatic switch represents an evolutionarily conserved strategy for bacterial stress adaptation, with broad implications for combating antimicrobial tolerance. SIGNIFICANCE STATEMENTAntibiotic tolerance, a key cause of treatment failure in chronic infections, is often linked to bacterial phenotypic heterogeneity, yet the underlying regulatory mechanisms remain largely understood. This study uncovers a fundamental survival strategy employed by intracellular pathogen Salmonella, where host-derived manganese acts as a critical signal to induce a reversible, dormant state. We demonstrate that stress-induced Mn2+ influx activates widespread protein UMPylation, which functions as a molecular glue to drive the aggregation of essential proteins, thereby halting bacterial growth and conferring antibiotic tolerance. Crucially, this state is reversible upon stress relief through Mn2+ efflux and subsequent de-UMPylation, enabling rapid bacterial resuscitation. As an evolutionarily conserved mechanism of adaptive tolerance, this metal-ion-responsive switch, mediated by reversible protein UMPylation, identifies a key target for developing therapeutics against persistent bacterial infections.

microbiology↗

bioRxiv2026

Accessible and reproducible mesoscale fMRI at 5.0 T: A Pulseq-based open framework for human laminar mapping

Mesoscale, layer-specific functional MRI (fMRI) enables noninvasive access to cortical microcircuitry, yet widespread adoption has been constrained by a reliance on ultra-high field ([≥]7.0 T) systems and proprietary pulse sequences. To bridge this gap and enhance accessibility, we developed an open-source framework at 5.0 T for mapping laminar brain activity. This framework integrates a Pulseq-based 3D vascular space occupancy (VASO) sequence with an end-to-end data acquisition and analysis pipeline. At matched sub-millimeter resolution (0.8 mm in-plane), the Pulseq-based 3D implementation increased slab coverage by [~]1.82-fold and improved temporal signal-to-noise ratio by [~]1.50-fold relative to a vendor-provided 2D-VASO sequence. Validated using a finger-tapping paradigm, individual cerebral blood volume-weighted (VASO) laminar activation profiles consistently revealed the canonical "double-peak" pattern, with distinct superficial and deep peaks in the primary motor cortex. These profiles exhibited excellent cross-visit reliability (r = 0.80), and peak depths showed good spatial reliability (ICC = 0.69 for deep layers; ICC = 0.58 for superficial layers). Between-subject reproducibility was high (r = 0.86). Deploying the identical Pulseq protocol at an independent imaging site reproduced the characteristic double-peak laminar profiles (r = 0.63). At the group level, 5.0 T laminar profiles closely matched established 7.0 T findings, robustly resolving both deep and superficial peaks despite the lower field strength. Notably, for each participant, a single 13-minute VASO run was sufficient to resolve reliable laminar activation patterns that exhibited high consistency with multi-run averages (r = 0.78), highlighting the potential for high-throughput population studies or clinical research settings. The Pulseq-based 3D VASO sequence file, image reconstruction pipeline, and data analysis scripts are openly available to facilitate the adoption of this framework. This work establishes a practical route towards more accessible and reproducible mesoscale fMRI for studying human laminar functional architecture.

neuroscience↗

bioRxiv2026

Prophylactic Lipoxin A4 Attenuates Clostridioides difficile Infection by Augmenting Epithelial Barrier and Resolving Inflammation

Clostridioides difficile infection (CDI) is a leading healthcare-associated diarrhea with high recurrence rates, partially due to antibiotic-induced dysbiosis and dysregulated host inflammation. Specialized pro-resolving mediators (SPMs), such as Lipoxin A4 (LXA4), offer promise in controlling excessive inflammation and promoting tissue repair, yet their role in CDI remains unexplored. Here, we developed a compact, gas-tight gut-on-a-chip (GOC) system that reconciles the anaerobic requirements of C. difficile with the oxic needs of human intestinal epithelium, enabling physiologically relevant co-culture within a standard incubator. A CDI in vitro model was established based on this GOC system. Using the model, we demonstrated that prophylactic administration of LXA4 significantly preserved epithelial barrier integrity, attenuated pro-inflammatory cytokine secretion (IL-8 and IFN-{gamma}), and reduced bacterial colonization. Transcriptomic analysis revealed that LXA4 pretreatment upregulated genes involved in cell junction organization while downregulated immune activation pathways. These protective effects were validated in a murine CDI model, where LXA4 pretreatment reduced weight loss, pathological damage, and fecal bacterial burden. Furthermore, prophylactic administration of LXA4 synergized with vancomycin treatment further enhanced antibiotic efficacy while allowing a 50% dose reduction without compromising therapeutic outcomes. Our study establishes a robust approach for CDI research and highlights the prophylactic and adjuvant potential of inflammation-resolving strategies, offering a novel approach to mitigate CDI incidence and improve treatment outcomes.

bioengineering↗

bioRxiv2026

A genetically encoded ionic-stress sensor reveals protons as a sleep driver

Dynamic ionic changes are hallmarks of physiological and behavioral state transitions, including sleep in animals. Although biosensors for specific cellular ions are widely available, real-time monitoring of overall ionic strength in living organisms remains challenging. Here, we present a genetically encoded nuclear translocation ionic sensor (GENTIS) that enables direct visualization of ionic stress in vivo. Using GENTIS in C. elegans, we uncover rhythmic elevations in ionic strength during larval molting transitions that coincide with the lethargus sleep. Cytosolic proton ionic increase through inhibition of v-ATPase is sufficient to induce GENTIS nuclear translocation and evoke behavioral quiescence, characterized by reduced feeding and activation of sleep-active neurons. Apical membrane v-ATPases undergo disassembly during lethargus and under sleep-inducing stress conditions, leading to proton accumulation. Notably, this proton-mediated sleep is suppressed by proton buffering with ammonium. Together, these findings establish GENTIS as a powerful tool for tracking ionic strength dynamics in living organisms and support protons as a physiological driver of sleep.

animal behavior and cognition↗

bioRxiv2026

The causes and consequences of human-specific DNA methylation

The vast collection of human-specific traits- such as our unique morphology, cognition, behavior, and diseases- has long been attributed to gene expression divergence between us and our closest living relatives, chimpanzees. Theory suggests that changes to cis-regulatory elements such as promoters and enhancers may drive evolutionary adaptation, and DNA methylation is a key factor in transcriptional cis-regulation. However, we still lack an understanding of 1) how species-specific methylation patterns arise; 2) their downstream effects; and 3) whether they are a common target of natural selection. In this study, we investigated these three questions. By combining a novel hypothesis testing framework with DNA methylation data from six human and chimpanzee cell types, as well as fused interspecies hybrid cells, we disentangled cis- vs. trans-acting methylation divergence across the genome. Across cell types, we found that methylation divergence is primarily driven in cis, which can be linked in some cases to nearby sequence variants such as CpG gains and losses. Although less common, regions with trans-acting methylation divergence were enriched for specific transcription factor (TF) binding motifs, suggesting a role of TFs such as FOXM1 in these differences. Having established these causes of methylation divergence, we then examined the functional consequences of differential methylation. Although methylation lacks a consistent relationship with transcription, we observed that associations between methylation and gene expression are stronger for genes with cis-regulatory divergence. Moreover, we identified lineage-specific selection shaping promoter methylation at the level of entire pathways including those associated with human-specific traits such as speech, cognition, and susceptibility to infection with hepatitis C. Collectively, our findings provide a mechanistic framework suggesting that DNA methylation may occupy a key position, mediating the effects of both cis- and trans-acting factors on transcriptional networks, including those contributing to human-specific traits.

evolutionary biology↗

bioRxiv2026

Adaptive evolution to thermal stress underpins climate resilience in a cosmopolitan arthropod

Adaptive evolution enables species to survive and thrive under changing environmental conditions. In the face of accelerating global climate change, thermal stress represents a major challenge to the persistence of terrestrial arthropods. Understanding the genetic mechanisms underlying thermal adaptation is therefore critical for predicting species evolutionary potential and future success. Here, we combine experimental evolution, phenotypic assays, and multi-omics analyses to investigate the adaptive responses of the diamondback moth (Plutella xylostella), a globally destructive pest of cruciferous crops, to contrasting thermal environments. Populations evolved under hot (32{degrees}C/27{degrees}C) and cold (15{degrees}C/10{degrees}C) regimes exhibited distinct life history and fitness traits relative to those maintained under favorable conditions (26{degrees}C). The hot strain showed accelerated development, higher fecundity, and increased survival under extreme heat, while the cold strain exhibited lower supercooling and freezing points, indicating enhanced cold hardiness. Integrated transcriptomic and metabolomic analyses revealed extensive transcriptional reprogramming and convergent metabolic adjustments, notably a reduction in lipid metabolism to conserve energy under thermal stress. Crucially, non-synonymous mutations in PxSODC enhance superoxide scavenging efficiency, enabling effective oxidative stress management at lower gene expression levels. Furthermore, we identified epigenetic regulation via DNA methylation as a key mediator of this thermal tolerance. Together, these coordinated mutational, epigenetic, and metabolic insights highlight this arthropods capacity for global dispersal and likely persistence under climate change, establishing a framework for understanding equivalent effects in other species.

evolutionary biology↗

bioRxiv2025

Generative AI Enables Breast Cancer Genomic Subtype Prediction from Histology Images

Breast cancer subtyping is essential for precision oncology, influencing prognosis, treatment selection, and clinical trial design. The Integrative Subtype Classification (IC) categorizes breast tumors into groups with distinct long-term outcomes based on genomic and correlated transcriptomic features. This method relies on sequencing data, which, despite decreasing costs, is not always available in research or clinical settings. Here we introduce PATH-IC, a computational pathology model that predicts ER+ breast cancer IC subtype risk of relapse categories from routine histology data. We enhance the current state-of-the-art computational pathology approach with BERGERON, which leverages generative AI to correct class imbalance and reduce overfitting, showing that synthetic data improves PATH-ICs performance by the equivalent of 41% more real training samples. PATH-IC achieves a testing AUROC of 0.814, with predictions correlating to Oncotype DX scores and long-term relapse risk. Using attention-based model interpretation and CRAWFORD, a novel embedding-to-image foundation model, we demonstrate that PATH-IC identifies expected tumor microenvironment patterns for IC subtypes and highlights heterochromatin condensation as a key feature of high-risk tumors. Matched single-cell spatial transcriptomics confirm IC subtype-specific gene expression patterns identified by PATH-IC, including active metabolic, proliferative, and proteostasis pathways in the high-risk group. PATH-IC advances computational pathology through generative AI, enabling subtype inference from histopathology data.

cancer biology↗

bioRxiv2025

SuperFocus enables whole-slide cell-level spatial omics from spot-based measurements and histology across modalities

Histopathology and molecular pathology are currently distinct diagnostic modalities for the most part, one revealing tissue morphology at cellular resolution and the other providing molecular measurements with limited or no spatial context. Projecting genome-scale molecular information onto histopathology images at single-cell resolution across whole tissue sections represents a long-sought goal for next-generation pathology. Here we present SuperFocus, a modality-agnostic computational platform that generates histopathology-integrated single-cell spatial multi-omics from spot-based spatial measurements acquired on the same or an adjacent section without requiring external reference data. SuperFocus combines constrained cascading imputation with feature-level and cell-level quality-control scores to reduce spurious predictions and quantify confidence. On a ground-truth spatial transcriptomics benchmark dataset, SuperFocus improves key accuracy metrics by 28-73% over existing methods. Across Patho-DBiT, spatial ATAC-RNA, spatial CITE-seq and Visium-MALDI-MSI (SMA) datasets, SuperFocus enables cell-resolved analyses of MALT lymphoma microenvironments, gene regulatory programs in human hippocampus, lipotoxic hepatocyte states in human MASH, and transcriptomic-metabolomic states linked to neurotransmission and neuroinflammation in Parkinsonian mouse brain. Overall, SuperFocus enables scalable whole-slide single-cell spatial multi-omics integrated with histopathology, bridging histology and genome-scale molecular profiling for next-generation molecular pathology.

bioinformatics↗

bioRxiv2025

Efficient Generation of Humanized Cloned Cattle via Embryonic Stem Cell-Mediated Dual-Round Site-Specific Editing

Transgenic cloning allows genetic modification in cattle, yet its translational potential is constrained by low cloning efficiency and limited gene-editing capacity, due largely to the restricted proliferative ability of somatic donor cells. Pluripotent stem cells (PSCs), with their unlimited self-renewal ability, represent a promising alternative to overcome these bottlenecks. Here, we first established stable bovine epiblast stem cell lines (bEpiSCs) and achieved site-specific integration of the HCSN2-HLF-GFP cassette into the {beta}-casein locus, with high transfection (72.07%) and correct integration efficiency (80.48%). Following Cre LoxP mediated excision of the selectable marker, we obtained marker free HCSN2-HLF bEpiSCs that retained pluripotency markers, tri lineage differentiation potential, and supported target protein expression in immortalized bovine mammary epithelial cells. When used as nuclear donors, these cells produced blastocysts with quality comparable to in vitro fertilization (IVF) embryos. Notably, cloning with HCSN2-HLF bEpiSCs resulted in the birth of twelve calves, with significantly higher birth and survival rates (30.71% and 16.74%, respectively) than those obtained using fibroblasts. Both PCR-based genotyping of the edited loci and microsatellite kinship analysis confirmed that all cloned calves were genetically derived from the HCSN2-HLF bEpiSCs. Collectively, this study establishes an efficient embryonic stem cell based platform for targeted gene editing and rapid production of multigene edited cloned cattle, offering a scalable strategy with strong potential for industrial translation.

bioengineering↗

bioRxiv2025

A Novel Three-Layer Methodology for Heterogeneity Analysis: from metrics, through statistical tests and modeling to applications

O_LIDiversity and heterogeneity are related but distinct and often conflated concepts. Diversity quantifies the number or relative abundance of discrete objects (e.g. species), whereas heterogeneity includes interactions among them (i.e. in networks) and between them and their environments. Although estimation, testing, and inference of diversity is well established and understood in ecology, comparable methods for heterogeneity are themselves diverse and rarely applied consistently or coherently. C_LIO_LIWe propose a consistent and coherent methodology for estimation, testing, and inference of heterogeneity of ecological networks. Estimation of heterogeneity is scalable from individuals to populations using the variance-to-mean (V/M) ratio and extensions of Taylors power law (TPL) to analyzing networks. Bootstrapping is used to partition heterogeneous and random clusters, whereas permutation tests are used to compare individual- and network-level heterogeneity. Inference includes the identification of "important" (e.g. dominant, foundation, keystone) species and "rich clubs" in heterogeneous networks, detection of biomarkers, and analysis of heterogeneity-stability relationships. C_LIO_LIWe demonstrate this methodology using the global Earth Microbiome Project dataset. The method could reliably distinguish heterogeneous nodes and networks; identified significant differences in heterogeneity among microbial assemblages in different habitats and in specific sites within habitats; and supported established principles of host filtering, species sorting, and niche partitioning. C_LIO_LIOur methods for estimation, testing, and inference of heterogeneity are modular, scalable, and applicable to a wide range of ecological systems. They also provide a quantitative method for understanding how evolutionary and ecological forces jointly shape both topology and heterogeneity in ecological networks. C_LI

ecology↗

bioRxiv2025

High-confidence structural predictions of extrachromosomal DNA with ecDNAInspector

Extrachromosomal DNA (ecDNA) are circularized genomic elements that reside outside canonical chromosomes. ecDNA amplify oncogene copy number, enhance chromatin accessibility, and act as mobile enhancers through cis- and trans-regulatory interactions, collectively boosting oncogene expression. ecDNA has been implicated in tumor progression, intratumoral heterogeneity, and poor patient prognosis. Despite various lines of evidence that ecDNA promotes aggressive disease, the mechanisms and selective pressures leading to ecDNA formation and propagation remain poorly understood as are their structures. While several computational tools have been developed to infer ecDNA presence or absence from short read sequencing data, accurate identification of large or complex ecDNA structures remains challenging. Here we introduce ecDNAInspector, a novel computational framework to systematically assess the confidence of ecDNA predictions from existing inference tools. Leveraging abundant short-read whole genome sequencing (WGS) data from population-scale cohorts, we demonstrate that ecDNAInspector accurately identifies high-confidence ecDNA calls, improving interpretability and facilitating the association with clinical features. As an illustrative example, applied to a cohort of 250 breast cancers, ecDNAInspector identifies associations between ecDNA structure and molecular subgroups of disease. These findings are supported by orthogonal omic data and experimental characterization of ecDNA captured in representative cell lines. ecDNAInspector provides a scalable, data-driven approach to characterize ecDNA structure, enabling integrative studies of the clinical and biological impact of this non-mendelian mode of oncogene amplification and inheritance.

cancer biology↗

bioRxiv2025

Inferring Local Protein Structural Similarity from Sequence Alone

Detecting structural similarity at the local level between proteins is central to understanding function and evolution, yet most approaches require 3D models. In this work, we show that protein language models (pLMs), solely using sequence data as input, implicitly capture fine-grained structural signals that can be leveraged to identify such similarities. By mean-pooling residue embeddings over sliding windows and comparing them across proteins with cosine similarity, we find diagonal patterns that reflect locally aligned regions even without sequence identity. Building on this insight, we introduce a framework for detecting locally aligned structural regions directly from sequences, supporting the development of scalable methods for structural annotation and comparison.

bioinformatics↗

bioRxiv2025

Predicting individual incubation of opioid craving by whole-brain functional connectivity

A high risk of relapse triggered by craving during abstinence remains a main challenge in opioid addiction treatment. Multiple brain regions have been implicated in opioid craving, but the brain-wide neural mechanisms underlying this process remain poorly understood. Using resting-state fMRI and connectome-based predictive modeling, we identified a whole-brain connectome that predicted the time-dependent increases (incubation) in oxycodone craving in individual rats after voluntary abstinence induced by exposure to an electric barrier. Incubation of oxycodone craving was operationally defined as the increase in non-reinforced lever pressing during relapse tests from early (day 1) to late (day 15) abstinence (incubation score). We found that changes in whole-brain functional connectivity during abstinence, but not during oxycodone self-administration, predicted the incubation score. Greater decreases in functional connectivity were associated with higher incubation scores. The predictive connectome involved complex interactions across multiple brain systems, including frontal-striatal, frontal-insula, insula-striatal, and hippocampal and sensorimotor circuits. To test causality of the predictive connectome, we examined the effect of pharmacological inactivation of dorsomedial striatum (DMS), which significantly decreased oxycodone seeking after electric barrier-induced abstinence. DMS inactivation increased connectivity strength within the predictive connectome, supporting a causal role of this connectome in incubation of oxycodone craving. The predictive connectome did not predict food-reward seeking after electric barrier-induced abstinence, indicating specificity to oxycodone craving. Our findings identify a brain-wide connectome marker that predicts individual differences in the incubation of opioid craving and provide potential targets for developing personalized interventions and monitoring therapeutic outcomes in opioid addiction treatment. Significance statementRelapse driven by craving remains a central challenge in treating opioid addiction. Using resting-state fMRI and connectome-based predictive modeling, we identified a whole-brain connectivity pattern that predicted incubation of oxycodone craving after voluntary abstinence in rats. Connectivity changes during abstinence predicted the magnitude of incubation, measured by increased oxycodone seeking from early to late abstinence. The predictive connectome involved interactions among cortical, basal ganglia, insular, hippocampal and sensorimotor systems. Pharmacological inactivation of the dorsomedial striatum modulated connectivity strength within this network, demonstrating a causal role of the connectome in incubation of craving. These findings identify a brain-wide marker of incubation of opioid craving and provide a translational framework for developing targeted interventions and monitoring therapeutic outcomes in opioid addiction treatment.

neuroscience↗