Biology subjects

Liu, Q.

Publications and source records attributed to Liu, Q..

At least 19 recordsLinked to original sources

bioRxiv2026

Induced microfold cells are required for the function of pulmonary tertiary lymphoid organs that promote mucosal immunity

Tertiary lymphoid organs (TLOs) arise in response to infection and inflammation. We show that influenza-induced lung TLOs are comprised of inducible M cell (iM cell)-inducible bronchus-associated lymphoid tissue (iBALT) functional composites. iM cells are necessary for the appropriate architecture of adjacent immune follicles, which contain clonally expanded and sequence-diversified B cells. iM cell ablation results in fewer antigen-specific germinal center B cells, T follicular helper cells, and T helper memory cells, and a decrease in secretory IgA and IgG production accompanied by a loss of viral control. We find that the iM cells arise from epithelial progenitors that respond to a RANKL signal from infiltrating T cells, consistent with a model in which the patchy inflammation typical of pneumonia results in the unpredictable and varying distribution of coupled iM cells and adjacent iBALT. We propose that iM cells could be harnessed to promote infection control and enhance mucosal vaccination.

immunology↗

bioRxiv2026

Multiscale, Survey-and-Target Optical Super-Resolution Histology

Super-resolution microscopy reveals fine subcellular tissue architecture, yet extending this capability to histological screening requires balancing spatial resolution with imaging area, axial access, throughput, and instrumental complexity. Here we present multiscale optical super-resolution histology (MOSH), an integrated optical-computational platform that combines tissue-scale, extended-depth-of-focus super-resolution surveys with coordinate-guided three-dimensional interrogation of selected targets. MOSH leverages stationary multifocal excitation, continuous sample translation, tunable axial refocusing, and parallelized reconstruction within a shared spatial-coordinate framework in a conventional epi-fluorescence setting. This survey-and-target strategy preserves tissue-scale context, directs volumetric acquisition to selected subcellular structures, and generates virtual H&E images that retain resolution-enhanced structural information. Across cultured cells, cytological preparations, conventional tissue sections, and intact unsectioned biopsies, MOSH achieved approximately twofold resolution enhancement, continuous lateral coverage beyond the native field of view, and tunable axial survey ranges up to 20 m. Bridging tissue-scale histological screening and targeted, quantitative nanoscale interrogation, MOSH establishes a scalable framework for multiscale analysis of biological and clinical specimens.

bioengineering↗

bioRxiv2026

Joint reconstruction of multi-contrast relaxation-diffusion imaging

Diffusion MRI across multiple gradient directions, b-values and echo times (TEs) provides complementary tissue contrast but requires extensive sampling. Joint k-space and q-space undersampling requires reconstruction that preserves diffusion- and TE-dependent signal variation while exploiting shared anatomical information. Here we introduce DIFFUSE (Diffusion-relaxometry Implicit Field Fitting from Undersampled k-q Space across Echoes), a subject-specific joint k-q-TE image reconstruction framework. It fits a single implicit neural representation (INR) directly to complementary undersampled multi-coil k-space measurements across diffusion encodings and echo times. The INR (a self-supervised neural network) maps spatial location, b-value and TE to baseline b0 signals and spherical harmonic coefficients for directional diffusion-weighted image reconstruction. The measurement-specific forward model incorporates phase, coil sensitivities and motion and eddy current related transforms. We evaluated DIFFUSE using retrospective Human Connectome Project data, controlled motion simulations and in vivo multi-shell and multi-TE acquisitions. At sixfold in-plane acceleration, DIFFUSE reduced normalized mean squared error by 57-73% relative to SENSE across two diffusion shells in the retrospective experiment. In the controlled motion experiment, motion-aware DIFFUSE reduced normalized mean squared error by 66-72% relative to static DIFFUSE. The prospective study demonstrated multi-shell reconstruction at 1.3 mm isotropic resolution with significantly high signal-to-noise ratio (SNR). In a separate multi-TE experiment, a single representation jointly reconstructed all nine contrasts corresponding to 3 b-value/TE combinations. DIFFUSE thus provides a common reconstruction strategy for accelerated protocols spanning multiple diffusion encodings and echo times.

neuroscience↗

bioRxiv2026

DNA sequence sets the destination of spontaneous epimutations in plants

Plant genomes gain and lose cytosine methylation spontaneously, far faster than DNA mutates, and these epimutations are inherited. The genome is thought to set a steady state around which methylation fluctuates. Here we show that this steady state, the set point, is written in the DNA of each CG site: REED, a deep-learning model trained on one methylome, reads it. In pedigrees of seven Arabidopsis thaliana accessions, gains concentrated at high set points and losses at low ones, and the equilibrium implied by their rates matched the set point, tracking it within genes. Stress sped epimutation but barely shifted its destination. The set point predicted germline epimutations in rice and changes accumulated in the wild and in a 330-year-old tree. Its rules, cooperation between nearby CG sites and transcription-factor motifs that lower methylation, are shared across flowering plants, so a genome and one methylome reveal where epigenetic variation is heading.

plant biology↗

bioRxiv2026

Reducing transitory starch in cassava impairs photosynthesis and carbon export to storage roots

Optimizing source-sink relations is a central strategy for improving crop yield. In potato, reducing transitory leaf starch synthesis can redirect carbon to storage organs without impairing growth. To test whether this strategy is transferable to cassava, we suppressed the major small subunit of ADP-GLUCOSE PYROPHOSPHORYLASE, MeAPS1a. Transgenic plants were nearly devoid of transitory leaf starch and showed impaired photosynthetic carbon fixation. Under greenhouse conditions, shoots acclimated to the reduced carbon supply and exhibited only moderate biomass losses, and the data indicated reduced carbon supply to developing storage roots. This was reflected by lower leaf sucrose levels, reduced storage root biomass and starch accumulation, and downregulation of carbohydrate-responsive genes, including the major AGPase large subunit MeAPL3. Under field conditions, the absence of transitory starch was even more detrimental, causing severe growth inhibition and drastic reductions in both shoot and root biomass. We also found that MeAPS1a-derived small interfering RNAs moved systemically from leaves to roots, with lower root siRNA abundance in older, larger plants than in younger, smaller plants. Together, our results demonstrate that transitory starch is indispensable for sustaining carbon flux to cassava storage roots, highlighting species-specific constraints on engineering source-sink relations.

plant biology↗

bioRxiv2026

StoichRNA: A Scalable Framework to Map and Optimize Combinatorial RNA Systems at Single-Cell Resolution

RNA-based genetic cargos have emerged as versatile tools in both fundamental research and translational medicine, with growing applications in regenerative medicine, gene therapy, cell therapy, and vaccination. As the field advances beyond single-RNA delivery, coordinated engineering of multiple RNA species is enabling increasingly sophisticated and combinatorial biological functions. However, this shift raises a fundamental challenge: what combination and stoichiometry of RNA species should be delivered together to achieve a desired cellular function? As the number of RNA components increases, the design space expands combinatorially, making conventional optimization strategies labor-intensive and poorly scalable. Here, we introduce StoichRNA, a framework for scalable exploration of multi-RNA stoichiometry in a single experiment. Individual RNA species are delivered independently, each coupled to a distinct fluorescent marker that reports its intracellular abundance. Because uptake is stochastic, each cell acquires a unique combination and copy number of RNA components. Across a sufficiently large population, cells collectively sample a broad stoichiometric landscape, allowing single-cell readouts such as flow cytometry to quantitatively map system output as a function of intracellular RNA composition. We first establish the conceptual validity of this approach by showing that StoichRNA-derived single-cell data can recover and predict the behavior of fixed-ratio RNA formulations. We then apply this framework to representative RNA circuits, demonstrating that StoichRNA can reveal design principles and identify improved stoichiometries that would be difficult to uncover through conventional workflows. Finally, we extend StoichRNA optimization to translationally relevant settings, including control of cell-state engineering in human induced pluripotent stem cells and tuning of RNA-based logic circuits for precision medicine applications. Together, these results establish StoichRNA as a generalizable strategy for engineering combinatorial RNA systems and provide a scalable solution to a central bottleneck in RNA circuit design.

synthetic biology↗

bioRxiv2026

LineageFlow: Growth-Corrected State Dynamics for Probabilistic Cell-Fate Prediction

Single-cell lineage tracing links early transcriptional states to the fates of their descendants, but forecasting clonal fate remains challenging because differentiation and unequal cell expansion jointly shape descendant populations. Here we introduce LineageFlow, a computational framework that combines continuous-time cell-state transitions, relative net growth and state-conditioned generative flows to predict future fate distributions and transcriptional profiles from early single-cell measurements. In clone-disjoint evaluations of lineage-traced hematopoiesis, LineageFlow improves fate prediction and expression-distribution accuracy over matched computational baselines. A separately retrained analysis of cord-blood lineage data shows a similar advantage at a longer prediction horizon. Controlled simulations show that accounting for differential growth improves fate prediction when descendant states expand at different rates, while offering little benefit under equal growth. These findings highlight the importance of modeling state transitions and relative expansion together when predicting the fate of unseen clones.

bioinformatics↗

bioRxiv2026

DeepD enables efficient genome modeling and controllable long-context sequence generation across domains of life

Genome foundation models hold great potential for advancing biological research, but modeling diverse complex genomes and generating controllable genome-scale sequences remain challenging. We present DeepD, a phylogeny-informed sparse mixture-of-experts model operating at single-nucleotide resolution over a 1-million-token context while reducing active computation. Pretrained on unselected whole-genome sequences across all domains of life, DeepD learns genic and intergenic sequence features and supports broad sequence-to-function prediction across species. DeepD performs well in human protein fitness and disease variants effect prediction, such as BRCA1 and BRCA2, with particular strength in regulatory regions, such as promoter and transcription-factor binding sites. Expert routing captures exon-intron boundaries, transposable elements, and distal cis-regulatory elements. DeepD also generates experimentally validated core promoters with specified transcriptional strengths. Finally, the Context-Anchor-Body strategy enables generation of complete yeast chromosome sequences and megabase-scale Arabidopsis segments, preserving gene structure and order while introducing variations within natural ranges. Together, DeepD is an efficient genome foundation model that unifies accurate sequence-to-function prediction with controllable genome-scale generation, advancing predictive and generative design of complex genomes.

bioinformatics↗

bioRxiv2026

Facilitating Valid Statistical Inference in Biomedical Image Synthesis

Generative AI for medical image synthesis is increasingly used to impute missing data and augment biomedical datasets. Yet despite achieving high computational similarity to their real-world counterparts, synthetic images remain uncommon in clinical research practice, in part because of concerns about their biological validity. Here, we evaluate whether synthetic images support reliable downstream statistical analysis and biological inference in magnetic resonance imaging (MRI) and multiplexed immunofluorescence (mIF) imaging. We find that associations between patient phenotypes and synthesized images can be severely attenuated. This is because synthesis methods typically omit clinically relevant covariates and therefore generate images that, although visually realistic, mask key biological differences. Furthermore, naive analyses treat synthesized images as if they were observed, ignoring the statistical uncertainty introduced by synthesis. Drawing on multiple imputation theory, we introduce Phenotype Aware Multiple Synthesis (PAMS), an analysis framework that requires no model retraining and instead adjusts synthesized output to recover valid biological estimates with appropriately calibrated uncertainty. Applied to MRI and mIF data, PAMS reduced estimation bias and restored nominal confidence interval coverage. Because it is compatible with existing synthesis pipelines, PAMS offers a practical and rigorous route to statistically and biologically valid use of synthesized images in biomedical imaging research.

bioinformatics↗

bioRxiv2026

Microglia-derived Small Extracellular Vesicles Promote Bone Marrow Monocyte Expansion and Recruitment in Glioblastoma.

Glioblastoma (GBM) remains one of the deadliest human cancers and is characterized by a profoundly immunosuppressive tumor microenvironment (TME). Although tumor-associated microglia (TAMg) promote GBM progression, their role in regulating immune cell recruitment remains poorly understood. Here, we demonstrate that TAMg-derived small extracellular vesicles (sEVs) mediate bone marrow-derived monocyte recruitment into the GBM TME. We find that these sEVs traffic to the bone marrow, where they selectively target monocytes and their progenitors. Mechanistically, sEV internalization activates MAPK signaling and inflammatory transcriptional programs, driving the differentiation and expansion of inflammatory monocytes. Together, our findings establish microglia-derived sEVs as a previously unrecognized mechanism of communication between the GBM TME and the peripheral immune system, thereby promoting the establishment of an immunosuppressive TME that drives GBM progression.

cancer biology↗

bioRxiv2026

Engineering Outstanding Water Retention and Toughness through Gradient Cellular Architecture and Delignification: A Case Study of Palm Fronds

Delignification can engineer wood into high-performance multi-scaled materials, building up from its elementary fibrils of nanocellulose across its micro-scaled cellular architecture. However, the potential to engineer other, structurally diverse, non-woody biomass remains largely unexplored. Non-wood plants can add additional hierarchical structures at the mm- and cm-scale. Here, we explore such unique hierarchical architecture as found in date palm fronds, where the vasculature promotes the formation of near-mm scaled vasculature bundles. We also show that along the growth direction, the multi-scale structure varies substantially, i.e. considering the frond base, petiole, and rachis. These tissues are systematically investigated to establish correlations between cellular structure, delignification pathways, and the combination of water dynamics to mechanical performance. A range of property spaces are showcased, highlighting excellent water-storage capacity (~1212%) in the never-dried porous state and high toughness after air-dry densification. Alkaline, acidic-bleaching, and combined bleach-cold alkali treatments are employed to tailor chemical composition and microstructure selectively. The combined route facilitates nanocellulose liberation and redistribution, generating a hierarchical network that improves moisture retention. These findings establish a structure-processing-property relationship for unconventional plant ultra-structure biomass towards multi-scaled materials with unique features from the nano- to the cm-scales.

plant biology↗

bioRxiv2026

HSF1 inhibition potentiates the antifungal activity and proteome instability induced by the fungal Hsp110 inhibitor 2H

Fungal infections remain a major cause of morbidity and mortality worldwide; however, therapeutic options are limited and are increasingly compromised by antifungal resistance. Heat Shock Protein 110 kDa (Hsp110) is an essential molecular chaperone required for protein homeostasis (proteostasis) and fungal viability and has recently emerged as a promising antifungal target. We previously identified 2H, a first-in-class inhibitor of fungal Hsp110 proteins that exhibits broad-spectrum antifungal activity through disruption of proteostasis. Heat Shock Transcription Factor 1 (HSF1), the master regulator of cellular stress responses and chaperone expression, has also been proposed as a potential antifungal target, although direct pharmacological evidence supporting this strategy remains limited. Here, we investigated the effects of combining the HSF1 inhibitor DTHIB with the fungal Hsp110 inhibitor 2H in Cryptococcus neoformans and Candida albicans. Although DTHIB alone exhibited minimal antifungal activity, it significantly enhanced the growth-inhibitory and fungicidal effects of 2H. Notably, this potentiating effect was specific to 2H and was not observed with fluconazole. Consistent with the roles of Hsp110 and HSF1 in proteostasis, 2H induced extensive accumulation of detergent-insoluble protein aggregates in C. neoformans, whereas HSF1 inhibition further increased both the abundance and diversity of aggregated proteins. Quantitative proteomic analyses demonstrated that 2H induces proteome-wide protein aggregation, impacting proteins involved in a broad range of essential cellular processes, with high-molecular-weight proteins exhibiting particular susceptibility. Together, these findings provide pharmacological evidence supporting HSF1 as a promising antifungal sensitization target and demonstrate the utility of 2H as a chemical probe for studying Hsp110-dependent proteostasis. Simultaneous disruption of both HSF1-mediated stress responses and Hsp110-dependent proteostasis promotes extensive proteome instability and enhances antifungal activity, highlighting a potential new strategy for antifungal drug development.

microbiology↗

bioRxiv2026

Transcriptional perturbation effects remain structured beyond the leading global trend

Prior work has described a global transcriptional response to gene perturbation, with many perturbations moving cells along a common direction. We find that perturbations contribute differently to this leading global trend and that substantial organization remains beyond it, including transferable perturbation-specific effects across datasets and a common hierarchy of responsive genes across perturbations. These patterns make simple strategies surprisingly competitive even in predicting effects beyond the leading global component.

genomics↗

bioRxiv2026

Contrasting gene gain and loss shape metabolic diversification in the Ralstonia solanacearum species complex

Metabolic repertoires are continually remodeled by gene acquisition and loss, but how these contrasting processes jointly generate phenotypic diversity within bacterial species complexes remains poorly understood. Here, we elucidated the genetic and evolutionary basis of the classical biovar phenotypes of the Ralstonia solanacearum species complex (RSSC), which are defined by differential utilization of three hexitols (sorbitol, mannitol, and dulcitol) and three disaccharides (lactose, maltose, and cellobiose). Although this classification has been used for over half a century, the genetic determinants and evolutionary origins of these phenotypes have not been systematically resolved. Machine-learning analysis of genomic data from 250 RSSC strains with documented biovar phenotypes, combined with genome-wide association analysis and gene deletion, identified the smoEFGK gene cluster and flavin adenine dinucleotide (FAD)-dependent glucose dehydrogenase (FADGDH) as the major determinants of hexitol and disaccharide utilization, respectively. Comparative phylogenomics revealed fundamentally different evolutionary histories of these loci. An ancestral smoEFGK system was lost early in RSSC evolution and subsequently reacquired by the ancestor of phylotype I through horizontal transfer from a Burkholderia-related lineage, whereas vertically inherited FADGDH underwent recurrent, independent losses across multiple RSSC lineages. Extending the analysis to 1,082 RSSC strains further showed that disaccharide-utilization capacity was significantly depleted among monocot-associated strains after accounting for phylogenetic relatedness and geographic origin. Together, these findings reveal how contrasting trajectories of gene gain and loss can reshape bacterial metabolic repertoires, generate lineage-associated phenotypic diversity, and potentially contribute to ecological differentiation.

genomics↗

bioRxiv2026

Causal event structure shapes convergent reactivation signatures in human cortex and language model

Humans adapt to changing environments by understanding the latent causal structure of events, and modern large language models (LLMs) increasingly exhibit analogous abilities. However, elucidating how causal knowledge is represented and integrated in the human brain and in LLMs poses a substantial challenge for cognitive science. To address this, we combine topological analysis of representational manifolds with neuroscientific analyses of reactivation. We find that event-level causal relations modulate temporally structured neural reactivation in the human brain, particularly in the angular gyrus, precuneus and prefrontal cortex. By contrast, LLMs express these relations through positionally structured representational reactivation. Despite these differences, the resulting reactivation patterns exhibit similar intrinsic manifold structure across the two systems. Together, these findings suggest that the human brain and LLM share a common principle for organizing causal knowledge.

neuroscience↗

bioRxiv2026

AbRefine: Framework-Conditioned Refinement for Structure-Guided Antibody CDR Design

Structure-guided antibody CDR design constrains residue selection using backbone geometry, but structural evidence alone may leave multiple amino acids plausible. We investigate whether the fixed antibody framework provides complementary sequence information for resolving this ambiguity. We introduce AbRefine, an information-aware fusion framework that combines backbone-conditioned structural predictions with framework-conditioned sequence distributions derived from a pretrained antibody language model with all designed CDRs masked. Its Information-Aware Fusion (IAF) module learns residue-specific fusion weights from the uncertainty, disagreement, and information content of the two predictive distributions, while keeping both pretrained experts frozen. Controlled interventions show that the signal is target specific: matched frameworks outperform shuffled frameworks by 7.37 percentage points, and the largest gain occurs at positions with high structural uncertainty and informative framework evidence (+5.77$ points). Compared with static fusion, IAF further improves AbMPNN recovery by +1.53 points (95\% CI +0.93$ to +2.14$) and significantly reduces native negative log-likelihood for both AbMPNN and AntiFold, without detectable degradation in fold-back structural compatibility. These results show that framework context provides information beyond backbone geometry and that learning its residue-specific utility enables effective adaptive fusion for antibody CDR design.

bioinformatics↗

bioRxiv2026

High-SNR Whole-Brain Mesoscale Diffusion MRI Using Rotating-View Acquisitions (ROVER-dMRI)

Purpose: We aim to acquire and reconstruct high signal-to-noise ratio (SNR), whole-brain, mesoscale (isotropic submillimeter) diffusion MRI (dMRI) in vivo in a clinically feasible scan time. Methods: We present ROVER-dMRI, a unified rotating-view acquisition and super-resolution reconstruction framework. Thick slices (up to 10 mm)acquired across many rotated orientations improve the base SNR, and a shared multi-resolution hash-encoded implicit neural representation (INR) with per-direction MLP head and a physics-informed, Bloch-simulated slice-profile forward model reconstructs these views into a continuous isotropic volume (up to 13x super-resolution) in a self-supervised manner. ROVER-dMRI was evaluated in vivo on human brain (0.78 mm isotropic and 0.5 mm isotropic) and monkey brain (0.6 mm isotropic) across MRI platforms with varying gradient performance, and benchmarked against gSlider-SMS and least-squares super-resolution reconstruction (LS-SRR). Results: At 0.78 mm isotropic resolution, ROVER-dMRI diffusion- weighted images achieved higher SNR than the vendor product sequence, whose images were limited by the noise floor, while its directionally encoded color (DEC) maps showed lower noise than gSlider. When reducing the number of views from 20 to 13 (reducing the scan time by 65% ), ROVER-dMRI acquisition/reconstruction preserved all fine details in the fiber orientations. In the monkey brain, its DEC map matched a 60-direction gSlider acquisition at only 40% of the scan time. ROVER-dMRI preserved coherent fiber-orientation-distribution (FOD) structure, including crossing fibers, and recovered fine details in the cerebellum at 0.5 mm isotropic, that are not visible at 0.78 mm resolution. Compared to state-of- the-art 0.5 mm methods, ROVER-dMRI reduced scan time by 73% while acquiring more diffusion directions. Conclusion: ROVER-dMRI offers a practical route to high-SNR whole-brain mesoscale diffusion imaging in vivo, with the pulse sequence and reconstruction pipeline released as open source. KEYWORDS: diffusion MRI, super-resolution, implicit neural representation, hash encoding, physics informed learning

neuroscience↗

bioRxiv2026

Redox oscillation frequency reshapes antibiotic resistance-gene expression through metabolic and ecological selection in a freshwater community

Aquatic environments can maintain and express antibiotic resistance genes even without antibiotic pressure, but the conditions that govern their activity remain poorly understood. Redox oscillations are pervasive in natural and engineered waters and strongly alter microbial metabolism, yet whether their frequency shapes resistance expression is unknown. We reanalyzed paired metagenomes and metatranscriptomes from a published chemostat experiment in which a sulfidic stream community was cycled between oxic and anoxic conditions at high, medium and low frequency (HF, MF, LF), with strain-resolved population genomics of the dominant Pseudomonas lineage. Resistance genes were broadly distributed across treatments, whereas transcription concentrated under fast cycling, reaching a median 168 TPM under HF against 38 under LF although no antibiotic was present, so genetic potential and expression diverged. Fast cycling induced ROS-generating flavoproteins, antioxidant defenses and transcription-coupled DNA repair, and Pseudomonas supplied 99% of resistance-gene transcripts under HF, chiefly through its intrinsic efflux systems. Within the dominant Pseudomonas genome, however, the four resistance genes carried no sequence variants under HF, where coverage was deepest, and no fixed difference from the reference in any sample. Genome-wide selection remained purifying throughout (pooled pN/pS 0.72 under HF against 0.12 under LF, both below 1), and the relaxation trended toward redox and central metabolism. Redox frequency therefore raises resistance expression by selecting a metabolically versatile, high-efflux lineage, while the resistance sequences themselves stay conserved. Resistance activity in aquatic systems responds to an abiotic driver, which argues for weighing expression alongside gene presence in environmental surveillance.

microbiology↗