Biology subjects

Zheng, R.

Publications and source records attributed to Zheng, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Aging, HIV Status, and Sex Differentially Influence Antiretroviral Therapy Disposition Pathways in Rectosigmoid Tissue and Peripheral Blood Mononuclear Cells

People living with human immunodeficiency virus (HIV) are increasingly surviving into older age due to the effectiveness of antiretroviral (ARV) therapy (ART), creating a growing population in whom age-related changes may influence treatment outcomes. Although aging is known to affect drug disposition, the molecular mechanisms by which aging alters ARV pharmacology remain incompletely understood, particularly within tissue compartments, including the rectosigmoid region of the colon, that serve as sites of HIV transmission and persistence. To address this knowledge gap, we used proteomics by liquid chromatography-mass spectrometry (LC-MS) to evaluate the impact of age and HIV status on proteins and pathways relevant to ARV disposition in peripheral blood mononuclear cells (PBMC) and rectosigmoid colon tissue obtained from young (18-50) HIV-seronegative adults, older (65-80) HIV-seronegative adults, and older virally suppressed people living with HIV. We found that aging and HIV infection differentially influenced proteins predominantly within nucleos(t)ide reverse transcriptase inhibitor (NRTI) pathways, in rectosigmoid tissue and PBMC. HIV-associated effects to the proteome were more prominent in PBMC, whereas aging produced broader alterations in ARV disposition-related proteins in rectosigmoid tissue, including proteins involved in drug transport, intracellular drug activation, dephosphorylation, and nucleotide homeostasis. Several age-associated changes were further influenced by biologic sex, suggesting that aging may not affect ARV disposition uniformly across populations. To place these findings within a broader context, pathway analyses were performed that identified extensive tissue-specific remodeling associated with aging and HIV infection involving drug disposition, immune regulation, cellular metabolism, and disease-related pathways. Notably, aging was associated with alterations in pathways relevant to nucleotide metabolism and gastrointestinal tissue function, suggesting that processes beyond traditional drug transport and metabolism pathways may contribute to variability in ARV disposition with advancing age. Together, these findings identify aging as an important modifier of pathways that regulate ARV disposition within compartments relevant to HIV transmission and persistence. While the clinical consequences of our findings are unknown, these data provide a mechanistic foundation for future studies investigating whether age-associated changes influence the effectiveness of ART and HIV pre-exposure prophylaxis in older adults.

pharmacology and toxicology↗

bioRxiv2026

A decision-based criterion and a resolution threshold for ranking genomic prediction models within trials

Breeding programmes choose genomic prediction models by accuracy rankings, and the metric behind a ranking decides which model wins. Selecting the best genotypes within a trial depends only on the order of the predictions, so a metric that guides the choice must be unchanged by any order-preserving change of the predictions and respond when their order is reversed. Of the 22 official metrics of the Genomes to Fields maize competition, one meets both conditions, the within-environment rank correlation; the within-environment Pearson correlation is protected against rescaling only; and twenty fail, including the root mean squared error that decided the 2022 ranking. Switching between the competitions two official metrics reverses 27 % of team pairs, and an exact decomposition shows why pooled metrics mislead: in three constructed panels, 94-95 % of the attainable square of pooled correlation is carried by the environment means. The accuracy difference a trial can resolve falls as about 3/{surd}N with its N genotype-environment cells: at least 0.03 at the competitions design and 0.04-0.12 once calibrated on real panels, against 0.003 between the first two teams; tests that two correlations differ declare far smaller differences, which do not reliably change selection. In soybean, where the two most accurate methods stand clear of most others by more than the trial resolves, ranking by either within-environment correlation selects better material, 98-99 % of the attainable selection gain in held-out environments against 13-34 % for error-magnitude rankings; in common bean and a Chinese maize population, where the leading methods lie within the resolvable gap, all rules pick material of similar value. GPverdict runs these checks on any programmes cross-validated predictions.

plant biology↗

bioRxiv2026

Long-form hibernation-promoting factor coordinates 100S assembly and translational repression

Ribosome hibernation mediated by long-form hibernation-promoting factor (HPF) requires coordination of ribosome dimerization and translational repression, but how these functions are integrated remains unclear. Here, we present cryo-EM structures of native 100S from a deep-sea Bacillus isolate, revealing a conformationally heterogeneous 100S population. Beyond the established roles of the HPF N-terminal domain (NTD) in decoding-center occlusion and the C-terminal domain (CTD) in dimerization, our analyses reveal additional contributions from the Linker and a charged CTD surface near the 16S rRNA 3' terminus containing the anti-Shine-Dalgarno (aSD) sequence. Linker substitutions differentially affect translational repression while retaining 100S formation, with distinct effects of the two Linker segments. Mutation of the charged CTD surface enhances translational repression while retaining robust 100S formation, suggesting an additional role beyond CTD-mediated ribosome dimerization. Functional analyses in an E. coli cell-free translation system further show that the inhibitory activity of individual HPF regions varies with translational context. Together, these findings support a modular model in which the NTD, Linker, and CTD make distinct but interconnected contributions to 100S assembly and translational repression.

biochemistry↗

bioRxiv2026

Unified modeling of mutation-induced protein stability responses with OmegaTherm

Accurate prediction of mutation-induced protein stability responses, including changes in folding free energy and melting temperature, is critical for protein engineering. Existing models represent these related but non-equivalent measurements separately and often focus on single mutations. Here we present OmegaTherm, a sequence-based framework that jointly learns both stability responses for single and multiple mutations. OmegaTherm introduces a shared protein language model to capture transferable mutation context, measurement-specific encoders to preserve measurement identity, and paired-measurement-informed distillation to learn complementary knowledge from heterogeneous data. An antisymmetric prediction architecture further enforces physical consistency between forward and reverse mutations. Across independent benchmarks, OmegaTherm achieves state-of-the-art performance in quantitative prediction, mutation classification and protein-level ranking. Beyond benchmark prediction, OmegaTherm-derived stability landscapes correlate strongly with fitness landscapes from deep mutational scanning, enrich favorable variants and reveal function-associated mutation-sensitive regions. These results establish unified representation learning as an effective strategy for integrating related but non-equivalent biochemical measurements.

bioinformatics↗

bioRxiv2026

A Comparison of Brain Metabolic Connectivity Methods Topology, Cognition, Age, Structure, and Genetics

Brain metabolic connectivity is increasingly studied using positron emission tomography (PET), but multiple analytical approaches exist with unknown comparability. Here we compared metabolic connectivity approaches in 80 participants who underwent simultaneous dynamic PET with 18F-fluorodeoxyglucose and functional magnetic resonance imaging (fMRI) scans and cognitive testing. Six connectivity approaches were compared, including the traditional across-subject method (often labelled metabolic covariance) and five approaches that estimate individual-level connectivity: across-subject metabolic covariance with individual scores derived using leave one out (LOO); functional PET (fPET) within-subject correlations of regional timeseries; and three approaches that use distance-based measures to estimate individual-level connectivity based on within-region tracer distribution and kinetic compartment C1 and C2 time activity curves. fMRI connectivity was also calculated. Each method showed distinct topological, behavioural, structural and genetic signatures. fPET connectivity demonstrated the strongest cognitive associations and largest age-related declines, with unique predictive power for cognition independent of age. Tracer distribution showed strong age group discrimination although with mixed direction of age and cognitive effects. Metabolic covariance (LOO) and kinetic C2 showed no significant age group differences and their cognition associations reflected age-related variance. Gene expression associations implicated a shared core glycolytic-insulin machinery in all approaches, but the direction of the association with regional connectivity diverged across approaches and unique gene signatures further distinguished most methods. Collectively, these results indicate that the optimal connectivity approach depends on the research question. fPET connectivity with dynamic scans offers utility for cognition and ageing studies, tracer distribution for single-scan designs with careful interpretation of age and cognition relationships, kinetic approaches for novel biological insights into glucose kinetics and fMRI for multi-modal integration. Metabolic covariance (LOO) should be used with caution for brain-behaviour investigations. Researchers should weigh trade-offs between physiological specificity, analytical complexity, acquisition requirements and interpretability when selecting a metabolic connectivity approach.

neuroscience↗

bioRxiv2026

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

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

cancer biology↗

bioRxiv2026

Logic-Gated Fluorogenic RNA Reporters for Multiplexed Live-Cell Imaging

Multiplexed imaging of biomolecular networks in living cells is limited by the small number of spectral separable fluorophores and the need to monitor dynamic processes in real time. Here, we present logicFRIES, a fluorogenic RNA (FR)-based platform that enables eight-plex live-cell imaging through programmable, logic-gated activation coupled with sequential fluorescence imaging. By integrating small-molecule-binding RNA aptamers into dye-activating fluorogenic RNAs, we engineered trigger-responsive FR reporters. This design implements an AND-gated mechanism in which fluorescence activation requires both a cognate trigger molecule and its corresponding fluorogenic dye, thereby expanding multiplexing capacity without adding new fluorophores. Using three membrane-permeable triggers, tetracycline, ASP2905, and guanine, we generated three distinct trigger-defined activation states for each engineered Broccoli and Pepper FR. Combined with orthogonal Corn/DFHO and DNB/TMR-DN reporter pairs, logicFRIES supports eight-plex imaging through sequential trigger/dye addition, imaging, and wash-based stripping cycles. We demonstrate robust, specific, and reversible fluorescence switching of these multiplexed FR reporters in living HEK293T and SKBR3 cells. Overall, logicFRIES extends live-cell imaging beyond conventional spectral limitations and provides a modular foundation for potentially developing multiplexed sensors targeting endogenous RNAs, proteins, and small molecules in complex cellular systems.

molecular biology↗

bioRxiv2026

Development and Analytical Validation of a Smartphone-Based Quantitative Lateral Flow Immunoassay for Serum Cystatin-C

Cystatin-C is an important renal function biomarker, and conventional quantification requires centralized laboratory analyzers, which limits timely testing in primary care and resource-limited settings. To address this need, we developed and validated a simple, rapid, and quantitative smartphone-based (SP) lateral flow immunoassay (LFIA) for measuring serum Cystatin-C. The SP-LFIA platform consists of a colorimetric LFIA strip and a custom SP reader with uniform LED illumination and macro lens for image capture. Quantitative image analysis of the colorimetric signal is performed by a dedicated application using a pre-defined third order polynomial calibration model. Following systematic optimization, the assay demonstrated a wide quantitative range of 0.32-8.00 mg/L, with a limit of detection of 0.15 mg/L. Analytical validation conducted according to CLSI guidelines showed excellent precision, with intra- and inter-assay coefficients of variation below 10%, and no significant interference from bilirubin, triglycerides, hemoglobin, or rheumatoid factor. Accelerated stability testing confirmed robust strip performance after storage at 50 {degrees}C for 28 days. Method comparison using 100 clinical serum samples showed high agreement with a commercial PETIA reference method (R{superscript 2} = 0.993) and minimal bias. These results indicate that the developed smartphone-based LFIA provides a reliable, cost-effective, and practical tool for point-of-care Cystatin-C monitoring.

biochemistry↗

bioRxiv2026

Transferable spatial omics deconvolution with SpaRank

By resolving cell-type compositions from multi-cellular spatial measurements, deconvolution is central to resolving the cellular landscape of complex tissues. Existing deconvolution methods fit continuous expression values and are therefore sensitive to batch effects between single-cell references and spatial data, requiring retraining for each new context. Here we present SpaRank, a context-aware framework that performs spatial deconvolution by representing spots as ranked feature sequences. Adapting the rank-based encodings of single-cell foundation models, this formulation is inherently robust to technical variation, enabling a pretrain-transfer paradigm. On simulated benchmarks, SpaRank achieves strong deconvolution accuracy, robustness to expression perturbations, and substantial computational efficiency. On experimental datasets, pretrained models generalize across diverse biological contexts: a model pretrained on a multi-organ lymphoid atlas accurately resolved cell-type distributions across distinct tissues and sequencing platforms; likewise, a model pretrained on an integrated breast atlas delineated cell-type compositions across normal and malignant disease states. Furthermore, the framework naturally extends to multimodal spatial deconvolution by employing gated fusion to adaptively integrate diverse omics signals, improving accuracy over single-modality approaches. Overall, SpaRank establishes a transferable deconvolution paradigm, enabling unified cellular atlases to support direct, context-aware inference across diverse biological states and profiling modalities.

bioinformatics↗

bioRxiv2026

A feed-forward loop between niche adenosine and Gzmk⁺ CD8 T cells propagates systemic inflammaging

The causal link between the aging microenvironment and T cell aging remains elusive. Here, we demonstrate that adenosine within aging tissues actively reprograms CD8+ T cells into a pro-aging Granzyme K+ (Gzmk+) population. Mechanistically, senescent cells create an adenosine-rich niche via p16-dependent CD39 upregulation, triggering A2aR signaling to induce Gzmk+ T cell differentiation. Once released, Gzmk promotes systemic inflammaging through PAR1 and complement activation. Crucially, targeting this axis--either via genetic Gzmk ablation or pharmacological A2aR blockade--reverses multi-organ aging phenotypes and significantly extends healthy lifespan in mice. Human analysis reveals age-dependent Gzmk+ T cell accumulation in multi organs, while coffee intake (an A2aR antagonist) inversely correlates with plasma Gzmk levels. Our findings uncover how metabolic niche changes drive T cell aging and establish the adenosine-Gzmk axis as a pivotal therapeutic target for combating age-related diseases.

immunology↗

bioRxiv2026

Geometric-aware and interpretable deep learning for single-cell batch correction via explicit disentanglement and optimal transport

Single-cell RNA sequencing enables high-resolution characterization of cellular heterogeneity, yet integrating datasets from diverse sources remains challenging due to batch effects. Current methods rely on implicit feature disentanglement and and lack geometric constraints, often result in under-correction, over-correction, or compromised biological fidelity. Here, we present iDLC, an interpretable deep learning framework that performs dual-level correction through explicit feature disentanglement and optimal transport-regularized adversarial alignment. iDLC separates biological and technical components within a structured latent space, then leverages high-confidence mutual nearest neighbor pairs to guide geometrically constrained distribution alignment. Systematic evaluation across pancreatic cancer datasets with varying batch effect intensities, multi-source human immune cells, and large-scale cross-species atlases demonstrates that iDLC robustly eliminates complex batch effects while preserving fine-grained cell subtypes, continuous developmental trajectories, and rare populations. The framework scales efficiently to datasets exceeding one million cells and consistently outperforms existing methods in both batch correction and biological conservation metrics. iDLC provides a principled and reliable tool for constructing unified single-cell reference atlases across diverse experimental conditions and biological systems.

bioinformatics↗

bioRxiv2026

Programmable and Switchable RNA Scaffolds for Synthetic Condensate Engineering in Mammalian Cells

The ability to engineer synthetic biomolecular condensates in living cells offers new opportunities to control intracellular organization, yet robust and programmable RNA-based systems have remained limited. Here, we introduce genetically encoded, modular platforms that generate RNA-driven condensates using nanostar-derived scaffolds. Systematic comparison of repeat-based and de novo designs identified nanostar variants that reliably assemble nuclear condensates in mammalian cells. Unexpectedly, condensate formation in cells is governed primarily by double-stranded RNA stems that recruit endogenous RNA-binding proteins, rather than by the kissing-loop interactions that drive assembly in vitro. This mechanistic shift highlights the divergence between cellular and in vitro environments and accounts for the limited orthogonality among scaffolds. Sequence refinement to reduce nonspecific pairing improved homotypic assembly and enhanced orthogonality. We further demonstrated functional compartmentalization by recruiting protein and RNA clients to modulate their stability and activity, and we incorporated an acyclovir-responsive allosteric switch to achieve reversible, small-molecule control of condensation. Together, this work establishes a versatile RNA-based toolkit for constructing programmable cellular compartments, advancing strategies for controlling RNA-protein organization and enabling new biosensing and therapeutic applications.

bioengineering↗

bioRxiv2026

GRAVITY: Dynamic gene regulatory network-enhanced RNA velocity modeling for trajectory inference and biological discovery

RNA velocity techniques have emerged as efficient tools for unraveling the complex trajectories of cell development and differentiation. However, most of existing RNA velocity approaches are constrained by estimating transcriptional parameters for each gene in isolation and neglects the regulatory relationships among genes, which limits the ability to jointly resolve the dynamic rewiring of gene regulation and the underlying gene transcriptional kinetics across cell state transitions. To address these limitations, we present GRAVITY, a novel deep learning framework that explicitly integrates regulatory dynamics into transcriptional kinetics inference and utilizes a refined two-stage optimization strategy. Benchmarking across various simulated and real single-cell RNA sequencing datasets demonstrates that GRAVITY accurately infers both cellular and gene trajectories, along with their associated kinetic parameters. Most importantly, GRAVITY uncovers terminal cell states L5/L6 in embryonic brain development dataset. Furthermore, GRAVITY not only provides mechanistic insights by identifying the driver regulatory factors and modules governing cell fate, but also enables the systematic in silico simulation of cellular velocity changes induced by targeted regulatory perturbations.

bioinformatics↗

bioRxiv2026

BiHiTo: Biomolecular Hierarchy-inspired Tokenization

Three-dimensional atomic arrangements of biomolecules are key to demystifying biological functions. The rapid expansion of accessible structural data, driven by advances in AI for science, highlights the critical challenge of efficiently modeling large-scale biomolecular structures, which are high-dimensional systems shaped by biological assembly principles. To address this, we introduce BiHiTo, a multi-level Biomolecular Hierarchy-inspired Tokenizer that intrinsically mimics natural biological assembly hierarchies. Specifically, we design a multi-codebook quantizer that mirrors the natural hierarchy of biomolecular structure, enabling simultaneous capture of representations spanning atomic motifs to global conformational variations. This hierarchical alignment markedly improves the biological interpretability and reconstruction fidelity of biomolecular structure.Extensive experiments demonstrate that BiHiTo delivers state-of-the-art performance and robust generalization across molecular dynamics trajectories and macromolecular complexes, facilitating advances in structure generation and dynamic conformation exploration. In the reconstruction of the CASP14 and OOD test set FastFolding protein multi-conformation data, our method achieves a 17% and 51% reduction in RMSD compared to Bio2Token, respectively.

molecular biology↗

bioRxiv2026

A dual-factor complex governs archaeal ribosome hibernation by sensing energy status

Efficient coupling of cellular energy status to ribosome regulation is fundamental for cellular survival. Here, we identify the Archaeal Ribosome regulatory Complex (ARC)--a previously uncharacterized dual-factor complex comprising ARC-P and ARC-A. Cryo-EM reveals that ARC anchors to the small ribosomal subunit, establishing a stringent blockade of the tRNA and mRNA paths via an electrostatic wedge mechanism. This hibernation state is modulated by energy status: the complex remains stably associated under low-energy conditions, whereas ATP binding triggers rapid mobilization. Genetic analyses demonstrate that ARC acts as a translational brake essential for preserving ribosomal structural integrity, with ARC-A serving as the primary inhibitory factor. Notably, while the loss of ARC accelerates growth, it impairs recovery in proportion to stress severity. Phylogenomic analysis identifies ARC as an evolutionarily novel and widely distributed dual-factor system, revealing a conserved regulatory strategy for metabolic-translational coupling across diverse archaea. Our findings define a fundamental strategy where modular complexes integrate metabolic sensing with mechanical sequestration to balance growth and persistence.

biochemistry↗

bioRxiv2026

BaCas12a3 represents a new subtype of type V CRISPR effector with collateral activity toward tRNA

The CRISPR-Cas12 family encompasses diverse RNA-guided nucleases with both DNA-targeting and RNA-targeting subtypes. They can trigger antiviral activities mainly through either direct elimination invading nucleic acids, or activating broad collateral cleavage to induce abortive infection. Here, we report a novel type V CRISPR effector BaCas12a3 that causes growth inhibition through a unique tRNA-cleavage mechanism. Plasmid interference assays indicated that BaCas12a3 inhibits host growth arrest without invoking the DNA damage response, suggesting that the immune responses may not involve double strand breaks of DNA. Indeed, biochemical characterization of the BaCas12a3-crRNA ribonucleoprotein (RNP) unraveled that the effector is an RNA-activating nuclease that cleaves 3' terminus of tRNAs. Cryo-EM structures of BaCas12a3 reveal a conserved bilobed architecture featuring a unique nucleic acid-loading (NL) domain adjacent to the RuvC catalytic center. Structural and mutagenesis analyses show that the NL domain, together with a zinc ribbon domain, form a gated substrate groove. Target RNA binding induces conformational changes that open this groove and expose the RuvC active site, enabling specific tRNA cleavage while preventing other non-specific degradation. Our findings identified the NL domain aside the RuvC active site responsible for the tRNA recognition in BaCas12a3, expanding the functional diversity of CRISPR immunity.

molecular biology↗

bioRxiv2025

Concurrent saliency and intentional maps in posterior parietal cortex

AbstractThe goal for motor action represents the desired state of the individual when interacting with the world. Investigating how the goal is generated requires clarifying spatial extrapolation that involves perceptual and action processes. Here, we recorded neural activity from monkeys manually intercepting a circularly moving target being occluded. The saliency and intentional maps, which developed to locate the target of interest and the intended action goal, were thus distinguished in neural activity recorded from the lateral intraparietal cortex (LIP) and parietal reach region (PRR). Dynamic internal representations spanning from visual registration to state estimation were unveiled in both areas, with the goal for the interception being inferred as the visual consequence of the movement. The motion extrapolation was found to be an intrinsic part of movement planning, so that the continuous and stable spatial extrapolation from motion to action was prevalent in both areas. The goal estimation based on the sensory extrapolation rationalized the concurrency and correlation of saliency and intentional maps, which were found to switch their dominance by reorganizing the read-out dimensions of the spatial manifold. The extrapolation achieved at both the perceptual and motor stages not only bridged the saliency and intentional maps, but also revealed the form of the state estimation in guiding predictive sensorimotor control.

neuroscience↗

bioRxiv2025

Microbial metabolism of complex organic matter across diverse deep-sea ecosystems

The deep sea is home to a vast and largely unexplored microbial biosphere, along with significant amounts of complex organic matter (COM). However, the ability of deep-sea microbes to metabolize complex organic matter across diverse regions remains poorly understood. Here, we combine 16S rRNA gene amplicon sequencing, metagenomics, and metatranscriptomics to comprehensively characterize prokaryotic communities across different years and habitats (cold seeps, hydrothermal vents, and seamounts). Our results reveal spatio-temporal community heterogeneity driven by geochemical gradients, alongside widespread genomic and transcriptomic potential for COM metabolism. Notably, the PVC (Planctomycetota-Verrucomicrobiota-Chlamydiota) superphylum exhibits extensive polysaccharide degradation capabilities, with Planctomycetota strain WC338 and Lentisphaerota strain WC36 isolated via laminarin enrichment. Growth and transcriptome data confirm their obligate laminarin dependence and robust degradation capacity, employing distinct enzymes (GH16 and GH2), whose broad distribution across diverse PVC superphylum lineages underscores their prevalence. Furthermore, we demonstrate that laminarin acts as a highly effective selective substrate for enriching and isolating the deep-sea PVC superphylum bacteria. Collectively, these findings delineate specialized adaptations within the PVC superphylum for polysaccharide degradation, significantly expanding our understanding of deep-sea microbial roles in global carbon cycling.

microbiology↗