Biology subjects

Xing, Z.

Publications and source records attributed to Xing, Z..

At least 19 recordsLinked to original sources

bioRxiv2026

A lactate-HIF1-VDR positive feedback loop drives protective SPP1+ macrophage differentiation to inhibit schistosomiasis induced liver fibrosis

Hepatic fibrosis remains a major cause of morbidity and mortality in patients with schistosomiasis without effective therapy. Here we show that vitamin D receptor (VDR) signaling in macrophages is essential for limiting fibrosis, acting through a previously unrecognized metabolic epigenetic circuit. Hepatic macrophages exhibit the highest VDR expression among liver resident cells, and pharmacological VDR activation with paricalcitol selectively expands a protective SPP1+ macrophage subset derived from circulating monocytes. Myeloid specific VDR knockout exacerbates fibrosis and abrogates paricalcitol's hepatoprotective effects, whereas SPP1+ macrophage depletion worsens disease. Mechanistically, VDR activation synergizes with hypoxia and lactate to drive SPP1 expression via glycolytic reprogramming. Moreover, lactate and HIF1alpha cooperatively induce VDR transcription by binding to the Vdr promoter and enhancing histone lactylation, forming a positive feedback loop that amplifies the antifibrotic response. Our findings establish the lactate-HIF1alpha-VDR-SPP1 axis as an endogenous defense mechanism and identify macrophage VDR as a promising therapeutic target for fibrotic liver diseases.

immunology↗

bioRxiv2026

Ourotide: decoding the hierarchical peptide recognition for generative design

The historical dichotomy between small-molecule pocket and extended protein interfaces misrepresents the physical reality of peptide recognition.1,2 Here we show that peptide binding is not a simple structural intermediate but a distinctly multimodal landscape comprising small-molecule-like pockets, protein-like interfaces, and a previously unrecognized third mode. This third regime is governed by a hierarchical subpocket architecture where a flattened surface achieves near-complete peptide engagement through spatially partitioned hydrophobic components. To maintain stability, an incompletely enclosed dominant anchor cooperates with highly hydrated auxiliary subpockets and an asymmetric receptor coupling mechanism that concentrates energy in an adjacent continuous water network. Because this unique binding mode suffers from extreme data scarcity, standard deep learning models fail to capture its physics.3,4 To resolve this, we mapped these specific geometric signatures to mine structurally faithful training distributions from global protein interactomes. Based on these data, we trained Ourotide, a deep learning framework coupling conditional geometric flow matching with interface-aware affinity learning. Evaluated across peptides up to 65 residues, Ourotide outperforms generalist models in backbone accuracy, interface recovery, and affinity prediction. This approach suggests that overcoming data scarcity in the physical sciences requires physics-guided data augmentation rather than naive statistical scaling.

bioinformatics↗

bioRxiv2026

A Glutamine antagonist-modulated tumor microenvironment unleashes enzalutamides immunotherapeutic effects

Androgen receptor (AR) antagonists, such as enzalutamide, suppress prostate cancer (PCa) cells to achieve temporary therapeutic effects. In addition to tumor cell-autonomous suppressive function, AR antagonists can also potentially exert anti-tumor effects via mitigating cytotoxic T cells exhaustion. However, strategies for effectively harnessing enzalutamides immunotherapeutic effects remain elusive. In studying a recently described glutamine antagonist prodrug (DRP-104) in PCa models, we found that despite the initial response, tumors ultimately became resistant. Intriguingly, compared to the untreated (DRP-104 treatment-naive) tumors, the resistant tumors became highly susceptible to enzalutamide in vivo. Additionally, treating tumors with DRP-104 and enzalutamide simultaneously also yielded superior therapeutic efficacy. We demonstrated that DRP-104 therapy promoted the infiltration of CD8+ T cells as well as regulatory T cells (Treg) in responsive tumors, and the tumor-infiltrating Treg were mostly depleted upon enzalutamide treatments. Mechanistically, we showed that Treg differentiation from mouse CD4+ T cells was attenuated by enzalutamide. We further demonstrated that Treg induction was accompanied by the interaction between AR and aryl hydrocarbon receptor (AhR), the nuclear receptor indispensable for Treg differentiation, in the nuclei of CD4+ T cells, and this interaction was diminished by enzalutamide. In further support of AR signaling in Treg biogenesis, analysis of available gene expression datasets found that AR expression was elevated in Treg when compared to CD4+ T cells in human peripheral blood mononuclear cells (PBMCs). In addition, it was positively correlated with Treg module scores in several human cancer types. Finally, using an anti-GPC3 (Glypican 3) vaccination model, we demonstrated that CD4+ T cells subjected to Treg induction in the presence of enzalutamide were less effective in protecting GPC3-expressing tumor cells from CD8+ T cells cytotoxic killing. Collectively, these results suggest that AR promotes Treg s differentiation and/or immunosuppressive functions, and nominate enzalutamide as a Treg-mitigating agent for potentiating immunotherapies. Our results also demonstrate that an otherwise unintended, Treg-promoting property of DRP-104 can be leveraged to unleash the immune-regulatory function of enzalutamide for the treatment of PCa.

cancer biology↗

bioRxiv2026

Architectural Confinement and Seasonal Forcing Shape Cross-Domain Pathogen Assemblages in Complex Built Environments

Airborne microbial pathogens in built environments (BEs) may pose potential health threats, yet the ecological mechanisms governing their assembly and persistence across interconnected architectural spaces are less studied. Here, we conducted a year-long, multi-spatial investigation across a university campus complex, sampling the exhaust outlets of three indoor environments and the adjacent outdoor inlets. By integrating the 16S rRNA gene and ITS sequencing from 437 paired bacterial-fungal samples, we characterized the spatiotemporal dynamics of airborne opportunistic pathogen-containing genera. Our study showed that spatial filtering emerged as the dominant determinant of pathogen community structure, with confined elevator environments serving as reservoirs of pathogens with limited but continuous microbial influx from surrounding spaces. Superimposed on this spatial baseline, bacterial and fungal pathogens displayed distinct seasonal dynamics. Opportunistic fungal pathogens such as Fusarium peaked during autumn and winter, possibly driven by enhanced aerosol persistence and dispersal under cooler, drier conditions. In contrast, bacterial pathogens exhibited greater temporal resilience, with key taxa such as Listeria transcribed actively during winter, predisposing them to increased relative abundance during spring. Cross-domain ecological networks further revealed the dynamic interactions among pathogens, centered on the skeletal structure mediated by the keystone fungal pathogen Aspergillus, suggesting that coordinated microbial interactions reinforced pathogen persistence across seasons. Together, our findings support a unified ecological framework in which pathogen dynamics within BEs arise from the coupled effects of spatial filtering, climatic modulation, and biotic interactions. These results provide a foundation for transitioning from static environmental control toward predictive pathogen management in BEs. ImportanceBEs are the primary settings of human microbial exposure, yet the ecological principles governing the persistence of airborne pathogens across interconnected indoor spaces remain poorly resolved. Most previous indoor studies have focused on isolated environments or single microbial groups, limiting our understanding of how pathogens are maintained and redistributed within complex architectural systems. By integrating bacterial and fungal community dynamics across spatial environments over four seasons, this year-long study demonstrates that the ecology of airborne pathogens is not static, but is instead dictated by a complex interplay of spatial, climatic, and biological forces. Our findings identify enclosed, high-transit elevator spaces as critical hotspots for pathogen accumulation and highlight the role of seasonal ecological reorganization in driving airborne health risks. More broadly, this work establishes a systems-level ecological framework for understanding airborne pathogen dynamics in BEs and provides a conceptual basis for developing more adaptive strategies for indoor microbial risk management.

microbiology↗

bioRxiv2026

STAnalyzer: Transparent Spatial Transcriptomics Analysis via an Agentic Architecture

Spatial transcriptomics enables high-resolution profiling of gene expression within spatial contexts, yet its potential is often hindered by fragmented toolchains, intricate parameters, and cognitive bottlenecks of interpreting high-dimensional data. While recent Large Language Model agents have attempted to automate this process, they remain constrained by rigid execution logic, lack multimodal feedback for self-correction, and operate in epistemic isolation from established biological knowledge. Here, we present STAnalyzer, an intelligent multi-agent framework designed to automate the end-to-end analytical lifecycle--from raw data processing to biological hypothesis generation. Transcending traditional pipelines, STAnalyzer employs a collaborative intelligence architecture to achieve three core capabilities: (1) Intent-Driven Orchestration, which dynamically translates natural language queries into rigorous bioinformatics workflows; (2) Multi-Modal Self-Refinement, which autonomously ensures analytical robustness through closed-loop synthesis of evidence from visual patterns and statistical metrics; and (3) Evidence-based Cross-Validation, which bridges the gap between data-driven correlations and biological causation by anchoring findings in ground-truth literature and structured databases. By eliminating manual analytical bottlenecks and ensuring rigorous evidentiary traceability and transparency, STAnalyzer makes high-resolution spatial omics more accessible to a broader research community. It provides a robust and scalable framework for cross-platform automated analysis and accelerated biological discovery, translating massive spatial datasets into verifiable biological insights.

bioinformatics↗

bioRxiv2026

Chiral Histidine-Modified Gold Nanoclusters Loaded into Cationic Lipid Nanoparticles for Treatment of Biofilm-associated Infections

Antimicrobial resistance has heightened the risk of device-associated infections, in which Staphylococcus aureus (S. aureus) biofilms persist through extracellular-matrix protection and marked physiological heterogeneity. Gold nanoclusters (AuNCs) can disrupt biofilms via redox-associated stress, but limited in vivo stability and poor local retention constrain their translational potential. Here, we synthesized chiral histidine-modified AuNCs (D-, L-, and DL-AuNCs) and formulated them into mildly cationic lipid nanoparticles (AuNCs@LNP) by microfluidic assembly to enhance biofilm engagement while preserving nanocluster identity. The resulting particles were [~]100-120 nm with high gold loading and a modest charge reversal. In established S. aureus USA300 biofilms, free DL-AuNCs reduced viable burden by up to 5.6 log10 CFU/mL, suppressed metabolic activity, and increased ROS signaling. Notably, LNP encapsulation maintained bactericidal activity and further improved killing by [~]1 log10 CFU at matched Au dose, while substantially enhancing biomass removal (crystal violet residual biomass: 63.77% vs 45.17%), consistent with carrier-mediated matrix destabilization. In a subcutaneous implant infection model, a single perilesional dose of DL-AuNCs@LNP reduced implant-associated bacterial burden by 2.6 log10 CFU per implant versus PBS. These results establish a modular antibiofilm platform that couples nanocluster-driven killing with lipid-facilitated biofilm disruption and defines an efficacy-tolerability window to guide optimization of locally delivered antibiofilm nanomedicines. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=61 SRC="FIGDIR/small/707929v2_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@119134forg.highwire.dtl.DTLVardef@142e7b2org.highwire.dtl.DTLVardef@1799550org.highwire.dtl.DTLVardef@139c33a_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2026

Metabolic stress reveals widespread accumulation of cap-unmethylated RNAs

RNA polymerase II transcripts are capped with N7-methylguanosine (mG), a conserved modification essential for mRNA function. Although traditionally viewed as constitutive, we developed a mass spectrometry-based method to demonstrate that in both yeast and mammalian cells, a substantial population of mRNAs lack cap methylation in response to SAM-limiting conditions and oxidative stress, which may be frequently encountered across organisms. Through developing two transcriptome-wide approaches, we found that methylation is enriched on specific transcripts and uncovered an unexpected connection between histone H3K36me3 and cap methylation, with both marks preferentially associated with stress-responsive MAPK signaling pathways. Strikingly, cap-unmethylated mRNAs exhibit features of canonical mRNAs--they are polyadenylated, exported to the cytosol, and translated. Enforced cap methylation reduces cell growth under SAM limitation, suggesting that unmethylated mRNAs confer an adaptive advantage during stress. These findings establish mRNA cap methylation as a dynamic, regulated modification and a previously underappreciated layer of gene expression control.

cell biology↗

bioRxiv2025

Multi-Timescale Neural Adaptation Failure as a Mechanistic Signature of Major Depression

Major depression (MD) is usually characterized in terms of momentary abnormalities, such as negative bias or emotional blunting, derived from paradigms that measure only the first response to a stimulus. Yet depressive experience in daily life is repetitive and cumulative. Here we test the hypothesis that a failure of neural adaptation across repetitions is a core feature of MD. Using a repeated negative-stimulus paradigm, we quantified adaptation at micro (within-trial) and macro (across-session) scales via electroencephalography in 28 MD patients and 34 healthy controls, measuring both conventional event-related potentials and large-scale traveling-wave propagation. MD patients exhibited pervasive maladaptation: neural rigidity at the micro scale (absent repetition suppression) and progressive regulatory depletion at the macro scale (escalating emotional reactivity with declining cognitive control). Across analyses, adaptation-based metrics revealed larger group differences than single-shot indices, and classifiers built on adaptation features--especially traveling-wave dynamics--outperformed single-shot models (task AUC = 0.83 vs 0.78) and generalized to two independent datasets (up to AUC = 0.91). These findings challenge conventional symptom-based characterizations of MD, establishing adaptation failure--not initial reactivity--as the mechanistically primary pathological signature. This reframing provides a unified mechanistic explanation for rumination and cognitive inflexibility while offering a standardized, low-cost biomarker requiring minimal electrodes ([≤]8 channels), enabling large-scale screening in resource-limited settings.

neuroscience↗

bioRxiv2025

Mucosal vaccine immunity induced by a new auxotrophic Pseudomonas aeruginosa strain is linked to Th17 and IgA responses

Pseudomonas aeruginosa (P.a) is a Gram-negative opportunistic pathogen that poses a major global health threat, particularly in immunocompromised individuals, patients with cystic fibrosis, and those with burn injuries or ventilator-associated pneumonia. Despite intense efforts, no licensed vaccine is currently available for human use. In this context, live attenuated vaccines (LAVs) represent a promising but underexplored approach, offering the potential to elicit robust, long-lasting, and multifaceted immune responses including that of inducing trained immunity. Here, we sub-cultured {Delta}LasB PAO1 (a P.a strain that we have shown previously shown to have reduced virulence) in artificial sputum medium (ASM), a culture medium mimicking CF sputum in which bacteria often show auxotrophy. We showed that such a strain (designed here V for vaccine) was auxotrophic, less virulent, and had characteristics of CF-like strains. Crucially, V was able to induce both local (IgA) and systemic humoral responses as well as memory Th17 immune responses, and could, when administered intra-tracheally (but not intra-muscularly), fully protected mice against a lethal PAO1 infection. Overall, the present study demonstrates that our vaccine formulation, in addition to providing an advantageous auxotrophic phenotype adapted to the CF setting, was efficient, when given mucosally, in preferentially inducing secretory IgA and Th17 pathway at mucosal surfaces, a critical barrier that neutralizes pathogens before tissue invasion.

immunology↗

bioRxiv2025

Quantitative Prevalence, Chemical Speciation, and ECM-Embedded Networks of Biogenic Silicon in Animal Tissues Revealed by Refined Analytical Methods

Silicon, abundant in Earths biosphere, plays crucial roles in many organisms. However, its biological functions in animals remain inadequately characterized, primarily constrained by methodological limitations in quantifying, identifying, and visualizing biogenic silicon in tissues. Here, we refined the molybdenum blue colorimetric method (MB) for more accurate silicon quantification, developed integrated mass spectrometry techniques to delineate chemical natures of biogenic silicon, and optimized micro-X-ray fluorescence (micro-XRF) imaging for spatial mapping in animals. Employing these methodologies, we demonstrated that silicon abundance in animals substantially exceeds prior estimates--surpassing essential elements like iron. We also revealed the chemical architectures of diverse silicon compounds and visualized tissue-specific distributions in mice and humans, indicating its predominant localization within extracellular matrix (ECM) and suggesting the formation of silicified networks. These findings establish silicon as a structurally and quantitatively significant element with profound biological implications, warranting its recognition as an essential factor in human physiology and pathology.

biochemistry↗

bioRxiv2025

DANCE 2.0: Transforming single-cell analysis from black box to transparent workflow

Preprocessing is a critical step in single-cell data analysis, yet current practices remain largely a black-box, trial-and-error process driven by user intuition, legacy defaults, and ad hoc heuristics. The optimal combination of steps such as normalization, gene selection, and dimensionality reduction varies across tasks, model architectures, and dataset characteristics, hindering reproducibility and method development. We present DANCE 2.0, an automated and interpretable preprocessing platform featuring two key modules: the Method-Aware Preprocessing (MAP) module, which discovers optimal pipelines for task-specific methods via hierarchical search, and the Dataset-Aware Preprocessing (DAP) module, which recommends pipelines for new datasets via similarity-based matching to a reference atlas. Together, MAP and DAP execute over 325,000 pipeline searches across six major tasks - clustering, cell type annotation, imputation, joint embedding, spatial domain identification, and cell type deconvolution - yielding robust and generalizable recommendations. MAP-recommended pipelines consistently outperform original method defaults, with substantial gains across all tasks. Beyond automation, DANCE 2.0 reveals interpretable preprocessing patterns across tasks, methods, and datasets, transforming preprocessing into a transparent, data-driven process. All resources are openly available at https://github.com/OmicsML/dance to support broad community adoption and future methodological advances.

bioinformatics↗

bioRxiv2025

Identification of a neural basis for energy expenditure in the arcuate hypothalamus

Given the evolutionary instinct for caloric intake and the tendency for weight rebound after discontinuing dietary interventions or medications, increasing energy expenditure emerges as an alternative obesity treatment. However, neural regulation of energy expenditure remains poorly understood. Here, we report that a hypothalamic neuronal subtype, characterized by Crabp1 expression, establishes connections with multiple hypothalamic nuclei to regulate energy expenditure in mice. Inactivation of Crabp1 neurons reduces physical activity, body temperature, and adaptive thermogenesis, leading to an obese phenotype. Conversely, activation of these neurons increases energy expenditure and mitigates diet-induced obesity. Structural and functional analyses reveal that Crabp1 neurons promote energy metabolism through a "one-to-many" projection pattern. While Crabp1 neurons are rapidly activated by cold exposure and physical activity, prolonged light exposure abrogates their firing and may mediate light-induced metabolic disorder. Together, we reveal a neural basis that integrate various physiological and environmental stimuli to control energy expenditure and body weight.

neuroscience↗

bioRxiv2024

DDA-BERT: leveraging transformer architecture pre-training for data-dependent acquisition mass spectrometry-based proteomics

In data-dependent acquisition mass spectrometry (DDA-MS)-based proteomics, machine learning based rescoring methods are often employed to integrate multiple scores measuring quality of peptide-spectrum matches (PSMs) from different aspects. Existing rescoring tools face limitations incurred by manual feature extraction, shallow machine learning models, and limited training data. Here, we introduce DDA-BERT, a transformer-based end-to-end deep learning model trained with 95 million human spectra for PSM rescoring. DDA-BERT demonstrates superior performance across various sample types, mass spectrometer platforms, trace sample proteomics, and multiple species proteome data. It consistently outperforms state-of-the-art methods of its kind for DDA-MS spectra analysis with up to 103.5% increased protein group identifications. For single cell proteomics, DDA-BERT identifies up to 85.6% more protein groups compared to existing tools. In addition, DDA-BERT can be effectively extended to proteome of non-human species. DDA-BERT offers a robust and scalable solution for enhancing PSM rescoring in proteomics.

bioinformatics↗

bioRxiv2024

Horizontally acquired CSP genes contribute to wheat adaptation and improvement

Although horizontal gene transfer (HGT) often facilitates environmental adaptation of recipient organisms, whether and how they might affect crop evolution and domestication is unclear. Here we show that three genes encoding cold shock proteins (CSPs) were transferred from bacteria to the last common ancestor of Triticeae, a tribe of the grass family that includes several major staple crops such as wheat, barley, and rye. The acquired CSP genes in wheat (TaCSPs) are functionally conserved with their bacterial homologs by encoding a nucleic acid binding protein. Experimental evidence indicates that TaCSP genes positively regulate drought response and improve photosynthetic efficiency under water deficit conditions, by directly targeting a type 1 metallothionein gene to increase ROS scavenging, which in turn contributed to the geographic expansion of wheat. We identified an elite CSP haplotype in Aegilops-tauschii, introduction of which to wheat significantly increased drought tolerance, photosynthetic efficiency and grain yields. These findings not only provide major insights into the role of HGT in crop adaptation and domestication, but also demonstrate that novel microbial genes introduced through HGT offer a stable and naturally optimized resource for transgenic crop breeding and improvement.

plant biology↗

bioRxiv2024

Robotic Imaging and Machine Learning Analysis of Seed Germination: Dissecting the Influence of ABA and DOG1 on Germination Uniformity

Seed germination research has evolved over the years, increasingly incorporating technology. Recent advances in phenotyping platforms have increased the accessibility of high throughput phenotyping technologies to more labs, leading to valuable insights into germination biology. These platforms benefit researchers by limiting manual labor and increasing the temporal resolution of imaging. Each of the platforms developed presents unique benefits and challenges, from scalability to price to computing resources. Performing experiments involving thousands of seeds remains a daunting task due to the limitations of current phenotyping platforms and image analysis pipelines. To overcome these challenges, we introduce SPENCER (Seed Phenotype Evaluation and Germination Curve Estimation Robot), a high-throughput phenotyping platform. SPENCER accommodates 32 rectangular petri plates, capable of assessing up to 8000 Arabidopsis seeds per experiment. Our design allows for high quality images while maintaining optimal humidity, crucial for precise germination assessment over longer experiments. The image analysis workflow incorporates advanced image analysis using semantic segmentation models trained for Arabidopsis and lettuce, providing researchers with accessible, reproducible, and efficient tools. We applied SPENCER to investigate the relative roles of DELAY OF GERMINATION 1 (DOG1) and abscisic acid (ABA) in Arabidopsis dormancy. DOG1 mutants exhibited rapid germination, whereas ANT application had a greater impact on the slower-germinating Ler ecotype. Our findings suggest that DOG1 plays a significant role in dormancy, particularly in non-dormant accessions, while ABAs influence is more pronounced under stress conditions. Additionally, we explored germination uniformity, another agriculurally relevant trait, observing parallels with germination timing. SPENCER offers a powerful and accessible tool for dissecting complex biological traits in conjunction with chemical and genetic manipulations. Its scalability and versatility make it suitable for large-scale genetic and chemical germination screens.

plant biology↗

bioRxiv2024

Functional genomic screening in Komagataella phaffii enabled by high-activity CRISPR-Cas9 library

CRISPR-based high-throughput genome-wide loss-of-function screens are a valuable approach to functional genetics and strain engineering. The yeast Komagataella phaffii is a host of particular interest in the biopharmaceutical industry and as a metabolic engineering host for proteins and metabolites. Here, we design and validate a highly active 6-fold coverage genome-wide sgRNA library for this biotechnologically important yeast containing 30,848 active sgRNAs targeting over 99% of its coding sequences. Conducting fitness screens in the absence of functional non-homologous end joining (NHEJ), the dominant DNA repair mechanism in K. phaffii, provides a quantitative means to assess the activity of each sgRNA in the library. This approach allows for the experimental validation of each guides targeting activity, leading to more precise screening outcomes. We used this approach to conduct growth screens with glucose as the sole carbon source and identify essential genes. Comparative analysis of the called gene sets identified a core set of K. phaffii essential genes, many of which relate to protein production, secretion, and glycosylation. The high activity, genome-wide CRISPR library developed here enables functional genomic screening in K. phaffii, applied here to gene essentiality classification, and promises to enable other genetic screens. HighlightsO_LIDesigned and validated a high activity genome-wide CRISPR-Cas9 library for K. phaffii C_LIO_LIDisabling NHEJ DNA repair enables the generation of genome-wide guide activity profiles C_LIO_LIActivity-corrected fitness screens identify a high confidence set of essential genes in K. phaffii C_LIO_LIProtein production, secretion, and glycosylation pathways are essential in K. phaffii but not in other yeasts C_LI

systems biology↗

bioRxiv2023

metaExpertPro: a computational workflow for metaproteomics spectral library construction and data-independent acquisition mass spectrometry data analysis

BackgroundAnalysis of mass spectrometry-based metaproteomic data, in particular large-scale data-independent acquisition MS (DIA-MS) data, remains a computational challenge. Here, we aim to develop a software tool for efficiently constructing spectral libraries and analyzing extensive datasets of DIA-based metaproteomics. ResultsWe present a computational pipeline called metaExpertPro for metaproteomics data analysis. This pipeline encompasses spectral library generation using data-dependent acquisition MS (DDA-MS), protein identification and quantification using DIA-MS, functional and taxonomic annotation, as well as quantitative matrix generation for both microbiota and hosts. To enhance accessibility and ease of use, all modules and dependencies are encapsulated within a Docker container. By integrating FragPipe and DIA-NN, metaExpertPro offers compatibility with both Orbitrap-based and PASEF-based DDA and DIA data. To evaluate the depth and accuracy of identification and quantification, we conducted extensive assessments using human fecal samples and benchmark tests. Performance tests conducted on human fecal samples demonstrated that metaExpertPro quantified an average of 45,000 peptides in a 60-minute diaPASEF injection. Notably, metaExpertPro outperformed three existing software tools by characterizing a higher number of peptides and proteins. Importantly, metaExpertPro maintained a low factual False Discovery Rate (FDR) of less than 5% for protein groups across four benchmark tests. Applying a filter of five peptides per genus, metaExpertPro achieved relatively high accuracy (F-score = 0.67-0.90) in genus diversity and demonstrated a high correlation (rSpearman = 0.73-0.82) between the measured and true genus relative abundance in benchmark tests. Additionally, the quantitative results at the protein, taxonomy, and function levels exhibited high reproducibility and consistency across the commonly adopted public human gut microbial protein databases IGC and UHGP. In a metaproteomic analysis of dyslipidemia patients, metaExpertPro revealed characteristic alterations in microbial functions and potential interactions between the microbiota and the host. ConclusionsmetaExpertPro presents a robust one-stop computational solution for constructing metaproteomics spectral libraries, analyzing DIA-MS data, and annotating taxonomic as well as functional data.

microbiology↗

bioRxiv2022

Structural Insights into Branch Site Proofreading by Human Spliceosome

Selection of the pre-mRNA branch site (BS) by U2 snRNP is crucial to prespliceosome (A complex) assembly. The RNA helicase PRP5 proofreads BS selection; but the underlying mechanism remains unclear. Here we report the atomic structures of two sequential complexes leading to prespliceosome assembly: human 17S U2 snRNP and a cross-exon pre-A complex. PRP5 is anchored on 17S U2 snRNP mainly through occupation of the RNA path of SF3B1 by an acidic loop of PRP5; the helicase domain of PRP5 associates with U2 snRNA; the BS-interacting stem loop (BSL) of U2 snRNA is shielded by the splicing factor TAT-SF1, unable to engage the BS. In the pre-A complex, an initial U2/BS duplex is formed; the translocated helicase domain of PRP5 stays with U2 snRNA; the acidic loop still occupies the RNA path. The pre-A conformation is specifically stabilized by the splicing factors SF1, DNAJC8 and SF3A2. Cancer-derived mutations in SF3B1 damage its association with PRP5, compromising BS proofreading. Together, these findings reveal key insights into prespliceosome assembly and BS selection/proofreading by PRP5.

biochemistry↗