Biology subjects

Mann, M.

Publications and source records attributed to Mann, M..

At least 19 recordsLinked to original sources

bioRxiv2026

FastaLake selects compact metaproteomic databases from large sequence reservoirs by de novo peptide lookup

Metaproteomics analyses require protein sequence databases, yet the microbial proteins present in complex communities are not known in advance. Searching reference catalogs containing hundreds of millions of candidate sequences reduces identification sensitivity, while generating sample-specific databases through metagenomic sequencing adds time and cost. We introduce FastaLake, a computational workflow that uses full-length de novo peptide predictions by AlphaNovo to retrieve complete proteins from broad sequence resources and selects compact databases for conventional searching. In a defined eight-member community, FastaLake approached reference-search peptide yields without knowing its members. Using public sequence catalogs, FastaLake identified about as many peptides as matched metagenomic databases in two stool cohorts and 20-35% more in intestinal fluid at the same nominal 1% peptide false discovery rate. The resulting proteomes reveal personal functional profiles that persist across intestinal regions despite differences in leading species between intestinal fluid and stool. DNA, RNA and protein profiles agreed on broad differences between patients but only weakly on individual functions. All 726 acquisition-specific databases were smaller than the human reference proteome. Compact databases allowed non-specific searches of all 347 CapScan acquisitions, revealing histidine enrichment at peptide C termini in intestinal fluid. FastaLake enables metaproteomics analysis without matched metagenomic sequencing by constructing search databases from sequence resources and de novo peptide sequencing.

bioinformatics↗

bioRxiv2026

Intraindividual longitudinal changes of the proteome reveal dynamic markers of biological aging

Proteomic aging clocks have emerged as biomarkers of aging, but most rely on cross-sectional data. Here, we present a proteomic aging clock based on longitudinal measurements of liquid chromatography-mass spectrometry plasma proteomics at up to three time points in the population-based KORA cohort, comprising 9,105 samples from 4,100 individuals in the final dataset aged 25-88 years. The model predicts chronological age and yields a measure of proteomic age acceleration associated with lifestyle and clinical risk factors. Risk factor-stratified analyses reveal shared proteomic signatures among cardiometabolic exposures and distinct signatures for smoking and physical activity. Within individuals, dyslipidemia and impaired kidney function track transitions to increases in age acceleration, while higher age acceleration is associated with increased all-cause mortality. Mediation analysis suggests that risk factors are linked to mortality indirectly through age acceleration. Affected proteins and pathways highlight extracellular matrix remodeling, complement and coagulation pathways, and liver-derived plasma proteins as key contributors to aging. Mendelian randomization identifies proteins with evidence consistent with a causal role in aging-related traits. Together, these findings are consistent with biological aging being dynamically responsive to changes in modifiable risk factors.

bioinformatics↗

bioRxiv2026

Conserved mycobacteriophage protein uses structural mimicry to displace host initiation factors

Compact viral genomes encode sophisticated strategies to reprogram host gene expression, often by co-opting rather than replacing the host RNA polymerase. How bacteriophages achieve such control in Mycobacteria remains largely unknown. Here, time-resolved interaction proteomics of mycobacteriophage D29 infection identifies a conserved two-protein module, gp53-gp52, that binds the host RNA polymerase. Affinity purification confirmed association of both proteins with the polymerase, while native mass spectrometry demonstrated that gp53 displaces the host sigma factor. Structural modelling places gp53 at the sigma-binding surface of RNA polymerase, consistent with molecular mimicry of sigma-factor engagement. The module is conserved throughout Cluster A mycobacteriophages and occurs in fused or split genomic architectures. These findings suggest sigma-factor displacement as a mechanism of transcriptional takeover in viruses of mycobacteria and show how a compact viral module can repurpose an essential host molecular machine

microbiology↗

bioRxiv2026

A genome-scale ORF screen for resilience factors that protect the RPE and restore vision in aged mice

Aging is a complex phenomenon, yet therapeutic discovery has largely relied on testing individual hypothesis-driven targets. Here, we performed a genome-scale open reading frame (ORF) screen for oxidative-stress resistance in retinal pigment epithelium (RPE) cells, which are vulnerable to oxidative damage during aging. The screen identified nine protective ORFs including antioxidant factors and an epithelial-to-mesenchymal transition regulator. Notably, we also identified three factors that reduce transcriptomic age including the cardiac transcription factor NKX2-5. NKX2-5 conferred robust oxidative resilience in vitro and in vivo. Domain dissection revealed that its homeodomain is dispensable for resilience, enabling development of NKX2-5{Delta}HD, which retained protective activity without detectable toxicity over 14 months. In middle-aged mice, subretinal NKX2-5{Delta}HD restored visual and electrophysiological function, and its systemic delivery improved grip strength and reduced frailty in late-aged mice. These findings highlight the value of unbiased, genome-scale gain-of-function screening, as resilience factors normally expressed in other contexts can be reengineered as potential therapeutics for age-related decline in other tissues.

genetics↗

bioRxiv2026

AlphaPeptTools: scverse-native analysis of mass spectrometry-based proteomics

Mass spectrometry (MS)-based proteomics now routinely profiles proteomes at scale, but extracting biological insight and integrating complementary modalities remains challenging. Here, we present AlphaPeptTools, a Python package for the analysis of MS-proteomics data built on the AnnData data structure of the scverse. AlphaPeptTools implements scalable and modular strategies for MS-data ingestion, quality control, preprocessing, and statistical analysis while seamlessly integrating with the scverse ecosystem, unlocking multi-level, spatial, and multimodal analyses.

bioinformatics↗

bioRxiv2026

In the same cell, the proteome defines cellular state and the transcriptome marks transitions

Bulk transcriptome and proteome correlate only modestly, but this has not been investigated in the same cell or across cell-state changes. Here we introduce a scalable technology that quantifies thousands of proteins and transcripts in the same cell, separating RNA from protein by tip-based C18 capture and pairing full-length RNA sequencing with latest-generation mass spectrometry. In HeLa cells, transcript and protein abundances agree on the broad ranking within a cell (r = 0.45), but do not co-vary across the population (r = 0.038). In pluripotency transitions, only a third of matched transcripts and proteins change synchronously, yet the transcription factors defining each state stay tightly co-regulated. Transcript variance is several-fold larger than protein variance, reflecting transcriptional bursting and mRNA sampling noise. The proteome is thus the stable, low-noise definition of cell state, while the transcriptome marks cellular transitions; consequently, the proteome defines cell-state from far fewer cells.

systems biology↗

bioRxiv2026

The diverse functions of viral microproteins

A large fraction of the uncharacterized proteome consists of microproteins. Viruses, rich in microproteins with potent activities, provide a unique system for exploring their functions and evolution. Here, we systematically annotate viral microproteins through a phenotype-first approach: construction of a >50,000-element small ORF library spanning the microprotein-coding potential of human-infecting viruses, followed by broad screens that enrich candidates for high-resolution profiling by Perturb-seq, mass spectrometry, AI-guided structure prediction, and mechanistic studies. This atlas identified >2,000 active microproteins that modulate host pathways through diverse mechanisms, including molecular mimicry, hijacking of key regulators, and altering subcellular localization. We further show the oncogenic potential of BNLF2b, an uncharacterized Epstein-Barr virus microprotein linked to nasopharyngeal carcinoma. Three principles emerge: viral microproteins are diverse and often multifunctional, show widespread convergence toward shared functions, and serve as reservoirs of evolutionary innovation. These findings elucidate how viral microproteins occupy a large functional landscape, establish them as versatile tools for host manipulation, and provide a framework for functional annotation of uncharacterized proteins, contributing toward predictive systems virology.

systems biology↗

bioRxiv2026

Deep Visual Proteomics links vascular smooth muscle cell phenotypes to atherosclerotic plaque stability

Vascular smooth muscle cells (VSMCs) drive atherosclerosis through phenotypic switching, yet their spatial organization and protein signatures within plaques remain poorly characterized. Here, we applied Deep Visual Proteomics (DVP) to dissect more than 500 VSMC neighborhoods across 24 human carotid plaques and profile VSMC plasticity in disease. To functionally interpret these tissue proteomes, we built a reference atlas of primary VSMCs driven toward five phenotypes by TGF-{beta}, PDGF-BB, osteogenic stimuli, IL-1{beta}, or cholesterol, quantifying over 10,000 proteins. We integrated tissue and reference proteomes with a deep-learning framework that assigns functional phenotypes to each neighborhood. This revealed spatially distinct phenotype distributions and a shift toward dedifferentiated states in unstable plaques. Knockdown of four candidates (TNC, TNFAIP2, AEBP1, PLK1) validated operational roles during phenotypic switching. Our approach functionally annotates spatial proteomes and links VSMC plasticity to plaque instability in carotid artery disease.

systems biology↗

bioRxiv2026

Machine learning-guided discovery of a conserved plasmid proteomic signature enables MALDI-TOF MS detection of pOXA-48-carrying Enterobacterales

OXA-48 carbapenemases are among the most widespread and important resistance mechanisms in Enterobacterales. Yet detecting carbapenemases by conventional workflows necessitates additional testing, thus delaying optimization of therapy and implementation of infection control measures. Here, we present a machine learning approach that identifies the conserved pOXA-48 plasmid directly from routine MALDI-TOF spectra acquired for species identification. The model detects pOXA-48 carriers with an AUROC of 0.96-0.98 across two independent hospital cohorts and instrument platforms, indicating near-perfect discrimination. Using bottom-up proteomics, plasmid conjugation, and plasmid curing, we link the discriminative MALDI-TOF spectral features to proteins encoded on pOXA-48, with DUF1496 domain-containing protein producing the most discriminative spectral feature. Our approach reframes the resistance prediction task from inferring a resistance phenotype to detecting a conserved plasmid through its expressed proteomic signature and has the potential to enable rapid MALDI-TOF MS-based diagnostics for a wide range of plasmid-based resistance determinants.

microbiology↗

bioRxiv2026

Cell-type-resolved spatial proteogenomics from matched genome and proteome of the same cells

The genome and proteome of the same cells are rarely measured together, which is especially consequential in cancer, where somatic mutations vary across clones and drive disease. We show that a single standard proteomics extraction tip can retain peptides on-tip after digestion while genomic DNA passes into the normally discarded flowthrough. Combined with Deep Visual Proteomics, flowthrough co-isolation enables cell-type-resolved spatial proteogenomics from archival FFPE tissue, demonstrated in melanoma.

biochemistry↗

bioRxiv2026

In situ polymerized monolith tips for reproducible, format-flexible proteomic sample preparation applied to biofluids

Sample preparation increasingly sets the throughput and reproducibility of mass spectrometry (MS)-based proteomics. StageTips (stop-and-go extraction tips) and variants thereof have long been common implements to purify samples, and we recently extended the concept to solid-phase extraction capture (SPEC) tips, in which the entire digestion takes place in sub-microliter volumes. Here we replace the hand-packed bed with a strong anion-exchange (SAX) monolith photopolymerized directly inside the pipette tip from a defined recipe (pSPEC). A liquid-handling robot casts 384 tunable tips in minutes, at low cost and in any format, adding negligibly to the workflows variance. Across biofluids, pSPEC added [~]20% more identifications than in-solution plasma and reached 3,500 protein groups from a single injection of healthy urine and 4,800 from saliva at 100 samples per day, depths usually requiring depletion or fractionation. The same light-cast chemistry should extend to single cells, affinity capture, and population-scale studies.

biochemistry↗

bioRxiv2026

HIP-MS: An ultra-high-throughput, sensitive, and versatile affinity enrichment platform for static and dynamic interactome profiling

Although protein-protein interactions govern virtually all cellular processes, systematic interactome mapping by affinity enrichment mass spectrometry (AE-MS) is constrained by manual sample preparation and lengthy liquid chromatography (LC)-MS/MS acquisition. Here we present High-throughput Interactome Profiling by MS (HIP-MS), an automated, end-to-end pipeline that overcomes these limitations. It leverages the compact, high-affinity ALFA tag for on-plate nanobody capture in 384-well format, combined with on-plate tryptic digestion. It can process almost 10,000 samples per week from protein expression up to MS measurement and can be combined with ultra-fast gradient LC-MS acquisition of 500 samples per day. HIP-MS remains sensitive down to low-microgram lysate inputs, a 4,000-fold reduction compared to recent large-scale screens. Our pipeline recovers complexes from diverse cellular compartments and resolves endogenous membrane receptor signaling. HIP-MS establishes a scalable foundation for systematic interrogation of protein interactions across conditions, perturbations, and time, and for the generation of large-scale datasets for computational modeling.

systems biology↗

bioRxiv2026

A cell type-resolved proteomic atlas of the human body

Proteins define what cells do, yet their systematic quantification across human cell types has remained out of reach. Using Deep Visual Proteomics on tissue from a healthy female donor, we built an atlas of 27 cell types across 14 tissues, quantifying two-thirds of all human protein-coding genes, with up to 8,500 per population. The proteome partitions bimodally into a universal core and highly specialized programs. Integration into the Human Protein Atlas Single Cell Resource enabled comparison of RNA and protein abundance at cell type resolution, revealing that concordance depends on pathway rather than cellular identity. This resolution uncovered cancer-testis antigens in oocytes invisible to bulk profiling. Our openly accessible resource provides a foundation for cell type-resolved proteomics in health and disease.

biochemistry↗

bioRxiv2026

Reprogramming insulin receptor activation with a de novo agonist to overcome severe insulin resistance

Computational protein engineering provides a powerful approach to address longstanding clinical challenges. Severe insulin resistance syndromes caused by mutations in the insulin receptor (IR) are life-threatening disorders for which effective long-term therapies remain lacking. Here, we define the in vivo activity and therapeutic potential of RF-409, a de novo-designed IR agonist that activates the receptor through a mechanism distinct from insulin. RF-409 exhibits markedly prolonged circulation compared to insulin and produces sustained improvements in glucose homeostasis without detectable adverse effects on body composition or liver function. In a patient-derived IR D707A mouse model of severe insulin resistance, RF-409--but not insulin--activates the mutant receptor, restoring glucose regulation and ameliorating hyperglycemia, hyperinsulinemia, lipoatrophy, and pancreatic atrophy. Mechanistically, RF-409 engages the IR through a noncanonical binding geometry while stabilizing an active conformation resembling that induced by insulin. Phosphoproteomic profiling shows that RF-409 elicits broadly insulin-like signaling with distinct temporal features in receptor-proximal regulation. Together, these findings establish a framework for reactivating dysfunctional receptors and suggest broader applications beyond rare receptoropathies, including diabetes and liver disease.

pharmacology and toxicology↗

bioRxiv2026

Scanning DIA on the ZenoTOF 8600 system enables ultra-sensitive and quantitative proteomics from single cells to post-translational modifications in a compact platform

Mass spectrometry-based proteomics increasingly demands platforms that combine quantitative rigor with the discovery capabilities of accurate mass systems. Here we present the ZenoTOF 8600 system, a compact mass spectrometry system that integrates enhanced ion capture and transmission optics with an optical detection system, Zeno trap-enhanced MS/MS, electron-activated dissociation, and scanning quadrupole data-independent acquisition (ZT Scan DIA). We show that ZT Scan DIA outperforms conventional variable-window DIA (Zeno SWATH DIA) in both identifications and quantitative reproducibility, and demonstrate the platforms versatility across proteomics applications: thousands of protein groups from bulk samples at up to 500 samples per day, single-cell proteomics yielding up to 4,700 proteins, accurate ratio recovery in mixed-species quantitative benchmarks, low-attomole targeted quantitation, and detection of disease-relevant phosphorylation in a Parkinsons disease cellular model using complementary CID and EAD fragmentation. The instruments compact footprint makes it attractive for settings where both analytical breadth and operational robustness are required.

biophysics↗

bioRxiv2026

NLRP3 activators disrupt the endocytic AP2 complex and plasma membrane signaling

Organellar perturbations are linked to NLRP3 inflammasome activation, however, it remains unclear whether unrelated agonists converge on a common upstream pathway. Here, we traced intracellular organelle and protein movements by differential ultracentrifugation combined with mass spectrometry-based proteomics. We show that NLRP3 activators uniformly disrupt the endocytic Adaptor Protein 2 (AP2) complex, whereas other subcellular rearrangements are stimulus-specific. We discovered Dynasore as a K-efflux-independent NLRP3 activator that engages this signaling node irrespective of endocytosis inhibition. Pharmacological and genetic perturbation of AP2 renders cells unresponsive to extracellular cues, blunting GPCR signaling, cAMP production, and chemotaxis, thereby enforcing a frozen signaling state that propagates NLRP3 inflammasome activation. Collectively, our study reveals a common surveillance checkpoint linking impaired plasma membrane signaling to the execution of inflammation and cell death. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/707400v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@178eb1dorg.highwire.dtl.DTLVardef@193cb02org.highwire.dtl.DTLVardef@1f50080org.highwire.dtl.DTLVardef@1f3e3e4_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2026

Multi-platform plasma proteomics reveals orthogonal metabolic signatures distinguishing elite athletic phenotypes

Exercise confers profound health benefits, yet the molecular mechanisms linking physical activity to health and longevity are incompletely known. Here we applied three mass spectrometry (MS)-based and one aptamer-based proteomics workflows to elite athletes with contrasting metabolic phenotypes, sampled before and after maximal exhaustive exercise. MS detected larger effect sizes and resolved isoforms; aptamers extended proteome coverage but with unannotated proteoform biases. Acute exercise induced coordinated platelet degranulation, neutrophil activation, and extracellular matrix turnover, with peptide topology analysis providing direct evidence for vesicular release. Chronic adaptations organized along two orthogonal axes: a muscle mass gradient marked by hypertrophy signaling and attenuated systemic inflammation, and an oxidative capacity gradient characterized by metabolic health-associated proteins (APOA4, IGFBP2, ITLN1) and dampened IGF-I signaling. Exploratory biological age analysis suggested younger adipose age in athletes. The plasma proteome provides an integrated readout of exercise adaptation, linking cardiorespiratory fitness to metabolic health and healthy aging.

biochemistry↗

bioRxiv2026

Deep Learning-Driven Fragment Ion Selection for Improved Quantification in MS based Proteomics

Quantitative proteomics relies on accurate selection of fragment ions for quantification, yet most current algorithms apply simple strategies such as median intensity or single quality filters. Modern data-independent acquisition (DIA) searches generate rich features such as fragment ion correlations, retention time and many others that could be leveraged to assess fragment quality. We introduce QuantSelect, a novel strategy to select optimal fragments by systematically integrating these features via self-supervised deep learning. QuantSelect uses a regularized, weighted-variance loss on intensity traces normalized via our directLFQ algorithm. This allows learning a fragment quality score without ground truth labels, enabling on-the-fly training on label-free DIA datasets. Integrated within our alphaDIA pipeline, QuantSelect significantly improves quantitative accuracy and in some cases substantially corrects protein intensity estimation. Sensitivity in differential expression improved by 68% in a mixed-species benchmarking dataset and by 18% in single-cell data. QuantSelect provides a practical framework for data-driven fragment selection that improves accuracy, precision and downstream inference in DIA proteomics.

bioinformatics↗