Biology subjects

Gharibi, H.

Publications and source records attributed to Gharibi, H..

At least 19 recordsLinked to original sources

bioRxiv2026

Multi-omics uncovers interaction in the vaginal microbiome and a type II secretion/Tad pilus system in Gardnerella vaginalis

The vaginal microbiome is a critical determinant of womens health. We investigated the genetic basis of common vaginal microbiome species and their biofilm formation. Genomic analysis of Gardnerella vaginalis (Gv) revealed a fundamental phylogenetic split correlating with high- versus low-biofilm phenotypes, driven by clade-specific genomic islands and allelic variants. In a dual-species coculture model of five key vaginal bacteria, Gv achieved numerical dominance, triggering extensive, asymmetric proteomic reprogramming in partner species while showing limited shifts itself. Proteins from biofilm-associated modules showed functional divergence, supported by AI-predicted structural variations in a type II secretion/Tad pilus system, which is first discovered from Gv strains. Integrated metabolomics identified a methyl-{beta}-carboline compound that is elevated in cocultures containing Prevotella bivia (Pb). This compound acts as a potent and selective inhibitor of Gv and Pb biofilms, sparing Lactobacillus crispatus. This work establishes a direct genomic basis for Gv virulence and demonstrates how interspecies interactions govern community dynamics and antimicrobial metabolite production. HighlightsO_LIComprehensive genomic resource comparing with high-quality long-read whole genomes and reference Gardnerella vaginalis and Lactobacillus iners strains. C_LIO_LIIntegrated multi-omics and functional analysis on the most common vaginal microbiome species using 16S rRNA gene sequencing, proteomics, metabolomics, and in vitro assays. C_LIO_LIKey phenotypes quantified, including biofilm formation and polymicrobial interactions. C_LIO_LIConserved Type II Secretion/Tad Pilus System identified across all Gardnerella vaginalis strains, with AI-predicted structural modeling. C_LIO_LIEvaluation of growth inhibition using metabolites against a panel of relevant microbes, including vaginal microbes and opportunistic pathogens. C_LI

microbiology↗

bioRxiv2026

Transcriptomic-guided compound prioritization and proteomics validation for HNRNPU deficiency identify signalling correction

Heterogeneous nuclear ribonucleoprotein U (HNRNPU) deficiency is a rare genetic cause of neurodevelopmental disorders (NDDs) lacking targeted therapies. Here, we developed a transcriptomic-guided compound prioritization pipeline using Connectivity Map (CMap) analysis on multi-model transcriptomic signatures from HNRNPU-deficient human cells and mouse models. Ten compounds were selected through manual curation and functionally screened in patient-derived HNRNPU-deficient neuroepithelial stem (NES) cells with earlier observed cellular phenotypes. Two of the compounds, AS601245 and Lenalidomide, significantly reduced the elevated neural progenitor population during differentiation, and their combination further decreased primary cilia incidence, indicating partial rescue of the patient-specific cellular phenotypes. To understand the mechanisms underlying the partial rescue, we employed proteome integral solubility alteration (PISA) and expression proteomics. PISA assay identified TMEM150C and GSK3A as proximal targets of combined treatment. Additionally, we observed reversal of multiple biological pathways including downregulation of Wnt signalling and upregulation of mitochondrial pathways and transmembrane proteins. Altogether, we established a computational-experimental pipeline for transcriptomic-guided drug repurposing for a monogenic NDD, and demonstrated that the network-level modulation partially rescues the delayed neural differentiation in HNRNPU-deficient neural cells.

molecular biology↗

bioRxiv2026

Peptide-to-protein data aggregation using Fisher's method improves target identification in chemical proteomics

Protein-level statistical tests in proteomics aimed at obtaining p-value are conventionally made on protein abundances aggregated from peptide data. This integral approach overlooks peptide-level heterogeneity and ignores important information coded in individual peptide data, while protein p-value can also be obtained by Fishers method of combining peptide p-values using chi-square statistics. Here we test this latter approach across diverse chemical proteomics datasets based on assessments of protein expression, solubility and protease accessibility. Using the top four peptides ranked by their p-values consistently outperformed protein-level analysis and avoided biases introduced by inclusion of deviant peptides or imputation of missing peptide values. Fishers method provides a simple and robust strategy, improving identification of regulated/shifted proteins in diverse proteomics assays.

bioinformatics↗

bioRxiv2026

Low-nanogram Fourier Transform Isotopic Ratio Mass Spectrometry of Proteins

Stable carbon and nitrogen isotope ratios are widely used in the life sciences to investigate diet, trophic interactions, and metabolic fluxes, but conventional isotope ratio mass spectrometry requires milligram-scale samples, limiting its applicability to small or rare biological specimens. Fourier Transform Isotopic Ratio Mass Spectrometry (FT IsoR MS) enables amino acid-resolved isotope analysis in a proteomics-compatible workflow and has previously been demonstrated at the microgram scale. Here, we assess the lower sample limit of FT IsoR MS by integrating it with single-cell proteomics-style sample preparation. Using human HeLa cells cultured in 13C-glucose-enriched and control media, we show that reliable relative {delta}13C measurements can be obtained from as few as 50 cells, corresponding to <10 ng of total protein, with a precision of approximately {+/-}9{per thousand}. The observed amino acid-specific labeling patterns are metabolically coherent and consistent with bulk measurements, while smaller cell numbers ([≤]10 cells) do not yield statistically robust results. These findings establish the practical sensitivity threshold of FT IsoR MS at the low-nanogram level and demonstrate its suitability for isotope-resolved analyses of small cell populations, micro-organoids, and other low-input biological samples, thereby extending stable isotope analysis toward single-cell-scale applications.

biochemistry↗

bioRxiv2025

High-ratiO partiaL proteolysiS with carriER proteome (HOLSER) Enables Global Structure Profiling and Site-resolved Elucidation of Ligand-Protein Interactions

Understanding how cellular proteins interact with their environment, including endogenous and exogeneous molecules, is critical for elucidating mechanisms of cellular regulation and drug action. Partial proteolysis-based techniques offer peptide-level resolution of ligand-induced conformational changes but are limited by modest proteome coverage and depth, as well as sensitivity to the experimental conditions. To overcome these limitations, we developed higH ratiO partiaL proteolysiS with carriER proteome (HOLSER), an efficient workflow that features extended digestion time for reduced peptide yield variability as well as tandem mass tag multiplexing that includes full digests for enhanced proteome depth and sequence coverage as well as higher precision of peptide abundance measurements. We demonstrate HOLSER capabilities of probing structural changes on the scale of specific binding sites for kinase target mapping, individual protein domains for structural mapping of the FKBP-mTOR complex in response to rapamycin as well as global proteome structure profiling.

biochemistry↗

bioRxiv2025

Towards a species search engine: KISSE offers a rigorous statistical framework for bone collagen tandem mass spectrometry data

DNA and bone collagen are two key sources of resilient molecular markers used to identify species from their remains. Collagen is more stable than DNA, and thus it is preferred for ancient and degraded samples. Current mass spectrometry-based collagen sequencing approaches are empirical and lack a rigorous statistical framework. Based on the well-developed approaches to protein identification in shotgun proteomics, we introduce a first approximation of the species search engine (SSE). Our SSE named KISSE is based on a species-specific library of collagenous peptides that uses both peptide sequences and their relative abundances. The developed statistical model can identify the species and the probability of correct identification, as well as determine the likelihood of the analyzed species not being in the library. We discuss the advantages and limitations of the proposed approach and the possibility of extending it to other tissues.

zoology↗

bioRxiv2025

Above-Filter Digestion Proteomics reveals drug targets and localizes ligand binding site

While a number of efficient chemical proteomics methods are available for determining protein targets of pharmaceutical drugs, each approach exhibits its own "blind spot" and development of complementary techniques is needed. Here, we introduce the Above-Filter Digestion Proteomics (AFDIP) approach based on the monitoring of the rate of trypsin digestion. Digestion rate is decreased at the site of ligand binding to its target protein, while other sites may simultaneously experience an increase the digestion rate. Molecular dynamics (MD) simulations revealed that this increase may be due to allosteric structural changes related to backbone flexibility. We showcase the utility of AFDIP for deconvoluting the targets of versatile drugs and metabolites. Like other techniques, AFDIP allows for a two-dimensional analysis, with the second dimension based on drug concentration. AFDIP exhibits structural resolution, identifying drug target binding sites within [≤]10 [A], and for larger proteins often [≤]5 [A] of the crystallography-determined binding sites. Compared to earlier introduced limited proteolysis approaches, AFDIP provides easier sample preparation, deeper proteome analysis and broader sequence coverage. AFDIP is expected to find its place among the most efficient chemical proteomics methods currently available.

biochemistry↗

bioRxiv2024

ThermoTargetMiner as a proteome integral solubility alteration target database for prospective drugs against lung cancer

Knowledge of the targets of therapeutic compounds is vital for understanding their action mechanisms and side effects, but such valuable data is seldom available. The multiple complementary techniques needed for comprehensive target characterization must combine data reliability with sufficient analysis throughput. Here, we leveraged the Proteome Integral Solubility Alteration (PISA) assay to characterize the targets of 67 approved and experimental compounds against two main lung cancer subtypes, which is now provided through the website https://thermotargetminer.serve.scilifelab.se/app/thermotargetminer. Novel target candidates (pro-targets) were found for 77% of the tested molecules. Comparison of the protein solubility shifts in lysate vs. living cells highlighted the targets directly interacting with the compounds. We verified that the drug PEITC exerts cytotoxicity through the inhibition of a pro-target PAFAH1B. As PISA is now joining the arsenal of fast and reliable target characterization techniques, the presented database, ThermoTargetMiner, will become a useful resource in lung cancer research.

biochemistry↗

bioRxiv2024

Standardizing protein corona characterization in nanomedicine: a multi-center study to enhance reproducibility and data homogeneity

Our recent findings reveal substantial variability in the characterization of identical protein corona across different proteomics facilities, demonstrating that protein corona datasets are not easily comparable between independent studies. We have shown that heterogeneity in the final composition of the identical protein corona mainly originates from variations in sample preparation protocols, liquid chromatography mass spectrometry (LC-MS) workflows, and raw data processing. Here, to address this issue, we developed standardized protocols and unified sample preparation workflows, and distributed identical protein corona digests to several proteomics centers that performed better in our previous study. Additionally, we examined the influence of using similar mass spectrometry instruments on data homogeneity. Furthermore, we evaluated whether standardizing database search parameters and data processing workflows could enhance data uniformity. More specifically, our new findings reveal a remarkable, stepwise improvement in protein corona data consistency across various proteomics facilities. Streamlining the whole workflow results in a dramatic increase in protein ID overlaps from 11% for good centers to 40% across core facilities that utilized similar instruments and were subjected to a uniform database search. This comprehensive analysis identifies key factors contributing to data heterogeneity in mass spectrometry-based proteomics of protein corona and plasma-related samples. By streamlining these processes, our findings significantly advance the potential for consistent and reliable nanomedicine-based diagnostics and therapeutics across different studies.

bioengineering↗

bioRxiv2024

Deep Plasma Proteome Profiling by Modulating Single Nanoparticle Protein Corona with Small Molecules

The protein corona, a dynamic biomolecular layer that forms on nanoparticle (NP) surfaces upon exposure to biological fluids is emerging as a valuable diagnostic tool for improving plasma proteome coverage analyzed by liquid chromatography-mass spectrometry (LC-MS/MS). Here, we show that spiking small molecules, including metabolites, lipids, vitamins, and nutrients (namely, glucose, triglyceride, diglycerol, phosphatidylcholine, phosphatidylethanolamine, L--phosphatidylinositol, inosine 5'-monophosphate, and B complex), into plasma can induce diverse protein corona patterns on otherwise identical NPs, significantly enhancing the depth of plasma proteome profiling. The protein coronas on polystyrene NPs when exposed to plasma treated with an array of small molecules (n=10) allowed for detection of 1793 proteins marking an 8.25-fold increase in the number of quantified proteins compared to plasma alone (218 proteins) and a 2.63-fold increase relative to the untreated protein corona (681 proteins). Furthermore, we discovered that adding 1000 {micro}g/ml phosphatidylcholine could singularly enable the detection of 897 proteins. At this specific concentration, phosphatidylcholine selectively depleted the four most abundant plasma proteins, including albumin, thus reducing the dynamic range of plasma proteome and enabling the detection of proteins with lower abundance. By employing an optimized data-independent acquisition (DIA) approach, the inclusion of phosphatidylcholine led to the detection of 1436 proteins in a single plasma sample. Our molecular dynamic results revealed that phosphatidylcholine interacts with albumin via hydrophobic interactions, h-bonds, and water-bridges. Addition of phosphatidylcholine also enabled the detection of 337 additional proteoforms compared to untreated protein corona using a top-down proteomics approach. These significant achievements are made utilizing only a single NP type and one small molecule to analyze a single plasma sample, setting a new standard in plasma proteome profiling. Given the critical role of plasma proteomics in biomarker discovery and disease monitoring, we anticipate widespread adoption of this methodology for identification and clinical translation of proteomic biomarkers into FDA approved diagnostics.

bioengineering↗

bioRxiv2024

Gel-Assisted Proteome Position Integral Shift (GAPPIS) Assay Returns Molecular Weight to Shotgun Proteomics and Identifies Novel Caspase 3 Substrates

Here we present a high-throughput virtual top-down proteomics approach that restores the molecular weight (MW) information in shotgun proteomics, and demonstrate its utility in studying proteolytic events in programmed cell death. With Gel-Assisted Proteome Position Integral Shift (GAPPIS), we quantified over 7000 proteins in staurosporine-induced apoptotic HeLa cells and identified 84 proteins exhibiting in a statistically significant manner at least two of the following features: 1) a negative MW shift; 2) an elevated ratio in a pair of a semi-tryptic and tryptic peptide, 3) a negative shift in the standard deviation of MW estimated for different peptides, and 4) a negative shift in skewness of the same data. Of these proteins, 58 molecules were novel caspase 3 substrates. Further analysis identified the preferred cleavage sites consistent with the known caspase cleavages after the DXXD motif. As a powerful tool for high-throughput MW analysis simultaneously with the conventional expression analysis, GAPPIS assay can prove useful in studying a broad range of biological processes involving proteolytic events.

biochemistry↗

bioRxiv2024

Do light eaters live shorter lives? The case of ultralight Caenorhabditis elegans

The idea that ingesting heavy stable isotopes can increase longevity emerged shortly after the discovery of deuterium in the early 1930s and has been extensively tested since then on animals. Here we present the first experimental evidence for the opposite. Growing C. elegans on bacteria E. coli that are in turn fed on a diet depleted of heavy isotopes of C, H, N and O produced ultralight worms that grow and mature faster but have a shorter lifespan. Based on the differences in expression and solubility of proteins, we established an aging pseudo-time scale. Notably, the newly born ultralight worms appear to be significantly "younger" than their normal counterparts, while at day 10 they are significantly "older". Pathway analysis revealed involvement of mitochondria; analysis of reactive oxygen species (ROS) confirmed significant ROS overproduction in ultralight worms that increases further with age. These findings provide a new modality of affecting the lifespan in this important animal model of human diseases and aging.

biochemistry↗

bioRxiv2023

Multifaceted proteome analysis at solubility, redox, and expression dimensions for target identification

Multifaceted interrogation of the proteome deepens the system-wide understanding of biological systems; however, mapping the redox changes in the proteome has so far been significantly more challenging than expression and solubility/stability analyses. Here, we devise the first high- throughput redox proteomics approach integrated with expression analysis (REX) and combine it with Proteome Integral Solubility Alteration (PISA) assay. The whole PISA-REX experiment with up to four biological replicates can be multiplexed into a single tandem mass tag TMTpro set. For benchmarking this compact tool, we analyzed HCT116 cells treated with auranofin, showing great improvement compared with previous such studies. Then we applied PISA-REX to study proteome remodeling upon stimulation of human monocytes by interferon . We also studied the proteome changes in plasmacytoid dendritic cells isolated from wild type vs. Ncf1- mutant mice treated with interferon , showing that NCF1 deficiency enhances the STAT1 pathway and modulates the expression, solubility and redox state of interferon-induced proteins. Providing comprehensive multifaceted information on the proteome, the compact PISA-REX has the potential to become an industry standard in proteomics and to open new windows into the biology of health and disease.

biochemistry↗

bioRxiv2023

DOPA residues endow collagen with radical scavenging capacity

Here we uncover collagen, the main structural protein of all connective tissues, as a redox-active material. We identify dihydroxyphenylalanine (DOPA) residues, post-translational oxidation products of tyrosine residues, to be common in collagen derived from different connective tissues. We observe that these DOPA residues endow collagen with substantial radical scavenging capacity. When reducing radicals, DOPA residues work as redox relay: they convert to the quinone and generate hydrogen peroxide. In this dual function, DOPA outcompetes its amino acid precursors and ascorbic acid. Our results establish DOPA residues as redox-active side chains of collagens, probably protecting connective tissues against radicals formed under mechanical stress and/or inflammation.

biophysics↗

bioRxiv2023

Massive solubility changes of neuronal proteins upon simulated traumatic brain injury reveal the role of shockwave in irreversible damage

The immediate molecular consequences of traumatic brain injuries or TBI are poorly understood. Here, we simulated TBI using an innovative laboratory apparatus that employs a 5.1 kg dummy head holding neuronal cells and generating a [≤]4,000 g-force acceleration upon impact. Dynamic impact led to both reduction in neuron viability and massive solubility changes in the proteome profiled using Proteome Integral Solubility Alteration (PISA) assay. The affected proteins mapped not only to the expected pathways like cell adhesion, collagen and laminin structures, as well as response to stress, but also to other dense protein networks, such as immune response, complement and coagulation cascades. The cellular effects are found to be mainly due to the shockwave rather than the g-force acceleration. Soft materials could reduce the impact severity only until being fully compressed. This study shows way to develop a proteome-based meter for measuring irreversible shockwave-induced cell damage and provides a resource for identifying TBI protein biomarkers and potential drug targets for developing products aiming at primary prevention and intervention.

neuroscience↗

bioRxiv2022

Multi-Omics Analysis of Magnetically Levitated Plasma Biomolecules

We recently discovered that superparamagnetic iron oxide nanoparticles (SPIONs) can levitate plasma biomolecules in the magnetic levitation (MagLev) system and cause formation of ellipsoidal biomolecular bands. To better understand the composition of the levitated biomolecules in various bands, we comprehensively characterized them by multi-omics analyses. To probe whether the biomolecular composition of the levitated ellipsoidal bands correlates with the health of plasma donors, we used plasma from individuals who had various types of multiple sclerosis (MS), as a model disease with significant clinical importance. Our findings reveal that, while the composition of proteins does not show much variability, there are significant differences in the lipidome and metabolome profiles of each magnetically levitated ellipsoidal band. By comparing the lipidome and metabolome compositions of various plasma samples, we found that the levitated biomolecular ellipsoidal bands do contain information on the health status of the plasma donors. More specifically, we demonstrate that there are particular lipids and metabolites in various layers of each specific plasma pattern that significantly contribute to the discrimination of different MS subtypes, i.e., relapsing-remitting MS (RRMS), secondary-progressive MS (SPMS), and primary-progressive MS (PPMS). These findings will pave the way for utilization of MagLev of biomolecules in biomarker discovery and diagnosis of this and other complex disorders.

bioengineering↗

bioRxiv2022

Mapping the GALNT1 substrate landscape with versatile proteomics tools

O-GalNAc type glycosylation is a common post-translational modification (PTM) of proteins catalyzed by polypeptide GalNAc transferases, but the substrate specificity of these transferases is poorly understood. Here we develop a strategy based on integral thermal proteome solubility profiling to identify and prioritize the protein substrates of polypeptide N-acetylgalactosaminyltransferase 1 (GALNT1). Combined with glycoprotein enrichment followed by HCD and soft EThcD gas-phase fragmentation technique, we uncover hundreds of novel GALNT1 substrates in two model human cell lines. GALNT1-mediated O-glycosylation is more common on Thr than Ser residues, with a strong preference for Pro at positions +3 and +4 in respect to O-glycosylation. These results implicate GALNT1 in potentially regulating proteins in several diverse pathways, including some unexpected processes, such as TCA cycle and DNA transcription. This study depicts a roadmap for identification of functional substrates for glycosyltransferases, facilitating fundamental insight into the role of glycosylation in homeostasis and disease.

biochemistry↗

bioRxiv2021

Redox regulation of PTPN22 affects the severity of T cell dependent autoimmune inflammation

Chronic autoimmune diseases are associated with mutations in PTPN22, a modifier of T cell receptor signaling. As with all protein tyrosine phosphatases the activity of PTPN22 is redox regulated, but if or how such regulation can modulate inflammatory pathways in vivo is not known. To determine this, we created a mouse with a cysteine-to-serine mutation at position 129 in PTPN22 (C129S), a residue proposed to alter the redox regulatory properties of PTPN22 by forming a disulfide with the catalytic C227 residue. The C129S mutant mouse showed a stronger T cell-dependent inflammatory response and development of T cell dependent autoimmune arthritis due to enhanced TCR signaling and activation of T cells, an effect neutralized by a mutation in Ncf1, a component of the NOX2 complex. Activity assays with purified proteins suggest that the functional results can be explained by an increased sensitivity to oxidation of the C129S mutated PTPN22 protein. We also observed that the disulfide of native PTPN22 can be directly reduced by the thioredoxin system, while the C129S mutant lacking this disulfide was less amenable to reductive reactivation. In conclusion, we show that PTPN22 functionally interacts with Ncf1 and is regulated by oxidation via the non-catalytic C129 residue and oxidation-prone PTPN22 leads to increased severity in the development of T cell-dependent autoimmunity. Significance statementA hitherto unstudied aspect of PTPN22 biology is its regulation by cell redox states. Here we created a mouse model where PTPN22 was mutated to respond differentially to redox levels in vivo and found that PTPN22 function is regulated by reactive oxygen species and that redox regulation of PTPN22 impacts T-cell-dependent autoimmune inflammation.

immunology↗