A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action.

Current machine learning techniques enable robust association of biological signals with measured phenotypes, but these approaches are incapable of identifying causal relationships. Here, we develop an integrated "white-box" biochemical screening, network modeling, and machine learning approach for revealing causal mechanisms and apply this approach to understanding antibiotic efficacy. We counter-screen diverse metabolites against bactericidal antibiotics in Escherichia coli and simulate their corresponding metabolic states using a genome-scale metabolic network model. Regression of the measured screening data on model simulations reveals that purine biosynthesis participates in antibiotic lethality, which we validate experimentally. We show that antibiotic-induced adenine limitation increases ATP demand, which elevates central carbon metabolism activity and oxygen consumption, enhancing the killing effects of antibiotics. This work demonstrates how prospective network modeling can couple with machine learning to identify complex causal mechanisms underlying drug efficacy.

Integrative omics indicate FMRP sequesters mRNA from translation and deadenylation in human neuronal cells.

How fragile X syndrome protein (FMRP) binds mRNAs and regulates mRNA metabolism remains unclear. Our previous work using human neuronal cells focused on mRNAs targeted for nonsense-mediated mRNA decay (NMD), which we showed are generally bound by FMRP and destabilized upon FMRP loss. Here, we identify >400 high-confidence FMRP-bound mRNAs, only ∼35% of which are NMD targets. Integrative transcriptomics together with SILAC-LC-MS/MS reveal that FMRP loss generally results in mRNA destabilization and more protein produced per FMRP target. We use our established RIP-seq technology to show that FMRP footprints are independent of protein-coding potential, target GC-rich and structured sequences, and are densest in 5' UTRs. Regardless of where within an mRNA FMRP binds, we find that FMRP protects mRNAs from deadenylation and directly binds the cytoplasmic poly(A)-binding protein. Our results reveal how FMRP sequesters polyadenylated mRNAs into stabilized and translationally repressed complexes, whose regulation is critical for neurogenesis and synaptic plasticity.

Multi-omics analysis reveals the potential pathogenesis and therapeutic targets of diabetic kidney disease.

Clinicians have long been interested in understanding the molecular basis of diabetic kidney disease (DKD)and its potential treatment targets. Its pathophysiology involves protein phosphorylation, one of the most recognizable post-transcriptional modifications, that can take part in many cellular functions and control different metabolic processes. In order to recognize the molecular and protein changes of DKD kidney, this study applied Tandem liquid chromatography-mass spectrometry (LC-MS/MS) and Next-Generation Sequencing, along with Tandem Mass Tags (TMT) labeling techniques to evaluate the mRNA, protein and modified phosphorylation sites between DKD mice and model ones. Based on Gene Ontology (GO) and KEGG pathway analyses of transcriptome and proteome, The molecular changes of DKD include accumulation of extracellular matrix, abnormally activated inflammatory microenvironment, oxidative stress and lipid metabolism disorders, leading to glomerulosclerosis and tubulointerstitial fibrosis. Oxidative stress has been emphasized as an important factor in DKD and progression to ESKD, which is directly related to podocyte injury, albuminuria and renal tubulointerstitial fibrosis. A histological study of phosphorylation further revealed that kinases were crucial. Three groups of studies have found that RAS signaling pathway, RAP1 signaling pathway, AMPK signaling pathway, PPAR signaling pathway and HIF-1 signaling pathway were crucial for the pathogenesis of DKD. Through this approach, it was discovered that targeting specific molecules, proteins, kinases and critical pathways could be a promising approach for treating DKD.

Systematic Proteome Profiling of Maternal Plasma for Development of Preeclampsia Biomarkers.

Preeclampsia (PE) is a hypertensive disorder of pregnancy with various clinical symptoms. However, traditional markers for the disease including high blood pressure and proteinuria are poor indicators of the related adverse outcomes. Here, we performed systematic proteome profiling of plasma samples obtained from pregnant women with PE to identify clinically effective diagnostic biomarkers. Proteome profiling was performed using TMT-based liquid chromatography-mass spectrometry (LC-MS/MS) followed by subsequent verification by multiple reaction monitoring (MRM) analysis on normal and PE maternal plasma samples. Functional annotations of differentially expressed proteins (DEPs) in PE were predicted using bioinformatic tools. The diagnostic accuracies of the biomarkers for PE were estimated according to the area under the receiver-operating characteristics curve (AUC). A total of 1307 proteins were identified, and 870 proteins of them were quantified from plasma samples. Significant differences were evident in 138 DEPs, including 71 upregulated DEPs and 67 downregulated DEPs in the PE group, compared with those in the control group. Upregulated proteins were significantly associated with biological processes including platelet degranulation, proteolysis, lipoprotein metabolism, and cholesterol efflux. Biological processes including blood coagulation and acute-phase response were enriched for down-regulated proteins. Of these, 40 proteins were subsequently validated in an independent cohort of 26 PE patients and 29 healthy controls. APOM, LCN2, and QSOX1 showed high diagnostic accuracies for PE detection (AUC >0.9 and p < 0.001, for all) as validated by MRM and ELISA. Our data demonstrate that three plasma biomarkers, identified by systematic proteomic profiling, present a possibility for the assessment of PE, independent of the clinical characteristics of pregnant women.

Galectin-3-Binding Protein Inhibits Extracellular Heparan 6-O-Endosulfatase Sulf-2.

Human extracellular 6-O-endosulfatases Sulf-1 and Sulf-2 are the only enzymes that post-synthetically alter the 6-O sulfation of heparan sulfate proteoglycans (HSPG), which regulates interactions of HSPG with many proteins. Oncogenicity of Sulf-2 in different cancers has been documented, and we have shown that Sulf-2 is associated with poor survival outcomes in head and neck squamous cell carcinoma (HNSCC). Despite its importance, limited information is available on direct protein-protein interactions of the Sulf-2 protein in the tumor microenvironment. In this study, we used monoclonal antibody (mAb) affinity purification and mass spectrometry to identify galectin-3-binding protein (LG3BP) as a highly specific binding partner of Sulf-2 in the conditioned media of HNSCC cell lines. We validated their direct interaction in vitro using recombinant proteins and have shown that the chondroitin sulfate (CS) covalently bound to the Sulf-2 influences the binding to LG3BP. We confirmed the importance of the CS chain for the interaction by generating a mutant Sulf-2 protein that lacks the CS. Importantly, we have shown that the LG3BP inhibits Sulf-2 activity in vitro in a concentration-dependent manner. As a consequence, the addition of LG3BP to a spheroid cell culture inhibited the invasion of the HNSCC cells into Matrigel. Thus, Sulf-2 interaction with LG3BP may regulate the physiological activity of the Sulf-2 enzyme as well as its activity in the tumor microenvironment.

Pupylation-Based Proximity-Tagging of FERONIA-Interacting Proteins in Arabidopsis.

The plasma membrane-localized receptor kinase FERONIA (FER) plays critical roles in a remarkable variety of biological processes throughout the life cycle of Arabidopsis thaliana. Revealing the molecular connections of FER that underlie these processes starts with identifying the proteins that interact with FER. We applied pupylation-based interaction tagging (PUP-IT) to survey cellular proteins in proximity to FER, encompassing weak and transient interactions that can be difficult to capture for membrane proteins. We reproducibly identified 581, 115, and 736 specific FER-interacting protein candidates in protoplasts, seedlings, and flowers, respectively. We also confirmed 14 previously characterized FER-interacting proteins. Protoplast transient gene expression expedited the testing of new gene constructs for PUP-IT analyses and the validation of candidate proteins. We verified the proximity labeling of five selected candidates that were not previously characterized as FER-interacting proteins. The PUP-IT method could be a valuable tool to survey and validate protein-protein interactions for targets of interest in diverse subcellular compartments in plants.

Direct sequencing of total Saccharomyces cerevisiae tRNAs by LC-MS/MS.

Among RNAs, transfer RNAs (tRNAs) contain the widest variety of abundant posttranscriptional chemical modifications. These modifications are crucial for tRNAs to participate in protein synthesis, promoting proper tRNA structure and aminoacylation, facilitating anticodon:codon recognition, and ensuring the reading frame maintenance of the ribosome. While tRNA modifications were long thought to be stoichiometric, it is becoming increasingly apparent that these modifications can change dynamically in response to the cellular environment. The ability to broadly characterize the fluctuating tRNA modification landscape will be essential for establishing the molecular level contributions of individual sites of tRNA modification. The locations of modifications within individual tRNA sequences can be mapped using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). In this approach, a single tRNA species is purified, treated with ribonucleases, and the resulting single-stranded RNA products are subject to LC-MS/MS analysis. The application of LC-MS/MS to study tRNAs is limited by the necessity of analyzing one tRNA at a time, because the digestion of total tRNA mixtures by commercially available ribonucleases produces many short digestion products unable to be uniquely mapped back to a single site within a tRNA. We overcame these limitations by taking advantage of the highly structured nature of tRNAs to prevent the full digestion by single-stranded RNA-specific ribonucleases. Folding total tRNA prior to digestion allowed us to sequence Saccharomyces cerevisiae tRNAs with up to 97% sequence coverage for individual tRNA species by LC-MS/MS. This method presents a robust avenue for directly detecting the distribution of modifications in total tRNAs.

O-Glycomic and Proteomic Signatures of Spontaneous and Butyrate-Stimulated Colorectal Cancer Cell Line Differentiation.

Gut microbiota of the gastrointestinal tract provide health benefits to the human host via bacterial metabolites. Bacterial butyrate has beneficial effects on intestinal homeostasis and is the preferred energy source of intestinal epithelial cells, capable of inducing differentiation. It was previously observed that changes in the expression of specific proteins as well as protein glycosylation occur with differentiation. In this study, specific mucin O-glycans were identified that mark butyrate-induced epithelial differentiation of the intestinal cell line CaCo-2 (Cancer Coli-2), by applying porous graphitized carbon nano-liquid chromatography with electrospray ionization tandem mass spectrometry. Moreover, a quantitative proteomic approach was used to decipher changes in the cell proteome. It was found that the fully differentiated butyrate-stimulated cells are characterized by a higher expression of sialylated O-glycan structures, whereas fucosylation is downregulated with differentiation. By performing an integrative approach, we generated hypotheses about the origin of the observed O-glycome changes. These insights pave the way for future endeavors to study the dynamic O-glycosylation patterns in the gut, either produced via cellular biosynthesis or through the action of bacterial glycosidases as well as the functional role of these patterns in homeostasis and dysbiosis at the gut-microbiota interface.

Global profiling of protein lactylation in Caenorhabditis elegans.

Lactylation, as a novel posttranslational modification, is essential for studying the functions and regulation of proteins in physiological and pathological processes, as well as for gaining in-depth knowledge on the occurrence and development of many diseases, including tumors. However, few studies have examined the protein lactylation of one whole organism. Thus, we studied the lactylation of global proteins in Caenorhabditis elegans to obtain an in vivo lactylome. Using an MS-based platform, we identified 1836 Class I (localization probabilities > 0.75) lactylated sites in 487 proteins. Bioinformatics analysis showed that lactylated proteins were mainly located in the cytoplasm and involved in the tricarboxylic acid cycle (TCA cycle) and other metabolic pathways. Then, we evaluated the conservation of lactylation in different organisms. In total, 41 C. elegans proteins were lactylated and homologous to lactylated proteins in humans and rats. Moreover, lactylation on H4K80 was conserved in three species. An additional 238 lactylated proteins were identified in C. elegans for the first time. This study establishes the first lactylome database in C. elegans and provides a basis for studying the role of lactylation.

On the excessive use of coefficient of variation as a metric of quantitation quality in proteomics.

The coefficient of variation (CV) is often used in proteomics as a proxy to characterize the performance of a quantitation method and/or the related software. In this note, we question the excessive reliance on this metric in quantitative proteomics that may result in erroneous conclusions. We support this note using a ground-truth Human-Yeast-E. coli dataset demonstrating in a number of cases that erroneous data processing methods may lead to a low CV which has nothing to do with these methods' performances in quantitation.

Identification of a rare [Gγ(Aγδß)0] -thalassemia using tandem mass spectrometry.

Thalassemias are a group of inherited monogenic disorders characterized by defects in the synthesis of one or more of the globin chain subunits of the hemoglobin tetramer. Delta-beta (δß-) thalassemia has large deletions in the ß globin gene cluster involving δ- and ß-globin genes, leading to absent or reduced synthesis of both δ- and ß-globin chains. Here, we used direct globin-chain analysis using tandem mass spectrometry for the diagnosis of δß-thalassemia. Two cases from unrelated families were recruited for the study based on clinical and hematological evaluation. Peptides obtained after trypsin digestion of proteins extracted from red blood cell pellets from two affected individuals and their parents were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Mass spectrometric analysis revealed a severe reduction in δ, ß, and Aγ globin proteins with increased Gγ globin protein in the affected individuals. The diagnosis of Gγ(Aγδß)0 -thalassemia in the homozygous state in the affected individuals and in the heterozygous state in the parents was made from our results. The diagnosis was confirmed at the genetic level using multiplex ligation-dependent probe amplification (MLPA). Our findings demonstrate the utility of direct globin protein quantitation using LC-MS/MS to quantify individual globin proteins reflecting changes in globin production. This approach can be utilized for accurate and timely diagnosis of hemoglobinopathies, including rare variants, where existing diagnostic methods provide inconclusive results.

Oktoberfest: Open-source spectral library generation and rescoring pipeline based on Prosit.

Machine learning (ML) and deep learning (DL) models for peptide property prediction such as Prosit have enabled the creation of high quality in silico reference libraries. These libraries are used in various applications, ranging from data-independent acquisition (DIA) data analysis to data-driven rescoring of search engine results. Here, we present Oktoberfest, an open source Python package of our spectral library generation and rescoring pipeline originally only available online via ProteomicsDB. Oktoberfest is largely search engine agnostic and provides access to online peptide property predictions, promoting the adoption of state-of-the-art ML/DL models in proteomics analysis pipelines. We demonstrate its ability to reproduce and even improve our results from previously published rescoring analyses on two distinct use cases. Oktoberfest is freely available on GitHub (https://github.com/wilhelm-lab/oktoberfest) and can easily be installed locally through the cross-platform PyPI Python package.

Dysregulated proteome and N-glycoproteome in ALG1-deficient fibroblasts.

Asparagine-linked glycosylation 1 protein is a ß-1,4-mannosyltransferase, is encoded by the ALG1 gene, which catalyzes the first step of mannosylation in N-glycosylation. Pathogenic variants in ALG1 cause a rare autosomal recessive disorder termed as ALG1-CDG. We performed a quantitative proteomics and N-glycoproteomics study in fibroblasts derived from patients with one homozygous and two compound heterozygous pathogenic variants in ALG1. Several proteins that exhibited significant upregulation included insulin-like growth factor II and pleckstrin, whereas hyaluronan and proteoglycan link protein 1 was downregulated. These proteins are crucial for cell growth, survival and differentiation. Additionally, we observed a decrease in the expression of mitochondrial proteins and an increase in autophagy-related proteins, suggesting mitochondrial and cellular stress. N-glycoproteomics revealed the reduction in high-mannose and complex/hybrid glycopeptides derived from numerous proteins in patients explaining that defect in ALG1 has broad effects on glycosylation. Further, we detected an increase in several short oligosaccharides, including chitobiose (HexNAc2) trisaccharides (Hex-HexNAc2) and novel tetrasaccharides (NeuAc-Hex-HexNAc2) derived from essential proteins including LAMP1, CD44 and integrin. These changes in glycosylation were observed in all patients irrespective of their gene variants. Overall, our findings not only provide novel molecular insights into understanding ALG1-CDG but also offer short oligosaccharide-bearing peptides as potential biomarkers.

Quantitative proteomics investigating the intrinsic adaptation mechanism of Aeromonas hydrophila to streptomycin.

Aeromonas hydrophila, a prevalent pathogen in the aquaculture industry, poses significant challenges due to its drug-resistant strains. Moreover, residues of antibiotics like streptomycin, extensively employed in aquaculture settings, drive selective bacterial evolution, leading to the progressive development of resistance to this agent. However, the underlying mechanism of its intrinsic adaptation to antibiotics remains elusive. Here, we employed a quantitative proteomics approach to investigate the differences in protein expression between A. hydrophila under streptomycin (SM) stress and nonstress conditions. Notably, bioinformatics analysis unveiled the potential involvement of metal pathways, including metal cluster binding, iron-sulfur cluster binding, and transition metal ion binding, in influencing A. hydrophila's resistance to SM. Furthermore, we evaluated the sensitivity of eight gene deletion strains related to streptomycin and observed the potential roles of petA and AHA_4705 in SM resistance. Collectively, our findings enhance the understanding of A. hydrophila's response behavior to streptomycin stress and shed light on its intrinsic adaptation mechanism.

Identification of the native Torpedo californica nicotinic acetylcholine receptor's glycan composition after a multi-step sequential purification method using MALDI-ToF MS.

The Cys-loop pentameric ligand-gated ion channels comprise a dynamic group of proteins that have been extensively studied for decades, yielding a wealth of findings at both the structural and functional levels. The nicotinic acetylcholine receptor (nAChR) is no exception, as it is part of this large protein family involved in proper organismal function. Our efforts have successfully produced a highly pure nAChR in detergent complex (nAChR-DC), enabling more robust studies to be conducted on it, including beginning to experiment with high-throughput crystallization. Our homogeneous product has been identified and extensively characterized with 100% identity using Nano Lc MS/MS and MALDI ToF/ToF for each nAChR subunit. Additionally, the N-linked glycans in the Torpedo californica-nAChR (Tc-nAChR) subunits have been identified. To study this, the Tc-nAChR subunits were digested with PNGase F and the released glycans were analyzed by MALDI-ToF. The MS results showed the presence of high-mannose N-glycan in all native Tc-nAChR subunits. Specifically, the oligommanose population Man8-9GlcNac2 with peaks at m/z 1742 and 1904 ([M + Na]+ ions) were observed.

Integrative Analyses Reveal the Correlation Between the Airway Microbiome and Host Metabolism in Severe Community-acquired Pneumonia.

Community-acquired pneumonia (CAP) is a significant global health concern, responsible for high mortality and morbidity. Recent research has revealed a potential link between disordered microbiome and metabolism in pneumonia, although the precise relationship between these factors and severe CAP remains unclear. To address this knowledge gap, we conducted a comprehensive analysis utilizing 16S sequencing and LC-MS/MS metabolomics data to characterize the microbial profile in sputum and metabolic profile in serum in patients with severe community-acquired pneumonia (sCAP). Our analysis identified 13 genera through LEfSe analysis and 15 metabolites meeting specific criteria (P < 0.05, VIP ≥ 2, and |Log2(FC)| ≥ 2). The findings of this study demonstrate the presence of altered coordination between the microbiome of the lower respiratory tract and host metabolism in patients with sCAP. The observed concentration trends of specific metabolites across different disease stages further support the potential involvement of the serum metabolism in the development of sCAP. These correlations between the airway microbiome and host metabolism in sCAP patients have important implications for optimizing early diagnosis and developing individualized therapeutic strategies.

Use of Proteomics for Dietary Reconstruction: A Case Study Using Animal Teeth from Ancient Mesopotamia.

This research examines animal teeth from Early Dynastic (2900-2350 BCE) Mesopotamia (Southern Iraq) to assess animal management practices and identify consumption patterns in animal diets. The objective to answer larger questions about food management and environmental resilience in ancient early complex societies in the Near East was achieved by the use of mass spectrometry-based proteomics for dietary reconstruction. Dietary MS, a revolutionary new methodology applying proteomics techniques to archeological sample sets to reconstruct ancient animal diet. A developed protein extraction technique followed by liquid chromatography tandem mass spectrometry allowed for the identification of the specific plant species consumed in order to highlight variable herd management strategies, resource optimization, for each taxon over time. It also provided information about overall health and indications of disease. This is the first study to apply a full suite of analyses to the region and provides the foundations of a necessary long-term view of human interaction within an environment, through both time and space.

Low-Invasive Sampling Method with Tape-Disc Sampling for the Taxonomic Identification of Archeological and Paleontological Bones by Proteomics.

Collagen from paleontological bones is an important organic material for isotopic measurement, radiocarbon analysis, and paleoproteomic analysis to provide information on diet, dating, taxonomy, and phylogeny. Current paleoproteomic methods are destructive and require from a few milligrams to several tens of milligrams of bone for analysis. In many cultures, bones are raw materials for artifacts that are conserved in museums, which hampers damage to these precious objects during sampling. Here, we describe a low-invasive sampling method that identifies collagen, taxonomy, and post-translational modifications from Holocene and Upper Pleistocene bones dated to 130,000 and 150 BC using dermatological skin tape discs for sampling. The sampled bone micropowders were digested following our highly optimized enhanced filter-aided sample preparation protocol and then analyzed by MALDI FTICR MS and LC-MS/MS for identifying the genus taxa of the bones. We show that this low-invasive sampling does not deteriorate the bones and achieves results similar to those obtained by more destructive sampling. Moreover, this sampling method can be carried out at archeological sites or in museums.

IsoForma: An R Package for Quantifying and Visualizing Positional Isomers in Top-Down LC-MS/MS Data.

Proteoforms, the different forms of a protein with sequence variations including post-translational modifications (PTMs), execute vital functions in biological systems, such as cell signaling and epigenetic regulation. Advances in top-down mass spectrometry (MS) technology have permitted the direct characterization of intact proteoforms and their exact number of modification sites, allowing for the relative quantification of positional isomers (PI). Protein positional isomers refer to a set of proteoforms with identical total mass and set of modifications, but varying PTM site combinations. The relative abundance of PI can be estimated by matching proteoform-specific fragment ions to top-down tandem MS (MS2) data to localize and quantify modifications. However, the current approaches heavily rely on manual annotation. Here, we present IsoForma, an open-source R package for the relative quantification of PI within a single tool. Benchmarking IsoForma's performance against two existing workflows produced comparable results and improvements in speed. Overall, IsoForma provides a streamlined process for quantifying PI, reduces the analysis time, and offers an essential framework for developing customized proteoform analysis workflows. The software is open source and available at https://github.com/EMSL-Computing/isoforma-lib.

O-Glycome Profiling of Breast Cancer Cell Lines to Understand Breast Cancer Brain Metastasis.

Breast cancer is the second leading cause of cancer-related death among women and a major source of brain metastases. Despite the increasing incidence of brain metastasis from breast cancer, the underlying mechanisms remain poorly understood. Altered glycosylation is known to play a role in various diseases including cancer metastasis. However, profiling studies of O-glycans and their isomers in breast cancer brain metastasis (BCBM) are scarce. This study analyzed the expression of O-glycans and their isomers in human breast cancer cell lines (MDA-MB-231, MDA-MB-361, HTB131, and HTB22), a brain cancer cell line (CRL-1620), and a brain metastatic breast cancer cell line (MDA-MB-231BR) using nanoLC-MS/MS, identifying 27 O-glycan compositions. We observed significant upregulation in the expression of HexNAc1Hex1NeuAc2 and HexNAc2Hex3, whereas the expression of HexNAc1Hex1NeuAc1 was downregulated in MDA-MB-231BR compared to other cell lines. In our isomeric analysis, we observed notable alterations in the isomeric forms of the O-glycan structure HexNAc1Hex1NeuAc1 in a comparison of different cell lines. Our analysis of O-glycans and their isomers in cancer cells demonstrated that changes in their distribution can be related to the metastatic process. We believe that our investigation will contribute to an enhanced comprehension of the significance of O-glycans and their isomers in BCBM.