Mass Spectrometry Datasets Research Articles

Kidney mRNA-protein expression correlation: what can we learn from the Human Protein Atlas?

The Human Protein Atlas, with more than 10 million immunohistochemical images showing tissue- and cell-specific protein expression levels and subcellular localization information, is widely used in kidney research. The Human Protein Atlas contains comprehensive data on multi-tissue transcript and protein abundance, allowing for comparisons across tissues. However, while visual and intuitive to interpret, immunohistochemistry is limited by its semi-quantitative nature. This can lead to mismatches in protein expression measurements across different platforms. We performed a comparison of the Human Protein Atlas' kidney-specific RNA sequencing and immunohistochemistry data to determine whether the mRNA and protein abundance levels are concordant. Our study shows that there is a discordance between mRNA and protein expression in the kidney based on the Human Protein Atlas data. Using an external validation mass spectrometry dataset, we show that more than 500 proteins undetected by immunohistochemistry are robustly measured by mass spectrometry. The Human Protein Atlas transcriptome data, on the other hand, exhibit similar transcript detection levels as other kidney RNA-seq datasets. Discordance in mRNA-protein expression could be due to both biological and technical reasons, such as transcriptional dynamics, translation rates, protein half-lives, and measurement errors. This is further complicated by the heterogeneity of the kidney tissue itself, which can increase the discordance if the cell populations or tissue compartment samples do not match. As such, shedding light on the mRNA-protein relationship of the kidney-specific Human Protein Atlas data can provide context to our scientific inferences onrenal gene and protein quantification.

Generative Adversarial Autoencoder Integration Voting Algorithm Based on Mass Spectrometry Data

Mass spectrometry technology is often used in disease prevention and diagnosis and treatment. However, the number of mass spectrometry data features is huge and the features vary greatly between different diseases, making multi-disease diagnosis. The task of judgment is complex and difficult. To address the above problems, this paper proposes a generative adversarial autoencoder integrated voting algorithm msDAGVote based on mass spectrometry data.The generative adversarial network based on dual autoencoders is used as the feature extraction framework of msDAGVote. After inputting mass spectrometry data for training, the generator subnetwork is used for feature extraction.Finally, the constructed features were screened by integrating the voting feature selection algorithm, and the optimal feature subset was used for multi-disease diagnosis.The results were evaluated on mass spectrometry datasets of different disease types. The experimental data showed that the features extracted by msDAGVote outperformed the comparison methods and significantly reduced the classification time.The required number of features and excellent disease classification and diagnosis capabilities are required. The classification AUC exceeds 0.98 on 6 datasets and The set exceeds 0.87.

Protein Modifications During Early Embryo Development.

Infertility is a global reproductive health burden. Assisted reproductive technologies (ARTs) have been widely used to help patients become pregnant. Few embryos develop to the blastocyst stage with ARTs, leading to relatively low live birth rates. Protein modifications play crucial roles in nearly every aspect of cell biology, including reproductive processes. The aim of this study was to explore the characteristics of protein modifications during embryonic development. Proteomic data from humans and mice were acquired from the integrated proteome resources (iProX) of ProteomeXchange (PXD024267) and a tandem mass tag (TMT)-mass spectrometry dataset. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were applied for functional annotation. Protein-protein interactions (PPIs) of the modification-related genes were revealed by the STRING database. Modified proteins during mouse embryogenesis were visualized through heatmaps of hierarchically clustering using k-means. We identified modification-related proteins in human embryo development and characterized them through heatmaps, GO analysis, KEGG analysis, and PPI network analysis. We found that the 4-cell stage to the 8-cell stage might be the demarcation period for modification-related protein expression patterns during embryo development. Using quantitative mass spectrometry, we elucidated the methylation, acetylation, and ubiquitination events that occur during mouse embryogenesis to validate our findings in human embryonic development to some extent. The results of our study suggest that the posttranslational modifications (PTMs) of human preimplantation embryos might exhibit the same trends as those in mice to exert synergistic and fine-tuned regulatory effects during embryonic development.

PyMS-Vis, an Open-Source Python Application for Visualizing and Investigating Deconvoluted Top-Down Mass Spectrometric Experiments: A Histone Proteoform Case Study.

We report the development of an open-source Python application that provides quantitative and qualitative information from deconvoluted liquid-chromatography top-down mass spectrometry (LC-TDMS) data sets. This simple-to-use program allows users to search masses-of-interest across multiple LC-TDMS runs and provides visualization of their ion intensities and elution characteristics while quantifying their abundances relative to one another. Focusing on proteoform-rich histone proteins from the green microalga Chlamydomonas reinhardtii, we were able to quantify proteoform abundances across different growth conditions and replicates in minutes instead of hours typically needed for manual spreadsheet-based analysis. This resulted in extending previously published qualitive observations on Chlamydomonas histone proteoforms into quantitative ones, leading to an exciting new discovery on alpha-amino termini processing exclusive to histone H2A family members. Lastly, the script was intentionally developed with readability and customizability in mind so that fellow mass spectrometrists can modify the code to suit their lab-specific needs.

Prediction of inherited metabolic disorders using tandem mass spectrometry data with the help of artificial neural networks.

Tandem mass spectrometry is helpful in diagnosing amino acid metabolism disorders, organic acidemias, and fatty acid oxidation disorders and can provide rapid and accurate diagnosis for inborn errors of metabolism. The aim of this study was to predict inborn errors of metabolism in children with the help of artificial neural networks using tandem mass spectrometry data. Forty-seven and 13 parameters of tandem mass spectrometry datasets obtained from 2938 different patients were respectively taken into account to train and test the artificial neural networks. Different artificial neural network models were established to obtain better prediction performances. The obtained results were compared with each other for fair comparisons. The best results were obtained by using the rectified linear unit activation function. One, two, and three hidden layers were considered for artificial neural network models established with both 47 and 13 parameters. The sensitivity of model B2 for definitive inherited metabolic disorders was found to be 80%. The accuracy rates of model A3 and model B2 are 99.3% and 99.2%, respectively. The area under the curve value of model A3 was 0.87, while that of model B2 was 0.90. The results showed that the proposed artificial neural networks are capable of predicting inborn errors of metabolism very accurately. Therefore, developing new technologies to identify and predict inborn errors of metabolism will be very useful.

HDXBoxeR: an R package for statistical analysis and visualization of multiple Hydrogen-Deuterium Exchange Mass-Spectrometry datasets of different protein states.

Hydrogen-Deuterium Exchange Mass Spectrometry (HDX-MS) is a powerful protein characterization technique that provides insights into protein dynamics and flexibility at the peptide level. However, analyzing HDX-MS data presents a significant challenge due to the wealth of information it generates. Each experiment produces data for hundreds of peptides, often measured in triplicate across multiple time points. Comparisons between different protein states create distinct datasets containing thousands of peptides that require matching, rigorous statistical evaluation, and visualization. Our open-source R package, HDXBoxeR, is a comprehensive tool designed to facilitate statistical analysis and comparison of multiple sets among samples and time points for different protein states, along with data visualization. HDXBoxeR is accessible as the R package (https://cran.r-project.org/web//packages/HDXBoxeR) and GitHub: mkajano/HDXBoxeR.

Fast Targeted Metabolomics for Analyzing Metabolic Diversity of Bacterial Indole Derivatives in ME/CFS Gut Microbiome.

Disruptions in microbial metabolite interactions due to gut microbiome dysbiosis and metabolomic shifts may contribute to Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and other immune-related conditions. The aryl hydrocarbon receptor (AhR), activated upon binding various tryptophan metabolites, modulates host immune responses. This study investigates whether the metabolic diversity-the concentration distribution-of bacterial indole pathway metabolites can differentiate bacterial strains and classify ME/CFS samples. A fast targeted liquid chromatography-parallel reaction monitoring method at a rate of 4 minutes per sample was developed for large-scale analysis. This method revealed significant metabolic differences in indole derivatives among B. uniformis strains cultured from human isolates. Principal component analysis identified two major components (PC1, 68.9%; PC2, 18.7%), accounting for 87.6% of the variance and distinguishing two distinct B. uniformis clusters. The metabolic difference between clusters was particularly evident in the relative contributions of indole-3-acrylate and indole-3-aldehyde. We further measured concentration distributions of indole derivatives in ME/CFS by analyzing fecal samples from 10 patients and 10 healthy controls using the fast targeted metabolomics method. An AdaBoost-LOOCV model achieved moderate classification success with a mean LOOCV accuracy of 0.65 (Control: precision of 0.67, recall of 0.60, F1-score of 0.63; ME/CFS: precision of 0.64, recall of 0.7000, F1-score of 0.67). These results suggest that the metabolic diversity of indole derivatives from tryptophan degradation, facilitated by the fast targeted metabolomics and machine learning, is a potential biomarker for differentiating bacterial strains and classifying ME/CFS samples. Mass spectrometry datasets are accessible at the National Metabolomics Data Repository (ST002308, DOI: 10.21228/M8G13Q; ST003344, DOI: 10.21228/M8RJ9N; ST003346, DOI: 10.21228/M8RJ9N).

How mass spectrometry can be exploited to study AMPK.

AMP-activated protein kinase (AMPK) is a key regulator of metabolism and a recognised target for the treatment of metabolic diseases such as Type 2 diabetes (T2D). Here, we review how mass spectrometry (MS) can be used to study short-term control by AMPK via protein phosphorylation and long-term control due to changes in protein expression. We discuss how MS can quantify AMPK subunit levels in tissues from different species. We propose hydrogen-deuterium exchange (HDX)-MS to investigate molecular mechanisms of AMPK activation and thermoproteomic profiling (TPP) to assess off-target effects of pharmacological AMPK activators/inhibitors. Lastly, because large MS data sets are generated, we consider different approaches that can be used for their interpretation.

DeTELpy: Python package for high-throughput detection of amino acid substitutions in mass spectrometry datasets.

Errors in the processing of genetic information during protein synthesis can lead to phenotypic mutations, such as amino acid substitutions, e.g. by transcription or translation errors. While genetic mutations can be readily identified using DNA sequencing, and mutations due to transcription errors by RNA sequencing, translation errors can only be identified proteome-wide using mass spectrometry. Here, we provide a Python package implementation of a high-throughput pipeline to detect amino acid substitutions in mass spectrometry datasets. Our tools enable users to process hundreds of mass spectrometry datasets in batch mode to detect amino acid substitutions and calculate codon-specific and site-specific translation error rates. deTELpy will facilitate the systematic understanding of amino acid misincorporation rates (translation error rates), and the inference of error models across organisms and under stress conditions, such as drug treatment or disease conditions. deTELpy is implemented in Python 3 and is freely available with detailed documentation and practical examples at https://git.mpi-cbg.de/tothpetroczylab/detelpy and https://pypi.org/project/deTELpy/ and can be easily installed via pip install deTELpy.

Simultaneous discovery of compounds dominated by either molding kinetics or geographical region of origin for moisture damaged cacao beans using orthogonally applied tile-based fisher ratio analysis of GC×GC-TOFMS data

Herein, two “orthogonal” characteristics of moisture damaged cacao beans (temporally dependent molding kinetics versus the time-independent geographical region of origin) are simultaneously analyzed in a comprehensive two-dimensional (2D) gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) dataset using tile-based Fisher ratio (F-ratio) analysis. Cacao beans from six geographical regions were analyzed once a day for six days following the initiation of moisture damage to trigger the molding process. Thus, there are two “extremes” to the experimental sample class design: six time points for the molding kinetics versus the six geographical regions of origin, resulting in a 6 × 6 element signal array referred to as a composite chemical fingerprint (CCF) for each analyte. Usually, this study would involve initial generation of two separate hit lists using F-ratio analysis, one hit list from inputting the data with the six time point classes, then another hit list from inputting the dataset from the perspective of geographic region of origin. However, analysis of two separate hit lists with the intent to distill them down to one hit list is extremely time-consuming and fraught with shortcomings due to the challenges associated with attempting to match analytes across two hit lists. To address this challenge, tile-based F-ratio analysis is “orthogonally applied” to each analyte CCF to simultaneously determine two F-ratios at the chromatographic 2D location (F-ratiokinetic and F-ratioregion) for each hit, by ranking a single hit list using the higher of the two F-ratios resulting in the discovery of 591 analytes. Further, using a pseudo-null distribution approach, at the 99.9% threshold over 400 analytes were deemed suitable for PCA classification. Using a more stringent 99.999% threshold, over 100 analytes were explored more deeply using PARAFAC to provide a purified mass spectrum.

Differential prolyl hydroxylation by six Physcomitrella prolyl-4 hydroxylases

Hydroxylation of prolines to 4-trans-hydroxyproline (Hyp) is mediated by prolyl-4 hydroxylases (P4Hs). In plants, Hyps occur in Hydroxyproline-rich glycoproteins (HRGPs), and are frequently O-glycosylated. While both modifications are important, e.g. for cell wall stability, they are undesired in plant-made pharmaceuticals. Sequence motifs for prolyl-hydroxylation were proposed but did not include data from mosses, such as Physcomitrella. We identified six moss P4Hs by phylogenetic reconstruction. Our analysis of 73 Hyps in 24 secretory proteins from multiple mass spectrometry datasets revealed that prolines near other prolines, alanine, serine, threonine and valine were preferentially hydroxylated. About 95 % of Hyps were predictable with combined established methods. In our data, AOV was the most frequent pattern. A combination of 443 AlphaFold models and MS data with 3000 prolines found Hyps mainly on protein surfaces in disordered regions. Moss-produced human erythropoietin (EPO) exhibited O-glycosylation with arabinose chains on two Hyps. This modification was significantly reduced in a p4h1 knock-out (KO) Physcomitrella mutant. Quantitative proteomics with different p4h mutants revealed specific changes in protein amounts, and a modified prolyl-hydroxylation pattern, suggesting a differential function of the Physcomitrella P4Hs. Quantitative RT-PCR revealed a differential effect of single p4h KOs on the expression of the other five p4h genes, suggesting a partial compensation of the mutation. AlphaFold-Multimer models for Physcomitrella P4H1 and its target EPO peptide superposed with the crystal structure of Chlamydomonas P4H1 suggested significant amino acids in the active centre of the enzyme and revealed differences between P4H1 and the other Physcomitrella P4Hs.

FLiPPR: A Processor for Limited Proteolysis (LiP) Mass Spectrometry Data Sets Built on FragPipe.

Here, we present FLiPPR, or FragPipe LiP (limited proteolysis) Processor, a tool that facilitates the analysis of data from limited proteolysis mass spectrometry (LiP-MS) experiments following primary search and quantification in FragPipe. LiP-MS has emerged as a method that can provide proteome-wide information on protein structure and has been applied to a range of biological and biophysical questions. Although LiP-MS can be carried out with standard laboratory reagents and mass spectrometers, analyzing the data can be slow and poses unique challenges compared to typical quantitative proteomics workflows. To address this, we leverage FragPipe and then process its output in FLiPPR. FLiPPR formalizes a specific data imputation heuristic that carefully uses missing data in LiP-MS experiments to report on the most significant structural changes. Moreover, FLiPPR introduces a data merging scheme and a protein-centric multiple hypothesis correction scheme, enabling processed LiP-MS data sets to be more robust and less redundant. These improvements strengthen statistical trends when previously published data are reanalyzed with the FragPipe/FLiPPR workflow. We hope that FLiPPR will lower the barrier for more users to adopt LiP-MS, standardize statistical procedures for LiP-MS data analysis, and systematize output to facilitate eventual larger-scale integration of LiP-MS data.

Intra-Individual Paired Mass Spectrometry Dataset for Decoding Solar-Induced Proteomic Changes in Facial Skin

Photoaging is the premature aging of the skin caused by prolonged exposure to solar radiation. The visual alterations manifest as wrinkles, reduced skin elasticity, uneven skin tone, as well as other signs that surpass the expected outcomes of natural aging. Beyond these surface changes, there is a complex interplay of molecular alterations, encompassing shifts in cellular function, DNA damage, and protein composition disruptions. This data descriptor introduces a unique dataset derived from ten individuals, each with a minimum of 18 years of professional experience as a driver, who are asymmetrically and chronically exposed to solar radiation due to their driving orientation. Skin samples were independently collected from each side of the face using a microdermabrasion-like procedure and analyzed on an Exploris 240 mass spectrometer. Our adapted proteomic statistical framework leverages the sample pairing to provide robust insights. This dataset delves into the molecular differences in exposed skin and serves as a foundational resource for interdisciplinary research in photodermatology, targeted skincare treatments, and computational modelling of skin health.

HormonomicsDB: a novel workflow for the untargeted analysis of plant growth regulators and hormones.

Metabolomics is the simultaneous determination of all metabolites in a system. Despite significant advances in the field, compound identification remains a challenge. Prior knowledge of the compound classes of interest can improve metabolite identification. Hormones are a small signaling molecules, which function in coordination to direct all aspects of development, function and reproduction in living systems and which also pose challenges as environmental contaminants. Hormones are inherently present at low levels in tissues, stored in many forms and mobilized rapidly in response to a stimulus making them difficult to measure, identify and quantify. An in-depth literature review was performed for known hormones, their precursors, metabolites and conjugates in plants to generate the database and an RShiny App developed to enable web-based searches against the database. An accompanying liquid chromatography - mass spectrometry (LC-MS) protocol was developed with retention time prediction in Retip. A meta-analysis of 14 plant metabolomics studies was used for validation. We developed HormonomicsDB, a tool which can be used to query an untargeted mass spectrometry (MS) dataset against a database of more than 200 known hormones, their precursors and metabolites. The protocol encompasses sample preparation, analysis, data processing and hormone annotation and is designed to minimize degradation of labile hormones. The plant system is used a model to illustrate the workflow and data acquisition and interpretation. Analytical conditions were standardized to a 30 min analysis time using a common solvent system to allow for easy transfer by a researcher with basic knowledge of MS. Incorporation of synthetic biotransformations enables prediction of novel metabolites. HormonomicsDB is suitable for use on any LC-MS based system with compatible column and buffer system, enables the characterization of the known hormonome across a diversity of samples, and hypothesis generation to reveal knew insights into hormone signaling networks.

Graph-pMHC: graph neural network approach to MHC class II peptide presentation and antibody immunogenicity.

Antigen presentation on MHC class II (pMHCII presentation) plays an essential role in the adaptive immune response to extracellular pathogens and cancerous cells. But it can also reduce the efficacy of large-molecule drugs by triggering an anti-drug response. Significant progress has been made in pMHCII presentation modeling due to the collection of large-scale pMHC mass spectrometry datasets (ligandomes) and advances in machine learning. Here, we develop graph-pMHC, a graph neural network approach to predict pMHCII presentation. We derive adjacency matrices for pMHCII using Alphafold2-multimer and address the peptide-MHC binding groove alignment problem with a simple graph enumeration strategy. We demonstrate that graph-pMHC dramatically outperforms methods with suboptimal inductive biases, such as the multilayer-perceptron-based NetMHCIIpan-4.0 (+20.17% absolute average precision). Finally, we create an antibody drug immunogenicity dataset from clinical trial data and develop a method for measuring anti-antibody immunogenicity risk using pMHCII presentation models. Our model increases receiver operating characteristic curve (ROC)-area under the ROC curve (AUC) by 2.57% compared to just filtering peptides by hits in OASis alone for predicting antibody drug immunogenicity.

Abstract 7082: Epidemiology meets epitranscriptomics: Exploring the cancer-related role of a novel TERT-antisense transcript and its regulation by RNA methylation

Abstract Human telomerase reverse transcriptase (TERT) maintains telomeres at chromosome ends and its dysregulation is implicated in various human health conditions. Multiple GWAS signals are detected within the TERT genomic region at chr 5p15.33. As a potential proxy for these signals, we investigated a highly polymorphic variable number tandem repeat (VNTR)2-1 within intron 2 of TERT, with 57-143 copies of a 42 bp repeat unit per allele. By functional annotation of VNTR2-1, we predicted that it generates an intronic transcript antisense to TERT with a variable-size open reading frame (ORF) of 2502-5211 bp (834-2038 aa). Specific to the TERT-antisense transcript, we predicted N6-methyladenosine (m6A) consensus motifs within each 42 bp unit of VNTR2-1, with longer VNTR2-1 regions carrying more m6A motifs. In a stranded RNA-seq dataset of African pediatric Burkitt lymphoma (BL) tumors, which have high TERT expression, we detected the VNTR2-1-containing TERT-antisense transcript in a subset of tumors (n=35/113, 30.9%), confirming the expression of this novel transcript. VNTR2-1 expression was also experimentally validated by 5’-RACE/Nanopore-seq in Raji (BL cell line) and UMUC3 (bladder cancer cell line). Additionally, the VNTR2-1 transcript was visualized in UMUC3 cells using RNA-FISH. The consensus 42 bp VNTR2-1 repeat sequence and its m6A-deficient variants were cloned into a m6A-deficient dual-luciferase reporter vector (m-psiCHECK-2) and expressed in UMUC3 cells, showing an m6A-motif dose-dependent response driving luciferase protein expression. Transient treatment with STM2457, an inhibitor of the m6A writer METTL3, reduced the expression of the luciferase reporter, confirming that m6A motifs within VNTR2-1 are methylated by METTL3. Additionally, Long-term cigarette smoke condensate (CSC) treatment of UMUC3 reduced luciferase expression, suggesting alterations of VNTR2-1 m6A due to smoke exposure. We explored potential peptides produced from VNTR2-1 transcript by analysis of public mass spectrometry datasets using PepQuery and identified several unique VNTR2-1 peptide fragments in human tumors. Next, we cloned a synthetic FLAG-tagged gene containing three VNTR2-1 consensus repeats with native Kozak sequence and translation start/stop codons. Western blotting confirmed the translation of the VNTR2-1 peptide that was ablated by start codon mutation, suggesting that the native Kozak sequence is sufficient for VNTR2-1 translation. These results support VNTR2-1 as a peptide-producing transcript. In conclusion, we identified a novel intronic transcript produced by VNTR2-1 antisense to TERT. This m6A-regulated transcript could function through several mechanisms, such as a lncRNA or translated peptide, potentially regulating TERT expression and telomerase function in health and disease conditions, including cancer. Citation Format: Brenen W. Papenberg, Oscar Florez-Vargas, Michelle Ho, Kaitlin Forsythe, Chi Zhang, Chia-Han Lee, Sam Mbulaiteye, Pedro J. Batista, Ludmila Prokunina-Olsson. Epidemiology meets epitranscriptomics: Exploring the cancer-related role of a novel TERT-antisense transcript and its regulation by RNA methylation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7082.

AlphaPept: a modern and open framework for MS-based proteomics

In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making efficient analysis a principal challenge. A plethora of different computational tools can process the MS data to derive peptide and protein identification and quantification. However, during the last years there has been dramatic progress in computer science, including collaboration tools that have transformed research and industry. To leverage these advances, we develop AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Numba for just-in-time compilation on CPU and GPU achieves hundred-fold speed improvements. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while accessing the latest advances. We provide an easy on-ramp for community contributions through the concept of literate programming, implemented in Jupyter Notebooks. Large datasets can rapidly be processed as shown by the analysis of hundreds of proteomes in minutes per file, many-fold faster than acquisition. AlphaPept can be used to build automated processing pipelines with web-serving functionality and compatibility with downstream analysis tools. It provides easy access via one-click installation, a modular Python library for advanced users, and via an open GitHub repository for developers.

SAM68 directs STING signaling to apoptosis in macrophages

DNA is a danger signal sensed by cGAS to engage signaling through STING to activate innate immune functions. The best-studied downstream responses to STING activation include expression of type I interferon and inflammatory genes, but STING also activates other pathways, including apoptosis. Here, we report that STING-dependent induction of apoptosis in macrophages occurs through the intrinsic mitochondrial pathway and is mediated via IRF3 but acts independently of gene transcription. By intersecting four mass spectrometry datasets, we identify SAM68 as crucial for the induction of apoptosis downstream of STING activation. SAM68 is essential for the full activation of apoptosis. Still, it is not required for STING-mediated activation of IFN expression or activation of NF-κB. Mechanistic studies reveal that protein trafficking is required and involves SAM68 recruitment to STING upon activation, with the two proteins associating at the Golgi or a post-Golgi compartment. Collectively, our work identifies SAM68 as a STING-interacting protein enabling induction of apoptosis through this DNA-activated innate immune pathway.

EndoGenius: Optimized Neuropeptide Identification from Mass Spectrometry Datasets.

Neuropeptides represent a unique class of signaling molecules that have garnered much attention but require special consideration when identifications are gleaned from mass spectra. With highly variable sequence lengths, neuropeptides must be analyzed in their endogenous state. Further, neuropeptides share great homology within families, differing by as little as a single amino acid residue, complicating even routine analyses and necessitating optimized computational strategies for confident and accurate identifications. We present EndoGenius, a database searching strategy designed specifically for elucidating neuropeptide identifications from mass spectra by leveraging optimized peptide-spectrum matching approaches, an expansive motif database, and a novel scoring algorithm to achieve broader representation of the neuropeptidome and minimize reidentification. This work describes an algorithm capable of reporting more neuropeptide identifications at 1% false-discovery rate than alternative software in five Callinectes sapidus neuronal tissue types.

Exploring the Molecular Tapestry: Organ-Specific Peptide and Protein Ultrafiltrates and Their Role in Therapeutics.

This study aims to characterize the proteome composition of organ-derived protein extracts from rabbits. Protein isolation was performed using soft homogenization and size exclusion via ultrafiltration. The proteome analysis of the ultrafiltrates was conducted using gel electrophoresis, and the mass spectrometry data were subjected to gene ontology analysis. Proteomic profiling revealed comprehensive protein profiles associated with RNA regulation, fatty acid binding, inflammatory response, oxidative stress, and metabolism. Additionally, our results demonstrate the presence of abundant small proteins, as observed in the mass spectrometry datasets. Small proteins and peptides are crucial in transcription modulation and various biological processes. The protein networks identified in the ultrafiltrates have the potential to enhance and complement biological therapeutic interventions. Data are available via ProteomeXchange with identifier PXD050039.