Mass Spectrometry Datasets Research Articles

Fitness Effects of Phenotypic Mutations at Proteome-Scale Reveal Optimality of Translation Machinery.

Errors in protein translation can lead to non-genetic, phenotypic mutations, including amino acid misincorporations. While phenotypic mutations can increase protein diversity, the systematic characterization of their proteome-wide frequencies and their evolutionary impact has been lacking. Here, we developed a mechanistic model of translation errors to investigate how selection acts on protein populations produced by amino acid misincorporations. We fitted the model to empirical observations of misincorporations obtained from over a hundred mass spectrometry datasets of E. coli and S. cerevisiae. We found that on average 20% to 23% of proteins synthesized in the cell are expected to harbor at least one amino acid misincorporation, and that deleterious misincorporations are less likely to occur. Combining misincorporation probabilities and the estimated fitness effects of amino acid substitutions in a population genetics framework, we found 74% of mistranslation events in E. coli and 94% in S. cerevisiae to be neutral. We further show that the set of available synonymous tRNAs is subject to evolutionary pressure, as the presence of missing tRNAs would increase codon-anticodon cross-reactivity and misincorporation error rates. Overall, we find that the translation machinery is likely optimal in E. coli and S. cerevisiae and that both local solutions at the level of codons and a global solution such as the tRNA pool can mitigate the impact of translation errors. We provide a framework to study the evolutionary impact of codon-specific translation errors and a method for their proteome-wide detection across organisms and conditions.

Investigating the impacts of solid phase extraction on dissolved organic matter optical signatures and the pairing with high‐resolution mass spectrometry data across a freshwater stream network

AbstractAdvancing our understanding of dissolved organic matter (DOM) chemistry in aquatic systems necessitates the integration of data streams from multiple analytical platforms. Some measurements require pretreatment with solid phase extraction (SPE), while others are performed directly on whole water samples. Evidence has suggested that SPE will be biased against select DOM fractions, leading to concerns over the ability to establish data linkages across platforms with variable needs for SPE pretreatment, such as those from optical measurements and those that provide high‐resolution molecular information. Here, we directly addressed this concern by assessing the impact of SPE on DOM optical properties through excitation–emission matrices with parallel factor analysis (PARAFAC) for 47 samples across a stream network within a single watershed reflective of variable DOM sources. PARAFAC data was further paired with molecular information obtained by Fourier transform ion cyclotron resonance mass spectrometry (FTICR‐MS). A comparison of PARAFAC models first revealed no systematic qualitative differences in major components between whole water DOM and DOM isolated by SPE (SPE‐DOM); however, quantitative biases against select components were observed. Further linkages with FTICR‐MS data revealed that the molecular fingerprint associated with each PARAFAC component was consistent between the whole water DOM and SPE‐DOM. Our results suggest that bulk scale linkages across these analytical platforms could be inferred irrespective of the observed quantitative biases resulting from SPE for samples within this example watershed. This work represents a key step toward the systematic evaluation of linkages between optical and high‐resolution mass spectrometry datasets in freshwater lotic environments.

Neural network establishes co-occurrence links between transformation products of the contaminant and the soil microbiome

It remains challenging to establish reliable links between transformation products (TPs) of contaminants and corresponding microbes. This challenge arises due to the sophisticated experimental regime required for TP discovery and the compositional nature of 16S rRNA gene amplicon sequencing and mass spectrometry datasets, which can potentially confound statistical inference. In this study, we present a new strategy by combining the use of 2H-labeled Stable Isotope-Assisted Metabolomics (2H-SIAM) with a neural network-based algorithm (i.e., MMvec) to explore links between TPs of pyrene and the soil microbiome. The links established by this novel strategy were further validated using different approaches. Briefly, a metagenomic study provided indirect evidence for the established links, while the identification of pyrene degraders from soils, and a DNA-based stable isotope probing (DNA-SIP) study offered direct evidence. The comparison among different approaches, including Pearson's and Spearman's correlations, further confirmed the superior performance of our strategy. In conclusion, we summarize the unique features of the combined use of 2H-SIAM and MMvec. This study not only addresses the challenges in linking TPs to microbes but also introduces an innovative and effective approach for such investigations.Environmental Implication: Taxonomically diverse bacteria performing successive metabolic steps of the contaminant were firstly depicted in the environmental matrix.

Cyanopeptolins and Anabaenopeptins Are the Dominant Cyanopeptides from Planktothrix Strains Collected in Canadian Lakes.

Common bloom-forming cyanobacteria produce complex strain-specific mixtures of secondary metabolites. The beneficial and toxic properties of these metabolite mixtures have attracted both research and public health interest. The advancement of mass spectrometry-based platforms and metabolomics data processing has accelerated the identification of new metabolites and feature dereplication from microbial sources. The objective of this study was to use metabolomics data processing to decipher the intracellular cyanopeptide diversity of six Planktothrix strains collected from Canadian lakes. Data-dependent acquisition experiments were used to collect a non-targeted high-resolution mass spectrometry dataset. Principal component analysis and factor loadings were used to visualize cyanopeptide variation between strains and identified features contributing to the observed variation. GNPS molecular networking was subsequently used to show the diversity of cyanopeptides produced by the Planktothrix strains. Each strain produced a unique mixture of cyanopeptides, and a total of 225 cyanopeptides were detected. Planktothrix sp. CPCC 735 produced the most (n = 68) cyanopeptides, and P. rubescens CPCC 732 produced the fewest (n = 27). Microcystins and anabaenopeptins were detected from all strains. Cyanopeptolins, microviridins and aeruginosins were detected from five, four and two strains, respectively. Cyanopeptolin (n = 80) and anabaenopeptin (n = 61) diversity was the greatest, whereas microcystins (n = 21) were the least diverse. Interestingly, three of the P. rubescens strains had different cyanopeptide profiles, despite being collected from the same lake at the same time. This study highlights the diversity of cyanopeptides produced by Planktothrix and further hints at the underestimated cyanopeptide diversity from subpopulations of chemotypic cyanobacteria in freshwater lakes.

From MS/MS library implementation to molecular networks: Exploring oxylipin diversity with NEO-MSMS

Oxylipins, small polar molecules derived from the peroxidation of polyunsaturated fatty acids (PUFAs), serve as biomarkers for many diseases and play crucial roles in human physiology and inflammation. Despite their significance, many non-enzymatic oxygenated metabolites of PUFAs (NEO-PUFAs) remain poorly reported, resulting in a lack of public datasets of experimental data and limiting their dereplication in further studies. To overcome this limitation, we constructed a high-resolution tandem mass spectrometry (MS/MS) dataset comprising pure NEO-PUFAs (both commercial and self-synthesized) and in vitro free radical-induced oxidation of diverse PUFAs. By employing molecular networking techniques with this dataset and the existent ones in public repositories, we successfully mapped a wide range of NEO-PUFAs, expanding the strategies for annotating oxylipins, and NEO-PUFAs and offering a novel workflow for profiling these molecules in biological samples.

Bioassay-Driven, Fractionation-Empowered, Focused Metabolomics for Discovering Bacterial Activators of Aryl Hydrocarbon Receptor.

Aryl hydrocarbon receptor (AhR) is a transcription factor that regulates gene expression upon ligand activation, enabling microbiota-dependent induction, training, and function of the host immune system. A spectrum of metabolites, encompassing indole and tryptophan derivatives, have been recognized as activators. This work introduces an integrated, mass spectrometry-centric workflow that employs a bioassay-guided, fractionation-based methodology for the identification of AhR activators derived from human bacterial isolates. By leveraging the workflow efficiency, the complexities inherent in metabolomics profiling are significantly reduced, paving the way for an in-depth and focused mass spectrometry analysis of bioactive fractions isolated from bacterial culture supernatants. Validation of AhR activator candidates used multiple criteria─MS/MS of the synthetic reference compound, bioassay of AhR activity, and elution time confirmation using a C-13 isotopic reference─and was demonstrated for N-formylkynurenine (NFK). The workflow reported provides a roadmap update for improved efficiency of identifying bioactive metabolites using mass spectrometry-based metabolomics. Mass spectrometry datasets are accessible at the National Metabolomics Data Repository (PR001479, Project DOI: 10.21228/M8JM7Q).

The 2023 Report on the Proteome from the HUPO Human Proteome Project.

Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1-4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.

Leveraging open science machine learning challenges for data constrained planetary mission instruments

Abstract We set up two open-science machine learning (ML) challenges focusing on building models to automatically analyse mass spectrometry (MS) data for Mars exploration. ML challenges provide an excellent way to engage a diverse set of experts with benchmark training data, explore a wide range of ML and data science approaches, and identify promising models based on empirical results, as well as to get independent external analyses to compare with those of the internal team. These two challenges were proof-of-concept projects to analyse the feasibility of combining data collected from different instruments in a single ML application. We selected MS data from (1) commercial instruments and (2) the Sample Analysis at Mars (an instrument suite that includes a mass spectrometer subsystem onboard the Curiosity rover) testbed. These challenges, organized with DrivenData, gathered more than 1150 unique participants from all over the world, and obtained more than 600 solutions contributing powerful models to the analysis of rock and soil samples relevant to planetary science using various MS data sets. These two challenges demonstrated the suitability and value of multiple ML approaches to classifying planetary analogue data sets from both commercial and flight-like instruments. We present the processes from the problem identification, challenge set-ups, and challenge results that gathered creative and diverse solutions from worldwide participants, in some cases with no backgrounds in MS. We also present the potential and limitations of these solutions for ML application in future planetary missions. Our longer term goal is to deploy these powerful methods onboard the spacecraft to autonomously guide space operations and reduce ground-in-the-loop reliance.

Extended similarity methods for efficient data mining in imaging mass spectrometry.

Imaging mass spectrometry is a label-free imaging modality that allows for the spatial mapping of many compounds directly in tissues. In an imaging mass spectrometry experiment, a raster of the tissue surface produces a mass spectrum at each sampled x, y position, resulting in thousands of individual mass spectra, each comprising a pixel in the resulting ion images. However, efficient analysis of imaging mass spectrometry datasets can be challenging due to the hyperspectral characteristics of the data. Each spectrum contains several thousand unique compounds at discrete m/z values that result in unique ion images, which demands robust and efficient algorithms for searching, statistical analysis, and visualization. Some traditional post-processing techniques are fundamentally ill-equipped to dissect these types of data. For example, while principal component analysis (PCA) has long served as a useful tool for mining imaging mass spectrometry datasets to identify correlated analytes and biological regions of interest, the interpretation of the PCA scores and loadings can be non-trivial. The loadings often contain negative peaks in the PCA-derived pseudo-spectra, which are difficult to ascribe to underlying tissue biology. Herein, we have utilized extended similarity indices to streamline the interpretation of imaging mass spectrometry data. This novel workflow uses PCA as a pixel-selection method to parse out the most and least correlated pixels, which are then compared using the extended similarity indices. The extended similarity indices complement PCA by removing all non-physical artifacts and streamlining the interpretation of large volumes of imaging mass spectrometry spectra simultaneously. The linear complexity, O(N), of these indices suggests that large imaging mass spectrometry datasets can be analyzed in a 1 : 1 scale of time and space with respect to the size of the input data. The extended similarity indices algorithmic workflow is exemplified here by identifying discrete biological regions of mouse brain tissue.

An improved cancer diagnosis algorithm for protein mass spectrometry based on PCA and a one-dimensional neural network combining ResNet and SENet.

Cancer is one of the most serious health problems worldwide. Because cancer has no specific symptoms in its early stages, it is often not diagnosed until it is in advanced stages, reducing the likelihood of successful treatment. Therefore, early diagnosis of cancer is a formidable challenge. Mass spectrometry-based proteomics offers a robust technical foundation for cancer diagnosis. However, mass spectrometry data are characterized by high dimensionality, large data volume, and noise interference, which can lead to diagnostic errors in clinical applications. To address this challenge, an improved algorithm combining principal component analysis (PCA) with a convolutional neural network (CNN) algorithm (denoted as PCA-1DSE-ResCNN) was proposed to assist in analyzing high-dimensional mass spectral data. The algorithm initially reduced the dimensionality of the data through the PCA technique. Subsequently, the convolutional neural network algorithm (1DSE-ResCNN) integrating residual blocks and squeeze-and-excitation blocks was used as a classifier. This approach can not only alleviate the issues of overfitting and gradient vanishing caused by deep network layers but also reduce redundant information, enabling the algorithm to effectively learn high-dimensional data features and deal with nonlinear relationships. To validate the effectiveness of the algorithm, the high-dimensional ovarian cancer mass spectrometry dataset was selected as an example to examine its application performance in early diagnosis of ovarian cancer. The experimental results demonstrated that the PCA-1DSE-ResCNN algorithm outperforms other methods in terms of accuracy, specificity, and sensitivity on three high-dimensional ovarian cancer datasets. This study will contribute to the rapid diagnosis and early detection of cancer.

Transfer learning improves pMHC kinetic stability and immunogenicity predictions

The cellular immune response comprises several processes, with the most notable ones being the binding of the peptide to the Major Histocompability Complex (MHC), the peptide-MHC (pMHC) presentation to the surface of the cell, and the recognition of the pMHC by the T-Cell Receptor. Identifying the most potent peptide targets for MHC binding, presentation and T-cell recognition is vital for developing peptide-based vaccines and T-cell-based immunotherapies. Data-driven tools that predict each of these steps have been developed, and the availability of mass spectrometry (MS) datasets has facilitated the development of accurate Machine Learning (ML) methods for class-I pMHC binding prediction. However, the accuracy of ML-based tools for pMHC kinetic stability prediction and peptide immunogenicity prediction is uncertain, as stability and immunogenicity datasets are not abundant. Here, we use transfer learning techniques to improve stability and immunogenicity predictions, by taking advantage of a large number of binding affinity and MS datasets. The resulting models, TLStab and TLImm, exhibit comparable or better performance than state-of-the-art approaches on different stability and immunogenicity test sets respectively. Our approach demonstrates the promise of learning from the task of peptide binding to improve predictions on downstream tasks. The source code of TLStab and TLImm is publicly available at https://github.com/KavrakiLab/TL-MHC.

Mapping protein states and interactions across the tree of life with co-fractionation mass spectrometry

We present CFdb, a harmonized resource of interaction proteomics data from 411 co-fractionation mass spectrometry (CF-MS) datasets spanning 21,703 fractions. Meta-analysis of this resource charts protein abundance, phosphorylation, and interactions throughout the tree of life, including a reference map of the human interactome. We show how large-scale CF-MS data can enhance analyses of individual CF-MS datasets, and exemplify this strategy by mapping the honey bee interactome.

A biotin targeting chimera (BioTAC) system to map small molecule interactomes in situ

Understanding how small molecules bind to specific protein complexes in living cells is critical to understanding their mechanism-of-action. Unbiased chemical biology strategies for direct readout of protein interactome remodelling by small molecules would provide advantages over target-focused approaches, including the ability to detect previously unknown ligand targets and complexes. However, there are few current methods for unbiased profiling of small molecule interactomes. To address this, we envisioned a technology that would combine the sensitivity and live-cell compatibility of proximity labelling coupled to mass spectrometry, with the specificity and unbiased nature of chemoproteomics. In this manuscript, we describe the BioTAC system, a small-molecule guided proximity labelling platform that can rapidly identify both direct and complexed small molecule binding proteins. We benchmark the system against µMap, photoaffinity labelling, affinity purification coupled to mass spectrometry and proximity labelling coupled to mass spectrometry datasets. We also apply the BioTAC system to provide interactome maps of Trametinib and analogues. The BioTAC system overcomes a limitation of current approaches and supports identification of both inhibitor bound and molecular glue bound complexes.

An intelligent strategy integrating polygonal mass defect filtering and preferred ion lists based on high-resolution mass spectrometry for the characterization of chemical compounds in Phellodendri Chinensis Cortex

Advanced analytical methods are often required for detailed and comprehensive interpretation of the complex traditional Chinese medicine system. Phellodendri Chinensis Cortex (PCC) is a common herb involving alkaloids as its main active constituent. The systemic characterization of alkaloids in PCC was carried out by an intelligent process combining polygonal mass defect filtering (p-MDF) with preferred ion lists based on high-resolution mass spectrometry. Firstly, the combination of p-MDF and preferred ion lists was used for data acquisition. Secondly, the mass spectrometry data sets were classified by the deep learning-assisted MDF method for manual annotation, including alkaloids and other components. The characteristic diagnosis ions and neutral loss fragments were also used to assist the manual annotation. Subsequently, the Global Natural Product Social platform was employed for automated annotation. A total of 116 compounds from PCC were identified, containing 86 alkaloids, 1 amino acid, 9 phenylpropanoids, 2 triterpenoids, 13 fatty acids and 5 others. The established strategy provided a powerful guide for the chemical characterization of complex herbal substrates.

Identification of Emerging Novel Psychoactive Substances by Retrospective Analysis of Population-Scale Mass Spectrometry Data Sets.

Over the last two decades, hundreds of new psychoactive substances (NPSs), also known as "designer drugs", have emerged on the illicit drug market. The toxic and potentially fatal effects of these compounds oblige laboratories around the world to screen for NPS in seized materials and biological samples, commonly using high-resolution mass spectrometry. However, unambiguous identification of a NPS by mass spectrometry requires comparison to data from analytical reference materials, acquired on the same instrument. The sheer number of NPSs that are available on the illicit market, and the pace at which new compounds are introduced, means that forensic laboratories must make difficult decisions about which reference materials to acquire. Here, we asked whether retrospective suspect screening of population-scale mass spectrometry data could provide a data-driven platform to prioritize emerging NPSs for assay development. We curated a suspect database of precursor and diagnostic fragment ion masses for 83 emerging NPSs and used this database to retrospectively screen mass spectrometry data from 12,727 urine drug screens from one Canadian province. We developed integrative computational strategies to prioritize the most reliable identifications and tracked the frequency of these identifications over a 3 year study period between August 2019 and August 2022. The resulting data were used to guide the acquisition of new reference materials, which were in turn used to validate a subset of the retrospective identifications. Last, we took advantage of matching clinical reports for all 12,727 samples to systematically benchmark the accuracy of our retrospective data analysis approach. Our work opens up new avenues to enable the rapid detection of emerging illicit drugs through large-scale reanalysis of mass spectrometry data.

EPCO-13. CNMF-SNS, A FRAMEWORK FOR UNSUPERVISED INTEGRATION OF MULTIOMICS DATA, IDENTIFIES INVASION AND RECURRENCE ASSOCIATED PROGRAMS IN GBM

Abstract Glioblastoma (GBM) is an aggressive cancer of the brain for which novel therapies are urgently needed. Heterogeneity in GBM underlies tumor evolution and resistance to therapy and involves genetic diversity of tumor cells, spatially distinct niches harbouring stem-like cells, and immune cells spanning anti- and pro-tumor states. No single profiling methodology can capture all these facets of tumor biology in the same space and time, necessitating multi-omics data collection and computational methods suited for multi-omic integration across and within cohorts. We therefore set out to develop a generalizable framework for integration that can bridge across cohorts and data types. Using consensus non-negative matrix factorization (cNMF), we discover gene expression programs (GEPs) corresponding to cell types and/or states in an unsupervised fashion. Rather than choosing a single rank (number of programs), we identify both high and low-resolution programs for each dataset, and then integrate programs from all datasets and ranks into a solution network space (SNS) on which graph algorithms identify communities of highly similar GEPs identified in all or some datasets. These communities are characterized by gene set enrichment analyses and association with sample metadata. SNS communities can be used to transfer sample metadata across datasets, enabling multi-omics and multi-cohort integration, even in the absence of shared samples or cells. We showcase the utility of cNMF-SNS in integrating multiomics datasets of GBM from single-cell to bulk RNA-Seq and mass spectrometry proteomics. We further demonstrate the power of cNMF-SNS to define and cross-annotate programs identified in 5 complementary mass spectrometry datasets of GBM, layering on survival, driver gene mutation status, clinical features, and imaging features. We demonstrate robust mapping of biologically and clinically relevant processes, highlighting how programs for tumor cell invasion and therapy resistance can be mined for novel therapeutic targets. cNMF-SNS is available at https://github.com/MorrissyLab/cNMF-SNS.

Multimodal Atlas of Paired Diagnosis and Relapse AML Samples Enables Novel Therapeutic Targeting of Surface Antigens

Note: A.H. and M.U. share co-first authorship. AML is an aggressive clonal malignancy characterized by combinations of chromosomal abnormalities, gene mutations, and cell surface antigen (Ag) expression profiles. This heterogeneity contributes to refractory or relapsed disease that presents a major challenge when treating AML. Previous single cell sequencing studies of primary AML samples have provided remarkable insight into the clonal architecture of AML cell populations and how their mutational, transcriptomic, and surface Ag profiles vary between patients. However, information addressing clonal shifts during progression from diagnosis to relapse in large cohorts is lacking. Here, we adapted single cell RNA sequencing with surface Ag feature barcoding to analyze more than 450,000 cells from 28 paired AML patient bone marrow mononuclear cell samples collected at diagnosis and relapse (56 samples). To our knowledge, it is the largest and most comprehensive single cell AML atlas to date. This atlas contains rich clinical metadata including cytogenetics, mutation status of canonical AML genes, treatment history, and survival information. We leveraged these data to identify potential correlations with clonal heterogeneity during progression to relapse and to propose novel strategies for targeting the surface of AML cells. The feature barcoding panel consists of a comprehensive list of 81 surface Ags reported in clinicaltrials.gov or mined from literature, including large proteomics mass spectrometry datasets of AML patient bone marrow samples (deBoer et. al. Cancer Cell 2018; Jayavelu et al, Cancer Cell 2022). Feature barcoding produces antibody derived tag (ADT) counts which are interpreted as relative values but alone does not indicate absolute number of Ags per cell. We addressed this limitation by measuring absolute Ag density of AML Ags, CD33, CLL1, CD123, and EMR2 in patient samples using the flow cytometric QuantiBRITE assay. Focusing on these four Ags, we modeled the relationship between ADT expression and antibodies per cell (ABC) Ag density from QuantiBRITE. The model was applied to impute absolute Ag density for the remaining 77 Ags in all samples. UMAP visualization after sample transcriptome integration revealed distinct clustering of CD45-dim myeloblasts, T cells, B cells, and erythroid cells. Longitudinal analysis identified 28 surface Ags that were differentially expressed (absolute log 2FC > 1, p < 0.01) between relapse and diagnosis myeloblasts across multiple patients (Fig. 1A). An additional 19 Ags were differentially expressed in no more than one patient while the other 19 had no significant change in any patient, highlighting the inter-and intra- heterogeneity of AML at diagnosis and relapse in these patients. Targeting multiple antigens simultaneously may help address the issue of antigen heterogeneity of tumor cells and help avoid potential antigen escape. Because multi-targeting therapies are a potential solution to this, weleveraged the single-cell resolution of our atlas to systematically identify Ags that in combination provide maximum coverage and are significantly co-expressed in myeloblasts. From this analysis, we found 48 and 52 co-expressed Ag pairs in diagnosis and relapse blasts, respectively (r > 0.4, p < 0.01; Fig. 1B). Of note, CD44 and EMR2 were co-expressed at both diagnosis (r = 0.49 p < 0.01) and relapse (r = 0.3, p < 0.01) with >90% of their blasts expressing CD44, EMR2, or both at targetable antigen levels of >1000 Ags/cell. Furthermore, 10 samples had >40% of their blasts co-expressing both Ags simultaneously at >1000 Ags/cell at diagnosis or relapse. 6 of these patients had a >10% increase in co-expression at relapse, 3 had >10% decrease at relapse, and one patient had no co-expression change. Our comprehensive profiling of AML has provided high resolution insight on cell surface at diagnosis and during disease progression and produced novel hypotheses about targetable combinations of surface Ags based on their expression changes between timepoints, co-expression, and surface densities. Furthermore, our reference atlas can be further dissected to characterize the transcriptome and mutational profiles of myeloblasts at diagnosis and relapse thereby prodding an important resource to create surface antigen protein - transcript correlations within AML blasts and to trace molecular signatures during disease progression.

FRI289 It Takes A Village: Musashi And Its Associated Proteins In The Adenohypophysis

Abstract Disclosure: K.L. Bronson: None. L. Hardy: None. A. Lagasse: None. A.M. MacNicol: None. Musashi is an RNA-binding protein that regulates stem and progenitor cell fates. Interestingly, we have reported expression of Musashi pituitary stem cells as expected but also in the differentiated anterior pituitary hormone-producing cell lineages as well as. The adult anterior pituitary relies on the functional cellular plasticity of its hormone-producing cell populations to maintain endocrine homeostasis when physiological demands change. Both Musashi isoforms (Musashi1 and Musashi2) are highly conserved across species and have two N-terminal RNA-recognition motifs (RRMs) and a disordered C-terminus containing two regulatory phosphorylation sites. Musashi can selectively activate or repress the translation of mRNAs depending on the transcript and cellular context. While Musashi phosphorylation is required for translational activation, Musashi does not possess inherent enzymatic activity; therefore, co-associated proteins must mediate Musashi-directed translational control. Musashi immunoprecipitation studies initially identified 50 co-associated proteins in Xenopus oocytes, but only a subset of these were directly implicated in Musashi-dependent translational activation (e.g. Elavl1 and Pabp4). The identity of Musashi-associated proteins in the anterior pituitary and their requirement for Musashi function are not known. To this end, we have immunoprecipitated Musashi from whole male mouse pituitaries for mass spectrometry analysis. 206 proteins significantly (adj p-value < 0.05) associated with Musashi1 and Musashi2, and seven of these overlapped with the Xenopus dataset: Elavl1, Pabp4, Hnrnpc, Mov10, Pura, Rps27, and Upf1. Three proteins from the mouse mass spectrometry dataset were selected for further analyses. Sam68 is a multi-functional protein that sensitizes ovaries to FSH and LH, as well as mediates the activation of leptin and insulin signaling in a variety of tissues. Ago2 is a component of the RNA-induced silencing complex that is necessary for miRNA-directed translational control and is known to associate with Musashi1. The AU-rich element binding protein Elavl1 (HuR) contributes to Musashi-dependent activation of target mRNAs in Xenopus and also stabilizes Gnrhr mRNA in LβT2 cells and sensitizes them to GnRH signaling. We will present our ongoing studies on the requirement for these protein interactions for Musashi-directed mRNA translational regulation. Presentation: Friday, June 16, 2023

High-throughput Proteomics Identifies THEMIS2 as Independent Biomarker of Treatment-free Survival in Untreated CLL.

It remains challenging in chronic lymphocytic leukemia (CLL) to distinguish between patients with favorable and unfavorable time-to-first treatment (TTFT). Additionally, the downstream protein correlates of well-known molecular features of CLL are not always clear. To address this, we selected 40 CLL patients with TTFT ≤24 months and compared their B cell intracellular protein expression with 40 age- and sex-matched CLL patients with TTFT >24 months using mass spectrometry. In total, 3268 proteins were quantified in the cohort. Immunoglobulin heavy-chain variable (IGHV) mutational status and trisomy 12 were most impactful on the CLL proteome. Comparing cases to controls, 5 proteins were significantly upregulated, whereas 3 proteins were significantly downregulated. Of these, only THEMIS2, a signaling protein acting downstream of the B cell receptor, was significantly associated with TTFT, independently of IGHV and TP53 mutational status (hazard ratio, 2.49 [95% confidence interval, 1.62-3.84]; P < 0.001). This association was validated on the mRNA and protein level by quantitative polymerase chain reaction and ELISA, respectively. Analysis of 2 independently generated RNA sequencing and mass spectrometry datasets confirmed the association between THEMIS2 expression and clinical outcome. In conclusion, we present a comprehensive characterization of the proteome of untreated CLL and identify THEMIS2 expression as a putative biomarker of TTFT.

Multiple urinary peptides display distinct sex-specific distribution.

Previous studies have established the association of sex with gene and protein expression. This study investigated the association of sex with the abundance of endogenous urinary peptides, using capillary electrophoresis-coupled to mass spectrometry (CE-MS) datasets from 2008 healthy individuals and patients with type II diabetes, divided in one discovery and two validation cohorts. Statistical analysis using the Mann-Whitney test, adjusted for multiple testing, revealed 143 sex-associated peptides in the discovery cohort. Of these, 90 peptides were associated with sex in at least one of the validation cohorts and showed agreement in their regulation trends across all cohorts. The 90 sex-associated peptides were fragments of 29 parental proteins. Comparison with previously published transcriptomics data demonstrated that the genes encoding 16 of these parental proteins had sex-biased expression. The 143 sex-associated peptides were combined into a support vector machine-based classifier that could discriminate males from females in two independent sets of healthy individuals and patients with type II diabetes, with an AUC of 89% and 81%, respectively. Collectively, the urinary peptidome contains multiple sex-associated differences, which may enable a better understanding of sex-biased molecular mechanisms and the development of more accurate diagnostic, prognostic, or predictive classifiers for each individual sex.