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Properties, Origin, and Consistency of Truncated Proteoforms Across Top-Down Proteomic Studies
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Quantitative proteomics reveals extensive lysine ubiquitination and transcription factor stability states in Arabidopsis
Abstract Protein activity, abundance, and stability can be regulated by post-translational modification including ubiquitination. Ubiquitination is conserved among eukaryotes and plays a central role in modulating cellular function; yet, we lack comprehensive catalogs of proteins that are modified by ubiquitin in plants. In this study, we describe an antibody-based approach to enrich ubiquitinated peptides coupled with isobaric labeling to enable quantification of up to 18-multiplexed samples. This approach identified 17,940 ubiquitinated lysine sites arising from 6,453 proteins from Arabidopsis (Arabidopsis thaliana) primary roots, seedlings, and rosette leaves. Gene ontology analysis indicated that ubiquitinated proteins are associated with numerous biological processes including hormone signaling, plant defense, protein homeostasis, and metabolism. We determined ubiquitinated lysine residues that directly regulate the stability of three transcription factors, CRYPTOCHROME-INTERACTING BASIC-HELIX-LOOP-HELIX 1 (CIB1), CIB1 LIKE PROTEIN 2 (CIL2), and SENSITIVE TO PROTON RHIZOTOXICITY1 (STOP1) using in vivo degradation assays. Furthermore, codon mutation of CIB1 to create a K166R conversion to prevent ubiquitination, via CRISPR/Cas9-derived adenosine base editing, led to an early flowering phenotype and increased expression of FLOWERING LOCUS T (FT). These comprehensive site-level ubiquitinome profiles provide a wealth of data for future functional studies related to modulation of biological processes mediated by this post-translational modification in plants.
Development and implementation of high-throughput proteomic and metabolomics assays by using advanced chromatographic and mass spectrometric systems (CRADA Final Report)
The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOFMS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Overall, the project advanced rapid metabolomics by combining liquid chromatography, ion mobility spectrometry, and data-independent mass spectrometry with machine learning. This multidimensional approach uses retention time, collision cross-section, precursor mass, and fragment-ion information to distinguish chemically similar metabolites that can be difficult to resolve using conventional liquid- or gas-chromatography methods. The resulting workflow also provided automated metabolite-identification error estimates, addressing a recognized need for statistical confidence measures in metabolomics.
Proteomic Profiling of Hematopoietic Stem/Progenitor Cells after a Whole Body Exposure of CBA/CaJ Mice to Titanium (48Ti) Ions
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Functional Meta-Analysis of the Proteomic Responses of Arabidopsis Seedlings to the Spaceflight Environment Reveals Multi-Dimensional Sources of Variability across Spaceflight Experiments
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Energy flux couples sulfur isotope fractionation to proteomic and metabolite profiles in Desulfovibrio vulgaris
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Growth in spaceflight hardware results in alterations to the transcriptome and proteome
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Proteomic Retrieval from Nucleic Acid Depleted Space-Flown Human Cells
Compared to experiments utilizing humans in microgravity, cell-based approaches to questions about subsystems of the human system afford multiple advantages, such as crew safety and the ability to achieve statistical significance. To maximize the science return from flight samples, an optimized method was developed to recover protein from samples depleted of nucleic acid. This technique allows multiple analyses on a single cellular sample and when applied to future cellular investigations could accelerate solutions to significant biomedical barriers to human space exploration. Cell cultures grown in American Fluoroseal bags were treated with an RNA stabilizing agent (RNAlater - Ambion), which enabled both RNA and immunoreactive protein analyses. RNA was purified using an RNAqueous(registered TradeMark) kit (Ambion) and the remaining RNA free supernatant was precipitated with 5% trichloroacetic acid. The precipitate was dissolved in SDS running buffer and tested for protein content using a bicinchoninic acid assay (1) (Sigma). Equal loads of protein were placed on SDS-PAGE gels and either stained with CyproOrange (Amersham) or transferred using Western Blotting techniques (2,3,4). Protein recovered from RNAlater-treated cells and stained with protein stain, was measured using Imagequant volume measurements for rectangles of equal size. BSA treated in this way gave quantitative data over the protein range used (Fig 1). Human renal cortical epithelial (HRCE) cells (5,6,7) grown onboard the International Space Station (ISS) during Increment 3 and in ground control cultures exhibited similar immunoreactivity profiles for antibodies to the Vitamin D receptor (VDR) (Fig 2), the beta isoform of protein kinase C (PKC ) (Fig 3), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (Fig 4). Parallel immunohistochemical studies on formalin-fixed flight and ground control cultures also showed positive immunostaining for VDR and other biomarkers (Fig 5). These results are consistent with data from additional antigenic recovery experiments performed on human Mullerian tumor cells cultured in microgravity (8).
Genomics and Proteomics Based Security Protocols for Secure Network Architectures
A hardware design that integrates live and algorithmic inhabitants to produce patterns of gene expression in vivo and in silico Protocols and algorithms based upon the processes of regulation of gene expression to produce cryptographic representations of genes, RNA, proteins, and gene expression to perform authentication and confidentiality functions for computers and networks. A network concept of operations integrating all of the above into existing legacy networks.
Benchmarking Biomolecular Foundation Models for Cross-Modal Genomics-Proteomics
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Increased inflammation as well as decreased endoplasmic reticulum stress and translation differentiate pancreatic islets from donors with pre-symptomatic stage 1 type 1 diabetes and non-diabetic donors
Aims/hypothesis Progression to type 1 diabetes is associated with genetic factors, the presence of autoantibodies and a decline in beta cell insulin secretion in response to glucose. Very little is known regarding the molecular changes that occur in human insulin-secreting beta cells prior to the onset of type 1 diabetes. Herein, we applied an unbiased proteomics approach to identify changes in proteins and potential mechanisms of islet dysfunction in islet-autoantibody-positive organ donors with pre-symptomatic stage 1 type 1 diabetes (HbA1c ≤42 mmol/mol [6.0%]). We aimed to identify pathways in islets that are indicative of beta cell dysfunction. Methods Multiple islet sections were collected through laser microdissection of frozen pancreatic tissues from organ donors positive for single or multiple islet autoantibodies (AAb + , n=5), and age (±2 years)- and sex-matched non-diabetic (ND) control donors (n=5) obtained from the Network for Pancreatic Organ donors with Diabetes (nPOD). Islet sections were subjected to MS-based proteomics and analysed with label-free quantification followed by pathway and functional annotations. Results Analyses resulted in ~4500 proteins identified with low false discovery rate (<1%), with 2165 proteins reliably quantified in every islet sample. We observed large inter-donor variations that presented a challenge for statistical analysis of proteome changes between donor groups. We therefore focused on only the donors with stage 1 type 1 diabetes who were positive for multiple autoantibodies (mAAb + , n=3) and genetic risk compared with their matched ND controls (n=3) for the final statistical analysis. Approximately 10% of the proteins (n=202) were significantly different (unadjusted p<0.025, q<0.15) for mAAb + vs ND donor islets. The significant alterations clustered around major functions for upregulation in the immune response and glycolysis, and downregulation in endoplasmic reticulum (ER) stress response as well as protein translation and synthesis. The observed proteome changes were further supported by several independent published datasets, including a proteomics dataset from in vitro proinflammatory cytokine-treated human islets and single-cell RNA-seq datasets from AAb + individuals. Conclusions/interpretation In situ human islet proteome alterations in stage 1 type 1 diabetes centred around several major functional categories, including an expected increase in immune response genes (elevated antigen presentation/HLA), with decreases in protein synthesis and ER stress response, as well as compensatory metabolic response. The dataset serves as a proteomics resource for future studies on beta cell changes during type 1 diabetes progression and pathogenesis. Data availability The LC-MS raw datasets that support the findings of this study have been deposited in the online repository: MassIVE (https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp) with accession no. MSV000090212.
Novel Cell-Type-Specific Drought-Responsive Proteins in Root Tips of Field-Grown Perennial Switchgrass
The root-tip region of plants, including the root cap, forms the most basal terminal of the root and exhibits a high degree of cellular complexity in terms of morphology, cytological function, and interaction with environmental cues in the soil. Cells in this region follow a developmental trajectory, transitioning from stem cells to meristematic cells, and ultimately to fully differentiated cell types. However, our understanding of root-tip cell-type specific proteomic responses to abiotic stresses, such as drought, particularly under field conditions, remains limited. This study aimed to identify spatially resolved, cell type-specific proteomes in switchgrass (Panicum virgatum) root tips under drought stress. Root tips were collected from seven-year-old, field-grown switchgrass ‘Alamo’ plants excavated under both well-watered and long-term drought conditions. Cell type-specific proteins were identified using laser capture microdissection (LCM) coupled with nanoPOTS (Nanodroplet Processing in One Pot for Trace Samples) and nano-LC-MS proteomics analysis. Five distinct cell types were targeted: (1) cells in the quiescent center and stem cell niche (QuC), (2) protodermal epidermal cells (PEC) in the meristematic zone, (3) epidermal cells in the transition and elongation zones above the root cap (Epi), (4) peripheral root cap cells (PRC), forming 2–3 layers below the PEC and 1–2 layers above the root border cells, and (5) columella root cap cells (Col) comprising of the columella initials and a single underlying layer of cells undergoing active growth. Principal component analysis (PCA) revealed clear separation among the five targeted cell types, confirming distinct proteomic profiles. Proteins predominantly enriched in each cell type were linked to distinct cellular functions, with QuC cells showing involvement in chromosomal behavior, DNA replication, and mitosis—key processes for stem cell niche regulation. Drought stress resulted in alterations of proteostasis, as evidenced by significant decreases in ribosomal proteins and increases in protein synthesis inhibitors. Moreover, drought stress induced unique cell-type–specific proteins involved in phytohormone biosynthesis and signaling pathways, including auxin, cytokinin, and jasmonic acid. In particular, QuC cells were more highly enriched in proteins associated with DNA repair and mitotic processes. Metabolic pathways related to amino acids, carbohydrates, and lipids were differentially affected in a cell-type–dependent manner, whereas general stress-responsive proteins exhibited consistent changes across all five cell types. Overall, this study provides unique spatially resolved, cell-type-specific proteomic profiles in root tips, representing a significant advancement in our understanding of the cellular mechanisms underlying plant responses to drought stress in natural field conditions.
Cell Population–resolved Multiomics Atlas of the Developing Lung
The lung is a vital organ that undergoes extensive morphological and functional changes during postnatal development. To disambiguate how different cell populations contribute to organ development, we performed proteomic and transcriptomic analyses of four sorted cell populations from the lung of human subjects aged 0 to 8 years-old with a focus on early life. The cell populations analyzed included epithelial, endothelial, mesenchymal, and immune cells. Our results revealed distinct molecular signatures for each of the sorted cell populations that enable the description of molecular shifts occurring in these populations during post-natal development. Here, we confirmed that the proteome of the different cell populations was distinct regardless of age and identified functions specific to each population. We identified a series of cell population protein markers, including those located at the cell surface, that show differential expression and distribution on RNA in situ hybridization and immunofluorescence imaging. We validated the spatial distribution of AT1 and endothelial cell surface markers. Temporal analyses of the proteome of each of the four populations revealed processes modulated during postnatal development and disambiguating results obtained on whole tissue proteome. Finally, the proteome was compared to a transcriptomics survey performed on the same lung samples to evaluate processes under post-transcriptional control.
Evaluation of normalization strategies for mass spectrometry-based multi-omics datasets
Introduction Data normalization is crucial for multi-omics integration, reducing systematic errors and maximizing the likelihood of discovering true biological variation. Most studies assess normalization for a single omics type or use datasets from separate experiments. Few address time-course data, where normalization might bias temporal differentiation. In this study, we compared common normalization methods and a machine learning approach, Systematical Error Removal using Random Forest (SERRF), using multi-omics datasets generated from the same experiment—even from the same cell lysate. Objectives To develop a straightforward process to assess normalization effects and identify the most robust methods across multi-omics datasets. Methods We analyzed metabolomics, lipidomics, and proteomics datasets from primary human cardiomyocytes and motor neurons exposed to acetylcholine-active compounds over time. Normalization effectiveness was evaluated based on improvement in QC features consistency and observing the change in treatment and time-related variance. Results Probabilistic Quotient Normalization (PQN) and Locally Estimated Scatterplot Smoothing (LOESS) QC were identified as optimal for metabolomics and lipidomics, while PQN, Median, and LOESS normalization excelled for proteomics. These methods consistently enhanced QC feature consistency in metabolomics and lipidomics, and preserved time-related variance or treatment-related variance in proteomics, demonstrating their effectiveness and robustness. SERRF normalization, applied only to metabolomics in this study, outperformed other methods in some datasets but inadvertently masked treatment-related variance in others. Conclusion Our evaluation identified PQN and LoessQC as the top methods for metabolomics and lipidomics, and PQN, Median, and Loess normalization for proteomics, in multi-omics integration in a temporal study.