iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates

Repression of mRNA translation initiation by GIGYF1 via disrupting the eIF3-eIF4G1 interaction

Viruses can selectively repress the translation of mRNAs involved in the antiviral response. RNA viruses exploit the Grb10-interacting GYF (glycine-tyrosine-phenylalanine) proteins 2 (GIGYF2) and eukaryotic translation initiation factor 4E (eIF4E) homologous protein 4EHP to selectively repress the translation of transcripts such as Ifnb1, which encodes the antiviral cytokine interferon-β (IFN-β). Herein, we reveal that GIGYF1, a paralog of GIGYF2, robustly represses cellular mRNA translation through a distinct 4EHP-independent mechanism. Upon recruitment to a target mRNA, GIGYF1 binds to subunits of eukaryotic translation initiation factor 3 (eIF3) at the eIF3-eIF4G1 interaction interface. This interaction disrupts the eIF3 binding to eIF4G1, resulting in transcript-specific translational repression. Depletion of GIGYF1 induces a robust immune response by derepressing IFN-β production. Our study highlights a unique mechanism of translational regulation by GIGYF1 that involves sequestering eIF3 and abrogating its binding to eIF4G1. This mechanism has profound implications for the host response to viral infections.

Waves of regulated protein expression and phosphorylation rewire the proteome to drive gametogenesis in budding yeast

Sexually reproducing eukaryotes employ a developmentally regulated cell division program-meiosis-to generate haploid gametes from diploid germ cells. To understand how gametes arise, we generated a proteomic census encompassing the entire meiotic program of budding yeast. We found that concerted waves of protein expression and phosphorylation modify nearly all cellular pathways to support meiotic entry, meiotic progression, and gamete morphogenesis. Leveraging this comprehensive resource, we pinpointed dynamic changes in mitochondrial components and showed that phosphorylation of the FoF1-ATP synthase complex is required for efficient gametogenesis. Furthermore, using cryoET as an orthogonal approach to visualize mitochondria, we uncovered highly ordered filament arrays of Ald4ALDH2, a conserved aldehyde dehydrogenase that is highly expressed and phosphorylated during meiosis. Notably, phosphorylation-resistant mutants failed to accumulate filaments, suggesting that phosphorylation regulates context-specific Ald4ALDH2 polymerization. Overall, this proteomic census constitutes a broad resource to guide the exploration of the unique sequence of events underpinning gametogenesis.

The TSC22D, WNK, and NRBP gene families exhibit functional buffering and evolved with Metazoa for cell volume regulation

The ability to sense and respond to osmotic fluctuations is critical for the maintenance of cellular integrity. We used gene co-essentiality analysis to identify an unappreciated relationship between TSC22D2, WNK1, and NRBP1 in regulating cell volume homeostasis. All of these genes have paralogs and are functionally buffered for osmo-sensing and cell volume control. Within seconds of hyperosmotic stress, TSC22D, WNK, and NRBP family members physically associate into biomolecular condensates, a process that is dependent on intrinsically disordered regions (IDRs). A close examination of these protein families across metazoans revealed that TSC22D genes evolved alongside a domain in NRBPs that specifically binds to TSC22D proteins, which we have termed NbrT (NRBP binding region with TSC22D), and this co-evolution is accompanied by rapid IDR length expansion in WNK-family kinases. Our study reveals that TSC22D, WNK, and NRBP genes evolved in metazoans to co-regulate rapid cell volume changes in response to osmolarity.

Meta-Analysis of Rice Phosphoproteomics Data to Understand Variation in Cell Signaling Across the Rice Pan-Genome

Phosphorylation is the most studied post-translational modification, and has multiple biological functions. In this study, we have reanalyzed publicly available mass spectrometry proteomics data sets enriched for phosphopeptides from Asian rice (Oryza sativa). In total we identified 15,565 phosphosites on serine, threonine, and tyrosine residues on rice proteins. We identified sequence motifs for phosphosites, and link motifs to enrichment of different biological processes, indicating different downstream regulation likely caused by different kinase groups. We cross-referenced phosphosites against the rice 3,000 genomes, to identify single amino acid variations (SAAVs) within or proximal to phosphosites that could cause loss of a site in a given rice variety and clustered the data to identify groups of sites with similar patterns across rice family groups. The data has been loaded into UniProt Knowledge-Base─enabling researchers to visualize sites alongside other data on rice proteins, e.g., structural models from AlphaFold2, PeptideAtlas, and the PRIDE database─enabling visualization of source evidence, including scores and supporting mass spectra.

Soil Metaproteomics for Microbial Community Profiling: Methodologies and Challenges

Soil represents a complex and dynamic ecosystem, hosting a myriad of microorganisms that coexist and play vital roles in nutrient cycling and organic matter transformation. Among these microorganisms, bacteria and fungi are key members of the microbial community, profoundly influencing the fate of nitrogen, sulfur, and carbon in terrestrial environments. Understanding the intricacies of soil ecosystems and the biological processes orchestrated by microbial communities necessitates a deep dive into their composition and metabolic activities. The advent of next-generation sequencing and 'omics' techniques, such as metagenomics and metaproteomics, has revolutionized our understanding of microbial ecology and the functional dynamics of soil microbial communities. Metagenomics enables the identification of microbial community composition in soil, while metaproteomics sheds light on the current biological functions performed by these communities. However, metaproteomics presents several challenges, both technical and computational. Factors such as the presence of humic acids and variations in extraction methods can influence protein yield, while the absence of high-resolution mass spectrometry and comprehensive protein databases limits the depth of protein identification. Notwithstanding these limitations, metaproteomics remains a potent tool for unraveling the intricate biological processes and functions of soil microbial communities. In this review, we delve into the methodologies and challenges of metaproteomics in soil research, covering aspects such as protein extraction, identification, and bioinformatics analysis. Furthermore, we explore the applications of metaproteomics in soil bioremediation, highlighting its potential in addressing environmental challenges.

Functional surface expression of immunoglobulin cleavage systems in a candidate Mycoplasma vaccine chassis

The Mycoplasma Immunoglobulin Binding/Protease (MIB-MIP) system is a candidate 'virulence factor present in multiple pathogenic species of the Mollicutes, including the fast-growing species Mycoplasma feriruminatoris. The MIB-MIP system cleaves the heavy chain of host immunoglobulins, hence affecting antigen-antibody interactions and potentially facilitating immune evasion. In this work, using -omics technologies and 5'RACE, we show that the four copies of the M. feriruminatoris MIB-MIP system have different expression levels and are transcribed as operons controlled by four different promoters. Individual MIB-MIP gene pairs of M. feriruminatoris and other Mollicutes were introduced in an engineered M. feriruminatoris strain devoid of MIB-MIP genes and were tested for their functionality using newly developed oriC-based plasmids. The two proteins are functionally expressed at the surface of M. feriruminatoris, which confirms the possibility to display large membrane-associated proteins in this bacterium. However, functional expression of heterologous MIB-MIP systems introduced in this engineered strain from phylogenetically distant porcine Mollicutes like Mesomycoplasma hyorhinis or Mesomycoplasma hyopneumoniae could not be achieved. Finally, since M. feriruminatoris is a candidate for biomedical applications such as drug delivery, we confirmed its safety in vivo in domestic goats, which are the closest livestock relatives to its native host the Alpine ibex.

Quantitative proteomic analysis of plasma membranes from the fish pathogen Saprolegnia parasitica reveals promising targets for disease control

The phylum Oomycota contains economically important pathogens of animals and plants, including Saprolegnia parasitica, the causal agent of the fish disease saprolegniasis. Due to intense fish farming and banning of the most effective control measures, saprolegniasis has re-emerged as a major challenge for the aquaculture industry. Oomycete cells are surrounded by a polysaccharide-rich cell wall matrix that, in addition to being essential for cell growth, also functions as a protective "armor." Consequently, the enzymes responsible for cell wall synthesis provide potential targets for disease control. Oomycete cell wall biosynthetic enzymes are predicted to be plasma membrane proteins. To identify these proteins, we applied a quantitative (iTRAQ) mass spectrometry-based proteomics approach to the plasma membrane of the hyphal cells of S. parasitica, providing the first complete plasma membrane proteome of an oomycete species. Of significance is the identification of 65 proteins enriched in detergent-resistant microdomains (DRMs). In silico analysis showed that DRM-enriched proteins are mainly involved in molecular transport and β-1,3-glucan synthesis, potentially contributing to pathogenesis. Moreover, biochemical characterization of the glycosyltransferase activity in these microdomains further supported their role in β-1,3-glucan synthesis. Altogether, the knowledge gained in this study provides a basis for developing disease control measures targeting specific plasma membrane proteins in S. parasitica.IMPORTANCEThe significance of this research lies in its potential to combat saprolegniasis, a detrimental fish disease, which has resurged due to intensive fish farming and regulatory restrictions. By targeting enzymes responsible for cell wall synthesis in Saprolegnia parasitica, this study uncovers potential avenues for disease control. Particularly noteworthy is the identification of several proteins enriched in membrane microdomains, offering insights into molecular mechanisms potentially involved in pathogenesis. Understanding the role of these proteins provides a foundation for developing targeted disease control measures. Overall, this research holds promise for safeguarding the aquaculture industry against the challenges posed by saprolegniasis.

Novel FFPE proteomics method suggests prolactin induced protein as hormone induced cytoskeleton remodeling spatial biomarker

Robotically assisted proteomics provides insights into the regulation of multiple proteins achieving excellent spatial resolution. However, developing an effective method for spatially resolved quantitative proteomics of formalin fixed paraffin embedded tissue (FFPE) in an accessible and economical manner remains challenging. We introduce non-robotic In-insert FFPE proteomics approach, combining glass insert FFPE tissue processing with spatial quantitative data-independent mass spectrometry (DIA). In-insert approach identifies 450 proteins from a 5 µm thick breast FFPE tissue voxel with 50 µm lateral dimensions covering several tens of cells. Furthermore, In-insert approach associated a keratin series and moesin (MOES) with prolactin-induced protein (PIP) indicating their prolactin and/or estrogen regulation. Our data suggest that PIP is a spatial biomarker for hormonally triggered cytoskeletal remodeling, potentially useful for screening hormonally affected hotspots in breast tissue. In-insert proteomics represents an alternative FFPE processing method, requiring minimal laboratory equipment and skills to generate spatial proteotype repositories from FFPE tissue.

Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.

DrugMap: A quantitative pan-cancer analysis of cysteine ligandability

Cysteine-focused chemical proteomic platforms have accelerated the clinical development of covalent inhibitors for a wide range of targets in cancer. However, how different oncogenic contexts influence cysteine targeting remains unknown. To address this question, we have developed "DrugMap," an atlas of cysteine ligandability compiled across 416 cancer cell lines. We unexpectedly find that cysteine ligandability varies across cancer cell lines, and we attribute this to differences in cellular redox states, protein conformational changes, and genetic mutations. Leveraging these findings, we identify actionable cysteines in NF-κB1 and SOX10 and develop corresponding covalent ligands that block the activity of these transcription factors. We demonstrate that the NF-κB1 probe blocks DNA binding, whereas the SOX10 ligand increases SOX10-SOX10 interactions and disrupts melanoma transcriptional signaling. Our findings reveal heterogeneity in cysteine ligandability across cancers, pinpoint cell-intrinsic features driving cysteine targeting, and illustrate the use of covalent probes to disrupt oncogenic transcription-factor activity.

Mapping the global interactome of the ARF family reveals spatial organization in cellular signaling pathways

The ADP-ribosylation factors (ARFs) and ARF-like (ARL) GTPases serve as essential molecular switches governing a wide array of cellular processes. In this study, we used proximity-dependent biotin identification (BioID) to comprehensively map the interactome of 28 out of 29 ARF and ARL proteins in two cellular models. Through this approach, we identified ∼3000 high-confidence proximal interactors, enabling us to assign subcellular localizations to the family members. Notably, we uncovered previously undefined localizations for ARL4D and ARL10. Clustering analyses further exposed the distinctiveness of the interactors identified with these two GTPases. We also reveal that the expression of the understudied member ARL14 is confined to the stomach and intestines. We identified phospholipase D1 (PLD1) and the ESCPE-1 complex, more precisely, SNX1, as proximity interactors. Functional assays demonstrated that ARL14 can activate PLD1 in cellulo and is involved in cargo trafficking via the ESCPE-1 complex. Overall, the BioID data generated in this study provide a valuable resource for dissecting the complexities of ARF and ARL spatial organization and signaling.

Evolution of chromosome-arm aberrations in breast cancer through genetic network rewiring

The basal breast cancer subtype is enriched for triple-negative breast cancer (TNBC) and displays consistent large chromosomal deletions. Here, we characterize evolution and maintenance of chromosome 4p (chr4p) loss in basal breast cancer. Analysis of The Cancer Genome Atlas data shows recurrent deletion of chr4p in basal breast cancer. Phylogenetic analysis of a panel of 23 primary tumor/patient-derived xenograft basal breast cancers reveals early evolution of chr4p deletion. Mechanistically we show that chr4p loss is associated with enhanced proliferation. Gene function studies identify an unknown gene, C4orf19, within chr4p, which suppresses proliferation when overexpressed-a member of the PDCD10-GCKIII kinase module we name PGCKA1. Genome-wide pooled overexpression screens using a barcoded library of human open reading frames identify chromosomal regions, including chr4p, that suppress proliferation when overexpressed in a context-dependent manner, implicating network interactions. Together, these results shed light on the early emergence of complex aneuploid karyotypes involving chr4p and adaptive landscapes shaping breast cancer genomes.

SeFilter-DIA: Squeeze-and-Excitation Network for Filtering High-Confidence Peptides of Data-Independent Acquisition Proteomics

Mass spectrometry is crucial in proteomics analysis, particularly using Data Independent Acquisition (DIA) for reliable and reproducible mass spectrometry data acquisition, enabling broad mass-to-charge ratio coverage and high throughput. DIA-NN, a prominent deep learning software in DIA proteome analysis, generates peptide results but may include low-confidence peptides. Conventionally, biologists have to manually screen peptide fragment ion chromatogram peaks (XIC) for identifying high-confidence peptides, a time-consuming and subjective process prone to variability. In this study, we introduce SeFilter-DIA, a deep learning algorithm, aiming at automating the identification of high-confidence peptides. Leveraging compressed excitation neural network and residual network models, SeFilter-DIA extracts XIC features and effectively discerns between high and low-confidence peptides. Evaluation of the benchmark datasets demonstrates SeFilter-DIA achieving 99.6% AUC on the test set and 97% for other performance indicators. Furthermore, SeFilter-DIA is applicable for screening peptides with phosphorylation modifications. These results demonstrate the potential of SeFilter-DIA to replace manual screening, providing an efficient and objective approach for high-confidence peptide identification while mitigating associated limitations.

Regulation of urea cycle by reversible high-stoichiometry lysine succinylation

The post-translational modification lysine succinylation is implicated in the regulation of various metabolic pathways. However, its biological relevance remains uncertain due to methodological difficulties in determining high-impact succinylation sites. Here, using stable isotope labelling and data-independent acquisition mass spectrometry, we quantified lysine succinylation stoichiometries in mouse livers. Despite the low overall stoichiometry of lysine succinylation, several high-stoichiometry sites were identified, especially upon deletion of the desuccinylase SIRT5. In particular, multiple high-stoichiometry lysine sites identified in argininosuccinate synthase (ASS1), a key enzyme in the urea cycle, are regulated by SIRT5. Mutation of the high-stoichiometry lysine in ASS1 to succinyl-mimetic glutamic acid significantly decreased its enzymatic activity. Metabolomics profiling confirms that SIRT5 deficiency decreases urea cycle activity in liver. Importantly, SIRT5 deficiency compromises ammonia tolerance, which can be reversed by the overexpression of wild-type, but not succinyl-mimetic, ASS1. Therefore, lysine succinylation is functionally important in ammonia metabolism.

ESRP1 controls biogenesis and function of a large abundant multiexon circRNA

While the majority of circRNAs are formed from infrequent back-splicing of exons from protein coding genes, some can be produced at quite high level and in a regulated manner. We describe the regulation, biogenesis and function of circDOCK1(2-27), a large, abundant circular RNA that is highly regulated during epithelial-mesenchymal transition (EMT) and whose formation depends on the epithelial splicing regulator ESRP1. CircDOCK1(2-27) synthesis in epithelial cells represses cell motility both by diverting transcripts from DOCK1 mRNA production to circRNA formation and by direct inhibition of migration by the circRNA. HITS-CLIP analysis and CRISPR-mediated deletions indicate ESRP1 controls circDOCK1(2-27) biosynthesis by binding a GGU-containing repeat region in intron 1 and detaining its splicing until Pol II completes its 157 kb journey to exon 27. Proximity-dependent biotinylation (BioID) assay suggests ESRP1 may modify the RNP landscape of intron 1 in a way that disfavours communication of exon 1 with exon 2, rather than physically bridging exon 2 to exon 27. The X-ray crystal structure of RNA-bound ESRP1 qRRM2 domain reveals it binds to GGU motifs, with the guanines embedded in clamp-like aromatic pockets in the protein.

WOMBAT-P: Benchmarking Label-Free Proteomics Data Analysis Workflows

The inherent diversity of approaches in proteomics research has led to a wide range of software solutions for data analysis. These software solutions encompass multiple tools, each employing different algorithms for various tasks such as peptide-spectrum matching, protein inference, quantification, statistical analysis, and visualization. To enable an unbiased comparison of commonly used bottom-up label-free proteomics workflows, we introduce WOMBAT-P, a versatile platform designed for automated benchmarking and comparison. WOMBAT-P simplifies the processing of public data by utilizing the sample and data relationship format for proteomics (SDRF-Proteomics) as input. This feature streamlines the analysis of annotated local or public ProteomeXchange data sets, promoting efficient comparisons among diverse outputs. Through an evaluation using experimental ground truth data and a realistic biological data set, we uncover significant disparities and a limited overlap in the quantified proteins. WOMBAT-P not only enables rapid execution and seamless comparison of workflows but also provides valuable insights into the capabilities of different software solutions. These benchmarking metrics are a valuable resource for researchers in selecting the most suitable workflow for their specific data sets. The modular architecture of WOMBAT-P promotes extensibility and customization. The software is available at https://github.com/wombat-p/WOMBAT-Pipelines.

Detection of the Arabidopsis Proteome and Its Post-translational Modifications and the Nature of the Unobserved (Dark) Proteome in PeptideAtlas

This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource (build 2023-10) providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected post-translational modifications (PTMs), and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying ∼0.6 million unique peptides and 18,267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins, and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome: the "dark" proteome. This dark proteome is highly enriched for E3 ligases, transcription factors, and for certain (e.g., CLE, IDA, PSY) but not other (e.g., THIONIN, CAP) signaling peptides families. A machine learning model trained on RNA expression data and protein properties predicts the probability that proteins will be detected. The model aids in discovery of proteins with short half-life (e.g., SIG1,3 and ERF-VII TFs) and for developing strategies to identify the missing proteins. PeptideAtlas is linked to TAIR, tracks in JBrowse, and several other community proteomics resources.

Multidimensional Cross-Linking and Real-Time Informatics for Multiprotein Interaction Studies

Chemical cross-linking combined with mass spectrometry is a technique used to study protein structures and identify protein complexes. Traditionally, chemical cross-linkers contain two reactive groups, allowing them to covalently bond a pair of proximal residues, either within a protein or between two proteins. The output of a cross-linking experiment is a list of interacting site pairs that provide structural constraints for modeling of new structures and complexes. Due to the binary reactive nature of cross-linking reagents, only pairs of interacting sites can be directly observed, and assembly of higher-order structures typically requires prior knowledge of complex composition or iterative docking to produce a putative model. Here, we describe a new tetrameric cross-linker bearing four amine-reactive groups, allowing it to covalently link up to four proteins simultaneously and a real-time instrument method to facilitate the identification of these tetrameric cross-links. We applied this new cross-linker to isolated mitochondria and identified a number of higher-order cross-links in various OXPHOS complexes and ATP synthase, demonstrating its utility in characterizing complex interfaces. We also show that higher-order cross-links can be used to effectively filter models of large protein assemblies generated by using Alphafold. Higher-dimensional cross-linking provides a new avenue for characterizing multiple protein interfaces, even in complex samples such as intact mitochondria.

Trace Sample Proteome Quantification by Data-Dependent Acquisition without Dynamic Exclusion

Despite continuous technological improvements in sample preparation, mass-spectrometry-based proteomics for trace samples faces the challenges of sensitivity, quantification accuracy, and reproducibility. Herein, we explored the applicability of turboDDA (a method that uses data-dependent acquisition without dynamic exclusion) for quantitative proteomics of trace samples. After systematic optimization of acquisition parameters, we compared the performance of turboDDA with that of data-dependent acquisition with dynamic exclusion (DEDDA). By benchmarking the analysis of trace unlabeled human cell digests, turboDDA showed substantially better sensitivity in comparison with DEDDA, whether for unfractionated or high pH fractionated samples. Furthermore, through designing an iTRAQ-labeled three-proteome model (i.e., tryptic digest of protein lysates from yeast, human, and E. coli) to document the interference effect, we evaluated the quantification interference, accuracy, reproducibility of iTRAQ labeled trace samples, and the impact of PIF (precursor intensity fraction) cutoff for different approaches (turboDDA and DEDDA). The results showed that improved quantification accuracy and reproducibility could be achieved by turboDDA, while a more stringent PIF cutoff resulted in more accurate quantification but less peptide identification for both approaches. Finally, the turboDDA strategy was applied to the differential analysis of limited amounts of human lung cancer cell samples, showing great promise in trace proteomics sample analysis.

The Identification of Nuclear FMRP Isoform Iso6 Partners

A deficiency of FMRP, a canonical RNA-binding protein, causes the development of Fragile X Syndrome (FXS), which is characterised by multiple phenotypes, including neurodevelopmental disorders, intellectual disability, and autism. Due to the alternative splicing of the encoding FMR1 gene, multiple FMRP isoforms are produced consisting of full-length predominantly cytoplasmic (i.e., iso1) isoforms involved in translation and truncated nuclear (i.e., iso6) isoforms with orphan functions. However, we recently implicated nuclear FMRP isoforms in DNA damage response, showing that they negatively regulate the accumulation of anaphase DNA genomic instability bridges. This finding provided evidence that the cytoplasmic and nuclear functions of FMRP are uncoupled played by respective cytoplasmic and nuclear isoforms, potentially involving specific interactions. While interaction partners of cytoplasmic FMRP have been reported, the identity of nuclear FMRP isoform partners remains to be established. Using affinity purification coupled with mass spectrometry, we mapped the nuclear interactome of the FMRP isoform iso6 in U2OS. In doing so, we found FMRP nuclear interaction partners to be involved in RNA processing, pre-mRNA splicing, ribosome biogenesis, DNA replication and damage response, chromatin remodeling and chromosome segregation. By comparing interactions between nuclear iso6 and cytoplasmic iso1, we report a set of partners that bind specifically to the nuclear isoforms, mainly proteins involved in DNA-associated processes and proteasomal proteins, which is consistent with our finding that proteasome targets the nuclear FMRP iso6. The specific interactions with the nuclear isoform 6 are regulated by replication stress, while those with the cytoplasmic isoform 1 are largely insensitive to such stress, further supporting a specific role of nuclear isoforms in DNA damage response induced by replicative stress, potentially regulated by the proteasome.

A meta-analysis of rice phosphoproteomics data to understand variation in cell signalling across the rice pan-genome

Phosphorylation is the most studied post-translational modification, and has multiple biological functions. In this study, we have re-analysed publicly available mass spectrometry proteomics datasets enriched for phosphopeptides from Asian rice (Oryza sativa). In total we identified 15,522 phosphosites on serine, threonine and tyrosine residues on rice proteins. We identified sequence motifs for phosphosites, and link motifs to enrichment of different biological processes, indicating different downstream regulation likely caused by different kinase groups. We cross-referenced phosphosites against the rice 3,000 genomes, to identify single amino acid variations (SAAVs) within or proximal to phosphosites that could cause loss of a site in a given rice variety. The data was clustered to identify groups of sites with similar patterns across rice family groups, for example those highly conserved in Japonica, but mostly absent in Aus type rice varieties - known to have different responses to drought. These resources can assist rice researchers to discover alleles with significantly different functional effects across rice varieties. The data has been loaded into UniProt Knowledge-Base - enabling researchers to visualise sites alongside other data on rice proteins e.g. structural models from AlphaFold2, PeptideAtlas and the PRIDE database - enabling visualisation of source evidence, including scores and supporting mass spectra.

Efficient Analysis of Proteome-Wide FPOP Data by FragPipe

Monitoring protein structure before and after environmental alterations (e.g., different cell states) can give insights into the role and function of proteins. Fast photochemical oxidation of proteins (FPOP) coupled with mass spectrometry (MS) allows for monitoring of structural rearrangements by exposing proteins to OH radicals that oxidize solvent-accessible residues, indicating protein regions undergoing movement. Some of the benefits of FPOP include high throughput and a lack of scrambling due to label irreversibility. However, the challenges of processing FPOP data have thus far limited its proteome-scale uses. Here, we present a computational workflow for fast and sensitive analysis of FPOP data sets. Our workflow, implemented as part of the FragPipe computational platform, combines the speed of the MSFragger search with a unique hybrid search method to restrict the large search space of FPOP modifications. Together, these features enable more than 10-fold faster FPOP searches that identify 150% more modified peptide spectra than previous methods. We hope this new workflow will increase the accessibility of FPOP to enable more protein structure and function relationships to be explored.

DrugMap: A quantitative pan-cancer analysis of cysteine ligandability

Cysteine-focused chemical proteomic platforms have accelerated the clinical development of covalent inhibitors of a wide-range of targets in cancer. However, how different oncogenic contexts influence cysteine targeting remains unknown. To address this question, we have developed DrugMap , an atlas of cysteine ligandability compiled across 416 cancer cell lines. We unexpectedly find that cysteine ligandability varies across cancer cell lines, and we attribute this to differences in cellular redox states, protein conformational changes, and genetic mutations. Leveraging these findings, we identify actionable cysteines in NFκB1 and SOX10 and develop corresponding covalent ligands that block the activity of these transcription factors. We demonstrate that the NFκB1 probe blocks DNA binding, whereas the SOX10 ligand increases SOX10-SOX10 interactions and disrupts melanoma transcriptional signaling. Our findings reveal heterogeneity in cysteine ligandability across cancers, pinpoint cell-intrinsic features driving cysteine targeting, and illustrate the use of covalent probes to disrupt oncogenic transcription factor activity.

A comprehensive spectral assay library to quantify the Halobacterium salinarum NRC-1 proteome by DIA/SWATH-MS

Data-Independent Acquisition (DIA) is a mass spectrometry-based method to reliably identify and reproducibly quantify large fractions of a target proteome. The peptide-centric data analysis strategy employed in DIA requires a priori generated spectral assay libraries. Such assay libraries allow to extract quantitative data in a targeted approach and have been generated for human, mouse, zebrafish, E. coli and few other organisms. However, a spectral assay library for the extreme halophilic archaeon Halobacterium salinarum NRC-1, a model organism that contributed to several notable discoveries, is not publicly available yet. Here, we report a comprehensive spectral assay library to measure 2,563 of 2,646 annotated H. salinarum NRC-1 proteins. We demonstrate the utility of this library by measuring global protein abundances over time under standard growth conditions. The H. salinarum NRC-1 library includes 21,074 distinct peptides representing 97% of the predicted proteome and provides a new, valuable resource to confidently measure and quantify any protein of this archaeon. Data and spectral assay libraries are available via ProteomeXchange (PXD042770, PXD042774) and SWATHAtlas (SAL00312-SAL00319).

Spectroscape enables real-time query and visualization of a spectral archive in proteomics

In proteomics, spectral archives organize the enormous amounts of publicly available peptide tandem mass spectra by similarity, offering opportunities for error correction and novel discoveries. Here we adapt an indexing algorithm developed by Facebook for organizing online multimedia resources to tandem mass spectra and achieve practically instantaneous retrieval and clustering of approximate nearest neighbors in a large spectral archive. An interactive web-based graphical user interface enables the user to view a query spectrum in its clustered neighborhood, which facilitates contextual validation of peptide identifications and exploration of the dark proteome.

KsgA facilitates ribosomal small subunit maturation by proofreading a key structural lesion

Ribosome assembly is orchestrated by many assembly factors, including ribosomal RNA methyltransferases, whose precise role is poorly understood. Here, we leverage the power of cryo-EM and machine learning to discover that the E. coli methyltransferase KsgA performs a 'proofreading' function in the assembly of the small ribosomal subunit by recognizing and partially disassembling particles that have matured but are not competent for translation. We propose that this activity allows inactive particles an opportunity to reassemble into an active state, thereby increasing overall assembly fidelity. Detailed structural quantifications in our datasets additionally enabled the expansion of the Nomura assembly map to highlight rRNA helix and r-protein interdependencies, detailing how the binding and docking of these elements are tightly coupled. These results have wide-ranging implications for our understanding of the quality-control mechanisms governing ribosome biogenesis and showcase the power of heterogeneity analysis in cryo-EM to unveil functionally relevant information in biological systems.

IQMMA: Efficient MS1 Intensity Extraction Pipeline Using Multiple Feature Detection Algorithms for DDA Proteomics

One of the key steps in data dependent acquisition (DDA) proteomics is detection of peptide isotopic clusters, also called "features", in MS1 spectra and matching them to MS/MS-based peptide identifications. A number of peptide feature detection tools became available in recent years, each relying on its own matching algorithm. Here, we provide an integrated solution, the intensity-based Quantitative Mix and Match Approach (IQMMA), which integrates a number of untargeted peptide feature detection algorithms and returns the most probable intensity values for the MS/MS-based identifications. IQMMA was tested using available proteomic data acquired for both well-characterized (ground truth) and real-world biological samples, including a mix of Yeast and E. coli digests spiked at different concentrations into the Human K562 digest used as a background, and a set of glioblastoma cell lines. Three open-source feature detection algorithms were integrated: Dinosaur, biosaur2, and OpenMS FeatureFinder. None of them was found optimal when applied individually to all the data sets employed in this work; however, their combined use in IQMMA improved efficiency of subsequent protein quantitation. The software implementing IQMMA is freely available at https://github.com/PostoenkoVI/IQMMA under Apache 2.0 license.

Improving quantitation accuracy in isobaric-labeling mass spectrometry experiments with spectral library searching and feature-based peptide-spectrum match filter

Isobaric labeling relative quantitation is one of the dominating proteomic quantitation technologies. Traditional quantitation pipelines for isobaric-labeled mass spectrometry data are based on sequence database searching. In this study, we present a novel quantitation pipeline that integrates sequence database searching, spectral library searching, and a feature-based peptide-spectrum-match (PSM) filter using various spectral features for filtering. The combined database and spectral library searching results in larger quantitation coverage, and the filter removes PSMs with larger quantitation errors, retaining those with higher quantitation accuracy. Quantitation results show that the proposed pipeline can improve the overall quantitation accuracy at the PSM and protein levels. To our knowledge, this is the first study that utilizes spectral library searching to improve isobaric labeling-based quantitation. For users to conveniently perform the proposed pipeline, we have implemented the feature-based filter being executable on both Windows and Linux platforms; its executable files, user manual, and sample data sets are freely available at https://ms.iis.sinica.edu.tw/comics/Software_FPF.html . Furthermore, with the developed filter, the proposed pipeline is fully compatible with the Trans-Proteomic Pipeline.

metaSpectraST: an unsupervised and database-independent analysis workflow for metaproteomic MS/MS data using spectrum clustering

BACKGROUND

The high diversity and complexity of the microbial community make it a formidable challenge to identify and quantify the large number of proteins expressed in the community. Conventional metaproteomics approaches largely rely on accurate identification of the MS/MS spectra to their corresponding short peptides in the digested samples, followed by protein inference and subsequent taxonomic and functional analysis of the detected proteins. These approaches are dependent on the availability of protein sequence databases derived either from sample-specific metagenomic data or from public repositories. Due to the incompleteness and imperfections of these protein sequence databases, and the preponderance of homologous proteins expressed by different bacterial species in the community, this computational process of peptide identification and protein inference is challenging and error-prone, which hinders the comparison of metaproteomes across multiple samples.

RESULTS

We developed metaSpectraST, an unsupervised and database-independent metaproteomics workflow, which quantitatively profiles and compares metaproteomics samples by clustering experimentally observed MS/MS spectra based on their spectral similarity. We applied metaSpectraST to fecal samples collected from littermates of two different mother mice right after weaning. Quantitative proteome profiles of the microbial communities of different mice were obtained without any peptide-spectrum identification and used to evaluate the overall similarity between samples and highlight any differentiating markers. Compared to the conventional database-dependent metaproteomics analysis, metaSpectraST is more successful in classifying the samples and detecting the subtle microbiome changes of mouse gut microbiomes post-weaning. metaSpectraST could also be used as a tool to select the suitable biological replicates from samples with wide inter-individual variation.

CONCLUSIONS

metaSpectraST enables rapid profiling of metaproteomic samples quantitatively, without the need for constructing the protein sequence database or identification of the MS/MS spectra. It maximally preserves information contained in the experimental MS/MS spectra by clustering all of them first and thus is able to better profile the complex microbial communities and highlight their functional changes, as compared with conventional approaches. tag the videobyte in this section as ESM4 Video Abstract.

Lifespan-extending interventions induce consistent patterns of fatty acid oxidation in mouse livers

Aging manifests as progressive deteriorations in homeostasis, requiring systems-level perspectives to investigate the gradual molecular dysregulation of underlying biological processes. Here, we report systemic changes in the molecular regulation of biological processes under multiple lifespan-extending interventions. Differential Rank Conservation (DIRAC) analyses of mouse liver proteomics and transcriptomics data show that mechanistically distinct lifespan-extending interventions (acarbose, 17α-estradiol, rapamycin, and calorie restriction) generally tighten the regulation of biological modules. These tightening patterns are similar across the interventions, particularly in processes such as fatty acid oxidation, immune response, and stress response. Differences in DIRAC patterns between proteins and transcripts highlight specific modules which may be tightened via augmented cap-independent translation. Moreover, the systemic shifts in fatty acid metabolism are supported through integrated analysis of liver transcriptomics data with a mouse genome-scale metabolic model. Our findings highlight the power of systems-level approaches for identifying and characterizing the biological processes involved in aging and longevity.

A joint proteomic and genomic investigation provides insights into the mechanism of calcification in coccolithophores

Coccolithophores are globally abundant, calcifying microalgae that have profound effects on marine biogeochemical cycles, the climate, and life in the oceans. They are characterized by a cell wall of CaCO3 scales called coccoliths, which may contribute to their ecological success. The intricate morphologies of coccoliths are of interest for biomimetic materials synthesis. Despite the global impact of coccolithophore calcification, we know little about the molecular machinery underpinning coccolithophore biology. Working on the model Emiliania huxleyi, a globally distributed bloom-former, we deploy a range of proteomic strategies to identify coccolithogenesis-related proteins. These analyses are supported by a new genome, with gene models derived from long-read transcriptome sequencing, which revealed many novel proteins specific to the calcifying haptophytes. Our experiments provide insights into proteins involved in various aspects of coccolithogenesis. Our improved genome, complemented with transcriptomic and proteomic data, constitutes a new resource for investigating fundamental aspects of coccolithophore biology.

Borrelia PeptideAtlas: A proteome resource of common Borrelia burgdorferi isolates for Lyme research

Lyme disease, caused by an infection with the spirochete Borrelia burgdorferi, is the most common vector-borne disease in North America. B. burgdorferi strains harbor extensive genomic and proteomic variability and further comparison is key to understanding the spirochetes infectivity and biological impacts of identified sequence variants. To achieve this goal, both transcript and mass spectrometry (MS)-based proteomics was applied to assemble peptide datasets of laboratory strains B31, MM1, B31-ML23, infective isolates B31-5A4, B31-A3, and 297, and other public datasets, to provide a publicly available Borrelia PeptideAtlas http://www.peptideatlas.org/builds/borrelia/. Included is information on total proteome, secretome, and membrane proteome of these B. burgdorferi strains. Proteomic data collected from 35 different experiment datasets, with a total of 855 mass spectrometry runs, identified 76,936 distinct peptides at a 0.1% peptide false-discovery-rate, which map to 1,221 canonical proteins (924 core canonical and 297 noncore canonical) and covers 86% of the total base B31 proteome. The diverse proteomic information from multiple isolates with credible data presented by the Borrelia PeptideAtlas can be useful to pinpoint potential protein targets which are common to infective isolates and may be key in the infection process.

Calcineurin associates with centrosomes and regulates cilia length maintenance

Calcineurin, or protein phosphatase 2B (PP2B), the Ca2+ and calmodulin-activated phosphatase and target of immunosuppressants, has many substrates and functions that remain uncharacterized. By combining rapid proximity-dependent labeling with cell cycle synchronization, we mapped the spatial distribution of calcineurin in different cell cycle stages. While calcineurin-proximal proteins did not vary significantly between interphase and mitosis, calcineurin consistently associated with multiple centrosomal and/or ciliary proteins. These include POC5, which binds centrins in a Ca2+-dependent manner and is a component of the luminal scaffold that stabilizes centrioles. We show that POC5 contains a calcineurin substrate motif (PxIxIT type) that mediates calcineurin binding in vivo and in vitro. Using indirect immunofluorescence and ultrastructure expansion microscopy, we demonstrate that calcineurin colocalizes with POC5 at the centriole, and further show that calcineurin inhibitors alter POC5 distribution within the centriole lumen. Our discovery that calcineurin directly associates with centriolar proteins highlights a role for Ca2+ and calcineurin signaling at these organelles. Calcineurin inhibition promotes elongation of primary cilia without affecting ciliogenesis. Thus, Ca2+ signaling within cilia includes previously unknown functions for calcineurin in maintenance of cilia length, a process that is frequently disrupted in ciliopathies.

A central chaperone-like role for 14-3-3 proteins in human cells

14-3-3 proteins are highly conserved regulatory proteins that interact with hundreds of structurally diverse clients and act as central hubs of signaling networks. However, how 14-3-3 paralogs differ in specificity and how they regulate client protein function are not known for most clients. Here, we map the interactomes of all human 14-3-3 paralogs and systematically characterize the effect of disrupting these interactions on client localization. The loss of 14-3-3 binding leads to the coalescence of a large fraction of clients into discrete foci in a client-specific manner, suggesting a central chaperone-like function for 14-3-3 proteins. Congruently, the engraftment of 14-3-3 binding motifs to nonclients can suppress their aggregation or phase separation. Finally, we show that 14-3-3s negatively regulate the localization of the RNA-binding protein SAMD4A to cytoplasmic granules and inhibit its activity as a translational repressor. Our work suggests that 14-3-3s have a more prominent role as chaperone-like molecules than previously thought.

Comparison of Database Searching Programs for the Analysis of Single-Cell Proteomics Data

Single-cell proteomics is emerging as an important subfield in the proteomics and mass spectrometry communities, with potential to reshape our understanding of cell development, cell differentiation, disease diagnosis, and the development of new therapies. Compared with significant advancements in the "hardware" that is used in single-cell proteomics, there has been little work comparing the effects of using different "software" packages to analyze single-cell proteomics datasets. To this end, seven popular proteomics programs were compared here, applying them to search three single-cell proteomics datasets generated by three different platforms. The results suggest that MSGF+, MSFragger, and Proteome Discoverer are generally more efficient in maximizing protein identifications, that MaxQuant is better suited for the identification of low-abundance proteins, that MSFragger is superior in elucidating peptide modifications, and that Mascot and X!Tandem are better for analyzing long peptides. Furthermore, an experiment with different loading amounts was carried out to investigate changes in identification results and to explore areas in which single-cell proteomics data analysis may be improved in the future. We propose that this comparative study may provide insight for experts and beginners alike operating in the emerging subfield of single-cell proteomics.

Interactomes of Glycogen Synthase Kinase-3 Isoforms

Functional differentiation of the two isoforms of the protein-serine/threonine kinase, glycogen synthase kinase-3 (GSK-3), is an unsettled area of research. The isoforms are highly similar in structure and are largely redundant, though there is also evidence for specific roles. Identification of isoform-specific protein interactors may elucidate the differences in function and provide insight into isoform-selective regulation. We therefore sought to identify novel GSK-3 interaction partners and to examine differences in the interactomes of the two isoforms using both affinity purification and proximity-dependent biotinylation (BioID) mass spectrometry methods. While the interactomes of the two isomers are highly similar in HEK293 cells, BioID in HeLa cells yielded a variety of preys that are preferentially associated with one of the two isoforms. DCP1B, which favored GSK-3α, and MISP, which favored GSK-3β, were evaluated for reciprocal interactions. The differences in interactions between isoforms may help in understanding the distinct functions and regulation of the two isoforms as well as offer avenues for the development of isoform-specific strategies.

Developmental mRNA clearance by PIWI-bound endo-siRNAs in Paramecium

The clearance of untranslated mRNAs by Argonaute proteins is essential for embryonic development in metazoans. However, it is currently unknown whether similar processes exist in unicellular eukaryotes. The ciliate Paramecium tetraurelia harbors a vast array of PIWI-clade Argonautes involved in various small RNA (sRNA) pathways, many of which have not yet been investigated. Here, we investigate the function of a PIWI protein, Ptiwi08, whose expression is limited to a narrow time window during development, concomitant with the start of zygotic transcription. We show that Ptiwi08 acts in an endogenous small interfering RNA (endo-siRNA) pathway involved in the clearance of untranslated mRNAs. These endo-siRNAs are found in clusters that are strictly antisense to their target mRNAs and are a subset of siRNA-producing clusters (SRCs). Furthermore, the endo-siRNAs are 2'-O-methylated by Hen1 and require Dcr1 for their biogenesis. Our findings suggest that sRNA-mediated developmental mRNA clearance extends beyond metazoans and may be a more widespread mechanism than previously anticipated.

Kindlin2 enables EphB/ephrinB bi-directional signaling to support vascular development

Direct contact between cells expressing either ephrin ligands or Eph receptor tyrosine kinase produces diverse developmental responses. Transmembrane ephrinB ligands play active roles in transducing bi-directional signals downstream of EphB/ephrinB interaction. However, it has not been well understood how ephrinB relays transcellular signals to neighboring cells and what intracellular effectors are involved. Here, we report that kindlin2 can mediate bi-directional ephrinB signaling through binding to a highly conserved NIYY motif in the ephrinB2 cytoplasmic tail. We show this interaction is important for EphB/ephrinB-mediated integrin activation in mammalian cells and for blood vessel morphogenesis during zebrafish development. A mixed two-cell population study revealed that kindlin2 (in ephrinB2-expressing cells) modulates transcellular EphB4 activation by promoting ephrinB2 clustering. This mechanism is also operative for EphB2/ephrinB1, suggesting that kindlin2-mediated regulation is conserved for EphB/ephrinB signaling pathways. Together, these findings show that kindlin2 enables EphB4/ephrinB2 bi-directional signal transmission.

Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. By leveraging progress in proteomic technologies and network-based methodologies, over the past decade, we developed VESPA-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and used it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogation of tumor-specific enzyme/substrate interactions accurately inferred kinase and phosphatase activity, based on their inferred substrate phosphorylation state, effectively accounting for signal cross-talk and sparse phosphoproteome coverage. The analysis elucidated time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring that was experimentally confirmed by CRISPRko assays, suggesting broad applicability to cancer and other diseases.

Meiotic nuclear pore complex remodeling provides key insights into nuclear basket organization

Nuclear pore complexes (NPCs) are large proteinaceous assemblies that mediate nuclear compartmentalization. NPCs undergo large-scale structural rearrangements during mitosis in metazoans and some fungi. However, our understanding of NPC remodeling beyond mitosis remains limited. Using time-lapse fluorescence microscopy, we discovered that NPCs undergo two mechanistically separable remodeling events during budding yeast meiosis in which parts or all of the nuclear basket transiently dissociate from the NPC core during meiosis I and II, respectively. Meiosis I detachment, observed for Nup60 and Nup2, is driven by Polo kinase-mediated phosphorylation of Nup60 at its interface with the Y-complex. Subsequent reattachment of Nup60-Nup2 to the NPC core is facilitated by a lipid-binding amphipathic helix in Nup60. Preventing Nup60-Nup2 reattachment causes misorganization of the entire nuclear basket in gametes. Strikingly, meiotic nuclear basket remodeling also occurs in the distantly related fission yeast, Schizosaccharomyces pombe. Our study reveals a conserved and developmentally programmed aspect of NPC plasticity, providing key mechanistic insights into the nuclear basket organization.

Scribe: Next Generation Library Searching for DDA Experiments

Spectrum library searching is a powerful alternative to database searching for data dependent acquisition experiments, but has been historically limited to identifying previously observed peptides in libraries. Here we present Scribe, a new library search engine designed to leverage deep learning fragmentation prediction software such as Prosit. Rather than relying on highly curated DDA libraries, this approach predicts fragmentation and retention times for every peptide in a FASTA database. Scribe embeds Percolator for false discovery rate correction and an interference tolerant, label-free quantification integrator for an end-to-end proteomics workflow. By leveraging expected relative fragmentation and retention time values, we find that library searching with Scribe can outperform traditional database searching tools both in terms of sensitivity and quantitative precision. Scribe and its graphical interface are easy to use, freely accessible, and fully open source.

Improved Analysis of Cross-Linking Mass Spectrometry Data with Kojak 2.0, Advanced by Integration into the Trans-Proteomic Pipeline

Fragmentation ion spectral analysis of chemically cross-linked proteins is an established technology in the proteomics research repertoire for determining protein interactions, spatial orientation, and structure. Here we present Kojak version 2.0, a major update to the original Kojak algorithm, which was developed to identify cross-linked peptides from fragment ion spectra using a database search approach. A substantially improved algorithm with updated scoring metrics, support for cleavable cross-linkers, and identification of cross-links between 15N-labeled homomultimers are among the newest features of Kojak 2.0 presented here. Kojak 2.0 is now integrated into the Trans-Proteomic Pipeline, enabling access to dozens of additional tools within that suite. In particular, the PeptideProphet and iProphet tools for validation of cross-links improve the sensitivity and accuracy of correct cross-link identifications at user-defined thresholds. These new features improve the versatility of the algorithm, enabling its use in a wider range of experimental designs and analysis pipelines. Kojak 2.0 remains open-source and multiplatform.

NeuroLINCS Proteomics: Defining human-derived iPSC proteomes and protein signatures of pluripotency

The National Institute of Health (NIH) Library of integrated network-based cellular signatures (LINCS) program is premised on the generation of a publicly available data resource of cell-based biochemical responses or "signatures" to genetic or environmental perturbations. NeuroLINCS uses human inducible pluripotent stem cells (hiPSCs), derived from patients and healthy controls, and differentiated into motor neuron cell cultures. This multi-laboratory effort strives to establish i) robust multi-omic workflows for hiPSC and differentiated neuronal cultures, ii) public annotated data sets and iii) relevant and targetable biological pathways of spinal muscular atrophy (SMA) and amyotrophic lateral sclerosis (ALS). Here, we focus on the proteomics and the quality of the developed workflow of hiPSC lines from 6 individuals, though epigenomics and transcriptomics data are also publicly available. Known and commonly used markers representing 73 proteins were reproducibly quantified with consistent expression levels across all hiPSC lines. Data quality assessments, data levels and metadata of all 6 genetically diverse human iPSCs analysed by DIA-MS are parsable and available as a high-quality resource to the public.

Characterization of the Functional Interplay between the BRD7 and BRD9 Homologues in mSWI/SNF Complexes

Bromodomains (BRDs) are a family of evolutionarily conserved domains that are the main readers of acetylated lysine (Kac) residues on proteins. Recently, numerous BRD-containing proteins have been proven essential for transcriptional regulation in numerous contexts. This is exemplified by the multi-subunit mSWI/SNF chromatin remodeling complexes, which incorporate up to 10 BRDs within five distinct subunits, allowing for extensive integration of Kac signaling to inform transcriptional regulation. As dysregulated transcription promotes oncogenesis, we sought to characterize how BRD-containing subunits contribute molecularly to mSWI/SNF functions. By combining genome editing, functional proteomics, and cellular biology, we found that loss of any single BRD-containing mSWI/SNF subunit altered but did not fully disrupt the various mSWI/SNF complexes. In addition, we report that the downregulation of BRD7 is common in invasive lobular carcinoma and modulates the interactome of its homologue, BRD9. We show that these alterations exacerbate sensitivities to inhibitors targeting epigenetic regulators─notably, inhibitors targeting the BRDs of non-mSWI/SNF proteins. Our results highlight the interconnections between distinct mSWI/SNF complexes and their far-reaching impacts on transcriptional regulation in human health and disease. The mass spectrometry data generated have been deposited to MassIVE and ProteomeXchange and assigned the identifiers MSV000089357, MSV000089362, and PXD033572.

MCC is a centrosomal protein that relocalizes to non-centrosomal apical sites during intestinal cell differentiation

The gene mutated in colorectal cancer (MCC) encodes a coiled-coil protein implicated, as its name suggests, in the pathogenesis of hereditary human colon cancer. To date, however, the contributions of MCC to intestinal homeostasis and disease remain unclear. Here, we examine the subcellular localization of MCC, both at the mRNA and protein levels, in the adult intestinal epithelium. Our findings reveal that Mcc transcripts are restricted to proliferating crypt cells, including Lgr5+ stem cells, where the Mcc protein is distinctly associated with the centrosome. Upon intestinal cellular differentiation, Mcc is redeployed to the apical domain of polarized villus cells where non-centrosomal microtubule organizing centers (ncMTOCs) are positioned. Using intestinal organoids, we show that the shuttling of the Mcc protein depends on phosphorylation by casein kinases 1δ and ε, which are critical modulators of WNT signaling. Together, our findings support a role for MCC in establishing and maintaining the cellular architecture of the intestinal epithelium as a component of both the centrosome and ncMTOC.

Impact of different oral treatments on the composition of the supragingival plaque microbiome

Background

Dental plaque consists of a diverse microbial community embedded in a complex structure of exopolysaccharides. Dental biofilms form a natural barrier against pathogens but lead to oral diseases in a dysbiotic state.

Objective

Using a metaproteome approach combined with a standard plaque-regrowth study, this pilot study examined the impact of different concentrations of lactoperoxidase (LPO) on early plaque formation, and active biological processes.

Design

Sixteen orally healthy subjects received four local treatments as a randomized single-blind study based on a cross-over design. Two lozenges containing components of the LPO-system in different concentrations were compared to a placebo and Listerine®. The newly formed dental plaque was analyzed by mass spectrometry (nLC-MS/MS). 

Results

On average 1,916 metaproteins per sample were identified, which could be assigned to 116 genera and 1,316 protein functions. Listerine® reduced the number of metaproteins and their relative abundance, confirming the plaque inhibiting effect. The LPO-lozenges triggered mainly higher metaprotein abundances of early and secondary colonizers as well as bacteria associated with dental health but also periodontitis. Functional information indicated plaque biofilm growth.

Conclusion

In conclusion, the mechanisms on plaque biofilm formation of Listerine® and the LPO-system containing lozenges are different. In contrast to Listerine®, the lozenges led to a higher bacterial diversity.

CDK12 is hyperactivated and a synthetic-lethal target in BRAF-mutated melanoma

Melanoma is the deadliest form of skin cancer and considered intrinsically resistant to chemotherapy. Nearly all melanomas harbor mutations that activate the RAS/mitogen-activated protein kinase (MAPK) pathway, which contributes to drug resistance via poorly described mechanisms. Herein we show that the RAS/MAPK pathway regulates the activity of cyclin-dependent kinase 12 (CDK12), which is a transcriptional CDK required for genomic stability. We find that melanoma cells harbor constitutively high CDK12 activity, and that its inhibition decreases the expression of long genes containing multiple exons, including many genes involved in DNA repair. Conversely, our results show that CDK12 inhibition promotes the expression of short genes with few exons, including many growth-promoting genes regulated by the AP-1 and NF-κB transcription factors. Inhibition of these pathways strongly synergize with CDK12 inhibitors to suppress melanoma growth, suggesting promising drug combinations for more effective melanoma treatment.

The rate and role of pseudogenes of the Mycobacterium tuberculosis complex

Whole-genome sequence analyses have significantly contributed to the understanding of virulence and evolution of the Mycobacterium tuberculosis complex (MTBC), the causative pathogens of tuberculosis. Most MTBC evolutionary studies are focused on single nucleotide polymorphisms and deletions, but rare studies have evaluated gene content, whereas none has comprehensively evaluated pseudogenes. Accordingly, we describe an extensive study focused on quantifying and predicting possible functions of MTBC and Mycobacterium canettii pseudogenes. Using NCBI's PGAP-detected pseudogenes, we analysed 25 837 pseudogenes from 158 MTBC and M. canetii strains and combined transcriptomics and proteomics of M. tuberculosis H37Rv to gain insights about pseudogenes' expression. Our results indicate significant variability concerning rate and conservancy of in silico predicted pseudogenes among different ecotypes and lineages of tuberculous mycobacteria and pseudogenization of important virulence factors and genes of the metabolism and antimicrobial resistance/tolerance. We show that in silico predicted pseudogenes contribute considerably to MTBC genetic diversity at the population level. Moreover, the transcription machinery of M. tuberculosis can fully transcribe most pseudogenes, indicating intact promoters and recent pseudogene evolutionary emergence. Proteomics of M. tuberculosis and close evaluation of mutational lesions driving pseudogenization suggest that few in silico predicted pseudogenes are likely capable of neofunctionalization, nonsense mutation reversal, or phase variation, contradicting the classical definition of pseudogenes. Such findings indicate that genome annotation should be accompanied by proteomics and protein function assays to improve its accuracy. While indels and insertion sequences are the main drivers of the observed mutational lesions in these species, population bottlenecks and genetic drift are likely the evolutionary processes acting on pseudogenes' emergence over time. Our findings unveil a new perspective on MTBC's evolution and genetic diversity.

Defects in autophagy lead to selective in vivo changes in turnover of cytosolic and organelle proteins in Arabidopsis

Identification of autophagic protein cargo in plants in autophagy-related genes (ATG) mutants is complicated by changes in protein synthesis and protein degradation. To detect autophagic cargo, we measured protein degradation rate in shoots and roots of Arabidopsis (Arabidopsis thaliana) atg5 and atg11 mutants. These data show that less than a quarter of proteins changing in abundance are probable cargo and revealed roles of ATG11 and ATG5 in degradation of specific glycolytic enzymes and of other cytosol, chloroplast, and ER-resident proteins, and a specialized role for ATG11 in degradation of proteins from mitochondria and chloroplasts. Protein localization in transformed protoplasts and degradation assays in the presence of inhibitors confirm a role for autophagy in degrading glycolytic enzymes. Autophagy induction by phosphate (Pi) limitation changed metabolic profiles and the protein synthesis and degradation rates of atg5 and atg11 plants. A general decrease in the abundance of amino acids and increase in secondary metabolites in autophagy mutants was consistent with altered catabolism and changes in energy conversion caused by reduced degradation rate of specific proteins. Combining measures of changes in protein abundance and degradation rates, we also identify ATG11 and ATG5-associated protein cargo of low Pi-induced autophagy in chloroplasts and ER-resident proteins involved in secondary metabolism.

Global and precise identification of functional miRNA targets in mESCs by integrative analysis

MicroRNA (miRNA) loaded Argonaute (AGO) complexes regulate gene expression via direct base pairing with their mRNA targets. Previous works suggest that up to 60% of mammalian transcripts might be subject to miRNA‐mediated regulation, but it remains largely unknown which fraction of these interactions are functional in a specific cellular context. Here, we integrate transcriptome data from a set of miRNA‐depleted mouse embryonic stem cell (mESC) lines with published miRNA interaction predictions and AGO‐binding profiles. Using this integrative approach, combined with molecular validation data, we present evidence that < 10% of expressed genes are functionally and directly regulated by miRNAs in mESCs. In addition, analyses of the stem cell‐specific miR‐290‐295 cluster target genes identify TFAP4 as an important transcription factor for early development. The extensive datasets developed in this study will support the development of improved predictive models for miRNA‐mRNA functional interactions.