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Dysfunctional extracellular matrices (ECM) contribute to aging and disease. Repairing dysfunctional ECM could potentially prevent age-related pathologies. Interventions promoting longevity also impact ECM gene expression. However, the role of ECM composition changes in healthy aging remains unclear. Here we perform proteomics and in-vivo monitoring to systematically investigate ECM composition (matreotype) during aging in C. elegans revealing three distinct collagen dynamics. Longevity interventions slow age-related collagen stiffening and prolong the expression of collagens that are turned over. These prolonged collagen dynamics are mediated by a mechanical feedback loop of hemidesmosome-containing structures that span from the exoskeletal ECM through the hypodermis, basement membrane ECM, to the muscles, coupling mechanical forces to adjust ECM gene expression and longevity via the transcriptional co-activator YAP-1 across tissues. Our results provide in-vivo evidence that coordinated ECM remodeling through mechanotransduction is required and sufficient to promote longevity, offering potential avenues for interventions targeting ECM dynamics.

Protein complexes constitute the primary functional modules of cellular activity. To respond to perturbations, complexes undergo changes in their abundance, subunit composition, or state of modification. Understanding the function of biological systems requires global strategies to capture this contextual state information. Methods based on cofractionation paired with mass spectrometry have demonstrated the capability for deep biological insight, but the scope of studies using this approach has been limited by the large measurement time per biological sample and challenges with data analysis. There has been little uptake of this strategy into the broader life science community despite its rich biological information content. We present a rapid integrated experimental and computational workflow to assess the reorganization of protein complexes across multiple cellular states. The workflow combines short gradient chromatography and DIA/SWATH mass spectrometry with a data analysis toolset to quantify changes in a complex organization. We applied the workflow to study the global protein complex rearrangements of THP-1 cells undergoing monocyte to macrophage differentiation and subsequent stimulation of macrophage cells with lipopolysaccharide. We observed substantial proteome reorganization on differentiation and less pronounced changes in macrophage stimulation. We establish our integrated differential pipeline for rapid and state-specific profiling of protein complex organization.

While several computational methods have been developed to predict the functional relevance of phosphorylation sites, experimental analysis of the interdependency between protein phosphorylation and Protein-Protein Interactions (PPIs) remains challenging. Here, we describe an experimental strategy to establish interdependencies between protein phosphorylation and complex formation. This strategy is based on three main steps: (i) systematically charting the phosphorylation landscape of a target protein; (ii) assigning distinct proteoforms of the target protein to different protein complexes by native complex separation (AP-BNPAGE) and protein correlation profiling; and (iii) analyzing proteoforms and complexes in cells lacking regulators of the target protein. We applied this strategy to YAP1, a transcriptional co-activator for the control of organ size and tissue homeostasis that is highly phosphorylated and among the most connected proteins in human cells. We identified multiple YAP1 phosphosites associated with distinct complexes and inferred how both are controlled by Hippo pathway members. We detected a PTPN14/LATS1/YAP1 complex and suggest a model how PTPN14 inhibits YAP1 via augmenting WW domain-dependent complex formation and phosphorylation by LATS1/2.

Analyzing proteins from single cells by tandem mass spectrometry (MS) has recently become technically feasible. While such analysis has the potential to accurately quantify thousands of proteins across thousands of single cells, the accuracy and reproducibility of the results may be undermined by numerous factors affecting experimental design, sample preparation, data acquisition and data analysis. We expect that broadly accepted community guidelines and standardized metrics will enhance rigor, data quality and alignment between laboratories. Here we propose best practices, quality controls and data-reporting recommendations to assist in the broad adoption of reliable quantitative workflows for single-cell proteomics. Resources and discussion forums are available at https://single-cell.net/guidelines .

Bacterial fitness depends on adaptability to changing environments. In rich growth medium, which is replete with amino acids, Escherichia coli primarily expresses protein synthesis machineries, which comprise ~40% of cellular proteins and are required for rapid growth. Upon transition to minimal medium, which lacks amino acids, biosynthetic enzymes are synthesized, eventually reaching ~15% of cellular proteins when growth fully resumes. We applied quantitative proteomics to analyse the timing of enzyme expression during such transitions, and established a simple positive relation between the onset time of enzyme synthesis and the fractional enzyme reserve maintained by E. coli while growing in rich media. We devised and validated a coarse-grained kinetic model that quantitatively captures the enzyme recovery kinetics in different pathways, solely on the basis of proteomes immediately preceding the transition and well after its completion. Our model enables us to infer regulatory strategies underlying the as-needed gene expression programme adopted by E. coli.

Protein concentrations are set by a complex interplay between gene-specific regulatory processes and systemic factors, including cell volume and shared gene expression machineries. Elucidating this interplay is crucial for discerning and designing gene regulatory systems. We quantitatively characterized gene-specific and systemic factors that affect transcription and translation genome-wide for Escherichia coli across many conditions. The results revealed two design principles that make regulation of gene expression insulated from concentrations of shared machineries: RNA polymerase activity is fine-tuned to match translational output, and translational characteristics are similar across most messenger RNAs (mRNAs). Consequently, in bacteria, protein concentration is set primarily at the promoter level. A simple mathematical formula relates promoter activities and protein concentrations across growth conditions, enabling quantitative inference of gene regulation from omics data.

In molecular biology, it is a general assumption that the ensemble of expressed molecules, their activities and interactions determine biological function, cellular states and phenotypes. Stable protein complexes-or macromolecular machines-are, in turn, the key functional entities mediating and modulating most biological processes. Although identifying protein complexes and their subunit composition can now be done inexpensively and at scale, determining their function remains challenging and labor intensive. This study describes Protein Complex Function predictor (PCfun), the first computational framework for the systematic annotation of protein complex functions using Gene Ontology (GO) terms. PCfun is built upon a word embedding using natural language processing techniques based on 1 million open access PubMed Central articles. Specifically, PCfun leverages two approaches for accurately identifying protein complex function, including: (i) an unsupervised approach that obtains the nearest neighbor (NN) GO term word vectors for a protein complex query vector and (ii) a supervised approach using Random Forest (RF) models trained specifically for recovering the GO terms of protein complex queries described in the CORUM protein complex database. PCfun consolidates both approaches by performing a hypergeometric statistical test to enrich the top NN GO terms within the child terms of the GO terms predicted by the RF models. The documentation and implementation of the PCfun package are available at https://github.com/sharmavaruns/PCfun. We anticipate that PCfun will serve as a useful tool and novel paradigm for the large-scale characterization of protein complex function.

Accurate measurements of cellular protein concentrations are invaluable to quantitative studies of gene expression and physiology in living cells. Here, we developed a versatile mass spectrometric workflow based on data-independent acquisition proteomics (DIA/SWATH) together with a novel protein inference algorithm (xTop). We used this workflow to accurately quantify absolute protein abundances in Escherichia coli for > 2,000 proteins over > 60 growth conditions, including nutrient limitations, non-metabolic stresses, and non-planktonic states. The resulting high-quality dataset of protein mass fractions allowed us to characterize proteome responses from a coarse (groups of related proteins) to a fine (individual) protein level. Hereby, a plethora of novel biological findings could be elucidated, including the generic upregulation of low-abundant proteins under various metabolic limitations, the non-specificity of catabolic enzymes upregulated under carbon limitation, the lack of large-scale proteome reallocation under stress compared to nutrient limitations, as well as surprising strain-dependent effects important for biofilm formation. These results present valuable resources for the systems biology community and can be used for future multi-omics studies of gene regulation and metabolic control in E. coli.

Polycomb repressive complexes 1 and 2 (PRC1 and PRC2) cooperate to determine cell identity by epigenetic gene expression regulation. However, the mechanism of PRC2 recruitment by means of recognition of PRC1-mediated H2AK119ub1 remains poorly understood. Our PRC2 cryo-electron microscopy structure with cofactors JARID2 and AEBP2 bound to a H2AK119ub1-containing nucleosome reveals a bridge helix in EZH2 that connects the SET domain, H3 tail, and nucleosomal DNA. JARID2 and AEBP2 each interact with one ubiquitin and the H2A-H2B surface. JARID2 stimulates PRC2 through interactions with both the polycomb protein EED and the H2AK119-ubiquitin, whereas AEBP2 has an additional scaffolding role. The presence of these cofactors partially overcomes the inhibitory effect that H3K4me3 and H3K36me3 exert on core PRC2 (in the absence of cofactors). Our results support a key role for JARID2 and AEBP2 in the cross-talk between histone modifications and PRC2 activity.

Proteases are among the largest protein families and critical regulators of biochemical processes like apoptosis and blood coagulation. Knowledge of proteases has been expanded by the development of proteomic approaches, however, technology for multiplexed screening of proteases within native environments is currently lacking behind. Here we introduce a simple method to profile protease activity based on isolation of protease products from native lysates using a 96FASP filter, their analysis in a mass spectrometer and a custom data analysis pipeline. The method is significantly faster, cheaper, technically less demanding, easy to multiplex and produces accurate protease fingerprints. Using the blood cascade proteases as a case study, we obtain protease substrate profiles that can be used to map specificity, cleavage entropy and allosteric effects and to design protease probes. The data further show that protease substrate predictions enable the selection of potential physiological substrates for targeted validation in biochemical assays.

Transcription by RNA polymerase V (Pol V) in plants is required for RNA-directed DNA methylation, leading to transcriptional gene silencing. Global chromatin association of Pol V requires components of the DDR complex DRD1, DMS3 and RDM1, but the assembly process of this complex and the underlying mechanism for Pol V recruitment remain unknown. Here we show that all DDR complex components co-localize with Pol V, and we report the cryoEM structures of two complexes associated with Pol V recruitment-DR (DMS3-RDM1) and DDR' (DMS3-RDM1-DRD1 peptide), at 3.6 Å and 3.5 Å resolution, respectively. RDM1 dimerization at the center frames the assembly of the entire complex and mediates interactions between DMS3 and DRD1 with a stoichiometry of 1 DRD1:4 DMS3:2 RDM1. DRD1 binding to the DR complex induces a drastic movement of a DMS3 coiled-coil helix bundle. We hypothesize that both complexes are functional intermediates that mediate Pol V recruitment.

The number of publications in the field of chemical cross-linking combined with mass spectrometry (XL-MS) to derive constraints for protein three-dimensional structure modeling and to probe protein-protein interactions has increased during the last years. As the technique is now becoming routine for in vitro and in vivo applications in proteomics and structural biology there is a pressing need to define protocols as well as data analysis and reporting formats. Such consensus formats should become accepted in the field and be shown to lead to reproducible results. This first, community-based harmonization study on XL-MS is based on the results of 32 groups participating worldwide. The aim of this paper is to summarize the status quo of XL-MS and to compare and evaluate existing cross-linking strategies. Our study therefore builds the framework for establishing best practice guidelines to conduct cross-linking experiments, perform data analysis, and define reporting formats with the ultimate goal of assisting scientists to generate accurate and reproducible XL-MS results.

In eukaryotes, the heterohexameric origin recognition complex (ORC) coordinates replication onset by facilitating the recruitment and loading of the minichromosome maintenance 2-7 (Mcm2-7) replicative helicase onto DNA to license origins. Drosophila ORC can adopt an autoinhibited configuration that is predicted to prevent Mcm2-7 loading; how the complex is activated and whether other ORC homologs can assume this state are not known. Using chemical cross-linking and mass spectrometry, biochemical assays, and electron microscopy (EM), we show that the autoinhibited state of Drosophila ORC is populated in solution, and that human ORC can also adopt this form. ATP binding to ORC supports a transition from the autoinhibited state to an active configuration, enabling the nucleotide-dependent association of ORC with both DNA and Cdc6. An unstructured N-terminal region adjacent to the conserved ATPase domain of Orc1 is shown to be required for high-affinity ORC-DNA interactions, but not for activation. ORC optimally binds DNA duplexes longer than the predicted footprint of the ORC ATPases associated with a variety of cellular activities (AAA+) and winged-helix (WH) folds; cryo-EM analysis of Drosophila ORC bound to DNA and Cdc6 indicates that ORC contacts DNA outside of its central core region, bending the DNA away from its central DNA-binding channel. Our findings indicate that ORC autoinhibition may be common to metazoans and that ORC-Cdc6 remodels origin DNA before Mcm2-7 recruitment and loading.

Transcriptionally repressive histone H3 lysine 27 methylation by Polycomb repressive complex 2 (PRC2) is essential for cellular differentiation and development. Here we report cryo-electron microscopy structures of human PRC2 in a basal state and two distinct active states while in complex with its cofactors JARID2 and AEBP2. Both cofactors mimic the binding of histone H3 tails. JARID2, methylated by PRC2, mimics a methylated H3 tail to stimulate PRC2 activity, whereas AEBP2 interacts with the RBAP48 subunit, mimicking an unmodified H3 tail. SUZ12 interacts with all other subunits within the assembly and thus contributes to the stability of the complex. Our analysis defines the complete architecture of a functionally relevant PRC2 and provides a structural framework to understand its regulation by cofactors, histone tails, and RNA.

Despite decades of accumulated knowledge about proteins and their post-translational modifications (PTMs), numerous questions remain regarding their molecular composition and biological function. One of the most fundamental queries is the extent to which the combinations of DNA-, RNA- and PTM-level variations explode the complexity of the human proteome. Here, we outline what we know from current databases and measurement strategies including mass spectrometry-based proteomics. In doing so, we examine prevailing notions about the number of modifications displayed on human proteins and how they combine to generate the protein diversity underlying health and disease. We frame central issues regarding determination of protein-level variation and PTMs, including some paradoxes present in the field today. We use this framework to assess existing data and to ask the question, "How many distinct primary structures of proteins (proteoforms) are created from the 20,300 human genes?" We also explore prospects for improving measurements to better regularize protein-level biology and efficiently associate PTMs to function and phenotype.

The 26S proteasome is the macromolecular machine responsible for ATP/ubiquitin dependent degradation. As aberration in proteasomal degradation has been implicated in many human diseases, structural analysis of the human 26S proteasome complex is essential to advance our understanding of its action and regulation mechanisms. In recent years, cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for elucidating structural topologies of large protein assemblies, with its unique capability of studying protein complexes in cells. To facilitate the identification of cross-linked peptides, we have previously developed a robust amine reactive sulfoxide-containing MS-cleavable cross-linker, disuccinimidyl sulfoxide (DSSO). To better understand the structure and regulation of the human 26S proteasome, we have established new DSSO-based in vivo and in vitro XL-MS workflows by coupling with HB-tag based affinity purification to comprehensively examine protein-protein interactions within the 26S proteasome. In total, we have identified 447 unique lysine-to-lysine linkages delineating 67 interprotein and 26 intraprotein interactions, representing the largest cross-link dataset for proteasome complexes. In combination with EM maps and computational modeling, the architecture of the 26S proteasome was determined to infer its structural dynamics. In particular, three proteasome subunits Rpn1, Rpn6, and Rpt6 displayed multiple conformations that have not been previously reported. Additionally, cross-links between proteasome subunits and 15 proteasome interacting proteins including 9 known and 6 novel ones have been determined to demonstrate their physical interactions at the amino acid level. Our results have provided new insights on the dynamics of the 26S human proteasome and the methodologies presented here can be applied to study other protein complexes.