Genome-wide base editor screen identifies regulators of protein abundance in yeast.

Proteins are key molecular players in a cell, and their abundance is extensively regulated not just at the level of gene expression but also post-transcriptionally. Here, we describe a genetic screen in yeast that enables systematic characterization of how protein abundance regulation is encoded in the genome. The screen combines a CRISPR/Cas9 base editor to introduce point mutations with fluorescent tagging of endogenous proteins to facilitate a flow-cytometric readout. We first benchmarked base editor performance in yeast with individual gRNAs as well as in positive and negative selection screens. We then examined the effects of 16,452 genetic perturbations on the abundance of eleven proteins representing a variety of cellular functions. We uncovered hundreds of regulatory relationships, including a novel link between the GAPDH isoenzymes Tdh1/2/3 and the Ras/PKA pathway. Many of the identified regulators are specific to one of the eleven proteins, but we also found genes that, upon perturbation, affected the abundance of most of the tested proteins. While the more specific regulators usually act transcriptionally, broad regulators often have roles in protein translation. Overall, our novel screening approach provides unprecedented insights into the components, scale and connectedness of the protein regulatory network.

Island-specific evolution of a sex-primed autosome in a sexual planarian.

The sexual strain of the planarian Schmidtea mediterranea, indigenous to Tunisia and several Mediterranean islands, is a hermaphrodite1,2. Here we isolate individual chromosomes and use sequencing, Hi-C3,4 and linkage mapping to assemble a chromosome-scale genome reference. The linkage map reveals an extremely low rate of recombination on chromosome 1. We confirm suppression of recombination on chromosome 1 by genotyping individual sperm cells and oocytes. We show that previously identified genomic regions that maintain heterozygosity even after prolonged inbreeding make up essentially all of chromosome 1. Genome sequencing of individuals isolated in the wild indicates that this phenomenon has evolved specifically in populations from Sardinia and Corsica. We find that most known master regulators5-13 of the reproductive system are located on chromosome 1. We used RNA interference14,15 to knock down a gene with haplotype-biased expression, which led to the formation of a more pronounced female mating organ. On the basis of these observations, we propose that chromosome 1 is a sex-primed autosome primed for evolution into a sex chromosome.

Towards synthetic PETtrophy: Engineering Pseudomonas putida for concurrent polyethylene terephthalate (PET) monomer metabolism and PET hydrolase expression.

BACKGROUND: Biocatalysis offers a promising path for plastic waste management and valorization, especially for hydrolysable plastics such as polyethylene terephthalate (PET). Microbial whole-cell biocatalysts for simultaneous PET degradation and growth on PET monomers would offer a one-step solution toward PET recycling or upcycling. We set out to engineer the industry-proven bacterium Pseudomonas putida for (i) metabolism of PET monomers as sole carbon sources, and (ii) efficient extracellular expression of PET hydrolases. We pursued this approach for both PET and the related polyester polybutylene adipate co-terephthalate (PBAT), aiming to learn about the determinants and potential applications of bacterial polyester-degrading biocatalysts. RESULTS: P. putida was engineered to metabolize the PET and PBAT monomer terephthalic acid (TA) through genomic integration of four tphII operon genes from Comamonas sp. E6. Efficient cellular TA uptake was enabled by a point mutation in the native P. putida membrane transporter MhpT. Metabolism of the PET and PBAT monomers ethylene glycol and 1,4-butanediol was achieved through adaptive laboratory evolution. We then used fast design-build-test-learn cycles to engineer extracellular PET hydrolase expression, including tests of (i) the three PET hydrolases LCC, HiC, and IsPETase; (ii) genomic versus plasmid-based expression, using expression plasmids with high, medium, and low cellular copy number; (iii) three different promoter systems; (iv) three membrane anchor proteins for PET hydrolase cell surface display; and (v) a 30-mer signal peptide library for PET hydrolase secretion. PET hydrolase surface display and secretion was successfully engineered but often resulted in host cell fitness costs, which could be mitigated by promoter choice and altering construct copy number. Plastic biodegradation assays with the best PET hydrolase expression constructs genomically integrated into our monomer-metabolizing P. putida strains resulted in various degrees of plastic depolymerization, although self-sustaining bacterial growth remained elusive. CONCLUSION: Our results show that balancing extracellular PET hydrolase expression with cellular fitness under nutrient-limiting conditions is a challenge. The precise knowledge of such bottlenecks, together with the vast array of PET hydrolase expression tools generated and tested here, may serve as a baseline for future efforts to engineer P. putida or other bacterial hosts towards becoming efficient whole-cell polyester-degrading biocatalysts.

The impact of genomic variation on protein phosphorylation states and regulatory networks.

Genomic variation impacts on cellular networks by affecting the abundance (e.g., protein levels) and the functional states (e.g., protein phosphorylation) of their components. Previous work has focused on the former, while in this context, the functional states of proteins have largely remained neglected. Here, we generated high-quality transcriptome, proteome, and phosphoproteome data for a panel of 112 genomically well-defined yeast strains. Genetic effects on transcripts were generally transmitted to the protein layer, but specific gene groups, such as ribosomal proteins, showed diverging effects on protein levels compared with RNA levels. Phosphorylation states proved crucial to unravel genetic effects on signaling networks. Correspondingly, genetic variants that cause phosphorylation changes were mostly different from those causing abundance changes in the respective proteins. Underscoring their relevance for cell physiology, phosphorylation traits were more strongly correlated with cell physiological traits such as chemical compound resistance or cell morphology, compared with transcript or protein abundance. This study demonstrates how molecular networks mediate the effects of genomic variants to cellular traits and highlights the particular importance of protein phosphorylation.

From coarse to fine: the absolute Escherichia coli proteome under diverse growth conditions.

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.

Publisher Correction: OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The GTPase Nog1 co-ordinates the assembly, maturation and quality control of distant ribosomal functional centers.

Eukaryotic ribosome precursors acquire translation competence in the cytoplasm through stepwise release of bound assembly factors, and proofreading of their functional centers. In case of the pre-60S, these steps include removal of placeholders Rlp24, Arx1 and Mrt4 that prevent premature loading of the ribosomal protein eL24, the protein-folding machinery at the polypeptide exit tunnel (PET), and the ribosomal stalk, respectively. Here, we reveal that sequential ATPase and GTPase activities license release factors Rei1 and Yvh1 to trigger Arx1 and Mrt4 removal. Drg1-ATPase activity removes Rlp24 from the GTPase Nog1 on the pre-60S; consequently, the C-terminal tail of Nog1 is extracted from the PET. These events enable Rei1 to probe PET integrity and catalyze Arx1 release. Concomitantly, Nog1 eviction from the pre-60S permits peptidyl transferase center maturation, and allows Yvh1 to mediate Mrt4 release for stalk assembly. Thus, Nog1 co-ordinates the assembly, maturation and quality control of distant functional centers during ribosome formation.

Breast Cancer Classification Based on Proteotypes Obtained by SWATH Mass Spectrometry.

Accurate classification of breast tumors is vital for patient management decisions and enables more precise cancer treatment. Here, we present a quantitative proteotyping approach based on sequential windowed acquisition of all theoretical fragment ion spectra (SWATH) mass spectrometry and establish key proteins for breast tumor classification. The study is based on 96 tissue samples representing five conventional breast cancer subtypes. SWATH proteotype patterns largely recapitulate these subtypes; however, they also reveal varying heterogeneity within the conventional subtypes, with triple negative tumors being the most heterogeneous. Proteins that contribute most strongly to the proteotype-based classification include INPP4B, CDK1, and ERBB2 and are associated with estrogen receptor (ER) status, tumor grade status, and HER2 status. Although these three key proteins exhibit high levels of correlation with transcript levels (R > 0.67), general correlation did not exceed R = 0.29, indicating the value of protein-level measurements of disease-regulated genes. Overall, this study highlights how cancer tissue proteotyping can lead to more accurate patient stratification.

The in silico human surfaceome.

Cell-surface proteins are of great biomedical importance, as demonstrated by the fact that 66% of approved human drugs listed in the DrugBank database target a cell-surface protein. Despite this biomedical relevance, there has been no comprehensive assessment of the human surfaceome, and only a fraction of the predicted 5,000 human transmembrane proteins have been shown to be located at the plasma membrane. To enable analysis of the human surfaceome, we developed the surfaceome predictor SURFY, based on machine learning. As a training set, we used experimentally verified high-confidence cell-surface proteins from the Cell Surface Protein Atlas (CSPA) and trained a random forest classifier on 131 features per protein and, specifically, per topological domain. SURFY was used to predict a human surfaceome of 2,886 proteins with an accuracy of 93.5%, which shows excellent overlap with known cell-surface protein classes (i.e., receptors). In deposited mRNA data, we found that between 543 and 1,100 surfaceome genes were expressed in cancer cell lines and maximally 1,700 surfaceome genes were expressed in embryonic stem cells and derivative lines. Thus, the surfaceome diversity depends on cell type and appears to be more dynamic than the nonsurface proteome. To make the predicted surfaceome readily accessible to the research community, we provide visualization tools for intuitive interrogation (wlab.ethz.ch/surfaceome). The in silico surfaceome enables the filtering of data generated by multiomics screens and supports the elucidation of the surfaceome nanoscale organization.

Comparative 'omics analyses differentiate Mycobacterium tuberculosis and Mycobacterium bovis and reveal distinct macrophage responses to infection with the human and bovine tubercle bacilli.

Members of the Mycobacterium tuberculosis complex (MTBC) are the causative agents of tuberculosis in a range of mammals, including humans. A key feature of MTBC pathogens is their high degree of genetic identity yet distinct host tropism. Notably, while Mycobacterium bovis is highly virulent and pathogenic for cattle, the human pathogen M. tuberculosis is attenuated in cattle. Previous research also suggests that host preference amongst MTBC members has a basis in host innate immune responses. To explore MTBC host tropism, we present in-depth profiling of the MTBC reference strains M. bovis AF2122/97 and M. tuberculosis H37Rv at both the global transcriptional and the translational level via RNA-sequencing and SWATH MS. Furthermore, a bovine alveolar macrophage infection time course model was used to investigate the shared and divergent host transcriptomic response to infection with M. tuberculosis H37Rv or M. bovis AF2122/97. Significant differential expression of virulence-associated pathways between the two bacilli was revealed, including the ESX-1 secretion system. A divergent transcriptional response was observed between M. tuberculosis H37Rv and M. bovis AF2122/97 infection of bovine alveolar macrophages, in particular cytosolic DNA-sensing pathways at 48 h post-infection, and highlights a distinct engagement of M. bovis with the bovine innate immune system. The work presented here therefore provides a basis for the identification of host innate immune mechanisms subverted by virulent host-adapted mycobacteria to promote their survival during the early stages of infection.

Delayed effects of transcriptional responses in Mycobacterium tuberculosis exposed to nitric oxide suggest other mechanisms involved in survival.

Mycobacterium tuberculosis has succeeded as a human pathogen for tens of thousands of years thanks to its ability to resist and adapt to the adverse conditions it encounters upon infection. Bacterial adaptation to stress is commonly viewed in the context of transcriptional regulation, with the implicit expectation that an initial transcriptomic response is tightly coupled to an ensuing proteomic response. However, after challenging M. tuberculosis with nitric oxide we found that the rapid transcriptional responses, detectable within minutes of nitric oxide exposure, typically took several hours to manifest on the protein level. Furthermore, early proteomic responses were dominated by the degradation of a set of proteins, specifically those containing damaged iron-sulphur clusters. Overall, our findings are consistent with transcriptional responses participating mostly in late-stage recovery rather than in generating an immediate resistance to nitric oxide stress, suggesting that survival of M. tuberculosis under acute stress is contingent on mechanisms other than transcriptional regulation. These findings provide a revised molecular understanding of an important human pathogen.

Quantitative proteomics: challenges and opportunities in basic and applied research.

In this Perspective, we discuss developments in mass-spectrometry-based proteomic technology over the past decade from the viewpoint of our laboratory. We also reflect on existing challenges and limitations, and explore the current and future roles of quantitative proteomics in molecular systems biology, clinical research and personalized medicine.

Updated Reference Genome Sequence and Annotation of Mycobacterium bovis AF2122/97.

We report here an update to the reference genome sequence of the bovine tuberculosis bacillus Mycobacterium bovis AF2122/97, generated using an integrative multiomics approach. The update includes 42 new coding sequences (CDSs), 14 modified annotations, 26 single-nucleotide polymorphism (SNP) corrections, and disclosure that the RD900 locus, previously described as absent from the genome, is in fact present.

Automated SWATH Data Analysis Using Targeted Extraction of Ion Chromatograms.

Targeted mass spectrometry comprises a set of methods able to quantify protein analytes in complex mixtures with high accuracy and sensitivity. These methods, e.g., Selected Reaction Monitoring (SRM) and SWATH MS, use specific mass spectrometric coordinates (assays) for reproducible detection and quantification of proteins. In this protocol, we describe how to analyze, in a targeted manner, data from a SWATH MS experiment aimed at monitoring thousands of proteins reproducibly over many samples. We present a standard SWATH MS analysis workflow, including manual data analysis for quality control (based on Skyline) as well as automated data analysis with appropriate control of error rates (based on the OpenSWATH workflow). We also discuss considerations to ensure maximal coverage, reproducibility, and quantitative accuracy.

Systems-level Proteomics of Two Ubiquitous Leaf Commensals Reveals Complementary Adaptive Traits for Phyllosphere Colonization.

Plants are colonized by a diverse community of microorganisms, the plant microbiota, exhibiting a defined and conserved taxonomic structure. Niche separation based on spatial segregation and complementary adaptation strategies likely forms the basis for coexistence of the various microorganisms in the plant environment. To gain insights into organism-specific adaptations on a molecular level, we selected two exemplary community members of the core leaf microbiota and profiled their proteomes upon Arabidopsis phyllosphere colonization. The highly quantitative mass spectrometric technique SWATH MS was used and allowed for the analysis of over two thousand proteins spanning more than three orders of magnitude in abundance for each of the model strains. The data suggest that Sphingomonas melonis utilizes amino acids and hydrocarbon compounds during colonization of leaves whereas Methylobacterium extorquens relies on methanol metabolism in addition to oxalate metabolism, aerobic anoxygenic photosynthesis and alkanesulfonate utilization. Comparative genomic analyses indicates that utilization of oxalate and alkanesulfonates is widespread among leaf microbiota members whereas, aerobic anoxygenic photosynthesis is almost exclusively found in Methylobacteria. Despite the apparent niche separation between these two strains we also found a relatively small subset of proteins to be coregulated, indicating common mechanisms, underlying successful leaf colonization. Overall, our results reveal for two ubiquitous phyllosphere commensals species-specific adaptations to the host environment and provide evidence for niche separation within the plant microbiota.

Integrating highly quantitative proteomics and genome-scale metabolic modeling to study pH adaptation in the human pathogen Enterococcus faecalis.

Genome-scale metabolic models represent the entirety of metabolic reactions of an organism based on the annotation of the respective genome. These models commonly allow all reactions to proceed concurrently, disregarding the fact that at no point all proteins will be present in a cell. The metabolic reaction space can be constrained to a more physiological state using experimentally obtained information on enzyme abundances. However, high-quality, genome-wide protein measurements have been challenging and typically transcript abundances have been used as a surrogate for protein measurements. With recent developments in mass spectrometry-based proteomics, exemplified by SWATH-MS, the acquisition of highly quantitative proteome-wide data at reasonable throughput has come within reach. Here we present methodology to integrate such proteome-wide data into genome-scale models. We applied this methodology to study cellular changes in Enterococcus faecalis during adaptation to low pH. Our results indicate reduced proton production in the central metabolism and decreased membrane permeability for protons due to different membrane composition. We conclude that proteomic data constrain genome-scale models to a physiological state and, in return, genome-scale models are useful tools to contextualize proteomic data.

Microbial Proteome Profiling and Systems Biology: Applications to Mycobacterium tuberculosis.

Each year, 1.3 million people die from tuberculosis, an infectious disease caused by Mycobacterium tuberculosis. Systems biology-based strategies might significantly contribute to the knowledge-guided development of more effective vaccines and drugs to prevent and cure infectious diseases. To build models simulating the behaviour of a system in response to internal or external stimuli and to identify potential targets for therapeutic intervention, systems biology approaches require the acquisition of quantitative molecular profiles on many perturbed states. Here we review the current state of proteomic analyses in Mycobacterium tuberculosis and discuss the potential of recently emerging targeting mass spectrometry-based techniques which enable fast, sensitive and accurate protein measurements.

Absolute Proteome Composition and Dynamics during Dormancy and Resuscitation of Mycobacterium tuberculosis.

Mycobacterium tuberculosis remains a health concern due to its ability to enter a non-replicative dormant state linked to drug resistance. Understanding transitions into and out of dormancy will inform therapeutic strategies. We implemented a universally applicable, label-free approach to estimate absolute cellular protein concentrations on a proteome-wide scale based on SWATH mass spectrometry. We applied this approach to examine proteomic reorganization of M. tuberculosis during exponential growth, hypoxia-induced dormancy, and resuscitation. The resulting data set covering >2,000 proteins reveals how protein biomass is distributed among cellular functions during these states. The stress-induced DosR regulon contributes 20% to cellular protein content during dormancy, whereas ribosomal proteins remain largely unchanged at 5%-7%. Absolute protein concentrations furthermore allow protein alterations to be translated into changes in maximal enzymatic reaction velocities, enhancing understanding of metabolic adaptations. Thus, global absolute protein measurements provide a quantitative description of microbial states, which can support the development of therapeutic interventions.

Building high-quality assay libraries for targeted analysis of SWATH MS data.

Targeted proteomics by selected/multiple reaction monitoring (S/MRM) or, on a larger scale, by SWATH (sequential window acquisition of all theoretical spectra) MS (mass spectrometry) typically relies on spectral reference libraries for peptide identification. Quality and coverage of these libraries are therefore of crucial importance for the performance of the methods. Here we present a detailed protocol that has been successfully used to build high-quality, extensive reference libraries supporting targeted proteomics by SWATH MS. We describe each step of the process, including data acquisition by discovery proteomics, assertion of peptide-spectrum matches (PSMs), generation of consensus spectra and compilation of MS coordinates that uniquely define each targeted peptide. Crucial steps such as false discovery rate (FDR) control, retention time normalization and handling of post-translationally modified peptides are detailed. Finally, we show how to use the library to extract SWATH data with the open-source software Skyline. The protocol takes 2-3 d to complete, depending on the extent of the library and the computational resources available.