The proteomic landscape of genome-wide genetic perturbations.

Functional genomic strategies have become fundamental for annotating gene function and regulatory networks. Here, we combined functional genomics with proteomics by quantifying protein abundances in a genome-scale knockout library in Saccharomyces cerevisiae, using data-independent acquisition mass spectrometry. We find that global protein expression is driven by a complex interplay of (1) general biological properties, including translation rate, protein turnover, the formation of protein complexes, growth rate, and genome architecture, followed by (2) functional properties, such as the connectivity of a protein in genetic, metabolic, and physical interaction networks. Moreover, we show that functional proteomics complements current gene annotation strategies through the assessment of proteome profile similarity, protein covariation, and reverse proteome profiling. Thus, our study reveals principles that govern protein expression and provides a genome-spanning resource for functional annotation.

Inorganic sulfur fixation via a new homocysteine synthase allows yeast cells to cooperatively compensate for methionine auxotrophy.

The assimilation, incorporation, and metabolism of sulfur is a fundamental process across all domains of life, yet how cells deal with varying sulfur availability is not well understood. We studied an unresolved conundrum of sulfur fixation in yeast, in which organosulfur auxotrophy caused by deletion of the homocysteine synthase Met17p is overcome when cells are inoculated at high cell density. In combining the use of self-establishing metabolically cooperating (SeMeCo) communities with proteomic, genetic, and biochemical approaches, we discovered an uncharacterized gene product YLL058Wp, herein named Hydrogen Sulfide Utilizing-1 (HSU1). Hsu1p acts as a homocysteine synthase and allows the cells to substitute for Met17p by reassimilating hydrosulfide ions leaked from met17Δ cells into O-acetyl-homoserine and forming homocysteine. Our results show that cells can cooperate to achieve sulfur fixation, indicating that the collective properties of microbial communities facilitate their basic metabolic capacity to overcome sulfur limitation.

Rationalization of the Membrane Permeability Differences in a Series of Analogue Cyclic Decapeptides.

Cyclization and selected backbone N-methylations are found to be often necessary but not sufficient conditions for peptidic drugs to have a good bioavailability. Thus, the design of cyclic peptides with good passive membrane permeability and good solubility remains a challenge. The backbone scaffold of a recently published series of cyclic decapeptides with six selected backbone N-methylations was designed to favor the adoption of a closed conformation with ß-turns and four transannular hydrogen bonds. Although this conformation was indeed adopted by the peptides as determined by NMR measurements, substantial differences in the membrane permeability were observed. In this work, we aim to rationalize the impact of discrete side chain modifications on membrane permeability for six of these cyclic decapeptides. The thermodynamic and kinetic properties were investigated using molecular dynamics simulations and Markov state modeling in water and chloroform. The study highlights the influence that side-chain modifications can have on the backbone conformation. Peptides with a d-proline in the ß-turns were more likely to adopt, even in water, the closed conformation with transannular hydrogen bonds, which facilitates transition through the membrane. The population of the closed conformation in water was found to correlate positively with PAMPA log Pe.

Unexpected biases in the distribution of consecutive primes.

Although the sequence of primes is very well distributed in the reduced residue classes [Formula: see text], the distribution of pairs of consecutive primes among the permissible ϕ(q)(2) pairs of reduced residue classes [Formula: see text] is surprisingly erratic. This paper proposes a conjectural explanation for this phenomenon, based on the Hardy-Littlewood conjectures. The conjectures are then compared with numerical data, and the observed fit is very good.

Multiply Intercalator-Substituted Cu(II) Cyclen Complexes as DNA Condensers and DNA/RNA Synthesis Inhibitors.

Many drugs that are applied in anticancer therapy such as the anthracycline doxorubicin contain DNA-intercalating 9,10-anthraquinone (AQ) moieties. When Cu(II) cyclen complexes were functionalized with up to three (2-anthraquinonyl)methyl substituents, they efficiently inhibited DNA and RNA synthesis resulting in high cytotoxicity (selective for cancer cells) accompanied by DNA condensation/aggregation phenomena. Molecular modeling suggests an unusual bisintercalation mode with only one base pair between the two AQ moieties and the metal complex as a linker. A regioisomer, in which the AQ moieties point in directions unfavorable for such an interaction, had a much weaker biological activity. The ligands alone and corresponding Zn(II) complexes (used as redox inert control compounds) also exhibited lower activity.

Density-based cluster algorithms for the identification of core sets.

The core-set approach is a discretization method for Markov state models of complex molecular dynamics. Core sets are disjoint metastable regions in the conformational space, which need to be known prior to the construction of the core-set model. We propose to use density-based cluster algorithms to identify the cores. We compare three different density-based cluster algorithms: the CNN, the DBSCAN, and the Jarvis-Patrick algorithm. While the core-set models based on the CNN and DBSCAN clustering are well-converged, constructing core-set models based on the Jarvis-Patrick clustering cannot be recommended. In a well-converged core-set model, the number of core sets is up to an order of magnitude smaller than the number of states in a conventional Markov state model with comparable approximation error. Moreover, using the density-based clustering one can extend the core-set method to systems which are not strongly metastable. This is important for the practical application of the core-set method because most biologically interesting systems are only marginally metastable. The key point is to perform a hierarchical density-based clustering while monitoring the structure of the metric matrix which appears in the core-set method. We test this approach on a molecular-dynamics simulation of a highly flexible 14-residue peptide. The resulting core-set models have a high spatial resolution and can distinguish between conformationally similar yet chemically different structures, such as register-shifted hairpin structures.

The Impact of Acute Nutritional Interventions on the Plasma Proteome.

CONTEXT: Humans respond profoundly to changes in diet, while nutrition and environment have a great impact on population health. It is therefore important to deeply characterize the human nutritional responses. OBJECTIVE: Endocrine parameters and the metabolome of human plasma are rapidly responding to acute nutritional interventions such as caloric restriction or a glucose challenge. It is less well understood whether the plasma proteome would be equally dynamic, and whether it could be a source of corresponding biomarkers. METHODS: We used high-throughput mass spectrometry to determine changes in the plasma proteome of i) 10 healthy, young, male individuals in response to 2 days of acute caloric restriction followed by refeeding; ii) 200 individuals of the Ely epidemiological study before and after a glucose tolerance test at 4 time points (0, 30, 60, 120 minutes); and iii) 200 random individuals from the Generation Scotland study. We compared the proteomic changes detected with metabolome data and endocrine parameters. RESULTS: Both caloric restriction and the glucose challenge substantially impacted the plasma proteome. Proteins responded across individuals or in an individual-specific manner. We identified nutrient-responsive plasma proteins that correlate with changes in the metabolome, as well as with endocrine parameters. In particular, our study highlights the role of apolipoprotein C1 (APOC1), a small, understudied apolipoprotein that was affected by caloric restriction and dominated the response to glucose consumption and differed in abundance between individuals with and without type 2 diabetes. CONCLUSION: Our study identifies APOC1 as a dominant nutritional responder in humans and highlights the interdependency of acute nutritional response proteins and the endocrine system.

The human host response to monkeypox infection: a proteomic case series study.

The rapid rise of monkeypox (MPX) cases outside previously endemic areas prompts for a better understanding of the disease. We studied the plasma proteome of a group of MPX patients with a similar infection history and clinical manifestation typical for the current outbreak. We report that MPX in this case series is associated with a strong plasma proteomic response among nutritional and acute phase response proteins. Moreover, we report a correlation between plasma proteins and disease severity. Contrasting the MPX host response with that of COVID-19, we find a range of similarities, but also important differences. For instance, CFHR1 is induced in COVID-19, but suppressed in MPX, reflecting the different roles of the complement system in the two infectious diseases. Of note, the spatial overlap in response proteins suggested that a COVID-19 biomarker panel assay could be repurposed for MPX. Applying a targeted protein panel assay provided encouraging results and distinguished MPX cases from healthy controls. Hence, our results provide a first proteomic characterization of the MPX human host response and encourage further research on protein-panel assays in emerging infectious diseases.

A multiplex protein panel assay for severity prediction and outcome prognosis in patients with COVID-19: An observational multi-cohort study.

Background: Global healthcare systems continue to be challenged by the COVID-19 pandemic, and there is a need for clinical assays that can help optimise resource allocation, support treatment decisions, and accelerate the development and evaluation of new therapies. Methods: We developed a multiplexed proteomics assay for determining disease severity and prognosis in COVID-19. The assay quantifies up to 50 peptides, derived from 30 known and newly introduced COVID-19-related protein markers, in a single measurement using routine-lab compatible analytical flow rate liquid chromatography and multiple reaction monitoring (LC-MRM). We conducted two observational studies in patients with COVID-19 hospitalised at Charité - Universitätsmedizin Berlin, Germany before (from March 1 to 26, 2020, n=30) and after (from April 4 to November 19, 2020, n=164) dexamethasone became standard of care. The study is registered in the German and the WHO International Clinical Trials Registry (DRKS00021688). Findings: The assay produces reproducible (median inter-batch CV of 10.9%) absolute quantification of 47 peptides with high sensitivity (median LLOQ of 143 ng/ml) and accuracy (median 96.8%). In both studies, the assay reproducibly captured hallmarks of COVID-19 infection and severity, as it distinguished healthy individuals, mild, moderate, and severe COVID-19. In the post-dexamethasone cohort, the assay predicted survival with an accuracy of 0.83 (108/130), and death with an accuracy of 0.76 (26/34) in the median 2.5 weeks before the outcome, thereby outperforming compound clinical risk assessments such as SOFA, APACHE II, and ABCS scores. Interpretation: Disease severity and clinical outcomes of patients with COVID-19 can be stratified and predicted by the routine-applicable panel assay that combines known and novel COVID-19 biomarkers. The prognostic value of this assay should be prospectively assessed in larger patient cohorts for future support of clinical decisions, including evaluation of sample flow in routine setting. The possibility to objectively classify COVID-19 severity can be helpful for monitoring of novel therapies, especially in early clinical trials. Funding: This research was funded in part by the European Research Council (ERC) under grant agreement ERC-SyG-2020 951475 (to M.R) and by the Wellcome Trust (IA 200829/Z/16/Z to M.R.). The work was further supported by the Ministry of Education and Research (BMBF) as part of the National Research Node 'Mass Spectrometry in Systems Medicine (MSCoresys)', under grant agreements 031L0220 and 161L0221. J.H. was supported by a Swiss National Science Foundation (SNSF) Postdoc Mobility fellowship (project number 191052). This study was further supported by the BMBF grant NaFoUniMedCOVID-19 - NUM-NAPKON, FKZ: 01KX2021. The study was co-funded by the UK's innovation agency, Innovate UK, under project numbers 75594 and 56328.

A proteomic survival predictor for COVID-19 patients in intensive care.

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care.

A time-resolved proteomic and prognostic map of COVID-19.

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease.

On the Stability of the Water-Soluble Chlorophyll-Binding Protein (WSCP) Studied by Molecular Dynamics Simulations.

The water-soluble chlorophyll-binding protein (WSCP) is assumed to be not a part of the photosynthetic process. Applying molecular dynamics (MD) simulations, we aimed to obtain insight into the exceptional stability of WSCP. We analyzed dynamical features such as the hydrogen bond network, flexibility, and force distributions. The WSCP structure contains two cysteines at the interfaces of every protein chain, which are in close contact with the cysteines of the other dimer. We tested if a connection of these cysteines between different protein chains influences the dynamical behavior to investigate any influences on the thermal stability. We find that the hydrogen bond network is very stable regardless of the presence or absence of the hypothetical disulfide bridges and/or the chlorophyll units. Furthermore, it is found that the phytyl chains of the chlorophyll units are extremely flexible, much more than what is seen in crystal structures. Nonetheless, they seem to protect a photochemically active site of the chlorophylls over the complete simulation time. Finally, we also find that a cavity in the chlorophyll-surrounding sheath exists, which may allow access for individual small molecules to the core of WSCP.

Broad substrate tolerance of tubulin tyrosine ligase enables one-step site-specific enzymatic protein labeling.

The broad substrate tolerance of tubulin tyrosine ligase is the basic rationale behind its wide applicability for chemoenzymatic protein functionalization. In this context, we report that the wild-type enzyme enables ligation of various unnatural amino acids that are substantially bigger than and structurally unrelated to the natural substrate, tyrosine, without the need for extensive protein engineering. This unusual substrate flexibility is due to the fact that the enzyme's catalytic pocket forms an extended cavity during ligation, as confirmed by docking experiments and all-atom molecular dynamics simulations. This feature enabled one-step C-terminal biotinylation and fluorescent coumarin labeling of various functional proteins as demonstrated with ubiquitin, an antigen binding nanobody, and the apoptosis marker Annexin V. Its broad substrate tolerance establishes tubulin tyrosine ligase as a powerful tool for in vitro enzyme-mediated protein modification with single functional amino acids in a specific structural context.