Critical assessment of proteome-wide label-free absolute abundance estimation strategies

Proteome allocation is linked to transcriptional regulation through a modularized transcriptome

It has proved challenging to quantitatively relate the proteome to the transcriptome on a per-gene basis. Recent advances in data analytics have enabled a biologically meaningful modularization of the bacterial transcriptome. We thus investigate whether matched datasets of transcriptomes and proteomes from bacteria under diverse conditions can be modularized in the same way to reveal novel relationships between their compositions. We find that; (1) the modules of the proteome and the transcriptome are comprised of a similar list of gene products, (2) the modules in the proteome often represent combinations of modules from the transcriptome, (3) known transcriptional and post-translational regulation is reflected in differences between two sets of modules, allowing for knowledge-mapping when interpreting module functions, and (4) through statistical modeling, absolute proteome allocation can be inferred from the transcriptome alone. Quantitative and knowledge-based relationships can thus be found at the genome-scale between the proteome and transcriptome in bacteria.

Systematic engineering for production of anti-aging sunscreen compound in Pseudomonas putida

Sunscreen has been used for thousands of years to protect skin from ultraviolet radiation. However, the use of modern commercial sunscreen containing oxybenzone, ZnO, and TiO2 has raised concerns due to their negative effects on human health and the environment. In this study, we aim to establish an efficient microbial platform for production of shinorine, a UV light absorbing compound with anti-aging properties. First, we methodically selected an appropriate host for shinorine production by analyzing central carbon flux distribution data from prior studies alongside predictions from genome-scale metabolic models (GEMs). We enhanced shinorine productivity through CRISPRi-mediated downregulation and utilized shotgun proteomics to pinpoint potential competing pathways. Simultaneously, we improved the shinorine biosynthetic pathway by refining its design, optimizing promoter usage, and altering the strength of ribosome binding sites. Finally, we conducted amino acid feeding experiments under various conditions to identify the key limiting factors in shinorine production. The study combines meta-analysis of 13C-metabolic flux analysis, GEMs, synthetic biology, CRISPRi-mediated gene downregulation, and omics analysis to improve shinorine production, demonstrating the potential of Pseudomonas putida KT2440 as platform for shinorine production.

Rationally designed chromosome fusion does not prevent rapid growth of Vibrio natriegens

DNA replication is essential for the proliferation of all cells. Bacterial chromosomes are replicated bidirectionally from a single origin of replication, with replication proceeding at about 1000 bp per second. For the model organism, Escherichia coli, this translates into a replication time of about 40 min for its 4.6 Mb chromosome. Nevertheless, E. coli can propagate by overlapping replication cycles with a maximum short doubling time of 20 min. The fastest growing bacterium known, Vibrio natriegens, is able to replicate with a generation time of less than 10 min. It has a bipartite genome with chromosome sizes of 3.2 and 1.9 Mb. Is simultaneous replication from two origins a prerequisite for its rapid growth? We fused the two chromosomes of V. natriegens to create a strain carrying one chromosome with a single origin of replication. Compared to the parental, this strain showed no significant deviation in growth rate. This suggests that the split genome is not a prerequisite for rapid growth.

Lipid A in outer membrane vesicles shields bacteria from polymyxins

The continuous emergence of multidrug-resistant bacterial pathogens poses a major global healthcare challenge, with Klebsiella pneumoniae being a prominent threat. We conducted a comprehensive study on K. pneumoniae's antibiotic resistance mechanisms, focusing on outer membrane vesicles (OMVs) and polymyxin, a last-resort antibiotic. Our research demonstrates that OMVs protect bacteria from polymyxins. OMVs derived from Polymyxin B (PB)-stressed K. pneumoniae exhibited heightened protective efficacy due to increased vesiculation, compared to OMVs from unstressed Klebsiella. OMVs also shield bacteria from different bacterial families. This was validated ex vivo and in vivo using precision cut lung slices (PCLS) and Galleria mellonella. In all models, OMVs protected K. pneumoniae from PB and reduced the associated stress response on protein level. We observed significant changes in the lipid composition of OMVs upon PB treatment, affecting their binding capacity to PB. The altered binding capacity of single OMVs from PB stressed K. pneumoniae could be linked to a reduction in the lipid A amount of their released vesicles. Although the amount of lipid A per vesicle is reduced, the overall increase in the number of vesicles results in an increased protection because the sum of lipid A and therefore PB binding sites have increased. This unravels the mechanism of the altered PB protective efficacy of OMVs from PB stressed K. pneumoniae compared to control OMVs. The lipid A-dependent protective effect against PB was confirmed in vitro using artificial vesicles. Moreover, artificial vesicles successfully protected Klebsiella from PB ex vivo and in vivo. The findings indicate that OMVs act as protective shields for bacteria by binding to polymyxins, effectively serving as decoys and preventing antibiotic interaction with the cell surface. Our findings provide valuable insights into the mechanisms underlying antibiotic cross-protection and offer potential avenues for the development of novel therapeutic interventions to address the escalating threat of multidrug-resistant bacterial infections.

Identification of Cellular Protein Targets of a Half-Sandwich Iridium(III) Complex Reveals Its Dual Mechanism of Action via Both Electrophilic and Oxidative Stresses

Identification of intracellular targets of anticancer drug candidates provides key information on their mechanism of action. Exploiting the ability of the anticancer (C∧N)-chelated half-sandwich iridium(III) complexes to covalently bind proteins, click chemistry with a bioorthogonal azido probe was used to localize a phenyloxazoline-chelated iridium complex within cells and profile its interactome at the proteome-wide scale. Proteins involved in protein folding and actin cytoskeleton regulation were identified as high-affinity targets. Upon iridium complex treatment, the folding activity of Heat Shock Protein HSP90 was inhibited in vitro and major cytoskeleton disorganization was observed. A wide array of imaging and biochemical methods validated selected targets and provided a multiscale overview of the effects of this complex on live human cells. We demonstrate that it behaves as a dual agent, inducing both electrophilic and oxidative stresses in cells that account for its cytotoxicity. The proposed methodological workflow can open innovative avenues in metallodrug discovery.

Proteomics-The State of the Field: The Definition and Analysis of Proteomes Should Be Based in Reality, Not Convenience

With growing recognition and acknowledgement of the genuine complexity of proteomes, we are finally entering the post-proteogenomic era. Routine assessment of proteomes as inferred correlates of gene sequences (i.e., canonical 'proteins') cannot provide the necessary critical analysis of systems-level biology that is needed to understand underlying molecular mechanisms and pathways or identify the most selective biomarkers and therapeutic targets. These critical requirements demand the analysis of proteomes at the level of proteoforms/protein species, the actual active molecular players. Currently, only highly refined integrated or integrative top-down proteomics (iTDP) enables the analytical depth necessary to provide routine, comprehensive, and quantitative proteome assessments across the widest range of proteoforms inherent to native systems. Here we provide a broad perspective of the field, taking in historical and current realities, to establish a more balanced understanding of where the field has come from (in particular during the ten years since Proteomes was launched), current issues, and how things likely need to proceed if necessary deep proteome analyses are to succeed. We base this in our firm belief that the best proteomic analyses reflect, as closely as possible, the native sample at the moment of sampling. We also seek to emphasise that this and future analytical approaches are likely best based on the broad recognition and exploitation of the complementarity of currently successful approaches. This also emphasises the need to continuously evaluate and further optimize established approaches, to avoid complacency in thinking and expectations but also to promote the critical and careful development and introduction of new approaches, most notably those that address proteoforms. Above all, we wish to emphasise that a rigorous focus on analytical quality must override current thinking that largely values analytical speed; the latter would certainly be nice, if only proteoforms could thus be effectively, routinely, and quantitatively assessed. Alas, proteomes are composed of proteoforms, not molecular species that can be amplified or that directly mirror genes (i.e., 'canonical'). The problem is hard, and we must accept and address it as such, but the payoff in playing this longer game of rigorous deep proteome analyses is the promise of far more selective biomarkers, drug targets, and truly personalised or even individualised medicine.

The secreted protein Amuc_1409 from Akkermansia muciniphila improves gut health through intestinal stem cell regulation

Akkermansia muciniphila has received great attention because of its beneficial roles in gut health by regulating gut immunity, promoting intestinal epithelial development, and improving barrier integrity. However, A. muciniphila-derived functional molecules regulating gut health are not well understood. Microbiome-secreted proteins act as key arbitrators of host-microbiome crosstalk through interactions with host cells in the gut and are important for understanding host-microbiome relationships. Herein, we report the biological function of Amuc_1409, a previously uncharacterised A. muciniphila-secreted protein. Amuc_1409 increased intestinal stem cell (ISC) proliferation and regeneration in ex vivo intestinal organoids and in vivo models of radiation- or chemotherapeutic drug-induced intestinal injury and natural aging with male mice. Mechanistically, Amuc_1409 promoted E-cadherin/β-catenin complex dissociation via interaction with E-cadherin, resulting in the activation of Wnt/β-catenin signaling. Our results demonstrate that Amuc_1409 plays a crucial role in intestinal homeostasis by regulating ISC activity in an E-cadherin-dependent manner and is a promising biomolecule for improving and maintaining gut health.

A compositional data model to predict the isotope distribution for average peptides using a compositional spline model

We propose an updated approach for approximating the isotope distribution of average peptides given their monoisotopic mass. Our methodology involves in-silico cleavage of the entire UNIPROT database of human-reviewed proteins using Trypsin, generating a theoretical peptide dataset. The isotope distribution is computed using BRAIN. We apply a compositional data modelling strategy that utilizes an additive log-ratio transformation for the isotope probabilities followed by a penalized spline regression. Furthermore, due to the impact of the number of sulphur atoms on the course of the isotope distribution, we develop separate models for peptides containing zero up to five sulphur atoms. Additionally, we propose three methods to estimate the number of sulphur atoms based on an observed isotope distribution. The performance of the spline models and the sulphur prediction approaches is evaluated using a mean squared error and a modified Pearson's χ2 goodness-of-fit measure on an experimental UPS2 data set. Our analysis reveals that the variability in spectral accuracy, that is, the variability between MS1 scans, contributes more to the errors than the approximation of the theoretical isotope distribution by our proposed average peptide model. Moreover, we find that the accuracy of predicting the number of sulphur atoms based on the observed isotope distribution is limited by measurement accuracy.

Extracellular vesicle biomarkers for complement dysfunction in schizophrenia

Schizophrenia, a complex neuropsychiatric disorder, frequently experiences a high rate of misdiagnosis due to subjective symptom assessment. Consequently, there is an urgent need for innovative and objective diagnostic tools. In this study, we used cutting-edge extracellular vesicles' (EVs) proteome profiling and XGBoost-based machine learning to develop new markers and personalized discrimination scores for schizophrenia diagnosis and prediction of treatment response. We analysed plasma and plasma-derived EVs from 343 participants, including 100 individuals with chronic schizophrenia, 34 first-episode and drug-naïve patients, 35 individuals with bipolar disorder, 25 individuals with major depressive disorder and 149 age- and sex-matched healthy controls. Our innovative approach uncovered EVs-based complement changes in patients, specific to their disease-type and status. The EV-based biomarkers outperformed their plasma counterparts, accurately distinguishing schizophrenia individuals from healthy controls with an area under curve (AUC) of 0.895, 83.5% accuracy, 85.3% sensitivity and 82.0% specificity. Moreover, they effectively differentiated schizophrenia from bipolar disorder and major depressive disorder, with AUCs of 0.966 and 0.893, respectively. The personalized discrimination scores provided a personalized diagnostic index for schizophrenia and exhibited a significant association with patients' antipsychotic treatment response in the follow-up cohort. Overall, our study represents a significant advancement in the field of neuropsychiatric disorders, demonstrating the potential of EV-based biomarkers in guiding personalized diagnosis and treatment of schizophrenia.

Ultra-fast label-free quantification and comprehensive proteome coverage with narrow-window data-independent acquisition

Mass spectrometry (MS)-based proteomics aims to characterize comprehensive proteomes in a fast and reproducible manner. Here we present the narrow-window data-independent acquisition (nDIA) strategy consisting of high-resolution MS1 scans with parallel tandem MS (MS/MS) scans of ~200 Hz using 2-Th isolation windows, dissolving the differences between data-dependent and -independent methods. This is achieved by pairing a quadrupole Orbitrap mass spectrometer with the asymmetric track lossless (Astral) analyzer which provides >200-Hz MS/MS scanning speed, high resolving power and sensitivity, and low-ppm mass accuracy. The nDIA strategy enables profiling of >100 full yeast proteomes per day, or 48 human proteomes per day at the depth of ~10,000 human protein groups in half-an-hour or ~7,000 proteins in 5 min, representing 3× higher coverage compared with current state-of-the-art MS. Multi-shot acquisition of offline fractionated samples provides comprehensive coverage of human proteomes in ~3 h. High quantitative precision and accuracy are demonstrated in a three-species proteome mixture, quantifying 14,000+ protein groups in a single half-an-hour run.

The virulence regulator VirB from Shigella flexneri uses a CTP-dependent switch mechanism to activate gene expression

The transcriptional antisilencer VirB acts as a master regulator of virulence gene expression in the human pathogen Shigella flexneri. It binds DNA sequences (virS) upstream of VirB-dependent promoters and counteracts their silencing by the nucleoid-organizing protein H-NS. However, its precise mode of action remains unclear. Notably, VirB is not a classical transcription factor but related to ParB-type DNA-partitioning proteins, which have recently been recognized as DNA-sliding clamps using CTP binding and hydrolysis to control their DNA entry gate. Here, we show that VirB binds CTP, embraces DNA in a clamp-like fashion upon its CTP-dependent loading at virS sites and slides laterally on DNA after clamp closure. Mutations that prevent CTP-binding block VirB loading in vitro and abolish the formation of VirB nucleoprotein complexes as well as virulence gene expression in vivo. Thus, VirB represents a CTP-dependent molecular switch that uses a loading-and-sliding mechanism to control transcription during bacterial pathogenesis.

Development of Corynebacterium glutamicum as a monoterpene production platform

Monoterpenes are commonly known for their role in the flavors and fragrances industry and are also gaining attention for other uses like insect repellant and as potential renewable fuels for aviation. Corynebacterium glutamicum, a Generally Recognized as Safe microbe, has been a choice organism in industry for the annual million ton-scale bioproduction of amino acids for more than 50 years; however, efforts to produce monoterpenes in C. glutamicum have remained relatively limited. In this study, we report a further expansion of the C. glutamicum biosynthetic repertoire through the development and optimization of a mevalonate-based monoterpene platform. In the course of our plasmid design iterations, we increased flux through the mevalonate-based bypass pathway, measuring isoprenol production as a proxy for monoterpene precursor abundance and demonstrating the highest reported titers in C. glutamicum to date at 1504.6 mg/L. Our designs also evaluated the effects of backbone, promoter, and GPP synthase homolog origin on monoterpene product titers. Monoterpene production was further improved by disrupting competing pathways for isoprenoid precursor supply and by implementing a biphasic production system to prevent volatilization. With this platform, we achieved 321.1 mg/L of geranoids, 723.6 mg/L of 1,8-cineole, and 227.8 mg/L of linalool. Furthermore, we determined that C. glutamicum first oxidizes geraniol through an aldehyde intermediate before it is asymmetrically reduced to citronellol. Additionally, we demonstrate that the aldehyde reductase, AdhC, possesses additional substrate promiscuity for acyclic monoterpene aldehydes.

Maximizing Heterologous Expression of Engineered Type I Polyketide Synthases: Investigating Codon Optimization Strategies

Type I polyketide synthases (T1PKSs) hold enormous potential as a rational production platform for the biosynthesis of specialty chemicals. However, despite great progress in this field, the heterologous expression of PKSs remains a major challenge. One of the first measures to improve heterologous gene expression can be codon optimization. Although controversial, choosing the wrong codon optimization strategy can have detrimental effects on the protein and product levels. In this study, we analyzed 11 different codon variants of an engineered T1PKS and investigated in a systematic approach their influence on heterologous expression in Corynebacterium glutamicum, Escherichia coli, and Pseudomonas putida. Our best performing codon variants exhibited a minimum 50-fold increase in PKS protein levels, which also enabled the production of an unnatural polyketide in each of these hosts. Furthermore, we developed a free online tool (https://basebuddy.lbl.gov) that offers transparent and highly customizable codon optimization with up-to-date codon usage tables. In this work, we not only highlight the significance of codon optimization but also establish the groundwork for the high-throughput assembly and characterization of PKS pathways in alternative hosts.

Recent applications of quantitative mass spectrometry in biopharmaceutical process development and manufacturing

Biopharmaceutical products have seen rapid growth over the past few decades and continue to dominate the global pharmaceutical market. Aligning with the quality by design (QbD) framework and realization, recent advances in liquid chromatography-mass spectrometry (LC-MS) instrumentation and related techniques have enhanced biopharmaceutical characterization capabilities and have supported an increased development of biopharmaceutical products. Beyond its routine qualitative characterization, the quantitative feature of LC-MS has unique applications in biopharmaceutical process development and manufacturing. This review describes the recent applications and implications of the advancement of quantitative MS methods in biopharmaceutical process development, and characterization of biopharmaceutical product, product-related variants, and process-related impurities. We also provide insights on the emerging applications of quantitative MS in the lifecycle of biopharmaceutical product development including quality control in the Good Manufacturing Practice (GMP) environment and process analytical technology (PAT) practices during process development and manufacturing. Through collaboration with instrument and software vendors and regulatory agencies, we envision broader adoption of phase-appropriate quantitative MS-based methods for the analysis of biopharmaceutical products, which in turn has the potential to enable manufacture of higher quality products for patients.

Phenotypic and proteomic analysis of plasma extracellular vesicles highlights them as potential biomarkers of primary Sjögren syndrome

Sjögren syndrome (SjS) is an autoimmune disease characterized by the destruction of the exocrine gland epithelia, causing a dryness of mucosa called sicca symptoms, and whose main life-threatening complication is lymphoma. There is a need for new biomarkers in this disease, notably diagnostic biomarkers for patients with genuine sicca symptoms that do not meet current criteria, and prognostic biomarkers for patients at risk of lymphoma. Plasma extracellular vesicles (EVs) are promising biomarker candidates in several diseases, but their potential has not yet been explored in SjS. In this proof-of-concept study, we characterized EVs from primary SjS patients (pSS, n=12) at the phenotypic and proteomic levels, compared to EVs from healthy donor (HD, n=8) and systemic lupus erythematosus patients (SLE, n=12). Specific plasma EVs subpopulations, derived from neutrophils, endothelial, and epithelial cells, were found increased in pSS. We also identified a pSS proteomic signature in plasma EVs, including neutrophil-, epithelial-, and endothelial-related proteins, such as integrin alpha M (ITGAM), olfactomedin-4 (OLFM4), Ras-related protein RAB10, and CD36. Overall, our results support the relevance of plasma EVs as biomarkers in SjS.

Complete integration of carbene-transfer chemistry into biosynthesis

Biosynthesis is an environmentally benign and renewable approach that can be used to produce a broad range of natural and, in some cases, new-to-nature products. However, biology lacks many of the reactions that are available to synthetic chemists, resulting in a narrower scope of accessible products when using biosynthesis rather than synthetic chemistry. A prime example of such chemistry is carbene-transfer reactions1. Although it was recently shown that carbene-transfer reactions can be performed in a cell and used for biosynthesis2,3, carbene donors and unnatural cofactors needed to be added exogenously and transported into cells to effect the desired reactions, precluding cost-effective scale-up of the biosynthesis process with these reactions. Here we report the access to a diazo ester carbene precursor by cellular metabolism and a microbial platform for introducing unnatural carbene-transfer reactions into biosynthesis. The α-diazoester azaserine was produced by expressing a biosynthetic gene cluster in Streptomyces albus. The intracellularly produced azaserine was used as a carbene donor to cyclopropanate another intracellularly produced molecule-styrene. The reaction was catalysed by engineered P450 mutants containing a native cofactor with excellent diastereoselectivity and a moderate yield. Our study establishes a scalable, microbial platform for conducting intracellular abiological carbene-transfer reactions to functionalize a range of natural and new-to-nature products and expands the scope of organic products that can be produced by cellular metabolism.

Proteome allocation is linked to transcriptional regulation through a modularized transcriptome

It has proved challenging to quantitatively relate the proteome to the transcriptome on a per-gene basis. Recent advances in data analytics have enabled a biologically meaningful modularization of the bacterial transcriptome. We thus investigated whether matched datasets of transcriptomes and proteomes from bacteria under diverse conditions could be modularized in the same way to reveal novel relationships between their compositions. We found that; 1) the modules of the proteome and the transcriptome are comprised of a similar list of gene products, 2) the modules in the proteome often represent combinations of modules from the transcriptome, 3) known transcriptional and post-translational regulation is reflected in differences between two sets of modules, allowing for knowledge-mapping when interpreting module functions, and 4) through statistical modeling, absolute proteome allocation can be inferred from the transcriptome alone. Quantitative and knowledge-based relationships can thus be found at the genome-scale between the proteome and transcriptome in bacteria.

FlrA-independent production of flagellar proteins is required for proper flagellation in Shewanella putrefaciens

Flagella are multiprotein complexes whose assembly and positioning require complex spatiotemporal control. Flagellar assembly is thought to be controlled by several transcriptional tiers, which are mediated through various master regulators. Here, we revisited the regulation of flagellar genes in polarly flagellated gammaproteobacteria by the regulators FlrA, RpoN (σ⁵⁴ ) and FliA (σ²⁸ ) in Shewanella putrefaciens CN-32 at the transcript and protein level. We found that a number of regulatory and structural proteins were present in the absence of the main regulators, suggesting that initiation of flagella assembly and motor activation relies on the abundance control of only a few structural key components that are required for the formation of the MS- and C-ring and the flagellar type III secretion system. We identified FlrA-independent promoters driving expression of the regulators of flagellar number and positioning, FlhF and FlhG. Reduction of the gene expression levels from these promoters resulted in the emergence of hyperflagellation. This finding indicates that basal expression is required to adjust the flagellar counter in Shewanella. This is adding a deeper layer to the regulation of flagellar synthesis and assembly.

Characterization of peptide-protein relationships in protein ambiguity groups via bipartite graphs

In bottom-up proteomics, proteins are enzymatically digested into peptides before measurement with mass spectrometry. The relationship between proteins and their corresponding peptides can be represented by bipartite graphs. We conduct a comprehensive analysis of bipartite graphs using quantified peptides from measured data sets as well as theoretical peptides from an in silico digestion of the corresponding complete taxonomic protein sequence databases. The aim of this study is to characterize and structure the different types of graphs that occur and to compare them between data sets. We observed a large influence of the accepted minimum peptide length during in silico digestion. When changing from theoretical peptides to measured ones, the graph structures are subject to two opposite effects. On the one hand, the graphs based on measured peptides are on average smaller and less complex compared to graphs using theoretical peptides. On the other hand, the proportion of protein nodes without unique peptides, which are a complicated case for protein inference and quantification, is considerably larger for measured data. Additionally, the proportion of graphs containing at least one protein node without unique peptides rises when going from database to quantitative level. The fraction of shared peptides and proteins without unique peptides as well as the complexity and size of the graphs highly depends on the data set and organism. Large differences between the structures of bipartite peptide-protein graphs have been observed between database and quantitative level as well as between analyzed species. In the analyzed measured data sets, the proportion of protein nodes without unique peptides ranged from 6.4% to 55.0%. This highlights the need for novel methods that can quantify proteins without unique peptides. The knowledge about the structure of the bipartite peptide-protein graphs gained in this study will be useful for the development of such algorithms.

Baseline proteomics characterisation of the emerging host biomanufacturing organism Halomonas bluephagenesis

Despite its greener credentials, biomanufacturing remains financially uncompetitive compared with the higher carbon emitting, hydrocarbon-based chemical industry. Replacing traditional chassis such as E. coli with novel robust organisms, are a route to cost reduction for biomanufacturing. Extremophile bacteria such as the halophilic Halomonas bluephagenesis TD01 exemplify this potential by thriving in environments inherently inimical to other organisms, so reducing sterilisation costs. Novel chassis are inevitably less well annotated than established organisms. Rapid characterisation along with community data sharing will facilitate adoption of such organisms for biomanufacturing. The data record comprises a newly sequenced genome for the organism and evidence via LC-MS based proteomics for expression of 1160 proteins (30% of the proteome) including baseline quantification of 1063 proteins (27% of the proteome), and a spectral library enabling re-use for targeted LC-MS proteomics assays. Protein data are annotated with KEGG Orthology, enabling rapid matching of quantitative data to pathways of interest to biomanufacturing.

Measurement(s)	Genome • Proteomic Profile
Technology Type(s)	PacBio Sequel System • nanoflow liquid chromatography-electrospray ionisation mass spectrometry
Sample Characteristic - Organism	Halomonas sp. TD01

Proteomic Analysis of Human Milk Reveals Nutritional and Immune Benefits in the Colostrum from Mothers with COVID-19

Despite the well-known benefits of breastfeeding and the World Health Organization’s breastfeeding recommendations for COVID-19 infected mothers, whether these mothers should be encouraged to breastfeed is under debate due to concern about the risk of virus transmission and lack of evidence of breastmilk’s protective effects against the virus. Here, we provide a molecular basis for the breastfeeding recommendation through mass spectrometry (MS)-based proteomics and glycosylation analysis of immune-related proteins in both colostrum and mature breastmilk collected from COVID-19 patients and healthy donors. The total protein amounts in the COVID-19 colostrum group were significantly higher than in the control group. While casein proteins in COVID-19 colostrum exhibited significantly lower abundances, immune-related proteins, especially whey proteins with antiviral properties against SARS-CoV-2, were upregulated. These proteins were detected with unique site-specific glycan structures and improved glycosylation diversity that are beneficial for recognizing epitopes and blocking viral entry. Such adaptive differences in milk from COVID-19 mothers tended to fade in mature milk from the same mothers one month postpartum. These results suggest that feeding infants colostrum from COVID-19 mothers confers both nutritional and immune benefits, and provide molecular-level insights that aid breastmilk feeding decisions in cases of active infection.

Absolute Proteome Quantification in the Gas-Fermenting Acetogen Clostridium autoethanogenum

Microbes that can recycle one-carbon (C₁) greenhouse gases into fuels and chemicals are vital for the biosustainability of future industries. Acetogens are the most efficient known microbes for fixing carbon oxides CO₂ and CO. Understanding proteome allocation is important for metabolic engineering as it dictates metabolic fitness. Here, we use absolute proteomics to quantify intracellular concentrations for >1,000 proteins in the model acetogen Clostridium autoethanogenum grown autotrophically on three gas mixtures (CO, CO+H₂, or CO+CO₂+H₂). We detect the prioritization of proteome allocation for C₁ fixation and the significant expression of proteins involved in the production of acetate and ethanol as well as proteins with unclear functions. The data also revealed which isoenzymes are likely relevant in vivo for CO oxidation, H₂ metabolism, and ethanol production. The integration of proteomic and metabolic flux data demonstrated that enzymes catalyze high fluxes with high concentrations and high in vivo catalytic rates. We show that flux adjustments were dominantly accompanied by changing enzyme catalytic rates rather than concentrations.

IMPORTANCE Acetogen bacteria are important for maintaining biosustainability as they can recycle gaseous C₁ waste feedstocks (e.g., industrial waste gases and syngas from gasified biomass or municipal solid waste) into fuels and chemicals. Notably, the acetogen Clostridium autoethanogenum is being used as a cell factory in industrial-scale gas fermentation. Here, we perform reliable absolute proteome quantification for the first time in an acetogen. This is important as our work advances both rational metabolic engineering of acetogen cell factories and accurate in silico reconstruction of their phenotypes. Furthermore, this absolute proteomics data set serves as a reference toward a better systems-level understanding of the ancient metabolism of acetogens.

Proteomic Analysis Reveals Enzymes for β-D-Glucan Formation and Degradation in Levilactobacillus brevis TMW 1.2112

Bacterial exopolysaccharide (EPS) formation is crucial for biofilm formation, for protection against environmental factors, or as storage compounds. EPSs produced by lactic acid bacteria (LAB) are appropriate for applications in food fermentation or the pharmaceutical industry, yet the dynamics of formation and degradation thereof are poorly described. This study focuses on carbohydrate active enzymes, including glycosyl transferases (GT) and glycoside hydrolases (GH), and their roles in the formation and potential degradation of O2-substituted (1,3)-β-D-glucan of Levilactobacillus (L.) brevis TMW 1.2112. The fermentation broth of L. brevis TMW 1.2112 was analyzed for changes in viscosity, β-glucan, and D-glucose concentrations during the exponential, stationary, and early death phases. While the viscosity reached its maximum during the stationary phase and subsequently decreased, the β-glucan concentration only increased to a plateau. Results were correlated with secretome and proteome data to identify involved enzymes and pathways. The suggested pathway for β-glucan biosynthesis involved a β-1,3 glucan synthase (GT2) and enzymes from maltose phosphorylase (MP) operons. The decreased viscosity appeared to be associated with cell lysis as the β-glucan concentration did not decrease, most likely due to missing extracellular carbohydrate active enzymes. In addition, an operon was discovered containing known moonlighting genes, all of which were detected in both proteome and secretome samples.

Enhanced protein isoform characterization through long-read proteogenomics

BACKGROUND

The detection of physiologically relevant protein isoforms encoded by the human genome is critical to biomedicine. Mass spectrometry (MS)-based proteomics is the preeminent method for protein detection, but isoform-resolved proteomic analysis relies on accurate reference databases that match the sample; neither a subset nor a superset database is ideal. Long-read RNA sequencing (e.g., PacBio or Oxford Nanopore) provides full-length transcripts which can be used to predict full-length protein isoforms.

RESULTS

We describe here a long-read proteogenomics approach for integrating sample-matched long-read RNA-seq and MS-based proteomics data to enhance isoform characterization. We introduce a classification scheme for protein isoforms, discover novel protein isoforms, and present the first protein inference algorithm for the direct incorporation of long-read transcriptome data to enable detection of protein isoforms previously intractable to MS-based detection. We have released an open-source Nextflow pipeline that integrates long-read sequencing in a proteomic workflow for isoform-resolved analysis.

CONCLUSIONS

Our work suggests that the incorporation of long-read sequencing and proteomic data can facilitate improved characterization of human protein isoform diversity. Our first-generation pipeline provides a strong foundation for future development of long-read proteogenomics and its adoption for both basic and translational research.

Investigating Toxin Diversity and Abundance in Snake Venom Proteomes

Understanding snake venom proteomes is becoming increasingly important to understand snake venom biology, evolution and especially clinical effects of venoms and approaches to antivenom development. To explore the current state of snake venom proteomics and transcriptomics we investigated venom proteomic methods, associations between methodological and biological variability and the diversity and abundance of protein families. We reviewed available studies on snake venom proteomes from September 2017 to April 2021. This included 81 studies characterising venom proteomes of 79 snake species, providing data on relative toxin abundance for 70 species and toxin diversity (number of different toxins) for 37 species. Methodologies utilised in these studies were summarised and compared. Several comparative studies showed that preliminary decomplexation of crude venom by chromatography leads to increased protein identification, as does the use of transcriptomics. Combining different methodological strategies in venomic approaches appears to maximize proteome coverage. 48% of studies used the RP-HPLC →1D SDS-PAGE →in-gel trypsin digestion → ESI -LC-MS/MS pathway. Protein quantification by MS1-based spectral intensity was used twice as commonly as MS2-based spectral counting (33–15 studies). Total toxin diversity was 25–225 toxins/species, with a median of 48. The relative mean abundance of the four dominant protein families was for elapids; 3FTx–52%, PLA₂–27%, SVMP–2.8%, and SVSP–0.1%, and for vipers: 3FTx–0.5%, PLA₂–24%, SVMP–27%, and SVSP–12%. Viper venoms were compositionally more complex than elapid venoms in terms of number of protein families making up most of the venom, in contrast, elapid venoms were made up of fewer, but more toxin diverse, protein families. No relationship was observed between relative toxin diversity and abundance. For equivalent comparisons to be made between studies, there is a need to clarify the differences between methodological approaches and for acceptance of a standardised protein classification, nomenclature and reporting procedure. Correctly measuring and comparing toxin diversity and abundance is essential for understanding biological, clinical and evolutionary implications of snake venom composition.

The measurement and control of high-risk host cell proteins for polysorbate degradation in biologics formulation

Nonionic surfactant polysorbates, including PS-80 and PS-20, are commonly used in the formulation of biotherapeutic products for both preventing surface adsorption and acting as stabilizer against protein aggregation. Trace levels of residual host cell proteins (HCPs) with lipase or esterase enzymatic activity have been shown to degrade polysorbates in biologics formulation. The measurement and control of these low abundance, high-risk HCPs for polysorbate degradation are an industry-wide challenge to achieve desired shelf life of biopharmaceuticals in liquid formulation, especially for high-concentration formulation product development. Here, we reviewed the challenges, recent advances, and future opportunities of analytical method development, risk assessment, and control strategies for polysorbate degradation during formulation development with a focus on enzymatic degradation. Continued efforts to advance our understanding of polysorbate degradation in biologics formulation will help develop high-quality medicines for patients.

Comparison of Different Label-Free Techniques for the Semi-Absolute Quantification of Protein Abundance

In proteomics, it is essential to quantify proteins in absolute terms if we wish to compare results among studies and integrate high-throughput biological data into genome-scale metabolic models. While labeling target peptides with stable isotopes allow protein abundance to be accurately quantified, the utility of this technique is constrained by the low number of quantifiable proteins that it yields. Recently, label-free shotgun proteomics has become the “gold standard” for carrying out global assessments of biological samples containing thousands of proteins. However, this tool must be further improved if we wish to accurately quantify absolute levels of proteins. Here, we used different label-free quantification techniques to estimate absolute protein abundance in the model yeast Saccharomyces cerevisiae. More specifically, we evaluated the performance of seven different quantification methods, based either on spectral counting (SC) or extracted-ion chromatogram (XIC), which were applied to samples from five different proteome backgrounds. We also compared the accuracy and reproducibility of two strategies for transforming relative abundance into absolute abundance: a UPS2-based strategy and the total protein approach (TPA). This study mentions technical challenges related to UPS2 use and proposes ways of addressing them, including utilizing a smaller, more highly optimized amount of UPS2. Overall, three SC-based methods (PAI, SAF, and NSAF) yielded the best results because they struck a good balance between experimental performance and protein quantification.

Composition and Dynamics of Protein Complexes Measured by Quantitative Mass Spectrometry of Affinity-Purified Samples

Multiple protein complexes are fundamental parts of living systems. Identification of the components of these complexes and characterization of the molecular mechanisms that allow their formation, function, and regulation can be done by affinity purification of proteins and associated factors followed by mass spectrometry of peptides. Speed and specificity for the isolation of complexes from whole cell extracts improved over time, together with the reliable identification and quantification of proteins by mass spectrometry. Relative quantification of proteins in such samples can now be done to characterize even relatively nonabundant complexes. We describe here our experience with proteins fused with the Z domain, derived from staphylococcal protein A, and IgG affinity purification for the analysis of protein complexes involved in RNA metabolism in the budding yeast Saccharomyces cerevisiae. We illustrate the use of enrichment calculations for proteins in purified samples as a way to robust identification of protein partners. While the protocols presented here are specific for yeast, their principles can be applied to the study of protein complexes in any other organism.

Long noncoding RNA and protein abundance in lncRNPs

Although long noncoding RNAs (lncRNAs) are generally expressed at low levels, emerging evidence has revealed that many play important roles in gene regulation by a variety of mechanisms as they engage with proteins. Given that the abundance of proteins often greatly exceeds that of their interacting lncRNAs, quantification of the relative abundance, or even the exact stoichiometry in some cases, within lncRNA-protein complexes is helpful for understanding of the mechanism(s) of action of lncRNAs. We discuss methods used to examine lncRNA and protein expression at the single cell, subcellular, and suborganelle levels, the average and local lncRNA concentration in cells, as well as how lncRNAs can modulate the functions of their interacting proteins even at a low stoichiometric concentration.

Median-Based Absolute Quantification of Proteins Using Fully Unlabeled Generic Internal Standard (FUGIS)

By reporting the molar abundance of proteins, absolute quantification determines their stoichiometry in complexes, pathways, or networks. Typically, absolute quantification relies either on protein-specific isotopically labeled peptide standards or on a semiempirical calibration against the average abundance of peptides chosen from arbitrarily selected proteins. In contrast, a generic protein standard FUGIS (fully unlabeled generic internal standard) requires no isotopic labeling, chemical synthesis, or external calibration and is applicable to quantifying proteins of any organismal origin. The median intensity of the peptide peaks produced by the tryptic digestion of FUGIS is used as a single-point calibrant to determine the molar abundance of any codigested protein. Powered by FUGIS, median-based absolute quantification (MBAQ) outperformed other methods of untargeted proteome-wide absolute quantification.

Relevance of BRAF Subcellular Localization and Its Interaction with KRAS and KIT Mutations in Skin Melanoma

Although skin melanoma (SKM) represents only one-quarter of newly diagnosed skin malignant tumors, it presents a high mortality rate. Hence, new prognostic and therapeutic tools need to be developed. This study focused on investigating the prognostic value of the subcellular expression of BRAF, KRAS, and KIT in SKM in correlation with their gene-encoding interactions. In silico analysis of the abovementioned gene interactions, along with their mRNA expression, was conducted, and the results were validated at the protein level using immunohistochemical (IHC) stains. For IHC expression, the encoded protein expressions were checked on 96 consecutive SKMs and 30 nevi. The UALCAN database showed no prognostic value for the mRNA expression level of KRAS and BRAF and demonstrated a longer survival for patients with low mRNA expression of KIT in SKMs. IHC examinations of SKMs confirmed the UALCAN data and showed that KIT expression was inversely correlated with ulceration, Breslow index, mitotic rate, and pT stage. KRAS expression was also found to be inversely correlated with ulceration and perineural invasion. When the subcellular expression of BRAF protein was recorded (nuclear vs. cytoplasmatic vs. mixed nucleus + cytoplasm), a direct correlation was emphasized between nuclear positivity and lymphovascular or perineural invasion. The independent prognostic value was demonstrated for mixed expression of the BRAF protein in SKM. BRAF cytoplasmic predominance, in association with KIT's IHC positivity, was more frequently observed in early-stage nonulcerated SKMs, which displayed a low mitotic rate and a late death event. The present study firstly verified the possible prognostic value of BRAF subcellular localization in SKMs. A low mRNA expression or IHC cytoplasmic positivity for KIT and BRAF might be used as a positive prognostic parameter of SKM. SKM's BRAF nuclear positivity needs to be evaluated in further studies as a possible indicator of perineural and lymphovascular invasion.

Extending the Proteomic Characterization of Candida albicans Exposed to Stress and Apoptotic Inducers through Data-Independent Acquisition Mass Spectrometry

Candida albicans is a commensal fungus that causes systemic infections in immunosuppressed patients. In order to deal with the changing environment during commensalism or infection, C. albicans must reprogram its proteome. Characterizing the stress-induced changes in the proteome that C. albicans uses to survive should be very useful in the development of new antifungal drugs. We studied the C. albicans global proteome after exposure to hydrogen peroxide (H₂O₂) and acetic acid (AA), using a data-independent acquisition mass spectrometry (DIA-MS) strategy. More than 2,000 C. albicans proteins were quantified using an ion library previously constructed using data-dependent acquisition mass spectrometry (DDA-MS). C. albicans responded to treatment with H₂O₂ with an increase in the abundance of many proteins involved in the oxidative stress response, protein folding, and proteasome-dependent catabolism, which led to increased proteasome activity. The data revealed a previously unknown key role for Prn1, a protein similar to pirins, in the oxidative stress response. Treatment with AA resulted in a general decrease in the abundance of proteins involved in amino acid biosynthesis, protein folding, and rRNA processing. Almost all proteasome proteins declined, as did proteasome activity. Apoptosis was observed after treatment with H₂O₂ but not AA. A targeted proteomic study of 32 proteins related to apoptosis in yeast supported the results obtained by DIA-MS and allowed the creation of an efficient method to quantify relevant proteins after treatment with stressors (H₂O₂, AA, and amphotericin B). This approach also uncovered a main role for Oye32, an oxidoreductase, suggesting this protein as a possible apoptotic marker common to many stressors.

IMPORTANCE Fungal infections are a worldwide health problem, especially in immunocompromised patients and patients with chronic disorders. Invasive candidiasis, caused mainly by C. albicans, is among the most common fungal diseases. Despite the existence of treatments to combat candidiasis, the spectrum of drugs available is limited. For the discovery of new drug targets, it is essential to know the pathogen response to different stress conditions. Our study provides a global vision of proteomic remodeling in C. albicans after exposure to different agents, such as hydrogen peroxide, acetic acid, and amphotericin B, that can cause apoptotic cell death. These results revealed the significance of many proteins related to oxidative stress response and proteasome activity, among others. Of note, the discovery of Prn1 as a key protein in the defense against oxidative stress as well the increase in the abundance of Oye32 protein when apoptotic process occurred point them out as possible drug targets.

Quorum sensing governs a transmissive Legionella subpopulation at the pathogen vacuole periphery

The Gram‐negative bacterium Legionella pneumophila is the causative agent of Legionnaires' disease and replicates in amoebae and macrophages within a distinct compartment, the Legionella‐containing vacuole (LCV). The facultative intracellular pathogen switches between a replicative, non‐virulent and a non‐replicating, virulent/transmissive phase. Here, we show on a single‐cell level that at late stages of infection, individual motile (P_flaA‐GFP‐positive) and virulent (P_ralF‐ and P_sidC‐GFP‐positive) L. pneumophila emerge in the cluster of non‐growing bacteria within an LCV. Comparative proteomics of P_flaA‐GFP‐positive and P_flaA‐GFP‐negative L. pneumophila subpopulations reveals distinct proteomes with flagellar proteins or cell division proteins being preferentially produced by the former or the latter, respectively. Toward the end of an infection cycle (˜ 48 h), the P_flaA‐GFP‐positive L. pneumophila subpopulation emerges at the cluster periphery, predominantly escapes the LCV, and spreads from the bursting host cell. These processes are mediated by the Legionella quorum sensing (Lqs) system. Thus, quorum sensing regulates the emergence of a subpopulation of transmissive L. pneumophila at the LCV periphery, and phenotypic heterogeneity underlies the intravacuolar bi‐phasic life cycle of L. pneumophila.

Molecular Basis of GABA Hypofunction in Adolescent Schizophrenia-Like Animals

Schizophrenia is a neurodevelopmental disorder that NMDA receptor (NMDAR) hypofunction appears centrally involved. Schizophrenia typically emerges in adolescence or early adulthood. Electrophysiological and several neurochemical changes have linked the GABA deficits to abnormal behaviors induced by NMDAR hypofunction. However, few studies have systematically investigated the molecular basis of GABA deficits, especially during adolescence. To address this issue, we transiently administrated MK-801 to mice on PND 10, which exhibited schizophrenia-relevant deficits in adolescence. Slice recording showed reduced GABA transmission and PVI+ hypofunction, indicating GABAergic hypofunction. Cortical proteomic evaluation combined with analysis of single cell data from the Allen Brain showed that various metabolic processes were enriched in top ranks and differentially altered in excitatory neurons, GABAergic interneurons, and glial cells. Notably, the GABA-related amino acid metabolic process was disturbed in both astrocytes and interneurons, in which we found a downregulated set of GABA-related proteins (GAD65, SYNPR, DBI, GAT3, SN1, and CPT1A). They synergistically regulate GABA synthesis, release, reuptake, and replenishment. Their downregulation indicates impaired GABA cycle and homeostasis regulated by interneuron-astrocyte communication in adolescence. Our findings on molecular basis of GABA deficits could provide potential drug targets of GABAergic rescue for early prevention and intervention.

Proteome Profile of Myelin in the Zebrafish Brain

The velocity of nerve conduction along vertebrate axons depends on their ensheathment with myelin. Myelin membranes comprise specialized proteins well characterized in mice. Much less is known about the protein composition of myelin in non-mammalian species. Here, we assess the proteome of myelin biochemically purified from the brains of adult zebrafish (Danio rerio), considering its increasing popularity as model organism for myelin biology. Combining gel-based and gel-free proteomic approaches, we identified > 1,000 proteins in purified zebrafish myelin, including all known constituents. By mass spectrometric quantification, the predominant Ig-CAM myelin protein zero (MPZ/P0), myelin basic protein (MBP), and the short-chain dehydrogenase 36K constitute 12%, 8%, and 6% of the total myelin protein, respectively. Comparison with previously established mRNA-abundance profiles shows that expression of many myelin-related transcripts coincides with the maturation of zebrafish oligodendrocytes. Zebrafish myelin comprises several proteins that are not present in mice, including 36K, CLDNK, and ZWI. However, a surprisingly large number of ortholog proteins is present in myelin of both species, indicating partial evolutionary preservation of its constituents. Yet, the relative abundance of CNS myelin proteins can differ markedly as exemplified by the complement inhibitor CD59 that constitutes 5% of the total zebrafish myelin protein but is a low-abundant myelin component in mice. Using novel transgenic reporter constructs and cryo-immuno electron microscopy, we confirm the incorporation of CD59 into myelin sheaths. These data provide the first proteome resource of zebrafish CNS myelin and demonstrate both similarities and heterogeneity of myelin composition between teleost fish and rodents.

Personalized Profiling Reveals Donor- and Lactation-Specific Trends in the Human Milk Proteome and Peptidome

BACKGROUND

Human milk is the most genuine form of personalized nutrition, whereby its nutritional and bioactive constituents support the changing needs of the growing infant. Personalized proteome profiling strategies may provide insights into maternal-infant relationships. Proteins and endogenous peptides in human milk play an important role as nutrients for growth and have distinct functionality such as immune defense. Comprehensive monitoring of all of the human milk proteinaceous components, including endogenous peptides, is required to fully understand the changing role of the human milk proteome throughout lactation.

OBJECTIVE

We aimed to investigate the personalized nature of the human milk proteome and peptidome for individual mother-infant dyads.

METHODS

Two individual healthy milk donors, aged 29 and 32 y and both of a normal BMI, were longitudinally observed over weeks 1, 2, 3, 4, 6, 8, 10, 12, and 16 postpartum. Milk collection was standardized. Comprehensive variations in the human milk proteinaceous components were assessed using quantitative LC-MS/MS methods.

RESULTS

We longitudinally profiled the concentrations of >1300 milk proteins and 2000 endogenous milk peptides spanning 16 wk of lactation for 2 individual donors. We observed many gradual and alike changes in both donors related to temporal effects, for instance early lactation was marked by high concentrations of proteins and peptides involved in lactose synthesis and immune development. Uniquely, in 1 of the 2 donors, we observed a substantial anomaly in the milk composition, exclusively at week 6, likely indicating a response to inflammation and/or infection.

CONCLUSIONS

Here, we provide a resource for characterizing the lactational changes in the human milk proteome, encompassing thousands of proteins and endogenous peptides. Further, we demonstrate the feasibility and benefit of personalized profiling to monitor the influence of milk on the development of the newborn, as well as the health status of each individual mother-infant pair.

Bacterial dormancy: A subpopulation of viable but non-culturable cells demonstrates better fitness for revival

The viable but non culturable (VBNC) state is a condition in which bacterial cells are viable and metabolically active, but resistant to cultivation using a routine growth medium. We investigated the ability of V. parahaemolyticus to form VBNC cells, and to subsequently become resuscitated. The ability to control VBNC cell formation in the laboratory allowed us to selectively isolate VBNC cells using fluorescence activated cell sorting, and to differentiate subpopulations based on their metabolic activity, cell shape and the ability to cause disease in Galleria mellonella. Our results showed that two subpopulations (P1 and P2) of V. parahaemolyticus VBNC cells exist and can remain dormant in the VBNC state for long periods. VBNC subpopulation P2, had a better fitness for survival under stressful conditions and showed 100% revival under favourable conditions. Proteomic analysis of these subpopulations (at two different time points: 12 days (T12) and 50 days (T50) post VBNC) revealed that the proteome of P2 was more similar to that of the starting microcosm culture (T0) than the proteome of P1. Proteins that were significantly up or down-regulated between the different VBNC populations were identified and differentially regulated proteins were assigned into 23 functional groups, the majority being assigned to metabolism functional categories. A lactate dehydrogenase (lldD) protein, responsible for converting lactate to pyruvate, was significantly upregulated in all subpopulations of VBNC cells. Deletion of the lactate dehydrogenase (RIMD2210633:ΔlldD) gene caused cells to enter the VBNC state significantly more quickly compared to the wild-type, and adding lactate to VBNC cells aided their resuscitation and extended the resuscitation window. Addition of pyruvate to the RIMD2210633:ΔlldD strain restored the wild-type VBNC formation profile. This study suggests that lactate dehydrogenase may play a role in regulating the VBNC state.

Quantitative Mass Spectrometry-Based Proteomics: An Overview

In recent decades, mass spectrometry has moved more than ever before into the front line of protein-centered research. After being established at the qualitative level, the more challenging question of quantification of proteins and peptides using mass spectrometry has become a focus for further development. In this chapter, we discuss and review actual strategies and problems of the methods for the quantitative analysis of peptides, proteins, and finally proteomes by mass spectrometry. The common themes, the differences, and the potential pitfalls of the main approaches are presented in order to provide a survey of the emerging field of quantitative, mass spectrometry-based proteomics.

SECAT: Quantifying Protein Complex Dynamics across Cell States by Network-Centric Analysis of SEC-SWATH-MS Profiles

Protein-protein interactions (PPIs) play critical functional and regulatory roles in cellular processes. They are essential for macromolecular complex formation, which in turn constitutes the basis for protein interaction networks that determine the functional state of a cell. We and others have previously shown that chromatographic fractionation of native protein complexes in combination with bottom-up mass spectrometric analysis of consecutive fractions supports the multiplexed characterization and detection of state-specific changes of protein complexes. In this study, we extend co-fractionation and mass spectrometric data analysis to perform quantitative, network-based studies of proteome organization, via the size-exclusion chromatography algorithmic toolkit (SECAT). This framework explicitly accounts for the dynamic nature and rewiring of protein complexes across multiple cell states and samples, thus, elucidating molecular mechanisms that are differentially implemented across different experimental settings. Systematic analysis of multiple datasets shows that SECAT represents a highly scalable and effective methodology to assess condition/state-specific protein-network state. A record of this paper's transparent peer review process is included in the Supplemental Information.

Utility of Proteomics in Emerging and Re-Emerging Infectious Diseases Caused by RNA Viruses

Emerging and re-emerging infectious diseases due to RNA viruses cause major negative consequences for the quality of life, public health, and overall economic development. Most of the RNA viruses causing illnesses in humans are of zoonotic origin. Zoonotic viruses can directly be transferred from animals to humans through adaptation, followed by human-to-human transmission, such as in human immunodeficiency virus (HIV), severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and, more recently, SARS coronavirus 2 (SARS-CoV-2), or they can be transferred through insects or vectors, as in the case of Crimean-Congo hemorrhagic fever virus (CCHFV), Zika virus (ZIKV), and dengue virus (DENV). At the present, there are no vaccines or antiviral compounds against most of these viruses. Because proteins possess a vast array of functions in all known biological systems, proteomics-based strategies can provide important insights into the investigation of disease pathogenesis and the identification of promising antiviral drug targets during an epidemic or pandemic. Mass spectrometry technology has provided the capacity required for the precise identification and the sensitive and high-throughput analysis of proteins on a large scale and has contributed greatly to unravelling key protein-protein interactions, discovering signaling networks, and understanding disease mechanisms. In this Review, we present an account of quantitative proteomics and its application in some prominent recent examples of emerging and re-emerging RNA virus diseases like HIV-1, CCHFV, ZIKV, and DENV, with more detail with respect to coronaviruses (MERS-CoV and SARS-CoV) as well as the recent SARS-CoV-2 pandemic.

Campylobacter jejuni 11168H Exposed to Penicillin Forms Persister Cells and Cells With Altered Redox Protein Activity

The formation of persister cells is one mechanism by which bacteria can survive exposure to environmental stresses. We show that Campylobacter jejuni 11168H forms persister cells at a frequency of 10⁻³ after exposure to 100 × MIC of penicillin G for 24 h. Staining the cell population with a redox sensitive fluorescent dye revealed that penicillin G treatment resulted in the appearance of a population of cells with increased fluorescence. We present evidence, to show this could be a consequence of increased redox protein activity in, or associated with, the electron transport chain. These data suggest that a population of penicillin G treated C. jejuni cells could undergo a remodeling of the electron transport chain in order to moderate membrane hyperpolarization and intracellular alkalization; thus reducing the antibiotic efficacy and potentially assisting in persister cell formation.

iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics

Summary

We present an R package called iq to enable accurate protein quantification for label-free data-independent acquisition (DIA) mass spectrometry-based proteomics, a recently developed global approach with superior quantitative consistency. We implement the popular maximal peptide ratio extraction module of the MaxLFQ algorithm, so far only applicable to data-dependent acquisition mode using the software suite MaxQuant. Moreover, our implementation shows, for each protein separately, the validity of quantification over all samples. Hence, iq exports a state-of-the-art protein quantification algorithm to the emerging DIA mode in an open-source implementation.

Availability and implementation

The open-source R package is available on CRAN, https://github.com/tvpham/iq/releases and oncoproteomics.nl/iq.

Supplementary information

Supplementary data are available at Bioinformatics online.

Different Research Approaches in Unraveling the Venom Proteome of Naja ashei

The dynamic development of venomics in recent years has resulted in a significant increase in publicly available proteomic data. The information contained therein is often used for comparisons between different datasets and to draw biological conclusions therefrom. In this article, we aimed to show the possible differences that can arise, in the final results of the proteomic experiment, while using different research workflows. We applied two software solutions (PeptideShaker and MaxQuant) to process data from shotgun LC-MS/MS analysis of Naja ashei venom and collate it with the previous report concerning this species. We were able to provide new information regarding the protein composition of this venom but also present the qualitative and quantitative limitations of currently used proteomic methods. Moreover, we reported a rapid and straightforward technique for the separation of the fraction of proteins from the three-finger toxin family. Our results underline the necessary caution in the interpretation of data based on a comparative analysis of data derived from different studies.

Absolute Quantification of Proteins in the Eye of Drosophila melanogaster

Absolute (molar) quantification of proteins determines their molar ratios in complexes, networks, and metabolic pathways. MS Western workflow is employed to determine molar abundances of proteins potentially critical for morphogenesis and phototransduction (PT) in eyes of Drosophila melanogaster using a single chimeric 264 kDa protein standard that covers, in total, 197 peptides from 43 proteins. The majority of proteins are independently quantified with two to four proteotypic peptides with the coefficient of variation of less than 15%, better than 1000-fold dynamic range and sub-femtomole sensitivity. Here, the molar abundance of proteins of the PT machinery and of the rhabdomere, the photosensitive organelle, is determined in eyes of wild-type flies as well as in crumbs (crb) mutant eyes, which exhibit perturbed rhabdomere morphogenesis.

Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

Enzyme turnover numbers (k _cats) are essential for a quantitative understanding of cells. Because k _cats are traditionally measured in low-throughput assays, they can be inconsistent, labor-intensive to obtain, and can miss in vivo effects. We use a data-driven approach to estimate in vivo k _cats using metabolic specialist Escherichia coli strains that resulted from gene knockouts in central metabolism followed by metabolic optimization via laboratory evolution. By combining absolute proteomics with fluxomics data, we find that in vivo k _cats are robust against genetic perturbations, suggesting that metabolic adaptation to gene loss is mostly achieved through other mechanisms, like gene-regulatory changes. Combining machine learning and genome-scale metabolic models, we show that the obtained in vivo k _cats predict unseen proteomics data with much higher precision than in vitro k _cats. The results demonstrate that in vivo k _cats can solve the problem of inconsistent and low-coverage parameterizations of genome-scale cellular models.

The CNS Myelin Proteome: Deep Profile and Persistence After Post-mortem Delay

Myelin membranes are dominated by lipids while the complexity of their protein composition has long been considered to be low. However, numerous additional myelin proteins have been identified since. Here we revisit the proteome of myelin biochemically purified from the brains of healthy c56Bl/6N-mice utilizing complementary proteomic approaches for deep qualitative and quantitative coverage. By gel-free, label-free mass spectrometry, the most abundant myelin proteins PLP, MBP, CNP, and MOG constitute 38, 30, 5, and 1% of the total myelin protein, respectively. The relative abundance of myelin proteins displays a dynamic range of over four orders of magnitude, implying that PLP and MBP have overshadowed less abundant myelin constituents in initial gel-based approaches. By comparisons with published datasets we evaluate to which degree the CNS myelin proteome correlates with the mRNA and protein abundance profiles of myelin and oligodendrocytes. Notably, the myelin proteome displays only minor changes if assessed after a post-mortem delay of 6 h. These data provide the most comprehensive proteome resource of CNS myelin so far and a basis for addressing proteomic heterogeneity of myelin in mouse models and human patients with white matter disorders.

mRNAs, proteins and the emerging principles of gene expression control

Gene expression involves transcription, translation and the turnover of mRNAs and proteins. The degree to which protein abundances scale with mRNA levels and the implications in cases where this dependency breaks down remain an intensely debated topic. Here we review recent mRNA-protein correlation studies in the light of the quantitative parameters of the gene expression pathway, contextual confounders and buffering mechanisms. Although protein and mRNA levels typically show reasonable correlation, we describe how transcriptomics and proteomics provide useful non-redundant readouts. Integrating both types of data can reveal exciting biology and is an essential step in refining our understanding of the principles of gene expression control.

Proteome profile of peripheral myelin in healthy mice and in a neuropathy model

Proteome and transcriptome analyses aim at comprehending the molecular profiles of the brain, its cell-types and subcellular compartments including myelin. Despite the relevance of the peripheral nervous system for normal sensory and motor capabilities, analogous approaches to peripheral nerves and peripheral myelin have fallen behind evolving technical standards. Here we assess the peripheral myelin proteome by gel-free, label-free mass-spectrometry for deep quantitative coverage. Integration with RNA-Sequencing-based developmental mRNA-abundance profiles and neuropathy disease genes illustrates the utility of this resource. Notably, the periaxin-deficient mouse model of the neuropathy Charcot-Marie-Tooth 4F displays a highly pathological myelin proteome profile, exemplified by the discovery of reduced levels of the monocarboxylate transporter MCT1/SLC16A1 as a novel facet of the neuropathology. This work provides the most comprehensive proteome resource thus far to approach development, function and pathology of peripheral myelin, and a straightforward, accurate and sensitive workflow to address myelin diversity in health and disease.

The mannose 6-phosphate receptor targeted with porphyrin-based periodic mesoporous organosilica nanoparticles for rhabdomyosarcoma theranostics

Periodic mesoporous organosilica nanoparticles targeted to the mannose 6-phosphate receptor for rhabdomyosarcoma photodynamic therapy using a pulsed laser.

A Digital Pathology-Based Shotgun-Proteomics Approach to Biomarker Discovery in Colorectal Cancer

Background

Biomarkers in colorectal cancer are scarce, especially for patients with Stage 2 disease. The aim of our study was to identify potential prognostic biomarkers from colorectal cancers using a novel combination of approaches, whereby digital pathology is coupled to shotgun proteomics followed by validation of candidates by immunohistochemistry (IHC) using digital image analysis (DIA).

Methods and Results

Tissue cores were punched from formalin-fixed paraffin-embedded colorectal cancers from patients with Stage 2 and 3 disease (n = 26, each). Protein extraction and liquid chromatography-mass spectrometry (MS) followed by analysis using three different methods were performed. Fold changes were evaluated. The candidate biomarker was validated by IHC on a series of 413 colorectal cancers from surgically treated patients using a next-generation tissue microarray. DIA was performed by using a pan-cytokeratin serial alignment and quantifying staining within the tumor and normal tissue epithelium. Analysis was done in QuPath and Brightness_Max scores were used for statistical analysis and clinicopathological associations. MS identified 1947 proteins with at least two unique peptides. To reinforce the validity of the biomarker candidates, only proteins showing a significant (P < 0.05) fold-change using all three analysis methods were considered. Eight were identified, and of these, cathepsin B was selected for further validation. DIA revealed strong associations between higher cathepsin B expression and less aggressive tumor features, including tumor node metastasis stage and lymphatic vessel and venous vessel invasion (P < 0.001, all). Cathepsin B was associated with more favorable survival in univariate analysis only.

Conclusions

Our results present a novel approach to biomarker discovery that includes MS and digital pathology. Cathepsin B expression analyzed by DIA within the tumor epithelial compartment was identified as a strong feature of less aggressive tumor behavior and favorable outcome, a finding that should be further investigated on a more functional level.