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IntroductionWhile Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy.MethodsEvaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders.ResultsSix proteins initially identified in the ICI cohort correlated with predicted response in the untreated cohort. Additionally, three proteins correlated with patient survival, both at the protein, and at the transcript levels, in an independent immunotherapy treated cohort.DiscussionOur study identifies predictive biomarkers across three melanoma cohorts, suggesting their use in therapeutic decision-making.

Research data is accumulating rapidly and with it the challenge of fully reproducible science. As a consequence, implementation of high-quality management of scientific data has become a global priority. The FAIR (Findable, Accesible, Interoperable and Reusable) principles provide practical guidelines for maximizing the value of research data; however, processing data using workflows-systematic executions of a series of computational tools-is equally important for good data management. The FAIR principles have recently been adapted to Research Software (FAIR4RS Principles) to promote the reproducibility and reusability of any type of research software. Here, we propose a set of 10 quick tips, drafted by experienced workflow developers that will help researchers to apply FAIR4RS principles to workflows. The tips have been arranged according to the FAIR acronym, clarifying the purpose of each tip with respect to the FAIR4RS principles. Altogether, these tips can be seen as practical guidelines for workflow developers who aim to contribute to more reproducible and sustainable computational science, aiming to positively impact the open science and FAIR community.

Abstract Background There is a strong need for biomarkers to better characterize individuals with COPD and to take into account the heterogeneity of COPD. The blood protein sRAGE has been put forward as promising biomarker for COPD in general and emphysema in particular. Here, we measured plasma sRAGE levels using quantitative LC–MS and assessed whether the plasma sRAGE levels associate with (changes in) lung function, radiological emphysema parameters, and radiological subtypes of emphysema. Methods Three hundred and twenty-four COPD patients (mean FEV1: 63%predicted) and 185 healthy controls from the COPDGene study were selected. Plasma sRAGE was measured by immunoprecipitation in 96-well plate methodology to enrich sRAGE, followed by targeted quantitative liquid chromatography-mass spectrometry. Spirometry and HRCT scans (inspiration and expiration) with a 5-year follow-up were used; both subjected to high quality control standards. Results Lower sRAGE values significantly associated with the presence of COPD, the severity of airflow obstruction, the severity of emphysema on HRCT, the heterogeneous distribution of emphysema, centrilobular emphysema, and 5-year progression of emphysema. However, sRAGE values did not associate with airway wall thickness or paraseptal emphysema. Conclusions Rather than being a general COPD biomarker, sRAGE is especially a promising biomarker for centrilobular emphysema. Follow-up studies should elucidate whether sRAGE can be used as a biomarker for other COPD phenotypes as well.

Abstract Background In systems biology, it is important to reconstruct regulatory networks from quantitative molecular profiles. Gaussian graphical models (GGMs) are one of the most popular methods to this end. A GGM consists of nodes (representing the transcripts, metabolites or proteins) inter-connected by edges (reflecting their partial correlations). Learning the edges from quantitative molecular profiles is statistically challenging, as there are usually fewer samples than nodes (‘high dimensional problem’). Shrinkage methods address this issue by learning a regularized GGM. However, it remains open to study how the shrinkage affects the final result and its interpretation. Results We show that the shrinkage biases the partial correlation in a non-linear way. This bias does not only change the magnitudes of the partial correlations but also affects their order. Furthermore, it makes networks obtained from different experiments incomparable and hinders their biological interpretation. We propose a method, referred to as ‘un-shrinking’ the partial correlation, which corrects for this non-linear bias. Unlike traditional methods, which use a fixed shrinkage value, the new approach provides partial correlations that are closer to the actual (population) values and that are easier to interpret. This is demonstrated on two gene expression datasets from Escherichia coli and Mus musculus. Conclusions GGMs are popular undirected graphical models based on partial correlations. The application of GGMs to reconstruct regulatory networks is commonly performed using shrinkage to overcome the ‘high-dimensional problem’. Besides it advantages, we have identified that the shrinkage introduces a non-linear bias in the partial correlations. Ignoring this type of effects caused by the shrinkage can obscure the interpretation of the network, and impede the validation of earlier reported results.

Abstract Background Small‐cell lung cancer (SCLC) molecular subtypes have been primarily characterized based on the expression pattern of the following key transcription regulators: ASCL1 (SCLC‐A), NEUROD1 (SCLC‐N), POU2F3 (SCLC‐P) and YAP1 (SCLC‐Y). Here, we investigated the proteomic landscape of these molecular subsets with the aim to identify novel subtype‐specific proteins of diagnostic and therapeutic relevance. Methods Pellets and cell media of 26 human SCLC cell lines were subjected to label‐free shotgun proteomics for large‐scale protein identification and quantitation, followed by in‐depth bioinformatic analyses. Proteomic data were correlated with the cell lines’ phenotypic characteristics and with public transcriptomic data of SCLC cell lines and tissues. Results Our quantitative proteomic data highlighted that four molecular subtypes are clearly distinguishable at the protein level. The cell lines exhibited diverse neuroendocrine and epithelial–mesenchymal characteristics that varied by subtype. A total of 367 proteins were identified in the cell pellet and 34 in the culture media that showed significant up‐ or downregulation in one subtype, including known druggable proteins and potential blood‐based markers. Pathway enrichment analysis and parallel investigation of transcriptomics from SCLC cell lines outlined unique signatures for each subtype, such as upregulated oxidative phosphorylation in SCLC‐A, DNA replication in SCLC‐N, neurotrophin signalling in SCLC‐P and epithelial–mesenchymal transition in SCLC‐Y. Importantly, we identified the YAP1‐driven subtype as the most distinct SCLC subgroup. Using sparse partial least squares discriminant analysis, we identified proteins that clearly distinguish four SCLC subtypes based on their expression pattern, including potential diagnostic markers for SCLC‐Y (e.g. GPX8, PKD2 and UFO). Conclusions We report for the first time, the protein expression differences among SCLC subtypes. By shedding light on potential subtype‐specific therapeutic vulnerabilities and diagnostic biomarkers, our results may contribute to a better understanding of SCLC biology and the development of novel therapies.

Introduction Continuing inhaled corticosteroid (ICS) use does not benefit all patients with COPD, yet it is difficult to determine which patients may safely sustain ICS withdrawal. Although eosinophil levels can facilitate this decision, better biomarkers could improve personalised treatment decisions. Methods We performed transcriptional profiling of sputum to explore the molecular biology and compared the predictive value of an unbiased gene signature versus sputum eosinophils for exacerbations after ICS withdrawal in COPD patients. RNA-sequencing data of induced sputum samples from 43 COPD patients were associated with the time to exacerbation after ICS withdrawal. Expression profiles of differentially expressed genes were summarised to create gene signatures. In addition, we built a Bayesian network model to determine coregulatory networks related to the onset of COPD exacerbations after ICS withdrawal. Results In multivariate analyses, we identified a gene signature (LGALS12, ALOX15, CLC, IL1RL1, CD24, EMR4P) associated with the time to first exacerbation after ICS withdrawal. The addition of this gene signature to a multiple Cox regression model explained more variance of time to exacerbations compared to a model using sputum eosinophils. The gene signature correlated with sputum eosinophil as well as macrophage cell counts. The Bayesian network model identified three coregulatory gene networks as well as sex to be related to an early versus late/nonexacerbation phenotype. Conclusion We identified a sputum gene expression signature that exhibited a higher predictive value for predicting COPD exacerbations after ICS withdrawal than sputum eosinophilia. Future studies should investigate the utility of this signature, which might enhance personalised ICS treatment in COPD patients.

Abstract The MM500 study is an initiative to map the protein levels in malignant melanoma tumor samples, focused on in‐depth histopathology coupled to proteome characterization. The protein levels and localization were determined for a broad spectrum of diverse, surgically isolated melanoma tumors originating from multiple body locations. More than 15,500 proteoforms were identified by mass spectrometry, from which chromosomal and subcellular localization was annotated within both primary and metastatic melanoma. The data generated by global proteomic experiments covered 72% of the proteins identified in the recently reported high stringency blueprint of the human proteome. This study contributes to the NIH Cancer Moonshot initiative combining detailed histopathological presentation with the molecular characterization for 505 melanoma tumor samples, localized in 26 organs from 232 patients.

Abstract The MM500 meta‐study aims to establish a knowledge basis of the tumor proteome to serve as a complement to genome and transcriptome studies. Somatic mutations and their effect on the transcriptome have been extensively characterized in melanoma. However, the effects of these genetic changes on the proteomic landscape and the impact on cellular processes in melanoma remain poorly understood. In this study, the quantitative mass‐spectrometry‐based proteomic analysis is interfaced with pathological tumor characterization, and associated with clinical data. The melanoma proteome landscape, obtained by the analysis of 505 well‐annotated melanoma tumor samples, is defined based on almost 16 000 proteins, including mutated proteoforms of driver genes. More than 50 million MS/MS spectra were analyzed, resulting in approximately 13,6 million peptide spectrum matches (PSMs). Altogether 13 176 protein‐coding genes, represented by 366 172 peptides, in addition to 52 000 phosphorylation sites, and 4 400 acetylation sites were successfully annotated. This data covers 65% and 74% of the predicted and identified human proteome, respectively. A high degree of correlation (Pearson, up to 0.54) with the melanoma transcriptome of the TCGA repository, with an overlap of 12 751 gene products, was found. Mapping of the expressed proteins with quantitation, spatiotemporal localization, mutations, splice isoforms, and PTM variants was proven not to be predicted by genome sequencing alone. The melanoma tumor molecular map was complemented by analysis of blood protein expression, including data on proteins regulated after immunotherapy. By adding these key proteomic pillars, the MM500 study expands the knowledge on melanoma disease.

The article estimates the exposure of the population of Qatar to aflatoxins, fumonisins and ochratoxins, measured in food samples collected from the markets of Doha. The mycotoxin extractions were performed using the “Quick Easy Cheap Effective Rugged Safe” Method (QuEChERS) and the extract measured with LC–MS/MS. High contamination with aflatoxins was detected in Nuts and Spices (e.g. 534.15 ng/g and 371.6 ng/g respectively). The estimated daily intake (EDI) level was estimated using statistical data on average Qatari population. Additionally, the probability to exceed TDI was calculated for fumonisins and ochratoxins. The results indicate high exposure to aflatoxins (with alarming values of margin of exposure, i.e. MoE).The article points to the necessity of a regular assessment and reevaluation of the concentration limits allowed in food products accounting for statistics on the population (i.e. food consumption), economic growth of the country, and monitoring of mycotoxin contamination of food as much as technological, financial and human resources of a country allows. Keywords: Mycotoxin, Risk assessment, Aflatoxin, Ochratoxin, Fumonisin, Margin of exposure, Tolerable daily intake

Acinetobacter baumannii is an important pathogen of nosocomial infection worldwide, which can primarily cause pneumonia, bloodstream infection, and urinary tract infection. The increasing drug resistance rate of A. baumannii and the slow development of new antibacterial drugs brought great challenges for clinical treatment. Host immunity is crucial to the defense of A. baumannii infection, and understanding the mechanisms of immune response can facilitate the development of new therapeutic strategies. To characterize the system-level changes of host proteome in immune response, we used tandem mass tag (TMT) labeling quantitative proteomics to compare the proteome changes of lungs from A. baumannii infected mice with control mice 6 h after infection. A total of 6,218 proteins were identified in which 6,172 could be quantified. With threshold p < 0.05 and relative expression fold change > 1.2 or < 0.83, we found 120 differentially expressed proteins. Bioinformatics analysis showed that differentially expressed proteins after infection were associated with receptor recognition, NADPH oxidase (NOX) activation and antimicrobial peptides. These differentially expressed proteins were involved in the pathways including leukocyte transendothelial migration, phagocyte, neutrophil degranulation, and antimicrobial peptides. In conclusion, our study showed proteome changes in mouse lung tissue due to A. baumannii infection and suggested the important roles of NOX, neutrophils, and antimicrobial peptides in host response. Our results provide a potential list of protein candidates for the further study of host-bacteria interaction in A. baumannii infection. Data are available via ProteomeXchange with identifier PXD020640.

Mycotoxins are naturally occurring food toxins worldwide that can cause serious health effects. The measurement of mycotoxin biomarkers in biological fluids is needed to assess individuals’ exposure. The aim of this study was to investigate the incidence of mycotoxins in the Qatari population. Serum samples from 412 adults and urinary samples from 559 adults were analyzed for the presence of mycotoxin biomarkers. Multimycotoxin approaches have been applied, using liquid chromatography mass spectrometry methods. Samples were further analyzed for the oxidative stress markers and compared with regard to the incidence of mycotoxins. The presence of mycotoxins was identified in 37% of serum samples and in less than 20% of urine samples. It was found that 88% of positive of the samples were positive for only one mycotoxin, while 12% of positive samples had two or more mycotoxins. Trichothecenes and zearalenone metabolites were most commonly detected mycotoxins, followed by aflatoxins, roquefortine C and mycophenolic acid. The presence of mycotoxins was found to positively correlate with oxidative stress markers. The obtained results illustrate the importance of mycotoxin biomonitoring studies in humans and the need to elucidate the underlying mechanisms of mycotoxin-induced toxicity.

mTORC1 is known to mediate the signalling activity of amino acids. Here, the authors combine modelling with experiments and find that amino acids acutely stimulate mTORC2, IRS/PI3K and AMPK, independently of mTORC1. AMPK activation through CaMKKβ sustains autophagy under non-starvation conditions.

Prescription sequence symmetry analysis (PSSA), a case-only design introduced in 1996, has been increasingly used to identify unintentional drug effects, and has potential applications as a hypothesis-testing and a hypothesis-generating method, due to its easy application and effective control of time-invariant confounders. The aim of this study is to systematically compare effect estimates from the PSSA to effect estimates from conventional observational parallel group study designs, to assess the validity and constraints of the PSSA study design. We reviewed the MEDLINE, EMBASE, and Web of Science databases until February 2016 to identify studies that compared PSSA to a parallel group design. Data from the eligible articles was extracted and analyzed, including making a Bland-Altman plot and calculating the number of discrepancies between the designs. 63 comparisons (from two studies) were included in the review. There was a significant correlation (p < 0.001) between the effect estimates of the PSSA and the parallel group designs, but the bias indicated by the Bland-Altman plot (0.20) and the percentage of discrepancies (70-80%) showed that this correlation was not accompanied by a considerable similarity of the effect estimates. Overall, the effect estimates of the parallel group designs were higher than those of the PSSA, not necessarily further away from 1, and the parallel group designs also generated more significant signals. However, these results should be approached with caution, as the effect estimates were only retrieved from two separate studies. This review indicates that, even though PSSA has a lot of potential, the effect estimates generated by the PSSA are usually lower than the effect estimates generated by parallel group designs, and PSSA mostly has a lower power than the conventional study designs, but this is based on limited comparisons, and more comparisons are needed to make a proper conclusion.

In an in vitro experiment commercially available probiotic products were tested for the survival of bacteria under conditions of simulated human digestion either when used alone or mixed into yogurt. In the in vivo experiment the effects of feeding a whey- and milk-based yogurt prepared with the probiotic strain showing adequate survival in the in vitro experiment, was measured on body weight, feed con­sumption and immune response of rats (IgG and IgA level after immunisation), on the composition and volatile fatty acid production of the intestinal microbiota and on the structure of intestinal villi. The Lactobacillus acidophilus (LA-15) strain had inadequate surviving ability in rats. Bifidobacterium animalis ssp. lactis (BB-12) improved the composition of the intestinal microflora, whereas whey-containing product had a mild immunostimulating effect and exerted a favourable influence on the morphology of intestinal villi. The consumption of yogurts increased the depth of crypts in the ileum, which resulted in enhanced secretion and thus softer faeces.

Computational approaches have been major drivers behind the progress of proteomics in recent years. The aim of this white paper is to provide a framework for integrating computational proteomics into ELIXIR in the near future, and thus to broaden the portfolio of omics technologies supported by this European distributed infrastructure. This white paper is the direct result of a strategy meeting on ‘The Future of Proteomics in ELIXIR’ that took place in March 2017 in Tübingen (Germany), and involved representatives of eleven ELIXIR nodes. These discussions led to a list of priority areas in computational proteomics that would complement existing activities and close gaps in the portfolio of tools and services offered by ELIXIR so far. We provide some suggestions on how these activities could be integrated into ELIXIR’s existing platforms, and how it could lead to a new ELIXIR use case in proteomics. We also highlight connections to the related field of metabolomics, where similar activities are ongoing. This white paper could thus serve as a starting point for the integration of computational proteomics into ELIXIR. Over the next few months we will be working closely with all stakeholders involved, and in particular with other representatives of the proteomics community, to further refine this paper.

Cigarette smoking is the main risk factor for COPD (Chronic Obstructive Pulmonary Disease), yet only a subset of smokers develops COPD. Family members of patients with severe early-onset COPD have an increased risk to develop COPD and are therefore defined as "susceptible individuals". Here we perform unbiased analyses of proteomic profiles to assess how "susceptible individuals" differ from age-matched "non-susceptible individuals" in response to cigarette smoking. Epithelial lining fluid (ELF) was collected at baseline and 24 hours after smoking 3 cigarettes in young individuals susceptible or non-susceptible to develop COPD and older subjects with established COPD. Controls at baseline were older healthy smoking and non-smoking individuals. Five samples per group were pooled and analysed by stable isotope labelling (iTRAQ) in duplicate. Six proteins were selected and validated by ELISA or immunohistochemistry. After smoking, 23 proteins increased or decreased in young susceptible individuals, 7 in young non-susceptible individuals, and 13 in COPD in the first experiment; 23 proteins increased or decreased in young susceptible individuals, 32 in young non-susceptible individuals, and 11 in COPD in the second experiment. SerpinB3 and Uteroglobin decreased after acute smoke exposure in young non-susceptible individuals exclusively, whereas Peroxiredoxin I, S100A9, S100A8, ALDH3A1 (Aldehyde dehydrogenase 3A1) decreased both in young susceptible and non-susceptible individuals, changes being significantly different between groups for Uteroglobin with iTRAQ and for Serpin B3 with iTRAQ and ELISA measures. Peroxiredoxin I, SerpinB3 and ALDH3A1 increased in COPD patients after smoking. We conclude that smoking induces a differential protein response in ELF of susceptible and non-susceptible young individuals, which differs from patients with established COPD. This is the first study applying unbiased proteomic profiling to unravel the underlying mechanisms that induce COPD. Our data suggest that SerpinB3 and Uteroglobin could be interesting proteins in understanding the processes leading to COPD.