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Understanding how oncogenic mutations rewire regulatory-protein networks is important for rationalizing the mechanisms of oncogenesis and for individualizing anticancer treatments. We report a chemical phosphoproteomics method to elucidate the topology of kinase-signaling networks in mammalian cells. We identified >6,000 protein phosphorylation sites that can be used to infer >1,500 kinase–kinase interactions and devised algorithms that can reconstruct kinase network topologies from these phosphoproteomics data. Application of our methods to primary acute myeloid leukemia and breast cancer tumors quantified the relationship between kinase expression and activity, and enabled the identification of hitherto unknown kinase network topologies associated with drug-resistant phenotypes or specific genetic mutations. Using orthogonal methods we validated that PIK3CA wild-type cells adopt MAPK-dependent circuitries in breast cancer cells and that the kinase TTK is important in acute myeloid leukemia. Our phosphoproteomic signatures of network circuitry can identify kinase topologies associated with both phenotypes and genotypes of cancer cells. Improved methods to reconstruct global kinase-signaling networks from phosphoproteomics data are applied in cancer cells.

Biomarkers are needed for predicting the effectiveness of disease modifying antirheumatic drugs (DMARDs). Here, using functional lipid mediator profiling and deeply phenotyped patients with early rheumatoid arthritis (RA), we observe that peripheral blood  specialized pro-resolving mediator (SPM) concentrations are linked with both DMARD responsiveness and disease pathotype. Machine learning analysis demonstrates that baseline plasma concentrations of resolvin D4, 10S, 17S-dihydroxy-docosapentaenoic acid, 15R-Lipoxin (LX)A 4 and n-3 docosapentaenoic-derived Maresin 1 are predictive of DMARD responsiveness at 6 months. Assessment of circulating SPM concentrations 6-months after treatment initiation establishes that differences between responders and non-responders are maintained, with a decrease in SPM concentrations in patients resistant to DMARD therapy. These findings elucidate the potential utility of  plasma SPM concentrations as biomarkers of DMARD responsiveness in RA. Being able to predict the therapeutic benefit of disease modifying anti-rheumatic drugs (DMARDs) would be of great benefit and a stepping stone towards personalized medicine. Here the authors use machine learning and lipid mediator mass spectrometry to show specialized pro-resolving mediators are indicative of DMARD responsiveness among rheumatoid arthritis patients.

ObjectivesOnline health forums provide rich and untapped real-time data on population health. Through novel data extraction and natural language processing (NLP) techniques, we characterise the evolution of mental and physical health concerns relating to the COVID-19 pandemic among online health forum users.Setting and designWe obtained data from three leading online health forums: HealthBoards, Inspire and HealthUnlocked, from the period 1 January 2020 to 31 May 2020. Using NLP, we analysed the content of posts related to COVID-19.Primary outcome measures(1) Proportion of forum posts containing COVID-19 keywords; (2) proportion of forum users making their very first post about COVID-19; (3) proportion of COVID-19-related posts containing content related to physical and mental health comorbidities.ResultsData from 739 434 posts created by 53 134 unique users were analysed. A total of 35 581 posts (4.8%) contained a COVID-19 keyword. Posts discussing COVID-19 and related comorbid disorders spiked in early March to mid-March around the time of global implementation of lockdowns prompting a large number of users to post on online health forums for the first time. Over a quarter of COVID-19-related thread titles mentioned a physical or mental health comorbidity.ConclusionsWe demonstrate that it is feasible to characterise the content of online health forum user posts regarding COVID-19 and measure changes over time. The pandemic and corresponding public response has had a significant impact on posters’ queries regarding mental health. Social media data sources such as online health forums can be harnessed to strengthen population-level mental health surveillance.

Background/Aims: Immune and inflammatory cells respond to multiple pathological hits in the development of nonalcoholic steatohepatitis (NASH) and fibrosis. Relatively little is known about how their type and function change through the non-alcoholic fatty liver disease (NAFLD) spectrum. Here we used multi-dimensional mass cytometry and a tailored bioinformatic approach to study circulating immune cells sampled from healthy individuals and people with NAFLD.Methods: Cytometry by time of flight using 36 metal-conjugated antibodies was applied to peripheral blood mononuclear cells (PBMCs) from biopsy-proven NASH fibrosis (late disease), steatosis (early disease), and healthy patients. Supervised and unsupervised analyses were used, findings confirmed, and mechanisms assessed using independent healthy and disease PBMC samples.Results: Of 36 PBMC clusters, 21 changed between controls and disease samples. Significant differences were observed between diseases stages with changes in T cells and myeloid cells throughout disease and B cell changes in late stages. Semi-supervised gating and re-clustering showed that disease stages were associated with fewer monocytes with active signalling and more inactive NK cells; B and T cells bearing activation markers were reduced in late stages, while B cells bearing co-stimulatory molecules were increased. Functionally, disease states were associated with fewer activated mucosal-associated invariant T cells and reduced toll-like receptor-mediated cytokine production in late disease.Conclusions: A range of innate and adaptive immune changes begin early in NAFLD, and disease stages are associated with a functionally less active phenotype compared to controls. Further study of the immune response in NAFLD spectrum may give insight into mechanisms of disease with potential clinical application.

Phosphoproteomics allows one to measure the activity of kinases that drive the fluxes of signal transduction pathways involved in biological processes such as immune function, senescence and cell growth. However, deriving knowledge of signalling network circuitry from these data is challenging due to a scarcity of phosphorylation sites that define kinase-kinase relationships. To address this issue, we previously identified around 6,000 phosphorylation sites as markers of kinase-kinase relationships (that may be conceptualised as network edges), from which empirical cell-model-specific weighted kinase networks may be reconstructed. Here, we assess whether the application of community detection algorithms to such networks can identify new components linked to canonical signalling pathways. Phosphoproteomics data from acute myeloid leukaemia (AML) cells treated separately with PI3K, AKT, MEK and ERK inhibitors were used to reconstruct individual kinase networks. We used modularity maximisation to detect communities in each network, and selected the community containing the main target of the inhibitor used to treat cells. These analyses returned communities that contained known canonical signalling components. Interestingly, in addition to canonical PI3K/AKT/mTOR members, the community assignments returned TTK (also known as MPS1) as a likely component of PI3K/AKT/mTOR signalling. We drew similar insights from an external phosphoproteomics dataset from breast cancer cells treated with rapamycin and oestrogen. We confirmed this observation with wet-lab laboratory experiments showing that TTK phosphorylation was decreased in AML cells treated with AKT and MTOR inhibitors. This study illustrates the application of community detection algorithms to the analysis of empirical kinase networks to uncover new members linked to canonical signalling pathways.

Protein folding: choosing the right metal Metal ions are needed by at least a quarter of proteins — although metallochaperones insert the 'correct' metal ion into some metal-containing proteins, metallochaperones have not been found for a large proportion of metalloproteins. It seems likely that some metalloproteins acquire their metal ions directly from cellular pools — but some metal ions form more stable metal ion/protein-complexes than others, so it is not clear what cellular mechanisms enables a nascent protein to incorporate the correct metal ion. In this manuscript, the authors identified the most abundant Cu 2+ and Mn 2+ containing proteins in the periplasm of a cyanobacterium and determined that the cellular compartment in which each of those proteins fold is responsible for the insertion of the correct metal ion into the metalloprotein. This study identifies the most abundant Cu 2+ - and Mn 2+ -containing proteins in the periplasm of a cyanobacterium and determines that the cellular compartment in which each of those proteins fold is responsible for the insertion of the correct metal ion into the metalloprotein. Metals are needed by at least one-quarter of all proteins 1 , 2 . Although metallochaperones 3 , 4 , 5 , 6 , 7 , 8 insert the correct metal into some proteins, they have not been found for the vast majority, and the view is that most metalloproteins acquire their metals directly from cellular pools. However, some metals form more stable complexes with proteins than do others. For instance, as described in the Irving–Williams series 9 , Cu 2+ and Zn 2+ typically form more stable complexes than Mn 2+ . Thus it is unclear what cellular mechanisms manage metal acquisition by most nascent proteins. To investigate this question, we identified the most abundant Cu 2+ -protein, CucA (Cu 2+ -cupin A), and the most abundant Mn 2+ -protein, MncA (Mn 2+ -cupin A), in the periplasm of the cyanobacterium Synechocystis PCC 6803. Each of these newly identified proteins binds its respective metal via identical ligands within a cupin fold. Consistent with the Irving–Williams series, MncA only binds Mn 2+ after folding in solutions containing at least a 10 4 times molar excess of Mn 2+ over Cu 2+ or Zn 2+ . However once MncA has bound Mn 2+ , the metal does not exchange with Cu 2+ . MncA and CucA have signal peptides for different export pathways into the periplasm, Tat and Sec respectively. Export by the Tat pathway allows MncA to fold in the cytoplasm, which contains only tightly bound copper or Zn 2+ (refs 10–12 ) but micromolar Mn 2+ (ref. 13 ). In contrast, CucA folds in the periplasm to acquire Cu 2+ . These results reveal a mechanism whereby the compartment in which a protein folds overrides its binding preference to control its metal content. They explain why the cytoplasm must contain only tightly bound and buffered copper and Zn 2+ .

A challenging task in the study of the secretory pathway is the identification and localization of new proteins to increase our understanding of the functions of different organelles. Previous proteomic studies of the endomembrane system have been hindered by contaminating proteins, making it impossible to assign proteins to organelles. Here we have used the localization of organelle proteins by the isotope tagging technique in conjunction with isotope tags for relative and absolute quantitation and 2D liquid chromatography for the simultaneous assignment of proteins to multiple subcellular compartments. With this approach, the density gradient distributions of 689 proteins from Arabidopsis thaliana were determined, enabling confident and simultaneous localization of 527 proteins to the endoplasmic reticulum, Golgi apparatus, vacuolar membrane, plasma membrane, or mitochondria and plastids. This parallel analysis of endomembrane components has enabled protein steady-state distributions to be determined. Consequently, genuine organelle residents have been distinguished from contaminating proteins and proteins in transit through the secretory pathway.

The aim of this study was to determine whether volatile organic compounds (VOCs) present in the headspace of feces could be used to diagnose or distinguish between chronic diseases of the gastrointestinal tract and apparently healthy volunteers.MethodsA total of 87 people were recruited, divided between 4 categories: healthy volunteers (n = 19), Crohn’s disease (n = 22), ulcerative colitis (n = 20), and irritable bowel syndrome (n = 26). They each supplied fecal samples before, and except for the healthy volunteers, after treatment. Fecal samples were incubated in a sample bag with added purified air at 40°C and headspace samples were taken and concentrated on thermal sorption tubes. Gas chromatography–mass spectrometry then desorbed and analyzed these. The concentrations of a selection of high-abundance compounds were determined and assessed for differences in concentration between the groups.ResultsCrohn's disease samples showed significant elevations in the concentrations of ester and alcohol derivates of short-chain fatty acids and indole compared with the other groups; indole and phenol were elevated in ulcerative colitis and irritable bowel syndrome but not at a statistically significant level. After treatment, the levels of many of the VOCs were significantly reduced and were more similar to those concentrations in healthy controls.ConclusionsThe abundance of a number of VOCs in feces differs markedly between Crohn's disease and other gastrointestinal conditions. Following treatment, the VOC profile is altered to more closely resemble that of healthy volunteers.

The goal of this group is to define the reporting requirements associated with the statistical analysis (including univariate, multivariate, informatics, machine learning etc.) of metabolite data with respect to other measured/collected experimental data (often called meta-data). These definitions will embrace as many aspects of a complete metabolomics study as possible at this time. In chronological order this will include: Experimental Design, both in terms of sample collection/matching, and data acquisition scheduling of samples through whichever spectroscopic technology used; Deconvolution (if required); Pre-processing, for example, data cleaning, outlier detection, row/column scaling, or other transformations; Definition and parameterization of subsequent visualizations and Statistical/Machine learning Methods applied to the dataset; If required, a clear definition of the Model Validation Scheme used (including how data are split into training/validation/test sets); Formal indication on whether the data analysis has been Independently Tested (either by experimental reproduction, or blind hold out test set). Finally, data interpretation and the visual representations and hypotheses obtained from the data analyses.

Widespread mammographic screening programs and improved self-monitoring allow for breast cancer to be detected earlier than ever before. Breast-conserving surgery is a successful treatment for select women. However, up to 40% of women develop local recurrence after surgery despite apparently tumor-free margins. This suggests that morphologically normal breast may harbor early alterations that contribute to increased risk of cancer recurrence. We conducted a comprehensive transcriptomic and proteomic analysis to characterize 57 fresh-frozen tissues from breast cancers and matched histologically normal tissues resected proximal to (<2 cm) and distant from (5–10 cm) the primary tumor, using tissues from cosmetic reduction mammoplasties as baseline. Four distinct transcriptomic subtypes are identified within matched normal tissues: metabolic; immune; matrisome/epithelial–mesenchymal transition, and non-coding enriched. Key components of the subtypes are supported by proteomic and tissue composition analyses. We find that the metabolic subtype is associated with poor prognosis (p < 0.001, HR6.1). Examination of genes representing the metabolic signature identifies several genes able to prognosticate outcome from histologically normal tissues. A subset of these have been reported for their predictive ability in cancer but, to the best of our knowledge, these have not been reported altered in matched normal tissues. This study takes an important first step toward characterizing matched normal tissues resected at pre-defined margins from the primary tumor. Unlocking the predictive potential of unexcised tissue could prove key to driving the realization of personalized medicine for breast cancer patients, allowing for more biologically-driven analyses of tissue margins than morphology alone.