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Abstract Multisystem inflammatory syndrome in children (MIS-C) is a severe disease that emerged during the COVID-19 pandemic. Although recognized as an immune-mediated condition, the pathogenesis remains unresolved. Furthermore, the absence of a diagnostic test can lead to delayed immunotherapy. Using state-of-the-art mass-spectrometry proteomics, assisted by artificial intelligence (AI), we aimed to identify a diagnostic signature for MIS-C and to gain insights into disease mechanisms. We identified a highly specific 4-protein diagnostic signature in children with MIS-C. Furthermore, we identified seven clusters that differed between MIS-C and controls, indicating an interplay between apolipoproteins, immune response proteins, coagulation factors, platelet function, and the complement cascade. These intricate protein patterns indicated MIS-C as an immunometabolic condition with global hypercoagulability. Our findings emphasize the potential of AI-assisted proteomics as a powerful and unbiased tool for assessing disease pathogenesis and suggesting avenues for future interventions and impact on pediatric disease trajectories through early diagnosis.

Desmoplasia is a common feature of aggressive cancers, driven by a complex interplay of protein production and degradation. Basigin is a type 1 integral membrane receptor secreted in exosomes or released by ectodomain shedding from the cell surface. Given that soluble basigin is increased in the circulation of patients with a poor cancer prognosis, we explored the putative role of the ADAM12-generated basigin ectodomain in cancer progression. We show that recombinant basigin ectodomain binds β1 integrin and stimulates gelatin degradation and the migration of cancer cells in a matrix metalloproteinase (MMP)- and β1-integrin-dependent manner. Subsequent in vitro and in vivo experiments demonstrated the altered expression of extracellular matrix proteins, including fibronectin and collagen type 5. Thus, we found increased deposits of collagen type 5 in the stroma of nude mice tumors of the human tumor cell line MCF7 expressing ADAM12—mimicking the desmoplastic response seen in human cancer. Our findings indicate a feedback loop between ADAM12 expression, basigin shedding, TGFβ signaling, and extracellular matrix (ECM) remodeling, which could be a mechanism by which ADAM12-generated basigin ectodomain contributes to the regulation of desmoplasia, a key feature in human cancer progression.

The application of next-generation sequencing (NGS) has transformed cancer research. As costs have decreased, NGS has increasingly been applied to generate multiple layers of molecular data from the same samples, covering genomics, transcriptomics, and methylomics. Integrating these types of multi-omics data in a combined analysis is now becoming a common issue with no obvious solution, often handled on an ad hoc basis, with multi-omics data arriving in a tabular format and analyzed using computationally intensive statistical methods. These methods particularly ignore the spatial orientation of the genome and often apply stringent p-value corrections that likely result in the loss of true positive associations. Here, we present GENIUS (GEnome traNsformatIon and spatial representation of mUltiomicS data), a framework for integrating multi-omics data using deep learning models developed for advanced image analysis. The GENIUS framework is able to transform multi-omics data into images with genes displayed as spatially connected pixels and successfully extract relevant information with respect to the desired output. We demonstrate the utility of GENIUS by applying the framework to multi-omics datasets from the Cancer Genome Atlas. Our results are focused on predicting the development of metastatic cancer from primary tumors, and demonstrate how through model inference, we are able to extract the genes which are driving the model prediction and are likely associated with metastatic disease progression. We anticipate our framework to be a starting point and strong proof of concept for multi-omics data transformation and analysis without the need for statistical correction.

Abstract Deeper understanding of liver pathophysiology would benefit from a comprehensive quantitative proteome resource at cell type resolution to predict outcome and design therapy. Here, we quantify more than 150,000 sequence‐unique peptides aggregated into 10,000 proteins across total liver, the major liver cell types, time course of primary cell cultures, and liver disease states. Bioinformatic analysis reveals that half of hepatocyte protein mass is comprised of enzymes and 23% of mitochondrial proteins, twice the proportion of other liver cell types. Using primary cell cultures, we capture dynamic proteome remodeling from tissue states to cell line states, providing useful information for biological or pharmaceutical research. Our extensive data serve as spectral library to characterize a human cohort of non‐alcoholic steatohepatitis and cirrhosis. Dramatic proteome changes in liver tissue include signatures of hepatic stellate cell activation resembling liver cirrhosis and providing functional insights. We built a web‐based dashboard application for the interactive exploration of our resource ( www.liverproteome.org ).

Breast cancer heterogeneity in histology and molecular subtype influences metabolic and proliferative activity and hence the acid load on cancer cells. We hypothesized that acid-base transporters and intracellular pH (pHi) dynamics contribute inter-individual variability in breast cancer aggressiveness and prognosis. We show that Na+,HCO3– cotransport and Na+/H+ exchange dominate cellular net acid extrusion in human breast carcinomas. Na+/H+ exchange elevates pHi preferentially in estrogen receptor-negative breast carcinomas, whereas Na+,HCO3– cotransport raises pHi more in invasive lobular than ductal breast carcinomas and in higher malignancy grade breast cancer. HER2-positive breast carcinomas have elevated protein expression of Na+/H+ exchanger NHE1/SLC9A1 and Na+,HCO3– cotransporter NBCn1/SLC4A7. Increased dependency on Na+,HCO3– cotransport associates with severe breast cancer: enlarged CO2/HCO3–-dependent rises in pHi predict accelerated cell proliferation, whereas enhanced CO2/HCO3–-dependent net acid extrusion, elevated NBCn1 protein expression, and reduced NHE1 protein expression predict lymph node metastasis. Accordingly, we observe reduced survival for patients suffering from luminal A or basal-like/triple-negative breast cancer with high SLC4A7 and/or low SLC9A1 mRNA expression. We conclude that the molecular mechanisms of acid-base regulation depend on clinicopathological characteristics of breast cancer patients. NBCn1 expression and dependency on Na+,HCO3– cotransport for pHi regulation, measured in biopsies of human primary breast carcinomas, independently predict proliferative activity, lymph node metastasis, and patient survival.

Abstract Non‐alcoholic fatty liver disease (NAFLD) affects 25% of the population and can progress to cirrhosis with limited treatment options. As the liver secretes most of the blood plasma proteins, liver disease may affect the plasma proteome. Plasma proteome profiling of 48 patients with and without cirrhosis or NAFLD revealed six statistically significantly changing proteins (ALDOB, APOM, LGALS3BP, PIGR, VTN, and AFM), two of which are already linked to liver disease. Polymeric immunoglobulin receptor (PIGR) was significantly elevated in both cohorts by 170% in NAFLD and 298% in cirrhosis and was further validated in mouse models. Furthermore, a global correlation map of clinical and proteomic data strongly associated DPP4, ANPEP, TGFBI, PIGR, and APOE with NAFLD and cirrhosis. The prominent diabetic drug target DPP4 is an aminopeptidase like ANPEP, ENPEP, and LAP3, all of which are up‐regulated in the human or mouse data. Furthermore, ANPEP and TGFBI have potential roles in extracellular matrix remodeling in fibrosis. Thus, plasma proteome profiling can identify potential biomarkers and drug targets in liver disease.

Summary: Although thousands of solid tumors have been sequenced to date, a fundamental under-sampling bias is inherent in current methodologies. This is caused by a tissue sample input of fixed dimensions (e.g., 6 mm biopsy), which becomes grossly under-powered as tumor volume scales. Here, we demonstrate representative sequencing (Rep-Seq) as a new method to achieve unbiased tumor tissue sampling. Rep-Seq uses fixed residual tumor material, which is homogenized and subjected to next-generation sequencing. Analysis of intratumor tumor mutation burden (TMB) variability shows a high level of misclassification using current single-biopsy methods, with 20% of lung and 52% of bladder tumors having at least one biopsy with high TMB but low clonal TMB overall. Misclassification rates by contrast are reduced to 2% (lung) and 4% (bladder) when a more representative sampling methodology is used. Rep-Seq offers an improved sampling protocol for tumor profiling, with significant potential for improved clinical utility and more accurate deconvolution of clonal structure.

Summary: Glucagon is secreted from pancreatic α cells, and hypersecretion (hyperglucagonemia) contributes to diabetic hyperglycemia. Molecular heterogeneity in hyperglucagonemia is poorly investigated. By screening human plasma using high-resolution-proteomics, we identified several glucagon variants, among which proglucagon 1-61 (PG 1-61) appears to be the most abundant form. PG 1-61 is secreted in subjects with obesity, both before and after gastric bypass surgery, with protein and fat as the main drivers for secretion before surgery, but glucose after. Studies in hepatocytes and in β cells demonstrated that PG 1-61 dose-dependently increases levels of cAMP, through the glucagon receptor, and increases insulin secretion and protein levels of enzymes regulating glycogenolysis and gluconeogenesis. In rats, PG 1-61 increases blood glucose and plasma insulin and decreases plasma levels of amino acids in vivo. We conclude that glucagon variants, such as PG 1-61, may contribute to glucose regulation by stimulating hepatic glucose production and insulin secretion. : Wewer Albrechtsen et al. identify a glucagon-like molecule, PG 1-61, in humans using mass-spectrometry-based proteomics. PG 1-61 activates the glucagon receptor, stimulates insulin secretion, and activates key gluconeogenic enzymes. In dysmetabolic conditions, PG 1-61 is upregulated and may therefore serve as a marker of alpha cell stress. Keywords: glucagon, GLP-1, alpha cells, diabetes, proglucagon, L-cells, proteomics

Abstract Sustained weight loss is a preferred intervention in a wide range of metabolic conditions, but the effects on an individual's health state remain ill‐defined. Here, we investigate the plasma proteomes of a cohort of 43 obese individuals that had undergone 8 weeks of 12% body weight loss followed by a year of weight maintenance. Using mass spectrometry‐based plasma proteome profiling, we measured 1,294 plasma proteomes. Longitudinal monitoring of the cohort revealed individual‐specific protein levels with wide‐ranging effects of losing weight on the plasma proteome reflected in 93 significantly affected proteins. The adipocyte‐secreted SERPINF1 and apolipoprotein APOF1 were most significantly regulated with fold changes of −16% and +37%, respectively (P

Glucagon circulates in concentrations in the low picomolar range, which is demanding regarding the sensitivity of the methods for quantification applied. In addition, the differential and tissue specific proteolytic processing of the glucagon precursor and the presence in of several glucagon-like sequences, not only in the precursor of glucagon, but also in a number of other peptides of the glucagon-secretin family of peptides, put special demands on the specificity of the assays. Finally, experience has shown that unspecific interference of plasma components has presented additional problems. All of these problems have resulted in a lot of diverging results concerning measured and reported glucagon responses in both humans and experimental animals that have and still are causing considerable debate and controversy. There is very solid evidence that glucagon is an important hormone in human and mammalian metabolism, but its precise physiological role in glucose and lipid metabolism and in metabolic disease has been difficult to establish, not least because of these difficulties. It was our purpose with this review to discuss the methods of glucagon quantification and discuss pitfalls and sources of error. We also reviewed some of the dogmas regarding glucagon secretion in the light of the methodological difficulties.

Hundred years after the discovery of glucagon, its biology remains enigmatic. Accurate measurement of glucagon has been essential for uncovering its pathological hypersecretion that underlies various metabolic diseases including not only diabetes and liver diseases but also cancers (glucagonomas). The suggested key role of glucagon in the development of diabetes has been termed the bihormonal hypothesis. However, studying tissue-specific knockout of the glucagon receptor has revealed that the physiological role of glucagon may extend beyond blood-glucose regulation. Decades ago, animal and human studies reported an important role of glucagon in amino acid metabolism through ureagenesis. Using modern technologies such as metabolomic profiling, knowledge about the effects of glucagon on amino acid metabolism has been expanded and the mechanisms involved further delineated. Glucagon receptor antagonists have indirectly put focus on glucagon’s potential role in lipid metabolism, as individuals treated with these antagonists showed dyslipidemia and increased hepatic fat. One emerging field in glucagon biology now seems to include the concept of hepatic glucagon resistance. Here, we discuss the roles of glucagon in glucose homeostasis, amino acid metabolism, and lipid metabolism and present speculations on the molecular pathways causing and associating with postulated hepatic glucagon resistance.

The transmembrane glycoprotein basigin, a member of the immunoglobulin superfamily, stimulates matrix metalloproteinase (MMP)-mediated extracellular matrix (ECM) degradation and thereby drives cancer cell invasion. Basigin is proteolytically shed from the cell surface and high concentrations of soluble basigin in the blood dictates poor prognosis in cancer patients. A positive correlation between basigin and a disintegrin and metalloproteinase (ADAM)-12 in serum from prostate cancer patients has been reported. Yet, the functional relevance of this correlation is unknown. Here, we show that ADAM12 interacts with basigin and cleaves it in the juxtamembrane region. Specifically, overexpression of ADAM12 increases ectodomain shedding of an alkaline phosphatase-tagged basigin reporter protein from the cell surface. Moreover, CRISPR/Cas9-mediated knockout of ADAM12 in human HeLa carcinoma cells results in reduced shedding of the basigin reporter, which can be rescued by ADAM12 re-expression. We detected endogenous basigin fragments, corresponding to the expected size of the ADAM12-generated ectodomain, in conditioned media from ADAM12 expressing cancer cell-lines, as well as serum samples from a healthy pregnant donor and five bladder cancer patients, known to contain high ADAM12 levels. Supporting the cancer relevance of our findings, we identified several cancer-associated mutations in the basigin membrane proximal region. Subsequent in vitro expression showed that some of these mutants are more prone to ADAM12-mediated shedding and that the shed ectodomain can enhance gelatin degradation by cancer cells. In conclusion, we identified ADAM12 as a novel basigin sheddase with a potential implication in cancer.

Abstract Background Interpretation of gene expression microarrays requires a mapping from probe set to gene. On many Affymetrix gene expression microarrays, a given gene may be detected by multiple probe sets, which may deliver inconsistent or even contradictory measurements. Therefore, obtaining an unambiguous expression estimate of a pre-specified gene can be a nontrivial but essential task. Results We developed scoring methods to assess each probe set for specificity, splice isoform coverage, and robustness against transcript degradation. We used these scores to select a single representative probe set for each gene, thus creating a simple one-to-one mapping between gene and probe set. To test this method, we evaluated concordance between protein measurements and gene expression values, and between sets of genes whose expression is known to be correlated. For both test cases, we identified genes that were nominally detected by multiple probe sets, and we found that the probe set chosen by our method showed stronger concordance. Conclusions This method provides a simple, unambiguous mapping to allow assessment of the expression levels of specific genes of interest.

Abstract Background Genome scale expression profiling of human tumor samples is likely to yield improved cancer treatment decisions. However, identification of clinically predictive or prognostic classifiers can be challenging when a large number of genes are measured in a small number of tumors. Results We describe an unsupervised method to extract robust, consistent metagenes from multiple analogous data sets. We applied this method to expression profiles from five "double negative breast cancer" (DNBC) (not expressing ESR1 or HER2) cohorts and derived four metagenes. We assessed these metagenes in four similar but independent cohorts and found strong associations between three of the metagenes and agent-specific response to neoadjuvant therapy. Furthermore, we applied the method to ovarian and early stage lung cancer, two tumor types that lack reliable predictors of outcome, and found that the metagenes yield predictors of survival for both. Conclusions These results suggest that the use of multiple data sets to derive potential biomarkers can filter out data set-specific noise and can increase the efficiency in identifying clinically accurate biomarkers.