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SUMMARY: CCT is required to regulate interorganelle contact sites through kinesin‐1 dynamics, modulating peroxisome and mitochondria activity, and ultimately controlling lipid cell content and metabolism. Cell proteostasis includes gene transcription, protein translation, folding of de novo proteins, post‐translational modifications, secretion, degradation and recycling. By profiling the proteome of extracellular vesicles (EVs) from T cells, we have found the chaperonin complex CCT, involved in the correct folding of particular proteins. By limiting CCT cell‐content by siRNA, cells undergo altered lipid composition and metabolic rewiring towards a lipid‐dependent metabolism, with increased activity of peroxisomes and mitochondria. This is due to dysregulation of the dynamics of interorganelle contacts between lipid droplets, mitochondria, peroxisomes and the endolysosomal system. This process accelerates the biogenesis of multivesicular bodies leading to higher EV production through the dynamic regulation of microtubule‐based kinesin motors. These findings connect proteostasis with lipid metabolism through an unexpected role of CCT. [ABSTRACT FROM AUTHOR]

Background: Controversies on sub‐populations most sensitive to therapy and the best timing of starting the treatment still surround the use of immunomodulatory drugs in COVID‐19. Objectives: We designed a multicentre open‐label randomised controlled trial to test the effect of prompt adding of tofacitinib to standard therapy for hospitalised patients affected by mild/moderate COVID‐19 pneumonitis. Methods: Patients admitted to three Italian hospitals affected by COVID‐19 pneumonitis not requiring mechanical ventilation were randomised to receive standard treatment alone or tofacitinib (10 mg/bid) for 2 weeks, starting within the first 24 h from admission. Results: A total of 116 patients were randomised; 49 in the experimental arm completed the 14‐day treatment period, 9 discontinued tofacitinib as the disease worsened and were included in the analysis, and 1 died of respiratory failure. All 58 control patients completed the study. Clinical and demographic characteristics were similar between the study groups. In the tofacitinib group, 9/58 (15.5%) patients progressed to noninvasive ventilation (CPAP) to maintain SO2 > 93%, invasive mechanical ventilation or death by day 14 was 15.5%, significantly less than in the control group (20/58, 34.4%, RR 0,45, RRR −55%, NNT 5; p =.018). No differences in severe adverse effect incidence had been observed across the groups. Conclusion: High‐dose tofacitinib therapy in patients with COVID pneumonitis is safe and may prevent deterioration to respiratory failure. [ABSTRACT FROM AUTHOR]

Objectives: To assess the inter-operator concordance and the potential sources of discordance in defining response to sorafenib in hepatocellular carcinoma (HCC).Methods: All patients who received sorafenib between September 2008 and February 2015 were scrutinised for this retrospective study. Images were evaluated separately by three radiologists with different expertise in liver imaging (operator 1, >10 years; operator 2, 5 years; operator 3, no specific training in liver imaging), according to: response evaluation radiological criteria in solid tumours (RECIST) 1.1, modified RECIST (mRECIST) and response evaluation criteria in cancer of the liver (RECICL).Results: The overall response concordance between the more expert operators was good, irrespective of the criteria (RECIST 1.1, ĸ = 0.840; mRECIST, ĸ = 0.871; RECICL, ĸ = 0.819). Concordance between the less expert operator and the other colleagues was lower. The most evident discordance was in target lesion response assessment, with expert operators disagreeing mostly on lesion selection and less expert operators on lesion measurement. As a clinical correlate, overall survival was more tightly related with "progressive disease" as assessed by the expert compared to the same assessment performed by operator 3.Conclusions: Decision on whether a patient is a responder or progressor under sorafenib may vary among different operators, especially in case of a non-specifically trained radiologist. Regardless of the adopted criteria, patients should be evaluated by experienced radiologists to minimise variability in this critical instance.Key Points: • Inter-operator variability in the assessment of response to sorafenib is poorly known. • The concordance between operators with expertise in liver imaging was good. • Target lesions selection was the main source of discordance between expert operators. • Concordance with non-specifically trained operator was lower, independently from the response criteria. • The non-specifically trained operator was mainly discordant in measurements of target lesions. [ABSTRACT FROM AUTHOR]

Disparities in hepatocellular carcinoma (HCC) incidence and survival have been observed between ethnic groups including African-Americans (AA) and European-Americans (EA). The evaluation of the changes in the levels of metabolites in samples stratified by race could provide a snapshot of ethnically diverse disease related pathways and identify reliable biomarkers. In this study, we considered AA and EA to investigate metabolites that may be associated with HCC in a race-specific manner. The levels of 46 metabolites in plasma samples, collected from patients recruited at MedStar Georgetown University Hospital, were analyzed by Agilent GC-qMS in selected ion monitoring (SIM) mode. A least absolute shrinkage and selection operator (LASSO) regression model was applied to select metabolites with significant changes in HCC vs. cirrhosis in three groups: (1) AA and EA combined; (2) AA separately; and (3) EA separately. In addition, metabolites that distinguish HCC cases from cirrhosis in these three groups were selected by excluding those without HCV infection. The performances of the metabolites selected by LASSO in each group were evaluated through a leave-one-out cross-validation. We identified race-specific metabolites that differentiated HCC cases from cirrhotic controls, yielding better area under the receiver operating characteristics (ROC) curve (AUC) compared to alpha-fetoprotein (AFP), the serological marker widely used for the diagnosis of HCC. This study sheds light on metabolites that could potentially be used as biomarkers for HCC by monitoring their levels in high-risk population of cirrhotic patients in a race-specific manner. [ABSTRACT FROM AUTHOR]

Background: Metabolomics plays an important role in providing insight into the etiology and mechanisms of hepatocellular carcinoma (HCC). This is accomplished by a comprehensive analysis of patterns involved in metabolic alterations in human specimens. This study compares the levels of plasma metabolites in HCC cases versus cirrhotic patients and evaluates the ability of candidate metabolites in distinguishing the two groups. Also, it investigates the combined use of metabolites and clinical covariates for detection of HCC in patients with liver cirrhosis. Methods: Untargeted analysis of metabolites in plasma from 128 subjects (63 HCC cases and 65 cirrhotic controls) was conducted using gas chromatography coupled to mass spectrometry (GC-MS). This was followed by targeted evaluation of selected metabolites. LASSO regression was used to select a set of metabolites and clinical covariates that are associated with HCC. The performance of candidate biomarkers in distinguishing HCC from cirrhosis was evaluated through a leave-one-out cross-validation based on area under the receiver operating characteristics (ROC) curve. Results: We identified 11 metabolites and three clinical covariates that differentiated HCC cases from cirrhotic controls. Combining these features in a panel for disease classification using support vector machines (SVM) yielded better area under the ROC curve compared with alpha-fetoprotein (AFP). Conclusions: This study demonstrates the combination of metabolites and clinical covariates as an effective approach for early detection of HCC in patients with liver cirrhosis. [ABSTRACT FROM AUTHOR]

Background: A fundamental challenge in quantitation of biomolecules for cancer biomarker discovery is owing to the heterogeneous nature of human biospecimens. Although this issue has been a subject of discussion in cancer genomic studies, it has not yet been rigorously investigated in mass spectrometry based proteomic and metabolomic studies. Purification of mass spectometric data is highly desired prior to subsequent analysis, e.g., quantitative comparison of the abundance of biomolecules in biological samples. Methods: We investigated topic models to computationally analyze mass spectrometric data considering both integrated peak intensities and scan-level features, i.e., extracted ion chromatograms (EICs). Probabilistic generative models enable flexible representation in data structure and infer sample-specific pure resources. Scan-level modeling helps alleviate information loss during data preprocessing. We evaluated the capability of the proposed models in capturing mixture proportions of contaminants and cancer profiles on LC-MS based serum proteomic and GC-MS based tissue metabolomic datasets acquired from patients with hepatocellular carcinoma (HCC) and liver cirrhosis as well as synthetic data we generated based on the serum proteomic data. Results: The results we obtained by analysis of the synthetic data demonstrated that both intensity-level and scan-level purification models can accurately infer the mixture proportions and the underlying true cancerous sources with small average error ratios (< 7 %) between estimation and ground truth. By applying the topic model-based purification to mass spectrometric data, we found more proteins and metabolites with significant changes between HCC cases and cirrhotic controls. Candidate biomarkers selected after purification yielded biologically meaningful pathway analysis results and improved disease discrimination power in terms of the area under ROC curve compared to the results found prior to purification. Conclusions: We investigated topic model-based inference methods to computationally address the heterogeneity issue in samples analyzed by LC/GC-MS. We observed that incorporation of scan-level features have the potential to lead to more accurate purification results by alleviating the loss in information as a result of integrating peaks. We believe cancer biomarker discovery studies that use mass spectrometric analysis of human biospecimens can greatly benefit from topic model-based purification of the data prior to statistical and pathway analyses. [ABSTRACT FROM AUTHOR]

This study evaluates changes in metabolite levels in hepatocellular carcinoma (HCC) cases vs. patients with liver cirrhosis by analysis of human blood plasma using gas chromatography coupled with mass spectrometry (GC-MS). Untargeted metabolomic analysis of plasma samples from participants recruited in Egypt was performed using two GC-MS platforms: a GC coupled to single quadruple mass spectrometer (GC-qMS) and a GC coupled to a time-of-flight mass spectrometer (GC-TOFMS). Analytes that showed statistically significant changes in ion intensities were selected using ANOVA models. These analytes and other candidates selected from related studies were further evaluated by targeted analysis in plasma samples from the same participants as in the untargeted metabolomic analysis. The targeted analysis was performed using the GC-qMS in selected ion monitoring (SIM) mode. The method confirmed significant changes in the levels of glutamic acid, citric acid, lactic acid, valine, isoleucine, leucine, alpha tocopherol, cholesterol, and sorbose in HCC cases vs. patients with liver cirrhosis. Specifically, our findings indicate up-regulation of metabolites involved in branched-chain amino acid (BCAA) metabolism. Although BCAAs are increasingly used as a treatment for cancer cachexia, others have shown that BCAA supplementation caused significant enhancement of tumor growth via activation of mTOR/AKT pathway, which is consistent with our results that BCAAs are up-regulated in HCC. [ABSTRACT FROM AUTHOR]

Background Cardiac steatosis and apoptosis are key processes in diabetic cardiomyopathy, but the underlying mechanisms have not been elucidated, leading to a lack of effective therapy. The mineralocorticoid receptor blocker, eplerenone, has demonstrated anti-fibrotic actions in the diabetic heart. However, its effects on the fatty-acid accumulation and apoptotic responses have not been revealed. Methods Non-hypertensive Zucker Diabetic Fatty (ZDF) rats received eplerenone (25 mg/kg) or vehicle. Zucker Lean (ZL) rats were used as control (n = 10, each group). After 16 weeks, cardiac structure and function was examined, and plasma and hearts were isolated for biochemical and histological approaches. Cultured cardiomyocytes were used for in vitro assays to determine the direct effects of eplerenone on high fatty acid and high glucose exposed cells. Results In contrast to ZL, ZDF rats exhibited hyperglycemia, hyperlipidemia, insulin-resistance, cardiac steatosis and diastolic dysfunction. The ZDF myocardium also showed increased mitochondrial oxidation and apoptosis. Importantly, eplerenone mitigated these events without altering hyperglycemia. In cultured cardiomyocytes, high-concentrations of palmitate stimulated the fatty-acid uptake (in detriment of glucose assimilation), accumulation of lipid metabolites, mitochondrial dysfunction, and apoptosis. Interestingly, fatty-acid uptake, ceramides formation and apoptosis were also significantly ameliorated by eplerenone. Conclusions By blocking mineralocorticoid receptors, eplerenone may attenuate cardiac steatosis and apoptosis, and subsequent remodelling and diastolic dysfunction in obese/type-II diabetic rats. [ABSTRACT FROM AUTHOR]

The composition and physical properties of the extracellular matrix (ECM) critically influence tumor progression, but the molecular mechanisms underlying ECM layering are poorly understood. Tumor-stroma interaction critically depends on cell communication mediated by exosomes, small vesicles generated within multivesicular bodies (MVBs). We show that caveolin-1 (Cav1) centrally regulates exosome biogenesis and exosomal protein cargo sorting through the control of cholesterol content at the endosomal compartment/MVBs. Quantitative proteomics profiling revealed that Cav1 is required for exosomal sorting of ECM protein cargo subsets, including Tenascin-C (TnC), and for fibroblast-derived exosomes to efficiently deposit ECM and promote tumor invasion. Cav1-driven exosomal ECM deposition not only promotes local stromal remodeling but also the generation of distant ECM-enriched stromal niches in vivo. Cav1 acts as a cholesterol rheostat in MVBs, determining sorting of ECM components into specific exosome pools and thus ECM deposition. This supports a model by which Cav1 is a central regulatory hub for tumor-stroma interactions through a novel exosome-dependent ECM deposition mechanism. [ABSTRACT FROM AUTHOR]