Non-apoptotic FAS signaling controls mTOR activation and extrafollicular maturation in human B cells.

Defective FAS (CD95/Apo-1/TNFRSF6) signaling causes autoimmune lymphoproliferative syndrome (ALPS). Hypergammaglobulinemia is a common feature in ALPS with FAS mutations (ALPS-FAS), but paradoxically, fewer conventional memory cells differentiate from FAS-expressing germinal center (GC) B cells. Resistance to FAS-induced apoptosis does not explain this phenotype. We tested the hypothesis that defective non-apoptotic FAS signaling may contribute to impaired B cell differentiation in ALPS. We analyzed secondary lymphoid organs of patients with ALPS-FAS and found low numbers of memory B cells, fewer GC B cells, and an expanded extrafollicular (EF) B cell response. Enhanced mTOR activity has been shown to favor EF versus GC fate decision, and we found enhanced PI3K/mTOR and BCR signaling in ALPS-FAS splenic B cells. Modeling initial T-dependent B cell activation with CD40L in vitro, we showed that FAS competent cells with transient FAS ligation showed specifically decreased mTOR axis activation without apoptosis. Mechanistically, transient FAS engagement with involvement of caspase-8 induced nuclear exclusion of PTEN, leading to mTOR inhibition. In addition, FASL-dependent PTEN nuclear exclusion and mTOR modulation were defective in patients with ALPS-FAS. In the early phase of activation, FAS stimulation promoted expression of genes related to GC initiation at the expense of processes related to the EF response. Hence, our data suggest that non-apoptotic FAS signaling acts as molecular switch between EF versus GC fate decisions via regulation of the mTOR axis and transcription. The defect of this modulatory circuit may explain the observed hypergammaglobulinemia and low memory B cell numbers in ALPS.

Serine synthesis via reversed SHMT2 activity drives glycine depletion and acetaminophen hepatotoxicity in MASLD.

Metabolic dysfunction-associated steatotic liver disease (MASLD) affects one-third of the global population. Understanding the metabolic pathways involved can provide insights into disease progression and treatment. Untargeted metabolomics of livers from mice with early-stage steatosis uncovered decreased methylated metabolites, suggesting altered one-carbon metabolism. The levels of glycine, a central component of one-carbon metabolism, were lower in mice with hepatic steatosis, consistent with clinical evidence. Stable-isotope tracing demonstrated that increased serine synthesis from glycine via reverse serine hydroxymethyltransferase (SHMT) is the underlying cause for decreased glycine in steatotic livers. Consequently, limited glycine availability in steatotic livers impaired glutathione synthesis under acetaminophen-induced oxidative stress, enhancing acute hepatotoxicity. Glycine supplementation or hepatocyte-specific ablation of the mitochondrial SHMT2 isoform in mice with hepatic steatosis mitigated acetaminophen-induced hepatotoxicity by supporting de novo glutathione synthesis. Thus, early metabolic changes in MASLD that limit glycine availability sensitize mice to xenobiotics even at the reversible stage of this disease.

Circulating succinate-modifying metabolites accurately classify and reflect the status of fumarate hydratase-deficient renal cell carcinoma.

Germline or somatic loss-of-function mutations of fumarate hydratase (FH) predispose patients to an aggressive form of renal cell carcinoma (RCC). Since other than tumor resection there is no effective therapy for metastatic FH-deficient RCC, an accurate method for early diagnosis is needed. Although MRI or CT scans are offered, they cannot differentiate FH-deficient tumors from other RCCs. Therefore, finding noninvasive plasma biomarkers suitable for rapid diagnosis, screening, and surveillance would improve clinical outcomes. Taking advantage of the robust metabolic rewiring that occurs in FH-deficient cells, we performed plasma metabolomics analysis and identified 2 tumor-derived metabolites, succinyl-adenosine and succinic-cysteine, as excellent plasma biomarkers for early diagnosis. These 2 molecules reliably reflected the FH mutation status and tumor mass. We further identified the enzymatic cooperativity by which these biomarkers are produced within the tumor microenvironment. Longitudinal monitoring of patients demonstrated that these circulating biomarkers can be used for reporting on treatment efficacy and identifying recurrent or metastatic tumors.

Bioenergetic and Metabolic Impairments in Induced Pluripotent Stem Cell-Derived Cardiomyocytes Generated from Duchenne Muscular Dystrophy Patients.

Duchenne muscular dystrophy (DMD) is caused by mutations in the dystrophin gene and dilated cardiomyopathy (DCM) is a major cause of morbidity and mortality in DMD patients. We tested the hypothesis that DCM is caused by metabolic impairments by employing induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) generated from four DMD patients; an adult male, an adult female, a 7-year-old (7y) male and a 13-year-old (13y) male, all compared to two healthy volunteers. To test the hypothesis, we measured the bioenergetics, metabolomics, electrophysiology, mitochondrial morphology and mitochondrial activity of CMs, using respirometry, LC-MS, patch clamp, electron microscopy (EM) and confocal microscopy methods. We found that: (1) adult DMD CMs exhibited impaired energy metabolism and abnormal mitochondrial structure and function. (2) The 7y CMs demonstrated arrhythmia-free spontaneous firing along with "healthy-like" metabolic status, normal mitochondrial morphology and activity. In contrast, the 13y CMs were mildly arrhythmogenic and showed adult DMD-like bioenergetics deficiencies. (3) In DMD adult CMs, mitochondrial activities were attenuated by 45-48%, whereas the 7y CM activity was similar to that of healthy CMs. (4) In DMD CMs, but not in 7y CMs, there was a 75% decrease in the mitochondrial ATP production rate compared to healthy iPSC-CMs. In summary, DMD iPSC-CMs exhibit bioenergetic and metabolic impairments that are associated with rhythm disturbances corresponding to the patient's phenotype, thereby constituting novel targets for alleviating cardiomyopathy in DMD patients.

Metabolic adaptation of acute lymphoblastic leukemia to the central nervous system microenvironment is dependent on Stearoyl CoA desaturase.

Metabolic reprogramming is a key hallmark of cancer, but less is known about metabolic plasticity of the same tumor at different sites. Here, we investigated the metabolic adaptation of leukemia in two different microenvironments, the bone marrow and the central nervous system (CNS). We identified a metabolic signature of fatty-acid synthesis in CNS leukemia, highlighting Stearoyl-CoA desaturase (SCD1) as a key player. In vivo SCD1 overexpression increases CNS disease, whilst genetic or pharmacological inhibition of SCD1 decreases CNS load. Overall, we demonstrated that leukemic cells dynamically rewire metabolic pathways to suit local conditions and that targeting these adaptations can be exploited therapeutically.

A Systematic Analysis Workflow for High-Density Customized Protein Microarrays in Biomarker Screening.

High-density protein microarrays constitute a promising high-throughput platform for the characterization of protein expression patterns, biomarker discovery, and validation. Different types of protein microarrays have been described according to several features (such as content, format, and detection system) presenting advantages and disadvantages which are relevant for the specific application and purposes. Therefore, an experimental design is key for any screening based on protein microarrays assays; in fact, the data analysis strategy is directly related to the experimental design, type of protein microarray and consequently the final outcome, the data and results interpretation, is also directly linked. Here, it is proposed a systematic workflow for biomarker discovery based on tailor-made protein microarrays platforms which obtain comprehensively info for the functional protein characterization in high-throughput format.

Inferring new relations between medical entities using literature curated term co-occurrences.

OBJECTIVES: Identifying new relations between medical entities, such as drugs, diseases, and side effects, is typically a resource-intensive task, involving experimentation and clinical trials. The increased availability of related data and curated knowledge enables a computational approach to this task, notably by training models to predict likely relations. Such models rely on meaningful representations of the medical entities being studied. We propose a generic features vector representation that leverages co-occurrences of medical terms, linked with PubMed citations. MATERIALS AND METHODS: We demonstrate the usefulness of the proposed representation by inferring two types of relations: a drug causes a side effect and a drug treats an indication. To predict these relations and assess their effectiveness, we applied 2 modeling approaches: multi-task modeling using neural networks and single-task modeling based on gradient boosting machines and logistic regression. RESULTS: These trained models, which predict either side effects or indications, obtained significantly better results than baseline models that use a single direct co-occurrence feature. The results demonstrate the advantage of a comprehensive representation. DISCUSSION: Selecting the appropriate representation has an immense impact on the predictive performance of machine learning models. Our proposed representation is powerful, as it spans multiple medical domains and can be used to predict a wide range of relation types. CONCLUSION: The discovery of new relations between various medical entities can be translated into meaningful insights, for example, related to drug development or disease understanding. Our representation of medical entities can be used to train models that predict such relations, thus accelerating healthcare-related discoveries.