Systems-level temporal immune-metabolic profile in Crimean-Congo hemorrhagic fever virus infection.

Crimean-Congo hemorrhagic fever (CCHF) caused by CCHF virus (CCHFV) is one of the epidemic-prone diseases prioritized by the World Health Organisation as public health emergency with an urgent need for accelerated research. The trajectory of host response against CCHFV is multifarious and remains unknown. Here, we reported the temporal spectrum of pathogenesis following the CCHFV infection using genome-wide blood transcriptomics analysis followed by advanced systems biology analysis, temporal immune-pathogenic alterations, and context-specific progressive and postinfection genome-scale metabolic models (GSMM) on samples collected during the acute (T0), early convalescent (T1), and convalescent-phase (T2). The interplay between the retinoic acid-inducible gene-I-like/nucleotide-binding oligomerization domain-like receptor and tumor necrosis factor signaling governed the trajectory of antiviral immune responses. The rearrangement of intracellular metabolic fluxes toward the amino acid metabolism and metabolic shift toward oxidative phosphorylation and fatty acid oxidation during acute CCHFV infection determine the pathogenicity. The upregulation of the tricarboxylic acid cycle during CCHFV infection, compared to the noninfected healthy control and between the severity groups, indicated an increased energy demand and cellular stress. The upregulation of glycolysis and pyruvate metabolism potentiated energy generation through alternative pathways associated with the severity of the infection. The downregulation of metabolic processes at the convalescent phase identified by blood cell transcriptomics and single-cell type proteomics of five immune cells (CD4+ and CD8+ T cells, CD14+ monocytes, B cells, and NK cells) potentially leads to metabolic rewiring through the recovery due to hyperactivity during the acute phase leading to post-viral fatigue syndrome.

Serum immuno-oncology markers carry independent prognostic information in patients with newly diagnosed metastatic breast cancer, from a prospective observational study.

BACKGROUND: Metastatic breast cancer (MBC) is a challenging disease, and despite new therapies, prognosis is still poor for a majority of patients. There is a clinical need for improved prognostication where immuno-oncology markers can provide important information. The aim of this study was to evaluate serum immuno-oncology markers in MBC patients and their respective relevance for prediction of survival. PATIENTS AND METHODS: We investigated a broad panel of 92 immuno-oncology proteins in serum from 136 MBC patients included in a prospective observational study (NCT01322893) with long-term follow-up. Serum samples were collected before start of systemic therapy and analyzed using multiplex proximity extension assay (Olink Target 96 Immuno-Oncology panel). Multiple machine learning techniques were used to identify serum markers with highest importance for prediction of overall and progression-free survival (OS and PFS), and associations to survival were further evaluated using Cox regression analyses. False discovery rate was then used to adjust for multiple comparisons. RESULTS: Using random forest and random survival forest analyses, we identified the top nine and ten variables of highest predictive importance for OS and PFS, respectively. Cox regression analyses revealed significant associations (P < 0.005) of higher serum levels of IL-8, IL-10 and CAIX with worse OS in multivariable analyses, adjusted for established clinical prognostic factors including circulating tumor cells (CTCs). Similarly, high serum levels of IL-8, IL-10, ADA and CASP8 significantly associated with worse PFS. Interestingly, high serum levels of FasL significantly associated with improved OS and PFS. In addition, CSF-1, IL-6, MUC16, TFNSFR4 and CD244 showed suggestive evidence (P < 0.05) for an association to survival in multivariable analyses. After correction for multiple comparisons, IL-8 still showed strong evidence for correlation to survival. CONCLUSION: To conclude, we found six serum immuno-oncology markers that were significantly associated with OS and/or PFS in MBC patients, independently of other established prognostic factors including CTCs. Furthermore, an additional five serum immuno-oncology markers provided suggestive evidence for an independent association to survival. These findings highlight the relevance of immuno-oncology serum markers in MBC patients and support their usefulness for improved prognostication. Trial registration Clinical Trials (NCT01322893), registered March 25, 2011.

Metabolic Perturbation Associated With COVID-19 Disease Severity and SARS-CoV-2 Replication.

Viruses hijack host metabolic pathways for their replicative advantage. In this study, using patient-derived multiomics data and in vitro infection assays, we aimed to understand the role of key metabolic pathways that can regulate severe acute respiratory syndrome coronavirus-2 reproduction and their association with disease severity. We used multiomics platforms (targeted and untargeted proteomics and untargeted metabolomics) on patient samples and cell-line models along with immune phenotyping of metabolite transporters in patient blood cells to understand viral-induced metabolic modulations. We also modulated key metabolic pathways that were identified using multiomics data to regulate the viral reproduction in vitro. Coronavirus disease 2019 disease severity was characterized by increased plasma glucose and mannose levels. Immune phenotyping identified altered expression patterns of carbohydrate transporter, glucose transporter 1, in CD8+ T cells, intermediate and nonclassical monocytes, and amino acid transporter, xCT, in classical, intermediate, and nonclassical monocytes. In in vitro lung epithelial cell (Calu-3) infection model, we found that glycolysis and glutaminolysis are essential for virus replication, and blocking these metabolic pathways caused significant reduction in virus production. Taken together, we therefore hypothesized that severe acute respiratory syndrome coronavirus-2 utilizes and rewires pathways governing central carbon metabolism leading to the efflux of toxic metabolites and associated with disease severity. Thus, the host metabolic perturbation could be an attractive strategy to limit the viral replication and disease severity.

The acute effect of metabolic cofactor supplementation: a potential therapeutic strategy against non-alcoholic fatty liver disease.

The prevalence of non-alcoholic fatty liver disease (NAFLD) continues to increase dramatically, and there is no approved medication for its treatment. Recently, we predicted the underlying molecular mechanisms involved in the progression of NAFLD using network analysis and identified metabolic cofactors that might be beneficial as supplements to decrease human liver fat. Here, we first assessed the tolerability of the combined metabolic cofactors including l-serine, N-acetyl-l-cysteine (NAC), nicotinamide riboside (NR), and l-carnitine by performing a 7-day rat toxicology study. Second, we performed a human calibration study by supplementing combined metabolic cofactors and a control study to study the kinetics of these metabolites in the plasma of healthy subjects with and without supplementation. We measured clinical parameters and observed no immediate side effects. Next, we generated plasma metabolomics and inflammatory protein markers data to reveal the acute changes associated with the supplementation of the metabolic cofactors. We also integrated metabolomics data using personalized genome-scale metabolic modeling and observed that such supplementation significantly affects the global human lipid, amino acid, and antioxidant metabolism. Finally, we predicted blood concentrations of these compounds during daily long-term supplementation by generating an ordinary differential equation model and liver concentrations of serine by generating a pharmacokinetic model and finally adjusted the doses of individual metabolic cofactors for future human clinical trials.

Metabolic network-based stratification of hepatocellular carcinoma reveals three distinct tumor subtypes.

Hepatocellular carcinoma (HCC) is one of the most frequent forms of liver cancer, and effective treatment methods are limited due to tumor heterogeneity. There is a great need for comprehensive approaches to stratify HCC patients, gain biological insights into subtypes, and ultimately identify effective therapeutic targets. We stratified HCC patients and characterized each subtype using transcriptomics data, genome-scale metabolic networks and network topology/controllability analysis. This comprehensive systems-level analysis identified three distinct subtypes with substantial differences in metabolic and signaling pathways reflecting at genomic, transcriptomic, and proteomic levels. These subtypes showed large differences in clinical survival associated with altered kynurenine metabolism, WNT/ß-catenin-associated lipid metabolism, and PI3K/AKT/mTOR signaling. Integrative analyses indicated that the three subtypes rely on alternative enzymes (e.g., ACSS1/ACSS2/ACSS3, PKM/PKLR, ALDOB/ALDOA, MTHFD1L/MTHFD2/MTHFD1) to catalyze the same reactions. Based on systems-level analysis, we identified 8 to 28 subtype-specific genes with pivotal roles in controlling the metabolic network and predicted that these genes may be targeted for development of treatment strategies for HCC subtypes by performing in silico analysis. To validate our predictions, we performed experiments using HepG2 cells under normoxic and hypoxic conditions and observed opposite expression patterns between genes expressed in high/moderate/low-survival tumor groups in response to hypoxia, reflecting activated hypoxic behavior in patients with poor survival. In conclusion, our analyses showed that the heterogeneous HCC tumors can be stratified using a metabolic network-driven approach, which may also be applied to other cancer types, and this stratification may have clinical implications to drive the development of precision medicine.

TCSBN: a database of tissue and cancer specific biological networks.

Biological networks provide new opportunities for understanding the cellular biology in both health and disease states. We generated tissue specific integrated networks (INs) for liver, muscle and adipose tissues by integrating metabolic, regulatory and protein-protein interaction networks. We also generated human co-expression networks (CNs) for 46 normal tissues and 17 cancers to explore the functional relationships between genes as well as their relationships with biological functions, and investigate the overlap between functional and physical interactions provided by CNs and INs, respectively. These networks can be employed in the analysis of omics data, provide detailed insight into disease mechanisms by identifying the key biological components and eventually can be used in the development of efficient treatment strategies. Moreover, comparative analysis of the networks may allow for the identification of tissue-specific targets that can be used in the development of drugs with the minimum toxic effect to other human tissues. These context-specific INs and CNs are presented in an interactive website http://inetmodels.com without any limitation.

Identification of line-specific strategies for improving carotenoid production in synthetic maize through data-driven mathematical modeling.

Plant synthetic biology is still in its infancy. However, synthetic biology approaches have been used to manipulate and improve the nutritional and health value of staple food crops such as rice, potato and maize. With current technologies, production yields of the synthetic nutrients are a result of trial and error, and systematic rational strategies to optimize those yields are still lacking. Here, we present a workflow that combines gene expression and quantitative metabolomics with mathematical modeling to identify strategies for increasing production yields of nutritionally important carotenoids in the seed endosperm synthesized through alternative biosynthetic pathways in synthetic lines of white maize, which is normally devoid of carotenoids. Quantitative metabolomics and gene expression data are used to create and fit parameters of mathematical models that are specific to four independent maize lines. Sensitivity analysis and simulation of each model is used to predict which gene activities should be further engineered in order to increase production yields for carotenoid accumulation in each line. Some of these predictions (e.g. increasing Zmlycb/Gllycb will increase accumulated ß-carotenes) are valid across the four maize lines and consistent with experimental observations in other systems. Other predictions are line specific. The workflow is adaptable to any other biological system for which appropriate quantitative information is available. Furthermore, we validate some of the predictions using experimental data from additional synthetic maize lines for which no models were developed.

Network-based multi-omics integration reveals metabolic at-risk profile within treated HIV-infection.

Multiomics technologies improve the biological understanding of health status in people living with HIV on antiretroviral therapy (PWH). Still, a systematic and in-depth characterization of metabolic risk profile during successful long-term treatment is lacking. Here, we used multi-omics (plasma lipidomic, metabolomic, and fecal 16 S microbiome) data-driven stratification and characterization to identify the metabolic at-risk profile within PWH. Through network analysis and similarity network fusion (SNF), we identified three groups of PWH (SNF-1-3): healthy (HC)-like (SNF-1), mild at-risk (SNF-3), and severe at-risk (SNF-2). The PWH in the SNF-2 (45%) had a severe at-risk metabolic profile with increased visceral adipose tissue, BMI, higher incidence of metabolic syndrome (MetS), and increased di- and triglycerides despite having higher CD4+ T-cell counts than the other two clusters. However, the HC-like and the severe at-risk group had a similar metabolic profile differing from HIV-negative controls (HNC), with dysregulation of amino acid metabolism. At the microbiome profile, the HC-like group had a lower α-diversity, a lower proportion of men having sex with men (MSM) and was enriched in Bacteroides. In contrast, in at-risk groups, there was an increase in Prevotella, with a high proportion of MSM, which could potentially lead to higher systemic inflammation and increased cardiometabolic risk profile. The multi-omics integrative analysis also revealed a complex microbial interplay of the microbiome-associated metabolites in PWH. Those severely at-risk clusters may benefit from personalized medicine and lifestyle intervention to improve their dysregulated metabolic traits, aiming to achieve healthier aging.

Translatome profiling in fatal familial insomnia implicates TOR signaling in somatostatin neurons.

Selective neuronal vulnerability is common in neurodegenerative diseases but poorly understood. In genetic prion diseases, including fatal familial insomnia (FFI) and Creutzfeldt-Jakob disease (CJD), different mutations in the Prnp gene manifest as clinically and neuropathologically distinct diseases. Here we report with electroencephalography studies that theta waves are mildly increased in 21 mo old knock-in mice modeling FFI and CJD and that sleep is mildy affected in FFI mice. To define affected cell types, we analyzed cell type-specific translatomes from six neuron types of 9 mo old FFI and CJD mice. Somatostatin (SST) neurons responded the strongest in both diseases, with unexpectedly high overlap in genes and pathways. Functional analyses revealed up-regulation of neurodegenerative disease pathways and ribosome and mitochondria biogenesis, and down-regulation of synaptic function and small GTPase-mediated signaling in FFI, implicating down-regulation of mTOR signaling as the root of these changes. In contrast, responses in glutamatergic cerebellar neurons were disease-specific. The high similarity in SST neurons of FFI and CJD mice suggests that a common therapy may be beneficial for multiple genetic prion diseases.

Genome-scale metabolic models for natural and long-term drug-induced viral control in HIV infection.

Genome-scale metabolic models (GSMMs) can provide novel insights into metabolic reprogramming during disease progression and therapeutic interventions. We developed a context-specific system-level GSMM of people living with HIV (PLWH) using global RNA sequencing data from PBMCs with suppressive viremia either by natural (elite controllers, PLWHEC) or drug-induced (PLWHART) control. This GSMM was compared with HIV-negative controls (HC) to provide a comprehensive systems-level metabo-transcriptomic characterization. Transcriptomic analysis identified up-regulation of oxidative phosphorylation as a characteristic of PLWHART, differentiating them from PLWHEC with dysregulated complexes I, III, and IV. The flux balance analysis identified altered flux in several intermediates of glycolysis including pyruvate, α-ketoglutarate, and glutamate, among others, in PLWHART The in vitro pharmacological inhibition of OXPHOS complexes in a latent lymphocytic cell model (J-Lat 10.6) suggested a role for complex IV in latency reversal and immunosenescence. Furthermore, inhibition of complexes I/III/IV induced apoptosis, collectively indicating their contribution to reservoir dynamics.

Improvement of the performance of anticancer peptides using a drug repositioning pipeline.

The use of anticancer peptides (ACPs) as an alternative/complementary strategy to conventional chemotherapy treatments has been shown to decrease drug resistance and/or severe side effects. However, the efficacy of the positively-charged ACP is inhibited by elevated levels of negatively-charged cell-surface components which trap the peptides and prevent their contact with the cell membrane. Consequently, this decreases ACP-mediated membrane pore formation and cell lysis. Negatively-charged heparan sulphate (HS) and chondroitin sulphate (CS) have been shown to inhibit the cytotoxic effect of ACPs. In this study, we propose a strategy to promote the broad utilization of ACPs. In this context, we developed a drug repositioning pipeline to analyse transcriptomics data generated for four different cancer cell lines (A549, HEPG2, HT29, and MCF7) treated with hundreds of drugs in the LINCS L1000 project. Based on previous studies identifying genes modulating levels of the glycosaminoglycans (GAGs) HS and CS at the cell surface, our analysis aimed at identifying drugs inhibiting genes correlated with high HS and CS levels. As a result, we identified six chemicals as likely repositionable drugs with the potential to enhance the performance of ACPs. The codes in R and Python programming languages are publicly available in https://github.com/ElyasMo/ACPs_HS_HSPGs_CS. As a conclusion, these six drugs are highlighted as excellent targets for synergistic studies with ACPs aimed at lowering the costs associated with ACP-treatment.

Multi-omics insights into host-viral response and pathogenesis in Crimean-Congo hemorrhagic fever viruses for novel therapeutic target.

The pathogenesis and host-viral interactions of the Crimean-Congo hemorrhagic fever orthonairovirus (CCHFV) are convoluted and not well evaluated. Application of the multi-omics system biology approaches, including biological network analysis in elucidating the complex host-viral response, interrogates the viral pathogenesis. The present study aimed to fingerprint the system-level alterations during acute CCHFV-infection and the cellular immune responses during productive CCHFV-replication in vitro. We used system-wide network-based system biology analysis of peripheral blood mononuclear cells (PBMCs) from a longitudinal cohort of CCHF patients during the acute phase of infection and after one year of recovery (convalescent phase) followed by untargeted quantitative proteomics analysis of the most permissive CCHFV-infected Huh7 and SW13 cells. In the RNAseq analysis of the PBMCs, comparing the acute and convalescent-phase, we observed system-level host's metabolic reprogramming towards central carbon and energy metabolism (CCEM) with distinct upregulation of oxidative phosphorylation (OXPHOS) during CCHFV-infection. Upon application of network-based system biology methods, negative coordination of the biological signaling systems like FOXO/Notch axis and Akt/mTOR/HIF-1 signaling with metabolic pathways during CCHFV-infection were observed. The temporal quantitative proteomics in Huh7 showed a dynamic change in the CCEM over time and concordant with the cross-sectional proteomics in SW13 cells. By blocking the two key CCEM pathways, glycolysis and glutaminolysis, viral replication was inhibited in vitro. Activation of key interferon stimulating genes during infection suggested the role of type I and II interferon-mediated antiviral mechanisms both at the system level and during progressive replication.

Crimean-Congo hemorrhagic fever (CCHF) is an emerging disease that is increasingly spreading to new populations. The condition is now endemic in almost 30 countries in sub-Saharan Africa, South-Eastern Europe, the Middle East and Central Asia. CCHF is caused by a tick-borne virus and can cause uncontrolled bleeding. It has a mortality rate of up to 40%, and there are currently no vaccines or effective treatments available. All viruses depend entirely on their hosts for reproduction, and they achieve this through hijacking the molecular machinery of the cells they infect. However, little is known about how the CCHF virus does this and how the cells respond. To understand more about the relationship between the cell's metabolism and viral replication, Neogi, Elaldi et al. studied immune cells taken from patients during an infection and one year later. The gene activity of the cells showed that the virus prefers to hijack processes known as central carbon and energy metabolism. These are the main regulator of the cellular energy supply and the production of essential chemicals. By using cancer drugs to block these key pathways, Neogi, Elaldi et al. could reduce the viral reproduction in laboratory cells. These findings provide a clearer understanding of how the CCHF virus replicates inside human cells. By interfering with these processes, researchers could develop new antiviral strategies to treat the disease. One of the cancer drugs tested in cells, 2-DG, has been approved for emergency use against COVID-19 in some countries. Neogi, Elaldi et al. are now studying this further in animals with the hope of reaching clinical trials in the future.

Multi-omics personalized network analyses highlight progressive disruption of central metabolism associated with COVID-19 severity.

The clinical outcome and disease severity in coronavirus disease 2019 (COVID-19) are heterogeneous, and the progression or fatality of the disease cannot be explained by a single factor like age or comorbidities. In this study, we used system-wide network-based system biology analysis using whole blood RNA sequencing, immunophenotyping by flow cytometry, plasma metabolomics, and single-cell-type metabolomics of monocytes to identify the potential determinants of COVID-19 severity at personalized and group levels. Digital cell quantification and immunophenotyping of the mononuclear phagocytes indicated a substantial role in coordinating the immune cells that mediate COVID-19 severity. Stratum-specific and personalized genome-scale metabolic modeling indicated monocarboxylate transporter family genes (e.g., SLC16A6), nucleoside transporter genes (e.g., SLC29A1), and metabolites such as α-ketoglutarate, succinate, malate, and butyrate could play a crucial role in COVID-19 severity. Metabolic perturbations targeting the central metabolic pathway (TCA cycle) can be an alternate treatment strategy in severe COVID-19.

Mitoribosomal defects aggravate liver cancer via aberrant glycolytic flux and T cell exhaustion.

BACKGROUND: Mitochondria are involved in cancer energy metabolism, although the mechanisms underlying the involvement of mitoribosomal dysfunction in hepatocellular carcinoma (HCC) remain poorly understood. Here, we investigated the effects of mitoribosomal impairment-mediated alterations on the immunometabolic characteristics of liver cancer. METHODS: We used a mouse model of HCC, liver tissues from patients with HCC, and datasets from The Cancer Genome Atlas (TCGA) to elucidate the relationship between mitoribosomal proteins (MRPs) and HCC. In a mouse model, we selectively disrupted expression of the mitochondrial ribosomal protein CR6-interacting factor 1 (CRIF1) in hepatocytes to determine the impact of hepatocyte-specific impairment of mitoribosomal function on liver cancer progression. The metabolism and immunophenotype of liver cancer was assessed by glucose flux assays and flow cytometry, respectively. RESULTS: Single-cell RNA-seq analysis of tumor tissue and TCGA HCC transcriptome analysis identified mitochondrial defects associated with high-MRP expression and poor survival outcomes. In the mouse model, hepatocyte-specific disruption of the mitochondrial ribosomal protein CRIF1 revealed the impact of mitoribosomal dysfunction on liver cancer progression. Crif1 deficiency promoted programmed cell death protein 1 expression by immune cells in the hepatic tumor microenvironment. A [U-13C6]-glucose tracer demonstrated enhanced glucose entry into the tricarboxylic acid cycle and lactate production in mice with mitoribosomal defects during cancer progression. Mice with hepatic mitoribosomal defects also exhibited enhanced progression of liver cancer accompanied by highly exhausted tumor-infiltrating T cells. Crif1 deficiency induced an environment unfavorable to T cells, leading to exhaustion of T cells via elevation of reactive oxygen species and lactate production. CONCLUSIONS: Hepatic mitoribosomal defects promote glucose partitioning toward glycolytic flux and lactate synthesis, leading to T cell exhaustion and cancer progression. Overall, the results suggest a distinct role for mitoribosomes in regulating the immunometabolic microenvironment during HCC progression.

Trans cohort metabolic reprogramming towards glutaminolysis in long-term successfully treated HIV-infection.

Despite successful combination antiretroviral therapy (cART), persistent low-grade immune activation together with inflammation and toxic antiretroviral drugs can lead to long-lasting metabolic flexibility and adaptation in people living with HIV (PLWH). Our study investigated alterations in the plasma metabolic profiles by comparing PLWH on long-term cART(>5 years) and matched HIV-negative controls (HC) in two cohorts from low- and middle-income countries (LMIC), Cameroon, and India, respectively, to understand the system-level dysregulation in HIV-infection. Using untargeted and targeted LC-MS/MS-based metabolic profiling and applying advanced system biology methods, an altered amino acid metabolism, more specifically to glutaminolysis in PLWH than HC were reported. A significantly lower level of neurosteroids was observed in both cohorts and could potentiate neurological impairments in PLWH. Further, modulation of cellular glutaminolysis promoted increased cell death and latency reversal in pre-monocytic HIV-1 latent cell model U1, which may be essential for the clearance of the inducible reservoir in HIV-integrated cells.

Systems Biology in ELIXIR: modelling in the spotlight.

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

Systems Analysis Reveals Ageing-Related Perturbations in Retinoids and Sex Hormones in Alzheimer's and Parkinson's Diseases.

Neurodegenerative diseases, including Alzheimer's (AD) and Parkinson's diseases (PD), are complex heterogeneous diseases with highly variable patient responses to treatment. Due to the growing evidence for ageing-related clinical and pathological commonalities between AD and PD, these diseases have recently been studied in tandem. In this study, we analysed transcriptomic data from AD and PD patients, and stratified these patients into three subclasses with distinct gene expression and metabolic profiles. Through integrating transcriptomic data with a genome-scale metabolic model and validating our findings by network exploration and co-analysis using a zebrafish ageing model, we identified retinoids as a key ageing-related feature in all subclasses of AD and PD. We also demonstrated that the dysregulation of androgen metabolism by three different independent mechanisms is a source of heterogeneity in AD and PD. Taken together, our work highlights the need for stratification of AD/PD patients and development of personalised and precision medicine approaches based on the detailed characterisation of these subclasses.

The central role of the glutamate metabolism in long-term antiretroviral treated HIV-infected individuals with metabolic syndrome.

Metabolic syndrome (MetS) is a significant factor for cardiometabolic comorbidities in people living with HIV (PLWH) and a barrier to healthy aging. The long-term consequences of HIV-infection and combination antiretroviral therapy (cART) in metabolic reprogramming are unknown. In this study, we investigated metabolic alterations in well-treated PLWH with MetS to identify potential mechanisms behind the MetS phenotype using advanced statistical and machine learning algorithms. We included 200 PLWH from the Copenhagen Comorbidity in HIV-infection (COCOMO) study. PLWH were grouped into PLWH with MetS (n = 100) defined according to the International Diabetes Federation (IDF) consensus worldwide definition of the MetS or without MetS (n = 100). The untargeted plasma metabolomics was performed using ultra-high-performance liquid chromatography/mass spectrometry (UHPLC/MS/MS) and immune-phenotyping of Glut1 (glucose transporter), xCT (glutamate/cysteine transporter) and MCT1 (pyruvate/lactate transporter) by flow cytometry. We applied several conventional approaches, machine learning algorithms, and linear classification models to identify the biologically relevant metabolites associated with MetS in PLWH. Of the 877 identified biochemicals, 9% (76/877) differed significantly between PLWH with and without MetS (false discovery rate < 0.05). The majority belonged to amino acid metabolism (43%). A consensus identification by combining supervised and unsupervised methods indicated 11 biomarkers of MetS phenotype in PLWH. A weighted co-expression network identified seven communities of positively intercorrelated metabolites. A single community contained six of the potential biomarkers mainly related to glutamate metabolism. Transporter expression identified altered xCT and MCT in both lymphocytic and monocytic cells. Combining metabolomics and immune-phenotyping indicated altered glutamate metabolism associated with MetS in PLWH, which has clinical significance.

Integrative Lipidomics and Metabolomics for System-Level Understanding of the Metabolic Syndrome in Long-Term Treated HIV-Infected Individuals.

People living with HIV (PLWH) require life-long anti-retroviral treatment and often present with comorbidities such as metabolic syndrome (MetS). Systematic lipidomic characterization and its association with the metabolism are currently missing. We included 100 PLWH with MetS and 100 without MetS from the Copenhagen Comorbidity in HIV Infection (COCOMO) cohort to examine whether and how lipidome profiles are associated with MetS in PLWH. We combined several standard biostatistical, machine learning, and network analysis techniques to investigate the lipidome systematically and comprehensively and its association with clinical parameters. Additionally, we generated weighted lipid-metabolite networks to understand the relationship between lipidomic profiles with those metabolites associated with MetS in PLWH. The lipidomic dataset consisted of 917 lipid species including 602 glycerolipids, 228 glycerophospholipids, 61 sphingolipids, and 26 steroids. With a consensus approach using four different statistical and machine learning methods, we observed 13 differentially abundant lipids between PLWH without MetS and PLWH with MetS, which mainly belongs to diacylglyceride (DAG, n = 2) and triacylglyceride (TAG, n = 11). The comprehensive network integration of the lipidomics and metabolomics data suggested interactions between specific glycerolipids' structural composition patterns and key metabolites involved in glutamate metabolism. Further integration of the clinical data with metabolomics and lipidomics resulted in the association of visceral adipose tissue (VAT) and exposure to earlier generations of antiretroviral therapy (ART). Our integrative omics data indicated disruption of glutamate and fatty acid metabolism, suggesting their involvement in the pathogenesis of PLWH with MetS. Alterations in the lipid homeostasis and glutaminolysis need clinical interventions to prevent accelerated aging in PLWH with MetS.

Integrative transcriptomic analysis of tissue-specific metabolic crosstalk after myocardial infarction.

Myocardial infarction (MI) promotes a range of systemic effects, many of which are unknown. Here, we investigated the alterations associated with MI progression in heart and other metabolically active tissues (liver, skeletal muscle, and adipose) in a mouse model of MI (induced by ligating the left ascending coronary artery) and sham-operated mice. We performed a genome-wide transcriptomic analysis on tissue samples obtained 6- and 24 hr post MI or sham operation. By generating tissue-specific biological networks, we observed: (1) dysregulation in multiple biological processes (including immune system, mitochondrial dysfunction, fatty-acid beta-oxidation, and RNA and protein processing) across multiple tissues post MI and (2) tissue-specific dysregulation in biological processes in liver and heart post MI. Finally, we validated our findings in two independent MI cohorts. Overall, our integrative analysis highlighted both common and specific biological responses to MI across a range of metabolically active tissues.

The human body is like a state-of-the-art car, where each part must work together with all the others. When a car breaks down, most of the time the problem is not isolated to only one part, as it is an interconnected system. Diseases in the human body can also have systemic effects, so it is important to study their implications throughout the body. Most studies of heart attacks focus on the direct impact on the heart and the cardiovascular system. Learning more about how heart attacks affect rest of the body may help scientists identify heart attacks early or create improved treatments. Arif and Klevstig et al. show that heart attacks affect the metabolism throughout the body. In the experiments, mice underwent a procedure that mimics either a heart attack or a fake procedure. Then, Arif and Klevstig et al. compared the activity of genes in the heart, muscle, liver and fat tissue of the two groups of mice 6- and 24-hours after the operations. This revealed disruptions in the immune system, metabolism and the production of proteins. The experiments also showed that changes in the activity of four important genes are key to these changes. This suggests that this pattern of changes could be used as a way to identify heart attacks. The experiments show that heart attacks have important effects throughout the body, especially on metabolism. These discoveries may help scientists learn more about the underlying biological processes and develop new treatments that prevent the harmful systemic effects of heart attacks and boost recovery.