Risk stratification using multi-omics data deepens understanding of immunometabolism in successfully treated people with HIV (PWH) is inadequately explained. A personalized medicine approach integrating blood cell transcriptomics, plasma proteomics, and metabolomics is employed to identify the mechanisms of immunometabolic complications in prolonged treated PWH from the COCOMO cohort. Among the PWHs, 44% of PWH are at risk of experiencing immunometabolic complications identified using the network-based patient stratification method. Utilizing advanced machine learning techniques and a Bayesian classifier, five plasma protein biomarkers; Tubulin Folding Cofactor B (TBCB), Gamma-Glutamylcyclotransferase (GGCT), Taxilin Alpha (TXLNA), Pyridoxal Phosphate Binding Protein (PLPBP) and Large Tumor Suppressor Kinase 1 (LATS1) are identified as highly differentially abundant between healthy control (HC)-like and immunometabolically at-risk PWHs (all FDR<10⁻¹⁰). The personalized metabolic models predict metabolic perturbations, revealing disruptions in central carbon metabolic fluxes and host tryptophan metabolism in at-risk phenotype. Functional assays in primary cells and cortical forebrain organoids (FBOs) further validate this. Metabolic perturbations lead to persistent monocyte activation, thereby impairing their functions ex vivo. Furthermore, the chronic inflammatory plasma microenvironment contributes to synaptic dysregulation in FBOs. The endogenous plasma inflammatory microenvironment is responsible for chronic inflammation in treated immunometabolically complicated at-risk PWH who have a higher risk of cardiovascular and neuropsychiatric disorders.

Biomarker profiling from biofluids such as blood are widely measured in clinical research, using for example Olink proteomics panels. One such research focus area is cardiovascular disease (CVD), for which chronic sleep restriction (SR) is a risk factor. However, it remains unclear whether blood levels of commonly measured CVD biomarkers are sensitive to acute dynamic factors such as SR, physical exercise (PEx), and time of day. In this crossover design, 16 normal-weight, healthy men underwent three highly standardized in-lab nights of SR (4.25 h/night) and normal sleep (NS, 8.5 h/night) in randomized order, with 88 CVD blood protein biomarkers quantified using the Olink technology (and selected validation using ELISA) in the morning, evening, and immediately before and repeatedly after 30 min of high-intensity exercise. We found significant time-of-day-dependent changes in several CVD biomarkers. Whereas several proteins were exercise-induced across sleep conditions (such as the canonical exerkines IL- 6 and BDNF), exercise-induced proteomic dynamics differed in response to recurrent SR, compared with following NS. Moreover, SR compared with NS resulted in a biomarker profile previously associated with increased prospective risk of several CVDs across large-scale cohorts (such as higher circulating levels of IL-27 and LGALS9). Our findings highlight how dynamic physiology can modulate CVD biomarker levels. These results also underscore the need to consider sleep duration as a key determinant of cardiovascular health—an emphasis reflected in recent American Heart Association guidelines. Further studies in women, older individuals, and patients with prior CVD, and across different chronotypes and dietary schedules are warranted.

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.

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.

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, a-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.

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.

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.

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.

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.

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 metabolo-mics 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 sub-stantial role in coordinating the immune cells that mediate COVID-19 severity. Stratum-specific and person-alized genome-scale metabolic modeling indicated monocarboxylate transporter family genes (e.g., SLC16A6), nucleoside transporter genes (e.g., SLC29A1), and metabolites such as a-ketoglutarate, succi-nate, 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.