NLRC5 senses NAD+ depletion, forming a PANoptosome and driving PANoptosis and inflammation.

NLRs constitute a large, highly conserved family of cytosolic pattern recognition receptors that are central to health and disease, making them key therapeutic targets. NLRC5 is an enigmatic NLR with mutations associated with inflammatory and infectious diseases, but little is known about its function as an innate immune sensor and cell death regulator. Therefore, we screened for NLRC5's role in response to infections, PAMPs, DAMPs, and cytokines. We identified that NLRC5 acts as an innate immune sensor to drive inflammatory cell death, PANoptosis, in response to specific ligands, including PAMP/heme and heme/cytokine combinations. NLRC5 interacted with NLRP12 and PANoptosome components to form a cell death complex, suggesting an NLR network forms similar to those in plants. Mechanistically, TLR signaling and NAD+ levels regulated NLRC5 expression and ROS production to control cell death. Furthermore, NLRC5-deficient mice were protected in hemolytic and inflammatory models, suggesting that NLRC5 could be a potential therapeutic target.

VISH-Pred: an ensemble of fine-tuned ESM models for protein toxicity prediction.

Peptide- and protein-based therapeutics are becoming a promising treatment regimen for myriad diseases. Toxicity of proteins is the primary hurdle for protein-based therapies. Thus, there is an urgent need for accurate in silico methods for determining toxic proteins to filter the pool of potential candidates. At the same time, it is imperative to precisely identify non-toxic proteins to expand the possibilities for protein-based biologics. To address this challenge, we proposed an ensemble framework, called VISH-Pred, comprising models built by fine-tuning ESM2 transformer models on a large, experimentally validated, curated dataset of protein and peptide toxicities. The primary steps in the VISH-Pred framework are to efficiently estimate protein toxicities taking just the protein sequence as input, employing an under sampling technique to handle the humongous class-imbalance in the data and learning representations from fine-tuned ESM2 protein language models which are then fed to machine learning techniques such as Lightgbm and XGBoost. The VISH-Pred framework is able to correctly identify both peptides/proteins with potential toxicity and non-toxic proteins, achieving a Matthews correlation coefficient of 0.737, 0.716 and 0.322 and F1-score of 0.759, 0.696 and 0.713 on three non-redundant blind tests, respectively, outperforming other methods by over $10\%$ on these quality metrics. Moreover, VISH-Pred achieved the best accuracy and area under receiver operating curve scores on these independent test sets, highlighting the robustness and generalization capability of the framework. By making VISH-Pred available as an easy-to-use web server, we expect it to serve as a valuable asset for future endeavors aimed at discerning the toxicity of peptides and enabling efficient protein-based therapeutics.

The protein phosphatase PP6 promotes RIPK1-dependent PANoptosis.

BACKGROUND: The innate immune system serves as the first line of host defense. Transforming growth factor-ß-activated kinase 1 (TAK1) is a key regulator of innate immunity, cell survival, and cellular homeostasis. Because of its importance in immunity, several pathogens have evolved to carry TAK1 inhibitors. In response, hosts have evolved to sense TAK1 inhibition and induce robust lytic cell death, PANoptosis, mediated by the RIPK1-PANoptosome. PANoptosis is a unique innate immune inflammatory lytic cell death pathway initiated by an innate immune sensor and driven by caspases and RIPKs. While PANoptosis can be beneficial to clear pathogens, excess activation is linked to pathology. Therefore, understanding the molecular mechanisms regulating TAK1 inhibitor (TAK1i)-induced PANoptosis is central to our understanding of RIPK1 in health and disease. RESULTS: In this study, by analyzing results from a cell death-based CRISPR screen, we identified protein phosphatase 6 (PP6) holoenzyme components as regulators of TAK1i-induced PANoptosis. Loss of the PP6 enzymatic component, PPP6C, significantly reduced TAK1i-induced PANoptosis. Additionally, the PP6 regulatory subunits PPP6R1, PPP6R2, and PPP6R3 had redundant roles in regulating TAK1i-induced PANoptosis, and their combined depletion was required to block TAK1i-induced cell death. Mechanistically, PPP6C and its regulatory subunits promoted the pro-death S166 auto-phosphorylation of RIPK1 and led to a reduction in the pro-survival S321 phosphorylation. CONCLUSIONS: Overall, our findings demonstrate a key requirement for the phosphatase PP6 complex in the activation of TAK1i-induced, RIPK1-dependent PANoptosis, suggesting this complex could be therapeutically targeted in inflammatory conditions.

NINJ1 mediates inflammatory cell death, PANoptosis, and lethality during infection conditions and heat stress.

Innate immunity provides the first line of defense through multiple mechanisms, including pyrogen production and cell death. While elevated body temperature during infection is beneficial to clear pathogens, heat stress (HS) can lead to inflammation and pathology. Links between pathogen exposure, HS, cytokine release, and inflammation have been observed, but fundamental innate immune mechanisms driving pathology during pathogen exposure and HS remain unclear. Here, we use multiple genetic approaches to elucidate innate immune pathways in infection or LPS and HS models. Our results show that bacteria and LPS robustly increase inflammatory cell death during HS that is dependent on caspase-1, caspase-11, caspase-8, and RIPK3 through the PANoptosis pathway. Caspase-7 also contributes to PANoptosis in this context. Furthermore, NINJ1 is an important executioner of this cell death to release inflammatory molecules, independent of other pore-forming executioner proteins, gasdermin D, gasdermin E, and MLKL. In an in vivo HS model, mortality is reduced by deleting NINJ1 and fully rescued by deleting key PANoptosis molecules. Our findings suggest that therapeutic strategies blocking NINJ1 or its upstream regulators to prevent PANoptosis may reduce the release of inflammatory mediators and benefit patients.

A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer.

BACKGROUND: Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. METHODS: Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. RESULTS: A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. CONCLUSIONS: This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. TRIAL REGISTRATION: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

Comparative analysis identifies genetic and molecular factors associated with prognostic clusters of PANoptosis in glioma, kidney and melanoma cancer.

The importance of inflammatory cell death, PANoptosis, in cancer is increasingly being recognized. PANoptosis can promote or inhibit tumorigenesis in context-dependent manners, and a computational approach leveraging transcriptomic profiling of genes involved in PANoptosis has shown that patients can be stratified into PANoptosis High and PANoptosis Low clusters that have significant differences in overall survival for low grade glioma (LGG), kidney renal cell carcinoma (KIRC) and skin cutaneous melanoma (SKCM). However, the molecular mechanisms that contribute to differential prognosis between PANoptosis clusters require further elucidation. Therefore, we performed a comprehensive comparison of genetic, genomic, tumor microenvironment, and pathway characteristics between the PANoptosis High and PANoptosis Low clusters to determine the relevance of each component in driving the differential associations with prognosis for LGG, KIRC and SKCM. Across these cancer types, we found that activation of the proliferation pathway was significantly different between PANoptosis High and Low clusters. In LGG and SKCM, we also found that aneuploidy and immune cell densities and activations contributed to differences in PANoptosis clusters. In individual cancers, we identified important roles for barrier gene pathway activation (in SKCM) and the somatic mutation profiles of driver oncogenes as well as hedgehog signaling pathway activation (in LGG). By identifying these genetic and molecular factors, we can possibly improve the prognosis for at risk-stratified patient populations based on the PANoptosis phenotype in LGG, KIRC and SKCM. This not only advances our mechanistic understanding of cancer but will allow for the selection of optimal treatment strategies.

Inflammatory cell death, PANoptosis, screen identifies host factors in coronavirus innate immune response as therapeutic targets.

The COVID-19 pandemic, caused by the ß-coronavirus (ß-CoV) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), continues to cause significant global morbidity and mortality. While vaccines have reduced the overall number of severe infections, there remains an incomplete understanding of viral entry and innate immune activation, which can drive pathology. Innate immune responses characterized by positive feedback between cell death and cytokine release can amplify the inflammatory cytokine storm during ß-CoV-mediated infection to drive pathology. Therefore, there remains an unmet need to understand innate immune processes in response to ß-CoV infections to identify therapeutic strategies. To address this gap, here we used an MHV model and developed a whole genome CRISPR-Cas9 screening approach to elucidate host molecules required for ß-CoV infection and inflammatory cell death, PANoptosis, in macrophages, a sentinel innate immune cell. Our screen was validated through the identification of the known MHV receptor Ceacam1 as the top hit, and its deletion significantly reduced viral replication due to loss of viral entry, resulting in a downstream reduction in MHV-induced cell death. Moreover, this screen identified several other host factors required for MHV infection-induced macrophage cell death. Overall, these findings demonstrate the feasibility and power of using genome-wide PANoptosis screens in macrophage cell lines to accelerate the discovery of key host factors in innate immune processes and suggest new targets for therapeutic development to prevent ß-CoV-induced pathology.

Pancancer network analysis reveals key master regulators for cancer invasiveness.

BACKGROUND: Tumor invasiveness reflects numerous biological changes, including tumorigenesis, progression, and metastasis. To decipher the role of transcriptional regulators (TR) involved in tumor invasiveness, we performed a systematic network-based pan-cancer assessment of master regulators of cancer invasiveness. MATERIALS AND METHODS: We stratified patients in The Cancer Genome Atlas (TCGA) into invasiveness high (INV-H) and low (INV-L) groups using consensus clustering based on an established robust 24-gene signature to determine the prognostic association of invasiveness with overall survival (OS) across 32 different cancers. We devise a network-based protocol to identify TRs as master regulators (MRs) unique to INV-H and INV-L phenotypes. We validated the activity of MRs coherently associated with INV-H phenotype and worse OS across cancers in TCGA on a series of additional datasets in the Prediction of Clinical Outcomes from the Genomic Profiles (PRECOG) repository. RESULTS: Based on the 24-gene signature, we defined the invasiveness score for each patient sample and stratified patients into INV-H and INV-L clusters. We observed that invasiveness was associated with worse survival outcomes in almost all cancers and had a significant association with OS in ten out of 32 cancers. Our network-based framework identified common invasiveness-associated MRs specific to INV-H and INV-L groups across the ten prognostic cancers, including COL1A1, which is also part of the 24-gene signature, thus acting as a positive control. Downstream pathway analysis of MRs specific to INV-H phenotype resulted in the identification of several enriched pathways, including Epithelial into Mesenchymal Transition, TGF-ß signaling pathway, regulation of Toll-like receptors, cytokines, and inflammatory response, and selective expression of chemokine receptors during T-cell polarization. Most of these pathways have connotations of inflammatory immune response and feasibility for metastasis. CONCLUSION: Our pan-cancer study provides a comprehensive master regulator analysis of tumor invasiveness and can suggest more precise therapeutic strategies by targeting the identified MRs and downstream enriched pathways for patients across multiple cancers.

Whole-genome CRISPR screen identifies RAVER1 as a key regulator of RIPK1-mediated inflammatory cell death, PANoptosis.

Transforming growth factor-ß-activated kinase 1 (TAK1) is a central regulator of innate immunity, cell death, inflammation, and cellular homeostasis. Therefore, many pathogens carry TAK1 inhibitors (TAK1i). As a host strategy to counteract this, inhibition or deletion of TAK1 induces spontaneous inflammatory cell death, PANoptosis, through the RIPK1-PANoptosome complex, containing the NLRP3 inflammasome and caspase-8/FADD/RIPK3 as integral components; however, PANoptosis also promotes pathological inflammation. Therefore, understanding molecular mechanisms that regulate TAK1i-induced cell death is essential. Here, we report a genome-wide CRISPR screen in macrophages that identified TAK1i-induced cell death regulators, including polypyrimidine tract-binding (PTB) protein 1 (PTBP1), a known regulator of RIPK1, and a previously unknown regulator RAVER1. RAVER1 blocked alternative splicing of Ripk1, and its genetic depletion inhibited TAK1i-induced, RIPK1-mediated inflammasome activation and PANoptosis. Overall, our CRISPR screen identified several positive regulators of PANoptosis. Moreover, our study highlights the utility of genome-wide CRISPR-Cas9 screens in myeloid cells for comprehensive characterization of complex cell death pathways to discover therapeutic targets.

NLRP12-PANoptosome activates PANoptosis and pathology in response to heme and PAMPs.

Cytosolic innate immune sensors are critical for host defense and form complexes, such as inflammasomes and PANoptosomes, that induce inflammatory cell death. The sensor NLRP12 is associated with infectious and inflammatory diseases, but its activating triggers and roles in cell death and inflammation remain unclear. Here, we discovered that NLRP12 drives inflammasome and PANoptosome activation, cell death, and inflammation in response to heme plus PAMPs or TNF. TLR2/4-mediated signaling through IRF1 induced Nlrp12 expression, which led to inflammasome formation to induce maturation of IL-1ß and IL-18. The inflammasome also served as an integral component of a larger NLRP12-PANoptosome that drove inflammatory cell death through caspase-8/RIPK3. Deletion of Nlrp12 protected mice from acute kidney injury and lethality in a hemolytic model. Overall, we identified NLRP12 as an essential cytosolic sensor for heme plus PAMPs-mediated PANoptosis, inflammation, and pathology, suggesting that NLRP12 and molecules in this pathway are potential drug targets for hemolytic and inflammatory diseases.

An integrated tumor, immune and microbiome atlas of colon cancer.

The lack of multi-omics cancer datasets with extensive follow-up information hinders the identification of accurate biomarkers of clinical outcome. In this cohort study, we performed comprehensive genomic analyses on fresh-frozen samples from 348 patients affected by primary colon cancer, encompassing RNA, whole-exome, deep T cell receptor and 16S bacterial rRNA gene sequencing on tumor and matched healthy colon tissue, complemented with tumor whole-genome sequencing for further microbiome characterization. A type 1 helper T cell, cytotoxic, gene expression signature, called Immunologic Constant of Rejection, captured the presence of clonally expanded, tumor-enriched T cell clones and outperformed conventional prognostic molecular biomarkers, such as the consensus molecular subtype and the microsatellite instability classifications. Quantification of genetic immunoediting, defined as a lower number of neoantigens than expected, further refined its prognostic value. We identified a microbiome signature, driven by Ruminococcus bromii, associated with a favorable outcome. By combining microbiome signature and Immunologic Constant of Rejection, we developed and validated a composite score (mICRoScore), which identifies a group of patients with excellent survival probability. The publicly available multi-omics dataset provides a resource for better understanding colon cancer biology that could facilitate the discovery of personalized therapeutic approaches.

An integrated multi-omic approach demonstrates distinct molecular signatures between human obesity with and without metabolic complications: a case-control study.

OBJECTIVES: To examine the hypothesis that obesity complicated by the metabolic syndrome, compared to uncomplicated obesity, has distinct molecular signatures and metabolic pathways. METHODS: We analyzed a cohort of 39 participants with obesity that included 21 with metabolic syndrome, age-matched to 18 without metabolic complications. We measured in whole blood samples 754 human microRNAs (miRNAs), 704 metabolites using unbiased mass spectrometry metabolomics, and 25,682 transcripts, which include both protein coding genes (PCGs) as well as non-coding transcripts. We then identified differentially expressed miRNAs, PCGs, and metabolites and integrated them using databases such as mirDIP (mapping between miRNA-PCG network), Human Metabolome Database (mapping between metabolite-PCG network) and tools like MetaboAnalyst (mapping between metabolite-metabolic pathway network) to determine dysregulated metabolic pathways in obesity with metabolic complications. RESULTS: We identified 8 significantly enriched metabolic pathways comprising 8 metabolites, 25 protein coding genes and 9 microRNAs which are each differentially expressed between the subjects with obesity and those with obesity and metabolic syndrome. By performing unsupervised hierarchical clustering on the enrichment matrix of the 8 metabolic pathways, we could approximately segregate the uncomplicated obesity strata from that of obesity with metabolic syndrome. CONCLUSIONS: The data suggest that at least 8 metabolic pathways, along with their various dysregulated elements, identified via our integrative bioinformatics pipeline, can potentially differentiate those with obesity from those with obesity and metabolic complications.

AI-driven drug repurposing and binding pose meta dynamics identifies novel targets for monkeypox virus.

Monkeypox virus (MPXV) was confirmed in May 2022 and designated a global health emergency by WHO in July 2022. MPX virions are big, enclosed, brick-shaped, and contain a linear, double-stranded DNA genome as well as enzymes. MPXV particles bind to the host cell membrane via a variety of viral-host protein interactions. As a result, the wrapped structure is a potential therapeutic target. DeepRepurpose, an artificial intelligence-based compound-viral proteins interaction framework, was used via a transfer learning setting to prioritize a set of FDA approved and investigational drugs which can potentially inhibit MPXV viral proteins. To filter and narrow down the lead compounds from curated collections of pharmaceutical compounds, we used a rigorous computational framework that included homology modeling, molecular docking, dynamic simulations, binding free energy calculations, and binding pose metadynamics. We identified Elvitegravir as a potential inhibitor of MPXV virus using our comprehensive pipeline.

Pancancer transcriptomic profiling identifies key PANoptosis markers as therapeutic targets for oncology.

Resistance to programmed cell death (PCD) is a hallmark of cancer. While some PCD components are prognostic in cancer, the roles of many molecules can be masked by redundancies and crosstalks between PCD pathways, impeding the development of targeted therapeutics. Recent studies characterizing these redundancies have identified PANoptosis, a unique innate immune-mediated inflammatory PCD pathway that integrates components from other PCD pathways. Here, we designed a systematic computational framework to determine the pancancer clinical significance of PANoptosis and identify targetable biomarkers. We found that high expression of PANoptosis genes was detrimental in low grade glioma (LGG) and kidney renal cell carcinoma (KIRC). ZBP1, ADAR, CASP2, CASP3, CASP4, CASP8 and GSDMD expression consistently had negative effects on prognosis in LGG across multiple survival models, while AIM2, CASP3, CASP4 and TNFRSF10 expression had negative effects for KIRC. Conversely, high expression of PANoptosis genes was beneficial in skin cutaneous melanoma (SKCM), with ZBP1, NLRP1, CASP8 and GSDMD expression consistently having positive prognostic effects. As a therapeutic proof-of-concept, we treated melanoma cells with combination therapy that activates ZBP1 and showed that this treatment induced PANoptosis. Overall, through our systematic framework, we identified and validated key innate immune biomarkers from PANoptosis which can be targeted to improve patient outcomes in cancers.

Molecular mechanism of RIPK1 and caspase-8 in homeostatic type I interferon production and regulation.

Type I interferons (IFNs) are essential innate immune proteins that maintain tissue homeostasis through tonic expression and can be upregulated to drive antiviral resistance and inflammation upon stimulation. However, the mechanisms that inhibit aberrant IFN upregulation in homeostasis and the impacts of tonic IFN production on health and disease remain enigmatic. Here, we report that caspase-8 negatively regulates type I IFN production by inhibiting the RIPK1-TBK1 axis during homeostasis across multiple cell types and tissues. When caspase-8 is deleted or inhibited, RIPK1 interacts with TBK1 to drive elevated IFN production, leading to heightened resistance to norovirus infection in macrophages but also early onset lymphadenopathy in mice. Combined deletion of caspase-8 and RIPK1 reduces the type I IFN signaling and lymphadenopathy, highlighting the critical role of RIPK1 in this process. Overall, our study identifies a mechanism to constrain tonic type I IFN during homeostasis which could be targeted for infectious and inflammatory diseases.

Investigation of the Effect of Exendin-4 on Oleic Acid-Induced Steatosis in HepG2 Cells Using Fourier Transform Infrared Spectroscopy.

Non-alcoholic fatty liver disease (NAFLD) is a common liver lesion that is untreatable with medications. Glucagon-like peptide-1 receptor (GLP-1R) agonists have recently emerged as a potential NAFLD pharmacotherapy. However, the molecular mechanisms underlying these drugs' beneficial effects are not fully understood. Using Fourier transform infrared (FTIR) spectroscopy, we sought to investigate the biochemical changes in a steatosis cell model treated or not with the GLP-1R agonist Exendin-4 (Ex-4). HepG2 cells were made steatotic with 400 µM of oleic acid and then treated with 200 nM Ex-4 in order to reduce lipid accumulation. We quantified steatosis using the Oil Red O staining method. We investigated the biochemical alterations induced by steatosis and Ex-4 treatment using Fourier transform infrared (FTIR) spectroscopy and chemometric analyses. Analysis of the Oil Red O staining showed that Ex-4 significantly reduces steatosis. This reduction was confirmed by FTIR analysis, as the phospholipid band (C=O) at 1740 cm-1 in Ex-4 treated cells is significantly decreased compared to steatotic cells. The principal component analysis score plots for both the lipid and protein regions showed that the untreated and Ex-4-treated samples, while still separated, are clustered close to each other, far from the steatotic cells. The biochemical and structural changes induced by OA-induced lipotoxicity are at least partially reversed upon Ex-4 treatment. FTIR and chemometric analyses revealed that Ex-4 significantly reduces OA-induced lipid accumulation, and Ex-4 also restored the lipid and protein biochemical alterations caused by lipotoxicity-induced oxidative stress. In combination with chemometric analyses, FTIR spectroscopy may offer new approaches for investigating the mechanisms underpinning NAFLD.

Immune-related 3-lncRNA signature with prognostic connotation in a multi-cancer setting.

BACKGROUND: Advances in our understanding of the tumor microenvironment have radically changed the cancer field, highlighting the emerging need for biomarkers of an active, favorable tumor immune phenotype to aid treatment stratification and clinical prognostication. Numerous immune-related gene signatures have been defined; however, their prognostic value is often limited to one or few cancer types. Moreover, the area of non-coding RNA as biomarkers remains largely unexplored although their number and biological roles are rapidly expanding. METHODS: We developed a multi-step process to identify immune-related long non-coding RNA signatures with prognostic connotation in multiple TCGA solid cancer datasets. RESULTS: Using the breast cancer dataset as a discovery cohort we found 2988 differentially expressed lncRNAs between immune favorable and unfavorable tumors, as defined by the immunologic constant of rejection (ICR) gene signature. Mapping of the lncRNAs to a coding-non-coding network identified 127 proxy protein-coding genes that are enriched in immune-related diseases and functions. Next, we defined two distinct 20-lncRNA prognostic signatures that show a stronger effect on overall survival than the ICR signature in multiple solid cancers. Furthermore, we found a 3 lncRNA signature that demonstrated prognostic significance across 5 solid cancer types with a stronger association with clinical outcome than ICR. Moreover, this 3 lncRNA signature showed additional prognostic significance in uterine corpus endometrial carcinoma and cervical squamous cell carcinoma and endocervical adenocarcinoma as compared to ICR. CONCLUSION: We identified an immune-related 3-lncRNA signature with prognostic connotation in multiple solid cancer types which performed equally well and in some cases better than the 20-gene ICR signature, indicating that it could be used as a minimal informative signature for clinical implementation.

Dysregulated Metabolic Pathways in Subjects with Obesity and Metabolic Syndrome.

Background: Obesity coexists with variable features of metabolic syndrome, which is associated with dysregulated metabolic pathways. We assessed potential associations between serum metabolites and features of metabolic syndrome in Arabic subjects with obesity. Methods: We analyzed a dataset of 39 subjects with obesity only (OBO, n = 18) age-matched to subjects with obesity and metabolic syndrome (OBM, n = 21). We measured 1069 serum metabolites and correlated them to clinical features. Results: A total of 83 metabolites, mostly lipids, were significantly different (p < 0.05) between the two groups. Among lipids, 22 sphingomyelins were decreased in OBM compared to OBO. Among non-lipids, quinolinate, kynurenine, and tryptophan were also decreased in OBM compared to OBO. Sphingomyelin is negatively correlated with glucose, HbA1C, insulin, and triglycerides but positively correlated with HDL, LDL, and cholesterol. Differentially enriched pathways include lysine degradation, amino sugar and nucleotide sugar metabolism, arginine and proline metabolism, fructose and mannose metabolism, and galactose metabolism. Conclusions: Metabolites and pathways associated with chronic inflammation are differentially expressed in subjects with obesity and metabolic syndrome compared to subjects with obesity but without the clinical features of metabolic syndrome.

Characteristic MicroRNAs Linked to Dysregulated Metabolic Pathways in Qatari Adult Subjects With Obesity and Metabolic Syndrome.

Background: Obesity-associated dysglycemia is associated with metabolic disorders. MicroRNAs (miRNAs) are known regulators of metabolic homeostasis. We aimed to assess the relationship of circulating miRNAs with clinical features in obese Qatari individuals. Methods: We analyzed a dataset of 39 age-matched patients that includes 18 subjects with obesity only (OBO) and 21 subjects with obesity and metabolic syndrome (OBM). We measured 754 well-characterized human microRNAs (miRNAs) and identified differentially expressed miRNAs along with their significant associations with clinical markers in these patients. Results: A total of 64 miRNAs were differentially expressed between metabolically healthy obese (OBO) versus metabolically unhealthy obese (OBM) patients. Thirteen out of 64 miRNAs significantly correlated with at least one clinical trait of the metabolic syndrome. Six out of the thirteen demonstrated significant association with HbA1c levels; miR-331-3p, miR-452-3p, and miR-485-5p were over-expressed, whereas miR-153-3p, miR-182-5p, and miR-433-3p were under-expressed in the OBM patients with elevated HbA1c levels. We also identified, miR-106b-3p, miR-652-3p, and miR-93-5p that showed a significant association with creatinine; miR-130b-5p, miR-363-3p, and miR-636 were significantly associated with cholesterol, whereas miR-130a-3p was significantly associated with LDL. Additionally, miR-652-3p's differential expression correlated significantly with HDL and creatinine. Conclusions: MicroRNAs associated with metabolic syndrome in obese subjects may have a pathophysiologic role and can serve as markers for obese individuals predisposed to various metabolic diseases like diabetes.