Accelerated epigenetic aging and DNA methylation alterations in Berardinelli-Seip congenital lipodystrophy.

Berardinelli-Seip congenital lipodystrophy type 2 (CGL2) is a very rare human genetic disorder with potential significance to the understanding of the pathobiology of aging. CGL2 patients display characteristic progeroid features and suffer from type 2 diabetes, insulin resistance and fatty liver. In this study, we profiled genome-wide DNA methylation levels in CGL2 patients with BSCL2 mutations to study epigenetic age acceleration and DNA methylation alterations. This analysis revealed significant age acceleration in blood DNA of CGL2 patients using both first- and second-generation epigenetic clocks. We also observed a shortened lifespan of Caenorhabditis elegans following knockdown of the BSCL2 homolog seip-1 on a daf-16/forkhead box, class O mutant background. DNA methylation analysis revealed significant differentially methylated sites enriched for lyase activity, kinase regulator activity, protein kinase regulator activity and kinase activator activity. We could also observe significant hypomethylation in the promoter of the dual specificity phosphatase 22 gene when comparing CGL2 patients versus controls. We conclude that in line with the observed progeroid features, CGL2 patients exhibit significant epigenetic age acceleration and DNA methylation alterations that might affect pathways/genes of potential relevance to the disease.

Integrated epigenome, whole genome sequence and metabolome analyses identify novel multi-omics pathways in type 2 diabetes: a Middle Eastern study.

BACKGROUND: T2D is of high prevalence in the middle east and thus studying its mechanisms is of a significant importance. Using 1026 Qatar BioBank samples, epigenetics, whole genome sequencing and metabolomics were combined to further elucidate the biological mechanisms of T2D in a population with a high prevalence of T2D. METHODS: An epigenome-wide association study (EWAS) with T2D was performed using the Infinium 850K EPIC array, followed by whole genome-wide sequencing SNP-CpG association analysis (> 5.5 million SNPs) and a methylome-metabolome (CpG-metabolite) analysis of the identified T2D sites. RESULTS: A total of 66 T2D-CpG associations were identified, including 63 novel sites in pathways of fructose and mannose metabolism, insulin signaling, galactose, starch and sucrose metabolism, and carbohydrate absorption and digestion. Whole genome SNP associations with the 66 CpGs resulted in 688 significant CpG-SNP associations comprising 22 unique CpGs (33% of the 66 CPGs) and included 181 novel pairs or pairs in novel loci. Fourteen of the loci overlapped published GWAS loci for diabetes related traits and were used to identify causal associations of HK1 and PFKFB2 with HbA1c. Methylome-metabolome analysis identified 66 significant CpG-metabolite pairs among which 61 pairs were novel. Using the identified methylome-metabolome associations, methylation QTLs, and metabolic networks, a multi-omics network was constructed which suggested a number of metabolic mechanisms underlying T2D methylated genes. 1-palmitoyl-2-oleoyl-GPE (16:0/18:1) - a triglyceride-associated metabolite, shared a common network with 13 methylated CpGs, including TXNIP, PFKFB2, OCIAD1, and BLCAP. Mannonate - a food component/plant shared a common network with 6 methylated genes, including TXNIP, BLCAP, THBS4 and PEF1, pointing to a common possible cause of methylation in those genes. A subnetwork with alanine, glutamine, urea cycle (citrulline, arginine), and 1-carboxyethylvaline linked to PFKFB2 and TXNIP revealed associations with kidney function, hypertension and triglyceride metabolism. The pathway containing STYXL1-POR was associated with a sphingosine-ceramides subnetwork associated with HDL-C and LDL-C and point to steroid perturbations in T2D. CONCLUSIONS: This study revealed several novel methylated genes in T2D, with their genomic variants and associated metabolic pathways with several implications for future clinical use of multi-omics associations in disease and for studying therapeutic targets.

Unraveling the functional role of the orphan solute carrier, SLC22A24 in the transport of steroid conjugates through metabolomic and genome-wide association studies.

Variation in steroid hormone levels has wide implications for health and disease. The genes encoding the proteins involved in steroid disposition represent key determinants of interindividual variation in steroid levels and ultimately, their effects. Beginning with metabolomic data from genome-wide association studies (GWAS), we observed that genetic variants in the orphan transporter, SLC22A24 were significantly associated with levels of androsterone glucuronide and etiocholanolone glucuronide (sentinel SNPs p-value <1x10-30). In cells over-expressing human or various mammalian orthologs of SLC22A24, we showed that steroid conjugates and bile acids were substrates of the transporter. Phylogenetic, genomic, and transcriptomic analyses suggested that SLC22A24 has a specialized role in the kidney and appears to function in the reabsorption of organic anions, and in particular, anionic steroids. Phenome-wide analysis showed that functional variants of SLC22A24 are associated with human disease such as cardiovascular diseases and acne, which have been linked to dysregulated steroid metabolism. Collectively, these functional genomic studies reveal a previously uncharacterized protein involved in steroid homeostasis, opening up new possibilities for SLC22A24 as a pharmacological target for regulating steroid levels.

The role of gut microbiota in atopic asthma and allergy, implications in the understanding of disease pathogenesis.

Asthma is a clinical syndrome characterized by chronic airway inflammation. There is mounting evidence on the role of microbiota in the development of asthma. This review focuses on the role of microbiota in maintaining the integrity of the epithelia and their role in regulating the immune response. The review compiles data from multiple studies on the role of microbiota in the innate immune response and the development and differentiation of CD4+ T cells, a major component of the adaptive arm of the immune response. As a result of dysbiosis, invariant natural killer T cells may induce T helper 2 cell differentiation and immunoglobulin E isotype switching through the release of interleukin-4 and interleukin-13. Furthermore, degradation of immunoglobulin A antibodies, increased circulating mast cells and basophils, and inflammation are among other mechanisms by which dysbiosis can induce or exacerbate asthma. After explaining the underlying mechanisms, the review derives conclusions from studies that investigate dysbiosis in infancy and the development of asthma later in life. The review also includes studies that investigate asthmatic mothers and the development of asthma in children and the role of dysbiosis in that regard. Finally, the review explains the statistical relationship between eczema and asthma through multiple studies that investigate the role of dysbiosis in both atopic states. This review provides insight into the role of dysbiosis in asthma, and an understanding that is required to establish clinical trials which aim to modulate the gut microbiota as a means of preventing and treating asthma.

Targeting the gut microbiome: A brief report on the awareness, practice, and readiness to engage in clinical interventions in Qatar.

BACKGROUND: There has been a growing global interest in the role of gut microbiota in the pathogenesis of diseases and the potentials of targeting the microbiome in clinical interventions. Very few clinical studies in Qatar focused on gut microbiome. This study aimed to assess the awareness of healthcare professionals, scientists, and the general public on the role of gut microbiota in health and diseases and, more specifically, in disorders of the gut-brain axis such as neurodevelopmental disorders (NDDs) or gastrointestinal (GI) disorders. It also aimed to evaluate the readiness of the population to engage in clinical trials involving dietary interventions or fecal transplants. METHODS: A total of 156 participants were recruited to answer questionnaires-from healthcare professionals and scientists (HSs; n = 44) and the general public (n = 112). Participants from the general public self-reported their diagnosis of NDDs-autism or attention deficit hyperactivity disorder (n = 36)-or GI diseases or disorders (n = 18) or as having none of them (n = 58). Two questionnaires for HSs and for the general public were distributed, and basic descriptive and statistical analyses were conducted using the Fisher's exact test. RESULTS: Among the participating HSs, 95% admitted that they had minimum to no knowledge on the role of gut microbes in health and diseases, and only 15.9% felt that their peers were knowledgeable about it. Nevertheless, 97.7% of HSs thought that gut microbiota should be considered when devising treatment plans as 79.1% believed that gut dysbiosis is involved in the pathogenesis of diseases. For the general public, 54% stated that they have read about studies on the potential benefits of microbes in the prevention, treatment, and management of diseases, with a higher proportion of them belonging to the GI group (p = 0.0523). The GI group was also more aware of the existence of the use of fecal transplants for treating their condition (p = 0.01935). Awareness was also reflected in participants' attempts to engage in dietary changes, as 40% tried a dietary intervention, which has noticeably changed their or their child's symptoms. This study reported a highly significant association between being exposed to multiple antibiotic courses before three years of age and being part of the NDD group (p = 0.0003). Public readiness to engage in interventions that target the gut microbiome, such as intensive dietary interventions or even fecal transplants, was perceived by HSs to be lower than what was stated by the public, with 87.96% of public being ready to engage in intensive dietary interventions and 66.98% in fecal transplants. CONCLUSION: The study revealed that the role of gut microbes in health and diseases, and especially through the gut-brain axis, is still unclear in both the scientific community and general public. While acknowledging the importance of gut microbes, the lack of information regarding the link between lifestyle and gut microbes is considered to hold the public in the precontemplation/contemplation stages of the transtheoretical model of behavioral change. An interdisciplinary approach to new knowledge produced by microbiome studies is needed to run awareness campaigns and continue professional development activities on the benefits of lifestyle-based modulation of gut microbiome, thus engaging the general public in lifestyle changes and facilitating clinical research in human microbiome investigations in Qatar.

Metabolic profiling of elite athletes with different cardiovascular demand.

Intensive exercise of elite athletes can lead to physiological alterations in the cardiovascular system in response to increased stroke volume and blood pressure, known collectively as cardiovascular demand (CD). This study aimed to compare metabolic differences in elite athletes with high vs low/moderate CD and to reveal their underlying metabolic pathways as potential biomarker signatures for assessing health, performance, and recovery of elite athletes. Metabolic profiling of serum samples from 495 elite athletes from different sport disciplines (118 high CD and 377 low/moderate CD athletes) was conducted using non-targeted metabolomics-based mass spectroscopy combined with ultra-high-performance liquid chromatography. Results show that DAGs containing arachidonic were enriched in high CD together with branched-chain amino acids, plasminogens, phosphatidylcholines, and phosphatidylethanolamines, potentially indicating increased risk of cardiovascular disease in the high CD group. Gamma-glutamyl amino acids and glutathione metabolism were increased in low/moderate CD group, suggesting more efficient oxidative stress scavenging mechanisms than the high CD group. This first most comprehensive metabolic profiling of elite athletes provides an evidence that athletes with different CD show a unique metabolic signature that reflects energy generation and oxidative stress and potentially places the high CD group at a higher risk of cardiovascular disease. Further studies are warranted for confirmation and validation of findings in other sport groups in light of potential confounders related to limited available information about participants.

A genome-wide association meta-analysis on apolipoprotein A-IV concentrations.

Apolipoprotein A-IV (apoA-IV) is a major component of HDL and chylomicron particles and is involved in reverse cholesterol transport. It is an early marker of impaired renal function. We aimed to identify genetic loci associated with apoA-IV concentrations and to investigate relationships with known susceptibility loci for kidney function and lipids. A genome-wide association meta-analysis on apoA-IV concentrations was conducted in five population-based cohorts (n = 13,813) followed by two additional replication studies (n = 2,267) including approximately 10 M SNPs. Three independent SNPs from two genomic regions were significantly associated with apoA-IV concentrations: rs1729407 near APOA4 (P = 6.77 × 10 - 44), rs5104 in APOA4 (P = 1.79 × 10-24) and rs4241819 in KLKB1 (P = 5.6 × 10-14). Additionally, a look-up of the replicated SNPs in downloadable GWAS meta-analysis results was performed on kidney function (defined by eGFR), HDL-cholesterol and triglycerides. From these three SNPs mentioned above, only rs1729407 showed an association with HDL-cholesterol (P = 7.1 × 10 - 07). Moreover, weighted SNP-scores were built involving known susceptibility loci for the aforementioned traits (53, 70 and 38 SNPs, respectively) and were associated with apoA-IV concentrations. This analysis revealed a significant and an inverse association for kidney function with apoA-IV concentrations (P = 5.5 × 10-05). Furthermore, an increase of triglyceride-increasing alleles was found to decrease apoA-IV concentrations (P = 0.0078). In summary, we identified two independent SNPs located in or next the APOA4 gene and one SNP in KLKB1 The association of KLKB1 with apoA-IV suggests an involvement of apoA-IV in renal metabolism and/or an interaction within HDL particles. Analyses of SNP-scores indicate potential causal effects of kidney function and by lesser extent triglycerides on apoA-IV concentrations.

A genome-wide association meta-analysis on lipoprotein (a) concentrations adjusted for apolipoprotein (a) isoforms.

High lipoprotein (a) [Lp(a)] concentrations are an independent risk factor for cardiovascular outcomes. Concentrations are strongly influenced by apo(a) kringle IV repeat isoforms. We aimed to identify genetic loci associated with Lp(a) concentrations using data from five genome-wide association studies (n = 13,781). We identified 48 independent SNPs in the LPA and 1 SNP in the APOE gene region to be significantly associated with Lp(a) concentrations. We also adjusted for apo(a) isoforms to identify loci affecting Lp(a) levels independently from them, which resulted in 31 SNPs (30 in the LPA, 1 in the APOE gene region). Seven SNPs showed a genome-wide significant association with coronary artery disease (CAD) risk. A rare SNP (rs186696265; MAF ∼1%) showed the highest effect on Lp(a) and was also associated with increased risk of CAD (odds ratio = 1.73, P = 3.35 × 10-30). Median Lp(a) values increased from 2.1 to 91.1 mg/dl with increasing number of Lp(a)-increasing alleles. We found the APOE2-determining allele of rs7412 to be significantly associated with Lp(a) concentrations (P = 3.47 × 10-10). Each APOE2 allele decreased Lp(a) by 3.34 mg/dl corresponding to ∼15% of the population's mean values. Performing a gene-based test of association, including suspected Lp(a) receptors and regulators, resulted in one significant association of the TLR2 gene with Lp(a) (P = 3.4 × 10-4). In summary, we identified a large number of independent SNPs in the LPA gene region, as well as the APOE2 allele, to be significantly associated with Lp(a) concentrations.

Diagnostic and Prognostic Metabolites Identified for Joint Symptoms in the KORA Population.

This study aims at identifying metabolites that significantly associate with self-reported joint symptoms (diagnostic) and metabolites that can predict the change from a symptom-free status to the development of self-reported joint symptoms after a 7 years period (prognostic). More than 300 metabolites were analyzed for 2246 subjects from the longitudinal study of the KORA (Cooperative Health Research in the Region of Augsburg, Germany), specifically the fourth survey S4 and its 7-year follow-up study F4. Two types of self-reported symptoms, chronic joint inflammation and worn out joints, were used for the analyses. Diagnostic analysis identified dysregulated metabolites in cases with symptoms compared with controls. Prognostic analysis identified metabolites that differentiate subjects in S4 who remained symptom-free after 7 years (F4) from those who developed any combination of symptoms. 48 metabolites were identified as nominally significantly (p < 0.05) associated with the self-reported symptoms in the diagnostic analysis, among which steroids show Bonferroni significance. 45 metabolites were identified as nominally significantly associated with developing symptoms after 7 years, among which hippurate showed Bonferroni significance. We show that metabolic profiles of self-reported joint symptoms are in line with metabolites known to associate with various forms of arthritis and suggest that future studies may benefit from that by investigating the possible use of self-reporting/questionnaire along with metabolic markers for the early referral of patients for further diagnostic workup and treatment of arthritis.

Network-based approach for analyzing intra- and interfluid metabolite associations in human blood, urine, and saliva.

Most studies investigating human metabolomics measurements are limited to a single biofluid, most often blood or urine. An organism's biochemical pool, however, comprises complex transboundary relationships, which can only be understood by investigating metabolic interactions and physiological processes spanning multiple parts of the human body. Therefore, we here propose a data-driven network-based approach to generate an integrated picture of metabolomics associations over multiple fluids. We performed an analysis of 2251 metabolites measured in plasma, urine, and saliva, from 374 participants of the Qatar Metabolomics Study on Diabetes (QMDiab). Gaussian graphical models (GGMs) were used to estimate metabolite-metabolite interactions on different subsets of the data set. First, we compared similarities and differences of the metabolome and the association networks between the three fluids. Second, we investigated the cross-talk between the fluids by analyzing correlations occurring between them. Third, we propose a framework for the analysis of medically relevant phenotypes by integrating type 2 diabetes, sex, age, and body mass index into our networks. In conclusion, we present a generic, data-driven network-based approach for structuring and visualizing metabolite correlations within and between multiple body fluids, enabling unbiased interpretation of metabolomics multifluid data.

A systems view of type 2 diabetes-associated metabolic perturbations in saliva, blood and urine at different timescales of glycaemic control.

AIMS/HYPOTHESIS: Metabolomics has opened new avenues for studying metabolic alterations in type 2 diabetes. While many urine and blood metabolites have been associated individually with diabetes, a complete systems view analysis of metabolic dysregulations across multiple biofluids and over varying timescales of glycaemic control is still lacking. METHODS: Here we report a broad metabolomics study in a clinical setting, covering 2,178 metabolite measures in saliva, blood plasma and urine from 188 individuals with diabetes and 181 controls of Arab and Asian descent. Using multivariate linear regression we identified metabolites associated with diabetes and markers of acute, short-term and long-term glycaemic control. RESULTS: Ninety-four metabolite associations with diabetes were identified at a Bonferroni level of significance (p < 2.3 × 10(-5)), 16 of which have never been reported. Sixty-five of these diabetes-associated metabolites were associated with at least one marker of glycaemic control in the diabetes group. Using Gaussian graphical modelling, we constructed a metabolic network that links diabetes-associated metabolites from three biofluids across three different timescales of glycaemic control. CONCLUSIONS/INTERPRETATION: Our study reveals a complex network of biochemical dysregulation involving metabolites from different pathways of diabetes pathology, and provides a reference framework for future diabetes studies with metabolic endpoints.

Copy number variations in the genome of the Qatari population.

BACKGROUND: The populations of the Arabian Peninsula remain the least represented in public genetic databases, both in terms of single nucleotide variants and of larger genomic mutations. We present the first high-resolution copy number variation (CNV) map for a Gulf Arab population, using a hybrid approach that integrates array genotyping intensity data and next-generation sequencing reads to call CNVs in the Qatari population. METHODS: CNVs were detected in 97 unrelated Qatari individuals by running two calling algorithms on each of two primary datasets: high-resolution genotyping (Illumina Omni 2.5M) and high depth whole-genome sequencing (Illumina PE 100bp). The four call-sets were integrated to identify high confidence CNV regions, which were subsequently annotated for putative functional effect and compared to public databases of CNVs in other populations. The availability of genome sequence was leveraged to identify tagging SNPs in high LD with common deletions in this population, enabling their imputation from genotyping experiments in the future. RESULTS: Genotyping intensities and genome sequencing data from 97 Qataris were analyzed with four different algorithms and integrated to discover 16,660 high confidence CNV regions (CNVRs) in the total population, affecting ~28 Mb in the median Qatari genome. Up to 40% of all CNVs affected genes, including novel CNVs affecting Mendelian disease genes, segregating at different frequencies in the 3 major Qatari subpopulations, including those with Bedouin, Persian/South Asian, and African ancestry. Consistent with high consanguinity levels in the Bedouin subpopulation, we found an increased burden for homozygous deletions in this group. In comparison to known CNVs in the comprehensive Database of Genomic Variants, we found that 5% of all CNVRs in Qataris were completely novel, with an enrichment of CNVs affecting several known chromosomal disorder loci and genes known to regulate sugar metabolism and type 2 diabetes in the Qatari cohort. Finally, we leveraged the availability of genome sequence to find suitable tagging SNPs for common deletions in this population. CONCLUSION: We combine four independently generated datasets from 97 individuals to study CNVs for the first time at high-resolution in a Gulf Arab population.

Integrating Genome Sequencing and Untargeted Metabolomics in Monozygotic Twins with a Rare Complex Neurological Disorder.

Multi-omics approaches, which integrate genomics, transcriptomics, proteomics, and metabolomics, have emerged as powerful tools in the diagnosis of rare diseases. We used untargeted metabolomics and whole-genome sequencing (WGS) to gain a more comprehensive understanding of a rare disease with a complex presentation affecting female twins from a consanguineous family. The sisters presented with polymicrogyria, a Dandy-Walker malformation, respiratory distress, and multiorgan dysfunctions. Through WGS, we identified two rare homozygous variants in both subjects, a pathogenic variant in ADGRG1(p.Arg565Trp) and a novel variant in CNTNAP1(p.Glu910Val). These genes have been previously associated with autosomal recessive polymicrogyria and hypomyelinating neuropathy with/without contractures, respectively. The twins exhibited symptoms that overlapped with both of these conditions. The results of the untargeted metabolomics analysis revealed significant metabolic perturbations relating to neurodevelopmental abnormalities, kidney dysfunction, and microbiome. The significant metabolites belong to essential pathways such as lipids and amino acid metabolism. The identification of variants in two genes, combined with the support of metabolic perturbation, demonstrates the rarity and complexity of this phenotype and provides valuable insights into its underlying mechanisms.

A scoping review of artificial intelligence-based methods for diabetes risk prediction.

The increasing prevalence of type 2 diabetes mellitus (T2DM) and its associated health complications highlight the need to develop predictive models for early diagnosis and intervention. While many artificial intelligence (AI) models for T2DM risk prediction have emerged, a comprehensive review of their advancements and challenges is currently lacking. This scoping review maps out the existing literature on AI-based models for T2DM prediction, adhering to the PRISMA extension for Scoping Reviews guidelines. A systematic search of longitudinal studies was conducted across four databases, including PubMed, Scopus, IEEE-Xplore, and Google Scholar. Forty studies that met our inclusion criteria were reviewed. Classical machine learning (ML) models dominated these studies, with electronic health records (EHR) being the predominant data modality, followed by multi-omics, while medical imaging was the least utilized. Most studies employed unimodal AI models, with only ten adopting multimodal approaches. Both unimodal and multimodal models showed promising results, with the latter being superior. Almost all studies performed internal validation, but only five conducted external validation. Most studies utilized the area under the curve (AUC) for discrimination measures. Notably, only five studies provided insights into the calibration of their models. Half of the studies used interpretability methods to identify key risk predictors revealed by their models. Although a minority highlighted novel risk predictors, the majority reported commonly known ones. Our review provides valuable insights into the current state and limitations of AI-based models for T2DM prediction and highlights the challenges associated with their development and clinical integration.

Proteome-wide associations with short- and long-term weight loss and regain after Roux-en-Y gastric bypass surgery.

OBJECTIVE: Gastric bypass surgery results in long-term weight loss. Small studies have examined protein changes during rapid weight loss (up to 1 or 2 years post surgery). This study tested whether short-term changes were maintained after 12 years. METHODS: A 12-year follow-up, protein-wide association study of 1,297 SomaLogic aptamer-based plasma proteins compared short- (2-year) and long-term (12-year) protein changes in 234 individuals who had gastric bypass surgery with 144 nonintervened individuals with severe obesity. RESULTS: There were 51 replicated 12-year protein changes that differed between the surgery and nonsurgery groups. Adjusting for change in BMI, only 12 proteins remained significant, suggesting that BMI change was the primary reason for most protein changes and not non-BMI-related surgical effects. Protein changes were related to BMI changes during both weight-loss and weight-regain periods. The significant proteins were associated primarily with lipid, uric acid, or resting energy expenditure clinical variables and metabolic pathways. Eight protein changes were associated with 12-year diabetes remission, including apolipoprotein M, sex hormone binding globulin, and adiponectin (p < 3.5 × 10-5 ). CONCLUSIONS: This study showed that most short-term postsurgical changes in proteins were maintained at 12 years. Systemic protection pathways, including inflammation, complement, lipid, and adipocyte pathways, were related to the long-term benefits of gastric bypass surgery.

DNA methylation signatures in Blood DNA of Hutchinson-Gilford Progeria syndrome.

Hutchinson-Gilford Progeria Syndrome (HGPS) is an extremely rare genetic disorder caused by mutations in the LMNA gene and characterized by premature and accelerated aging beginning in childhood. In this study, we performed the first genome-wide methylation analysis on blood DNA of 15 patients with progeroid laminopathies using Infinium Methylation EPIC arrays including 8 patients with classical HGPS. We could observe DNA methylation alterations at 61 CpG sites as well as 32 significant regions following a 5 Kb tiling analysis. Differentially methylated probes were enriched for phosphatidylinositol biosynthetic process, phospholipid biosynthetic process, sarcoplasm, sarcoplasmic reticulum, phosphatase regulator activity, glycerolipid biosynthetic process, glycerophospholipid biosynthetic process, and phosphatidylinositol metabolic process. Differential methylation analysis at the level of promoters and CpG islands revealed no significant methylation changes in blood DNA of progeroid laminopathy patients. Nevertheless, we could observe significant methylation differences in classic HGPS when specifically looking at probes overlapping solo-WCGW partially methylated domains. Comparing aberrantly methylated sites in progeroid laminopathies, classic Werner syndrome, and Down syndrome revealed a common significantly hypermethylated region in close vicinity to the transcription start site of a long non-coding RNA located anti-sense to the Catenin Beta Interacting Protein 1 gene (CTNNBIP1). By characterizing epigenetically altered sites, we identify possible pathways/mechanisms that might have a role in the accelerated aging of progeroid laminopathies.

Metabolic and Metabo-Clinical Signatures of Type 2 Diabetes, Obesity, Retinopathy, and Dyslipidemia.

Macro- and microvascular complications of type 2 diabetes (T2D), obesity, and dyslipidemia share common metabolic pathways. In this study, using a total of 1,300 metabolites from 996 Qatari adults (57% with T2D) and 1,159 metabolites from an independent cohort of 2,618 individuals from the Qatar BioBank (11% with T2D), we identified 373 metabolites associated with T2D, obesity, retinopathy, dyslipidemia, and lipoprotein levels, 161 of which were novel. Novel metabolites included phospholipids, sphingolipids, lysolipids, fatty acids, dipeptides, and metabolites of the urea cycle and xanthine, steroid, and glutathione metabolism. The identified metabolites enrich pathways of oxidative stress, lipotoxicity, glucotoxicity, and proteolysis. Second, we identified 15 patterns we defined as "metabo-clinical signatures." These are clusters of patients with T2D who group together based on metabolite levels and reveal the same clustering in two or more clinical variables (obesity, LDL, HDL, triglycerides, and retinopathy). These signatures revealed metabolic pathways associated with different clinical patterns and identified patients with extreme (very high/low) clinical variables associated with extreme metabolite levels in specific pathways. Among our novel findings are the role of N-acetylmethionine in retinopathy in conjunction with dyslipidemia and the possible roles of N-acetylvaline and pyroglutamine in association with high cholesterol levels and kidney function.

A Comparative Pilot Study of Bacterial and Fungal Dysbiosis in Neurodevelopmental Disorders and Gastrointestinal Disorders: Commonalities, Specificities and Correlations with Lifestyle.

Gastrointestinal disorders (GIDs) are a common comorbidity in patients with neurodevelopmental disorders (NDDs), while anxiety-like behaviors are common among patients with gastrointestinal diseases. It is still unclear as to which microbes differentiate these two groups. This pilot study aims at proposing an answer by exploring both the bacteriome and the mycobiome in a cohort of 55 volunteers with NDD, GID or controls, while accounting for additional variables that are not commonly included such as probiotic intake and diet. Recruited participants answered a questionnaire and provided a stool sample using the Fisherbrand collection kit. Bacterial and fungal DNA was extracted using the Qiagen Stool minikit. Sequencing (16sRNA and ITS) and phylogenetic analyses were performed using the PE300 Illumina Miseq v3 sequencing. Statistical analysis was performed using the R package. Results showed a significant decrease in bacterial alpha diversity in both NDD and GID, but an increased fungal alpha diversity in NDD. Data pointed at a significant bacterial dysbiosis between the three groups, but the mycobiome dysbiosis is more pronounced in NDD than in GID. Fungi seem to be more affected by probiotics, diet and antibiotic exposure and are proposed to be the main key player in differentiation between NDD and GID dybiosis.

Author Correction: Metabolic GWAS of elite athletes reveals novel genetically-influenced metabolites associated with athletic performance.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.