Human macrophages are innate immune cells with diverse, functionally distinct phenotypes, namely, pro-inflammatory M1 and anti-inflammatory M2 macrophages. Both are involved in multiple... Show moreHuman macrophages are innate immune cells with diverse, functionally distinct phenotypes, namely, pro-inflammatory M1 and anti-inflammatory M2 macrophages. Both are involved in multiple physiological and pathological processes, including would healing, infection, and cancer. However, the metabolic differences between these phenotypes are largely unexplored at single-cell resolution. To address this knowledge gap, an untargeted live single-cell mass spectrometry-based metabolomic profiling coupled with a machine-learning data analysis approach was developed to investigate the metabolic profile of each phenotype at the single-cell level. Results show that M1 and M2 macrophages have distinct metabolic profiles, with differential levels of fatty acyls, glycerophospholipids, and sterol lipids, which are important components of plasma membrane and involved in multiple biological processes. Furthermore, we could discern several putatively annotated molecules that contribute to inflammatory response of macrophages. The combination of random forest and live single-cell metabolomics provided an in-depth profile of the metabolome of primary human M1 and M2 macrophages at the single-cell level for the first time, which will pave the way for future studies targeting the differentiation of other immune cells. Show less
We identify biomarkers for disease progression in three type 2 diabetes cohorts encompassing 2,973 individuals across three molecular classes, metabolites, lipids and proteins. Homocitrulline,... Show moreWe identify biomarkers for disease progression in three type 2 diabetes cohorts encompassing 2,973 individuals across three molecular classes, metabolites, lipids and proteins. Homocitrulline, isoleucine and 2-aminoadipic acid, eight triacylglycerol species, and lowered sphingomyelin 42:2;2 levels are predictive of faster progression towards insulin requirement. Of ~1,300 proteins examined in two cohorts, levels of GDF15/MIC-1, IL-18Ra, CRELD1, NogoR, FAS, and ENPP7 are associated with faster progression, whilst SMAC/DIABLO, SPOCK1 and HEMK2 predict lower progression rates. In an external replication, proteins and lipids are associated with diabetes incidence and prevalence. NogoR/RTN4R injection improved glucose tolerance in high fat-fed male mice but impaired it in male db/db mice. High NogoR levels led to islet cell apoptosis, and IL-18R antagonised inflammatory IL-18 signalling towards nuclear factor kappa-B in vitro. This comprehensive, multi-disciplinary approach thus identifies biomarkers with potential prognostic utility, provides evidence for possible disease mechanisms, and identifies potential therapeutic avenues to slow diabetes progression. Show less
Background Postoperative acute kidney injury (AKI) is a common complication of major gastrointestinal surgery with an impact on short- and long-term survival. No validated system for risk... Show moreBackground Postoperative acute kidney injury (AKI) is a common complication of major gastrointestinal surgery with an impact on short- and long-term survival. No validated system for risk stratification exists for this patient group. This study aimed to validate externally a prognostic model for AKI after major gastrointestinal surgery in two multicentre cohort studies.Methods The Outcomes After Kidney injury in Surgery (OAKS) prognostic model was developed to predict risk of AKI in the 7 days after surgery using six routine datapoints (age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker). Validation was performed within two independent cohorts: a prospective multicentre, international study ('IMAGINE') of patients undergoing elective colorectal surgery (2018); and a retrospective regional cohort study ('Tayside') in major abdominal surgery (2011-2015). Multivariable logistic regression was used to predict risk of AKI, with multiple imputation used to account for data missing at random. Prognostic accuracy was assessed for patients at high risk (greater than 20 per cent) of postoperative AKI.Results In the validation cohorts, 12.9 per cent of patients (661 of 5106) in IMAGINE and 14.7 per cent (106 of 719 patients) in Tayside developed 7-day postoperative AKI. Using the OAKS model, 558 patients (9.6 per cent) were classified as high risk. Less than 10 per cent of patients classified as low-risk developed AKI in either cohort (negative predictive value greater than 0.9). Upon external validation, the OAKS model retained an area under the receiver operating characteristic (AUC) curve of range 0.655-0.681 (Tayside 95 per cent c.i. 0.596 to 0.714; IMAGINE 95 per cent c.i. 0.659 to 0.703), sensitivity values range 0.323-0.352 (IMAGINE 95 per cent c.i. 0.281 to 0.368; Tayside 95 per cent c.i. 0.253 to 0.461), and specificity range 0.881-0.890 (Tayside 95 per cent c.i. 0.853 to 0.905; IMAGINE 95 per cent c.i. 0.881 to 0.899).Conclusion The OAKS prognostic model can identify patients who are not at high risk of postoperative AKI after gastrointestinal surgery with high specificity.Presented to Association of Surgeons in Training (ASiT) International Conference 2018 (Edinburgh, UK), European Society of Coloproctology (ESCP) International Conference 2018 (Nice, France), SARS (Society of Academic and Research Surgery) 2020 (Virtual, UK). Show less
Type 2 diabetes is a multifactorial disease with multiple underlying aetiologies. To address this heterogeneity, investigators of a previous study clustered people with diabetes according to five... Show moreType 2 diabetes is a multifactorial disease with multiple underlying aetiologies. To address this heterogeneity, investigators of a previous study clustered people with diabetes according to five diabetes subtypes. The aim of the current study is to investigate the etiology of these clusters by comparing their molecular signatures. In three independent cohorts, in total 15,940 individuals were clustered based on five clinical characteristics. In a subset, genetic (N = 12,828), metabolomic (N = 2,945), lipidomic (N = 2,593), and proteomic (N = 1,170) data were obtained in plasma. For each data type, each cluster was compared with the other four clusters as the reference. The insulin-resistant cluster showed the most distinct molecular signature, with higher branched-chain amino acid, diacylglycerol, and triacylglycerol levels and aberrant protein levels in plasma were enriched for proteins in the intracellular PI3K/Akt pathway. The obese cluster showed higher levels of cytokines. The mild diabetes cluster with high HDL showed the most beneficial molecular profile with effects opposite of those seen in the insulin-resistant cluster. This study shows that clustering people with type 2 diabetes can identify underlying molecular mechanisms related to pancreatic islets, liver, and adipose tissue metabolism. This provides novel biological insights into the diverse aetiological processes that would not be evident when type 2 diabetes is viewed as a homogeneous disease. Show less
Aims/hypothesis Five clusters based on clinical characteristics have been suggested as diabetes subtypes: one autoimmune and four subtypes of type 2 diabetes. In the current study we replicate and... Show moreAims/hypothesis Five clusters based on clinical characteristics have been suggested as diabetes subtypes: one autoimmune and four subtypes of type 2 diabetes. In the current study we replicate and cross-validate these type 2 diabetes clusters in three large cohorts using variables readily measured in the clinic.Methods In three independent cohorts, in total 15,940 individuals were clustered based on age, BMI, HbA(1c), random or fasting C-peptide, and HDL-cholesterol. Clusters were cross-validated against the original clusters based on HOMA measures. In addition, between cohorts, clusters were cross-validated by re-assigning people based on each cohort's cluster centres. Finally, we compared the time to insulin requirement for each cluster.Results Five distinct type 2 diabetes clusters were identified and mapped back to the original four All New Diabetics in Scania (ANDIS) clusters. Using C-peptide and HDL-cholesterol instead of HOMA2-B and HOMA2-IR, three of the clusters mapped with high sensitivity (80.6-90.7%) to the previously identified severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD) and mild obesity-related diabetes (MOD) clusters. The previously described ANDIS mild age-related diabetes (MARD) cluster could be mapped to the two milder groups in our study: one characterised by high HDL-cholesterol (mild diabetes with high HDL-cholesterol [MDH] cluster), and the other not having any extreme characteristic (mild diabetes [MD]). When these two milder groups were combined, they mapped well to the previously labelled MARD cluster (sensitivity 79.1%). In the cross-validation between cohorts, particularly the SIDD and MDH clusters cross-validated well, with sensitivities ranging from 73.3% to 97.1%. SIRD and MD showed a lower sensitivity, ranging from 36.1% to 92.3%, where individuals shifted from SIRD to MD and vice versa. People belonging to the SIDD cluster showed the fastest progression towards insulin requirement, while the MDH cluster showed the slowest progression.Conclusions/interpretation Clusters based on C-peptide instead of HOMA2 measures resemble those based on HOMA2 measures, especially for SIDD, SIRD and MOD. By adding HDL-cholesterol, the MARD cluster based upon HOMA2 measures resulted in the current clustering into two clusters, with one cluster having high HDL levels. Cross-validation between cohorts showed generally a good resemblance between cohorts. Together, our results show that the clustering based on clinical variables readily measured in the clinic (age, HbA(1c), HDL-cholesterol, BMI and C-peptide) results in informative clusters that are representative of the original ANDIS clusters and stable across cohorts. Adding HDL-cholesterol to the clustering resulted in the identification of a cluster with very slow glycaemic deterioration. Show less
Pagán-Jiménez, J.R.; Ali, A.; Santiago Marrero, C.G.; Hofman, C.L. 2020
Cellular heterogeneity is a phenomenon that is often observed but poorly understood. Single-cell metabolomics can provide insights into the phenotypical variations between individual cells. Recent... Show moreCellular heterogeneity is a phenomenon that is often observed but poorly understood. Single-cell metabolomics can provide insights into the phenotypical variations between individual cells. Recent advances in microfluidics, micromanipulation, image analysis, and automation allowed for high-throughput isolation of single cells in a minimally disruptive manner as to not affect the cell metabolism. Coupled with new innovations in mass spectrometry-based analytical techniques, single cell metabolomics stands at the cusp of becoming an established field. In this review, some of the recent single cell isolation platforms that are especially suited for metabolomics will be highlighted, as well as the recent trends in mass spectrometry-based single cell platforms. Additionally, some of the limitations of single-cell metabolomics and its recent applications will be briefly discussed. (c) 2019 Elsevier B.V. All rights reserved. Show less
Huisman, M.V.; Rothman, K.J.; Paquette, M.; Teutsch, C.; Diener, H.C.; Dubner, S.J.; ... ; Frappe, T 2017
Protecting groups play a key role in the synthesis of complex natural products.This holds especially true for the synthesis of oligosaccharides, of which the monomeric carbohydrate building blocks... Show moreProtecting groups play a key role in the synthesis of complex natural products.This holds especially true for the synthesis of oligosaccharides, of which the monomeric carbohydrate building blocks usually contain up to five different hydroxyl functions. The discrimination of these hydroxyl functions requires a careful protecting group strategy and typically involves multistep protocols.This thesis describes the prepartion, installation, their use in the synthesis of stereoselective glycosidic bonds. Although protecting groups primarily function to mask a given functionality on the carbohydrate core, they also have a profound effect on the overall reactivity of a carbohydrate building block and can control the stereochemical outcome of a glycosylation reaction. Furthermore protecting groups can be used to introduce extra functionality on the carbohydrate core, such as visualization and/or purification handles. Fluorous solid phase extraction (FSPE) is an emerging tecnique, in which compounds are seperated on the basis of flourous content. The compound bearing fluorous tags which are difficult to purify by routine methods can easily be purified. Show less
During the 19th century, Algeria became familiar to the Western world through the paintings of the French Orientalists and, towards the end of the century, through photographs of the elaborately... Show moreDuring the 19th century, Algeria became familiar to the Western world through the paintings of the French Orientalists and, towards the end of the century, through photographs of the elaborately adorned dancers of the Ouled Naïl. A confederation of tribes, the Ouled Naïl originate from the high desert region and can be found living in towns such as Bou Saada, Biskra and Chellala. Show less
