Explore the vast range of titles available.

Mapping protein–protein interactions is an invaluable tool for understanding protein function. Here, we report the first large‐scale study of protein–protein interactions in human cells using a mass spectrometry‐based approach. The study maps protein interactions for 338 bait proteins that were selected based on known or suspected disease and functional associations. Large‐scale immunoprecipitation of Flag‐tagged versions of these proteins followed by LC‐ESI‐MS/MS analysis resulted in the identification of 24 540 potential protein interactions. False positives and redundant hits were filtered out using empirical criteria and a calculated interaction confidence score, producing a data set of 6463 interactions between 2235 distinct proteins. This data set was further cross‐validated using previously published and predicted human protein interactions. In‐depth mining of the data set shows that it represents a valuable source of novel protein–protein interactions with relevance to human diseases. In addition, via our preliminary analysis, we report many novel protein interactions and pathway associations. Synopsis Understanding the roles and consequences of protein–protein interactions is a fundamental goal in cellular biology and a prerequisite for the development of molecular systems biology. The endeavor of cataloging protein interactions is primarily hindered by the throughput and reproducibility of existing technologies. Different techniques for mapping protein interactions are available, such as the two‐hybrid approach (Chien et al , 1991 ) and the LUMIER approach (Barrios‐Rodiles et al , 2005 ) and assay whether two proteins interact in a pair‐wise fashion. We have developed a high‐throughput platform combining immunoprecipitation and high‐throughput mass spectrometry (IP‐HTMS) to rapidly identify potentially novel protein interactions for a bait protein of interest. We (Ho et al , 2002 ) and others (Gavin et al , 2002 ) previously used this approach to map protein–protein interactions in yeast, creating invaluable data sets for yeast biology and extrapolation into mammalian biology. Mapping protein interactions in human cells has its own set of challenges owing to the number of potentially expressed genes, the number of different cell types, and the numbers of internal and external factors that impact the cellular system. Although a complete mapping of the human interactome is still beyond current capabilities, more focused studies are possible. Here we report the first large‐scale application of IP‐HTMS to the mapping of protein–protein interactions in human cells using 338 human bait proteins of significant biomedical interest. The complete data set is available from the Intact database ( http://www.ebi.ac.uk/intact/site/ ) (accession EBI‐1059370) or as a table of bait–prey pairs with associated confidence values ( Supplementary Table II ). There has been much focus and discussion over the last few years on the quality and reproducibility of interactions in high‐throughput protein–protein interaction datasets (e.g. von Mering et al , 2002 ). A guiding principle in our study has therefore been to implement stringent quality controls. The final data set includes protein interactions for 338 human bait proteins ( Supplementary Table I ). For over half of these baits, two or more replicate immunoprecipitation experiments were performed, requiring a total of 1034 individual immunoprecipitation experiments with associated SDS–PAGE. These experiments yielded over 16 000 gel bands for which over 400 000 MS/MS spectra were assigned peptide sequences. Approximately 1/5 of our immunoprecipitation experiments were control (no‐bait) experiments allowing us to build a comprehensive list of spurious and ubiquitously binding proteins that could then be filtered out of the interaction network. Another 1/5 of the experiments were directed towards a study of the reproducibility of prey protein identification using our platform. These 202 immunoprecipitation experiments, derived from 18 baits, were used to train a statistical model that associates interaction reproducibility with various observed experimental parameters, such as the number of peptides identified for the given prey protein. This model was used to assign confidence values (taking a value between 0 and 1) to each of the 6486 interactions in the data set. As the interaction confidence score is calculated solely from IP‐HTMS experimental parameters, an initial focus was to confirm that the confidence score was an accurate means of ranking the interactions for further study. We observed, for example, that known interactions in the data set have, on average, significantly higher interaction confidence scores. For example, the set of baits corresponding to core and regulatory components of the proteasome enabled reconstruction of a proteasome interaction network (Figure 6C ), comprising many known proteasome components and enriched for high‐scoring interactions. We also integrated the IP‐HTMS data set with several other genomic‐scale data including other protein–protein interaction data sets, gene co‐expression data, and annotations from the gene ontology project. In the latter case, we analyzed the frequency of co‐occurrence of both bait and prey protein in the same biological process or cellular component category (Figure 3 ). We find that there is significant enrichment of bait–prey pairs sharing the same annotation category, indicating a strong tendency for bait proteins to bind prey proteins with related functions. Integration with gene co‐expression data showed that interaction data sets, this one included, are enriched for gene pairs that are co‐expressed. This enabled identification of tightly clustered sets of protein interactors that are also co‐expressed at the mRNA level. For example, the LYAR bait protein (Ly1 antibody reactive clone) is a nucleolar protein of unknown function (Su et al , 1993 ). This bait identified a set of nucleolar‐localized prey proteins that are also very tightly co‐expressed (Figure 5 ). These results along with the other protein–protein interaction data sources provided a powerful means of cross‐validating the human IP‐HTMS data set and associated methodology. Our focus in this paper has been to prepare a quality‐controlled, large‐scale human protein interaction data set that will add significantly to our knowledge of the human protein interactome. Given the focus on baits of significant biomedical interest (through functional or disease associations), we anticipate that this data set alongside other sources of human protein–protein interactions will be an important starting point for functional characterization of disease‐related interactions and complexes. The IP‐HTMS platform utilized here shows great promise as an effective means of protein interaction discovery and we anticipate that future applications will include broadening to a larger set of disease associated proteins, to other cell lines and coupling with drug treatments. We present a dataset of 6486 interactions between 2371 distinct proteins from a large‐scale application of immunoprecipitation and high‐throughput mass‐spectrometry (IP‐HTMS) on 338 human bait proteins expressed in human cells. The dataset is cross‐validated using previously published and predicted human protein interactions. In depth mining of the dataset shows that it represents a valuable source of novel protein‐protein interactions with relevance to human diseases. In addition, our analysis reveals many novel protein interactions and pathway associations. Protein interactions in the dataset are accompanied by a confidence score which is derived by combining several experimental and protein identification analysis metrics.

The current Coronavirus disease 2019 or COVID-19 pandemic has infected over two million people and resulted in the death of over one hundred thousand people at the time of writing this review. The disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Even though multiple vaccines and treatments are under development so far, the disease is only slowing down under extreme social distancing measures that are difficult to maintain. SARS-COV-2 is an enveloped virus that is surrounded by a lipid bilayer. Lipids are fundamental cell components that play various biological roles ranging from being a structural building block to a signaling molecule as well as a central energy store. The role lipids play in viral infection involves the fusion of the viral membrane to the host cell, viral replication, and viral endocytosis and exocytosis. Since lipids play a crucial function in the viral life cycle, we asked whether drugs targeting lipid metabolism, such as statins, can be utilized against SARS-CoV-2 and other viruses. In this review, we discuss the role of lipid metabolism in viral infection as well as the possibility of targeting lipid metabolism to interfere with the viral life cycle.

Obesity and its associated complications like type 2 diabetes (T2D) are reaching epidemic stages. Increased food intake and lack of exercise are two main contributing factors. Recent work has been highlighting an increasingly more important role of gut microbiota in metabolic disorders. It’s well known that gut microbiota plays a major role in the development of food absorption and low grade inflammation, two key processes in obesity and diabetes. This review summarizes key discoveries during the past decade that established the role of gut microbiota in the development of obesity and diabetes. It will look at the role of key metabolites mainly the short chain fatty acids (SCFA) that are produced by gut microbiota and how they impact key metabolic pathways such as insulin signalling, incretin production as well as inflammation. It will further look at the possible ways to harness the beneficial aspects of the gut microbiota to combat these metabolic disorders and reduce their impact.

COVID-19 is a disease caused by the coronavirus SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2), known as a highly contagious disease, currently affecting more than 200 countries worldwide. The main feature of SARS-CoV-2 that distinguishes it from other viruses is the speed of transmission combined with higher risk of mortality from acute respiratory distress syndrome (ARDS). People with diabetes mellitus (DM), severe obesity, cardiovascular disease, and hypertension are more likely to get infected and are at a higher risk of mortality from COVID-19. Among elderly patients who are at higher risk of death from COVID-19, 26.8% have DM. Although the reasons for this increased risk are yet to be determined, several factors may contribute to type-2 DM patients’ increased susceptibility to infections. A possible factor that may play a role in increasing the risk in people affected by diabetes and/or obesity is the impaired innate and adaptive immune response, characterized by a state of chronic and low-grade inflammation that can lead to abrupt systemic metabolic alteration. SARS patients previously diagnosed with diabetes or hyperglycemia had higher mortality and morbidity rates when compared with patients who were under metabolic control. Similarly, obese individuals are at higher risk of developing complications from SARS-CoV-2. In this review, we will explore the current and evolving insights pertinent to the metabolic impact of coronavirus infections with special attention to the main pathways and mechanisms that are linked to the pathophysiology and treatment of diabetes.

Background Kuwait is amongst countries in the Gulf region with high income economy. According to the World Health Organisation (WHO), one in five adults in the Gulf region is obese. This study sought to evaluate the prevalence and magnitude of association between overweight, obesity, central obesity, and socio-demographic factors in Kuwait. Methods A population-based cross-sectional survey of diabetes and obesity in Kuwait – part of the Kuwait Diabetes Epidemiology Program – was conducted between 2011 and 2014, targeting adults aged 18–82 years using the WHO STEPwise approach to non-communicable disease surveillance. Body mass index (BMI) was calculated to classify overweight and obesity, and waist circumference (WC) used to express central obesity. Multivariable logistic regression was used to estimate relationships between socio-demographic factors, overweight (25.0–29.9 kg/m 2 ), obesity (≥30.0 kg/m 2 ) or central obesity (WC ≥ 80 cm women; WC ≥ 94 cm men). Results Records for gender (56% Men), age, BMI, governorate, and nationality existed for 4901 individuals. Mean age and BMI were 43 years and 30 kg/m 2 , respectively. Non-Kuwaiti nationals were more prevalent than Kuwaitis (76% vs 24%). Prevalence rates for overweight, obesity and central obesity were 40.6% (95%CI: 38.4–42.8%), 42.1% (95%CI: 40.0–44.3%) and 73.7% (95%CI: 71.7–75.6%), respectively. The youngest age group (18–29 years) had rates of 38.2% (95%CI: 29.2–47.7%), 27.2% (95%CI: 19.0–36.7%) and 49.9% (95%CI: 40.6–59.1%) for overweight, obesity and central obesity, respectively. In covariate-adjusted analyses, the odds of being overweight was 26% greater for men than for women. Conversely, women had a 54% (95%CI: 19–99%) and 7-fold (95%CI, 5–10-fold) greater odds of obesity/central obesity, respectively, than men. Greater educational attainment, physical activity, and non-Kuwaiti status were associated with lower odds of obesity/central obesity. History of smoking, elevated blood pressure, higher income, being married, greater age and female sex related to greater odds of obesity/central obesity. Conclusion Overweight was greater in men, obesity greater in women. Overweight and obesity prevalence were high in young adults aged 18–29 years, a significant public health concern. Efforts to integrate mandatory physical education to the school curriculum and promoting the creation of recreation spaces/parks to promote physical activities, will play a vital role in the early prevention of overweight/obesity in Kuwait.

ANGPTL8 is a liver and adipose tissue produced protein that regulates the level of triglyceride in plasma as well as glucose homeostasis. This study was designed to evaluate the level of ANGPTL8 in obese and non-obese subjects before and after exercise training. A total of 82 non-obese and 62 adult obese were enrolled in this study. Subjects underwent a three months of exercise training. Both full length and C-terminal 139-198 form of ANGPTL8 were measured by ELISA. Our data show that the full length ANGPTL8 level was increased in obese subjects (1150.04 ± 108.10 pg/mL) compared to non-obese (775.54 ± 46.12) pg/mL (p-Value = 0.002). C-terminal 139-198 form of ANGPTL8 was also increased in obese subjects 0.28 ± 0.04 ng/mL vs 0.20 ± 0.02 ng/mL in non-obese (p-value = 0.058). In obese subjects, the levels of both forms were reduced after three months of exercise training; full length was reduced from 1150.04 ± 108.10 pg/mL to 852.04 ± 51.95 pg/mL (p-Values 0.015) and c-terminal form was reduced from 0.28 ± 0.04 ng/mL to 0.19 ± 0.03 ng/mL (p-Value = 0.058). Interestingly, full length ANGPTL8 was positively associated with fasting blood glucose (FBG) in non-obese (r = 0.317, p-Value = 0.006) and obese subjects (r = 0.346, p-Value = 0.006) C-terminal 139-198 form of ANGPTL8 on the other hand, did not show any correlation in both groups. In conclusion, our data demonstrate that ANGPTL8 was increased in obesity and reduced after exercise training supporting the potential therapeutic benefit of reducing ANGPTL8. The various forms of ANGPTL8 associated differently with FBG suggesting that they have different roles in glucose homeostasis.

The emergence of effective vaccines for COVID-19 has been welcomed by the world with great optimism. Given their increased susceptibility to COVID-19, the question arises whether individuals with type-2 diabetes mellitus (T2DM) and other metabolic conditions can respond effectively to the mRNA-based vaccine. We aimed to evaluate the levels of anti-SARS-CoV-2 IgG and neutralizing antibodies in people with T2DM and/or other metabolic risk factors (hypertension and obesity) compared to those without. This study included 262 people (81 diabetic and 181 non-diabetic persons) that took two doses of BNT162b2 (Pfizer–BioNTech) mRNA vaccine. Both T2DM and non-diabetic individuals had a robust response to vaccination as demonstrated by their high antibody titers. However, both SARS-CoV-2 IgG and neutralizing antibodies titers were lower in people with T2DM. The mean ( ± 1 standard deviation) levels were 154 ± 49.1 vs. 138 ± 59.4 BAU/ml for IgG and 87.1 ± 11.6 vs. 79.7 ± 19.5% for neutralizing antibodies in individuals without diabetes compared to those with T2DM, respectively. In a multiple linear regression adjusted for individual characteristics, comorbidities, previous COVID-19 infection, and duration since second vaccine dose, diabetics had 13.86 BAU/ml (95% CI: 27.08 to 0.64 BAU/ml, p=0.041) less IgG antibodies and 4.42% (95% CI: 8.53 to 0.32%, p=0.036) fewer neutralizing antibodies than non-diabetics. Hypertension and obesity did not show significant changes in antibody titers. Taken together, both type-2 diabetic and non-diabetic individuals elicited strong immune responses to SARS-CoV-2 BNT162b2 mRNA vaccine; nonetheless, lower levels were seen in people with diabetes. Continuous monitoring of the antibody levels might be a good indicator to guide personalized needs for further booster shots to maintain adaptive immunity. Nonetheless, it is important that people get their COVID-19 vaccination especially people with diabetes.

Autosomal dominant polycystic kidney disease (ADPKD) is an inherited monogenic renal disease characterised by the accumulation of clusters of fluid-filled cysts in the kidneys and is caused by mutations in PKD1 or PKD2 genes. ADPKD genetic diagnosis is complicated by PKD1 pseudogenes located proximal to the original gene with a high degree of homology. The next generation sequencing (NGS) technology including whole exome sequencing (WES) and whole genome sequencing (WGS), is becoming more affordable and its use in the detection of ADPKD mutations for diagnostic and research purposes more widespread. However, how well does NGS technology compare with the Gold standard (Sanger sequencing) in the detection of ADPKD mutations? Is a question that remains to be answered. We have evaluated the efficacy of WES, WGS and targeted enrichment methodologies in detecting ADPKD mutations in the PKD1 and PKD2 genes in patients who were clinically evaluated by ultrasonography and renal function tests. Our results showed that WES detected PKD1 mutations in ADPKD patients with 50% sensitivity, as the reading depth and sequencing quality were low in the duplicated regions of PKD1 (exons 1–32) compared with those of WGS and target enrichment arrays. Our investigation highlights major limitations of WES in ADPKD genetic diagnosis. Enhancing reading depth, quality and sensitivity of WES in the PKD1 duplicated regions (exons 1–32) is crucial for its potential diagnostic or research applications.

Myeloperoxidase (MPO) is positively associated with obesity and diet-induced insulin resistance. Angiopoietin-like protein 6 (ANGPTL6) regulates metabolic processes and counteract obesity through increased energy expenditure. This study aims to evaluate the plasma MPO and ANGPTL6 levels in obese and diabetic individuals as well as MPO association with biochemical markers of obesity. A total of 238 participants were enrolled, including 137 control and 101 type 2 diabetes (T2D) patients. ANGPTL6 and MPO levels and other biomarkers were measured via ELISA. ANGPTL6 levels were significantly higher in the diabetic population and obese individuals. When the group was stratified based on T2D, ANGPTL6 levels were significantly higher in obese-diabetic participants compared with non-obese-diabetics, but obese-non-diabetic individuals had similar ANGPTL6 levels to their controls. MPO levels were higher in obese compared with non-obese participants but did not differ between T2D and control participants. MPO levels were upregulated in obese compared with non-obese in both diabetics and non-diabetics. MPO was positively associated with ANGPTL6, triglyceride, BMI, TNF-alpha, high-sensitivity C-reactive protein, interleukin-6, and plasminogen activator inhibitor-1. Taken together, our findings suggest that both MPO and ANGPTL6 may regulate obesity, although MPO exerts this effect independent of diabetes while ANGPTL6 may have a modulatory role in diabetes.

Wharton's Jelly- derived Mesenchymal stem cells (WJ-MSCs) have gained interest as an alternative source of stem cells for regenerative medicine because of their potential for self-renewal, differentiation and unique immunomodulatory properties. Although many studies have characterized various WJ-MSCs biologically, the expression profiles of the commonly used stemness markers have not yet been addressed. In this study, WJ-MSCs were isolated and characterized for stemness and surface markers expression. Flow cytometry, immunofluorescence and qRT-PCR analysis revealed predominant expression of CD29, CD44, CD73, CD90, CD105 and CD166 in WJ-MSCs, while the hematopoietic and endothelial markers were absent. Differential expression of CD 29, CD90, CD105 and CD166 following adipogenic, osteogenic and chondrogenic induction was observed. Furthermore, our results demonstrated a reduction in CD44 and CD73 expressions in response to the tri-lineage differentiation induction, suggesting that they can be used as reliable stemness markers, since their expression was associated with undifferentiated WJ-MSCs only.