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.

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.

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.

Betatrophin/ANGPTL8 is a newly identified hormone produced in liver and adipose tissue that has been shown to be induced as a result of insulin resistance and regulates lipid metabolism. Little is known about betatrophin level in humans and its association with T2D and metabolic risk factors. Plasma level of betatrophin was measured by ELISA in 1603 subjects: 1047 non-diabetic and 556 T2D subjects and its associations with metabolic risk factors in both non-diabetic and T2D were also studied. Our data show a significant difference in betatrophin levels between non-diabetic (731.3 (59.5–10625.0) pg/ml) and T2D (1710.5 (197.4–12361.1) p < 0.001. Betatrophin was positively correlated with age, BMI, waist/hip ratio, FBG, HbA1C, HOMA-IR and TG in the non-diabetic subjects. However, no association was observed with BMI, FBG, HbA1C or HOMA-IR in T2D subjects. TC and LDL showed negative association with betatrophin in T2D subjects. Multivariate analysis showed that subjects in the highest tertile of betatrophin had higher odds of having T2D (odd ratio [OR] = 6.15, 95% confidence interval [CI] = (3.15 – 12.01). Our data show strong positive associations between betatrophin and FBG and insulin resistance in non-diabetic subjects. However, correlations with FBG and insulin resistance were diminished in T2D subjects.

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