Untargeted metabolomics reveals alterations in metabolites of lipid metabolism and immune pathways in the serum of rats after long-term oral administration of Amalaki rasayana

Metabolism configures immune response across multi-systems: Lessons from COVID-19

Several studies over the last decade demonstrate the recruitment of immune cells, increased inflammatory cytokines, and chemokine in patients with metabolic diseases, including heart failure, parenchymal inflammation, obesity, tuberculosis, and diabetes mellitus. Metabolic rewiring of immune cells is associated with the severity and prevalence of these diseases. The risk of developing COVID-19/SARS-CoV-2 infection increases in patients with metabolic dysfunction (heart failure, diabetes mellitus, and obesity). Several etiologies, including fatigue, dyspnea, and dizziness, persist even months after COVID-19 infection, commonly known as Post-Acute Sequelae of CoV-2 (PASC) or long COVID. A chronic inflammatory state and metabolic dysfunction are the factors that contribute to long COVID. Here, this study explores the potential link between pathogenic metabolic and immune alterations across different organ systems that could underlie COVID-19 and PASC. These interactions could be utilized for targeted future therapeutic approaches.

Multifaceted effects of obesity on cancer immunotherapies: Bridging preclinical models and clinical data

Obesity, defined by excessive body fat, is a highly complex condition affecting numerous physiological processes, such as metabolism, proliferation, and cellular homeostasis. These multifaceted effects impact cells and tissues throughout the host, including immune cells as well as cancer biology. Because of the multifaceted nature of obesity, common parameters used to define it (such as body mass index in humans) can be problematic, and more nuanced methods are needed to characterize the pleiotropic metabolic effects of obesity. Obesity is well-accepted as an overall negative prognostic factor for cancer incidence, progression, and outcome. This is in part due to the meta-inflammatory and immunosuppressive effects of obesity. Immunotherapy is increasingly used in cancer therapy, and there are many different types of immunotherapy approaches. The effects of obesity on immunotherapy have only recently been studied with the demonstration of an "obesity paradox", in which some immune therapies have been demonstrated to result in greater efficacy in obese subjects despite the direct adverse effects of obesity and excess body fat acting on the cancer itself. The multifactorial characteristics that influence the effects of obesity (age, sex, lean muscle mass, underlying metabolic conditions and drugs) further confound interpretation of clinical data and necessitate the use of more relevant preclinical models mirroring these variables in the human scenario. Such models will allow for more nuanced mechanistic assessment of how obesity can impact, both positively and negatively, cancer biology, host metabolism, immune regulation, and how these intersecting processes impact the delivery and outcome of cancer immunotherapy.

Artificial intelligence in microbial natural product drug discovery: current and emerging role

Covering: up to the end of 2022Microorganisms are exceptional sources of a wide array of unique natural products and play a significant role in drug discovery. During the golden era, several life-saving antibiotics and anticancer agents were isolated from microbes; moreover, they are still widely used. However, difficulties in the isolation methods and repeated discoveries of the same molecules have caused a setback in the past. Artificial intelligence (AI) has had a profound impact on various research fields, and its application allows the effective performance of data analyses and predictions. With the advances in omics, it is possible to obtain a wealth of information for the identification, isolation, and target prediction of secondary metabolites. In this review, we discuss drug discovery based on natural products from microorganisms with the help of AI and machine learning.

Gallic Acid and Diabetes Mellitus: Its Association with Oxidative Stress

Diabetes mellitus (DM) is a severe chronic metabolic disease with increased mortality and morbidity. The pathological progression of DM is intimately connected with the formation and activation of oxidative stress (OS). Especially, the involvement of OS with hyperglycemia, insulin resistance, and inflammation has shown a vital role in the pathophysiological development of DM and related complications. Interestingly, accumulating studies have focused on the exploration of natural antioxidants for their improvement on DM. Of specific interest is gallic acid (GA), which is rich in many edible and herbal plants and has progressively demonstrated robust antioxidative and anti-inflammatory effects on metabolic disorders. To provide a better understanding of its potential therapeutic impacts and enhancement of human health care, the available research evidence supporting the effective antidiabetic properties of GA and relevant derivatives are needed to be summarized and discussed, with emphasis on its regulation on OS and inflammation against DM. This review aims to highlight the latest viewpoints and current research information on the role of OS in diabetes and to provide scientific support for GA as a potential antihypoglycemic agent for DM and its complications.

Deciphering the Importance of Glycosphingolipids on Cellular and Molecular Mechanisms Associated with Epithelial-to-Mesenchymal Transition in Cancer

Every living cell is covered with a dense and complex layer of glycans on the cell surface, which have important functions in the interaction between cells and their environment. Glycosphingolipids (GSLs) are glycans linked to lipid molecules that together with sphingolipids, sterols, and proteins form plasma membrane lipid rafts that contribute to membrane integrity and provide specific recognition sites. GSLs are subdivided into three major series (globo-, ganglio-, and neolacto-series) and are synthesized in a non-template driven process by enzymes localized in the ER and Golgi apparatus. Altered glycosylation of lipids are known to be involved in tumor development and metastasis. Metastasis is frequently linked with reversible epithelial-to-mesenchymal transition (EMT), a process involved in tumor progression, and the formation of new distant metastatic sites (mesenchymal-to-epithelial transition or MET). On a single cell basis, cancer cells lose their epithelial features to gain mesenchymal characteristics via mechanisms influenced by the composition of the GSLs on the cell surface. Here, we summarize the literature on GSLs in the context of reversible and cancer-associated EMT and discuss how the modification of GSLs at the cell surface may promote this process.

Systematic Review of Multi-Omics Approaches to Investigate Toxicological Effects in Macrophages

Insights into the modes of action (MoAs) of xenobiotics are of utmost importance for the definition of adverse outcome pathways (AOPs), which are essential for a mechanism-based risk assessment. A well-established strategy to reveal MoAs of xenobiotics is the use of omics. However, often an even more comprehensive approach is needed, which can be achieved using multi-omics. Since the immune system plays a central role in the defense against foreign substances and pathogens, with the innate immune system building a first barrier, we systematically reviewed multi-omics studies investigating the effects of xenobiotics on macrophages. Surprisingly, only nine publications were identified, combining proteomics with transcriptomics or metabolomics. We summarized pathways and single proteins, transcripts, or metabolites, which were described to be affected upon treatment with xenobiotics in the reviewed studies, thus revealing a broad range of effects. In summary, we show that macrophages are a relevant model system to investigate the toxicological effects induced by xenobiotics. Furthermore, the multi-omics approaches led to a more comprehensive overview compared to only one omics layer with slight advantages for combinations that complement each other directly, e.g., proteome and metabolome.