Alzheimer’s disease (AD) is reported to be closely linked with abnormal lipid metabolism. To gain a more comprehensive understanding of what causes AD and its subsequent development, we profiled the lipidome of postmortem (PM) human brains (neocortex) of people with a range of AD pathology (Braak 0–6). Using high-resolution mass spectrometry, we employed a semi-targeted, fully quantitative lipidomics profiling method (Lipidyzer) to compare the biochemical profiles of brain tissues from persons with mild AD (n = 15) and severe AD (AD; n = 16), and compared them with age-matched, cognitively normal controls (n = 16). Univariate analysis revealed that the concentrations of 420 lipid metabolites significantly (p < 0.05; q < 0.05) differed between AD and controls. A total of 49 lipid metabolites differed between mild AD and controls, and 439 differed between severe AD and mild AD. Interestingly, 13 different subclasses of lipids were significantly perturbed, including neutral lipids, glycerolipids, glycerophospholipids, and sphingolipids. Diacylglycerol (DAG) (14:0/14:0), triacylglycerol (TAG) (58:10/FA20:5), and TAG (48:4/FA18:3) were the most notably altered lipids when AD and control brains were compared (p < 0.05). When we compare mild AD and control brains, phosphatidylethanolamine (PE) (p-18:0/18:1), phosphatidylserine (PS) (18:1/18:2), and PS (14:0/22:6) differed the most (p < 0.05). PE (p-18:0/18:1), DAG (14:0/14:0), and PS (18:1/20:4) were identified as the most significantly perturbed lipids when AD and mild AD brains were compared (p < 0.05). Our analysis provides the most extensive lipid profiling yet undertaken in AD brain tissue and reveals the cumulative perturbation of several lipid pathways with progressive disease pathology. Lipidomics has considerable potential for studying AD etiology and identifying early diagnostic biomarkers.

Objective: Diabetes mellitus (DM) is a metabolic condition defined by hyperglycemia driven by insulin deficiency or decreased insulin activity. GLP-1, a gut enzyme, stimulates insulin production and reduces hepatic glucose synthesis to regulate diabetes. GLP-1 agonists enhance insulin sensitivity and decrease blood glucose to relieve symptoms of DM. These medications represent a novel paradigm to manage diabetes as they improve glycaemic control in type 2 diabetic patients. Snake venom proteins have been investigated as a potential medicinal strategy for diabetes treatment. These proteins contain a multitude of bioactive constituents, such as insulinotropic cytotoxins, which have been found to influence insulin secretion and glucose homeostasis. Methods: In the present study, the snake venom proteins long neurotoxin 1 Cytotoxin 7, Cytotoxin 2a, and Cytotoxin 10 were modeled and their therapeutic efficacy as GLP-1 analogs was determined by employing molecular docking techniques. The binding of snake venom protein towards GLP-1 receptors was compared against the positive controls (Exenatide, Liraglutide, Semaglutide, and Lixisenatide). Results: The results demonstrated that the cytotoxins (Cytotoxin 2a, Cytotoxin 7, and Cytotoxin 10) exhibited comparable binding with the positive controls and majorly interacted with the hydrophobic amino acids in the binding pocket of the GLP-1 receptor. The modeled snake venom toxins demonstrated beneficial physicochemical properties and advocated them to be a novel contender for the development of GLP-1 analogs. Conclusion: Despite its beneficial outcomes, the utilization of snake venom proteins as a therapeutic agent for diabetes is still in its initial stages, and additional research is required to assess their effectiveness and safety in patients.

Background: The intestinal mucus layer is comprised of heavily glycosylated mucins, including mucin 2 (MUC2), that serve as a nutrient source for certain bacterial members of the gut microbiota. Only a subset of gut commensals encode the glycoside hydrolases required to degrade mucin glycans. However, mucin-degrading microbes can release glycans and generate compounds that can cross-fed non-mucin degrading microbes, creating complex microbial networks. While pairwise studies have shown that mucin degradation drives cross-feeding and metabolite exchange, the broader impact of mucins on community structure and metabolic output remains poorly understood. Objective: In this study, we sought to identify how a defined microbial consortium of human commensals with varied mucin-degrading capacities responds to MUC2 to shape community composition and metabolic output. Methods: A defined consortium of human gut commensals with varied mucin-degrading capacities was cultivated in anaerobic bioreactors in the presence or absence of porcine MUC2. Community composition was assessed, and extracellular metabolites were quantified using targeted and untargeted metabolomic profiling. Results: MUC2 supplementation significantly altered community structure, promoting the expansion of Akkermansia muciniphila while reducing Prevotella. MUC2 also reshaped microbial metabolism, decreasing acetate levels while increasing propionate, butyrate, and formate. In addition, MUC2 supplementation altered amino acid utilization and vitamin metabolism and reduced several neuroactive compounds, including glutamate, γ-aminobutyric acid (GABA), and anthranilic acid, while increasing tryptamine levels. Conclusion: These findings demonstrate that mucins exert broad effects on microbial community structure and metabolic output. Collectively, this work highlights the central role of bacterial cross-feeding in shaping gut ecosystem function.