Fungus-growing ants engage in complex symbiotic relationships with their fungal crop, specialized fungal pathogens, and bacteria that provide chemical defenses. In an effort to understand the evolutionary origins of this multilateral system, we investigated bacteria isolated from fungi. One bacterial strain (Streptomyces sp. CLI2509) from the bracket fungus Hymenochaete rubiginosa, produced an unusual peptide, tryptorubin A, which contains heteroaromatic links between side chains that give it a rigid polycyclic globular structure. The three-dimensional structure was determined by NMR and MS, including a ¹³C-¹³C COSY of isotopically enriched material, degradation, derivatives, and computer modeling. Whole genome sequencing identified a likely pair of biosynthetic genes responsible for tryptorubin A’s linear hexapeptide backbone. The genome also revealed the close relationship between CLI2509 and Streptomyces sp. SPB78, which was previously implicated in an insect–bacterium symbiosis.

Rediscovery of known natural products hinders the discovery of new, unique scaffolds. Efforts have mostly focused on streamlining the determination of what compounds are known vs. unknown (dereplication), but an alternative strategy is to focus on what is different. Utilizing statistics and assuming that common actinobacterial metabolites are likely known, focus can be shifted away from dereplication and towards discovery. LC-MS-based principal component analysis (PCA) provides a perfect tool to distinguish unique vs. common metabolites, but the variability inherent within natural products leads to datasets that do not fit ideal standards. To simplify the analysis of PCA models, we developed a script that identifies only those masses or molecules that are unique to each strain within a group, thereby greatly reducing the number of data points to be inspected manually. Since the script is written in R, it facilitates integration with other metabolomics workflows and supports automated mass matching to databases such as Antibase.

Establishing the relative configuration of a bioactive natural product represents the most challenging part in determining its structure. Residual dipolar couplings (RDCs) are sensitive probes of the relative spatial orientation of internuclear vectors. We adapted a force field structure calculation methodology to allow free sampling of both R and S configurations of the stereocenters of interest. The algorithm uses a floating alignment tensor in a simulated annealing protocol to identify the conformations and configurations that best fit experimental RDC and distance restraints (from NOE and J-coupling data). A unique configuration (for rigid molecules) or a very small number of configurations (for less rigid molecules) of the structural models having the lowest chiral angle energies and reasonable magnitudes of the alignment tensor are provided as the best predictions of the unknown configuration. For highly flexible molecules, the progressive locking of their stereocenters into their statistically dominant R or S state dramatically reduces the number of possible relative configurations. The result is verified by checking that the same configuration is obtained by initiating the locking from different regions of the molecule. For all molecules tested having known configurations (with conformations ranging from mostly rigid to highly flexible), the method accurately determined the correct configuration.

Type 2 diabetes (T2D) is a global public health issue characterized by excess weight, abdominal obesity, dyslipidemia, hyperglycemia, and a progressive increase in insulin resistance. Human population studies of T2D development and its effects on systemic metabolism are confounded by many factors that cannot be controlled, complicating the interpretation of results and the identification of early biomarkers. Aged, sedentary, and overweight/obese non-human primates (NHPs) are one of the best animal models to mimic spontaneous T2D development in humans. We sought to identify and distinguish a set of plasma and/or fecal metabolite biomarkers, that have earlier disease onset predictability, and that could be evaluated for their predictability in subsequent T2D studies in human cohorts. In this study, a single plasma and fecal sample was collected from each animal in a colony of 57 healthy and dysmetabolic NHPs and analyzed for metabolomics and lipidomics. The samples were comprehensively analyzed using untargeted and targeted LC/MS/MS. The changes in each animal’s disease phenotype were monitored using IVGTT, HbA1c, and other clinical metrics, and correlated with their metabolic profile. The plasma and fecal lipids, as well as bile acid profiles, from Healthy, Dysmetabolic (Dys), and Diabetic (Dia) animals were compared. Following univariate and multivariate analyses, including adjustments for weight, age, and sex, several plasma lipid species were identified to be significantly different between these animal groups. Medium and long-chain plasma phosphatidylcholines (PCs) ranked highest at distinguishing Healthy from Dys animals, whereas plasma triglycerides (TG) primarily distinguished Dia from Dys animals. Random Forest (RF) analysis of fecal bile acids showed a reduction in the secondary bile acid glycoconjugate, GCDCA, in diseased animals (AUC 0.76[0.64, 0.89]). Moreover, metagenomics results revealed several bacterial species, belonging to the genera Roseburia, Ruminococcus, Clostridium, and Streptococcus, to be both significantly enriched in non-healthy animals and associated with secondary bile acid levels. In summary, our results highlight the detection of several elevated circulating plasma PCs and microbial species associated with fecal secondary bile acids in NHP dysmetabolic states. The lipids and metabolites we have identified may help researchers to differentiate individual NHPs more precisely between dysmetabolic and overtly diabetic states. This could help assign animals to study groups that are more likely to respond to potential therapies where a difference in efficacy might be anticipated between early vs. advanced disease.