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Animals experience climatic variation in their natural habitats, which may lead to variation in phenotypic responses among populations through local adaptation or phenotypic plasticity. In ectotherm arthropods, the expression of thermoprotective metabolites such as free amino acids, sugars, and polyols, in response to temperature stress, may facilitate temperature tolerance by regulating cellular homeostasis. If populations experience differences in temperatures, individuals may exhibit population-specific metabolite profiles through differential accumulation of metabolites that facilitate thermal tolerance. Such thermoprotective metabolites may originate from the animals themselves or from their associated microbiome, and hence microbial symbionts may contribute to shape the thermal niche of their host. The social spider Stegodyphus dumicola has extremely low genetic diversity, yet it occupies a relatively broad temperature range occurring across multiple climate zones in Southern Africa. We investigated whether the metabolome, including thermoprotective metabolites, differs between populations, and whether population genetic structure or the spider microbiome may explain potential differences. To address these questions, we assessed metabolite profiles, phylogenetic relationships, and microbiomes in three natural populations along a temperature gradient. The spider microbiomes in three genetically distinct populations of S. dumicola showed no significant population-specific pattern, and none of its dominating genera (Borrelia, Diplorickettsia, and Mycoplasma) are known to facilitate thermal tolerance in hosts. These results do not support a role of the microbiome in shaping the thermal niche of S. dumicola. Metabolite profiles of the three spider populations were significantly different. The variation was driven by multiple metabolites that can be linked to temperature stress (e.g., lactate, succinate, or xanthine) and thermal tolerance (e.g., polyols, trehalose, or glycerol): these metabolites had higher relative abundance in spiders from the hottest geographic region. These distinct metabolite profiles are consistent with a potential role of the metabolome in temperature response.
Over the last decades, thyroid hormone metabolites (THMs) received marked attention as it has been demonstrated that they are bioactive compounds. Their concentrations were determined by immunoassay or mass-spectrometry methods. Among those metabolites, 3,5-diiodothyronine (3,5-T2), occurs at low nanomolar concentrations in human serum, but might reach tissue concentrations similar to those of T4 and T3, at least based on data from rodent models. However, the immunoassay-based measurements in human sera revealed remarkable variations depending on antibodies used in the assays and thus need to be interpreted with caution. In clinical experimental approaches in euthyroid volunteers and hypothyroid patients using the immunoassay as the analytical tool no evidence of formation of 3,5-T2 from its putative precursors T4 or T3 was found, nor was any support found for the assumption that 3,5-T2 might represent a direct precursor for serum 3-T1-AM generated by combined deiodination and decarboxylation from 3,5-T2, as previously documented for mouse intestinal mucosa. We hypothesized that lowered endogenous production of 3,5-T2 in patients requiring T4 replacement therapy after thyroidectomy or for treatment of autoimmune thyroid disease, compared to production of 3,5-T2 in individuals with intact thyroid glands might contribute to the discontent seen in a subset of patients with this therapeutic regimen. So far, our observations do not support this assumption. However, the unexpected association between high serum 3,5-T2 and elevated urinary concentrations of metabolites related to coffee consumption requires further studies for an explanation. Elevated 3,5-T2 serum concentrations were found in several situations including impaired renal function, chronic dialysis, sepsis, non-survival in the ICU as well as post-operative atrial fibrillation (POAF) in studies using a monoclonal antibody-based chemoluminescence immunoassay. Pilot analysis of human sera using LC-linear-ion-trap-mass-spectrometry yielded 3,5-T2 concentrations below the limit of quantification in the majority of cases, thus the divergent results of both methods need to be reconciliated by further studies. Although positive anti-steatotic effects have been observed in rodent models, use of 3,5-T2 as a muscle anabolic, slimming or fitness drug, easily obtained without medical prescription, must be advised against, considering its potency in suppressing the HPT axis and causing adverse cardiac side effects. 3,5-T2 escapes regular detection by commercially available clinical routine assays used for thyroid function tests, which may be seriously disrupted in individuals self-administering 3,5-T2 obtained over-the counter or from other sources.
With the development of new functional genomics methods that can access the whole genome, transcriptome, proteome and metabolome more comprehensive insights in cellular processes are possible. Largely based on these advances, our knowledge about molecular constituents for many organisms is increasing at a tremendous rate. Until today, the genomes of several organisms including pathogenic bacteria are already sequenced and pave the way for metabolic network constructions. Interest in metabolomics, the global profiling of metabolites in a cell, tissue or organism, has been rapidly increased. A range of analytical techniques, including nuclear magnetic resonance (NMR) spectroscopy, gas chromatography–mass spectrometry (GC–MS), liquid chromatography–mass spectrometry (LC–MS), Fourier Transform mass spectrometry (FT–MS), high performance liquid chromatography (HPLC) are required in order to maximize the number of metabolites that can be identified in a matrix. With the help of microbial metabolomics (qualification and quantification of a huge variety of metabolites from a bacterium) deciphering of the bacterial metabolism is feasible. The metabolome pipeline or workflow encompasses the processes of (i) sample generation and preparation, (ii) establishment of analytical techniques (iii) collection of analytical data, raw data pre-processing, (iv) data analysis and (v) data integration into biological questions. The present work contributes to the above mentioned steps in a metabolomics workflow. A specific focus was set to the exo- and endometabolome analysis of Gram-positive bacteria