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Metabolomic and proteomic analyses of human plasma and serum samples harbor the power to advance our understanding of disease biology. Pre-analytical factors may contribute to variability and bias in the detection of analytes, especially when multiple labs are involved, caused by sample handling, processing time, and differing operating procedures. To better understand the impact of pre-analytical factors that are relevant to implementing a unified proteomic and metabolomic approach in a clinical setting, we assessed the influence of temperature, sitting times, and centrifugation speed on the plasma and serum metabolomes and proteomes from six healthy volunteers. We used targeted metabolic profiling (497 metabolites) and data-independent acquisition (DIA) proteomics (572 proteins) on the same samples generated with well-defined pre-analytical conditions to evaluate criteria for pre-analytical SOPs for plasma and serum samples. Time and temperature showed the strongest influence on the integrity of plasma and serum proteome and metabolome. While rapid handling and low temperatures (4°C) are imperative for metabolic profiling, the analyzed proteomics data set showed variability when exposed to temperatures of 4°C for more than 2 h, highlighting the need for compromises in a combined analysis. We formalized a quality control scoring system to objectively rate sample stability and tested this score using external data sets from other pre-analytical studies. Stringent and harmonized standard operating procedures (SOPs) are required for pre-analytical sample handling when combining proteomics and metabolomics of clinical samples to yield robust and interpretable data on a longitudinal scale and across different clinics. To ensure an adequate level of practicability in a clinical routine for metabolomics and proteomics studies, we suggest keeping blood samples up to 2 h on ice (4°C) prior to snap-freezing as a compromise between stability and operability. Finally, we provide the methodology as an open-source R package allowing the systematic scoring of proteomics and metabolomics data sets to assess the stability of plasma and serum samples.

Analyses of metabolic compounds inside cells or tissues provide high information content since they represent the endpoint of biological information flow and are a snapshot of the integration of many regulatory processes. However, quantification of the abundance of metabolites requires their careful extraction. We present a comprehensive study comparing ten extraction protocols in four human sample types (liver tissue, bone marrow, HL60, and HEK cells) aiming to detect and quantify up to 630 metabolites of different chemical classes. We show that the extraction efficiency and repeatability are highly variable across protocols, tissues, and chemical classes of metabolites. We used different quality metrics including the limit of detection and variability between replicates as well as the sum of concentrations as a global estimate of analytical repeatability of the extraction. The coverage of extracted metabolites depends on the used solvents, which has implications for the design of measurements of different sample types and metabolic compounds of interest. The benchmark dataset can be explored in an easy-to-use, interactive, and flexible online resource (R/shiny app MetaboExtract: http://www.metaboextract.shiny.dkfz.de) for context-specific selection of the optimal extraction method. Furthermore, data processing and conversion functionality underlying the shiny app are accessible as an R package: https://cran.r-project.org/package=MetAlyzer.

Metabolic profiling harbors the potential to better understand various disease entities such as cancer, diabetes, Alzheimer’s, Parkinson’s disease or COVID-19. To better understand such diseases and their intricate metabolic pathways in human studies, model animals are regularly used. There, standardized rearing conditions and uniform sampling strategies are prerequisites towards a successful metabolomic study that can be achieved through model organisms. Although metabolomic approaches have been employed on model organisms before, no systematic assessment of different conditions to optimize metabolite extraction across several organisms and sample types has been conducted. We address this issue using a highly standardized metabolic profiling assay analyzing 630 metabolites across three commonly used model organisms (Drosophila, mouse, and zebrafish) to find an optimal extraction protocol for various matrices. Focusing on parameters such as metabolite coverage, concentration and variance between replicates we compared seven extraction protocols. We found that the application of a combination of 75% ethanol and methyl tertiary-butyl ether (MTBE), while not producing the broadest coverage and highest concentrations, was the most reproducible extraction protocol. We were able to determine up to 530 metabolites in mouse kidney samples, 509 in mouse liver, 422 in zebrafish and 388 in Drosophila and discovered a core overlap of 261 metabolites in these four matrices. To enable other scientists to search for the most suitable extraction protocol in their experimental context and interact with this comprehensive data, we have integrated our data set in the open-source shiny app “MetaboExtract”. Hereby, scientists can search for metabolites or compound classes of interest, compare them across the different tested extraction protocols and sample types as well as find reference concentration values.

The liver is the central hub for processing and maintaining homeostatic levels of dietary nutrients especially essential amino acids such as tryptophan (Trp). Trp is required not only to sustain protein synthesis but also as a precursor for the production of NAD, neurotransmitters and immunosuppressive metabolites. In light of these roles of Trp and its metabolic products, maintaining homeostatic levels of Trp is essential for health and well-being. The liver regulates global Trp supply by the immunosuppressive enzyme tryptophan-2,3-dioxygenase (TDO2), which degrades Trp down the kynurenine pathway (KP). In the current study, we show that isolated primary hepatocytes when exposed to hypoxic environments, extensively rewire their Trp metabolism by reducing constitutive Tdo2 expression and differentially regulating other Trp pathway enzymes and transporters. Mathematical modelling of Trp metabolism in liver cells under hypoxia predicted decreased flux through the KP while metabolic flux through the tryptamine branch significantly increased. In line, the model also revealed an increased accumulation of tryptamines under hypoxia, at the expense of kynurenines. Metabolic measurements in hypoxic hepatocytes confirmed the predicted reduction in KP metabolites as well as accumulation of tryptamine. Tdo2 expression in cultured primary hepatocytes was reduced upon hypoxia inducible factor (HIF) stabilisation by dimethyloxalylglycine (DMOG), demonstrating that HIFs are involved in the hypoxic downregulation of hepatic Tdo2. DMOG abrogated hepatic luciferase signals in Tdo2 reporter mice, indicating that HIF stability also recapitulates hypoxic rewiring of Trp metabolism in vivo. Also in WT mice HIF stabilization drove homeostatic Trp metabolism away from the KP towards enhanced tryptamine production, leading to enhanced levels of tryptamine in liver, serum and brain. As tryptamines are the most potent hallucinogens known, the observed upregulation of tryptamine in response to hypoxic exposure of hepatocytes may be involved in the generation of hallucinations occurring at high altitude. KP metabolites are known to activate the aryl hydrocarbon receptor (AHR). The AHR-activating properties of tryptamines may explain why immunosuppressive AHR activity is maintained under hypoxia despite downregulation of the KP. In summary our results identify hypoxia as an important factor controlling Trp metabolism in the liver with possible implications for immunosuppressive AHR activation and mental disturbances.

Coral bleaching, i.e., the loss of photosynthetic algal endosymbionts, caused by ocean warming is now among the main factors driving global reef decline, making the elucidation of factors that contribute to thermotolerance important. Recent studies implicate high salinity as a contributing factor in cnidarians, potentially explaining the high thermotolerance of corals from the Arabian Seas. Here we characterized bacterial community composition under heat stress at different salinities using the coral model Aiptasia. Exposure of two Aiptasia host-algal symbiont pairings (H2-SSB01 and CC7-SSA01) to ambient (25°C) and heat stress (34°C) temperatures at low (36 PSU), intermediate (39 PSU), and high (42 PSU) salinities showed that bacterial community composition at high salinity was significantly different, concomitant with reduced bleaching susceptibility in H2-SSB01, not observed in CC7-SSA01. Elucidation of bacteria that showed increased relative abundance at high salinity, irrespective of heat stress, revealed candidate taxa that could potentially contribute to the observed increased thermotolerance. We identified 4 (H2-SSB01) and 3 (CC7-SSA01) bacterial taxa belonging to the orders Alteromonadales (1 OTU), Oligoflexales (1 OTU), Rhizobiales (2 OTUs), and Rhodobacterales (2 OTUs), suggesting that only few bacterial taxa are potential contributors to an increase in thermal tolerance at high salinities. These taxa have previously been implicated in nitrogen and DMSP cycling, processes that are considered to affect thermotolerance. Our study demonstrates microbiome restructuring in symbiotic cnidarians under heat stress at different salinities. As such, it underlines how host-associated bacterial communities adapt to prevailing environmental conditions with putative consequences for the environmental stress tolerance of the emergent metaorganism.