Medicina

The human gut microbiome establishes and matures during infancy, and dysregulation at this stage may lead to pathologies later in life. We conducted a multi-omics study comprising three generations of family members to investigate the early development of the gut microbiota. Fecal samples from 200 individuals, including infants (0-12 months old; 55% females, 45% males) and their respective mothers and grandmothers, were analyzed using two independent metabolomics platforms and metagenomics. For metabolomics, gas chromatography and capillary electrophoresis coupled to mass spectrometry were applied. For metagenomics, both 16S rRNA gene and shotgun sequencing were performed. Here we show that infants greatly vary from their elders in fecal microbiota populations, function, and metabolome. Infants have a less diverse microbiota than adults and present differences in several metabolite classes, such as short- and branched-chain fatty acids, which are associated with shifts in bacterial populations. These findings provide innovative biochemical insights into the shaping of the gut microbiome within the same generational line that could be beneficial in improving childhood health outcomes.

GC-MS for untargeted metabolomics is a well-established technique. Small molecules and molecules made volatile by derivatization can be measured and those compounds are key players in main biological pathways. This tutorial provides ready-to-use protocols for GC-MS-based metabolomics, using either the well-known low-resolution approach (GC-Q-MS) with nominal mass or the more recent high-resolution approach (GC-QTOF-MS) with accurate mass, discussing their corresponding strengths and limitations. Analytical procedures are covered for different types of biofluids (plasma/serum, bronchoalveolar lavage, urine, amniotic fluid) tissue samples (brain/hippocampus, optic nerve, lung, kidney, liver, pancreas) and samples obtained from cell cultures (adipocytes, macrophages, Leishmania promastigotes, mitochondria, culture media). Together with the sample preparation and data acquisition, data processing strategies are described specially focused on Agilent equipments, including deconvolution software and database annotation using spectral libraries. Manual curation strategies and quality control are also deemed. Finally, considerations to obtain a semiquantitative value for the metabolites are also described. As a case study, an illustrative example from one of our experiments at CEMBIO Research Centre, is described and findings discussed.

Plasmid-mediated antimicrobial resistance is one of the major threats to public health worldwide. The mechanisms involved in the plasmid/host coadaptation are still poorly characterized, and their understanding is crucial to comprehend the genesis and evolution of multidrug-resistant bacteria. With this purpose, we designed an experimental evolution using Haemophilus influenzae RdKW20 as the model strain carrying the ColE1-like plasmid pB1000. Five H. influenzae populations adapted previously to the culture conditions were transformed with pB1000 and subsequently evolved to compensate for the plasmid-associated fitness cost. Afterward, we performed an integrative multiomic analysis combining genomics, transcriptomics, and metabolomics to explore the molecular mechanisms involved in the compensatory evolution of the plasmid. Our results demonstrate that minimal modifications in the host are responsible for plasmid adaptation. Among all of them, the most enriched process was amino acid metabolism, especially those pathways related to serine, tryptophan, and arginine, eventually related to the genesis and resolution of plasmid dimers. Additional rearrangements occurred during the plasmid adaptation, such as an overexpression of the ribonucleotide reductases and metabolic modifications within specific membrane phospholipids. All these findings demonstrate that the plasmid compensation occurs through the combination of diverse host-mediated mechanisms, of which some are beyond genomic and transcriptomic modifications. IMPORTANCE The ability of bacteria to horizontally transfer genetic material has turned antimicrobial resistance into one of the major sanitary crises of the 21st century. Plasmid conjugation is considered the main mechanism responsible for the mobilization of resistance genes, and its understanding is crucial to tackle this crisis. It is generally accepted that the acquisition and maintenance of mobile genetic elements entail a fitness cost to its host, which is susceptible to be alleviated through a coadaptation process or compensatory evolution. Notwithstanding, despite recent major efforts, the underlying mechanisms involved in this adaptation remain poorly characterized. Analyzing the plasmid/host coadaptation from a multiomic perspective sheds light on the physiological processes involved in the compensation, providing a new understanding on the genesis and evolution of plasmid-mediated antimicrobial-resistant bacteria.

Leishmania donovani infection of macrophages drives profound changes in the metabolism of both the host macrophage and the parasite, which undergoes different phases of development culminating in replication and propagation. However, the dynamics of this parasite-macrophage cometabolome are poorly understood. In this study, a multiplatform metabolomics pipeline combining untargeted, high-resolution CE-TOF/MS and LC-QTOF/MS with targeted LC-QqQ/MS was followed to characterize the metabolome alterations induced in L. donovani-infected human monocyte-derived macrophages from different donors at 12, 36, and 72 h post-infection. The set of alterations known to occur during Leishmania infection of macrophages, substantially expanded in this investigation, characterized the dynamics of the glycerophospholipid, sphingolipid, purine, pentose phosphate, glycolytic, TCA, and amino acid metabolism. Our results showed that only citrulline, arginine, and glutamine exhibited constant trends across all studied infection time points, while most metabolite alterations underwent a partial recovery during amastigote maturation. We determined a major metabolite response pointing to an early induction of sphingomyelinase and phospholipase activities and correlated with amino acid depletion. These data represent a comprehensive overview of the metabolome alterations occurring during promastigote-to-amastigote differentiation and maturation of L. donovani inside macrophages that contributes to our understanding of the relationship between L. donovani pathogenesis and metabolic dysregulation.

Haemophilus influenzae is a gram-negative bacterium of relevant clinical interest. H. influenzae Rd KW20 was the first organism to be sequenced and for which a genome-scale metabolic model (GEM) was developed. However, current H. influenzae GEMs are unable to capture several aspects of metabolome nature related to metabolite pools. To directly and comprehensively characterize the endometabolome of H. influenzae Rd KW20, we performed a multiplatform MS-based metabolomics approach combining LC-MS, GC-MS and CE-MS. We obtained direct evidence of 15-20% of the endometabolome present in current H. influenzae GEMs and showed that polar metabolite pools are interconnected through correlating metabolite islands. Notably, we obtained high-quality evidence of 18 metabolites not previously included in H. influenzae GEMs, including the antimicrobial metabolite cyclo(Leu-Pro). Additionally, we comprehensively characterized and evaluated the quantitative composition of the phospholipidome of H. influenzae, revealing that the fatty acyl chain composition is largely independent of the lipid class, as well as that the probability distribution of phospholipids is mostly related to the conditional probability distribution of individual acyl chains. This finding enabled us to provide a rationale for the observed phospholipid profiles and estimate the abundance of low-level species, permitting the expansion of the phospholipidome characterization through predictive probabilistic modelling.

The mechanisms whereby Mycobacterium tuberculosis (Mtb) rewires the host metabolism in vivo are surprisingly unexplored. Here, we used three high-resolution mass spectrometry platforms to track altered lung metabolic changes associated with Mtb infection of mice. The multiplatform data sets were merged using consensus orthogonal partial least squaresdiscriminant analysis (cOPLS-DA), an algorithm that allows for the joint interpretation of the results from a single multivariate analysis. We show that Mtb infection triggers a temporal and progressive catabolic state to satisfy the continuously changing energy demand to control infection. This causes dysregulation of metabolic and oxido-reductive pathways culminating in Mtbassociated wasting. Notably, high abundances of trimethylamine-N-oxide (TMAO), produced by the host from the bacterial metabolite trimethylamine upon infection, suggest that Mtb could exploit TMAO as an electron acceptor under anaerobic conditions. Overall, these new pathway alterations advance our understanding of the link between Mtb pathogenesis and metabolic dysregulation and could serve as a foundation for new therapeutic intervention strategies. Mass spectrometry data has been deposited in the Metabolomics Workbench repository (data-set identifier: ST001328).