Simultaneous exploration of nutrients and pollutants in human milk and their impact on preterm infant growth: An integrative cross-platform approach.

Early nutritional management including fortified human breastmilk is currently recommended to fulfil the energy demands and counterbalance risks associated to preterm birth. However, little is known about the potential adverse effects of exposure to persistent organic pollutants (POPs) carried in human milk on preterm infant growth. We conducted a pilot study proving the application of an integrative analytical approach based on mass spectrometry (MS) coupled to advanced statistical models, favouring the comprehensive molecular profiling to support the identification of multiple biomarkers. We applied this workflow in the frame of a preterm infants' cohort to explore environmental determinants of growth. The combination of high resolution gas and liquid chromatography MS platforms generated a large molecular profile, including 102 pollutants and nutrients (targeted analysis) and 784 metabolites (non-targeted analysis). Data analysis consisted in a preliminary examination of associations between the signatures of POPs and the normalised growth of preterm infants, using multivariate linear regression adjusting for known confounding variables. A second analysis aimed to identify multidimensional biomarkers using a multiblock algorithm allowing the integration of multiple datasets in the growth model of preterm infants. The preliminary results did not suggest an impairment of preterm growth associated to the milk concentrations of POPs. The multiblock approach however revealed complex interrelated molecular networks of POPs, lipids, metabolites and amino acids in breastmilk associated to preterm infant growth, supporting the high potential of biomarkers exploration of this proposed workflow. Whereas the present study intended to identify simultaneously pollutant and nutrient exposure profiles associated to early preterm infant growth, this workflow may be easily adapted and applied to other matrices (e.g. serum) and research settings, favouring the functional exploration of environmental determinants of complex and multifactorial diseases.

Moderate High Caloric Maternal Diet Impacts Dam Breast Milk Metabotype and Offspring Lipidome in a Sex-Specific Manner.

Lactation is a critical period during which maternal sub- or over-nutrition affect milk composition and offspring development that can have lasting health effects. The consequences of moderate high-fat, high-simple carbohydrate diet (WD) consumption by rat dams, during gestation and lactation, on milk composition and offspring blood lipidome and its growth, at weaning, were investigated by using a comprehensive lipidomic study on mass-spectrometric platform combined to targeted fatty- and free amino-acids analysis. This holistic approach allowed clear-cut differences in mature milk-lipidomic signature according to maternal diet with a similar content of protein, lactose and leptin. The lower WD-milk content in total fat and triglycerides (TGs), particularly in TGs-with saturated medium-chain, and higher levels in both sphingolipid (SL) and TG species with unsaturated long-chain were associated to a specific offspring blood-lipidome with decreased levels in TGs-containing saturated fatty acid (FA). The sexual-dimorphism in the FA-distribution in TG (higher TGs-rich in oleic and linoleic acids, specifically in males) and SL species (increased levels in very long-chain ceramides, specifically in females) could be associated with some differences that we observed between males and females like a higher total body weight gain in females and an increased preference for fatty taste in males upon weaning.

Population genomics of divergence within an obligate pollination mutualism: Selection maintains differences between Joshua tree species.

PREMISE OF THE STUDY: Speciation is a complex process that can be shaped by many factors, from geographic isolation to interspecific interactions. In Joshua trees, selection from pollinators on style length has been hypothesized to contribute to the maintenance of differentiation between two hybridizing sister species. We used population genomics approaches to measure the extent of genetic differentiation between these species, test whether selection maintains differences between them, and determine whether genetic variants associated with style length show signatures of selection. METHODS: Using restriction-site-associated DNA (RAD)-sequencing, we identified 9516 single nucleotide polymorphisms (SNPs) across the Joshua tree genome. We characterized the genomic composition of trees in a narrow hybrid zone and used genomic scans to search for signatures of selection acting on these SNPs. We used a genome-wide association study to identify SNPs associated with variation in phenotypic traits, including style length, and asked whether those SNPs were overrepresented among the group under selection. KEY RESULTS: The two species were highly genetically differentiated (FST = 0.25), and hybrids were relatively rare in the hybrid zone. Approximately 20% of SNPs showed evidence of selection maintaining divergence. While SNPs associated with style length were overrepresented among those under selection (P << 0.0001), the same was true for SNPs associated with highly differentiated vegetative traits. CONCLUSIONS: The two species of Joshua tree are clearly genetically distinct, and selection is maintaining differences between them. We found that loci associated with differentiated traits were likely to be under selection. However, many traits other than style length appeared to be under selection. Together with the dearth of intermediate hybrids, these findings reveal that these taxa are more strongly diverged than previously suspected and that selection, likely on many targets, is maintaining separation where the two species meet and hybridize.

Quantifying nonadditive selection caused by indirect ecological effects.

In natural biological communities, species interact with many other species. Multiple species interactions can lead to indirect ecological effects that have important fitness consequences and can cause nonadditive patterns of natural selection. Given that indirect ecological effects are common in nature, nonadditive selection may also be quite common. As a result, quantifying nonadditive selection resulting from indirect ecological effects may be critical for understanding adaptation in natural communities composed of many interacting species. We describe how to quantify the relative strength of nonadditive selection resulting from indirect ecological effects compared to the strength of pairwise selection. We develop a clear method for testing for nonadditive selection caused by indirect ecological effects and consider how it might affect adaptation in multispecies communities. We use two case studies to illustrate how our method can be applied to empirical data sets. Our results suggest that nonadditive selection caused by indirect ecological effects may be common in nature. Our hope is that trait-based approaches, combined with multifactorial experiments, will result in more estimates of nonadditive selection that reveal the relative importance of indirect ecological effects for evolution in a community context.

Implementation of a semi-automated strategy for the annotation of metabolomic fingerprints generated by liquid chromatography-high resolution mass spectrometry from biological samples.

Metabolomics aims at detecting and semi-quantifying small molecular weight metabolites in biological samples in order to characterise the metabolic changes resulting from one or more given factors and/or to develop models based on diagnostic biomarker candidates. Nevertheless, whatever the objective of a metabolomic study, one critical step consists in the structural identification of mass spectrometric features revealed by statistical analysis and this remains a real challenge. Indeed, this requires both an understanding of the studied biological system, the correct use of various analytical information (retention time, molecular weight experimentally measured, isotopic golden rules, MS/MS fragment pattern interpretation…), or querying online databases. In gas chromatography-electro-ionisation (EI)-mass spectrometry, EI leads to a very reproducible fragmentation allowing establishment of universal EI mass spectra databases (for example, the NIST database -National Institute of Standards and Technology) and thus facilitates the identification step. Unfortunately, the situation is different when working with liquid chromatography-mass spectrometry (LC-MS) since atmospheric pressure ionisation exhibits high inter-instrument variability regarding fragmentation. Therefore, the constitution of LC-MS "in-house" spectral databases appears relevant in this context. The present study describes the procedure developed and applied to increment 133 and 130 metabolites in databanks dedicated to analyses performed with LC-HRMS in positive and negative electrospray ionisation, and the use of these databanks for annotating quickly untargeted metabolomics fingerprints. This study also describes the optimization of the parameters controlling the automatic processing in order to obtain a fast and reliable annotation of a maximum of organic compounds. This strategy was applied to bovine kidney samples collected from control animals or animals treated with steroid hormones. Thirty-eight compounds were identified successfully in the generated chemical phenotypes, among which five were found to be candidate markers of the administration of these anabolic agents, demonstrating the efficiency of the developed strategy to reveal and confirm metabolite structures according to the high-throughput objective expected from these integrative biological approaches.

Urinary metabolomic profiling from spontaneous tolerant kidney transplanted recipients shows enrichment in tryptophan-derived metabolites.

BACKGROUND: Operational tolerance is the holy grail in solid organ transplantation. Previous reports showed that the urinary compartment of operationally tolerant recipients harbor a specific and unique profile. We hypothesized that spontaneous tolerant kidney transplanted recipients (KTR) would have a specific urinary metabolomic profile associated to operational tolerance. METHODS: We performed metabolomic profiling on urine samples from healthy volunteers, stable KTR under standard and minimal immunosuppression and spontaneous tolerant KTR using liquid chromatography in tandem with mass spectrometry. Supervised and unsupervised multivariate computational analyses were used to highlight urinary metabolomic profile and metabolite identification thanks to workflow4metabolomic platform. FINDINGS: The urinary metabolome was composed of approximately 2700 metabolites. Raw unsupervised clustering allowed us to separate healthy volunteers and tolerant KTR from others. We confirmed by two methods a specific urinary metabolomic signature in tolerant KTR mainly driven by kynurenic acid independent of immunosuppressive drugs, serum creatinine and gender. INTERPRETATION: Kynurenic acid and tryptamine enrichment allowed the identification of putative pathways and metabolites associated with operational tolerance like IDO, GRP35 and AhR and indole alkaloids. FUNDING: This study was supported by the ANR, IRSRPL and CHU de Nantes.

Nandrolone and estradiol biomarkers identification in bovine urine applying a liquid chromatography high-resolution mass spectrometry metabolomics approach.

With the aim of specifically investigating patterns associated with three steroid treatments (17ß-nandrolone, 17ß-estradiol, and 17ß-nandrolone + 17ß-estradiol) in bovine, an reversed phase liquid chromatography (RPLC)-electrospray ionization (ESI)(+/-)-high-resolution mass spectrometry (HRMS) study was conducted to characterize the urinary profiles of involved animals. Although specific fingerprints with strong differences could be highlighted between urinary metabolite profiles within urine samples collected on control and treated animals, it appeared further that significant discriminations could also be observed between steroid treatments, evidencing thus specific patterns and candidate biomarkers associated to each treatment. An MS-2 structural elucidation step enabled level-1 identification of two biomarkers mainly involved in energy pathways, in relation to skeletal muscle functioning. These results make it possible to envisage a global strategy for the detection of anabolic practices involving steroids, while at the same time providing clues as to the compounds used, which would facilitate the confirmation stage to follow.

Associations between persistent organic pollutants and endometriosis: A multiblock approach integrating metabolic and cytokine profiling.

Humans are exposed daily to complex mixtures of chemical pollutants through their environment and diet, some of which have the potential to disrupt the bodies' natural endocrine functions and contribute to reproductive diseases like endometriosis. Increasing epidemiological and experimental evidence supports the association between endometriosis and certain persistent organic pollutants (POPs) like dioxins; however, little is known about the underlying linking mechanisms. The main objective of this study is to proof the methodological applicability and discovery potential of integrating ultra-trace mass spectrometry (MS) profiling of POP biomarkers and endogenous biomarker profiling (MS metabolomics and cytokines) in a case-control study for the etiological research of endometriosis. The approach is applied in a pilot clinical-based study conducted in France where women with and without surgically confirmed endometriosis were recruited. Serum samples were analysed with high-resolution MS for about 30 polychlorinated biphenyls (PCBs), organochlorinated pesticides and perfluoroalkyl substances (PFAS). About 600 serum metabolites and lipids were identified with targeted metabolomics using tandem MS with the Biocrates MxP® Quant 500 Kit. A panel of 4 pro-inflammatory cytokines were analysed using ELISA-based 4-PLEX analyser. Statistical analysis included a battery of variable selection approaches, multivariate logistic regression for single-chemical associations, Bayesian kernel machine regressions (BKMR) to identify mixture effects of POPs and a multiblock approach to identify shared biomarker signatures among high risk clusters. The results showed the positive associations between some POPs and endometriosis risk, including the pesticide trans-nonachlor Odds Ratio (95% Confidence Interval) 3.38 (2.06-5.98), p < 0.0001 and PCB 114 OR (95% CI) 1.83 (1.17-2.93), p = 0.009. The BKMR approach showed a tendency of a positive cumulative effect of the mixture, however trans-nonachlor exhibited significant associations within the mixture and interacted with other PCBs, strengthening the effects at highest concentrations. Finally, the multiblock analysis, relating the various blocks of data, revealed a latent cluster of women with higher risk of endometrioma presenting higher concentrations of trans-nonachlor, PCB 114 and dioxin-like toxic equivalents from PCBs, together with an increased inflammatory profile (i.e. elevated interleukin-8 and monocyte chemoattractant protein-1). It was also highlighted a specific metabolic pattern characterized by dysregulation of bile acid homeostasis and lipase activity. Further research will be required with larger sample size to confirm these findings and gain insight on the underlying mechanisms between POPs and endometriosis.

From a non-targeted metabolomics approach to a targeted biomarkers strategy to highlight testosterone abuse in equine. Illustration of a methodological transfer between platforms and laboratories.

In order to overcome the challenge associated with the screening of Anabolic-Androgenic Steroids abuses in animal competitions, a non-targeted liquid chromatography coupled to high resolution mass spectrometry based metabolomics approach was implemented on equine urine samples to highlight potential biomarkers associated with the administration of such compounds, using testosterone esters as model steroids. A statistical model relying on four potential biomarkers intensity could be defined to predict the status of the samples. With a routine application perspective, the monitoring of the highlighted potential biomarkers was first transferred into high-throughput liquid chromatography-selected reaction monitoring (LC-SRM). The model's performances and robustness of the approach were preserved and providing a first demonstration of metabolomics-based biomarkers integration within a targeted workflow using common benchtop MS instrumentation. In addition, with a view to the widespread implementation of such biomarker-based tools, we have transferred the method to a second laboratory with similar instrumentation. This proof of concept allows the development and application of biomarker-based strategies to meet current doping control needs.

Extending the Lipidome Coverage by Combining Different Mass Spectrometric Platforms: An Innovative Strategy to Answer Chemical Food Safety Issues.

From a general public health perspective, a strategy combining non-targeted and targeted lipidomics MS-based approaches is proposed to identify disrupted patterns in serum lipidome upon growth promoter treatment in pigs. Evaluating the relative contributions of the platforms involved, the study aims at investigating the potential of innovative analytical approaches to highlight potential chemical food safety threats. Serum samples collected during an animal experiment involving control and treated pigs, whose food had been supplemented with ractopamine, were extracted and characterised using three MS strategies: Non-targeted RP LC-HRMS; the targeted Lipidyzer™ platform (differential ion mobility associated with shotgun lipidomics) and a homemade LC-HRMS triglyceride platform. The strategy enabled highlighting specific lipid profile patterns involving various lipid classes, mainly in relation to cholesterol esters, sphingomyelins, lactosylceramide, phosphatidylcholines and triglycerides. Thanks to the combination of non-targeted and targeted MS approaches, various compartments of the pig serum lipidome could be explored, including commonly characterised lipids (Lipidyzer™), triglyceride isomers (Triglyceride platform) and unique lipid features (non-targeted LC-HRMS). Thanks to their respective characteristics, the complementarity of the three tools could be demonstrated for public health purposes, with enhanced coverage, level of characterization and applicability.