Optical dignatures of dissolved organic matter transformation in the global ocean

© The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Frontiers in Marine Science 2 (2016): 118, doi:10.3389/fmars.2015.00118.Characterization of dissolved organic matter (DOM) in terms of its composition and optical properties, with an eye toward ultimately understanding its deep ocean dynamics, is the currently active frontier in DOM research. We used UV-visible absorption spectroscopy and fluorescence excitation-emission matrix (EEM) spectroscopy to characterize DOM in the open ocean along sections of the U.S. CO2/CLIVAR Repeat Hydrography Project located in all the major ocean basins outside the Arctic. Despite large differences in fluorescence intensity between ocean basins, some variability patterns were similar throughout the global ocean, suggesting similar processes controlling the composition of the DOM. We find that commercially available single channel CDOM sensors are sensitive to the fluorescence of humic materials in the deep ocean and thermocline but not to the UVA-fluorescing and absorbing materials that characterize freshly produced CDOM in surface waters, revealing fundamental diversity in the DOM profile. In surface waters, UVA fluorescence and absorption signatures indicate the presence of freshly produced material and the process of bleaching removal, but in the upper mesopelagic and in the main thermocline these optical signatures are replaced by those of humic materials, with distribution patterns correlated to apparent oxygen utilization (AOU) and other signatures of remineralization. Empirical orthogonal function analysis (EOF) of the EEM data suggests the presence of two (unidentified) processes which convert “fresh” DOM to humic materials: one located in the surface ocean (shallower than 500 m) and one located in the main thermocline. These inferred humification processes represent less than 5% of the overall variability in oceanic humic DOM fluorescence, which appears to be dominated by terrestrial input and solar bleaching of humic materials.This research was supported by grants from NASA (grants NAG5-13277 and NNX14AG24G) and NSF (OCE-0241614 and OCE-0648541) to NN and D. A. Siegel

Metabolome-Informed Microbiome Analysis Refines Metadata Classifications and Reveals Unexpected Medication Transfer in Captive Cheetahs.

Even high-quality collection and reporting of study metadata in microbiome studies can lead to various forms of inadvertently missing or mischaracterized information that can alter the interpretation or outcome of the studies, especially with nonmodel organisms. Metabolomic profiling of fecal microbiome samples can provide empirical insight into unanticipated confounding factors that are not possible to obtain even from detailed care records. We illustrate this point using data from cheetahs from the San Diego Zoo Safari Park. The metabolomic characterization indicated that one cheetah had to be moved from the non-antibiotic-exposed group to the antibiotic-exposed group. The detection of the antibiotic in this second cheetah was likely due to grooming interactions with the cheetah that was administered antibiotics. Similarly, because transit time for stool is variable, fecal samples within the first few days of antibiotic prescription do not all contain detected antibiotics, and the microbiome is not yet affected. These insights significantly altered the way the samples were grouped for analysis (antibiotic versus no antibiotic) and the subsequent understanding of the effect of the antibiotics on the cheetah microbiome. Metabolomics also revealed information about numerous other medications and provided unexpected dietary insights that in turn improved our understanding of the molecular patterns on the impact on the community microbial structure. These results suggest that untargeted metabolomic data provide empirical evidence to correct records and aid in the monitoring of the health of nonmodel organisms in captivity, although we also expect that these methods may be appropriate for other social animals, such as cats.IMPORTANCE Metabolome-informed analyses can enhance omics studies by enabling the correct partitioning of samples by identifying hidden confounders inadvertently misrepresented or omitted from carefully curated metadata. We demonstrate here the utility of metabolomics in a study characterizing the microbiome associated with liver disease in cheetahs. Metabolome-informed reinterpretation of metagenome and metabolome profiles factored in an unexpected transfer of antibiotics, preventing misinterpretation of the data. Our work suggests that untargeted metabolomics can be used to verify, augment, and correct sample metadata to support improved grouping of sample data for microbiome analyses, here for nonmodel organisms in captivity. However, the techniques also suggest a path forward for correcting clinical information in microbiome studies more broadly to enable higher-precision analyses

Pelagic barite precipitation at micromolar ambient sulfate

The question of how significant barite deposits were able to form from early Earth’s low-sulfate seas remains controversial. Here, the authors show pelagic barite precipitation within a strongly barite-undersaturated ecosystem, highlighting the importance of particle-associated microenvironments

Publisher Correction : Pelagic barite precipitation at micromolar ambient sulfate

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Nature Communications 9 (2018): 305, doi:10.1038/s41467-017-02701-y.Correction to: Nature Communications https://doi.org/10.1038/s41467-017-01229-5, Article published online 07 November 201

Global chemical effects of the microbiome include new bile-acid conjugations

A mosaic of cross-phylum chemical interactions occurs between all metazoans and their microbiomes. A number of molecular families that are known to be produced by the microbiome have a marked effect on the balance between health and disease. Considering the diversity of the human microbiome (which numbers over 40,000 operational taxonomic units), the effect of the microbiome on the chemistry of an entire animal remains underexplored. Here we use mass spectrometry informatics and data visualization approaches to provide an assessment of the effects of the microbiome on the chemistry of an entire mammal by comparing metabolomics data from germ-free and specific-pathogen-free mice. We found that the microbiota affects the chemistry of all organs. This included the amino acid conjugations of host bile acids that were used to produce phenylalanocholic acid, tyrosocholic acid and leucocholic acid, which have not previously been characterized despite extensive research on bile-acid chemistry. These bile-acid conjugates were also found in humans, and were enriched in patients with inflammatory bowel disease or cystic fibrosis. These compounds agonized the farnesoid X receptor in vitro, and mice gavaged with the compounds showed reduced expression of bile-acid synthesis genes in vivo. Further studies are required to confirm whether these compounds have a physiological role in the host, and whether they contribute to gut diseases that are associated with microbiome dysbiosis

Association between fetal abdominal growth trajectories, maternal metabolite signatures early in pregnancy, and childhood growth and adiposity : prospective observational multinational INTERBIO-21st fetal study

Background Obesity predominantly affects populations in high-income countries and those countries facing epidemiological transition. The risk of childhood obesity is increased among infants who had overweight or obesity at birth, but in low-resource settings one in five infants are born small for gestational age. We aimed to study the relationships between: (1) maternal metabolite signatures; (2) fetal abdominal growth; and (3) postnatal growth, adiposity, and neurodevelopment. Methods In the prospective, multinational, observational INTERBIO-21st fetal study, conducted in maternity units in Pelotas (Brazil), Nairobi (Kenya), Karachi (Pakistan), Soweto (South Africa), Mae Sot (Thailand), and Oxford (UK), we enrolled women (≥18 years, with a BMI of less than 35 kg/m2, natural conception, and a singleton pregnancy) who initiated antenatal care before 14 weeks’ gestation. Ultrasound scans were performed every 5±1 weeks until delivery to measure fetal growth and feto–placental blood flow, and we used finite mixture models to derive growth trajectories of abdominal circumference. The infants’ health, growth, and development were monitored from birth to age 2 years. Early pregnancy maternal blood and umbilical cord venous blood samples were collected for untargeted metabolomic analysis. Findings From Feb 8, 2012, to Nov 30, 2019, we enrolled 3598 pregnant women and followed up their infants to 2 years of age. We identified four ultrasound-derived trajectories of fetal abdominal circumference growth that accelerated or decelerated within a crucial 20–25 week gestational age window: faltering growth, early accelerating growth, late accelerating growth, and median growth tracking. These distinct phenotypes had matching feto–placental blood flow patterns throughout pregnancy, and different growth, adiposity, vision, and neurodevelopment outcomes in early childhood. There were 709 maternal metabolites with positive effect for the faltering growth phenotype and 54 for the early accelerating growth phenotype; 31 maternal metabolites had a negative effect for the faltering growth phenotype and 76 for the early accelerating growth phenotype. Metabolites associated with the faltering growth phenotype had statistically significant odds ratios close to 1·5 (ie, suggesting upregulation of metabolic pathways of impaired fetal growth). The metabolites had a reciprocal relationship with the early accelerating growth phenotype, with statistically significant odds ratios close to 0.6 (ie, suggesting downregulation of fetal growth acceleration). The maternal metabolite signatures included 5-hydroxy-eicosatetraenoic acid, and 11 phosphatidylcholines linked to oxylipin or saturated fatty acid sidechains. The fungicide, chlorothalonil, was highly abundant in the early accelerating growth phenotype group. Interpretation Early pregnancy lipid biology associated with fetal abdominal growth trajectories is an indicator of patterns of growth, adiposity, vision, and neurodevelopment up to the age of 2 years. Our findings could contribute to the earlier identification of infants at risk of obesity. Funding Bill & Melinda Gates Foundation

American Gut: an Open Platform for Citizen Science Microbiome Research

McDonald D, Hyde E, Debelius JW, et al. American Gut: an Open Platform for Citizen Science Microbiome Research. mSystems. 2018;3(3):e00031-18

Open Access Repository-Scale Propagated Nearest Neighbor Suspect Spectral Library for Untargeted Metabolomics

Abstract Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of public MS/MS spectra. Annotations were propagated based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimer’s brain phenotype. The nearest neighbor suspect spectral library is openly available through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data

A community resource for paired genomic and metabolomic data mining

Genomics and metabolomics are widely used to explore specialized metabolite diversity. The Paired Omics Data Platform is a community initiative to systematically document links between metabolome and (meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.Peer reviewe