Biology subjects

Quinn, R. A.

Publications and source records attributed to Quinn, R. A..

8 recordsLinked to original sources

bioRxiv2026

From SCUBA to spectra: Broadly applicable methods for coral metabolomics research

Corals represent a complex assemblage consisting of a host cnidarian, symbiotic dinoflagellate microalgae, and associated microbiomes and viromes, collectively called the coral holobiont. Corals are foundational to tropical reefs, yet their global decline due to climate change and other stressors creates an uncertain future for this valuable ecosystem. Metabolomics is a powerful means to unravel biochemical interactions within the holobiont that underpin coral resilience and adaptation. However, the remote nature of reefs and the analytical demands of this technique often limit its application. Untargeted metabolomics presents analytical challenges that are amplified in complex samples like corals, such as identifying the biological source of metabolites. Here, we evaluate how different sample fixation methods and time delays before storage--unavoidable in field contexts--affect coral metabolome profiles. We further present a framework for mapping metabolites in holobiont samples to their coral host and algal symbiont origins and introduce a spectral library to improve and automate annotation of coral lipids. Additionally, we demonstrate how single samples can be used concurrently for metabolomics, DNA amplification, and proteomics. Together, our study provides a streamlined, field-adaptable workflow for coral metabolomics that enables larger-scale studies and broader adoption of metabolomics in coral reef research and conservation.

biochemistry↗

bioRxiv2022

Bile acid-CoA:amino acid N-acyltransferase gene knockout alters early life development, the gut microbiome and reveals unusual bile acid conjugates in mice

Bile acids are steroid detergents in bile that contribute to fat absorption, cell signaling and microbiome interactions in mammals. The final step in their synthesis is amino acid conjugation with either glycine or taurine to a cholic acid or chenodeoxycholic acid backbone in the liver by the enzyme bile acid-CoA:amino acid N-acyltransferase (BAAT). Here, we describe the microbial, chemical, and physiological consequences of BAAT gene deletion in mice. BAAT-/- mice were underweight after weaning but quickly exhibited catch-up growth. At 3-weeks-of-age, KO animals had increased phospholipid excretion and decreased subcutaneous fat pad mass, glycogen staining in hepatocytes and vitamin A stores in the liver, but these phenotypes were less marked in adulthood. Their bile acid (BA) pool was highly altered throughout the 8-weeks of life but was not completely devoid of conjugated BAs. These animals had 27-fold lower amounts of taurine-conjugated BAs than wildtype in their liver, but similar concentrations of glycine-conjugated BAs and higher microbially-conjugated BAs. The BA pool in BAAT-/- was enriched in a variety of unusual bile acids that were putatively sourced from cysteamine conjugation with subsequent oxidation and methylation of the sulfur group to mimic taurine. KO mice also had an altered microbiome, but most strongly in the first 3-weeks, indicating bile acid conjugation is important for proper microbiome development during the postnatal period. Finally, antibiotic treatment increased taurine, glycine, and the unusually conjugated BAs in BAAT-/- animals, indicating the microbiome was not the likely source of the conjugation. Instead, BA conjugation in KO animals was likely derived from the peroxisomal acyltransferases ACNAT1 and ACNAT2, which are duplications of BAAT in the mouse genome, but inactivated in humans. This study demonstrates that BA conjugation is important for early life development in mice and is facilitated by other host or microbial enzymes besides BAAT in a manner that results in molecular mimics of taurine that may rescue pathological phenotypes.

microbiology↗

bioRxiv2020

Gut metabolites are more predictive of disease- and cohoused- states than gut bacterial features in a mouse model of polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) impacts [~]10% of reproductive-aged women worldwide. In addition to infertility, women with PCOS suffer from metabolic dysregulation which increases their risk of developing type 2 diabetes, cardiovascular disease and non-alcoholic fatty liver disease. Studies have shown differences in the gut microbiome of women with PCOS compared to controls, a pattern replicated in mouse models. Recently, using a letrozole-induced mouse model of PCOS, we demonstrated that cohousing was protective against development of metabolic and reproductive phenotypes and showed via 16S amplicon sequencing that this protection correlated with time-dependent shifts in gut bacteria. Here, we applied untargeted metabolomics and shotgun metagenomics approaches to further analyze the longitudinal samples from the cohousing experiment. Analysis of beta diversity found that untargeted metabolites had the strongest correlation to both disease and cohoused states and that shifts in metabolite diversity were detected prior to shifts in bacterial diversity. In addition, log2-fold analyses found numerous metabolite features, particularly bile acids (BA), to be highly differentiated between placebo (P) and letrozole (LET), as well as cohoused LET versus LET. Our results indicate that changes in gut metabolites, particularly BAs, are associated with a PCOS-like phenotype in the LET mouse model as well as the protective effect of cohousing. Our results also suggest that transfer of metabolites via coprophagy occurs rapidly and may precipitate changes in bacterial diversity. This study joins a growing body of research highlighting changes in primary and secondary bile acids that may provide a link between host metabolism and gut microbes relevant to the pathology of PCOS. IMPORTANCEUsing a combination of untargeted metabolomics and metagenomics, we performed a comparative longitudinal analysis of the feces collected in a cohousing study with a PCOS mouse model. Our results showed that gut metabolite composition experienced earlier and more pronounced differentiation in both the disease model and cohoused mice compared with the microbial composition. Notably, statistical and machine learning approaches identified shifts in the relative abundance of primary and secondary BA, which have been implicated as modifiers of gut microbial growth and diversity. Network correlation analysis showed strong associations between particular BA and bacterial species, particularly members of Lactobacillus, and that these correlations were time and treatment dependent. Our results provide novel insights into host/microbe relationships related to hyperandrogenism in females and indicate that focused research into small molecule control of gut microbial diversity and host physiology may provide new therapeutic options for the treatment of PCOS.

microbiology↗

bioRxiv2020

Metabolomic signatures of coral bleaching history

Coral bleaching, a process where corals expel their photosynthetic symbionts, has a profound impact on the health and function of coral reefs. As global ocean temperatures continue to rise, bleaching poses the greatest threat to coral reef ecosystems. Here, untargeted metabolomics was used to analyze the biochemicals in pairs of adjacent corals from a patch reef in K[a]ne ohe Bay, Hawai i, where one colony in the pair bleached (in 2015) and recovered while the other did not bleach. There was a strong metabolomic signature of prior bleaching history four years after recovery found in both the host and its algal symbionts. Machine learning analysis determined that the strongest metabolite drivers of the difference in bleaching phenotype were a group of betaine lipids. Those with saturated fatty acids were significantly enriched in thermally tolerant corals and those with longer, unsaturated and diacyl forms were enriched in historically bleached corals. Host immune response molecules, Lyso-PAF and PAF, were also altered by bleaching history and were strongly correlated with symbiont community and algal-derived metabolites suggesting a role of coral immune modulation in symbiont choice and bleaching response. To validate these findings, we tested a separate in situ set of corals and were able to predict the bleaching phenotype with 100% accuracy. Furthermore, corals subjected to an experimental temperature stress had strong phenotype-specific responses in all components of the holobiont, which served to further increase the differences between historical bleaching phenotypes. Thus, we show that natural bleaching susceptibility is simultaneously manifested in the biochemistry of the coral animal and the algal symbiont and that this bleaching history results in different physiological responses to temperature stress. This work provides insight into the biochemical mechanisms involved in coral bleaching and presents a valuable new tool for resilience-based reef restoration.

ecology↗

bioRxiv2020

Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS.

Gas chromatography-mass spectrometry (GC-MS) represents an analytical technique with significant practical societal impact. Spectral deconvolution is an essential step for interpreting GC-MS data. No public GC-MS repositories that also enable repository-scale analysis exist, in part because deconvolution requires significant user input. We therefore engineered a scalable machine learning workflow for the Global Natural Product Social Molecular Networking (GNPS) analysis platform to enable the mass spectrometry community to store, process, share, annotate, compare, and perform molecular networking of GC-MS data. The workflow performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization, using a Fast Fourier Transform-based strategy to overcome scalability limitations. We introduce a "balance score" that quantifies the reproducibility of fragmentation patterns across all samples. We demonstrate the utility of the platform with breathomics analysis applied to the early detection of oesophago-gastric cancer, and by creating the first molecular spatial map of the human volatilome.

bioinformatics↗

bioRxiv2019

Revisiting microbe-metabolite interactions: doing better than random

I.Recently, Quinn and Erb et al [1] made the case that when used correctly, correlation and proportionality can outperform MMvec when identifying microbe-metabolite interactions. We revisit this comparison and show that the proposed correlation and proportionality are outperformed by MMvec on real data due to their inability to deal with sparsity commonly observed in microbiome and metabolome datasets.

bioinformatics↗

bioRxiv2019

The Molecular and Microbial Microenvironments in Chronically Diseased Lungs

To visualize the personalized distributions of pathogens, chemical environments including microbial metabolites, pharmaceuticals, and their metabolic products within and between human lungs afflicted with cystic fibrosis, we generated 3D microbiome and metabolome maps of six explanted lungs from three cystic fibrosis patients. These 3D spatial maps revealed that the chemical environments are variable between patients and within the lungs of each patient. Although the patients microbial ecosystems were defined by the dominant pathogen, their chemical diversity was not. Additionally, the chemical diversity between locales in lungs of the same individual sometimes exceeded inter-individual variation. Thus, the chemistry and microbiome of the explanted lungs appear to be not only personalized but also regiospecific. Previously undescribed analogs of microbial quinolones and antibiotic metabolites were also detected. Furthermore, mapping the chemical and microbial distributions allowed visualization of microbial community interactions, such as increased production of quorum sensing quinolones in locations where Pseudomonas was in contact with Staphylococcus and Granulicatella, consistent with in vitro observations of bacteria isolated from these patients. Visualization of microbe-metabolite associations within a host organ in early-stage CF disease in animal models will help elucidate a complex interplay between the presence of a given microbial structure, antibiotics, metabolism of antibiotics, microbial virulence factors, and host responses.\n\nImportanceMicrobial infections are now recognized to be polymicrobial and personalized in nature. A comprehensive analysis and understanding of the factors underlying the polymicrobial and personalized nature of infections remains limited, especially in the context of the host. By visualizing microbiomes and metabolomes of diseased human lungs, we describe how different the chemical environments are between hosts that are dominated by the same pathogen and how community interactions shape the chemical environment, or vice versa. We highlight that three-dimensional organ mapping are hypothesis building tools that allow us to design mechanistic studies aimed at addressing microbial responses to other microbes, the host, and pharmaceutical drugs.

microbiology↗

bioRxiv2019

Chemical Impacts of the Microbiome Across Scales Reveal Novel Conjugated Bile Acids

A mosaic of cross-phyla chemical interactions occurs between all metazoans and their microbiomes. In humans, the gut harbors the heaviest microbial load, but many organs, particularly those with a mucosal surface, associate with highly adapted and evolved microbial consortia1. The microbial residents within these organ systems are increasingly well characterized, yielding a good understanding of human microbiome composition, but we have yet to elucidate the full chemical impact the microbiome exerts on an animal and the breadth of the chemical diversity it contributes2. A number of molecular families are known to be shaped by the microbiome including short-chain fatty acids, indoles, aromatic amino acid metabolites, complex polysaccharides, and host lipids; such as sphingolipids and bile acids3-11. These metabolites profoundly affect host physiology and are being explored for their roles in both health and disease. Considering the diversity of the human microbiome, numbering over 40,000 operational taxonomic units12, a plethora of molecular diversity remains to be discovered. Here, we use unique mass spectrometry informatics approaches and data mapping onto a murine 3D-model13-15 to provide an untargeted assessment of the chemical diversity between germ-free (GF) and colonized mice (specific-pathogen free, SPF), and report the finding of novel bile acids produced by the microbiome in both mice and humans that have evaded characterization despite 170 years of research on bile acid chemistry16.

microbiology↗