Automated High-Throughput Quantification of Phenyl-γ-valerolactones and Creatinine in Urine by Laser Diode Thermal Desorption

Quantification of nutritional biomarkers is crucial to accurately assess the dietary intake of different classes of (poly)phenols in large epidemiological studies. High-throughput analysis is mandatory to apply this methodology in large cohorts. However, the current validated methods to quantify (poly)phenols metabolites in biological fluids use ultra performance liquid chromatography (UPLC), leading to analysis time of several minutes per sample. To significantly reduce the run time, we developed and validated a method to quantify in urine the flavan-3-ols biomarkers, phenyl-γ-valerolactones (PVLs), using laser diode thermal desorption (LDTD). This mass spectrometry source allows direct introduction of sample extracts, resulting in analysis time of less than 10 s per sample. Also, to encompass the problem associated with the cost and availability of sulfated and glucuronide analytical standards, urine samples were subjected to enzymatic hydrolysis. Creatinine was also quantified to normalize the results obtained from the urinary spot. Results obtained with LDTD-MS/MS were cross-validated by UPLC-MS/MS using 155 urine samples. Coefficient of correlation was above 0.975 for PVLs and creatinine. For all analytes, the accuracy was between 90% and 113% by LDTD-MS/MS. Altogether, sample preparation was fully automated to demonstrate the application potential of this method to large cohorts.

Gene-based microbiome representation enhances host phenotype classification

With the concomitant advances in both the microbiome and machine learning fields, the gut microbiome has become of great interest for the potential discovery of biomarkers to be used in the classification of the host health status. Shotgun metagenomics data derived from the human microbiome is composed of a high-dimensional set of microbial features. The use of such complex data for the modeling of host-microbiome interactions remains a challenge as retaining de novo content yields a highly granular set of microbial features. In this study, we compared the prediction performances of machine learning approaches according to different types of data representations derived from shotgun metagenomics. These representations include commonly used taxonomic and functional profiles and the more granular gene cluster approach. For the five case-control datasets used in this study (Type 2 diabetes, obesity, liver cirrhosis, colorectal cancer, and inflammatory bowel disease), gene-based approaches, whether used alone or in combination with reference-based data types, allowed improved or similar classification performances as the taxonomic and functional profiles. In addition, we show that using subsets of gene families from specific functional categories of genes highlight the importance of these functions on the host phenotype. This study demonstrates that both reference-free microbiome representations and curated metagenomic annotations can provide relevant representations for machine learning based on metagenomic data. IMPORTANCE Data representation is an essential part of machine learning performance when using metagenomic data. In this work, we show that different microbiome representations provide varied host phenotype classification performance depending on the dataset. In classification tasks, untargeted microbiome gene content can provide similar or improved classification compared to taxonomical profiling. Feature selection based on biological function also improves classification performance for some pathologies. Function-based feature selection combined with interpretable machine learning algorithms can generate new hypotheses that can potentially be assayed mechanistically. This work thus proposes new approaches to represent microbiome data for machine learning that can potentiate the findings associated with metagenomic data.

Gut metagenome profile of the Nunavik Inuit youth is distinct from industrial and non-industrial counterparts

Comparative metagenomics studies have highlighted differences in microbiome community structure among human populations over diverse lifestyles and environments. With their unique environmental and historical backgrounds, Nunavik Inuit have a distinctive gut microbiome with undocumented health-related implications. Using shotgun metagenomics, we explored the taxonomic and functional structure of the gut microbiome from 275 Nunavik Inuit ranging from 16 to 30-year-old. Whole-metagenome analyses revealed that Nunavik Inuit youths have a more diverse microbiome than their non-industrialized and industrialized counterparts. A comparison of k-mer content illustrated the uniqueness of the Nunavik gut microbiome. Short-chain fatty acids producing species, and carbohydrates degradation pathways dominated Inuit metagenomes. We identified a taxonomic and functional signature unique to the Nunavik gut microbiome contrasting with other populations using a random forest classifier. Here, we show that the Nunavik Inuit gut microbiome exhibits high diversity and a distinct community structure.

Short-term supplementation with ω-3 polyunsaturated fatty acids modulates primarily mucolytic species from the gut luminal mucin niche in a human fermentation system

Consumption of omega-3 polyunsaturated fatty acids (ω-3 PUFAs) eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) provides multifaceted health benefits. Recent studies suggest that ω-3 PUFAs modulate the gut microbiota by enhancing health-promoting bacteria, such as the mucin specialist Akkermansia muciniphila. However, these prebiotic properties have been poorly investigated and direct effects on the gut microbiome have never been explored dynamically across gut regions and niches (lumen vs. mucus-associated microbiota). Thus, we studied the effects of 1 week EPA- and DHA-enriched ω-3 fish-oil supplementation on the composition and functionality of the human microbiome in a Mucosal Simulator of the Human Intestinal Microbial Ecosystem (M-SHIME®). Gut microbial communities derived from one individual harvested in two different seasons were tested in duplicate. Luminal and outer mucus-associated microbiota of the ileum, ascending, transverse and descending colons were cultivated over 28 d from fecal inoculates and supplemented with ω-3 PUFAs for the last 7 d. We show that ω-3 PUFA supplementation modulates the microbiota in a gut region- and niche-dependent fashion. The outer mucus-associated microbiota displayed a higher resilience than the luminal mucin habitat to ω-3 PUFAs, with a remarkable blooming of Akkermansia muciniphila in opposition to a decrease of Firmicutes-mucolytic bacteria. The ω-3 PUFAs also induced a gradual and significant depletion of non-mucolytic Clostridia members in luminal habitats. Finally, increased concentrations of the short chain fatty acids (SCFA) propionate in colon regions at the end of the supplementation was associated positively with the bloom of Akkermansia muciniphila and members of the Desulfovibrionia class.

Purified recombinant enzymes efficiently hydrolyze conjugated urinary (poly)phenol metabolites

Purified recombinant enzymes are efficient at hydrolyzing microbial (poly)phenol metabolite phase II conjugates, and hence, can be used to accurately quantify them using unconjugated analytical standards.

A novel bioluminescent herpes simplex virus 1 for in vivo monitoring of herpes simplex encephalitis

Herpes simplex virus 1 (HSV-1) is responsible for herpes simplex virus encephalitis (HSE), associated with a 70% mortality rate in the absence of treatment. Despite intravenous treatment with acyclovir, mortality remains significant, highlighting the need for new anti-herpetic agents. Herein, we describe a novel neurovirulent recombinant HSV-1 (rHSV-1), expressing the fluorescent tdTomato and Gaussia luciferase (Gluc) enzyme, generated by the Clustered regularly interspaced short palindromic repeats (CRISPR)—CRISPR-associated protein 9 (Cas9) (CRISPR-Cas9) system. The Gluc activity measured in the cell culture supernatant was correlated (P = 0.0001) with infectious particles, allowing in vitro monitoring of viral replication kinetics. A significant correlation was also found between brain viral titers and Gluc activity in plasma (R² = 0.8510, P < 0.0001) collected from BALB/c mice infected intranasally with rHSV-1. Furthermore, evaluation of valacyclovir (VACV) treatment of HSE could also be performed by analyzing Gluc activity in mouse plasma samples. Finally, it was also possible to study rHSV-1 dissemination and additionally to estimate brain viral titers by in vivo imaging system (IVIS). The new rHSV-1 with reporter proteins is not only as a powerful tool for in vitro and in vivo antiviral screening, but can also be used for studying different aspects of HSE pathogenesis.

Biosynthesis of the Novel Endogenous 15-Lipoxygenase Metabolites N-13-Hydroxy-octodecadienoyl-ethanolamine and 13-Hydroxy-octodecadienoyl-glycerol by Human Neutrophils and Eosinophils

The endocannabinoids 2-arachidonoyl-glycerol and N-arachidonoyl-ethanolamine are lipids regulating many physiological processes, notably inflammation. Endocannabinoid hydrolysis inhibitors are now being investigated as potential anti-inflammatory agents. In addition to 2-arachidonoyl-glycerol and N-arachidonoyl-ethanolamine, the endocannabinoidome also includes other monoacylglycerols and N-acyl-ethanolamines such as 1-linoleoyl-glycerol (1-LG) and N-linoleoyl-ethanolamine (LEA). By increasing monoacylglycerols and/or N-acyl-ethanolamine levels, endocannabinoid hydrolysis inhibitors will likely increase the levels of their metabolites. Herein, we investigated whether 1-LG and LEA were substrates for the 15-lipoxygenase pathway, given that both possess a 1Z,4Z-pentadiene motif, near their omega end. We thus assessed how human eosinophils and neutrophils biosynthesized the 15-lipoxygenase metabolites of 1-LG and LEA. Linoleic acid (LA), a well-documented substrate of 15-lipoxygenases, was used as positive control. N-13-hydroxy-octodecadienoyl-ethanolamine (13-HODE-EA) and 13-hydroxy-octodecadienoyl-glycerol (13-HODE-G), the 15-lipoxygenase metabolites of LEA and 1-LG, were synthesized using Novozym 435 and soybean lipoxygenase. Eosinophils, which express the 15-lipoxygenase-1, metabolized LA, 1-LG, and LEA into their 13-hydroxy derivatives. This was almost complete after five minutes. Substrate preference of eosinophils was LA > LEA > 1-LG in presence of 13-HODE-G hydrolysis inhibition with methyl-arachidonoyl-fluorophosphonate. Human neutrophils also metabolized LA, 1-LG, and LEA into their 13-hydroxy derivatives. This was maximal after 15-30 s. Substrate preference was LA ≫ 1-LG > LEA. Importantly, 13-HODE-G was found in humans and mouse tissue samples. In conclusion, our data show that human eosinophils and neutrophils metabolize 1-LG and LEA into the novel endogenous 15-lipoxygenase metabolites 13-HODE-G and 13-HODE-EA. The full biological importance of 13-HODE-G and 13-HODE-EA remains to be explored.

Streamlining CRISPR spacer-based bacterial host predictions to decipher the viral dark matter

Thousands of new phages have recently been discovered thanks to viral metagenomics. These phages are extremely diverse and their genome sequences often do not resemble any known phages. To appreciate their ecological impact, it is important to determine their bacterial hosts. CRISPR spacers can be used to predict hosts of unknown phages, as spacers represent biological records of past phage–bacteria interactions. However, no guidelines have been established to standardize host prediction based on CRISPR spacers. Additionally, there are no tools that use spacers to perform host predictions on large viral datasets. Here, we developed a set of tools that includes all the necessary steps for predicting the hosts of uncharacterized phages. We created a database of >11 million spacers and a program to execute host predictions on large viral datasets. Our host prediction approach uses biological criteria inspired by how CRISPR–Cas naturally work as adaptive immune systems, which make the results easy to interpret. We evaluated the performance using 9484 phages with known hosts and obtained a recall of 49% and a precision of 69%. We also found that this host prediction method yielded higher performance for phages that infect gut-associated bacteria, suggesting it is well suited for gut-virome characterization.

Synthesis and molecular targets of N-13-hydroxy-octadienoyl-ethanolamine, a novel endogenous bioactive 15-lipoxygenase-derived metabolite of N-linoleoyl-ethanolamine found in the skin and saliva

N-Arachidonoyl-ethanolamine (AEA) is an endocannabinoid (eCB) and endogenous lipid mimicking many of the effects of Δ⁹-tetrahydrocannabinol, notably on brain functions, appetite, pain and inflammation. The eCBs and eCB-like compounds contain fatty acids, the main classes being the monoacylglycerols and the N-acyl-ethanolamines (NAEs). Thus, each long chain fatty acid likely exists under the form of a monoacylglycerol and NAE, as it is the case for arachidonic acid (AA) and linoleic acid (LA). Following their biosynthesis, AA and AEA can be further metabolized into additional eicosanoids, notably by the 15-lipoxygenase pathway. Thus, we postulated that NAEs possessing a 1Z,4Z-pentadiene motif, near their omega end, would be transformed into their 15-lipoxygenase metabolites. As a proof of concept, we investigated N-linoleoyl-ethanolamine (LAE). We successfully synthesized LEA and LEA-d₄ as well as their 15-lipoxygenase-derived derivatives, namely 13-hydroxy-9Z,11E-octadecadienoyl-N-ethanolamine (13-HODE-EA) and 13-HODE-EA-d₄, using Novozyme 435 immobilized on acrylic resin and soybean lipoxygenase respectively. We also show that both human 15-lipoxygenase-1 and -2 can biosynthesize 13-HODE-EA. Co-incubation of LEA and LA with either human 15-lipoxygenase led to the biosynthesis of 13-HODE-EA and 13-HODE in a ratio equal to or greater than 3:1, indicating that LEA is preferred to LA by these enzymes. Finally, we show that 13-HODE-EA is found in human saliva and skin and is a weak although selective TRPV1 agonist. The full biological importance of 13-HODE-EA remains to be explored.

Phenotypic and Genetic Characterization of the Cheese Ripening Yeast Geotrichum candidum

The yeast Geotrichum candidum (teleomorph Galactomyces candidus) is inoculated onto mold- and smear-ripened cheeses and plays several roles during cheese ripening. Its ability to metabolize proteins, lipids, and organic acids enables its growth on the cheese surface and promotes the development of organoleptic properties. Recent multilocus sequence typing (MLST) and phylogenetic analyses of G. candidum isolates revealed substantial genetic diversity, which may explain its strain-dependant technological capabilities. Here, we aimed to shed light on the phenotypic and genetic diversity among eight G. candidum and three Galactomyces spp. strains of environmental and dairy origin. Phenotypic tests such as carbon assimilation profiles, the ability to grow at 35°C and morphological traits on agar plates allowed us to discriminate G. candidum from Galactomyces spp. The genomes of these isolates were sequenced and assembled; whole genome comparison clustered the G. candidum strains into three subgroups and provided a reliable reference for MLST scheme optimization. Using the whole genome sequence as a reference, we optimized an MLST scheme using six loci that were proposed in two previous MLST schemes. This new MLST scheme allowed us to identify 15 sequence types (STs) out of 41 strains and revealed three major complexes named GeoA, GeoB, and GeoC. The population structure of these 41 strains was evaluated with STRUCTURE and a NeighborNet analysis of the combined six loci, which revealed recombination events between and within the complexes. These results hint that the allele variation conferring the different STs arose from recombination events. Recombination occurred for the six housekeeping genes studied, but most likely occurred throughout the genome. These recombination events may have induced an adaptive divergence between the wild strains and the cheesemaking strains, as observed for other cheese ripening fungi. Further comparative genomic studies are needed to confirm this phenomenon in G. candidum. In conclusion, the draft assembly of 11 G. candidum/Galactomyces spp. genomes allowed us to optimize a genotyping MLST scheme and, combined with the assessment of their ability to grow under different conditions, provides a reliable tool to cluster and eventually improves the selection of G. candidum strains.

Mass spectra alignment using virtual lock-masses

Mass spectrometry is a valued method to evaluate the metabolomics content of a biological sample. The recent advent of rapid ionization technologies such as Laser Diode Thermal Desorption (LDTD) and Direct Analysis in Real Time (DART) has rendered high-throughput mass spectrometry possible. It is used for large-scale comparative analysis of populations of samples. In practice, many factors resulting from the environment, the protocol, and even the instrument itself, can lead to minor discrepancies between spectra, rendering automated comparative analysis difficult. In this work, a sequence/pipeline of algorithms to correct variations between spectra is proposed. The algorithms correct multiple spectra by identifying peaks that are common to all and, from those, computes a spectrum-specific correction. We show that these algorithms increase comparability within large datasets of spectra, facilitating comparative analysis, such as machine learning.

Predicting Ion Mobility Collision Cross-Sections Using a Deep Neural Network: DeepCCS

Untargeted metabolomic measurements using mass spectrometry are a powerful tool for uncovering new small molecules with environmental and biological importance. The small molecule identification step, however, still remains an enormous challenge due to fragmentation difficulties or unspecific fragment ion information. Current methods to address this challenge are often dependent on databases or require the use of nuclear magnetic resonance (NMR), which have their own difficulties. The use of the gas-phase collision cross section (CCS) values obtained from ion mobility spectrometry (IMS) measurements were recently demonstrated to reduce the number of false positive metabolite identifications. While promising, the amount of empirical CCS information currently available is limited, thus predictive CCS methods need to be developed. In this article, we expand upon current experimental IMS capabilities by predicting the CCS values using a deep learning algorithm. We successfully developed and trained a prediction model for CCS values requiring only information about a compound's SMILES notation and ion type. The use of data from five different laboratories using different instruments allowed the algorithm to be trained and tested on more than 2400 molecules. The resulting CCS predictions were found to achieve a coefficient of determination of 0.97 and median relative error of 2.7% for a wide range of molecules. Furthermore, the method requires only a small amount of processing power to predict CCS values. Considering the performance, time, and resources necessary, as well as its applicability to a variety of molecules, this model was able to outperform all currently available CCS prediction algorithms.

Culture-enriched human gut microbiomes reveal core and accessory resistance genes

BACKGROUND

Low-abundance microorganisms of the gut microbiome are often referred to as a reservoir for antibiotic resistance genes. Unfortunately, these less-abundant bacteria can be overlooked by deep shotgun sequencing. In addition, it is a challenge to associate the presence of resistance genes with their risk of acquisition by pathogens. In this study, we used liquid culture enrichment of stools to assemble the genome of lower-abundance bacteria from fecal samples. We then investigated the gene content recovered from these culture-enriched and culture-independent metagenomes in relation with their taxonomic origin, specifically antibiotic resistance genes. We finally used a pangenome approach to associate resistance genes with the core or accessory genome of Enterobacteriaceae and inferred their propensity to horizontal gene transfer.

RESULTS

Using culture-enrichment approaches with stools allowed assembly of 187 bacterial species with an assembly size greater than 1 million nucleotides. Of these, 67 were found only in culture-enriched conditions, and 22 only in culture-independent microbiomes. These assembled metagenomes allowed the evaluation of the gene content of specific subcommunities of the gut microbiome. We observed that differentially distributed metabolic enzymes were associated with specific culture conditions and, for the most part, with specific taxa. Gene content differences between microbiomes, for example, antibiotic resistance, were for the most part not associated with metabolic enzymes, but with other functions. We used a pangenome approach to determine if the resistance genes found in Enterobacteriaceae, specifically E. cloacae or E. coli, were part of the core genome or of the accessory genome of this species. In our healthy volunteer cohort, we found that E. cloacae contigs harbored resistance genes that were part of the core genome of the species, while E. coli had a large accessory resistome proximal to mobile elements.

CONCLUSION

Liquid culture of stools contributed to an improved functional and comparative genomics study of less-abundant gut bacteria, specifically those associated with antibiotic resistance. Defining whether a gene is part of the core genome of a species helped in interpreting the genomes recovered from culture-independent or culture-enriched microbiomes.

The initial state of the human gut microbiome determines its reshaping by antibiotics

Microbiome studies have demonstrated the high inter-individual diversity of the gut microbiota. However, how the initial composition of the microbiome affects the impact of antibiotics on microbial communities is relatively unexplored. To specifically address this question, we administered a second-generation cephalosporin, cefprozil, to healthy volunteers. Stool samples gathered before antibiotic exposure, at the end of the treatment and 3 months later were analysed using shotgun metagenomic sequencing. On average, 15 billion nucleotides were sequenced for each sample. We show that standard antibiotic treatment can alter the gut microbiome in a specific, reproducible and predictable manner. The most consistent effect of the antibiotic was the increase of Lachnoclostridium bolteae in 16 out of the 18 cefprozil-exposed participants. Strikingly, we identified a subgroup of participants who were enriched in the opportunistic pathogen Enterobacter cloacae after exposure to the antibiotic, an effect linked to lower initial microbiome diversity and to a Bacteroides enterotype. Although the resistance gene content of participants' microbiomes was altered by the antibiotic, the impact of cefprozil remained specific to individual participants. Resistance genes that were not detectable prior to treatment were observed after a 7-day course of antibiotic administration. Specifically, point mutations in beta-lactamase blaCfxA-6 were enriched after antibiotic treatment in several participants. This suggests that monitoring the initial composition of the microbiome before treatment could assist in the prevention of some of the adverse effects associated with antibiotics or other treatments.

Targeted Proteomics of Human Metapneumovirus in Clinical Samples and Viral Cultures

The rapid, sensitive, and specific identification of infectious pathogens from clinical isolates is a critical need in the hospital setting. Mass spectrometry (MS) has been widely adopted for identification of bacterial pathogens, although polymerase chain reaction remains the mainstay for the identification of viral pathogens. Here, we explored the capability of MS for the detection of human metapneumovirus (HMPV), a common cause of respiratory tract infections in children. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) sequencing of a single HMPV reference strain (CAN97-83) was used to develop a multiple reaction monitoring (MRM) assay that employed stable isotope-labeled peptide internal standards for quantitation of HMPV. Using this assay, we confirmed the presence of HMPV in viral cultures from 10 infected patients and further assigned genetic lineage based on the presence/absence of variant peptides belonging to the viral matrix and nucleoproteins. Similar results were achieved for primary clinical samples (nasopharyngeal aspirates) from the same individuals. As validation, virus lineages, and variant coding sequences, were confirmed by next-generation sequencing of viral RNA obtained from the culture samples. Finally, separate dilution series of HMPV A and B lineages were used to further refine and assess the robustness of the assay and to determine limits of detection in nasopharyngeal aspirates. Our results demonstrate the applicability of MRM for identification of HMPV, and assignment of genetic lineage, from both viral cultures and clinical samples. More generally, this approach should prove tractable as an alternative to nucleic-acid based sequencing for the multiplexed identification of respiratory virus infections.

Genomic characterization of a large outbreak of Legionella pneumophila serogroup 1 strains in Quebec City, 2012

During the summer of 2012, a major Legionella pneumophila serogroup 1 outbreak occurred in Quebec City, Canada, which caused 182 declared cases of Legionnaire's disease and included 13 fatalities. Legionella pneumophila serogroup 1 isolates from 23 patients as well as from 32 cooling towers located in the vicinity of the outbreak were recovered for analysis. In addition, 6 isolates from the 1996 Quebec City outbreak and 4 isolates from patients unrelated to both outbreaks were added to allow comparison. We characterized the isolates using pulsed-field gel electrophoresis, sequence-based typing, and whole genome sequencing. The comparison of patients-isolated strains to cooling tower isolates allowed the identification of the tower that was the source of the outbreak. Legionella pneumophila strain Quebec 2012 was identified as a ST-62 by sequence-based typing methodology. Two new Legionellaceae plasmids were found only in the epidemic strain. The LVH type IV secretion system was found in the 2012 outbreak isolates but not in the ones from the 1996 outbreak and only in half of the contemporary human isolates. The epidemic strains replicated more efficiently and were more cytotoxic to human macrophages than the environmental strains tested. At least four Icm/Dot effectors in the epidemic strains were absent in the environmental strains suggesting that some effectors could impact the intracellular replication in human macrophages. Sequence-based typing and pulsed-field gel electrophoresis combined with whole genome sequencing allowed the identification and the analysis of the causative strain including its likely environmental source.

Improving the safety of Staphylococcus aureus polyvalent phages by their production on a Staphylococcus xylosus strain

Team1 (vB_SauM_Team1) is a polyvalent staphylococcal phage belonging to the Myoviridae family. Phage Team1 was propagated on a Staphylococcus aureus strain and a non-pathogenic Staphylococcus xylosus strain used in industrial meat fermentation. The two Team1 preparations were compared with respect to their microbiological and genomic properties. The burst sizes, latent periods, and host ranges of the two derivatives were identical as were their genome sequences. Phage Team1 has 140,903 bp of double stranded DNA encoding for 217 open reading frames and 4 tRNAs. Comparative genomic analysis revealed similarities to staphylococcal phages ISP (97%) and G1 (97%). The host range of Team1 was compared to the well-known polyvalent staphylococcal phages phi812 and K using a panel of 57 S. aureus strains collected from various sources. These bacterial strains were found to represent 18 sequence types (MLST) and 14 clonal complexes (eBURST). Altogether, the three phages propagated on S. xylosus lysed 52 out of 57 distinct strains of S. aureus. The identification of phage-insensitive strains underlines the importance of designing phage cocktails with broadly varying and overlapping host ranges. Taken altogether, our study suggests that some staphylococcal phages can be propagated on food-grade bacteria for biocontrol and safety purposes.