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.

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.

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.

Evolution of oseltamivir resistance mutations in Influenza A(H1N1) and A(H3N2) viruses during selection in experimentally infected mice.

The evolution of oseltamivir resistance mutations during selection through serial passages in animals is still poorly described. Herein, we assessed the evolution of neuraminidase (NA) and hemagglutinin (HA) genes of influenza A/WSN/33 (H1N1) and A/Victoria/3/75 (H3N2) viruses recovered from the lungs of experimentally infected BALB/c mice receiving suboptimal doses (0.05 and 1 mg/kg of body weight/day) of oseltamivir over two generations. The traditional phenotypic and genotypic methods as well as deep-sequencing analysis were used to characterize the potential selection of mutations and population dynamics of oseltamivir-resistant variants. No oseltamivir-resistant NA or HA changes were detected in the recovered A/WSN/33 viruses. However, we observed a positive selection of the I222T NA substitution in the recovered A/Victoria/3/75 viruses, with a frequency increasing over time and with an oseltamivir concentration from 4% in the initial pretherapy inoculum up to 28% after two lung passages. Although the presence of mixed I222T viral populations in mouse lungs only led to a minimal increase in oseltamivir 50% enzyme-inhibitory concentrations (IC50s) (by a mean of 5.7-fold) compared to that of the baseline virus, the expressed recombinant A/Victoria/3/75 I222T NA protein displayed a 16-fold increase in the oseltamivir IC50 level compared to that of the recombinant wild type (WT). In conclusion, the combination of serial in vivo passages under neuraminidase inhibitor (NAI) pressure and temporal deep-sequencing analysis enabled, for the first time, the identification and selection of the oseltamivir-resistant I222T NA mutation in an influenza H3N2 virus. Additional in vivo selection experiments with other antivirals and drug combinations might provide important information on the evolution of antiviral resistance in influenza viruses.

A new Microviridae phage isolated from a failed biotechnological process driven by Escherichia coli.

Bacteriophages are present in every environment that supports bacterial growth, including man made ecological niches. Virulent phages may even slow or, in more severe cases, interrupt bioprocesses driven by bacteria. Escherichia coli is one of the most widely used bacteria for large-scale bioprocesses; however, literature describing phage-host interactions in this industrial context is sparse. Here, we describe phage MED1 isolated from a failed industrial process. Phage MED1 (Microviridae family, with a single-stranded DNA [ssDNA] genome) is highly similar to the archetypal phage phiX174, sharing >95% identity between their genomic sequences. Whole-genome phylogenetic analysis of 52 microvirus genomes from public databases revealed three genotypes (alpha3, G4, and phiX174). Phage MED1 belongs to the phiX174 group. We analyzed the distribution of single nucleotide variants in MED1 and 18 other phiX174-like genomes and found that there are more missense mutations in genes G, B, and E than in the other genes of these genomes. Gene G encodes the spike protein, involved in host attachment. The evolution of this protein likely results from the selective pressure on phages to rapidly adapt to the molecular diversity found at the surface of their hosts.

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.

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.

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

Many strategies are used to quantify microbial (poly)phenol metabolites (MPMs) in urine. Currently, to obtain accurate results, the use of phase II conjugate analytical standards is deemed to be the gold standard. However, these standards are expensive or commercially unavailable. Quantification using an affordable and commercially available unconjugated analytical standard following hydrolysis with the crude preparation from Helix pomatia containing arylsulfatase and ß-glucuronidase was once considered to be an alternative, but previous studies have shown poor hydrolysis efficiency for conjugated MPMs. In this work, we evaluated the efficiency of purified recombinant enzymes and compared them with the preparation from H. pomatia using 75 urine samples. 38 conjugated MPMs were identified before hydrolysis, associated with 17 unconjugated MPMs. Rapid chemical synthesis of sulfated compounds was carried out to increase the confidence level for the identification of 13 sulfated MPMs. Recombinant enzymes had a mean hydrolysis efficiency of over 95% for 36 out of 38 conjugated MPMs with a hydrolysis time of 30 min. In comparison, the preparation from H. pomatia achieved similar efficiency for only 28 conjugated MPMs after 6 h of hydrolysis. When comparing the concentration of unconjugated MPMs released after enzymatic hydrolysis, recombinant enzymes were more or as effective for almost every MPM. These results demonstrate that accurate quantification of MPMs in urine can be quickly achieved using purified recombinant enzymes and represent an affordable alternative to the use of conjugated analytical standards, improving access to the analysis of the metabolism of (poly)phenols by the gut microbiota.

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.

A truncated anti-CRISPR protein prevents spacer acquisition but not interference.

CRISPR-Cas systems in prokaryotic cells provide an adaptive immunity against invading nucleic acids. For example, phage infection leads to addition of new immunity (spacer acquisition) and DNA cleavage (interference) in the bacterial model species Streptococcus thermophilus, which primarily relies on Cas9-containing CRISPR-Cas systems. Phages can counteract this defense system through mutations in the targeted protospacers or by encoding anti-CRISPR proteins (ACRs) that block Cas9 interference activity. Here, we show that S. thermophilus can block ACR-containing phages when the CRISPR immunity specifically targets the acr gene. This in turn selects for phage mutants carrying a deletion within the acr gene. Remarkably, a truncated acrIIA allele, found in a wild-type virulent streptococcal phage, does not block the interference activity of Cas9 but still prevents the acquisition of new immunities, thereby providing an example of an ACR specifically inhibiting spacer acquisition.

Genome Sequence of SN1, a Bacteriophage That Infects Sphaerotilus natans and Pseudomonas aeruginosa.

Phage SN1 infects Sphaerotilus natans and Pseudomonas aeruginosa strains. Its genome consists of 61,858 bp (64.3% GC) and 89 genes, including 32 with predicted functions. SN1 genome is very similar to Pseudomonas phage M6, which contains hypermodified thymidines. Genome analyses revealed similar base-modifying genes as those found in M6.

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.

In through the Out Door: A Functional Virulence Factor Secretion System Is Necessary for Phage Infection in Ralstonia solanacearum.

Bacteriophages put intense selective pressure on microbes, which must evolve diverse resistance mechanisms to survive continuous phage attacks. We used a library of spontaneous Bacteriophage Insensitive Mutants (BIMs) to learn how the plant pathogen Ralstonia solanacearum resists the virulent lytic podophage phiAP1. Phenotypic and genetic characterization of many BIMs suggested that the R. solanacearum Type II Secretion System (T2SS) plays a key role in phiAP1 infection. Using precision engineered mutations that permit T2SS assembly but either inactivate the T2SS GspE ATPase or sterically block the secretion portal, we demonstrated that phiAP1 needs a functional T2SS to infect R. solanacearum. This distinction between the static presence of T2SS components, which is necessary but not sufficient for phage sensitivity, and the energized and functional T2SS, which is sufficient, implies that binding interactions alone cannot explain the role of the T2SS in phiAP1 infection. Rather, our results imply that some aspect of the resetting of the T2SS, such as disassembly of the pseudopilus, is required. Because R. solanacearum secretes multiple virulence factors via the T2SS, acquiring resistance to phiAP1 also dramatically reduced R. solanacearum virulence on tomato plants. This acute fitness trade-off suggests this group of phages may be a sustainable control strategy for an important crop disease. IMPORTANCE Ralstonia solanacearum is a destructive plant pathogen that causes lethal bacterial wilt disease in hundreds of diverse plant hosts, including many economically important crops. Phages that kill R. solanacearum could offer effective and environmentally friendly wilt disease control, but only if the bacterium cannot easily evolve resistance. Encouragingly, most R. solanacearum mutants resistant to the virulent lytic phage phiAP1 no longer secreted multiple virulence factors and had much reduced fitness and virulence on tomato plants. Further analysis revealed that phage phiAP1 needs a functional type II secretion system to infect R. solanacearum, suggesting this podophage uses a novel infection mechanism.

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.

Complete Genome Sequences of 10 Lactococcal Skunavirus Phages Isolated from Cheddar Cheese Whey Samples in Canada.

We report the complete genome sequences of 10 virulent phages of the Skunavirus genus (Siphoviridae) that infect Lactococcus lactis strains used for cheddar cheese production in Canada. Their linear genomes range from 28,969 bp to 31,042 bp with GC contents of 34.1 to 35.1% and 55 to 60 predicted open reading frames (ORFs).

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.

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.

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.

A Lactococcal Phage Protein Promotes Viral Propagation and Alters the Host Proteomic Response During Infection.

The lactococcal virulent phage p2 is a model for studying the Skunavirus genus, the most prevalent group of phages causing milk fermentation failures in cheese factories worldwide. This siphophage infects Lactococcus lactis MG1363, a model strain used to study Gram-positive lactic acid bacteria. The structural proteins of phage p2 have been thoroughly described, while most of its non-structural proteins remain uncharacterized. Here, we developed an integrative approach, making use of structural biology, genomics, physiology, and proteomics to provide insights into the function of ORF47, the most conserved non-structural protein of unknown function among the Skunavirus genus. This small phage protein, which is composed of three α-helices, was found to have a major impact on the bacterial proteome during phage infection and to significantly reduce the emergence of bacteriophage-insensitive mutants.

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.