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Background Early life microbiota is an important determinant of immune and metabolic development and may have lasting consequences. The maternal gut microbiota during pregnancy or breastfeeding is important for defining infant gut microbiota. We hypothesized that maternal gut microbiota during pregnancy and breastfeeding is a critical determinant of infant immunity. To test this, pregnant BALB/c dams were fed vancomycin for 5 days prior to delivery (gestation; Mg), 14 days postpartum during nursing (Mn), or during gestation and nursing (Mgn), or no vancomycin (Mc). We analyzed adaptive immunity and gut microbiota in dams and pups at various times after delivery. Results In addition to direct alterations to maternal gut microbial composition, pup gut microbiota displayed lower α-diversity and distinct community clusters according to timing of maternal vancomycin. Vancomycin was undetectable in maternal and offspring sera, therefore the observed changes in the microbiota of stomach contents (as a proxy for breastmilk) and pup gut signify an indirect mechanism through which maternal intestinal microbiota influences extra-intestinal and neonatal commensal colonization. These effects on microbiota influenced both maternal and offspring immunity. Maternal immunity was altered, as demonstrated by significantly higher levels of both total IgG and IgM in Mgn and Mn breastmilk when compared to Mc. In pups, lymphocyte numbers in the spleens of Pg and Pn were significantly increased compared to Pc. This increase in cellularity was in part attributable to elevated numbers of both CD4+ T cells and B cells, most notable Follicular B cells. Conclusion Our results indicate that perturbations to maternal gut microbiota dictate neonatal adaptive immunity.

Microbiome research is providing important new insights into the metabolic interactions of complex microbial ecosystems involved in fields as diverse as the pathogenesis of human diseases, agriculture and climate change. Poor correlations typically observed between RNA and protein expression datasets make it hard to accurately infer microbial protein synthesis from metagenomic data. Additionally, mass spectrometry-based metaproteomic analyses typically rely on focused search sequence databases based on prior knowledge for protein identification that may not represent all the proteins present in a set of samples. Metagenomic 16S rRNA sequencing only targets the bacterial component, while whole genome sequencing is at best an indirect measure of expressed proteomes. Here we describe a novel approach, MetaNovo, that combines existing open-source software tools to perform scalable de novo sequence tag matching with a novel algorithm for probabilistic optimization of the entire UniProt knowledgebase to create tailored sequence databases for target-decoy searches directly at the proteome level, enabling metaproteomic analyses without prior expectation of sample composition or metagenomic data generation and compatible with standard downstream analysis pipelines. We compared MetaNovo to published results from the MetaPro-IQ pipeline on 8 human mucosal-luminal interface samples, with comparable numbers of peptide and protein identifications, many shared peptide sequences and a similar bacterial taxonomic distribution compared to that found using a matched metagenome sequence database-but simultaneously identified many more non-bacterial peptides than the previous approaches. MetaNovo was also benchmarked on samples of known microbial composition against matched metagenomic and whole genomic sequence database workflows, yielding many more MS/MS identifications for the expected taxa, with improved taxonomic representation, while also highlighting previously described genome sequencing quality concerns for one of the organisms, and identifying an experimental sample contaminant without prior expectation. By estimating taxonomic and peptide level information directly on microbiome samples from tandem mass spectrometry data, MetaNovo enables the simultaneous identification of peptides from all domains of life in metaproteome samples, bypassing the need for curated sequence databases to search. We show that the MetaNovo approach to mass spectrometry metaproteomics is more accurate than current gold standard approaches of tailored or matched genomic sequence database searches, can identify sample contaminants without prior expectation and yields insights into previously unidentified metaproteomic signals, building on the potential for complex mass spectrometry metaproteomic data to speak for itself.

Background : The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has swept the world and poses a significant global threat to lives and livelihoods, with 115 million confirmed cases and at least 2.5 million deaths from Coronavirus disease 2019 (COVID-19) in the first year of the pandemic. Developing tools to measure seroprevalence and understand protective immunity to SARS-CoV-2 is a priority. We aimed to develop a serological assay using plant-derived recombinant viral proteins, which represent important tools in less-resourced settings. Methods : We established an indirect ELISA using the S1 and receptor-binding domain (RBD) portions of the spike protein from SARS-CoV-2, expressed in Nicotiana benthamiana . We measured antibody responses in sera from South African patients ( n = 77) who had tested positive by PCR for SARS-CoV-2. Samples were taken a median of 6 weeks after the diagnosis, and the majority of participants had mild and moderate COVID-19 disease. In addition, we tested the reactivity of pre-pandemic plasma ( n = 58) and compared the performance of our in-house ELISA with a commercial assay. We also determined whether our assay could detect SARS-CoV-2-specific IgG and IgA in saliva. Results: We demonstrate that SARS-CoV-2-specific immunoglobulins are readily detectable using recombinant plant-derived viral proteins, in patients who tested positive for SARS-CoV-2 by PCR. Reactivity to S1 and RBD was detected in 51 (66%) and 48 (62%) of participants, respectively. Notably, we detected 100% of samples identified as having S1-specific antibodies by a validated, high sensitivity commercial ELISA, and optical density (OD) values were strongly and significantly correlated between the two assays. For the pre-pandemic plasma, 1/58 (1.7%) of samples were positive, indicating a high specificity for SARS-CoV-2 in our ELISA. SARS-CoV-2-specific IgG correlated significantly with IgA and IgM responses. Endpoint titers of S1- and RBD-specific immunoglobulins ranged from 1:50 to 1:3,200. S1-specific IgG and IgA were found in saliva samples from convalescent volunteers. Conclusion : We demonstrate that recombinant SARS-CoV-2 proteins produced in plants enable robust detection of SARS-CoV-2 humoral responses. This assay can be used for seroepidemiological studies and to measure the strength and durability of antibody responses to SARS-CoV-2 in infected patients in our setting.

Microbiome research is providing important new insights into the metabolic interactions of complex microbial ecosystems involved in fields as diverse as the pathogenesis of human diseases, agriculture and climate change. Poor correlations typically observed between RNA and protein expression datasets make it hard to accurately infer microbial protein synthesis from metagenomic data. Additionally, mass spectrometry-based metaproteomic analyses typically rely on focused search sequence databases based on prior knowledge for protein identification that may not represent all the proteins present in a set of samples. Metagenomic 16S rRNA sequencing only targets the bacterial component, while whole genome sequencing is at best an indirect measure of expressed proteomes. Here we describe a novel approach, MetaNovo, that combines existing open-source software tools to perform scalable de novo sequence tag matching with a novel algorithm for probabilistic optimization of the entire UniProt knowledgebase to create tailored sequence databases for target-decoy searches directly at the proteome level, enabling metaproteomic analyses without prior expectation of sample composition or metagenomic data generation and compatible with standard downstream analysis pipelines. We compared MetaNovo to published results from the MetaPro-IQ pipeline on 8 human mucosal-luminal interface samples, with comparable numbers of peptide and protein identifications, many shared peptide sequences and a similar bacterial taxonomic distribution compared to that found using a matched metagenome sequence database-but simultaneously identified many more non-bacterial peptides than the previous approaches. MetaNovo was also benchmarked on samples of known microbial composition against matched metagenomic and whole genomic sequence database workflows, yielding many more MS/MS identifications for the expected taxa, with improved taxonomic representation, while also highlighting previously described genome sequencing quality concerns for one of the organisms, and identifying an experimental sample contaminant without prior expectation. By estimating taxonomic and peptide level information directly on microbiome samples from tandem mass spectrometry data, MetaNovo enables the simultaneous identification of peptides from all domains of life in metaproteome samples, bypassing the need for curated sequence databases to search. We show that the MetaNovo approach to mass spectrometry metaproteomics is more accurate than current gold standard approaches of tailored or matched genomic sequence database searches, can identify sample contaminants without prior expectation and yields insights into previously unidentified metaproteomic signals, building on the potential for complex mass spectrometry metaproteomic data to speak for itself.

Microbiome research is providing important new insights into the metabolic interactions of complex microbial ecosystems involved in fields as diverse as the pathogenesis of human diseases, agriculture and climate change. Poor correlations typically observed between RNA and protein expression datasets make it hard to accurately infer microbial protein synthesis from metagenomic data. Additionally, mass spectrometry-based metaproteomic analyses typically rely on focussed search libraries based on prior knowledge for protein identification that may not represent all the proteins present in a set of samples. Metagenomic 16S rRNA sequencing will only target the bacterial component, while whole genome sequencing is at best an indirect measure of expressed proteomes. We describe a novel approach, MetaNovo, that combines existing open-source software tools to perform scalable de novo sequence tag matching with a novel algorithm for probabilistic optimization of the entire UniProt knowledgebase to create tailored databases for target-decoy searches directly at the proteome level, enabling analyses without prior expectation of sample composition or metagenomic data generation, and compatible with standard downstream analysis pipelines. We compared MetaNovo to published results from the MetaPro-IQ pipeline on 8 human mucosal-luminal interface samples, with comparable numbers of peptide and protein identifications, many shared peptide sequences and a similar bacterial taxonomic distribution compared to that found using a matched metagenome database - but simultaneously identified many more non-bacterial peptides than the previous approaches. MetaNovo was also benchmarked on samples of known microbial composition against matched metagenomic and whole genomic database workflows, yielding many more MS/MS identifications for the expected taxa, with improved taxonomic representation, while also highlighting previously described genome sequencing quality concerns for one of the organisms, and identifying a known sample contaminant without prior expectation. By estimating taxonomic and peptide level information directly on microbiome samples from tandem mass spectrometry data, MetaNovo enables the simultaneous identification of peptides from all domains of life in metaproteome samples, bypassing the need for curated sequence search databases. We show that the MetaNovo approach to mass spectrometry metaproteomics is more accurate than current gold standard approaches of tailored or matched genomic database searches, can identify sample contaminants without prior expectation and yields insights into previously unidentified metaproteomic signals, building on the potential for complex mass spectrometry metaproteomic data to speak for itself. The pipeline source code is available on GitHub1 and documentation is provided to run the software as a singularity-compatible docker image available from the Docker Hub2.

Metagenome-driven microbiome research is providing important new insights in fields as diverse as the pathogenesis of human disease, the metabolic interactions of complex microbial ecosystems involved in agriculture, and climate change. However, poor correlations typically observed between RNA and protein expression datasets even for single organisms make it hard to infer microbial protein expression with any accuracy from metagenomic data, thus restricting movement beyond microbial catalogues and into functional analysis of microbial effector molecules. By contrast, mass spectrometry analysis of microbiome data at the protein level in theory allows direct measurement of dynamic changes in microbial protein composition, localisation and modification that may mediate host/pathogen interactions in complex microbial ecosystems, but analysis of such metaproteomic datasets remains challenging. Here we describe a novel data analysis approach, MetaNovo, that searches complex datasets against the entire known protein universe, whilst still controlling false discovery rates, thus enabling metaproteomic data analyses without requiring prior expectation of likely sample composition or metagenomic data generation that classically inform construction of focussed, relatively small search libraries. MetaNovo directly identifies and quantifies the expressed metaproteomes, and estimates the microbial composition present in complex microbiome samples, using scalable de novo sequence tag matching and probabilistic optimization of very large, unbiased sequence databases prior to target-decoy search. We validated MetaNovo against the results obtained from the recently published MetaPro-IQ pipeline on 8 human mucosal-luminal interface samples, with comparable numbers of peptide and protein identifications being found when searching relatively small databases. We then showed that using an unbiased search of the entire release of UniProt (ca. 90 million protein sequences) MetaNovo was able to identify a similar bacterial taxonomic distribution compared to that found using a small, focused matched metagenome database, but now also simultaneously identified proteins present in the samples that are derived from other organisms missed by 16S or shotgun sequencing and by previous metaproteomic methods. Using MetaNovo to analyze a set of single-organism human neuroblastoma cell-line samples (SH-SY5Y) against UniProt we achieved a comparable MS/MS identification rate during target-decoy search to using the UniProt human Reference proteome, with 22583 (85.99 %) of the total set of identified peptides shared in common. Taxonomic analysis of 612 peptides not found in the canonical set of human proteins yielded 158 peptides unique to the Chordata phylum as potential human variant identifications. Of these, 40 had previously been predicted and 9 identified using whole genome sequencing in a proteogenomic study of the same cell-line. By estimating taxonomic and peptide level information on microbiome samples directly from tandem mass spectrometry data, MetaNovo enables simultaneous identification of human, bacterial, helminth, fungal, viral and other eukaryotic proteins in a sample, thus allowing correlations between changes in microbial protein abundance and change in the host proteome to be drawn based on a single analysis. Data are available via ProteomeXchange with identifier PXD014214. The MetaNovo software is available from GitHub and can be run as a standalone Singularity or Docker container available from the Docker Hub. Footnotes * Additional co-author added, supplementary data added.

Differences in Bacille Calmette-Guérin (BCG) immunogenicity and efficacy have been reported, but various strains of BCG are administered worldwide. Since BCG immunization may also provide protection against off-target antigens, we sought to identify the impact of different BCG strains on the ontogeny of vaccine-specific and heterologous vaccine immunogenicity in the first 9 months of life, utilizing two African birth cohorts. A total of 270 infants were studied: 84 from Jos, Nigeria (vaccinated with BCG-Bulgaria) and 187 from Cape Town, South Africa (154 vaccinated with BCG-Denmark and 33 with BCG-Russia). Infant whole blood was taken at birth, 7, 15, and 36 weeks and short-term stimulated (12 h) with BCG, Tetanus and Pertussis antigens. Using multiparameter flow cytometry, CD4+ T cell memory subset polyfunctionality was measured by analyzing permutations of TNF-α, IL-2, and IFN-γ expression at each time point. Data was analyzed using FlowJo, SPICE, R, and COMPASS. We found that infants vaccinated with BCG-Denmark mounted significantly higher frequencies of BCG-stimulated CD4+ T cell responses, peaking at week 7 after immunization, and possessed durable polyfunctional CD4+ T cells that were in a more early differentiated memory stage when compared with either BCG-Bulgaria and BCG-Russia strains. The latter responses had lower polyfunctional scores and tended to accumulate in a CD4+ T cell naïve-like state (CD45RA+CD27+). Notably, BCG-Denmark immunization resulted in higher magnitudes and polyfunctional cytokine responses to heterologous vaccine antigens (Tetanus and Pertussis). Collectively, our data show that BCG strain was the strongest determinant of both BCG-stimulated and heterologous vaccine stimulated T cell magnitude and polyfunctionality. These findings have implications for vaccine policy makers, manufacturers and programs worldwide and also suggest that BCG-Denmark, the first vaccine received in many African infants, has both specific and off-target effects in the first few months of life, which may provide an immune priming benefit to other EPI vaccines.

crAssphages are a class of bacteriophages that are highly abundant in the human gastrointestinal tract. Accordingly, crAssphage genomes have been identified in most human fecal viral metagenome studies. However, we currently have an incomplete understanding of factors impacting the transmission frequencies of these phages between mothers and infants, and the evolutionary pressures associated with such transmissions. Here, we use metagenome sequencing of stool-associated virus-like particles to identify the prevalence of crAssphage across ten South African mother-infant dyads that are discordant for HIV infection. We report the identification of a complete 97kb crAssphage genome, parts of which are detected at variable levels across each mother-infant dyad. We observed average nucleotide sequence identities of >99% for crAssphages from related mother-infant pairs but ~97% identities between crAssphages from unrelated mothers and infants: a finding strongly suggestive of vertical mother to infant transmission. We further analyzed patterns of nucleotide diversity across the crAssphage sequences described here, identifying particularly elevated positive selection in RNA polymerase and phage tail protein encoding genes, which we validated against a crAssphage genome from previous studies. Using 16S rRNA gene sequencing, we found that the relative abundances of Bacteroides thetaiotaomicron and Parabacteroides merdae (Order: Bacteroidales) were differentially correlated with crAssphage abundance. Together, our results reveal that crAssphages may be vertically transmitted from mothers to their infants and that hotspots of selection within crAssphage RNA polymerase and phage tail protein encoding genes are potentially mediated by interactions between crAssphages and their bacterial partners.