Bacterial Oncotraits Rather than Spatial Organization Are Associated with Dysplasia in Ulcerative Colitis.

BACKGROUND AND AIMS: Colonic bacterial biofilms are frequently present in ulcerative colitis [UC] and may increase dysplasia risk through pathogens expressing oncotraits. This prospective cohort study aimed to determine [1] the association of oncotraits and longitudinal biofilm presence with dysplasia risk in UC, and [2] the relation of bacterial composition with biofilms and dysplasia risk. METHODS: Faeces and left- and right-sided colonic biopsies were collected from 80 UC patients and 35 controls. Oncotraits [FadA of Fusobacterium, BFT of Bacteroides fragilis, colibactin [ClbB] and Intimin [Eae] of Escherichia coli] were assessed in faecal DNA with multiplex quantitative polymerase chain reaction [qPCR]. Biopsies were screened for biofilms [n = 873] with 16S rRNA fluorescent in situ hybridiation. Shotgun metagenomic sequencing [n = 265], and ki67-immunohistochemistry were performed. Associations were determined with a mixed-effects regression model. RESULTS: Biofilms were highly prevalent in UC patients [90.8%] with a median persistence of 3 years (interquartile range [IQR] 2-5 years). Biofilm-positive biopsies showed increased epithelial hypertrophy [p = 0.025] and a reduced Shannon diversity independent of disease status [p = 0.015], but were not significantly associated with dysplasia in UC: adjusted odds ratio [aOR] 1.45, 95% confidence interval [CI] 0.63-3.40. In contrast, ClbB independently associated with dysplasia [aOR 7.16, 95% CI 1.75-29.28], and FadA and Fusobacteriales were associated with a decreased dysplasia risk in UC [aOR 0.23, 95% CI 0.06-0.83, p <0.01]. CONCLUSIONS: Biofilms are a hallmark of UC; however, because of their high prevalence are a poor biomarker for dysplasia. In contrast, colibactin presence and FadA absence independently associate with dysplasia in UC and might therefore be valuable biomarkers for future risk stratification and intervention strategies.

Nutrition or nature: using elementary flux modes to disentangle the complex forces shaping prokaryote pan-genomes.

BACKGROUND: Microbial pan-genomes are shaped by a complex combination of stochastic and deterministic forces. Even closely related genomes exhibit extensive variation in their gene content. Understanding what drives this variation requires exploring the interactions of gene products with each other and with the organism's external environment. However, to date, conceptual models of pan-genome dynamics often represent genes as independent units and provide limited information about their mechanistic interactions. RESULTS: We simulated the stochastic process of gene-loss using the pooled genome-scale metabolic reaction networks of 46 taxonomically diverse bacterial and archaeal families as proxies for their pan-genomes. The frequency by which reactions are retained in functional networks when stochastic gene loss is simulated in diverse environments allowed us to disentangle the metabolic reactions whose presence depends on the metabolite composition of the external environment (constrained by "nutrition") from those that are independent of the environment (constrained by "nature"). By comparing the frequency of reactions from the first group with their observed frequencies in bacterial and archaeal families, we predicted the metabolic niches that shaped the genomic composition of these lineages. Moreover, we found that the lineages that were shaped by a more diverse metabolic niche also occur in more diverse biomes as assessed by global environmental sequencing datasets. CONCLUSION: We introduce a computational framework for analyzing and interpreting pan-reactomes that provides novel insights into the ecological and evolutionary drivers of pan-genome dynamics.

BCG-induced trained immunity enhances acellular pertussis vaccination responses in an explorative randomized clinical trial.

Acellular pertussis (aP) booster vaccines are central to pertussis immunization programs, although their effectiveness varies. The Bacille Calmette-Guérin (BCG) vaccine is a prototype inducer of trained immunity, which enhances immune responses to subsequent infections or vaccinations. While previous clinical studies have demonstrated that trained immunity can protect against heterologous infections, its effect on aP vaccines in humans is unknown. We conducted a clinical study in order to determine the immunological effects of trained immunity on pertussis vaccination. Healthy female volunteers were randomly assigned to either receive BCG followed by a booster dose of tetanus-diphteria-pertussis inactivated polio vaccine (Tdap-IPV) 3 months later (BCG-trained), BCG + Tdap-IPV concurrently, or Tdap-IPV followed by BCG 3 months later. Primary outcomes were pertussis-specific humoral, T- and B-cell responses and were quantified at baseline of Tdap-IPV vaccination and 2 weeks thereafter. As a secondary outcome in the BCG-trained cohort, ex vivo leukocyte responses were measured in response to unrelated stimuli before and after BCG vaccination. BCG vaccination 3 months prior to, but not concurrent with, Tdap-IPV improves pertussis-specific Th1-cell and humoral responses, and also increases total memory B cell responses. These responses were correlated with enhanced IL-6 and IL-1ß production at the baseline of Tdap-IPV vaccination in the BCG-trained cohort. Our study demonstrates that prior BCG vaccination potentiates immune responses to pertussis vaccines and that biomarkers of trained immunity are the most reliable correlates of those responses.

A Prioritized and Validated Resource of Mitochondrial Proteins in Plasmodium Identifies Unique Biology.

Plasmodium species have a single mitochondrion that is essential for their survival and has been successfully targeted by antimalarial drugs. Most mitochondrial proteins are imported into this organelle, and our picture of the Plasmodium mitochondrial proteome remains incomplete. Many data sources contain information about mitochondrial localization, including proteome and gene expression profiles, orthology to mitochondrial proteins from other species, coevolutionary relationships, and amino acid sequences, each with different coverage and reliability. To obtain a comprehensive, prioritized list of Plasmodium falciparum mitochondrial proteins, we rigorously analyzed and integrated eight data sets using Bayesian statistics into a predictive score per protein for mitochondrial localization. At a corrected false discovery rate of 25%, we identified 445 proteins with a sensitivity of 87% and a specificity of 97%. They include proteins that have not been identified as mitochondrial in other eukaryotes but have characterized homologs in bacteria that are involved in metabolism or translation. Mitochondrial localization of seven Plasmodium berghei orthologs was confirmed by epitope labeling and colocalization with a mitochondrial marker protein. One of these belongs to a newly identified apicomplexan mitochondrial protein family that in P. falciparum has four members. With the experimentally validated mitochondrial proteins and the complete ranked P. falciparum proteome, which we have named PlasmoMitoCarta, we present a resource to study unique proteins of Plasmodium mitochondria. IMPORTANCE The unique biology and medical relevance of the mitochondrion of the malaria parasite Plasmodium falciparum have made it the subject of many studies. However, we actually do not have a comprehensive assessment of which proteins reside in this organelle. Many omics data are available that are predictive of mitochondrial localization, such as proteomics data and expression data. Individual data sets are, however, rarely complete and can provide conflicting evidence. We integrated a wide variety of available omics data in a manner that exploits the relative strengths of the data sets. Our analysis gave a predictive score for the mitochondrial localization to each nuclear encoded P. falciparum protein and identified 445 likely mitochondrial proteins. We experimentally validated the mitochondrial localization of seven of the new mitochondrial proteins, confirming the quality of the complete list. These include proteins that have not been observed mitochondria before, adding unique mitochondrial functions to P. falciparum.

Optimized bacterial DNA isolation method for microbiome analysis of human tissues.

Recent advances in microbiome sequencing have rendered new insights into the role of the microbiome in human health with potential clinical implications. Unfortunately, the presence of host DNA in tissue isolates has hampered the analysis of host-associated bacteria. Here, we present a DNA isolation protocol for tissue, optimized on biopsies from resected human colons (~2-5 mm in size), which includes reduction of human DNA without distortion of relative bacterial abundance at the phylum level. We evaluated which concentrations of Triton and saponin lyse human cells and leave bacterial cells intact, in combination with DNAse treatment to deplete released human DNA. Saponin at a concentration of 0.0125% in PBS lysed host cells, resulting in a 4.5-fold enrichment of bacterial DNA while preserving the relative abundance of Firmicutes, Bacteroidetes, γ-Proteobacteria, and Actinobacteria assessed by qPCR. Our optimized protocol was validated in the setting of two large clinical studies on 521 in vivo acquired colon biopsies of 226 patients using shotgun metagenomics. The resulting bacterial profiles exhibited alpha and beta diversities that are similar to the diversities found by 16S rRNA amplicon sequencing. A direct comparison between shotgun metagenomics and 16S rRNA amplicon sequencing of 15 forceps tissue biopsies showed similar bacterial profiles and a similar Shannon diversity index between the sequencing methods. Hereby, we present the first protocol for enriching bacterial DNA from tissue biopsies that allows efficient isolation of all bacteria. Our protocol facilitates analysis of a wide spectrum of bacteria of clinical tissue samples improving their applicability for microbiome research.

Growth rate alterations of human colorectal cancer cells by 157 gut bacteria.

Several bacteria in the human gut microbiome have been associated with colorectal cancer (CRC) by high-throughput screens. In some cases, molecular mechanisms have been elucidated that drive tumorigenesis, including bacterial membrane proteins or secreted molecules that interact with the human cancer cells. For most gut bacteria, however, it remains unknown if they enhance or inhibit cancer cell growth. Here, we screened bacteria-free supernatants (secretomes) and inactivated cells of over 150 cultured bacterial strains for their effects on cell growth. We observed family-level and strain-level effects that often differed between bacterial cells and secretomes, suggesting that different molecular mechanisms are at play. Secretomes of Bacteroidaceae, Enterobacteriaceae, and Erysipelotrichaceae bacteria enhanced cell growth, while most Fusobacteriaceae cells and secretomes inhibited growth, contrasting prior findings. In some bacteria, the presence of specific functional genes was associated with cell growth rates, including the virulence genes TcdA, TcdB in Clostridiales and FadA in Fusobacteriaceae, which both inhibited growth. Bacteroidaceae cells that enhanced growth were enriched for genes of the cobalamin synthesis pathway, while Fusobacteriaceae cells that inhibit growth were enriched for genes of the ethanolamine utilization pathway. Together, our results reveal how different gut bacteria have wide-ranging effects on cell growth, contribute a better understanding of the effects of the gut microbiome on host cells, and provide a valuable resource for identifying candidate target genes for potential microbiome-based diagnostics and treatment strategies.

Metabolic models predict bacterial passengers in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is a complex multifactorial disease. Increasing evidence suggests that the microbiome is involved in different stages of CRC initiation and progression. Beyond specific pro-oncogenic mechanisms found in pathogens, metagenomic studies indicate the existence of a microbiome signature, where particular bacterial taxa are enriched in the metagenomes of CRC patients. Here, we investigate to what extent the abundance of bacterial taxa in CRC metagenomes can be explained by the growth advantage resulting from the presence of specific CRC metabolites in the tumor microenvironment. METHODS: We composed lists of metabolites and bacteria that are enriched on CRC samples by reviewing metabolomics experimental literature and integrating data from metagenomic case-control studies. We computationally evaluated the growth effect of CRC enriched metabolites on over 1500 genome-based metabolic models of human microbiome bacteria. We integrated the metabolomics data and the mechanistic models by using scores that quantify the response of bacterial biomass production to CRC-enriched metabolites and used these scores to rank bacteria as potential CRC passengers. RESULTS: We found that metabolic networks of bacteria that are significantly enriched in CRC metagenomic samples either depend on metabolites that are more abundant in CRC samples or specifically benefit from these metabolites for biomass production. This suggests that metabolic alterations in the cancer environment are a major component shaping the CRC microbiome. CONCLUSION: Here, we show with in sillico models that supplementing the intestinal environment with CRC metabolites specifically predicts the outgrowth of CRC-associated bacteria. We thus mechanistically explain why a range of CRC passenger bacteria are associated with CRC, enhancing our understanding of this disease. Our methods are applicable to other microbial communities, since it allows the systematic investigation of how shifts in the microbiome can be explained from changes in the metabolome.

Towards predicting the environmental metabolome from metagenomics with a mechanistic model.

The environmental metabolome and metabolic potential of microorganisms are dominant and essential factors shaping microbial community composition. Recent advances in genome annotation and systems biology now allow us to semiautomatically reconstruct genome-scale metabolic models (GSMMs) of microorganisms based on their genome sequence 1 . Next, growth of these models in a defined metabolic environment can be predicted in silico, mechanistically linking the metabolic fluxes of individual microbial populations to the community dynamics. A major advantage of GSMMs is that no training data is needed, besides information about the metabolic capacity of individual genes (genome annotation) and knowledge of the available environmental metabolites that allow the microorganism to grow. However, the composition of the environment is often not fully determined and remains difficult to measure 2 . We hypothesized that the relative abundance of different bacterial species, as measured by metagenomics, can be combined with GSMMs of individual bacteria to reveal the metabolic status of a given biome. Using a newly developed algorithm involving over 1,500 GSMMs of human-associated bacteria, we inferred distinct metabolomes for four human body sites that are consistent with experimental data. Together, we link the metagenome to the metabolome in a mechanistic framework towards predictive microbiome modelling.

From cultured to uncultured genome sequences: metagenomics and modeling microbial ecosystems.

Microorganisms and the viruses that infect them are the most numerous biological entities on Earth and enclose its greatest biodiversity and genetic reservoir. With strength in their numbers, these microscopic organisms are major players in the cycles of energy and matter that sustain all life. Scientists have only scratched the surface of this vast microbial world through culture-dependent methods. Recent developments in generating metagenomes, large random samples of nucleic acid sequences isolated directly from the environment, are providing comprehensive portraits of the composition, structure, and functioning of microbial communities. Moreover, advances in metagenomic analysis have created the possibility of obtaining complete or nearly complete genome sequences from uncultured microorganisms, providing important means to study their biology, ecology, and evolution. Here we review some of the recent developments in the field of metagenomics, focusing on the discovery of genetic novelty and on methods for obtaining uncultured genome sequences, including through the recycling of previously published datasets. Moreover we discuss how metagenomics has become a core scientific tool to characterize eco-evolutionary patterns of microbial ecosystems, thus allowing us to simultaneously discover new microbes and study their natural communities. We conclude by discussing general guidelines and challenges for modeling the interactions between uncultured microorganisms and viruses based on the information contained in their genome sequences. These models will significantly advance our understanding of the functioning of microbial ecosystems and the roles of microbes in the environment.

Comparative genomics of 274 Vibrio cholerae genomes reveals mobile functions structuring three niche dimensions.

BACKGROUND: Vibrio cholerae is a globally dispersed pathogen that has evolved with humans for centuries, but also includes non-pathogenic environmental strains. Here, we identify the genomic variability underlying this remarkable persistence across the three major niche dimensions space, time, and habitat. RESULTS: Taking an innovative approach of genome-wide association applicable to microbial genomes (GWAS-M), we classify 274 complete V. cholerae genomes by niche, including 39 newly sequenced for this study with the Ion Torrent DNA-sequencing platform. Niche metadata were collected for each strain and analyzed together with comprehensive annotations of genetic and genomic attributes, including point mutations (single-nucleotide polymorphisms, SNPs), protein families, functions and prophages. CONCLUSIONS: Our analysis revealed that genomic variations, in particular mobile functions including phages, prophages, transposable elements, and plasmids underlie the metadata structuring in each of the three niche dimensions. This underscores the role of phages and mobile elements as the most rapidly evolving elements in bacterial genomes, creating local endemicity (space), leading to temporal divergence (time), and allowing the invasion of new habitats. Together, we take a data-driven approach for comparative functional genomics that exploits high-volume genome sequencing and annotation, in conjunction with novel statistical and machine learning analyses to identify connections between genotype and phenotype on a genome-wide scale.