Whole genome sequencing (WGS) provides the highest resolution for genome-based species identification and can provide insight into the antimicrobial resistance and virulence potential of a single microbiological isolate during the diagnostic process. In contrast, metagenomic sequencing allows the analysis of DNA segments from multiple microorganisms within a community, either using an amplicon- or shotgun-based approach. However, WGS and shotgun metagenomic data are rarely combined, although such an approach may generate additive or synergistic information, critical for, e.g., patient management, infection control, and pathogen surveillance. To produce a combined workflow with actionable outputs, we need to understand the pre-to-post analytical process of both technologies. This will require specific databases storing interlinked sequencing and metadata, and also involves customized bioinformatic analytical pipelines. This review article will provide an overview of the critical steps and potential clinical application of combining WGS and metagenomics together for microbiological diagnosis.

Reads assignment to taxonomic units is a key step in microbiome analysis pipelines. To date, accurate taxonomy annotation of 16S reads, particularly at species rank, is still challenging due to the short size of read sequences and differently curated classification databases. The close phylogenetic relationship between species encountered in dairy products, however, makes it crucial to annotate species accurately to achieve sufficient phylogenetic resolution for further downstream ecological studies or for food diagnostics. Curated databases dedicated to the environment of interest are expected to improve the accuracy and resolution of taxonomy annotation. We provide a manually curated database composed of 10’290 full-length 16S rRNA gene sequences from prokaryotes tailored for dairy products analysis ( https://github.com/marcomeola/DAIRYdb ). The performance of the DAIRYdb was compared with the universal databases Silva, LTP, RDP and Greengenes. The DAIRYdb significantly outperformed all other databases independently of the classification algorithm by enabling higher accurate taxonomy annotation down to the species rank. The DAIRYdb accurately annotates over 90% of the sequences of either single or paired hypervariable regions automatically. The manually curated DAIRYdb strongly improves taxonomic annotation accuracy for microbiome studies in dairy environments. The DAIRYdb is a practical solution that enables automatization of this key step, thus facilitating the routine application of NGS microbiome analyses for microbial ecology studies and diagnostics in dairy products.

The success of fecal microbiota transplants (FMT) has provided the necessary proof-of-concept for microbiome therapeutics. Yet, feces-based therapies have many associated risks and uncertainties, and hence defined microbial consortia that modify the microbiome in a targeted manner have emerged as a promising safer alternative to FMT. The development of such live biotherapeutic products has important challenges, including the selection of appropriate strains and the controlled production of the consortia at scale. Here, we report on an ecology- and biotechnology-based approach to microbial consortium construction that overcomes these issues. We selected nine strains that form a consortium to emulate the central metabolic pathways of carbohydrate fermentation in the healthy human gut microbiota. Continuous co-culturing of the bacteria produces a stable and reproducible consortium whose growth and metabolic activity are distinct from an equivalent mix of individually cultured strains. Further, we showed that our function-based consortium is as effective as FMT in counteracting dysbiosis in a dextran sodium sulfate mouse model of acute colitis, while an equivalent mix of strains failed to match FMT. Finally, we showed robustness and general applicability of our approach by designing and producing additional stable consortia of controlled composition. We propose that combining a bottom-up functional design with continuous co-cultivation is a powerful strategy to produce robust functionally designed synthetic consortia for therapeutic use.

Mounting evidence points towards a pivotal role of gut microbiota in multiple sclerosis (MS) pathophysiology. Yet, whether disease-modifying treatments alter microbiota composition and whether microbiota shape treatment response and side-effects remain unclear. In this prospective observational pilot study, we assessed the effect of dimethyl fumarate (DMF) on gut microbiota and on host/microbial metabolomics in a cohort of 20 MS patients. Combining state-of-the-art microbial sequencing, metabolome mass spectrometry, and computational analysis, we identified longitudinal changes in gut microbiota composition under DMF-treatment and an increase in citric acid cycle metabolites. Notably, DMF-induced lymphopenia, a clinically relevant safety concern, was correlated with distinct baseline microbiome signatures in MS patients. We identified gastrointestinal microbiota as a key therapeutic target for metabolic properties of DMF. By characterizing gut microbial composition as a candidate risk factor for DMF-induced lymphopenia, we provide novel insights into the role of microbiota in mediating clinical side-effects.

Microbiomes are an essential contributor to the metabolic activity in the human gastrointestinal tract. The fermentation of otherwise indigestible nutritional components like dietary fibers relies on a complex interplay of metabolic pathways that are distributed across the individual bacteria. Yet, which of the bacteria are responsible for which parts of the distributed metabolism and how they should be grouped together is insufficiently understood. Here, we present the NicheMap™, an approach to map the different bacterial taxa that make up the gut microbiome onto the different functional niches of microbial carbohydrate fermentation. Our approach uses in vitro measurements of bacterial growth and metabolic activity to identify which bacterial taxa are responsible for which metabolic function in the relevant complex context of whole human fecal microbiomes. We identified ‘characteristic taxa’ selected for by a panel growth substrates representative of dietary components that are resistant to digestion by host enzymes. These characteristic taxa offer predictions of which bacteria are stimulated by the various components of human diet. We validated these predictions using microbiome data from a human nutritional supplementation study. We suggest a template of how bacterial taxonomic diversity is organized along the trophic cascade of intestinal carbohydrate fermentation. We anticipate that our results and our approach will provide a key contribution towards building a structure-function map for gut microbiomes. Having such a map on hand is an important step in moving the microbiome from a descriptive science to an interventional one.

Propionibacterium freudenreichii is crucial in Swiss-type cheese manufacture. Classic propionic acid fermentation yields the nutty flavor and the typical eyes. Co-metabolism of aspartate pronounces the flavor of the cheese; however, it also increases the size of the eyes, which can induce splitting and reduce the cheese quality. Aspartase (EC 4.3.1.1) catalyzes the deamination of aspartate, yielding fumarate and ammonia. The aspartase activity varies considerably among P. freudenreichii strains. Here, the correlation between aspartase activity and the locus of aspartase-encoding genes (aspA ) and dcuA encoding the C4-dicarboxylate transporter was investigated in 46 strains to facilitate strain selection for cheese culture. Low aspartase activity was correlated with a particular genomic rearrangement: low in vitro aspartase activity always occurred in strains with gene clusters aspA - dcuA where the dcuA was frameshifted, producing a stop codon or was disrupted by an ISL3-like element. The low aspartase activity could be due to the protein sequence of the aspartase or a dysfunctional DcuA. The highest values of aspartase activity were detected in strains with aspA1 - aspA2-dcuA with a DcuA sequence sharing 99.07 - 100% identity with the DcuA sequence of strain DSM 20271 T and an additional C4-dicarboxylate transporter belonging to the DcuAB family.