Pangenome analysis reveals the genetic basis for taxonomic classification of the Lactobacillaceae family.

Lactobacillaceae represent a large family of important microbes that are foundational to the food industry. Many genome sequences of Lactobacillaceae strains are now available, enabling us to conduct a comprehensive pangenome analysis of this family. We collected 3591 high-quality genomes from public sources and found that: 1) they contained enough genomes for 26 species to perform a pangenomic analysis, 2) the normalized Heap's coefficient λ (a measure of pangenome openness) was found to have an average value of 0.27 (ranging from 0.07 to 0.37), 3) the pangenome openness was correlated with the abundance and genomic location of transposons and mobilomes, 4) the pangenome for each species was divided into core, accessory, and rare genomes, that highlight the species-specific properties (such as motility and restriction-modification systems), 5) the pangenome of Lactiplantibacillus plantarum (which contained the highest number of genomes found amongst the 26 species studied) contained nine distinct phylogroups, and 6) genome mining revealed a richness of detected biosynthetic gene clusters, with functions ranging from antimicrobial and probiotic to food preservation, but ∼93% were of unknown function. This study provides the first in-depth comparative pangenomics analysis of the Lactobacillaceae family.

Microbial foods for improving human and planetary health.

The current food production system is negatively impacting planetary and human health. A transition to a sustainable and fair food system is urgently needed. Microorganisms are likely enablers of this process, as they can produce delicious and healthy microbial foods with low environmental footprints. We review traditional and current approaches to microbial foods, such as fermented foods, microbial biomass, and food ingredients derived from microbial fermentations. We discuss how future advances in science-driven fermentation, synthetic biology, and sustainable feedstocks enable a new generation of microbial foods, potentially impacting the sustainability, resilience, and health effects of our food system.

Derivation of Kokumi γ-Glutamyl Peptides and Volatile Aroma Compounds from Fermented Cereal Processing By-Products for Reducing Bitterness of Plant-Based Ingredients.

Current food production methods and consumption behaviours are unsustainable and contribute to environmental harm. One example is food waste-around 38% of food produced is wasted each year. Here, we show that two common food waste products, wheat bran and brewer's spent grain, can successfully be upcycled via miso fermentation. During the fermentation process, kokumi γ-glutamyl peptides, known to increase mouthfulness, are produced; these include γ-ECG (oxidized), γ-EVG, γ-EV, γ-EE, γ-EF, and γ-EL. The profiles of kokumi peptides and volatile aroma compounds are correlated with koji substrate, pH, and enzymatic activity, offering straightforward parameters that can be manipulated to increase the abundance of kokumi peptides during the fermentation process. Correlation analysis demonstrates that some volatile aroma compounds, such as fatty acid ethyl esters, are correlated with kokumi peptide abundance and may be responsible for fatty, greasy, and buttery aromas. Consumer sensory analysis conveys that the bitter taste of vegetables, such as that in endives, can be dampened when miso extract containing kokumi peptides is added. This suggests that kokumi peptides, along with aroma volatile compounds, can enhance the overall flavour of plant-based products. This study opens new opportunities for cereal processing by-product upcycling via fermentation, ultimately having the potential to promote a plant-based diet.

Dominant resistance and negative epistasis can limit the co-selection of de novo resistance mutations and antibiotic resistance genes.

To tackle the global antibiotic resistance crisis, antibiotic resistance acquired either vertically by chromosomal mutations or horizontally through antibiotic resistance genes (ARGs) have been studied. Yet, little is known about the interactions between the two, which may impact the evolution of antibiotic resistance. Here, we develop a multiplexed barcoded approach to assess the fitness of 144 mutant-ARG combinations in Escherichia coli subjected to eight different antibiotics at 11 different concentrations. While most interactions are neutral, we identify significant interactions for 12% of the mutant-ARG combinations. The ability of most ARGs to confer high-level resistance at a low fitness cost shields the selective dynamics of mutants at low drug concentrations. Therefore, high-fitness mutants are often selected regardless of their resistance level. Finally, we identify strong negative epistasis between two unrelated resistance mechanisms: the tetA tetracycline resistance gene and loss-of-function nuo mutations involved in aminoglycoside tolerance. Our study highlights important constraints that may allow better prediction and control of antibiotic resistance evolution.

Pharmacogenomics of GPCR Drug Targets.

Natural genetic variation in the human genome is a cause of individual differences in responses to medications and is an underappreciated burden on public health. Although 108 G-protein-coupled receptors (GPCRs) are the targets of 475 (∼34%) Food and Drug Administration (FDA)-approved drugs and account for a global sales volume of over 180 billion US dollars annually, the prevalence of genetic variation among GPCRs targeted by drugs is unknown. By analyzing data from 68,496 individuals, we find that GPCRs targeted by drugs show genetic variation within functional regions such as drug- and effector-binding sites in the human population. We experimentally show that certain variants of µ-opioid and Cholecystokinin-A receptors could lead to altered or adverse drug response. By analyzing UK National Health Service drug prescription and sales data, we suggest that characterizing GPCR variants could increase prescription precision, improving patients' quality of life, and relieve the economic and societal burden due to variable drug responsiveness. VIDEO ABSTRACT.

Adaptive Laboratory Evolution of Antibiotic Resistance Using Different Selection Regimes Lead to Similar Phenotypes and Genotypes.

Antibiotic resistance is a global threat to human health, wherefore it is crucial to study the mechanisms of antibiotic resistance as well as its emergence and dissemination. One way to analyze the acquisition of de novo mutations conferring antibiotic resistance is adaptive laboratory evolution. However, various evolution methods exist that utilize different population sizes, selection strengths, and bottlenecks. While evolution in increasing drug gradients guarantees high-level antibiotic resistance promising to identify the most potent resistance conferring mutations, other selection regimes are simpler to implement and therefore allow higher throughput. The specific regimen of adaptive evolution may have a profound impact on the adapted cell state. Indeed, substantial effects of the selection regime on the resulting geno- and phenotypes have been reported in the literature. In this study we compare the geno- and phenotypes of Escherichia coli after evolution to Amikacin, Piperacillin, and Tetracycline under four different selection regimes. Interestingly, key mutations that confer antibiotic resistance as well as phenotypic changes like collateral sensitivity and cross-resistance emerge independently of the selection regime. Yet, lineages that underwent evolution under mild selection displayed a growth advantage independently of the acquired level of antibiotic resistance compared to lineages adapted under maximal selection in a drug gradient. Our data suggests that even though different selection regimens result in subtle genotypic and phenotypic differences key adaptations appear independently of the selection regime.