The rise of the sourdough: Genome-scale metabolic modeling-based approach to design sourdough starter communities with tailored-made properties.

Sourdough starters harbor microbial consortia that benefit the final product's aroma and volume. The complex nature of these spontaneously developed communities raises challenges in predicting the fermentation phenotypes. Herein, we demonstrated for the first time in this field the potential of genome-scale metabolic modeling (GEMs) in the study of sourdough microbial communities. Broad in-silico modeling of microbial growth was applied on communities composed of yeast (Saccharomyces cerevisiae) and different Lactic Acid Bacteria (LAB) species, which mainly predominate in sourdough starters. Simulations of model-represented communities associated specific bacterial compositions with sourdough phenotypes. Based on ranking the phenotypic performances of different combinations, Pediococcus spp. - Lb. sakei group members were predicted to have an optimal effect considering the increase in S. cerevisiae growth abilities and overall CO2 secretion rates. Flux Balance Analysis (FBA) revealed mutual relationships between the Pediococcus spp. - Lb. sakei group members and S. cerevisiae through bidirectional nutrient dependencies, and further underlined that these bacteria compete with the yeast over nutrients to a lesser extent than the rest LAB species. Volatile compounds (VOCs) production was further modeled, identifying species-specific and community-related VOCs production profiles. The in-silico models' predictions were validated by experimentally building synthetic sourdough communities and assessing the fermentation phenotypes. The Pediococcus spp. - Lb. sakei group was indeed associated with increased yeast cell counts and fermentation rates, demonstrating a 25 % increase in the average leavening rates during the first 10 fermentation hours compared to communities with a lower representation of these group members. Overall, these results provide a possible novel strategy towards the de-novo design of sourdough starter communities with tailored-made characterizations, including a shortened leavening period.

Selective Terminal Functionalization of Linear Alkanes.

A two-step sequential strategy involving a biocatalytic dehydrogenation/remote hydrofunctionalization, as a unified and versatile approach to selectively convert linear alkanes into a large array of valuable functionalized aliphatic derivatives is reported. The dehydrogenation is carried out by a mutant strain of a bacteria Rhodococcus and the produced alkenes are subsequently engaged in a remote functionalization through a metal-catalyzed hydrometalation/migration sequence that subsequently react with a large variety of electrophiles. The judicious implementation of this combined biocatalytic and organometallic approach enabled us to develop a high-yielding protocol to site-selectively functionalize unreactive primary C-H bonds.

Periodic concentration-polarization-based formation of a biomolecule preconcentrate for enhanced biosensing.

Ionic concentration-polarization (CP)-based biomolecule preconcentration is an established method for enhancing the detection sensitivity of target biomolecules. However, the formed preconcentrated biomolecule plug rapidly sweeps over the surface-immobilized antibodies, resulting in a short-term overlap between the capture agent and the analyte, and subsequently suboptimal binding. To overcome this, we designed a setup allowing for the periodic formation of a preconcentrated biomolecule plug by activating the CP for predetermined on/off intervals. This work demonstrated the feasibility of cyclic CP actuation and optimized the sweeping conditions required to obtain the maximum retention time of a preconcentrated plug over a desired sensing region and enhanced detection sensitivity. The ability of this method to efficiently preconcentrate different analytes and to successfully increase immunoassay sensitivity underscore its potential in immunoassays serving the clinical and food testing industries.

Mapping Ethanol Tolerance in Budding Yeast Reveals High Genetic Variation in a Wild Isolate.

Ethanol tolerance, a polygenic trait of the yeast Saccharomyces cerevisiae, is the primary factor determining industrial bioethanol productivity. Until now, genomic elements affecting ethanol tolerance have been mapped only at low resolution, hindering their identification. Here, we explore the genetic architecture of ethanol tolerance, in the F6 generation of an Advanced Intercrossed Line (AIL) mapping population between two phylogenetically distinct, but phenotypically similar, S. cerevisiae strains (a common laboratory strain and a wild strain isolated from nature). Under ethanol stress, 51 quantitative trait loci (QTLs) affecting growth and 96 QTLs affecting survival, most of them novel, were identified, with high resolution, in some cases to single genes, using a High-Resolution Mapping Package of methodologies that provided high power and high resolution. We confirmed our results experimentally by showing the effects of the novel mapped genes: MOG1, MGS1, and YJR154W. The mapped QTLs explained 34% of phenotypic variation for growth and 72% for survival. High statistical power provided by our analysis allowed detection of many loci with small, but mappable effects, uncovering a novel "quasi-infinitesimal" genetic architecture. These results are striking demonstration of tremendous amounts of hidden genetic variation exposed in crosses between phylogenetically separated strains with similar phenotypes; as opposed to the more common design where strains with distinct phenotypes are crossed. Our findings suggest that ethanol tolerance is under natural evolutionary fitness-selection for an optimum phenotype that would tend to eliminate alleles of large effect. The study provides a platform for development of superior ethanol-tolerant strains using genome editing or selection.

Murine Genetic Background Has a Stronger Impact on the Composition of the Gut Microbiota than Maternal Inoculation or Exposure to Unlike Exogenous Microbiota.

The gut microbiota is a complex ecosystem, affected by both environmental factors and host genetics. Here, we aim at uncovering the bacterial taxa whose gut persistence is controlled by host genetic variation. We used a murine model based on inbred lines BALB/c and C57BL/6J and their F1 reciprocal hybrids (♀C57BL/6J × â™‚BALB/c; ♀BALB/c × â™‚C57BL/6J). To guarantee genetic similarity of F1 offspring, including the sex chromosomes, we used only female mice. Based on 16S rRNA gene sequencing, we found that the genetically different inbred lines present different microbiota, whereas their genetically identical F1 reciprocal hybrids presented similar microbiota. Moreover, the F1 microbial composition differed from that of both parental lines. Twelve taxa were shown to have genetically controlled gut persistence, while none were found to show maternal effects. Nine of these taxa were dominantly inherited by the C57BL/6J line. Cohousing of the parental inbred lines resulted in a temporary and minor shift in microbiota composition, which returned back to the former microbial composition following separation, indicating that each line tends to maintain a unique bacterial signature reflecting the line. Taken together, our findings indicate that mouse genetics has an effect on the microbial composition in the gut, which is greater than maternal effect and continuous exposure to different microbiota of the alternative line. Uncovering the bacterial taxa associated with host genetics and understanding their role in the gut ecosystem could lead to the development of genetically oriented probiotic products, as part of the personalized medicine approach.IMPORTANCE The gut microbiota play important roles for their host. The link between host genetics and their microbial composition has received increasing interest. Using a unique reciprocal cross model, generating genetically similar F1 hybrids with different maternal inoculation, we demonstrate the inheritance of gut persistence of 12 bacterial taxa. No taxa identified as maternally transmitted. Moreover, cohabitation of two genetically different inbred lines did not dramatically affect the microbiota composition. Taken together, our results demonstrate the importance of the genetic effect over maternal inoculation or effect of exposure to unlike exogenous microbiota. These findings may lead to the development of personalized probiotic products, specifically designed according to the genetic makeup.

Draft Genome Sequence of Lactobacillus johnsonii Strain 16, Isolated from Mice.

Here, we report the genome sequence of Lactobacillus johnsonii, a member of the gut lactobacilli. This draft genome of L. johnsonii strain 16 isolated from C57BL/6J mice enables the identification of bacterial genes responsible for host-specific gut persistence.

Biodiversity of Enterococcus faecalis based on genomic typing.

Enterococcus faecalis is a common inhabitant of the gastrointestinal tracts of different animals and is also found in other environments, such as plants, soil, food and water. The diverse nature of E. faecalis, which includes pathogenic, commensal and probiotic strains, calls for the development of tools for accurate discrimination and characterization at the strain level. Here we studied the genetic relationships among 106 E. faecalis strains isolated from diverse origins and possessing different degrees of virulence. Strain typing was conducted using a set of selected simple-sequence repeat (SSR) loci combined with multilocus sequence typing (MLST) analysis, which discriminated among the strains and separated them into three main clusters. While pathogenic and commensal isolates were dispersed along the dendrogram, probiotic and cheese-originated strains were highly associated with one specific cluster (cluster 1). The strain panel was further characterized by testing the occurrence of two virulence determinants (esp gene and ß-hemolysis). The two determinants showed low abundance among probiotic and cheese-originated strains within cluster 1 when compared to non-cluster 1 cheese-originated strains, indicating a possible association of cluster 1 with non-virulent strains. Our results further emphasize the importance and challenge of precise characterization of E. faecalis strains from various sources.

Indication for Co-evolution of Lactobacillus johnsonii with its hosts.

BACKGROUND: The intestinal microbiota, composed of complex bacterial populations, is host-specific and affected by environmental factors as well as host genetics. One important bacterial group is the lactic acid bacteria (LAB), which include many health-promoting strains. Here, we studied the genetic variation within a potentially probiotic LAB species, Lactobacillus johnsonii, isolated from various hosts. RESULTS: A wide survey of 104 fecal samples was carried out for the isolation of L. johnsonii. As part of the isolation procedure, terminal restriction fragment length polymorphism (tRFLP) was performed to identify L. johnsonii within a selected narrow spectrum of fecal LAB. The tRFLP results showed host specificity of two bacterial species, the Enterococcus faecium species cluster and Lactobacillus intestinalis, to different host taxonomic groups while the appearance of L. johnsonii and E. faecalis was not correlated with any taxonomic group. The survey ultimately resulted in the isolation of L. johnsonii from few host species. The genetic variation among the 47 L. johnsonii strains isolated from the various hosts was analyzed based on variation at simple sequence repeats (SSR) loci and multi-locus sequence typing (MLST) of conserved hypothetical genes. The genetic relationships among the strains inferred by each of the methods were similar, revealing three different clusters of L. johnsonii strains, each cluster consisting of strains from a different host, i.e. chickens, humans or mice. CONCLUSIONS: Our typing results support phylogenetic separation of L. johnsonii strains isolated from different animal hosts, suggesting specificity of L. johnsonii strains to their hosts. Taken together with the tRFLP results, that indicated the association of specific LAB species with the host taxonomy, our study supports co-evolution of the host and its intestinal lactic acid bacteria.

Genetic modification of iron metabolism in mice affects the gut microbiota.

The composition of the gut microbiota is affected by environmental factors as well as host genetics. Iron is one of the important elements essential for bacterial growth, thus we hypothesized that changes in host iron homeostasis, may affect the luminal iron content of the gut and thereby the composition of intestinal bacteria. The iron regulatory protein 2 (Irp2) and one of the genes mutated in hereditary hemochromatosis Hfe , are both proteins involved in the regulation of systemic iron homeostasis. To test our hypothesis, fecal metal content and a selected spectrum of the fecal microbiota were analyzed from Hfe-/-, Irp2-/- and their wild type control mice. Elevated levels of iron as well as other minerals in feces of Irp2-/- mice compared to wild type and Hfe-/- mice were observed. Interestingly significant variation in the general fecal-bacterial population-patterns was observed between Irp2-/- and Hfe-/- mice. Furthermore the relative abundance of five species, mainly lactic acid bacteria, was significantly different among the mouse lines. Lactobacillus (L.) murinus and L. intestinalis were highly abundant in Irp2-/- mice, Enterococcus faecium species cluster and a species most similar to Olsenella were highly abundant in Hfe-/- mice and L. johnsonii was highly abundant in the wild type mice. These results suggest that deletion of iron metabolism genes in the mouse host affects the composition of its intestinal bacteria. Further studying the relationship between gut microbiota and genetic mutations affecting systemic iron metabolism in human should lead to clinical implications.

Predominant effect of host genetics on levels of Lactobacillus johnsonii bacteria in the mouse gut.

The gut microbiota is strongly associated with the well-being of the host. Its composition is affected by environmental factors, such as food and maternal inoculation, while the relative impact of the host's genetics have been recently uncovered. Here, we studied the effect of the host genetic background on the composition of intestinal bacteria in a murine model, focusing on lactic acid bacteria (LAB) as an important group that includes many probiotic strains. Based on 16S rRNA gene genotyping, variation was observed in fecal LAB populations of BALB/c and C57BL/6J mouse lines. Lactobacillus johnsonii, a potentially probiotic bacterium, appeared at significantly higher levels in C57BL/6J versus BALB/c mouse feces. In the BALB/c gut, the L. johnsonii level decreased rapidly after oral administration, suggesting that some selective force does not allow its persistence at higher levels. The genetic inheritance of L. johnsonii levels was further tested in reciprocal crosses between the two mouse lines. The resultant F1 offspring presented similar L. johnsonii levels, confirming that mouse genetics plays a major role in determining these levels compared to the smaller maternal effect. Our findings suggest that mouse genetics has a major effect on the composition of the LAB population in general and on the persistence of L. johnsonii in the gut in particular. Concentrating on a narrow spectrum of culturable LAB enables the isolation and characterization of such potentially probiotic bacterial strains, which might be specifically oriented to the genetic background of the host as part of a personalized-medicine approach.