Genome -Scale Reconstruction of Metabolic Networks of Lactobacillus casei ATCC 334 and 12A

Lactobacillus casei strains are widely used in industry and the utility of this organism in these industrial applications is straindependent. Hence, tools capable of predicting strain specific phenotypes would have utility in the selection of strains forspecific industrial processes. Genome-scale metabolic models can be utilized to better understand genotype-phenotyperelationships and to compare different organisms. To assist in the selection and development of strains with enhancedindustrial utility, genome-scale models for L. casei ATCC 334, a well characterized strain, and strain 12A, a corn silage isolate,were constructed. Draft models were generated from RAST genome annotations using the Model SEED database and refined by evaluating ATP generating cycles, mass-and-charge-balances of reactions, and growth phenotypes. After the validation process was finished, we compared the metabolic networks of these two strains to identify metabolic, genetic and ortholog differences that may lead to different phenotypic behaviors. We conclude that the metabolic capabilities of the two networks are highly similar. The L. casei ATCC 334 model accounts for 1,040 reactions, 959 metabolites and 548 genes, while the L. casei 12A model accounts for 1,076 reactions, 979 metabolites and 640 genes. The developed L. casei ATCC 334 and 12A metabolic models will enable better understanding of the physiology of these organisms and be valuable tools in the development and selection of strains with enhanced utility in a variety of industrial applications

Improving End-User Trust in the Quality of Commercial Probiotic Products

In a rapidly growing global probiotic market, end-users have difficulty distinguishing between high quality and poor quality products. This ambiguity threatens the trust consumers and healthcare providers have in probiotic products. To address this problem, we recommend that companies undergo third-party evaluations to certify probiotic quality and label accuracy. In order to communicate about product quality to end-users, indication of certification on product labels is helpful, although not all manufacturers choose to use this approach. Herein we discuss: third-party certification, the process of setting standards for identity, purity, and quantification of probiotics; some emerging methodologies useful for quality assessment; and some technical challenges unique to managing quality of live microbial products. This review provides insights of an Expert Panel engaged in this process and aims to update the reader on relevant current scientific methodologies. Establishing validated methodologies for all aspects of quality assessment is an essential component of this process and can be facilitated by established organizations, such as United States Pharmacopeia. Emerging methodologies including whole genome sequencing and flow cytometry are poised to play important roles in these processes

Use of optical mapping to sort uropathogenic Escherichia coli strains into distinct subgroups

Optical maps were generated for 33 uropathogenic Escherichia coli (UPEC) isolates. For individual genomes, the NcoI restriction fragments aligned into a unique chromosome map for each individual isolate, which was then compared with the in silico restriction maps of all of the sequenced E. coli and Shigella strains. All of the UPEC isolates clustered separately from the Shigella strains as well as the laboratory and enterohaemorrhagic E. coli strains. Moreover, the individual strains appeared to cluster into distinct subgroups based on the dendrogram analyses. Phylogenetic grouping of these 33 strains showed that 32/33 were the B2 subgroup and 1/33 was subgroup A. To further characterize the similarities and differences among the 33 isolates, pathogenicity island (PAI), haemolysin and virulence gene comparisons were performed. A strong correlation was observed between individual subgroups and virulence factor genes as well as haemolysis activity. Furthermore, there was considerable conservation of sequenced-strain PAIs in the specific subgroups. Strains with different antibiotic-resistance patterns also appeared to sort into separate subgroups. Thus, the optical maps distinguished the UPEC strains from other E. coli strains and further subdivided the strains into distinct subgroups. This optical mapping procedure holds promise as an alternative way to subgroup all E. coli strains, including those involved in infections outside of the intestinal tract and epidemic strains with distinct patterns of antibiotic resistance

Genotyping by PCR and High-Throughput Sequencing of Commercial Probiotic Products Reveals Composition Biases.

Recent advances in microbiome research have brought renewed focus on beneficial bacteria, many of which are available in food and dietary supplements. Although probiotics have historically been defined as microorganisms that convey health benefits when ingested in sufficient viable amounts, this description now includes the stipulation well defined strains, encompassing definitive taxonomy for consumer consideration and regulatory oversight. Here, we evaluated 52 commercial dietary supplements covering a range of labeled species, and determined their content using plate counting, targeted genotyping. Additionally, strain identities were assessed using methods recently published by the United States Pharmacopeial Convention. We also determined the relative abundance of individual bacteria by high-throughput sequencing (HTS) of the 16S rRNA sequence using paired-end 2x250bp Illumina MiSeq technology. Using multiple methods, we tested the hypothesis that products do contain the quantitative amount of labeled bacteria, and qualitative list of labeled microbial species. We found that 17 samples (33%) were below label claim for CFU prior to their expiration dates. A multiplexed-PCR scheme showed that only 30/52 (58%) of the products contained a correctly labeled classification, with issues encompassing incorrect taxonomy, missing species and un-labeled species. The HTS revealed that many blended products consisted predominantly of Lactobacillus acidophilus and Bifidobacterium animalis subsp. lactis. These results highlight the need for reliable methods to qualitatively determine the correct taxonomy and quantitatively ascertain the relative amounts of mixed microbial populations in commercial probiotic products

Absolute Enumeration of Probiotic Strains Lactobacillus acidophilus NCFM® and Bifidobacterium animalis subsp. lactis Bl-04® via Chip-Based Digital PCR

The current standard for enumeration of probiotics to obtain colony forming units by plate counts has several drawbacks: long time to results, high variability and the inability to discern between bacterial strains. Accurate probiotic cell counts are important to confirm the delivery of a clinically documented dose for its associated health benefits. A method is described using chip-based digital PCR (cdPCR) to enumerate Bifidobacterium animalis subsp. lactis Bl-04 and Lactobacillus acidophilus NCFM both as single strains and in combination. Primers and probes were designed to differentiate the target strains against other strains of the same species using known single copy, genetic differences. The assay was optimized to include propidium monoazide pre-treatment to prevent amplification of DNA associated with dead probiotic cells as well as liberation of DNA from cells with intact membranes using bead beating. The resulting assay was able to successfully enumerate each strain whether alone or in multiplex. The cdPCR method had a 4 and 5% relative standard deviation (RSD) for Bl-04 and NCFM, respectively, making it more precise than plate counts with an industry accepted RSD of 15%. cdPCR has the potential to replace traditional plate counts because of its precision, strain specificity and the ability to obtain results in a matter of hours

CRISPR Immunity Drives Rapid Phage Genome Evolution in Streptococcus thermophilus

Many bacteria rely on CRISPR-Cas systems to provide adaptive immunity against phages, predation by which can shape the ecology and functioning of microbial communities. To characterize the impact of CRISPR immunization on phage genome evolution, we performed long-term bacterium-phage (Streptococcus thermophilus-phage 2972) coevolution experiments. We found that in this species, CRISPR immunity drives fixation of single nucleotide polymorphisms that accumulate exclusively in phage genome regions targeted by CRISPR. Mutation rates in phage genomes highly exceed those of the host. The presence of multiple phages increased phage persistence by enabling recombination-based formation of chimeric phage genomes in which sequences heavily targeted by CRISPR were replaced. Collectively, our results establish CRISPR-Cas adaptive immunity as a key driver of phage genome evolution under the conditions studied and highlight the importance of multiple coexisting phages for persistence in natural systems

CRISPR immunity drives rapid phage genome evolution in Streptococcus thermophilus.

UnlabelledMany bacteria rely on CRISPR-Cas systems to provide adaptive immunity against phages, predation by which can shape the ecology and functioning of microbial communities. To characterize the impact of CRISPR immunization on phage genome evolution, we performed long-term bacterium-phage (Streptococcus thermophilus-phage 2972) coevolution experiments. We found that in this species, CRISPR immunity drives fixation of single nucleotide polymorphisms that accumulate exclusively in phage genome regions targeted by CRISPR. Mutation rates in phage genomes highly exceed those of the host. The presence of multiple phages increased phage persistence by enabling recombination-based formation of chimeric phage genomes in which sequences heavily targeted by CRISPR were replaced. Collectively, our results establish CRISPR-Cas adaptive immunity as a key driver of phage genome evolution under the conditions studied and highlight the importance of multiple coexisting phages for persistence in natural systems.ImportancePhages remain an enigmatic part of the biosphere. As predators, they challenge the survival of host bacteria and archaea and set off an "arms race" involving host immunization countered by phage mutation. The CRISPR-Cas system is adaptive: by capturing fragments of a phage genome upon exposure, the host is positioned to counteract future infections. To investigate this process, we initiated massive deep-sequencing experiments with a host and infective phage and tracked the coevolution of both populations over hundreds of days. In the present study, we found that CRISPR immunity drives the accumulation of phage genome rearrangements (which enable longer phage survival) and escape mutations, establishing CRISPR as one of the fundamental drivers of phage evolution

Number of gene deletion sets found by CONGA under four different conditions between <i>i</i>Lca334_548 and <i>i</i>Lca12A_640.

<p>Number of gene deletion sets found by CONGA under four different conditions between <i>i</i>Lca334_548 and <i>i</i>Lca12A_640.</p

Metabolic differences in the two <i>L. casei</i> strains.

<p>(A): Pathway for the synthesis of tetrahydrofolate (THF) from 5, 10-methylenetetrahydrofolate (5,10-CH2-THF) and its role in purine biosynthesis. This pathway is common to both strains. (B): Additional pathway for the conversion of 5,10-CH₂-THF to THF active in the <i>i</i>Lca12A_640 model. With the exception of the panthtothenate transporter, the reactions are found in both models. (A and B): Thick arrows indicate flux in both models. Double arrows represent flux in the <i>i</i>Lca12A_640 model. The black ‘X’ indicates a gene deletion identified by CONGA lethal in <i>i</i>Lca334_548 but not <i>i</i>Lca12A_640, and gray arrows indicate inactive reactions arising from the deletion. The dashed arrow represents two separate steps. Reactions and metabolites corresponding to the given E.C. numbers and metabolite identifiers are given in the Supporting Material.</p

Carbohydrate utilization of <i>L. casei</i> ATCC 334 and 12A as determined by <i>in vivo</i> experiments and flux balance analysis of the models prior to and after model refinement.

<p>Discrepancies between experimental data and simulations are in bold.</p><p>G = Growth in the presence of the carbohydrate.</p><p>NG = No growth in the presence of the carbohydrate.</p><p>*For simulations, G represents increased biomass production in the presence of the carbohydrate; NG represents no change in biomass production in the presence of the carbohydrate.</p>+<p>In vivo data are based on the study cited by Broadbent et al. (2012) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0110785#pone.0110785-Broadbent1" target="_blank">[5]</a>.</p><p>Carbohydrate utilization of <i>L. casei</i> ATCC 334 and 12A as determined by <i>in vivo</i> experiments and flux balance analysis of the models prior to and after model refinement.</p