The 3,308,274-bp sequence of the chromosome of Lactobacillus plantarum strain WCFS1, a single colony isolate of strain NCIMB8826 that was originally isolated from human saliva, has been determined, and contains 3,052 predicted protein-encoding genes. Putative biological functions could be assigned to 2,120 (70%) of the predicted proteins. Consistent with the classification of L. plantarum as a facultative heterofermentative lactic acid bacterium, the genome encodes all enzymes required for the glycolysis and phosphoketolase pathways, all of which appear to belong to the class of potentially highly expressed genes in this organism, as was evident from the codon-adaptation index of individual genes. Moreover, L. plantarum encodes a large pyruvate-dissipating potential, leading to various end-products of fermentation. L. plantarum is a species that is encountered in many different environmental niches, and this flexible and adaptive behavior is reflected by the relatively large number of regulatory and transport functions, including 25 complete PTS sugar transport systems. Moreover, the chromosome encodes >200 extracellular proteins, many of which are predicted to be bound to the cell envelope. A large proportion of the genes encoding sugar transport and utilization, as well as genes encoding extracellular functions, appear to be clustered in a 600-kb region near the origin of replication. Many of these genes display deviation of nucleotide composition, consistent with a foreign origin. These findings suggest that these genes, which provide an important part of the interaction of L. plantarum with its environment, form a lifestyle adaptation region in the chromosome.

NCT01111864.

Prokaryotes encode adaptive immune systems, called CRISPR-Cas (clustered regularly interspaced short palindromic repeats-CRISPR associated), to provide resistance against mobile invaders, such as viruses and plasmids. Host immunity is based on incorporation of invader DNA sequences in a memory locus (CRISPR), the formation of guide RNAs from this locus, and the degradation of cognate invader DNA (protospacer). Invaders can escape type I-E CRISPRCas immunity in Escherichia coli K12 by making point mutations in the seed region of the protospacer or its adjacent motif (PAM), but hosts quickly restore immunity by integrating new spacers in a positive-feedback process termed "priming." Here, by using a randomized protospacer and PAM library and high-throughput plasmid loss assays, we provide a systematic analysis of the constraints of both direct interference and subsequent priming in E. coli. We have defined a high-resolution genetic map of direct interference by Cascade and Cas3, which includes five positions of the protospacer at 6-nt intervals that readily tolerate mutations. Importantly, we show that priming is an extremely robust process capable of using degenerate target regions, with up to 13 mutations throughout the PAM and protospacer region. Priming is influenced by the number of mismatches, their position, and is nucleotide dependent. Our findings imply that even outdated spacers containing many mismatches can induce a rapid primed CRISPR response against diversified or related invaders, giving microbes an advantage in the coevolutionary arms race with their invaders.adaptive immunity | phage resistance | crRNA | next-generation sequencing | horizontal gene transfer

BackgroundMicroorganisms in the human intestine (i.e. the gut microbiome) have an increasingly recognized impact on human health, including brain functioning. Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder associated with abnormalities in dopamine neurotransmission and deficits in reward processing and its underlying neuro-circuitry including the ventral striatum. The microbiome might contribute to ADHD etiology via the gut-brain axis. In this pilot study, we investigated potential differences in the microbiome between ADHD cases and undiagnosed controls, as well as its relation to neural reward processing.MethodsWe used 16S rRNA marker gene sequencing (16S) to identify bacterial taxa and their predicted gene functions in 19 ADHD and 77 control participants. Using functional magnetic resonance imaging (fMRI), we interrogated the effect of observed microbiome differences in neural reward responses in a subset of 28 participants, independent of diagnosis.ResultsFor the first time, we describe gut microbial makeup of adolescents and adults diagnosed with ADHD. We found that the relative abundance of several bacterial taxa differed between cases and controls, albeit marginally significant. A nominal increase in the Bifidobacterium genus was observed in ADHD cases. In a hypothesis-driven approach, we found that the observed increase was linked to significantly enhanced 16S-based predicted bacterial gene functionality encoding cyclohexadienyl dehydratase in cases relative to controls. This enzyme is involved in the synthesis of phenylalanine, a precursor of dopamine. Increased relative abundance of this functionality was significantly associated with decreased ventral striatal fMRI responses during reward anticipation, independent of ADHD diagnosis and age.ConclusionsOur results show increases in gut microbiome predicted function of dopamine precursor synthesis between ADHD cases and controls. This increase in microbiome function relates to decreased neural responses to reward anticipation. Decreased neural reward anticipation constitutes one of the hallmarks of ADHD.

In the Life Sciences ‘omics’ data is increasingly generated by different high-throughput technologies. Often only the integration of these data allows uncovering biological insights that can be experimentally validated or mechanistically modelled, i.e. sophisticated computational approaches are required to extract the complex non-linear trends present in omics data. Classification techniques allow training a model based on variables (e.g. SNPs in genetic association studies) to separate different classes (e.g. healthy subjects versus patients). Random Forest (RF) is a versatile classification algorithm suited for the analysis of these large data sets. In the Life Sciences, RF is popular because RF classification models have a high-prediction accuracy and provide information on importance of variables for classification. For omics data, variables or conditional relations between variables are typically important for a subset of samples of the same class. For example: within a class of cancer patients certain SNP combinations may be important for a subset of patients that have a specific subtype of cancer, but not important for a different subset of patients. These conditional relationships can in principle be uncovered from the data with RF as these are implicitly taken into account by the algorithm during the creation of the classification model. This review details some of the to the best of our knowledge rarely or never used RF properties that allow maximizing the biological insights that can be extracted from complex omics data sets using RF.