Rings Reconcile Genotypic and Phenotypic Evolution within the Proteobacteria.

Although prokaryotes are usually classified using molecular phylogenies instead of phenotypes after the advent of gene sequencing, neither of these methods is satisfactory because the phenotypes cannot explain the molecular trees and the trees do not fit the phenotypes. This scientific crisis still exists and the profound disconnection between these two pillars of evolutionary biology--genotypes and phenotypes--grows larger. We use rings and a genomic form of goods thinking to resolve this conundrum (McInerney JO, Cummins C, Haggerty L. 2011. Goods thinking vs. tree thinking. Mobile Genet Elements. 1:304-308; Nelson-Sathi S, et al. 2015. Origins of major archaeal clades correspond to gene acquisitions from bacteria. Nature 517:77-80). The Proteobacteria is the most speciose prokaryotic phylum known. It is an ideal phylogenetic model for reconstructing Earth's evolutionary history. It contains diverse free living, pathogenic, photosynthetic, sulfur metabolizing, and symbiotic species. Due to its large number of species (Whitman WB, Coleman DC, Wiebe WJ. 1998. Prokaryotes: the unseen majority. Proc Nat Acad Sci U S A. 95:6578-6583) it was initially expected to provide strong phylogenetic support for a proteobacterial tree of life. But despite its many species, sequence-based tree analyses are unable to resolve its topology. Here we develop new rooted ring analyses and study proteobacterial evolution. Using protein family data and new genome-based outgroup rooting procedures, we reconstruct the complex evolutionary history of the proteobacterial rings (combinations of tree-like divergences and endosymbiotic-like convergences). We identify and map the origins of major gene flows within the rooted proteobacterial rings (P < 3.6 × 10(-6)) and find that the evolution of the "Alpha-," "Beta-," and "Gammaproteobacteria" is represented by a unique set of rings. Using new techniques presented here we also root these rings using outgroups. We also map the independent flows of genes involved in DNA-, RNA-, ATP-, and membrane- related processes within the Proteobacteria and thereby demonstrate that these large gene flows are consistent with endosymbioses (P < 3.6 × 10(-9)). Our analyses illustrate what it means to find that a gene is present, or absent, within a gene flow, and thereby clarify the origin of the apparent conflicts between genotypes and phenotypes. Here we identify the gene flows that introduced photosynthesis into the Alpha-, Beta-, and Gammaproteobacteria from the common ancestor of the Actinobacteria and the Firmicutes. Our results also explain why rooted rings, unlike trees, are consistent with the observed genotypic and phenotypic relationships observed among the various proteobacterial classes. We find that ring phylogenies can explain the genotypes and the phenotypes of biological processes within large and complex groups like the Proteobacteria

CRISPR-Cas system:A new paradigm for bacterial stress response through genome rearrangement

Bacteria can receive genetic material from other bacteria or invading bacteriophages primarily through horizontal gene transfer. These genetic exchanges can result in genome rearrangement and the acquisition of novel traits that assist cells with stresses and adverse environmental conditions. Bacteria have a relatively small genome with >90% of sequences consisting of protein coding genes, stable RNA biomolecules, and gene regulatory sequences. The remaining genome fraction is primarily large repeat elements, such as retrotransposons, interspersed repeat elements, insertion sequences, and the more recently discovered clustered regularly interspaced short palindromic repeats (CRISPRs), with CRISPR-associated gene sequences (cas) that code for various Cas proteins. The CRISPR genetic locus is a series of direct repeats that are interspersed by unique spacer sequences. These unique spacer sequences represent signatures of bacteriophage genomes as the "working memory" for a bacterium to identify and destroy an invading phage genome that has previously infected the host. The protective function of the CRISPR-Cas systems are found in ∼40% of sequenced bacterial genomes, and it is often defined as bacterial acquired immunity. This chapter will elaborate the origin, structure, and function of CRISPR-Cas genetic systems acquired by bacteria, and their role in adaptive fitness while being subjected to environmental stress conditions

Metagenomic data of the bacterial community in coastal Gulf of Mexico sediment microcosms following exposure to Macondo oil (MC252)

The data in this article includes the sequences of bacterial 16S rRNA gene from metagenome of Macondo oil (MC252)-treated and non-oil-treated sediment microcosms, collected from coastal Gulf of Mexico and Bayou La Batre, USA. Metacommunity DNA was PCR amplified with 341F and 907R oligonucleotide primers, targeting V3–V5 regions of the 16S rRNA gene. Data were generated by using bacterial tag-encoded FLX-amplicon pyrosequencing (bTEFAP) methodology and then processed using bioinformatics tools such as QIIME. The data information is deposited to NCBI׳s BioProject and BioSample and raw sequence files are available via NCBI׳s Sequence Read Archive (SRA) database

Perturbation of the human gastrointestinal tract microbial ecosystem by oral drugs to treat chronic disease results in a spectrum of individual specific patterns of extinction and persistence of dominant microbial strains.

BackgroundOral drugs can have side effects such as diarrhea that indicate the perturbation of the gut microbial community. To further understand the dynamics of perturbation, we have assessed the strain relatedness of samples from previously published data sets from pre and post bowel evacuation, episodes of diarrhea, and administration of oral drugs to treat diabetes and rheumatoid arthritis.MethodsWe analyzed a total of published five data sets using our strain-tracking tool called Window-based Single Nucleotide Variant (SNV) Similarity (WSS) to identify related strains from the same individual.ResultsStrain-tracking analysis using the first data set from 8 individuals pre and 21-50 days post iso-osmotic bowel wash revealed almost all microbial strains were related in an individual between pre and post samples. Similarly, in a second study, strain-tracking analysis of 4 individuals pre and post sporadic diarrhea revealed the majority of strains were related over time (up to 44 weeks). In contrast, the analysis of a third data set from 22 individuals pre and post 3-day exposure of oral metformin revealed that no individuals had a related strain. In a fourth study, the data set taken at 2 and 4 months from 38 individuals on placebo or metformin revealed individual specific sharing of pre and post strains. Finally, the data set from 18 individuals with rheumatoid arthritis given disease-modifying antirheumatic drugs methotrexate or glycosides of the traditional Chinese medicinal component Tripterygium wilfordii showed individual specific sharing of pre and post strains up to 16 months.ConclusionOral drugs used to treat chronic disease can result in individual specific microbial strain change for the majority of species. Since the gut community provides essential functions for the host, our study supports personalized monitoring to assess the status of the dominant microbial strains after initiation of oral drugs to treat chronic disease

WSS, BSAP-3 and StrainPhlAn analyses for the FMT donor 18–0005 and recipient 18–0007 pair.

(A) The WSS scores were obtained by comparing 1) the donor’s sample to paired recipient’s pre- and post-FMT samples; and 2) the recipient’s pre-FMT sample to the same recipient’s post-FMT samples for donor 18–0005 and recipient 18–0007. The purple box indicates that strain was related to the recipient’s pre-FMT. BLAST result from the BSAP-3 gene analysis was also shown in a table, indicating “+” = BSAP-3 positive and “-” = BSAP-3 negative. (B) StrainPhlAn analysis on donor 18–0005 and recipient 18–0007 pair was done for Bacteroides vulgatus across all samples in this pair. A neighbor-joining (NJ) tree was constructed and the tree is drawn to scale with branch lengths. The distances were calculated using the Maximum Composite Likelihood method and are in the units of the number of base substitutions per site using MEGA X. The blue and purple boxes shown next to the NJ tree match the color boxes shown in Fig 8A. A white asterisk indicates a donor and recipient pre FMT sample.</p

Taxonomic profile.

Microbial community composition was profiled at the species level for (A) donor 18–0002, recipient 18–0018, (B) donor 18–0014, recipient 19–0024, (C) donor 18–0031, recipient 19–0013, (D) donor 18–0006, recipient 19–0002, and (E) donor 18–0005, recipient 18–0007 from Davar data set. The number shown in the table shows the relative abundances obtained using MetaPhlAn2. (XLSX)</p

StrainPhlAn analysis for <i>B</i>. <i>uniformis</i> from FMT donor 18–0014 and recipient 19–0024 pair.

StrainPhlAn analysis on FMT pair donor 18–0014 and recipient 19–0024 was done for B. uniformis across all samples in this pair. A neighbor-joining (NJ) tree was built and the tree is drawn to scale with branch lengths. Then, the distances were calculated using the Maximum Composite Likelihood method and are in the units of the number of base substitutions per site using MEGA X. The blue and purple color boxes shown next to the NJ tree match the color boxes shown in Fig 2A and the orange color boxes indicate a recombinant strain which is shown as both numerical numbers in Fig 2A. A white asterisk indicates a donor and recipient pre FMT sample.</p

StrainPhlAn analysis for <i>B</i>. <i>vulgatus</i> FMT donor 18–0002 and recipient 18–0018 pair.

StrainPhlAn analysis on donor 18–0002 and recipient 18–0018 pair was done for Bacteroides vulgatus across all samples in this pair. A neighbor-joining (NJ) tree was constructed and the tree is drawn to scale with branch lengths. The same steps described in the legend of Fig 3 were then applied to complete the tree using MEGA X. The blue and purple boxes shown next to the NJ tree match the color boxes shown in Fig 5A, the yellow boxes depict a new strain and orange boxes indicate a recombinant strain both shown as numerical numbers in Fig 5A. The yellow boxes showed consistent WSS scores related to donor or recipient and the orange boxes showed either similar WSS scores observed between donor and recipient or a higher score found in the recipient than donor. A white asterisk indicates a donor pre FMT sample.</p

Summarized WSS scores with the taxonomic profile for the FMT donor 18-0002- recipient 18–0018 pair.

(A) The WSS scores were determined by comparing 1) the donor’s sample to paired recipient’s pre- and post-FMT samples; and 2) the recipient’s pre-FMT sample to the same recipient’s post-FMT samples for donor 18–0002 and recipient 18–0018. Sample information for this pair was shown in S1 Table. The resultant WSS scores were grouped into different color boxes (see the figure key) or numerical numbers (when the score was below the cutoff value; NS = No Score). (B and C) Relative abundance was obtained for B. uniformis and B. vulgatus for this pair using MetaPhlAn2 analysis. For B. vulgatus, relative abundance for the Recipient was 0.03% and day 21 was 0.05%. For B. uniformis, relative abundance for the day 42 was 0.02% and day 342 was 0.01%.</p

Sharing of gut microbial strains between selected individual sets of twins cohabitating for decades.

BACKGROUND:Given the increasing realization of the important functions of the gut microbial community in human health, it is important to determine whether the increased age of the host coupled with inevitable environmental changes can alter the stability of individual microbial strains of the gut microbial community. Since early studies demonstrated that pairs of twins possess the related gut microbial communities, to gain insights into the temporal stability of the reservoir of gut microbial strains in humans, we have assessed the strain relatedness of samples from two previously published data sets that were obtained from twin children and adults (36-80 years old) who have been either living together or apart for different times. METHODS:We analyzed the two data sets; twin children (n = 24) and adults (n = 50) using our previously developed strain-tracking program called Window-based Single Nucleotide Variant (SNV) Similarity (WSS) that can distinguish a related strain pair from a non-related strain pair based on the overall genome-wide SNV similarity. To independently substantiate the identification of distinct microbial genomic variants (herein strains) observed from WSS analysis, we used analysis by StrainPhlAn. RESULTS:Analysis of the twin children data set revealed a significantly (P-value <0.05) higher number of the shared strain pairs with a predominance of Bacteroides vulgatus between individual sets of twin pairs than the twin adult data set. Additional analysis on the adult twins showed that twins who have been living apart less than 10 years shared significantly more related strain pairs than twins living apart between 10 to 60 years. Eighty-year-old twins who had been living together for 79 years then separated for 1 year showed the highest number of related strain pairs consisting of B. vulgatus, Eubacterium eligens, and Bifidobacterium adolescentis. The next highest number of related strain pairs was found in 56-year-old twins who had been living together for 51 years then separated for 5 years (B. vulgatus and Coprococcus eutactus as related strains), 73-year-old twins living together for 66 years and then separated for 7 years (Bacteroides uniformis and Clostrium sp. L2-50 as related strains) and 36-year-old twins separated for 19 years (shared strains of Alistipes shahii and E. eligens). Finally, a sporadic appearance of a single shared strain that did not show a correlation with time of separation was observed in three twin sets that had separation times between 22 to 54 years. CONCLUSION:We conclude from our strain-tracking analysis of twins that certain gut microbial strains can be shared between individuals in some cases for decades. Changes in the host environmental conditions over time can impact the stability landscape of the gut microbial community resulting in the appearance of new strains that could potentially impact microbe interactions that are essential for function in human health