Small RNAs >26 nt in length associate with AGO1 and are upregulated by nutrient deprivation in the alga Chlamydomonas.

Small RNAs (sRNAs) associate with ARGONAUTE (AGO) proteins forming effector complexes with key roles in gene regulation and defense responses against molecular parasites. In multicellular eukaryotes, extensive duplication and diversification of RNA interference (RNAi) components have resulted in intricate pathways for epigenetic control of gene expression. The unicellular alga Chlamydomonas reinhardtii also has a complex RNAi machinery, including 3 AGOs and 3 DICER-like proteins. However, little is known about the biogenesis and function of most endogenous sRNAs. We demonstrate here that Chlamydomonas contains uncommonly long (>26 nt) sRNAs that associate preferentially with AGO1. Somewhat reminiscent of animal PIWI-interacting RNAs, these >26 nt sRNAs are derived from moderately repetitive genomic clusters and their biogenesis is DICER-independent. Interestingly, the sequences generating these >26-nt sRNAs have been conserved and amplified in several Chlamydomonas species. Moreover, expression of these longer sRNAs increases substantially under nitrogen or sulfur deprivation, concurrently with the downregulation of predicted target transcripts. We hypothesize that the transposon-like sequences from which >26-nt sRNAs are produced might have been ancestrally targeted for silencing by the RNAi machinery but, during evolution, certain sRNAs might have fortuitously acquired endogenous target genes and become integrated into gene regulatory networks.

Divergent evolution of extreme production of variant plant monounsaturated fatty acids.

Metabolic extremes provide opportunities to understand enzymatic and metabolic plasticity and biotechnological tools for novel biomaterial production. We discovered that seed oils of many Thunbergia species contain up to 92% of the unusual monounsaturated petroselinic acid (18:1Δ6), one of the highest reported levels for a single fatty acid in plants. Supporting the biosynthetic origin of petroselinic acid, we identified a Δ6-stearoyl-acyl carrier protein (18:0-ACP) desaturase from Thunbergia laurifolia, closely related to a previously identified Δ6-palmitoyl-ACP desaturase that produces sapienic acid (16:1Δ6)-rich oils in Thunbergia alata seeds. Guided by a T. laurifolia desaturase crystal structure obtained in this study, enzyme mutagenesis identified key amino acids for functional divergence of Δ6 desaturases from the archetypal Δ9-18:0-ACP desaturase and mutations that result in nonnative enzyme regiospecificity. Furthermore, we demonstrate the utility of the T. laurifolia desaturase for the production of unusual monounsaturated fatty acids in engineered plant and bacterial hosts. Through stepwise metabolic engineering, we provide evidence that divergent evolution of extreme petroselinic acid and sapienic acid production arises from biosynthetic and metabolic functional specialization and enhanced expression of specific enzymes to accommodate metabolism of atypical substrates.

Evolution and emergence of primate-specific interferon regulatory factor 9.

The binding of interferon (IFN) to its receptors leads to formation of IFN-stimulated gene factor 3 (ISGF3) complex that activates the transcription of cellular IFN-regulated genes. IFN regulatory factor 9 (IRF9, also called ISGF3γ or p48) is a key component of ISGF3. However, there is limited knowledge regarding the molecular evolution of IRF9 among vertebrates. In this study, we have identified the existence of the IRF9 gene in cartilaginous fish (sharks). Among primates, several isoforms unique to old world moneys and great apes are identified. These IRF9 isoforms are named as primate-specific IRF9 (PS-IRF9) to distinguish from canonical IRF9. PS-IRF9 originates from a unique exon usage and differential splicing in the IRF9 gene. Although the N-terminus are identical for all IRF9s, the C-terminal regions of the PS-IRF9 are completely different from canonical IRF9. In humans, two PS-IRF9s are identified and their RNA transcripts were detected in human primary peripheral blood mononuclear cells. In addition, human PS-IRF9 proteins were detected in human cell lines. Sharing the N-terminal exons with the canonical IRF9 proteins, PS-IRF9 is predicted to bind to the same DNA sequences as the canonical IRF9 proteins. As the C-terminal regions of IRFs are the determinants of IRF functions, PS-IRF9 may offer unique biological functions and represent a novel signaling molecule involved in the regulation of the IFN pathway in a primate-specific manner.

A consensus-based ensemble approach to improve transcriptome assembly.

BACKGROUND: Systems-level analyses, such as differential gene expression analysis, co-expression analysis, and metabolic pathway reconstruction, depend on the accuracy of the transcriptome. Multiple tools exist to perform transcriptome assembly from RNAseq data. However, assembling high quality transcriptomes is still not a trivial problem. This is especially the case for non-model organisms where adequate reference genomes are often not available. Different methods produce different transcriptome models and there is no easy way to determine which are more accurate. Furthermore, having alternative-splicing events exacerbates such difficult assembly problems. While benchmarking transcriptome assemblies is critical, this is also not trivial due to the general lack of true reference transcriptomes. RESULTS: In this study, we first provide a pipeline to generate a set of the simulated benchmark transcriptome and corresponding RNAseq data. Using the simulated benchmarking datasets, we compared the performance of various transcriptome assembly approaches including both de novo and genome-guided methods. The results showed that the assembly performance deteriorates significantly when alternative transcripts (isoforms) exist or for genome-guided methods when the reference is not available from the same genome. To improve the transcriptome assembly performance, leveraging the overlapping predictions between different assemblies, we present a new consensus-based ensemble transcriptome assembly approach, ConSemble. CONCLUSIONS: Without using a reference genome, ConSemble using four de novo assemblers achieved an accuracy up to twice as high as any de novo assemblers we compared. When a reference genome is available, ConSemble using four genome-guided assemblies removed many incorrectly assembled contigs with minimal impact on correctly assembled contigs, achieving higher precision and accuracy than individual genome-guided methods. Furthermore, ConSemble using de novo assemblers matched or exceeded the best performing genome-guided assemblers even when the transcriptomes included isoforms. We thus demonstrated that the ConSemble consensus strategy both for de novo and genome-guided assemblers can improve transcriptome assembly. The RNAseq simulation pipeline, the benchmark transcriptome datasets, and the script to perform the ConSemble assembly are all freely available from: http://bioinfolab.unl.edu/emlab/consemble/ .

Next-generation transcriptome assembly and analysis: Impact of ploidy.

Whole genome duplications (WGD) occur widely in plants, but the effects of these events impact all branches of life. WGD events have major evolutionary impacts, often leading to major structural changes within the chromosomes and massive changes in gene expression that facilitate rapid speciation and gene diversification. Even for species that currently have diploid genomes, the impact of ancestral duplication events is still present in the genomes, especially in the context of highly similar gene families that are retained from WGD. However, the impact of these ploidies on various bioinformatics workflows has not been studied well. In this review, we overview biological significance of polyploidy in different organisms. We describe the impact of having polyploid transcriptomes on bioinformatics analyses, especially focusing on transcriptome assembly and transcript quantification. We discuss the benefits of using simulated benchmarking data when we examine the performance of various methods. We also present an example strategy to generate simulated allopolyploid transcriptomes and RNAseq datasets and how these benchmark datasets can be used to assess the performance of transcript assembly and quantification methods. Our benchmarking study shows that all transcriptome assembly methods are affected by having polyploid genomes. Quantification accuracy is also impacted by polyploidy depending on the method. These simulated datasets can be adapted for testing, such as, read mapping, variant calling, and differential expression using biologically realistic conditions.

MOCASSIN-prot: a multi-objective clustering approach for protein similarity networks.

Motivation: Proteins often include multiple conserved domains. Various evolutionary events including duplication and loss of domains, domain shuffling, as well as sequence divergence contribute to generating complexities in protein structures, and consequently, in their functions. The evolutionary history of proteins is hence best modeled through networks that incorporate information both from the sequence divergence and the domain content. Here, a game-theoretic approach proposed for protein network construction is adapted into the framework of multi-objective optimization, and extended to incorporate clustering refinement procedure. Results: The new method, MOCASSIN-prot, was applied to cluster multi-domain proteins from ten genomes. The performance of MOCASSIN-prot was compared against two protein clustering methods, Markov clustering (TRIBE-MCL) and spectral clustering (SCPS). We showed that compared to these two methods, MOCASSIN-prot, which uses both domain composition and quantitative sequence similarity information, generates fewer false positives. It achieves more functionally coherent protein clusters and better differentiates protein families. Availability and implementation: MOCASSIN-prot, implemented in Perl and Matlab, is freely available at http://bioinfolab.unl.edu/emlab/MOCASSINprot. Contact: emoriyama2@unl.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Whole genomic sequence analysis of Bacillus infantis: defining the genetic blueprint of strain NRRL B-14911, an emerging cardiopathogenic microbe.

BACKGROUND: We recently reported the identification of Bacillus sp. NRRL B-14911 that induces heart autoimmunity by generating cardiac-reactive T cells through molecular mimicry. This marine bacterium was originally isolated from the Gulf of Mexico, but no associations with human diseases were reported. Therefore, to characterize its biological and medical significance, we sought to determine and analyze the complete genome sequence of Bacillus sp. NRRL B-14911. RESULTS: Based on the phylogenetic analysis of 16S ribosomal RNA (rRNA) genes, sequence analysis of the 16S-23S rDNA intergenic transcribed spacers, phenotypic microarray, and matrix-assisted laser desorption ionization time-of-flight mass spectrometry, we propose that this organism belongs to the species Bacillus infantis, previously shown to be associated with sepsis in a newborn child. Analysis of the complete genome of Bacillus sp. NRRL B-14911 revealed several virulence factors including adhesins, invasins, colonization factors, siderophores and transporters. Likewise, the bacterial genome encodes a wide range of methyl transferases, transporters, enzymatic and biochemical pathways, and insertion sequence elements that are distinct from other closely related bacilli. CONCLUSIONS: The complete genome sequence of Bacillus sp. NRRL B-14911 provided in this study may facilitate genetic manipulations to assess gene functions associated with bacterial survival and virulence. Additionally, this bacterium may serve as a useful tool to establish a disease model that permits systematic analysis of autoimmune events in various susceptible rodent strains.

Transcriptional analysis of susceptible and resistant European corn borer strains and their response to Cry1F protoxin.

BACKGROUND: Despite a number of recent reports of insect resistance to transgenic crops expressing insecticidal toxins from Bacillus thuringiensis (Bt), little is known about the mechanism of resistance to these toxins. The purpose of this study is to identify genes associated with the mechanism of Cry1F toxin resistance in European corn borer (Ostrinia nubilalis Hübner). For this, we compared the global transcriptomic response of laboratory selected resistant and susceptible O. nubilalis strain to Cry1F toxin. We further identified constitutive transcriptional differences between the two strains. RESULTS: An O. nubilalis midgut transcriptome of 36,125 transcripts was assembled de novo from 106 million Illumina HiSeq and Roche 454 reads and used as a reference for estimation of differential gene expression analysis. Evaluation of gene expression profiles of midgut tissues from the Cry1F susceptible and resistant strains after toxin exposure identified a suite of genes that responded to the toxin in the susceptible strain (n = 1,654), but almost 20-fold fewer in the resistant strain (n = 84). A total of 5,455 midgut transcripts showed significant constitutive expression differences between Cry1F susceptible and resistant strains. Transcripts coding for previously identified Cry toxin receptors, cadherin and alkaline phosphatase and proteases were also differentially expressed in the midgut of the susceptible and resistant strains. CONCLUSIONS: Our current study provides a valuable resource for further molecular characterization of Bt resistance and insect response to Cry1F toxin in O. nubilalis and other pest species.

Comparative studies of differential gene calling using RNA-Seq data.

BACKGROUND: With its massive amount of data, gene-expression profiling by RNA-Seq has many advantanges compared with microarray experiments. RNA-Seq analysis, however, is fundamentally different from microarray data analysis. Techniques developed for analyzing microarray data thus cannot be directly applicable for the digital gene expression data. Several statistical methods have been developed for identifying differentially expressed genes specifically from RNA-Seq data over the past few years. RESULTS: In this study, we examined the performance of differential gene-calling methods using RNA-Seq data in practical situations. We focused on two representative methods: one parametric method, DESeq, and one nonparametric method, NOISeq. We examined their performance using both simulated and real datasets. Our simulation followed the RNA-Seq process and produced more realistic short read data. Both DESeq and NOISeq identified over-expressed genes more correctly than under-expressed genes. While DESeq was more likely to call longer genes as differentially expressed than shorter ones, NOISeq did not have such bias. When the underlying variation increased, both methods showed higher rates of false positives. When replicates were not available in the experiments, both methods showed lower rates of true positives and higher rates of false positives. CONCLUSIONS: The level of variation clearly affected the performance of both methods, showing the importance of understanding the variation in the data as well as having replications in RNA-Seq experiments. We showed that it is possible to obtain improved differential gene-calling results by combining the results obtained by the two methods. We suggested strategies to use these two methods individually or combined according to the characteristics of the data.

Reductive evolution and the loss of PDC/PAS domains from the genus Staphylococcus.

BACKGROUND: The Per-Arnt-Sim (PAS) domain represents a ubiquitous structural fold that is involved in bacterial sensing and adaptation systems, including several virulence related functions. Although PAS domains and the subclass of PhoQ-DcuS-CitA (PDC) domains have a common structure, there is limited amino acid sequence similarity. To gain greater insight into the evolution of PDC/PAS domains present in the bacterial kingdom and staphylococci in specific, the PDC/PAS domains from the genomic sequences of 48 bacteria, representing 5 phyla, were identified using the sensitive search method based on HMM-to-HMM comparisons (HHblits). RESULTS: A total of 1,007 PAS domains and 686 PDC domains distributed over 1,174 proteins were identified. For 28 Gram-positive bacteria, the distribution, organization, and molecular evolution of PDC/PAS domains were analyzed in greater detail, with a special emphasis on the genus Staphylococcus. Compared to other bacteria the staphylococci have relatively fewer proteins (6-9) containing PDC/PAS domains. As a general rule, the staphylococcal genomes examined in this study contain a core group of seven PDC/PAS domain-containing proteins consisting of WalK, SrrB, PhoR, ArlS, HssS, NreB, and GdpP. The exceptions to this rule are: 1) S. saprophyticus lacks the core NreB protein; 2) S. carnosus has two additional PAS domain containing proteins; 3) S. epidermidis, S. aureus, and S. pseudintermedius have an additional protein with two PDC domains that is predicted to code for a sensor histidine kinase; 4) S. lugdunensis has an additional PDC containing protein predicted to be a sensor histidine kinase. CONCLUSIONS: This comprehensive analysis demonstrates that variation in PDC/PAS domains among bacteria has limited correlations to the genome size or pathogenicity; however, our analysis established that bacteria having a motile phase in their life cycle have significantly more PDC/PAS-containing proteins. In addition, our analysis revealed a tremendous amount of variation in the number of PDC/PAS-containing proteins within genera. This variation extended to the Staphylococcus genus, which had between 6 and 9 PDC/PAS proteins and some of these appear to be previously undescribed signaling proteins. This latter point is important because most staphylococcal proteins that contain PDC/PAS domains regulate virulence factor synthesis or antibiotic resistance.

Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors.

In vertebrates, including humans, individuals harbor gut microbial communities whose species composition and relative proportions of dominant microbial groups are tremendously varied. Although external and stochastic factors clearly contribute to the individuality of the microbiota, the fundamental principles dictating how environmental factors and host genetic factors combine to shape this complex ecosystem are largely unknown and require systematic study. Here we examined factors that affect microbiota composition in a large (n = 645) mouse advanced intercross line originating from a cross between C57BL/6J and an ICR-derived outbred line (HR). Quantitative pyrosequencing of the microbiota defined a core measurable microbiota (CMM) of 64 conserved taxonomic groups that varied quantitatively across most animals in the population. Although some of this variation can be explained by litter and cohort effects, individual host genotype had a measurable contribution. Testing of the CMM abundances for cosegregation with 530 fully informative SNP markers identified 18 host quantitative trait loci (QTL) that show significant or suggestive genome-wide linkage with relative abundances of specific microbial taxa. These QTL affect microbiota composition in three ways; some loci control individual microbial species, some control groups of related taxa, and some have putative pleiotropic effects on groups of distantly related organisms. These data provide clear evidence for the importance of host genetic control in shaping individual microbiome diversity in mammals, a key step toward understanding the factors that govern the assemblages of gut microbiota associated with complex diseases.

Association of ISVsa3 with Multidrug Resistance in Salmonella enterica Isolates from Cattle (Bos taurus).

Salmonella enterica is, globally, an important cause of human illness with beef being a significant attributable source. In the human patient, systemic Salmonella infection requires antibiotic therapy, and when strains are multidrug resistant (MDR), no effective treatment may be available. MDR in bacteria is often associated with the presence of mobile genetic elements (MGE) that mediate horizontal spread of antimicrobial resistance (AMR) genes. In this study, we sought to determine the potential relationship of MDR in bovine Salmonella isolates with MGE. The present study involved 111 bovine Salmonella isolates obtained collectively from specimens derived from healthy cattle or their environments at Midwestern U.S. feedyards (2000-2001, n = 19), or specimens from sick cattle submitted to the Nebraska Veterinary Diagnostic Center (2010-2020, n = 92). Phenotypically, 33/111 isolates (29.7%) were MDR (resistant to ≥3 drug classes). Based on whole-genome sequencing (WGS; n = 41) and PCR (n = 111), a MDR phenotype was strongly associated (OR = 186; p < 0.0001) with carriage of ISVsa3, an IS91-like Family transposase. In all 41 isolates analyzed by WGS ((31 MDR and 10 non-MDR (resistant to 0-2 antibiotic classes)), MDR genes were associated with carriage of ISVsa3, most often on an IncC type plasmid carrying blaCMY-2. The typical arrangement was floR, tet(A), aph(6)-Id, aph(3″)-Ib, and sul2 flanked by ISVsa3. These results suggest that AMR genes in MDR S. enterica isolates of cattle are frequently associated with ISVsa3 and carried on IncC plasmids. Further research is needed to better understand the role of ISVsa3 in dissemination of MDR Salmonella strains.

Arabidopsis G-protein interactome reveals connections to cell wall carbohydrates and morphogenesis.

The heterotrimeric G-protein complex is minimally composed of Gα, Gß, and Gγ subunits. In the classic scenario, the G-protein complex is the nexus in signaling from the plasma membrane, where the heterotrimeric G-protein associates with heptahelical G-protein-coupled receptors (GPCRs), to cytoplasmic target proteins called effectors. Although a number of effectors are known in metazoans and fungi, none of these are predicted to exist in their canonical forms in plants. To identify ab initio plant G-protein effectors and scaffold proteins, we screened a set of proteins from the G-protein complex using two-hybrid complementation in yeast. After deep and exhaustive interrogation, we detected 544 interactions between 434 proteins, of which 68 highly interconnected proteins form the core G-protein interactome. Within this core, over half of the interactions comprising two-thirds of the nodes were retested and validated as genuine in planta. Co-expression analysis in combination with phenotyping of loss-of-function mutations in a set of core interactome genes revealed a novel role for G-proteins in regulating cell wall modification.

SuiteMSA: visual tools for multiple sequence alignment comparison and molecular sequence simulation.

BACKGROUND: Multiple sequence alignment (MSA) plays a central role in nearly all bioinformatics and molecular evolutionary applications. MSA reconstruction is thus one of the most heavily scrutinized bioinformatics fields. Evaluating the quality of MSA reconstruction is often hindered by the lack of good reference MSAs. The use of sequence evolution simulation can provide such reference MSAs. Furthermore, none of the MSA viewing/editing programs currently available allows the user to make direct comparisons between two or more MSAs. Considering the importance of MSA quality in a wide range of research, it is desirable if MSA assessment can be performed more easily. RESULTS: We have developed SuiteMSA, a java-based application that provides unique MSA viewers. Users can directly compare multiple MSAs and evaluate where the MSAs agree (are consistent) or disagree (are inconsistent). Several alignment statistics are provided to assist such comparisons. SuiteMSA also includes a graphical phylogeny editor/viewer as well as a graphical user interface for a sequence evolution simulator that can be used to construct reference MSAs. CONCLUSIONS: SuiteMSA provides researchers easy access to a sequence evolution simulator, reference alignments generated by the simulator, and a series of tools to evaluate the performance of the MSA reconstruction programs. It will help us improve the quality of MSAs, often the most important first steps of bioinformatics and other biological research.

Molecular evolution of urea amidolyase and urea carboxylase in fungi.

BACKGROUND: Urea amidolyase breaks down urea into ammonia and carbon dioxide in a two-step process, while another enzyme, urease, does this in a one step-process. Urea amidolyase has been found only in some fungal species among eukaryotes. It contains two major domains: the amidase and urea carboxylase domains. A shorter form of urea amidolyase is known as urea carboxylase and has no amidase domain. Eukaryotic urea carboxylase has been found only in several fungal species and green algae. In order to elucidate the evolutionary origin of urea amidolyase and urea carboxylase, we studied the distribution of urea amidolyase, urea carboxylase, as well as other proteins including urease, across kingdoms. RESULTS: Among the 64 fungal species we examined, only those in two Ascomycota classes (Sordariomycetes and Saccharomycetes) had the urea amidolyase sequences. Urea carboxylase was found in many but not all of the species in the phylum Basidiomycota and in the subphylum Pezizomycotina (phylum Ascomycota). It was completely absent from the class Saccharomycetes (phylum Ascomycota; subphylum Saccharomycotina). Four Sordariomycetes species we examined had both the urea carboxylase and the urea amidolyase sequences. Phylogenetic analysis showed that these two enzymes appeared to have gone through independent evolution since their bacterial origin. The amidase domain and the urea carboxylase domain sequences from fungal urea amidolyases clustered strongly together with the amidase and urea carboxylase sequences, respectively, from a small number of beta- and gammaproteobacteria. On the other hand, fungal urea carboxylase proteins clustered together with another copy of urea carboxylases distributed broadly among bacteria. The urease proteins were found in all the fungal species examined except for those of the subphylum Saccharomycotina. CONCLUSIONS: We conclude that the urea amidolyase genes currently found only in fungi are the results of a horizontal gene transfer event from beta-, gamma-, or related species of proteobacteria. The event took place before the divergence of the subphyla Pezizomycotina and Saccharomycotina but after the divergence of the subphylum Taphrinomycotina. Urea carboxylase genes currently found in fungi and other limited organisms were also likely derived from another ancestral gene in bacteria. Our study presented another important example showing plastic and opportunistic genome evolution in bacteria and fungi and their evolutionary interplay.

Complete Genome Sequence of Geobacter sp. Strain FeAm09, a Moderately Acidophilic Soil Bacterium.

A moderately acidophilic Geobacter sp. strain, FeAm09, was isolated from forest soil. The complete genome sequence is 4,099,068 bp with an average GC content of 61.1%. No plasmids were detected. The genome contains a total of 3,843 genes and 3,608 protein-coding genes, including genes supporting iron and nitrogen biogeochemical cycling.

Captive Common Marmosets (Callithrix jacchus) Are Colonized throughout Their Lives by a Community of Bifidobacterium Species with Species-Specific Genomic Content That Can Support Adaptation to Distinct Metabolic Niches.

The common marmoset (Callithrix jacchus) is an omnivorous New World primate whose diet in the wild includes large amounts of fruit, seeds, flowers, and a variety of lizards and invertebrates. Marmosets also feed heavily on tree gums and exudates, and they have evolved unique morphological and anatomical characteristics to facilitate gum feeding (gummivory). In this study, we characterized the fecal microbiomes of adult and infant animals from a captive population of common marmosets at the Callitrichid Research Center at the University of Nebraska at Omaha under their normal dietary and environmental conditions. The microbiomes of adult animals were dominated by species of Bifidobacterium, Bacteroides, Prevotella, Phascolarctobacterium, Megamonas, and Megasphaera. Culturing and genomic analysis of the Bifidobacterium populations from adult animals identified four known marmoset-associated species (B. reuteri, B. aesculapii, B. myosotis, and B. hapali) and three unclassified taxa of Bifidobacterium that are phylogenetically distinct. Species-specific quantitative PCR (qPCR) confirmed that these same species of Bifidobacterium are abundant members of the microbiome throughout the lives of the animals. Genomic loci in each Bifidobacterium species encode enzymes to support growth and major marmoset milk oligosaccharides during breastfeeding; however, metabolic islands that can support growth on complex polysaccharide substrates in the diets of captive adults (pectin, xyloglucan, and xylan), including loci in B. aesculapii that can support its unique ability to grow on arabinogalactan-rich tree gums, were species-specific. IMPORTANCEBifidobacterium species are recognized as important, beneficial microbes in the human gut microbiome, and their ability colonize individuals at different stages of life is influenced by host, dietary, environmental, and ecological factors, which is poorly understood. The common marmoset is an emerging nonhuman primate model with a short maturation period, making this model amenable to study the microbiome throughout a life history. Features of the microbiome in captive marmosets are also shared with human gut microbiomes, including abundant populations of Bifidobacterium species. Our studies show that several species of Bifidobacterium are dominant members of the captive marmoset microbiome throughout their life history. Metabolic capacities in genomes of the marmoset Bifidobacterium species suggest species-specific adaptations to different components of the captive marmoset diet, including the unique capacity in B. aesculapii for degradation of gum arabic, suggesting that regular dietary exposure in captivity may be important for preserving gum-degrading species in the microbiome.

Evolution of the Kdo2-lipid A biosynthesis in bacteria.

BACKGROUND: Lipid A is the highly immunoreactive endotoxic center of lipopolysaccharide (LPS). It anchors the LPS into the outer membrane of most Gram-negative bacteria. Lipid A can be recognized by animal cells, triggers defense-related responses, and causes Gram-negative sepsis. The biosynthesis of Kdo2-lipid A, the LPS substructure, involves with nine enzymatic steps. RESULTS: In order to elucidate the evolutionary pathway of Kdo2-lipid A biosynthesis, we examined the distribution of genes encoding the nine enzymes across bacteria. We found that not all Gram-negative bacteria have all nine enzymes. Some Gram-negative bacteria have no genes encoding these enzymes and others have genes only for the first four enzymes (LpxA, LpxC, LpxD, and LpxB). Among the nine enzymes, five appeared to have arisen from three independent gene duplication events. Two of such events happened within the Proteobacteria lineage, followed by functional specialization of the duplicated genes and pathway optimization in these bacteria. CONCLUSIONS: The nine-enzyme pathway, which was established based on the studies mainly in Escherichia coli K12, appears to be the most derived and optimized form. It is found only in E. coli and related Proteobacteria. Simpler and probably less efficient pathways are found in other bacterial groups, with Kdo2-lipid A variants as the likely end products. The Kdo2-lipid A biosynthetic pathway exemplifies extremely plastic evolution of bacterial genomes, especially those of Proteobacteria, and how these mainly pathogenic bacteria have adapted to their environment.

Biological sequence simulation for testing complex evolutionary hypotheses: indel-Seq-Gen version 2.0.

Sequence simulation is an important tool in validating biological hypotheses as well as testing various bioinformatics and molecular evolutionary methods. Hypothesis testing relies on the representational ability of the sequence simulation method. Simple hypotheses are testable through simulation of random, homogeneously evolving sequence sets. However, testing complex hypotheses, for example, local similarities, requires simulation of sequence evolution under heterogeneous models. To this end, we previously introduced indel-Seq-Gen version 1.0 (iSGv1.0; indel, insertion/deletion). iSGv1.0 allowed heterogeneous protein evolution and motif conservation as well as insertion and deletion constraints in subsequences. Despite these advances, for complex hypothesis testing, neither iSGv1.0 nor other currently available sequence simulation methods is sufficient. indel-Seq-Gen version 2.0 (iSGv2.0) aims at simulating evolution of highly divergent DNA sequences and protein superfamilies. iSGv2.0 improves upon iSGv1.0 through the addition of lineage-specific evolution, motif conservation using PROSITE-like regular expressions, indel tracking, subsequence-length constraints, as well as coding and noncoding DNA evolution. Furthermore, we formalize the sequence representation used for iSGv2.0 and uncover a flaw in the modeling of indels used in current state of the art methods, which biases simulation results for hypotheses involving indels. We fix this flaw in iSGv2.0 by using a novel discrete stepping procedure. Finally, we present an example simulation of the calycin-superfamily sequences and compare the performance of iSGv2.0 with iSGv1.0 and random model of sequence evolution.