Phylogenetic Pipeline Research Articles

Sequential Bayesian Phylogenetic Inference.

The ideal approach to Bayesian phylogenetic inference is to estimate all parameters of interest jointly in a single hierarchical model. However, this is often not feasible in practice due to the high computational cost. Instead, phylogenetic pipelines generally consist of sequential analyses, whereby a single point estimate from a given analysis is used as input for the next analysis (e.g., a single multiple sequence alignment is used to estimate a gene tree). In this framework, uncertainty is not propagated from step to step, which can lead to inaccurate or spuriously confident results. Here, we formally develop and test a sequential inference approach for Bayesian phylogenetic inference, which uses importance sampling to generate observations for the next step of an analysis pipeline from the posterior distribution produced in the previous step. Our sequential inference approach presented here not only accounts for uncertainty between analysis steps, but also allows for greater flexibility in software choice (and hence model availability) and can be computationally more efficient than the traditional joint inference approach when multiple models are being tested. We show that our sequential inference approach is identical in practice to the joint inference approach only if sufficient information in the data is present (a narrow posterior distribution) and/or sufficiently many importance samples are used. Conversely, we show that the common practice of using a single point estimate can be biased, e.g., a single phylogeny estimate to transform an unrooted phylogeny into a time-calibrated phylogeny. We demonstrate the theory of sequential Bayesian inference using both a toy example and an empirical case study of divergence-time estimation in insects using a relaxed clock model from transcriptome data. In the empirical example, we estimate three posterior distributions of branch lengths from the same data (DNA character matrix with a GTR+Γ+I substitution model, an amino acid data matrix with empirical substitution models, and an amino acid data matrix with the PhyloBayes CAT-GTR model). Finally, we apply three different node-calibration strategies and show that divergence-time estimates are affected by both the data source and underlying substitution process to estimate branch lengths as well as the node-calibration strategies. Thus, our new sequential Bayesian phylogenetic inference provides the opportunity to efficiently test different approaches for divergence time estimation, including branch-length estimation from other software.

Substitution spectra of SARS-CoV-2 genome from Pakistan reveals insights into the evolution of variants across the pandemic

Changing morbidity and mortality due to COVID-19 across the pandemic has been linked with factors such as the emergence of SARS-CoV-2 variants and vaccination. Mutations in the Spike glycoprotein enhanced viral transmission and virulence. We investigated whether SARS-CoV-2 mutation rates and entropy were associated COVID-19 in Pakistan, before and after the introduction of vaccinations. We analyzed 1,705 SARS-CoV-2 genomes using the Augur phylogenetic pipeline. Substitution rates and entropy across the genome, and in the Spike glycoprotein were compared between 2020, 2021 and 2022 (as periods A, B and C). Mortality was greatest in B whilst cases were highest during C. In period A, G clades were predominant, and substitution rate was 5.25 × 10–4 per site per year. In B, Delta variants dominated, and substitution rates increased to 9.74 × 10–4. In C, Omicron variants led to substitution rates of 5.02 × 10–4. Genome-wide entropy was the highest during B particularly, at Spike E484K and K417N. During C, genome-wide mutations increased whilst entropy was reduced. Enhanced SARS-CoV-2 genome substitution rates were associated with a period when more virulent SARS-CoV-2 variants were prevalent. Reduced substitution rates and stabilization of genome entropy was subsequently evident when vaccinations were introduced. Whole genome entropy analysis can help predict virus evolution to guide public health interventions.

Integrating full and partial genome sequences to decipher the global spread of canine rabies virus

Despite the rapid growth in viral genome sequencing, statistical methods face challenges in handling historical viral endemic diseases with large amounts of underutilized partial sequence data. We propose a phylogenetic pipeline that harnesses both full and partial viral genome sequences to investigate historical pathogen spread between countries. Its application to rabies virus (RABV) yields precise dating and confident estimates of its geographic dispersal. By using full genomes and partial sequences, we reduce both geographic and genetic biases that often hinder studies that focus on specific genes. Our pipeline reveals an emergence of the present canine-mediated RABV between years 1301 and 1403 and reveals regional introductions over a 700-year period. This geographic reconstruction enables us to locate episodes of human-mediated introductions of RABV and examine the role that European colonization played in its spread. Our approach enables phylogeographic analysis of large and genetically diverse data sets for many viral pathogens.

Using PhyloSuite for molecular phylogeny and tree-based analyses.

Phylogenetic analysis has entered the genomics (multilocus) era. For less experienced researchers, conquering the large number of software programs required for a multilocus-based phylogenetic reconstruction can be somewhat daunting and time-consuming. PhyloSuite, a software with a user-friendly GUI, was designed to make this process more accessible by integrating multiple software programs needed for multilocus and single-gene phylogenies and further streamlining the whole process. In this protocol, we aim to explain how to conduct each step of the phylogenetic pipeline and tree-based analyses in PhyloSuite. We also present a new version of PhyloSuite (v1.2.3), wherein we fixed some bugs, made some optimizations, and introduced some new functions, including a number of tree-based analyses, such as signal-to-noise calculation, saturation analysis, spurious species identification, and etc. The step-by-step protocol includes background information (i.e., what the step does), reasons (i.e., why do the step), and operations (i.e., how to do it). This protocol will help researchers quick-start their way through the multilocus phylogenetic analysis, especially those interested in conducting organelle-based analyses.

Phytest: quality control for phylogenetic analyses.

The ability to automatically conduct quality control checks on phylogenetic analyses is becoming more important with the increase in genetic sequencing and the use of real-time pipelines e.g. in the SARS-CoV-2 era. Implementations of real-time phylogenetic analyses require automated testing to make sure that problems in the data are caught automatically within analysis pipelines and in a timely manner. Here, we present Phytest (version 1.1) a tool for automating quality control checks on sequences, trees and metadata during phylogenetic analyses. Phytest is a phylogenetic analysis testing program that easily integrates into existing phylogenetic pipelines. We demonstrate the utility of Phytest with real-world examples. Phytest source code is available on GitHub (https://github.com/phytest-devs/phytest) and can be installed via PyPI with the command 'pip install phytest'. Extensive documentation can be found at https://phytest-devs.github.io/phytest/. Supplementary data are available at Bioinformatics online.

Mito-nuclear coevolution and phylogenetic artifacts: the case of bivalve mollusks

Mito-nuclear phylogenetic discordance in Bivalvia is well known. In particular, the monophyly of Amarsipobranchia (Heterodonta + Pteriomorphia), retrieved from mitochondrial markers, contrasts with the monophyly of Heteroconchia (Heterodonta + Palaeoheterodonta), retrieved from nuclear markers. However, since oxidative phosphorylation nuclear markers support the Amarsipobranchia hypothesis instead of the Heteroconchia one, interacting subunits of the mitochondrial complexes ought to share the same phylogenetic signal notwithstanding the genomic source, which is different from the signal obtained from other nuclear markers. This may be a clue of coevolution between nuclear and mitochondrial genes. In this work we inferred the phylogenetic signal from mitochondrial and nuclear oxidative phosphorylation markers exploiting different phylogenetic approaches and added two more datasets for comparison: genes of the glycolytic pathway and genes related to the biogenesis of regulative small noncoding RNAs. All trees inferred from mitochondrial and nuclear subunits of the mitochondrial complexes support the monophyly of Amarsipobranchia, regardless of the phylogenetic pipeline. However, not every single marker agrees with this topology: this is clearly visible in nuclear subunits that do not directly interact with the mitochondrial counterparts. Overall, our data support the hypothesis of a coevolution between nuclear and mitochondrial genes for the oxidative phosphorylation. Moreover, we suggest a relationship between mitochondrial topology and different nucleotide composition between clades, which could be associated to the highly variable gene arrangement in Bivalvia.

Reconstructing SARS-CoV-2 infection dynamics through the phylogenetic inference of unsampled sources of infection.

The COVID-19 pandemic has illustrated the importance of infection tracking. The role of asymptomatic, undiagnosed individuals in driving infections within this pandemic has become increasingly evident. Modern phylogenetic tools that take into account asymptomatic or undiagnosed individuals can help guide public health responses. We finetuned established phylogenetic pipelines using published SARS-CoV-2 genomic data to examine reasonable estimate transmission networks with the inference of unsampled infection sources. The system utilised Bayesian phylogenetics and TransPhylo to capture the evolutionary and infection dynamics of SARS-CoV-2. Our analyses gave insight into the transmissions within a population including unsampled sources of infection and the results aligned with epidemiological observations. We were able to observe the effects of preventive measures in Canada’s “Atlantic bubble” and in populations such as New York State. The tools also inferred the cross-species disease transmission of SARS-CoV-2 transmission from humans to lions and tigers in New York City’s Bronx Zoo. These phylogenetic tools offer a powerful approach in response to both the COVID-19 and other emerging infectious disease outbreaks.

Relative model selection of evolutionary substitution models can be sensitive to multiple sequence alignment uncertainty

BackgroundMultiple sequence alignments (MSAs) represent the fundamental unit of data inputted to most comparative sequence analyses. In phylogenetic analyses in particular, errors in MSA construction have the potential to induce further errors in downstream analyses such as phylogenetic reconstruction itself, ancestral state reconstruction, and divergence time estimation. In addition to providing phylogenetic methods with an MSA to analyze, researchers must also specify a suitable evolutionary model for the given analysis. Most commonly, researchers apply relative model selection to select a model from candidate set and then provide both the MSA and the selected model as input to subsequent analyses. While the influence of MSA errors has been explored for most stages of phylogenetics pipelines, the potential effects of MSA uncertainty on the relative model selection procedure itself have not been explored.ResultsWe assessed the consistency of relative model selection when presented with multiple perturbed versions of a given MSA. We find that while relative model selection is mostly robust to MSA uncertainty, in a substantial proportion of circumstances, relative model selection identifies distinct best-fitting models from different MSAs created from the same set of sequences. We find that this issue is more pervasive for nucleotide data compared to amino-acid data. However, we also find that it is challenging to predict whether relative model selection will be robust or sensitive to uncertainty in a given MSA.ConclusionsWe find that that MSA uncertainty can affect virtually all steps of phylogenetic analysis pipelines to a greater extent than has previously been recognized, including relative model selection.

Covid-19: Physical distancing of at least one metre is linked to large reduction in infection.

<h3>Abstract</h3> idCOV is a phylogenetic pipeline for quickly identifying the clades of SARS-CoV-2 virus isolates from raw sequencing data based on a selected clade-defining marker list. Using a public dataset, we show that idCOV can make equivalent calls as annotated by Nextstrain.org on all three common clade systems using user uploaded FastQ files directly. Web and equivalent command-line interfaces are available. It can be deployed on any Linux environment, including personal computer, HPC and the cloud. The source code is available at https://github.com/xz-stjude/idcov. A documentation for installation can be found at https://github.com/xz-stjude/idcov/blob/master/README.md.

Evaluation of A Phylogenetic Pipeline to Examine Transmission Networks in A Canadian HIV Cohort.

Modern computational methods using patient Human Immunodeficiency Virus type 1 (HIV-1) genetic sequences can model population-wide viral transmission dynamics. Accurate transmission inferences can play a critical role in the characterization of high-risk transmission clusters important for enhanced epidemiological control. We evaluated a phylogenetics-based analysis pipeline to infer person-to-person (P2P) infection dates and transmission relationships using 139 patient HIV-1 polymerase Sanger sequences curated by the Southern Alberta HIV Clinic. Parameter combinations tailored to HIV-1 transmissions were tuned with respect to inference accuracy. Inference accuracy was assessed using clinically confirmed P2P transmission patient data. The most accurate parameter settings correctly inferred 48.56% of the P2P relationships (95% confidence interval 63.89–33.33%), slightly lower than next-generation-sequencing methods. The infection date was correctly inferred 43.02% (95% confidence interval 49.89–35.63%). Several novel unsuspected transmission clusters of up to twelve patients were identified. An accuracy trade-off between inferring transmission relationships and infection dates was observed. Using clinically confirmed P2P transmission data as benchmark, our phylogenetic methods identified sufficient P2P transmission relationships using readily available low-resolution Sanger sequences. These approaches may give valuable information about HIV infection dynamics within a population and may be easily deployed to guide public health interventions, without a need for next generation sequencing technology.

PhyloPi: An affordable, purpose built phylogenetic pipeline for the HIV drug resistance testing facility.

IntroductionPhylogenetic analysis plays a crucial role in quality control in the HIV drug resistance testing laboratory. If previous patient sequence data is available sample swaps can be detected and investigated. As Antiretroviral treatment coverage is increasing in many developing countries, so is the need for HIV drug resistance testing. In countries with multiple languages, transcription errors are easily made with patient identifiers. Here a self-contained blastn integrated phylogenetic pipeline can be especially useful. Even though our pipeline can run on any unix based system, a Raspberry Pi 3 is used here as a very affordable and integrated solution.Performance benchmarksThe computational capability of this single board computer is demonstrated as well as the utility thereof in the HIV drug resistance laboratory. Benchmarking analysis against a large public database shows excellent time performance with minimal user intervention. This pipeline also contains utilities to find previous sequences as well as phylogenetic analysis and a graphical sequence mapping utility against the pol area of the HIV HXB2 reference genome. Sequence data from the Los Alamos HIV database was analyzed for inter- and intra-patient diversity and logistic regression was conducted on the calculated genetic distances. These findings show that allowable clustering and genetic distance between viral sequences from different patients is very dependent on subtype as well as the area of the viral genome being analyzed.AvailabilityThe Raspberry Pi image for PhyloPi, source code of the pipeline, sequence data, bash-, python- and R-scripts for the logistic regression, benchmarking as well as helper scripts are available at http://scholar.ufs.ac.za:8080/xmlui/handle/11660/7638 and https://github.com/ArmandBester/phylopi. The PhyloPi image and the source code are published under the GPLv3 license. A demo version of the PhyloPi pipeline is available at http://phylopi.hpc.ufs.ac.za/.

ParGenes: a tool for massively parallel model selection and phylogenetic tree inference on thousands of genes.

MotivationCoalescent- and reconciliation-based methods are now widely used to infer species phylogenies from genomic data. They typically use per-gene phylogenies as input, which requires conducting multiple individual tree inferences on a large set of multiple sequence alignments (MSAs). At present, no easy-to-use parallel tool for this task exists. Ad hoc scripts for this purpose do not only induce additional implementation overhead, but can also lead to poor resource utilization and long times-to-solution. We present ParGenes, a tool for simultaneously determining the best-fit model and inferring maximum likelihood (ML) phylogenies on thousands of independent MSAs using supercomputers.ResultsParGenes executes common phylogenetic pipeline steps such as model-testing, ML inference(s), bootstrapping and computation of branch support values via a single parallel program invocation. We evaluated ParGenes by inferring > 20 000 phylogenetic gene trees with bootstrap support values from Ensembl Compara and VectorBase alignments in 28 h on a cluster with 1024 nodes.Availability and implementationGNU GPL at https://github.com/BenoitMorel/ParGenes.Supplementary information Supplementary material is available at Bioinformatics online.

SICLE: a high-throughput tool for extracting evolutionary relationships from phylogenetic trees.

We present the phylogeny analysis software SICLE (Sister Clade Extractor), an easy-to-use, high-throughput tool to describe the nearest neighbors to a node of interest in a phylogenetic tree as well as the support value for the relationship. The application is a command line utility that can be embedded into a phylogenetic analysis pipeline or can be used as a subroutine within another C++ program. As a test case, we applied this new tool to the published phylome of Salinibacter ruber, a species of halophilic Bacteriodetes, identifying 13 unique sister relationships to S. ruber across the 4,589 gene phylogenies. S. ruber grouped with bacteria, most often other Bacteriodetes, in the majority of phylogenies, but 91 phylogenies showed a branch-supported sister association between S. ruber and Archaea, an evolutionarily intriguing relationship indicative of horizontal gene transfer. This test case demonstrates how SICLE makes it possible to summarize the phylogenetic information produced by automated phylogenetic pipelines to rapidly identify and quantify the possible evolutionary relationships that merit further investigation. SICLE is available for free for noncommercial use at http://eebweb.arizona.edu/sicle/.

ModelOMatic: fast and automated model selection between RY, nucleotide, amino acid, and codon substitution models.

Molecular phylogenetics is a powerful tool for inferring both the process and pattern of evolution from genomic sequence data. Statistical approaches, such as maximum likelihood and Bayesian inference, are now established as the preferred methods of inference. The choice of models that a researcher uses for inference is of critical importance, and there are established methods for model selection conditioned on a particular type of data, such as nucleotides, amino acids, or codons. A major limitation of existing model selection approaches is that they can only compare models acting upon a single type of data. Here, we extend model selection to allow comparisons between models describing different types of data by introducing the idea of adapter functions, which project aggregated models onto the originally observed sequence data. These projections are implemented in the program ModelOMatic and used to perform model selection on 3722 families from the PANDIT database, 68 genes from an arthropod phylogenomic data set, and 248 genes from a vertebrate phylogenomic data set. For the PANDIT and arthropod data, we find that amino acid models are selected for the overwhelming majority of alignments; with progressively smaller numbers of alignments selecting codon and nucleotide models, and no families selecting RY-based models. In contrast, nearly all alignments from the vertebrate data set select codon-based models. The sequence divergence, the number of sequences, and the degree of selection acting upon the protein sequences may contribute to explaining this variation in model selection. Our ModelOMatic program is fast, with most families from PANDIT taking fewer than 150 s to complete, and should therefore be easily incorporated into existing phylogenetic pipelines. ModelOMatic is available at https://code.google.com/p/modelomatic/.

A protocol for species delineation of public DNA databases, applied to the Insecta.

Public DNA databases are composed of data from many different taxa, although the taxonomic annotation on sequences is not always complete, which impedes the utilization of mined data for species-level applications. There is much ongoing work on species identification and delineation based on the molecular data itself, although applying species clustering to whole databases requires consolidation of results from numerous undefined gene regions, and introduces significant obstacles in data organization and computational load. In the current paper, we demonstrate an approach for species delineation of a sequence database. All DNA sequences for the insects were obtained and processed. After filtration of duplicated data, delineation of the database into species or molecular operational taxonomic units (MOTUs) followed a three-step process in which (i) the genetic loci L are partitioned, (ii) the species S are delineated within each locus, then (iii) species units are matched across loci to form the matrix L × S, a set of global (multilocus) species units. Partitioning the database into a set of homologous gene fragments was achieved by Markov clustering using edge weights calculated from the amount of overlap between pairs of sequences, then delineation of species units and assignment of species names were performed for the set of genes necessary to capture most of the species diversity. The complexity of computing pairwise similarities for species clustering was substantial at the cytochrome oxidase subunit I locus in particular, but made feasible through the development of software that performs pairwise alignments within the taxonomic framework, while accounting for the different ranks at which sequences are labeled with taxonomic information. Over 24 different homologs, the unidentified sequences numbered approximately 194,000, containing 41,525 species IDs (98.7% of all found in the insect database), and were grouped into 59,173 single-locus MOTUs by hierarchical clustering under parameters optimized independently for each locus. Species units from different loci were matched using a multipartite matching algorithm to form multilocus species units with minimal incongruence between loci. After matching, the insect database as represented by these 24 loci was found to be composed of 78,091 species units in total. 38,574 of these units contained only species labeled data, 34,891 contained only unlabeled data, leaving 4,626 units composed both of labeled and unlabeled sequences. In addition to giving estimates of species diversity of sequence repositories, the protocol developed here will facilitate species-level applications of modern-day sequence data sets. In particular, the L × S matrix represents a post-taxonomic framework that can be used for species-level organization of metagenomic data, and incorporation of these methods into phylogenetic pipelines will yield matrices more representative of species diversity.

The mitochondrial respiratory chain of the secondary green alga Euglena gracilis shares many additional subunits with parasitic Trypanosomatidae

The mitochondrion is an essential organelle for the production of cellular ATP in most eukaryotic cells. It is extensively studied, including in parasitic organisms such as trypanosomes, as a potential therapeutic target. Recently, numerous additional subunits of the respiratory-chain complexes have been described in Trypanosoma brucei and Trypanosoma cruzi. Since these subunits had apparently no counterparts in other organisms, they were interpreted as potentially associated with the parasitic trypanosome lifestyle. Here we used two complementary approaches to characterise the subunit composition of respiratory complexes in Euglena gracilis, a non-parasitic secondary green alga related to trypanosomes. First, we developed a phylogenetic pipeline aimed at mining sequence databases for identifying homologues to known respiratory-complex subunits with high confidence. Second, we used MS/MS proteomics after two-dimensional separation of the respiratory complexes by Blue Native- and SDS-PAGE both to confirm in silico predictions and to identify further additional subunits. Altogether, we identified 41 subunits that are restricted to E. gracilis, T. brucei and T. cruzi, along with 48 classical subunits described in other eukaryotes (i.e. plants, mammals and fungi). This moreover demonstrates that at least half of the subunits recently reported in T. brucei and T. cruzi are actually not specific to Trypanosomatidae, but extend at least to other Euglenozoa, and that their origin and function are thus not specifically associated with the parasitic lifestyle. Furthermore, preliminary biochemical analyses suggest that some of these additional subunits underlie the peculiarities of the respiratory chain observed in Euglenozoa.

The impact of automated filtering of BLAST-determined homologs in the phylogenetic detection of horizontal gene transfer from a transcriptome assembly

Phylomes (comprehensive sets of gene phylogenies for organisms) are built to investigate fundamental questions in genomics and evolutionary biology, such as those pertaining to the detection and characterization of horizontal gene transfer in microbes. To address these questions, phylome construction demands rigorous yet efficient phylogenetic methods. Currently, many sequence alignment and tree-building models can analyze several thousands of genes in a high-throughput manner. However, the phylogenetics is complicated by variability in sequence divergence and different taxon sampling among genes. In addition, homolog selection for automated approaches often relies on arbitrary sequence similarity thresholds that are likely inappropriate for all genes in a genome. To investigate the effects of automated homolog selection on the detection of horizontal gene transfer using phylogenomics, we constructed the phylome of a transcriptome assembly of Alexandrium tamarense, a microbial eukaryote with a history of horizontal and endosymbiotic gene transfer, using seven sequence similarity thresholds for selecting putative homologs to be included in phylogenetic analyses. We show that no single threshold recovered informative trees for the majority of A. tamarense unigenes compared to the pooled results from all pipeline iterations. As much as 29% of trees built could have misleading phylogenetic relationships that appear biased in favor of those otherwise indicative of horizontal gene transfer. Perhaps worse, nearly half of the unigenes were represented by a single tree built at just one threshold, making it difficult to assess the validity of phylogenetic relationships recovered in these cases. However, combining the results from several pipeline iterations maximizes the number of informative phylogenies. Moreover, when the same phylogenetic relationship for a given unigene is recovered in multiple pipeline iterations, conclusions regarding gene origin are more robust to methodological artifact. Using these methods, the majority of A. tamarense unigenes showed evolutionary relationships indicative of vertical inheritance. Nevertheless, many other unigenes revealed diverse phylogenetic associations, suggestive of possible gene transfer. This analysis suggests that caution should be used when interpreting the results from phylogenetic pipelines implementing a single similarity threshold. Our approach is a practical method to mitigate the problems associated with automated sequence selection in phylogenomics.

Reconstructing the Dynamics of HIV Evolution within Hosts from Serial Deep Sequence Data

At the early stage of infection, human immunodeficiency virus (HIV)-1 predominantly uses the CCR5 coreceptor for host cell entry. The subsequent emergence of HIV variants that use the CXCR4 coreceptor in roughly half of all infections is associated with an accelerated decline of CD4+ T-cells and rate of progression to AIDS. The presence of a ‘fitness valley’ separating CCR5- and CXCR4-using genotypes is postulated to be a biological determinant of whether the HIV coreceptor switch occurs. Using phylogenetic methods to reconstruct the evolutionary dynamics of HIV within hosts enables us to discriminate between competing models of this process. We have developed a phylogenetic pipeline for the molecular clock analysis, ancestral reconstruction, and visualization of deep sequence data. These data were generated by next-generation sequencing of HIV RNA extracted from longitudinal serum samples (median 7 time points) from 8 untreated subjects with chronic HIV infections (Amsterdam Cohort Studies on HIV-1 infection and AIDS). We used the known dates of sampling to directly estimate rates of evolution and to map ancestral mutations to a reconstructed timeline in units of days. HIV coreceptor usage was predicted from reconstructed ancestral sequences using the geno2pheno algorithm. We determined that the first mutations contributing to CXCR4 use emerged about 16 (per subject range 4 to 30) months before the earliest predicted CXCR4-using ancestor, which preceded the first positive cell-based assay of CXCR4 usage by 10 (range 5 to 25) months. CXCR4 usage arose in multiple lineages within 5 of 8 subjects, and ancestral lineages following alternate mutational pathways before going extinct were common. We observed highly patient-specific distributions and time-scales of mutation accumulation, implying that the role of a fitness valley is contingent on the genotype of the transmitted variant.

Regulatory elements of marine cyanobacteria.

The free-living, oxyphototroph bacteria of the group of Prochlorococcus populate widely the oceans. Genomic information of nine marine cyanobacteria was used to predict signals essential for regulation. We implemented a pipeline that automatically calculates BLASTp alignments of query genomes, selects a representative subset of orthologs and predicts motifs conserved in their upstream sequences. Next, similar motifs are clustered into groups which could contain profiles recognized by different transcription factors. The phylogenetic footprinting pipeline revealed a minimal conserved set of putative transcription factors, binding sites and regulons for the chosen marine cyanobacterial genomes. DNA-binding motifs for NtcA and LexA were correctly identified. The relevance of transcriptional regulation of predicted cis elements was supported experimentally.

Extensive inter-domain lateral gene transfer in the evolution of the human commensal Methanosphaera stadtmanae

Methanosphaera stadtmanae is a commensal methanogenic archaeon found in the human gut. As most of its niche-neighbors are bacteria, it is expected that lateral gene transfer (LGT) from bacteria might have contributed to the evolutionary history of this organism. We performed a phylogenomic survey of putative LGT events in M. stadtmanae, using a phylogenetic pipeline. Our analysis indicates that a substantial fraction of the proteins of M. stadtmanae are inferred to have been involved in inter-domain LGT. Laterally acquired genes have had a large contribution to surface functions, by providing novel glycosyltransferase functions. In addition, several ABC transporters seem to be of bacterial origin, including the molybdate transporter. Thus, bacterial genes contributed to the adaptation of M. stadtmanae to a host-dependent lifestyle by allowing a larger variation in surface structures and increasing transport efficiency in the gut niche which is diverse and competitive.