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PubMed2025

Characterization of microbial dark matter at scale with MetaSBT and taxonomy-aware Sequence Bloom Trees.

Metagenomics has become a powerful tool for studying microbial communities, allowing researchers to investigate microbial diversity within complex environmental samples. Recent advances in sequencing technology have enabled the recovery of near-complete microbial genomes directly from metagenomic samples, also known as metagenome-assembled genomes (MAGs). However, accurately characterizing these genomes remains a significant challenge due to the presence of sequencing errors, incomplete assembly, and contamination. Here we present MetaSBT, a new tool for organizing, indexing, and characterizing microbial reference genomes and MAGs. It is able to identify clusters of genomes at all seven taxonomic levels, from the kingdom all the way down to the species level, using the Sequence Bloom Tree (SBT) data structure that relies on Bloom Filters (BFs) to index massive amounts of genomes based on their k-mers composition. We have built an initial set of databases composed of over 190 thousand viral genomes from NCBI GenBank and public sources grouped into sequence consistent clusters at different taxonomic levels, making it the first software solution for the classification of viruses at different ranks, including still unknown ones. This results in the definition of over 40 thousand species clusters where ~80% do not match with any known viral species in reference databases to date. Furthermore, we show how our databases can be used as a new basis for existing quantitative metagenomic profilers to unlock the detection of unknown microbes and the estimation of their abundance in metagenomic samples. Finally, the framework is released open-source and, along with its public databases, is fully integrated into the Galaxy Platform enabling broad accessibility.

metagenome-assembled genomes↗
PubMed2004

Dealing with complexity: evolutionary engineering and genome shuffling.

Comparative analysis of the growing number of microbial genome sequences has shown a high plasticity of genomes and several mechanisms for the adaptation of microbial cells to changing environmental conditions have been discovered. By contrast, the underlying metabolic networks of microorganisms are under strict control and relatively rigid, which poses a significant challenge for rational metabolic engineering approaches. Recursive shuffling of whole genomes has recently been demonstrated as an effective new evolutionary whole-cell engineering approach for the rapid improvement of industrially important microbial phenotypes.

Bacterial Proteins↗
PubMed2005

The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes.

The release of the 1000th complete microbial genome will occur in the next two to three years. In anticipation of this milestone, the Fellowship for Interpretation of Genomes (FIG) launched the Project to Annotate 1000 Genomes. The project is built around the principle that the key to improved accuracy in high-throughput annotation technology is to have experts annotate single subsystems over the complete collection of genomes, rather than having an annotation expert attempt to annotate all of the genes in a single genome. Using the subsystems approach, all of the genes implementing the subsystem are analyzed by an expert in that subsystem. An annotation environment was created where populated subsystems are curated and projected to new genomes. A portable notion of a populated subsystem was defined, and tools developed for exchanging and curating these objects. Tools were also developed to resolve conflicts between populated subsystems. The SEED is the first annotation environment that supports this model of annotation. Here, we describe the subsystem approach, and offer the first release of our growing library of populated subsystems. The initial release of data includes 180 177 distinct proteins with 2133 distinct functional roles. This data comes from 173 subsystems and 383 different organisms.

Acyl Coenzyme A↗
PubMed2006

Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models.

BACKGROUND: Horizontal gene transfer (HGT) is considered a strong evolutionary force shaping the content of microbial genomes in a substantial manner. It is the difference in speed enabling the rapid adaptation to changing environmental demands that distinguishes HGT from gene genesis, duplications or mutations. For a precise characterization, algorithms are needed that identify transfer events with high reliability. Frequently, the transferred pieces of DNA have a considerable length, comprise several genes and are called genomic islands (GIs) or more specifically pathogenicity or symbiotic islands. RESULTS: We have implemented the program SIGI-HMM that predicts GIs and the putative donor of each individual alien gene. It is based on the analysis of codon usage (CU) of each individual gene of a genome under study. CU of each gene is compared against a carefully selected set of CU tables representing microbial donors or highly expressed genes. Multiple tests are used to identify putatively alien genes, to predict putative donors and to mask putatively highly expressed genes. Thus, we determine the states and emission probabilities of an inhomogeneous hidden Markov model working on gene level. For the transition probabilities, we draw upon classical test theory with the intention of integrating a sensitivity controller in a consistent manner. SIGI-HMM was written in JAVA and is publicly available. It accepts as input any file created according to the EMBL-format.It generates output in the common GFF format readable for genome browsers. Benchmark tests showed that the output of SIGI-HMM is in agreement with known findings. Its predictions were both consistent with annotated GIs and with predictions generated by different methods. CONCLUSION: SIGI-HMM is a sensitive tool for the identification of GIs in microbial genomes. It allows to interactively analyze genomes in detail and to generate or to test hypotheses about the origin of acquired genes.

Algorithms↗
PubMed2004

Protein interaction mapping on a functional shotgun sequence of Rickettsia sibirica.

Protein interaction maps can reveal novel pathways and functional complexes, allowing 'guilt by association' annotation of uncharacterized proteins. To address the need for large-scale protein interaction analyses, a bacterial two-hybrid system was coupled with a whole genome shotgun sequencing approach for microbial genome analysis. We report the first large-scale proteomics study using this system, integrating de novo genome sequencing with functional interaction mapping and annotation in a high-throughput format. We apply the approach by shotgun sequencing and annotating the genome of Rickettsia sibirica strain 246, an obligate intracellular human pathogen among the Spotted Fever Group rickettsiae. The bacteria invade endothelial cells and cause lysis after large amounts of progeny have accumulated. Little is known about specific Rickettsial virulence factors and their mode of pathogenicity. Analysis of the combined genomic sequence and protein-protein interaction data for a set of virulence related Type IV secretion system (T4SS) proteins revealed over 250 interactions and will provide insight into the mechanism of Rickettsial pathogenicity.

Bacterial Proteins↗
PubMed2001

[Persistent microbial infection and therapeutic vaccine].

Microbial persistent infection usually occurs due to invasion of the immune system, microbial genomic mutation or integration of microbial DNA with host chromosomal DNA. Defective host immune responses or immune tolerance are also related to persistent infection. Therapeutic vaccine is aimed at inducing specific humoral and cellular immune responses to terminate microbial persistent infection or to prevent progression of diseases. Different categories of therapeutic vaccines have been presented and the prospect of research as well as the development of novel effective therapeutic vaccines are discussed.

Bacterial Vaccines↗
PubMed2001

Region-specific transcriptional activity in the genome of Streptomyces coelicolor A3(2).

Transcriptional analysis of microbial genomes is an important component of functional genomics. Strategies such as hybridization of labeled total RNA against ordered clone libraries or differential-display approaches have already been carried out to identify expressed genes. We describe here an additional method which applies subtractive hybridization between genome-specific DNA and total RNA followed by a PCR approach to identify expressed microbial genes. With the new strategy, the expression of genes in the terminal regions of the linear Streptomyces coelicolor A3(2) chromosome and the accessory linear plasmid SCP1 was analyzed. The results indicate that the method is useful for the identification of expressed genes in actinomycetes and other microbial systems. We demonstrate for the first time that at least 24 genes in the chromosome end regions (silent regions) of S. coelicolor are actively expressed. In addition, several expressed SCP1 genes were identified, including a gene which shows high similarity to microbial dnaN genes and which seems to play a role in SCP1 maintenance.

Bacterial Proteins↗
PubMed2003

Minimal model for genome evolution and growth.

Textual analysis of typical microbial genomes reveals that they have the statistical characteristics of a DNA sequence of a much shorter length. This peculiar property supports an evolutionary model in which a genome evolves by random mutation but primarily grows by random segmental duplication. That genomes grew mostly by duplication is consistent with the observation that repeat sequences in all genomes are widespread and intragenomic and intergenomic homologous genes are preponderant across all life forms.

DNA, Bacterial↗
PubMed2005

C-di-GMP: the dawning of a novel bacterial signalling system.

Bis-(3'-5')-cyclic dimeric guanosine monophosphate (c-di-GMP) has come to the limelight as a result of the recent advances in microbial genomics and increased interest in multicellular microbial behaviour. Known for more than 15 years as an activator of cellulose synthase in Gluconacetobacter xylinus, c-di-GMP is emerging as a novel global second messenger in bacteria. The GGDEF and EAL domain proteins involved in c-di-GMP synthesis and degradation, respectively, are (almost) ubiquitous in bacterial genomes. These proteins affect cell differentiation and multicellular behaviour as well as interactions between the microorganisms and their eukaryotic hosts and other phenotypes. While the role of GGDEF and EAL domain proteins in bacterial physiology and behaviour has gained appreciation, and significant progress has been achieved in understanding the enzymology of c-di-GMP turnover, many questions regarding c-di-GMP-dependent signalling remain unanswered. Among these, the key questions are the identity of targets of c-di-GMP action and mechanisms of c-di-GMP-dependent regulation. This review discusses phylogenetic distribution of the c-di-GMP signalling pathway in bacteria, recent developments in biochemical and structural characterization of proteins involved in its metabolism, and biological processes affected by c-di-GMP. The accumulated data clearly indicate that a novel ubiquitous signalling system in bacteria has been discovered.

Bacteria↗
PubMedDate not supplied

How will bioinformatics influence metabolic engineering?

Ten microbial genomes have been fully sequenced to date, and the sequencing of many more genomes is expected to be completed before the end of the century. The assignment of function to open reading frames (ORFs) is progressing, and for some genomes over 70% of functional assignments have been made. The majority of the assigned ORFs relate to metabolic functions. Thus, the complete genetic and biochemical functions of a number of microbial cells may be soon available. From a metabolic engineering standpoint, these developments open a new realm of possibilities. Metabolic analysis and engineering strategies can now be built on a sound genomic basis. An important question that now arises; how should these tasks be approached? Flux-balance analysis (FBA) has the potential to play an important role. It is based on the fundamental principle of mass conservation. It requires only the stoichiometric matrix, the metabolic demands, and some strain specific parameters. Importantly, no enzymatic kinetic data is required. In this article, we show how the genomically defined microbial metabolic genotypes can be analyzed by FBA. Fundamental concepts of metabolic genotype, metabolic phenotype, metabolic redundancy and robustness are defined and examples of their use given. We discuss the advantage of this approach, and how FBA is expected to find uses in the near future. FBA is likely to become an important analysis tool for genomically based approaches to metabolic engineering, strain design, and development.

Computational Biology↗
PubMed2004

SIGI: score-based identification of genomic islands.

BACKGROUND: Genomic islands can be observed in many microbial genomes. These stretches of DNA have a conspicuous composition with regard to sequence or encoded functions. Genomic islands are assumed to be frequently acquired via horizontal gene transfer. For the analysis of genome structure and the study of horizontal gene transfer, it is necessary to reliably identify and characterize these islands. RESULTS: A scoring scheme on codon frequencies Score_G1G2(cdn) = log(f_G2(cdn) / f_G1(cdn)) was utilized. To analyse genes of a species G1 and to test their relatedness to species G2, scores were determined by applying the formula to log-odds derived from mean codon frequencies of the two genomes. A non-redundant set of nearly 400 codon usage tables comprising microbial species was derived; its members were used alternatively at position G2. Genes having at least one score value above a species-specific and dynamically determined cut-off value were analysed further. By means of cluster analysis, genes were identified that comprise clusters of statistically significant size. These clusters were predicted as genomic islands. Finally and individually for each of these genes, the taxonomical relation among those species responsible for significant scores was interpreted. The validity of the approach and its limitations were made plausible by an extensive analysis of natural genes and synthetic ones aimed at modelling the process of gene amelioration. CONCLUSIONS: The method reliably allows to identify genomic island and the likely origin of alien genes.

Algorithms↗
PubMed2006

Ancient lateral gene transfer in the evolution of Bdellovibrio bacteriovorus.

The recently sequenced genome of the predatory delta-proteobacterium Bdellovibrio bacteriovorus provides many insights into its metabolism and evolution. Because its genes are reasonably uniform in G+C content, it was suggested that B. bacteriovorus actively resists recombination with foreign DNA and horizontal transfer of DNA from other bacteria. To investigate this further, we carried out a variety of phylogenetic and comparative genomics analyses using data from >200 microbial genomes, including several published delta-proteobacteria. Although there might be little evidence for the extensive recent transfer of genes, we demonstrate that ancient lateral gene acquisition has shaped the B. bacteriovorus genome to a great extent.

Bdellovibrio↗
PubMed1998

Abundant microsatellite polymorphism in Saccharomyces cerevisiae, and the different distributions of microsatellites in eight prokaryotes and S. cerevisiae, result from strong mutation pressures and a variety of selective forces.

We examined the distributions of short tandemly repeated DNAs (microsatellites) in nine complete microbial genomes (Saccharomyces cerevisiae, Archaeoglobus fulgidus, Escherichia coli, Haemophilus influenzae, Helicobacter pylori, Methanococcus jannaschii, Mycoplasma pneumoniae, M. genitalium, and Synechocystis PCC6803.) These repeats contribute differently to the global features of these genomes, and we explore the evolutionary implications of these differences by empirical examination of length polymorphisms at 20 long triplet-repeats repeats in S. cerevisiae, and by comparison of observed and expected repeat distributions. All of a sample of 20 microsatellites found in S. cerevisiae are highly polymorphic in length, suggesting that mutation pressure overcomes overall selection for small genome size that will tend to shorten or eliminate unnecessary DNA. By comparison, prokaryotes have fewer long repeats than expected, except for a few statistically improbable repeats that appear to function in gene regulation. Finally, we find that in all these genomes there is an excess of repeats shorter than those traditionally considered to be microsatellites. This finding suggests that even in prokaryotes these repeats are being generated by mutational pressures. These results have important potential implications for understanding genome stability and evolution in these microbial species.

Bacteria↗
PubMed2026

A genomic catalog of Earth's bacterial and archaeal symbionts.

Microbial symbiosis drives the functional and phylogenomic diversification of life on Earth yet remains underexplored because of culturing challenges. This study used machine learning (ML) to predict symbiotic lifestyles in more than a hundred thousand microbial genomes from diverse environmental metagenome samples and reference genomes. Predictions were performed using symclatron, an ML framework developed to identify genomic signatures of symbionts. Predictions were deposited in a catalog we established called Symbiont Genomes (SymGs). The results indicate that 15-23% of uncultivated microorganisms likely engage in symbiotic relationships with other organisms, categorized as host-associated or obligate intracellular lifestyles, and are present in half of all known bacterial and archaeal phyla. We also identify genomic signatures of symbiotic lifestyles, including the loss of certain metabolic functions and the differential presence of metabolic modules that may enable host-dependent living. The symclatron software and the SymGs catalog represent valuable resources for studying symbioses, potentially facilitating future mechanistic investigations and engineering of host-microorganism associations.

Journal Article↗
PubMed2000

A family of removable cassettes designed to obtain antibiotic-resistance-free genomic modifications of Escherichia coli and other bacteria.

Modifications of microbial genomes often require the use of the antibiotic-resistance (Anb(R))-encoding genes and other easily selectable markers. We have developed a set of such selectable markers (Cm(R), Km(R) and Gm(R)), which could easily be inserted into the genome and subsequently removed by using the Cre/loxP site-specific recombination system of bacteriophage P1. In this manner the same marker could be used more than once in the same background, while the resulting strain could or would remain Anb(R) marker-free. Three plasmids were constructed, each containing a cassette consisting of the Cm(R), Km(R), or Gm(R) gene flanked by two parallel loxP sites and two polylinkers (MCS). To test insertion and excision, cassettes were inserted into the lacZ or galE genes carried on an origamma/pir-dependent suicide plasmid, which contained a dominant Sm(R) gene. The cassettes were crossed into the E. coli genome by homologous recombination (allelic exchange), in a manner analogous to that described by Pósfai et al. [Nucl. Acids Res. 22 (1994) 2392-2398], selecting for the Cm(R), Km(R), or Gm(R), for the LacZ(-) or GalE(-) and for the Sm(S) phenotypes (the latter to assure allelic exchange rather than insertion of the entire plasmid). When required, after selecting the strain with the desired modification, the Cm(R), Km(R), or Gm(R) marker was excised by supplying the Cre function. Cre was provided by the thermosensitive plasmid pJW168, which was transformed into the Anb(R) host at 30 degrees C, and was subsequently eliminated at 42 degrees C. Thus the Anb(R) marker was removed, whereas the lacZ or galE gene remained interrupted by the retained loxP site.

Acetyltransferases↗
PubMed1999

Genetic comparison of Bacillus anthracis and its close relatives using amplified fragment length polymorphism and polymerase chain reaction analysis.

Amplified fragment length polymorphism (AFLP) analysis allows a rapid, relatively simple analysis of a large portion of a microbial genome, providing information about the species and its phylogenetic relationship to other microbes (Vos et al. 1995). The method simply surveys the genome for length and sequence polymorphisms. The AFLP pattern identified can be used for comparison to the genomes of other species. Unlike other methods, it does not rely on analysis of a single genetic locus that may bias the interpretation of results and does not require any prior knowledge of the targeted organism. Moreover, a standard set of reagents can be applied to any species without using species-specific information or molecular probes. We are using AFLP analysis to rapidly identify different bacterial species. A comparison of AFLP profiles generated from a large battery of Bacillus anthracis strains shows very little variability among different isolates (Keim et al. 1997). By contrast, there is a significant difference between AFLP profiles generated for any B. anthracis strain and even the most closely related Bacillus species. Sufficient variability is apparent among all known microbial species to allow phylogenetic analysis based on large numbers of genetically unlinked loci. These striking differences among AFLP profiles allow unambiguous identification of previously identified species and phylogenetic placement of newly characterized isolates relative to known species based on a large number of independent genetic loci. Data generated thus far show that the method provides phylogenetic analyses that are consistent with other widely accepted phylogenetic methods. However, AFLP analysis provides a more detailed analysis of the targets and samples a much larger portion of the genome. Consequently, it provides an inexpensive, rapid means of characterizing microbial isolates to further differentiate among strains and closely related microbial species. Such information cannot be rapidly generated by other means. AFLP sample analysis quickly generates a very large amount of molecular information about microbial genomes. However, this information cannot be analysed rapidly using manual methods. We are developing a large archive of electronic AFLP signatures that is being used to identify isolates collected from medical, veterinary, forensic and environmental samples. We are also developing the computational packages necessary to rapidly and unambiguously analyse the AFLP profiles and conduct a phylogenetic comparison of these data relative to information already in our database. We will use this archive and the associated algorithms to determine the species identity of previously uncharacterized isolates and place them phylogenetically relative to other microbes based on their AFLP signatures. This study provides significant new information about microbes with environmental, veterinary and medical significance. This information can be used in further studies to understand the relationships among these species and the factors that distinguish them from one another. It should also allow the identification of unique factors that contribute to important microbial traits, including pathogenicity and virulence. We are also using AFLP data to identify, isolate and sequence DNA fragments that are unique to particular microbial species and strains. The fragment patterns and sequence information provide insights into the complexity and organization of bacterial genomes relative to one another. They also provide the information necessary for the development of species-specific polymerase chain reaction primers that can be used to interrogate complex samples for the presence of B. anthracis, other microbial pathogens or their remnants.

Bacillus↗
PubMed2005

Bioinformatics in microbial biotechnology--a mini review.

The revolutionary growth in the computation speed and memory storage capability has fueled a new era in the analysis of biological data. Hundreds of microbial genomes and many eukaryotic genomes including a cleaner draft of human genome have been sequenced raising the expectation of better control of microorganisms. The goals are as lofty as the development of rational drugs and antimicrobial agents, development of new enhanced bacterial strains for bioremediation and pollution control, development of better and easy to administer vaccines, the development of protein biomarkers for various bacterial diseases, and better understanding of host-bacteria interaction to prevent bacterial infections. In the last decade the development of many new bioinformatics techniques and integrated databases has facilitated the realization of these goals. Current research in bioinformatics can be classified into: (i) genomics--sequencing and comparative study of genomes to identify gene and genome functionality, (ii) proteomics--identification and characterization of protein related properties and reconstruction of metabolic and regulatory pathways, (iii) cell visualization and simulation to study and model cell behavior, and (iv) application to the development of drugs and anti-microbial agents. In this article, we will focus on the techniques and their limitations in genomics and proteomics. Bioinformatics research can be classified under three major approaches: (1) analysis based upon the available experimental wet-lab data, (2) the use of mathematical modeling to derive new information, and (3) an integrated approach that integrates search techniques with mathematical modeling. The major impact of bioinformatics research has been to automate the genome sequencing, automated development of integrated genomics and proteomics databases, automated genome comparisons to identify the genome function, automated derivation of metabolic pathways, gene expression analysis to derive regulatory pathways, the development of statistical techniques, clustering techniques and data mining techniques to derive protein-protein and protein-DNA interactions, and modeling of 3D structure of proteins and 3D docking between proteins and biochemicals for rational drug design, difference analysis between pathogenic and non-pathogenic strains to identify candidate genes for vaccines and anti-microbial agents, and the whole genome comparison to understand the microbial evolution. The development of bioinformatics techniques has enhanced the pace of biological discovery by automated analysis of large number of microbial genomes. We are on the verge of using all this knowledge to understand cellular mechanisms at the systemic level. The developed bioinformatics techniques have potential to facilitate (i) the discovery of causes of diseases, (ii) vaccine and rational drug design, and (iii) improved cost effective agents for bioremediation by pruning out the dead ends. Despite the fast paced global effort, the current analysis is limited by the lack of available gene-functionality from the wet-lab data, the lack of computer algorithms to explore vast amount of data with unknown functionality, limited availability of protein-protein and protein-DNA interactions, and the lack of knowledge of temporal and transient behavior of genes and pathways.

Journal Article↗
PubMed2004

To BAC or not to BAC: marine ecogenomics.

Most microbes in the ocean are still resistant to our collective cultivation efforts. Environmental microbial genomics provides science with the means for accessing and assessing the genomes, diversity, evolution and population dynamics of uncultured microorganisms--the ocean's hidden majority.

Chromosomes, Artificial, Bacterial↗