Use of single-point genome signature tags as a universal tagging method for microbial genome surveys

We developed single-point genome signature tags (SP-GSTs), a generally applicable, high-throughput sequencing-based method that targets specific genes to generate identifier tags from well-defined points in a genome. The technique yields identifier tags that can distinguish between closely related bacterial strains and allow for the identification of microbial community members. SP-GSTs are determined by three parameters: (i) the primer designed to recognize a conserved gene sequence, (ii) the anchoring enzyme recognition sequence, and (iii) the type IIS restriction enzyme which defines the tag length. We evaluated the SP-GST method in silico for bacterial identification using the genes rpoC, uvrB, and recA and the 16S rRNA gene. The best distinguishing tags were obtained with the restriction enzyme Csp6I upstream of the 16S rRNA gene, which discriminated all organisms in our data set to at least the genus level and most organisms to the species level. The method was successfully used to generate Csp6I-based tags upstream of the 16S rRNA gene and allowed us to discriminate between closely related strains of Bacillus cereus and Bacillus anthracis. This concept was further used successfully to identify the individual members of a defined microbial community.     

A functional gene array for detection of bacterial virulence elements

Emerging known and unknown pathogens create profound threats to public health. Platforms for rapid detection and characterization of microbial agents are critically needed to prevent and respond to disease outbreaks. Available detection technologies cannot provide broad functional information about known or novel organisms. As a step toward developing such a system, we have produced and tested a series of high-density functional gene arrays to detect elements of virulence and antibiotic resistance mechanisms. Our first generation array targets genes from Escherichia coli strains K12 and CFT073, Enterococcus faecalis and Staphylococcus aureus. We determined optimal probe design parameters for gene family detection and discrimination. When tested with organisms at varying phylogenetic distances from the four target strains, the array detected orthologs for the majority of targeted gene families present in bacteria belonging to the same taxonomic family. In combination with whole-genome amplification, the array detects femtogram concentrations of purified DNA, either spiked in to an aerosol sample background, or in combinations from one or more of the four target organisms. This is the first report of a high density NimbleGen microarray system targeting microbial antibiotic resistance and virulence mechanisms. By targeting virulence gene families as well as genes unique to specific biothreat agents, these arrays will provide important data about the pathogenic potential and drug resistance profiles of unknown organisms in environmental samples.     

Identification of genome-scale metabolic network models using experimentally measured flux profiles

Genome-scale metabolic network models can be reconstructed for well-characterized organisms using genomic annotation and literature information. However, there are many instances in which model predictions of metabolic fluxes are not entirely consistent with experimental data, indicating that the reactions in the model do not match the active reactions in the in vivo system. We introduce a method for determining the active reactions in a genome-scale metabolic network based on a limited number of experimentally measured fluxes. This method, called optimal metabolic network identification (OMNI), allows efficient identification of the set of reactions that results in the best agreement between in silico predicted and experimentally measured flux distributions. We applied the method to intracellular flux data for evolved Escherichia coli mutant strains with lower than predicted growth rates in order to identify reactions that act as flux bottlenecks in these strains. The expression of the genes corresponding to these bottleneck reactions was often found to be downregulated in the evolved strains relative to the wild-type strain. We also demonstrate the ability of the OMNI method to diagnose problems in E. coli strains engineered for metabolite overproduction that have not reached their predicted production potential. The OMNI method applied to flux data for evolved strains can be used to provide insights into mechanisms that limit the ability of microbial strains to evolve towards their predicted optimal growth phenotypes. When applied to industrial production strains, the OMNI method can also be used to suggest metabolic engineering strategies to improve byproduct secretion. In addition to these applications, the method should prove to be useful in general for reconstructing metabolic networks of ill-characterized microbial organisms based on limited amounts of experimental data.     

Untangling multi-gene families in plants by integrating proteomics into functional genomics

The classification and study of gene families is emerging as a constructive tool for fast tracking the elucidation of gene function. A multitude of technologies can be employed to undertake this task including comparative genomics, gene expression studies, sub-cellular localisation studies and proteomic analysis. Here we focus on the growing role of proteomics in untangling gene families in model plant species. Proteomics can specifically identify the products of closely related genes, can determine their abundance, and coupled to affinity chromatography and sub-cellular fractionation studies, it can even provide location within cells and functional assessment of specific proteins. Furthermore global gene expression analysis can then be used to place a specific family member in the context of a cohort of co-expressed genes. In model plants with established reverse genetic resources, such as catalogued T-DNA insertion lines, this gene specific information can also be readily used for a wider assessment of specific protein function or its capacity for compensation through assessing whole plant phenotypes. In combination, these resources can explore partitioning of function between members and assess the level of redundancy within gene families.     

Rapid development of multiple nuclear loci for phylogenetic analysis using genomic resources: an example from squamate reptiles

Recently, as genome-scale data have become available for more organisms, the development of phylogenetic markers from nuclear protein-coding loci (NPCL) has become more tractable. However, new methods are needed to efficiently sort the large number of genes from genomic databases into more limited sets appropriate for particular phylogenetic questions, while avoiding introns and paralogs. Here we describe a general methodology for identifying candidate single-copy NPCL from genomic databases. Our method uses information from reference genomes to identify genes with relatively large continuous protein-coding regions (i.e., 700bp). BLAST comparisons are used to help avoid genes with paralogous copies or close relatives (i.e., gene families) that might confound phylogenetic analyses. Exon boundary information is used to identify appropriately spaced potential priming sites. Using this method, we have developed over 25 novel NPCL, which span a variety of desirable evolutionary rates for phylogenetic analyses. Although targeted for higher-level phylogenetics of squamate reptiles, many of these loci appear to be useful across and within other vertebrate clades (e.g., amphibians), and some are relatively rapidly evolving and may be useful for closely-related species (e.g., within genera). This general method can be used whenever large-scale genomic data are available for an appropriate reference species (not necessarily within the focal clade). The method is also well suited for the development of intron regions for lower-level phylogenetic and phylogeographic studies. We provide an online database of alignments and suggested primers for approximately 85 NPCL that should be useful across vertebrates.     

Protist.guru: A Comparative Transcriptomics Database for Protists

SummaryDuring the last few decades, the study of microbial ecology has been enabled by molecular and genomic data. DNA sequencing has revealed the surprising extent of microbial diversity and how microbial processes run global ecosystems. However, significant gaps in our understanding of the microbial world remain, and one example is that microbial eukaryotes, or protists, are still largely neglected. To address this gap, we used gene expression data from 17 protist species to create protist.guru: an online database equipped with tools for identifying co-expressed genes, gene families, and co-expression clusters enriched for specific biological functions. Here, we show how our database can be used to reveal genes involved in essential pathways, such as the synthesis of secondary carotenoids in Haematococcus lacustris. We expect protist.guru to serve as a valuable resource for protistologists, as well as a catalyst for discoveries and new insights into the biological processes of microbial eukaryotes.AvailabilityThe database and co-expression networks are freely available from http://protist.guru/. The expression matrices and sample annotations are found in the supplementary data.     

任意引物PCR及其应用研究进展

任意引物PCR技术又称为随机扩增多态性DNA技术,它是在PCR技术基础上发展起来的一项分子检测技术。它具有简便、快速,一套引物可用于多个物种的分析,不需预知分析对象的核酸序列,可以显示差异表达基因等特点,已广泛应用于病原微生物的分型鉴定、物种亲源关系分析、遗传育种研究和特异表达基因的克隆与鉴定等方面。     

Signatures of optimal codon usage in metabolic genes inform budding yeast ecology

Reverse ecology is the inference of ecological information from patterns of genomic variation. One rich, heretofore underutilized, source of ecologically relevant genomic information is codon optimality or adaptation. Bias toward codons that match the tRNA pool is robustly associated with high gene expression in diverse organisms, suggesting that codon optimization could be used in a reverse ecology framework to identify highly expressed, ecologically relevant genes. To test this hypothesis, we examined the relationship between optimal codon usage in the classic galactose metabolism (GAL) pathway and known ecological niches for 329 species of budding yeasts, a diverse subphylum of fungi. We find that optimal codon usage in the GAL pathway is positively correlated with quantitative growth on galactose, suggesting that GAL codon optimization reflects increased capacity to grow on galactose. Optimal codon usage in the GAL pathway is also positively correlated with human-associated ecological niches in yeasts of the CUG-Ser1 clade and with dairy-associated ecological niches in the family Saccharomycetaceae. For example, optimal codon usage of GAL genes is greater than 85% of all genes in the genome of the major human pathogen Candida albicans (CUG-Ser1 clade) and greater than 75% of genes in the genome of the dairy yeast Kluyveromyces lactis (family Saccharomycetaceae). We further find a correlation between optimization in the GALactose pathway genes and several genes associated with nutrient sensing and metabolism. This work suggests that codon optimization harbors information about the metabolic ecology of microbial eukaryotes. This information may be particularly useful for studying fungal dark matter—species that have yet to be cultured in the lab or have only been identified by genomic material.

Bias toward codons that match the tRNA pool is robustly associated with high gene expression in diverse organisms, suggesting that codon optimization could be used in a reverse ecology framework to identify ecologically relevant genes. This study finds that this is indeed the case for 329 species of budding yeasts, suggesting that codon optimization harbors information about the metabolic ecology of microbial eukaryotes.     

The evolutionary landscape of cytosolic microbial sensors in humans

Host-pathogen interactions are generally initiated by host recognition of microbial components or danger signals triggered by microbial invasion. This recognition involves germline-encoded microbial sensors or pattern-recognition receptors (PRRs). By studying the way in which natural selection has driven the evolution of these microbial sensors in humans, we can identify genes playing an essential role and distinguish them from other, more redundant genes. We characterized the sequence diversity of the NOD-like receptor family, including the NALP and NOD/IPAF subfamilies, in various populations worldwide and compared this diversity with that of other PRR families, such as Toll-like receptors (TLRs) and RIG-I-like receptors (RLRs). We found that most NALPs had evolved under strong selective constraints, suggesting that their functions are essential and possibly much broader than previously thought. Conversely, most NOD/IPAF subfamily members were subject to more relaxed selective constraints, suggesting greater redundancy. Furthermore, some NALP genes, including NLRP1, NLRP14, and CIITA, were found to have evolved adaptively. We identified those variants conferring a selective advantage on some human populations as the most likely targets of positive selection. More generally, the strength of selection differed considerably between the major families of microbial sensors. Endosomal TLRs and most NALPs were found to evolve under stronger purifying selection than most NOD/IPAF subfamily members and cell-surface TLRs and RLRs, suggesting some degree of redundancy in the signaling pathways triggered by these molecules. This study provides novel perspectives and experimentally testable hypotheses concerning the relative biological relevance of the various families of microbial sensors in humans.     

Use of Single-Point Genome Signature Tags as a Universal Tagging Method for Microbial Genome Surveys

We developed single-point genome signature tags (SP-GSTs), a generally applicable, high-throughput sequencing-based method that targets specific genes to generate identifier tags from well-defined points in a genome. The technique yields identifier tags that can distinguish between closely related bacterial strains and allow for the identification of microbial community members. SP-GSTs are determined by three parameters: (i) the primer designed to recognize a conserved gene sequence, (ii) the anchoring enzyme recognition sequence, and (iii) the type IIS restriction enzyme which defines the tag length. We evaluated the SP-GST method in silico for bacterial identification using the genes rpoC, uvrB, and recA and the 16S rRNA gene. The best distinguishing tags were obtained with the restriction enzyme Csp6I upstream of the 16S rRNA gene, which discriminated all organisms in our data set to at least the genus level and most organisms to the species level. The method was successfully used to generate Csp6I-based tags upstream of the 16S rRNA gene and allowed us to discriminate between closely related strains of Bacillus cereus and Bacillus anthracis. This concept was further used successfully to identify the individual members of a defined microbial community.     

Differential network expression during drug and stress response

MOTIVATION: The application of microarray chip technology has led to an explosion of data concerning the expression levels of the genes in an organism under a plethora of conditions. One of the major challenges of systems biology today is to devise generally applicable methods of interpreting this data in a way that will shed light on the complex relationships between multiple genes and their products. The importance of such information is clear, not only as an aid to areas of research like drug design, but also as a contribution to our understanding of the mechanisms behind an organism's ability to react to its environment. RESULTS: We detail one computational approach for using gene expression data to identify response networks in an organism. The method is based on the construction of biological networks given different sets of interaction information and the reduction of the said networks to important response sub-networks via the integration of the gene expression data. As an application, the expression data of known stress responders and DNA repair genes in Mycobacterium tuberculosis is used to construct a generic stress response sub-network. This is compared to similar networks constructed from data obtained from subjecting M.tuberculosis to various drugs; we are thus able to distinguish between generic stress response and specific drug response. We anticipate that this approach will be able to accelerate target identification and drug development for tuberculosis in the future. CONTACT: chris@lanl.gov SUPPLEMENTARY INFORMATION: Supplementary Figures 1 through 6 on drug response networks and differential network analyses on cerulenin, chlorpromazine, ethionamide, ofloxacin, thiolactomycin and triclosan. Supplementary Tables 1 to 3 on predicted protein interactions. http://www.santafe.edu/~chris/DifferentialNW.     

Incorporating 16S Gene Copy Number Information Improves Estimates of Microbial Diversity and Abundance

The abundance of different SSU rRNA (“16S”) gene sequences in environmental samples is widely used in studies of microbial ecology as a measure of microbial community structure and diversity. However, the genomic copy number of the 16S gene varies greatly – from one in many species to up to 15 in some bacteria and to hundreds in some microbial eukaryotes. As a result of this variation the relative abundance of 16S genes in environmental samples can be attributed both to variation in the relative abundance of different organisms, and to variation in genomic 16S copy number among those organisms. Despite this fact, many studies assume that the abundance of 16S gene sequences is a surrogate measure of the relative abundance of the organisms containing those sequences. Here we present a method that uses data on sequences and genomic copy number of 16S genes along with phylogenetic placement and ancestral state estimation to estimate organismal abundances from environmental DNA sequence data. We use theory and simulations to demonstrate that 16S genomic copy number can be accurately estimated from the short reads typically obtained from high-throughput environmental sequencing of the 16S gene, and that organismal abundances in microbial communities are more strongly correlated with estimated abundances obtained from our method than with gene abundances. We re-analyze several published empirical data sets and demonstrate that the use of gene abundance versus estimated organismal abundance can lead to different inferences about community diversity and structure and the identity of the dominant taxa in microbial communities. Our approach will allow microbial ecologists to make more accurate inferences about microbial diversity and abundance based on 16S sequence data.