Genomics and early cellular evolution. The origin of the DNA world.

The sequencing of several genomes from each of the three domains of life (Archaea, Bacteria and Eukarya) has provided a huge amount of data that can be used to gain insight about early cellular evolution. Some features of the universal tree of life based on rRNA polygenies have been confirmed, such as the division of the cellular living world into three domains. The monophyly of each domain is supported by comparative genomics. However, the hyperthermophilic nature of the 'last universal common ancestor' (LUCA) is not confirmed. Comparative genomics has revealed that gene transfers have been (and still are) very frequent in genome evolution. Nevertheless, a core of informational genes appears more resistant to transfer, testifying for a close relationship between archaeal and eukaryal informational processes. This observation can be explained either by a common unique history between Archaea and Eukarya or by an atypical evolution of these systems in Bacteria. At the moment, comparative genomics still does not allow to choose between a simple LUCA, possibly with an RNA genome, or a complex LUCA, with a DNA genome and informational mechanisms similar to those of Archaea and Eukarya. Further comparative studies on informational mechanisms in the three domains should help to resolve this critical question. The role of viruses in the origin and evolution of DNA genomes also appears an area worth of active investigations. I suggest here that DNA and DNA replication mechanisms appeared first in the virus world before being transferred into cellular organisms.     

Discovering novel biology by in silico archaeology

Archaea are prokaryotes that evolved in parallel with bacteria. Since the discovery of the distinct status of the Archaea, extensive physiological and biochemical research has been conducted to elucidate the molecular basis of their remarkable lifestyle and their unique biology. Here, we discuss how in-depth comparative genomics has been used to improve the annotation of archaeal genomes. Combined with experimental verification, bioinformatic analysis contributes to the ongoing discovery of novel metabolic conversions and control mechanisms, and as such to a better understanding of the intriguing biology of the Archaea.     

A study of archaeal enzymes involved in polar lipid synthesis linking amino acid sequence information, genomic contexts and lipid composition

Cellular membrane lipids, of which phospholipids are the major constituents, form one of the characteristic features that distinguish Archaea from other organisms. In this study, we focused on the steps in archaeal phospholipid synthetic pathways that generate polar lipids such as archaetidylserine, archaetidylglycerol, and archaetidylinositol. Only archaetidylserine synthase (ASS), from Methanothermobacter thermautotrophicus, has been experimentally identified. Other enzymes have not been fully examined. Through database searching, we detected many archaeal hypothetical proteins that show sequence similarity to members of the CDP alcohol phosphatidyltransferase family, such as phosphatidylserine synthase (PSS), phosphatidylglycerol synthase (PGS) and phosphatidylinositol synthase (PIS) derived from Bacteria and Eukarya. The archaeal hypothetical proteins were classified into two groups, based on the sequence similarity. Members of the first group, including ASS from M. thermautotrophicus, were closely related to PSS. The rough agreement between PSS homologue distribution within Archaea and the experimentally identified distribution of archaetidylserine suggested that the hypothetical proteins are ASSs. We found that an open reading frame (ORF) tends to be adjacent to that of ASS in the genome, and that the order of the two ORFs is conserved. The sequence similarity of phosphatidylserine decarboxylase to the product of the ORF next to the ASS gene, together with the genomic context conservation, suggests that the ORF encodes archaetidylserine decarboxylase, which may transform archaetidylserine to archaetidylethanolamine. The second group of archaeal hypothetical proteins was related to PGS and PIS. The members of this group were subjected to molecular phylogenetic analysis, together with PGSs and PISs and it was found that they formed two distinct clusters in the molecular phylogenetic tree. The distribution of members of each cluster within Archaea roughly corresponded to the experimentally identified distribution of archaetidylglycerol or archaetidylinositol. The molecular phylogenetic tree patterns and the correspondence to the membrane compositions suggest that the two clusters in this group correspond to archaetidylglycerol synthases and archaetidylinositol synthases. No archaeal hypothetical protein with sequence similarity to known phosphatidylcholine synthases was detected in this study.     

Space efficient computation of rare maximal exact matches between multiple sequences.

In this article, we propose a new method for computing rare maximal exact matches between multiple sequences. A rare match between k sequences S(1), ... , S(k) is a string that occurs at most t(i)-times in the sequence S(i), where the t(i) > 0 are user-defined thresholds. First, the suffix tree of one of the sequences (the reference sequence) is built, and then the other sequences are matched separately against this suffix tree. Second, the resulting pairwise exact matches are combined to multiple exact matches. A clever implementation of this method yields a very fast and space efficient program. This program can be applied in several comparative genomics tasks, such as the identification of synteny blocks between whole genomes.     

Computational Biology Methods and Their Application to the Comparative Genomics of Endocellular Symbiotic Bacteria of Insects

Comparative genomics has become a real tantalizing challenge in the postgenomic era. This fact has been mostly magnified by the plethora of new genomes becoming available in a daily bases. The overwhelming list of new genomes to compare has pushed the field of bioinformatics and computational biology forward toward the design and development of methods capable of identifying patterns in a sea of swamping data noise. Despite many advances made in such endeavor, the ever-lasting annoying exceptions to the general patterns remain to pose difficulties in generalizing methods for comparative genomics. In this review, we discuss the different tools devised to undertake the challenge of comparative genomics and some of the exceptions that compromise the generality of such methods. We focus on endosymbiotic bacteria of insects because of their genomic dynamics peculiarities when compared to free-living organisms.     

A database of phylogenetically atypical genes in archaeal and bacterial genomes,identified using the <Emphasis Type="Italic">DarkHorse</Emphasis> algorithm

Background  

The process of horizontal gene transfer (HGT) is believed to be widespread in Bacteria and Archaea, but little comparative data is available addressing its occurrence in complete microbial genomes. Collection of high-quality, automated HGT prediction data based on phylogenetic evidence has previously been impractical for large numbers of genomes at once, due to prohibitive computational demands. DarkHorse, a recently described statistical method for discovering phylogenetically atypical genes on a genome-wide basis, provides a means to solve this problem through lineage probability index (LPI) ranking scores. LPI scores inversely reflect phylogenetic distance between a test amino acid sequence and its closest available database matches. Proteins with low LPI scores are good horizontal gene transfer candidates; those with high scores are not.     

Comparative phylogeography as an integrative approach to historical biogeography

Phylogeography has become a powerful approach for elucidating contemporary geographical patterns of evolutionary subdivision within species and species complexes. A recent extension of this approach is the comparison of phylogeographic patterns of multiple co-distributed taxonomic groups, or 'comparative phylogeography.' Recent comparative phylogeographic studies have revealed pervasive and previously unrecognized biogeographic patterns which suggest that vicariance has played a more important role in the historical development of modern biotic assemblages than current taxonomy would indicate. Despite the utility of comparative phylogeography for uncovering such 'cryptic vicariance', this approach has yet to be embraced by some researchers as a valuable complement to other approaches to historical biogeography. We address here some of the common misconceptions surrounding comparative phylogeography, provide an example of this approach based on the boreal mammal fauna of North America, and argue that together with other approaches, comparative phylogeography can contribute importantly to our understanding of the relationship between earth history and biotic diversification.     

Thermophiles like hot T

A plethora of mechanisms confer protein stability in thermophilic microorganisms and, recently, it was suggested that these mechanisms might be divided along evolutionary lines. Here, a multi-genome comparison shows that there is a statistically significant increase in the proportion of NTN codons correlated with increasing optimal growth temperature for both Bacteria and Archaea. NTN encodes exclusively non-polar, hydrophobic amino acids and indicates a common underlying use of hydrophobicity for stabilizing proteins in Bacteria and Archaea that transcends evolutionary origins. However, some microorganisms do not follow this trend, suggesting that alternate mechanisms (e.g. intracellular electrolytes) might be used for protein stabilization. These studies highlight the usefulness of large-scale comparative genomics to uncover novel relationships that are not immediately obvious from protein structure studies alone.     

A novel approach for nontargeted data analysis for metabolomics. Large-scale profiling of tomato fruit volatiles

To take full advantage of the power of functional genomics technologies and in particular those for metabolomics, both the analytical approach and the strategy chosen for data analysis need to be as unbiased and comprehensive as possible. Existing approaches to analyze metabolomic data still do not allow a fast and unbiased comparative analysis of the metabolic composition of the hundreds of genotypes that are often the target of modern investigations. We have now developed a novel strategy to analyze such metabolomic data. This approach consists of (1) full mass spectral alignment of gas chromatography (GC)-mass spectrometry (MS) metabolic profiles using the MetAlign software package, (2) followed by multivariate comparative analysis of metabolic phenotypes at the level of individual molecular fragments, and (3) multivariate mass spectral reconstruction, a method allowing metabolite discrimination, recognition, and identification. This approach has allowed a fast and unbiased comparative multivariate analysis of the volatile metabolite composition of ripe fruits of 94 tomato (Lycopersicon esculentum Mill.) genotypes, based on intensity patterns of >20,000 individual molecular fragments throughout 198 GC-MS datasets. Variation in metabolite composition, both between- and within-fruit types, was found and the discriminative metabolites were revealed. In the entire genotype set, a total of 322 different compounds could be distinguished using multivariate mass spectral reconstruction. A hierarchical cluster analysis of these metabolites resulted in clustering of structurally related metabolites derived from the same biochemical precursors. The approach chosen will further enhance the comprehensiveness of GC-MS-based metabolomics approaches and will therefore prove a useful addition to nontargeted functional genomics research.     

FOUNDING A NEW SCIENCE: MIND GENOMICS

We present in this article our vision for a new science, modeled on the emerging science of genomics and the technology of informatics. Our goal in this new science is to better understand how people react to ideas in a formal and structured way, using the principles of stimulus–response (from experimental psychology), conjoint analysis (from consumer research and statistics), Internet‐based testing (from marketing research) and multiple tests to identify patterns of mind‐sets (patterned after genomics). We show how this formal approach can then be used to construct new, innovative ideas in business. We demonstrate the approach using the development of new ideas for an electronic color palette for cosmetic products to be used by consumers.     

Comparative genomics of archaea: how much have we learned in six years,and what's next?

Archaea comprise one of the three distinct domains of life (with bacteria and eukaryotes). With 16 complete archaeal genomes sequenced to date, comparative genomics has revealed a conserved core of 313 genes that are represented in all sequenced archaeal genomes, plus a variable 'shell' that is prone to lineage-specific gene loss and horizontal gene exchange. The majority of archaeal genes have not been experimentally characterized, but novel functional pathways have been predicted.     

Prediction of functionally related proteins by comparative genomics in silico

The review considers the computational prediction of functionally related proteins by comparative genomics. Growing possibilities of biotechnology for genome sequencing lead to generation of sequences for millions of genes. However, functions of majority of these genes remain unknown, and can be determined experimentally only for a few of them. Therefore, accurate and robust methods for in silico prediction (annotation) of gene functions are needed. We describe here the main techniques of comparative genomics, including the standard method based on transferring functions between homologous sequences and also context-based methods, including phylogenetic profiles and gene-neighbor approaches. Modern methods of comparative genomics allow obtaining correct functional annotations for more than a half of all organism proteins.     

Determining relevant features to recognize electron density patterns in x-ray protein crystallography

High-throughput computational methods in X-ray protein crystallography are indispensable to meet the goals of structural genomics. In particular, automated interpretation of electron density maps, especially those at mediocre resolution, can significantly speed up the protein structure determination process. TEXTAL(TM) is a software application that uses pattern recognition, case-based reasoning and nearest neighbor learning to produce reasonably refined molecular models, even with average quality data. In this work, we discuss a key issue to enable fast and accurate interpretation of typically noisy electron density data: what features should be used to characterize the density patterns, and how relevant are they? We discuss the challenges of constructing features in this domain, and describe SLIDER, an algorithm to determine the weights of these features. SLIDER searches a space of weights using ranking of matching patterns (relative to mismatching ones) as its evaluation function. Exhaustive search being intractable, SLIDER adopts a greedy approach that judiciously restricts the search space only to weight values that cause the ranking of good matches to change. We show that SLIDER contributes significantly in finding the similarity between density patterns, and discuss the sensitivity of feature relevance to the underlying similarity metric.     

主要禾谷类作物比较基因组学研究策略与进展

随着拟南芥、水稻等模式植物基因组测序计划的完成, 比较基因组学作为一门新兴学科, 近年来发展迅速, 为植物基因组的进化、结构和功能研究开辟了新的途径。文章综述了比较基因组学在作物比较遗传作图、基因结构区域的微共线性、ESTs和蛋白质水平的比较以及基于比较基因组学的基因和QTL的克隆等方面内容与研究进展, 分析了不同水平上比较基因组学研究策略的原理、特点、可行性, 以期为利用模式生物的基因和基因组数据、采用比较基因组学策略克隆作物重要性状功能基因、阐明基因组结构与进化提供帮助。     

Metabolic symbiosis at the origin of eukaryotes

Thirty years after Margulis revived the endosymbiosis theory for the origin of mitochondria and chloroplasts, two novel symbiosis hypotheses for the origin of eukaryotes have been put forward. Both propose that eukaryotes arose through metabolic symbiosis (syntrophy) between eubacteria and methanogenic Archaea. They also propose that this was mediated by interspecies hydrogen transfer and that, initially, mitochondria were anaerobic. These hypotheses explain the mosaic character of eukaryotes (i.e. an archaeal-like genetic machinery and a eubacterial-like metabolism), as well as distinct eukaryotic characteristics (which are proposed to be products of symbiosis). Combined data from comparative genomics, microbial ecology and the fossil record should help to test their validity.     

On the way to building an integrated computational environment for the study of developmental patterns and genetic diseases

Genetic diseases and developmental patterns should be studied on several levels: from macroscale (organs and tissues) to nanoscale (cells, genes, proteins). Due to the overwhelming complexity of the life science data, it is common that disparate data pieces are meticulously stored but never fully analyzed or correlated. We have begun to develop a novel methodology based on virtual reality techniques for the study of these phenomena. Our key approach to knowledge integration is a top-down mapping of data onto visual contexts. For each organism that we want to study, a structural model is created and used as a visual "wireframe" onto which other data types are superimposed in a top-down assembly. Data analysis tools, visual controls, and queries are enabled so that users can interactively explore data. Our visualization technology gives users an opportunity to map disparate data onto a common model, and search visually for hitherto unknown patterns and correlations contained within the data. It is our goal to eventually transform genomics research from measuring various data pieces analytically into a fully interactive exploration of combined data in a 4D immersive visual environment that best matches a researcher's intuition.     

Vom Phänotyp zum Genotyp

Forward Genomics – a comparative genomics approach to link phenotype to genotype Despite availability of several sequenced genomes, we know very little about the specific changes in the DNA that underlie phenotypic differences between species. The main reason is that species differ by both numerous genomic and phenotypic changes. A new comparative genomics method addresses this question by for phenotypes with independent evolutionary losses by searching for genomic regions that exhibit an elevated number of mutations in exactly these phenotype‐loss species. The near future sequencing of thousands of novel genomes will make it possible to use comparative genomics to systematically search for such DNA changes that are associated with phenotypic differences.     

INTEGRATING LANDSCAPE GENOMICS AND SPATIALLY EXPLICIT APPROACHES TO DETECT LOCI UNDER SELECTION IN CLINAL POPULATIONS

Uncovering the genetic basis of adaptation hinges on the ability to detect loci under selection. However, population genomics outlier approaches to detect selected loci may be inappropriate for clinal populations or those with unclear population structure because they require that individuals be clustered into populations. An alternate approach, landscape genomics, uses individual‐based approaches to detect loci under selection and reveal potential environmental drivers of selection. We tested four landscape genomics methods on a simulated clinal population to determine their effectiveness at identifying a locus under varying selection strengths along an environmental gradient. We found all methods produced very low type I error rates across all selection strengths, but elevated type II error rates under “weak” selection. We then applied these methods to an AFLP genome scan of an alpine plant, Campanula barbata, and identified five highly supported candidate loci associated with precipitation variables. These loci also showed spatial autocorrelation and cline patterns indicative of selection along a precipitation gradient. Our results suggest that landscape genomics in combination with other spatial analyses provides a powerful approach for identifying loci potentially under selection and explaining spatially complex interactions between species and their environment.