Lunjiang Ling

Evolution and migration history of the Chinese population inferred from Chinese Y-chromosome evidence.

AbstractY-chromosomes from 76 Chinese men covering 33 ethnical minorities throughout China as well as the Han majority were collected as genetic material for the study of Chinese nonrecombinant Y-chromosome (NRY) phylogeny. Of the accepted worldwide NRY haplogroups, three (haplogroups D, C, O) were significant in this sample, extending previous assessments of Chinese genetic diversity. Based on geographic, linguistic, and ethnohistorical information, the 33 Chinese ethnical minorities in our survey were divided into the following four subgroups: North, Tibet, West, and South. Inferred from the distribution of the newfound immediate ancestor lineage haplogroup O*, which has M214 but not M175, we argue that the southern origin scenario of this most common Chinese Y haplogroup is not very likely. We tentatively propose a West/North-origin hypothesis, suggesting that haplogroup O originated in West/North China and mainly evolved in China and thence spread further throughout eastern Eurasia. The nested cladistic analysis revealed in detail a multilayered, multidirectional, and continuous history of ethnic admixture that has shaped the contemporary Chinese population. Our results give some new clues to the evolution and migration of the Chinese population and its subsequence moving about in this land, which are in accordance with the historical records.

Conservation analysis of small RNA genes in Escherichia coli

MOTIVATION Small RNA (sRNA) genes in Escherichia coli have been in focus recently, as 44 out of 55 experimentally confirmed sRNA genes have been precisely located in the genome. The object of this study is to analyze quantitatively the conservation of these sRNA genes and compare it with the conservation of protein-encoding genes, function-unknown regions and tRNA genes. RESULTS The results show that within an evolutionary distance of 0.26, both sRNA genes and protein-encoding genes display a similar tendency in their degrees of conservation at the nucleotide level. In addition, the conservation of sRNA genes is much stronger than function-unknown regions, but much weaker than tRNA genes. Based on the conservation of studied sRNA genes, we also give clues to estimate the total number of sRNA genes in E.coli. SUPPLEMENTARY INFORMATION Supplementary information is available at http://www.bioinfo.org.cn/SM/sRNAconservation.htm

Phylogeny Based on Whole Genome as inferred from Complete Information Set Analysis.

Previous molecular phylogeny algorithms mainly rely onmulti-sequence alignments of cautiously selected characteristic sequences,thus not directly appropriate for whole genome phylogeny where eventssuch as rearrangements make full-length alignments impossible. Weintroduce here the concept of Complete Information Set (CIS) and itsmeasurement implementation as evolution distance without reference tosizes. As method proof-test, the 16s rRNA sequences of 22 completelysequenced Bacteria and Archaea species are used to reconstruct aphylogenetic tree, which is generally consistent with the commonlyaccepted one. Based on whole genome, our further efforts yield a highlyrobust whole genome phylogenetic tree, supporting separate monophyleticcluster of species with similar phenotype as well as the early evolution ofthermophilic Bacteria and late diverging of Eukarya. The purpose of thiswork is not to contradict or confirm previous phylogeny standards butrather to bring a brand-new algorithm and tool to the phylogeny researchcommunity. The software to estimate the sequence distance and materialsused in this study are available upon request to corresponding author.

Modeling and Docking of the Three-Dimensional Structure of the Human Melanocortin 4 Receptor

A three-dimensional structure of the human melanocortin 4 receptor (hMC4R) is constructed in this study using a computer-aided molecular modeling approach. Human melanocortin 4 receptor is a G Protein-Coupled Receptor (GPCR). We structurally aligned transmembrane helices with bovine rhodopsin transmembrane domains, simulated both intracellular and extracellular loop domains on homologous loop regions in other proteins of known 3D structure and modeled the C terminus on the corresponding part of bovine rhodopsin. Then tandem minimization and dynamics calculations were run to refine the crude structure. The simulative model was tested by docking with a triplet peptide (RFF) ligand. It was found that the ligand is located among transmembrane regions TM3, TM4, TM5, and TM6 of hMC4R. In consistence with mutational and biochemical data, binding site is mainly formed as a hydrophobic and negatively charged pocket. The model constructed here might provide a structural framework for making rational predictions in relevant fields.

Date of origin of the SARS coronavirus strains

BackgroundA new respiratory infectious epidemic, severe acute respiratory syndrome (SARS), broke out and spread throughout the world. By now the putative pathogen of SARS has been identified as a new coronavirus, a single positive-strand RNA virus. RNA viruses commonly have a high rate of genetic mutation. It is therefore important to know the mutation rate of the SARS coronavirus as it spreads through the population. Moreover, finding a date for the last common ancestor of SARS coronavirus strains would be useful for understanding the circumstances surrounding the emergence of the SARS pandemic and the rate at which SARS coronavirus diverge.MethodsWe propose a mathematical model to estimate the evolution rate of the SARS coronavirus genome and the time of the last common ancestor of the sequenced SARS strains. Under some common assumptions and justifiable simplifications, a few simple equations incorporating the evolution rate (K) and time of the last common ancestor of the strains (T0) can be deduced. We then implemented the least square method to estimate K and T0 from the dataset of sequences and corresponding times. Monte Carlo stimulation was employed to discuss the results.ResultsBased on 6 strains with accurate dates of host death, we estimated the time of the last common ancestor to be about August or September 2002, and the evolution rate to be about 0.16 base/day, that is, the SARS coronavirus would on average change a base every seven days. We validated our method by dividing the strains into two groups, which coincided with the results from comparative genomics.ConclusionThe applied method is simple to implement and avoid the difficulty and subjectivity of choosing the root of phylogenetic tree. Based on 6 strains with accurate date of host death, we estimated a time of the last common ancestor, which is coincident with epidemic investigations, and an evolution rate in the same range as that reported for the HIV-1 virus.

Gene's Functional Arrangement as a Measure of thePhylogenetic Relationships of Microorganisms

With the development of genome sequencing more whole genomes of microorganisms were completed, many methods wereintroduced to reconstruct the phylogenetic tree of those microorganismswith the information extracted from the whole genomes through variousways of transforming or mapping the whole genome sequences into otherforms which can describe the evolutionary distance in a new way. We thinkit might be possible that there exists information buried in the wholegenome transferred along lineage, which remains stable and is moreessential than sequence conservation of individual genes or the arrangementof some genes of a selected set. We need to find one measurement that caninvolve as many phylogenetic features as possible that are beyond thegenome sequence itself. We converted each genome sequence of themicroorganisms into another linear sequence to represent the functionalstructure of the sequence, and we used a new information function tocalculate the discrepancy of sequences and to get one distance matrix of thegenomes, and built one phylogenetic tree with a neighbor joining method.The resulting tree shows that the major lineages are consistent with theresult based on their 16srRNA sequences. Our method discovered onephylogenetic feature derived from the genome sequences and the encodedgenes that can rebuild the phylogenetic tree correctly. The mapping of onegenome sequence to its new form representing the relative positions of thefunctional genes provides a new way to measure the phylogeneticrelationships, and with the more specific classification of gene functions theresult could be more sensitive.

A new method for protein domain recognition

Abstract A fuzzy cluster method is presented to recognize protein domains. This algorithm can identify domains globally. A protein structure set was used to test the algorithm. Among 219 proteins, 66.7% yielded results that agreed with the reference definitions, 30.6% showed minor differences, and only 2.7% (six proteins) showed major differences with the reference. The new method is more than 20 times fast than previous algorithms.

Phylogeny of SARS-CoV as inferred from complete genome comparison

SARS-CoV, as the pathogeny of severe acute respiratory syndrome (SARS), is a mystery that the origin of the virus is still unknown even a few isolates of the virus were completely sequenced. To explore the genesis of SARS-CoV, the FDOD method previously developed by us was applied to comparing complete genomes from 12 SARS-CoV isolates to those from 12 previously identified coronaviruses and an unrooted phylogenetic tree was constructed. Our results show that all SARS-CoV isolates were clustered into a clique and previously identified coronaviruses formed the other clique. Meanwhile, the three groups of coronaviruses depart from each other clearly in our tree that is consistent with the results of prevenient papers. Differently, from the topology of the phylogenetic tree we found that SARS-CoV is more close to group 1 within genus coronavirus. The topology map also shows that the 12 SARS-CoV isolates may be divided into two groups determined by the association with the SARS-CoV from the Hotel M in Hong Kong that may give some information about the infectious relationship of the SARS.

Analysis of correlations between protein complex and protein-protein interaction and mRNA expression

Protein-protein interaction is a physical interaction of two proteins in living cells. In budding yeastSaccharomyces cerevisiae, large-scale protein-protein interaction data have been obtained through high-throughput yeast two-hybrid systems (Y2H) and protein complex purification techniques based on mass-spectrometry. Here, we collect 11855 interactions between total 2617 proteins. Through seriate genome-wide mRNA expression data, similarity between two genes could be measured. Protein complex data can also be obtained publicly and can be translated to pair relationship that any two proteins can only exist in the same complex or not. Analysis of protein complex data, protein-protein interaction data and mRNA expression data can elucidate correlations between them. The results show that proteins that have interactions or similar expression patterns have a higher possibility to be in the same protein complex than randomized selected proteins, and proteins which have interactions and similar expression patterns are even more possible to exist in the same protein complex. The work indicates that comprehensive integration and analysis of public large-scale bioinformatical data, such as protein complex data, protein-protein interaction data and mRNA expression data, may help to uncover their relationships and common biological information underlying these data. The strategies described here may help to integrate and analyze other functional genomic and proteomic data, such as gene expression profiling, protein-localization mapping and large-scale phenotypic data, both in yeast and in other organisms.

MOLECULAR DYNAMICS SIMULATION OF SITE-DIRECTED MUTAGENESIS OF HIV-1 TAT TRANS-ACTIVATOR

The Cys-rich domain, core region and basic domain are highly conserved and very important to thetrans-activation activity of HIV-1 Tattrans-activator. The three-dimensional structures of 6 mutants of HIV-1 Tat protein were constructed with the methods of molecular dynamics simulation. The variations of the structures of the mutants have been analyzed and the factors that led to abolishment oftrans-activation activity have been discussed.