Comparing Patterns of Natural Selection Across Species Using Selective Signatures

Comparing gene expression profiles over many different conditions has led to insights that were not obvious from single experiments. In the same way, comparing patterns of natural selection across a set of ecologically distinct species may extend what can be learned from individual genome-wide surveys. Toward this end, we show how variation in protein evolutionary rates, after correcting for genome-wide effects such as mutation rate and demographic factors, can be used to estimate the level and types of natural selection acting on genes across different species. We identify unusually rapidly and slowly evolving genes, relative to empirically derived genome-wide and gene family-specific background rates for 744 core protein families in 30 gamma-proteobacterial species. We describe the pattern of fast or slow evolution across species as the 'selective signature' of a gene. Selective signatures represent a profile of selection across species that is predictive of gene function: pairs of genes with correlated selective signatures are more likely to share the same cellular function, and genes in the same pathway can evolve in concert. For example, glycolysis and phenylalanine metabolism genes evolve rapidly in Idiomarina loihiensis, mirroring an ecological shift in carbon source from sugars to amino acids. In a broader context, our results suggest that the genomic landscape is organized into functional modules even at the level of natural selection, and thus it may be easier than expected to understand the complex evolutionary pressures on a cell

Characterization of the unfolding pathway of the cell-cycle protein p13suc1 by molecular dynamics simulations: implications for domain swapping

AbstractBackground: The p13suc1 gene product is a member of the cks (cyclin-dependent protein kinase subunit) protein family and has been implicated in regulation of the cell cycle. Various crystal structures of suc1 are available, including a globular, monomeric form and a β-strand exchanged dimer. It has been suggested that conversions between these forms, and perhaps others, may be important in the regulation of the cell cycle.Results: We have undertaken molecular dynamics simulations of protein unfolding to investigate the conformational properties of suc1. Unfolding transition states were identified for each of four simulations. These states contain some native secondary structure, primarily helix α1 and the core of the β sheet. The hydrophobic core is loosely packed. Further unfolding leads to an intermediate state that is slightly more expanded than the transition state, but with considerably fewer nonlocal, tertiary packing contacts and less secondary structure. The helices are fluctuating but partially formed in the denatured state and β2 and β4 remain associated.Conclusions: It appears that suc1 folds by a nucleation-condensation mechanism, similar to that observed for two-state folding proteins. However, suc1 forms an intermediate during unfolding and contains considerable residual structure in the denatured state. The stability of the β2–β4 residual structure is surprising, because β4 is the strand involved in domain swapping. This stability suggests that the domain-swapping event, if physiologically relevant, may require the assistance of additional factors in vivo or occur early in the folding process

Inferring Correlation Networks from Genomic Survey Data

High-throughput sequencing based techniques, such as 16S rRNA gene profiling, have the potential to elucidate the complex inner workings of natural microbial communities - be they from the world's oceans or the human gut. A key step in exploring such data is the identification of dependencies between members of these communities, which is commonly achieved by correlation analysis. However, it has been known since the days of Karl Pearson that the analysis of the type of data generated by such techniques (referred to as compositional data) can produce unreliable results since the observed data take the form of relative fractions of genes or species, rather than their absolute abundances. Using simulated and real data from the Human Microbiome Project, we show that such compositional effects can be widespread and severe: in some real data sets many of the correlations among taxa can be artifactual, and true correlations may even appear with opposite sign. Additionally, we show that community diversity is the key factor that modulates the acuteness of such compositional effects, and develop a new approach, called SparCC (available at https://bitbucket.org/yonatanf/sparcc), which is capable of estimating correlation values from compositional data. To illustrate a potential application of SparCC, we infer a rich ecological network connecting hundreds of interacting species across 18 sites on the human body. Using the SparCC network as a reference, we estimated that the standard approach yields 3 spurious species-species interactions for each true interaction and misses 60% of the true interactions in the human microbiome data, and, as predicted, most of the erroneous links are found in the samples with the lowest diversity.United States. Dept. of Energy (Contract DE-AC02-05CH11231

OpWise: Operons aid the identification of differentially expressed genes in bacterial microarray experiments

BACKGROUND: Differentially expressed genes are typically identified by analyzing the variation between replicate measurements. These procedures implicitly assume that there are no systematic errors in the data even though several sources of systematic error are known. RESULTS: OpWise estimates the amount of systematic error in bacterial microarray data by assuming that genes in the same operon have matching expression patterns. OpWise then performs a Bayesian analysis of a linear model to estimate significance. In simulations, OpWise corrects for systematic error and is robust to deviations from its assumptions. In several bacterial data sets, significant amounts of systematic error are present, and replicate-based approaches overstate the confidence of the changers dramatically, while OpWise does not. Finally, OpWise can identify additional changers by assigning genes higher confidence if they are consistent with other genes in the same operon. CONCLUSION: Although microarray data can contain large amounts of systematic error, operons provide an external standard and allow for reasonable estimates of significance. OpWise is available at

Comparing Patterns of Natural Selection across Species Using Selective Signatures

Comparing gene expression profiles over many different conditions has led to insights that were not obvious from single experiments. In the same way, comparing patterns of natural selection across a set of ecologically distinct species may extend what can be learned from individual genome-wide surveys. Toward this end, we show how variation in protein evolutionary rates, after correcting for genome-wide effects such as mutation rate and demographic factors, can be used to estimate the level and types of natural selection acting on genes across different species. We identify unusually rapidly and slowly evolving genes, relative to empirically derived genome-wide and gene family-specific background rates for 744 core protein families in 30 c-proteobacterial species. We describe the pattern of fast or slow evolution across species as the"selective signature" of a gene. Selective signatures represent aprofile of selection across species that is predictive of gene function: pairs of genes with correlated selective signatures are more likely to share the same cellular function, and genes in the same pathway can evolve in concert. For example,glycolysis and phenylalanine metabolism genes evolve rapidly in Idiomarina loihiensis, mirroring an ecological shift in carbon source from sugars to amino acids. In a broader context, our results suggest that the genomic landscape is organized into functional modules even at the level of natural selection, and thus it may be easier than expected to understand the complex evolutionary pressures on a cell

A novel method for accurate operon predictions in all sequenced prokaryotes

We combine comparative genomic measures and the distance separating adjacent genes to predict operons in 124 completely sequenced prokaryotic genomes. Our method automatically tailors itself to each genome using sequence information alone, and thus can be applied to any prokaryote. For Escherichia coli K12 and Bacillus subtilis, our method is 85 and 83% accurate, respectively, which is similar to the accuracy of methods that use the same features but are trained on experimentally characterized transcripts. In Halobacterium NRC-1 and in Helicobacter pylori, our method correctly infers that genes in operons are separated by shorter distances than they are in E.coli, and its predictions using distance alone are more accurate than distance-only predictions trained on a database of E.coli transcripts. We use microarray data from six phylogenetically diverse prokaryotes to show that combining intergenic distance with comparative genomic measures further improves accuracy and that our method is broadly effective. Finally, we survey operon structure across 124 genomes, and find several surprises: H.pylori has many operons, contrary to previous reports; Bacillus anthracis has an unusual number of pseudogenes within conserved operons; and Synechocystis PCC 6803 has many operons even though it has unusually wide spacings between conserved adjacent genes

On the function of lead (Pb) in machining brass alloys

Lead has traditionally been added to brass alloys to achieve high machinability, but the exact mechanisms at work are still debated. Lead-free brass alternatives could be developed if these mechanisms were better understood. Accordingly, machinability characteristics were investigated for two brass alloys with similar mechanical properties and phase composition, but with very different machining characteristics because one has 3 wt.% lead (CuZn38Pb3) while the other has only 0.1 wt.% (CuZn42). The effect of the lead was investigated using infrared temperature measurement, electron microscopy, secondary ion mass spectroscopy, quick-stop methods, and high-speed filming. Neither melting of lead nor its deposition on the tool rake surface takes place during machining thus confirming its limited lubrication and tribological effects. Instead, the main role of lead is to promote discontinuous chip formation. Lead deforms to flake-like shapes that act as crack initiation points when the workpiece material passes through the primary deformation zone. This effect prevents the development of stable tool–chip contact, thus lowering cutting forces, friction, and process temperature

Analisis Faktor yang Mempengaruhi Pendapatan USAhatani Sayuran di Kecamatan Sungai Gelam Kabupaten Muaro Jambi

Penelitian ini bertujuan untuk melihat besarnya pendapatan USAhatani sayuran di Kecamatan Sungai Gelam Kabupaten Muaro Jambi. Pemilihan lokasi penelitian dilakukan dengan sengaja atas dasar pertimbangan bahwa di Kecamatan Sungai Gelam merupakan salah satu daerah yang mengusahakan sayuran terbesar di Kabupaten Muaro Jambi.Sampel dalam penelitian ini adalah petani sayuran di Kecamatan Sungai Gelam Kabupaten Muaro Jambi.Penelitian dilakukan dari tanggal 10 September 2014 sampai dengan tanggal 10 Oktober 2014 dengan menggunakan metode simple random sampling. Hasil penelitian ini menunjukkan bahwa Rata – rata pendapatan USAhatani sayuran petani responden di daerah penelitian yaitu Rp. 21.673.293,87 /Tahun dengan rata – rata luas lahan sebesar 0,26 ha. Data ini menunjukkan bahwa kegiatan USAhatani sayuran yang dilakukan petani di Kecamatan Sungai Gelam Masih berskala kecil.Pendapatan USAhatani sayuran di daerah penelitian secara nyata dipengaruhi oleh variabel luas lahan dan modal dengan nilai koefisien positif.Hal ini berarti semakin tinggi luas lahan dan modal yang digunakan, maka pendapatan USAhatani sayuran tinggi.Sedangkan tenaga kerja tidak memberikan pengaruh secara nyata terhadap pendapatan USAhatani sayuran