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

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

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

High resolution time series reveals cohesive but short-lived communities in coastal plankton

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Nature Communications 9 (2018): 266, doi:10.1038/s41467-017-02571-4.Because microbial plankton in the ocean comprise diverse bacteria, algae, and protists that are subject to environmental forcing on multiple spatial and temporal scales, a fundamental open question is to what extent these organisms form ecologically cohesive communities. Here we show that although all taxa undergo large, near daily fluctuations in abundance, microbial plankton are organized into clearly defined communities whose turnover is rapid and sharp. We analyze a time series of 93 consecutive days of coastal plankton using a technique that allows inference of communities as modular units of interacting taxa by determining positive and negative correlations at different temporal frequencies. This approach shows both coordinated population expansions that demarcate community boundaries and high frequency of positive and negative associations among populations within communities. Our analysis thus highlights that the environmental variability of the coastal ocean is mirrored in sharp transitions of defined but ephemeral communities of organisms.This work was supported by grants from the U.S. National Science Foundation (OCE-1441943) to M.F.P. and the U.S. Department of Energy (DE-SC0008743) to M.F.P. and E.J.A. A.M.M.-P. was partially supported by the Ramon Areces foundation through a postdoctoral fellowship. D.J.M. was supported by the U.S. National Science Foundation (OCE-1314642) and National Institute of Environmental Health Sciences (1P01ES021923-01) through the Woods Hole Center for Oceans and Human Health

Dissimilatory Metabolism of Nitrogen Oxides in Bacteria: Comparative Reconstruction of Transcriptional Networks

Bacterial response to nitric oxide (NO) is of major importance since NO is an obligatory intermediate of the nitrogen cycle. Transcriptional regulation of the dissimilatory nitric oxides metabolism in bacteria is diverse and involves FNR-like transcription factors HcpR, DNR, and NnrR; two-component systems NarXL and NarQP; NO-responsive activator NorR; and nitrite-sensitive repressor NsrR. Using comparative genomics approaches, we predict DNA-binding motifs for these transcriptional factors and describe corresponding regulons in available bacterial genomes. Within the FNR family of regulators, we observed a correlation of two specificity-determining amino acids and contacting bases in corresponding DNA recognition motif. Highly conserved regulon HcpR for the hybrid cluster protein and some other redox enzymes is present in diverse anaerobic bacteria, including Clostridia, Thermotogales, and delta-proteobacteria. NnrR and DNR control denitrification in alpha- and beta-proteobacteria, respectively. Sigma-54-dependent NorR regulon found in some gamma- and beta-proteobacteria contains various enzymes involved in the NO detoxification. Repressor NsrR, which was previously known to control only nitrite reductase operon in Nitrosomonas spp., appears to be the master regulator of the nitric oxides' metabolism, not only in most gamma- and beta-proteobacteria (including well-studied species such as Escherichia coli), but also in Gram-positive Bacillus and Streptomyces species. Positional analysis and comparison of regulatory regions of NO detoxification genes allows us to propose the candidate NsrR-binding motif. The most conserved member of the predicted NsrR regulon is the NO-detoxifying flavohemoglobin Hmp. In enterobacteria, the regulon also includes two nitrite-responsive loci, nipAB (hcp-hcr) and nipC (dnrN), thus confirming the identity of the effector, i.e. nitrite. The proposed NsrR regulons in Neisseria and some other species are extended to include denitrification genes. As the result, we demonstrate considerable interconnection between various nitrogen-oxides-responsive regulatory systems for the denitrification and NO detoxification genes and evolutionary plasticity of this transcriptional network

‘Hygienic’ Lymphocytes Convey Increased Cancer Risk

Risk of developing inflammation-associated cancers has increased in industrialized countries during the past 30 years. One possible explanation is societal hygiene practices with use of antibiotics and Caesarian births that provide too few early life exposures of beneficial microbes. Building upon a ‘hygiene hypothesis’ model whereby prior microbial exposures lead to beneficial changes in CD4+ lymphocytes, here we use an adoptive cell transfer model and find that too few prior microbe exposures alternatively result in increased inflammation-associated cancer growth in susceptible recipient mice. Specifically, purified CD4+ lymphocytes collected from ‘restricted flora’ donors increases multiplicity and features of malignancy in intestinal polyps of recipient Apc[superscript Min/+] mice, coincident with increased inflammatory cell infiltrates and instability of the intestinal microbiota. We conclude that while a competent immune system serves to maintain intestinal homeostasis and good health, under hygienic rearing conditions CD4+ lymphocytes instead exacerbate inflammation-associated tumorigenesis, subsequently contributing to more frequent cancers in industrialized societies.National Institutes of Health (U.S.) (Grant P30-ES002109)National Institutes of Health (U.S.) (Grant U01 CA164337)National Institutes of Health (U.S.) (Grant RO1CA108854

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 γ-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