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      The bovine lactation genome: insights into the evolution of mammalian milk


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          Comparison of milk protein and mammary genes in the bovine genome with those from other mammals gives insights into the evolution of lactation.



          The newly assembled Bos taurus genome sequence enables the linkage of bovine milk and lactation data with other mammalian genomes.


          Using publicly available milk proteome data and mammary expressed sequence tags, 197 milk protein genes and over 6,000 mammary genes were identified in the bovine genome. Intersection of these genes with 238 milk production quantitative trait loci curated from the literature decreased the search space for milk trait effectors by more than an order of magnitude. Genome location analysis revealed a tendency for milk protein genes to be clustered with other mammary genes. Using the genomes of a monotreme (platypus), a marsupial (opossum), and five placental mammals (bovine, human, dog, mice, rat), gene loss and duplication, phylogeny, sequence conservation, and evolution were examined. Compared with other genes in the bovine genome, milk and mammary genes are: more likely to be present in all mammals; more likely to be duplicated in therians; more highly conserved across Mammalia; and evolving more slowly along the bovine lineage. The most divergent proteins in milk were associated with nutritional and immunological components of milk, whereas highly conserved proteins were associated with secretory processes.


          Although both copy number and sequence variation contribute to the diversity of milk protein composition across species, our results suggest that this diversity is primarily due to other mechanisms. Our findings support the essentiality of milk to the survival of mammalian neonates and the establishment of milk secretory mechanisms more than 160 million years ago.

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          Cluster analysis and display of genome-wide expression patterns.

          A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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            An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans.

            Two small temporal RNAs (stRNAs), lin-4 and let-7, control developmental timing in Caenorhabditis elegans. We find that these two regulatory RNAs are members of a large class of 21- to 24-nucleotide noncoding RNAs, called microRNAs (miRNAs). We report on 55 previously unknown miRNAs in C. elegans. The miRNAs have diverse expression patterns during development: a let-7 paralog is temporally coexpressed with let-7; miRNAs encoded in a single genomic cluster are coexpressed during embryogenesis; and still other miRNAs are expressed constitutively throughout development. Potential orthologs of several of these miRNA genes were identified in Drosophila and human genomes. The abundance of these tiny RNAs, their expression patterns, and their evolutionary conservation imply that, as a class, miRNAs have broad regulatory functions in animals.
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              The delayed rise of present-day mammals.

              Did the end-Cretaceous mass extinction event, by eliminating non-avian dinosaurs and most of the existing fauna, trigger the evolutionary radiation of present-day mammals? Here we construct, date and analyse a species-level phylogeny of nearly all extant Mammalia to bring a new perspective to this question. Our analyses of how extant lineages accumulated through time show that net per-lineage diversification rates barely changed across the Cretaceous/Tertiary boundary. Instead, these rates spiked significantly with the origins of the currently recognized placental superorders and orders approximately 93 million years ago, before falling and remaining low until accelerating again throughout the Eocene and Oligocene epochs. Our results show that the phylogenetic 'fuses' leading to the explosion of extant placental orders are not only very much longer than suspected previously, but also challenge the hypothesis that the end-Cretaceous mass extinction event had a major, direct influence on the diversification of today's mammals.

                Author and article information

                Genome Biol
                Genome Biology
                BioMed Central
                24 April 2009
                : 10
                : 4
                : R43
                [1 ]Department of Food Science and Technology, University of California Davis, One Shields Avenue, Davis, CA 95616, USA
                [2 ]Department of Molecular Biology and Biochemistry, Simon Fraser University, University Drive, Burnaby, BC, V5A 1S6, Canada
                [3 ]Department of Physiology and Biophysics, University of Colorado Denver, Anschutz Medical Center, E. 19th Ave, Aurora CO 80045, USA
                [4 ]Department of Animal Science, Michigan State University, East Lansing, MI 48824-1225, USA
                [5 ]Department of Animal Science, University of California Davis, One Shields Avenue, Davis, CA 95616, USA
                [6 ]Department of Structural Biology and Bioinformatics, University of Geneva Medical School, rue Michel-Servet, 1211 Geneva, Switzerland
                [7 ]CSIRO Livestock Industries, Queensland Bioscience Precinct, Carmody Road, St Lucia, Queensland 4067, Australia
                [8 ]Center for Biomolecular Science and Engineering, University of California Santa Cruz, High St, Santa Cruz, CA 95064, USA
                [9 ]Dairy Science and Technology, AgResearch, Ruakura Research Centre, East Street, Hamilton, 3240, New Zealand
                [10 ]Division of Biostatistics and Gladstone Institutes, University of California San Francisco, Owens St, San Francisco, CA 94158, USA
                [11 ]Bioinformatics, Mathematics and Statistics, AgResearch, Invermay Agricultural Centre, Puddle Alley, Mosgiel 9053, New Zealand
                [12 ]Department of Genetic Medicine and Development, University of Geneva Medical School, rue Michel-Servet, 1211 Geneva, Switzerland
                [13 ]Swiss Institute of Bioinformatics, rue Michel-Servet, 1211 Geneva, Switzerland
                [14 ]Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
                [15 ]Nestlé Research Centre, Vers-chez-les-Blanc CH-1000, Lausanne 26, Switzerland
                [16 ]Department of Pediatrics, Children's Nutrition Research Center, Baylor College of Medicine, Bates Street, Houston TX 77030, USA
                Copyright © 2009 Lemay et al.; licensee BioMed Central Ltd.

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.




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