1. Evol Dev. 2015 Jan;17(1):120-6. doi: 10.1111/ede.12113. From shavenbaby to the naked valley: trichome formation as a model for evolutionary developmental biology. Arif S(1), Kittelmann S, McGregor AP. Author information: (1)Friedrich Meischer Laboratory of the Max Planck Society, Spemannstrasse 39, Tuebingen, 72076, Germany. Microtrichia or trichomes are non-sensory actin protrusions produced by the epidermal cells of many insects. Studies of trichome formation in Drosophila have over the last 30 years provided key insights towards our understanding of gene regulation, gene regulatory networks (GRNs), development, the genotype to phenotype map, and the evolution of these processes. Here we review classic studies that have used trichome formation as a model to shed light on Drosophila development as well as recent research on the architecture of the GRN underlying trichome formation. This includes the findings that both small peptides and microRNAs play important roles in the regulation and evolution of this network. In addition, we review research on the evolution of trichome patterns that has provided novel insights into the function and architecture of cis-regulatory modules, and into the genetic basis of morphological change. We conclude that further research on these apparently simple and often functionally enigmatic structures will continue to provide new and important knowledge about development and evolution. © 2014 Wiley Periodicals, Inc. PMID: 25627718 [PubMed - in process] 2. Dev Dyn. 2015 Jan 23. doi: 10.1002/dvdy.24255. [Epub ahead of print] Making quantitative morphological variation from basic developmental processes: Where are we? The case of the Drosophila wing. Alexis MV(1), Isaac SC, David H. Author information: (1)Department of Biological Science, Florida State University, Tallahassee, Florida, United States, 32306; Genomics, Bioinformatics and Evolution Group. Department de Genètica i Microbiologia, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, 08193, Spain. One of the aims of evolutionary developmental biology is to discover the developmental origins of morphological variation. The discipline has mainly focused on qualitative morphological differences (e.g., presence or absence of a structure) between species. Studies addressing subtle, quantitative variation are less common. The Drosophila wing is a model for the study of development and evolution, making it suitable to investigate the developmental mechanisms underlying the subtle quantitative morphological variation observed in nature. Previous reviews have focused on the processes involved in wing differentiation, patterning and growth. Here, we investigate what is known about how the wing achieves its final shape, and what variation in development is capable of generating the variation in wing shape observed in nature. Three major developmental stages need to be considered: larval development, pupariation, and pupal development. The major cellular processes involved in the determination of tissue size and shape are cell proliferation, cell death, oriented cell division and oriented cell intercalation. We review how variation in temporal and spatial distribution of growth and transcription factors affects these cellular mechanisms, which in turn affects wing shape. We then discuss which aspects of the wing morphological variation are predictable on the basis of these mechanisms. This article is protected by copyright. All rights reserved. ©2015. Wiley Periodicals, Inc. PMID: 25619644 [PubMed - as supplied by publisher] 3. Science. 2015 Jan 23;347(6220):395-9. doi: 10.1126/science.1261735. Human evolution. Human-like hand use in Australopithecus africanus. Skinner MM(1), Stephens NB(2), Tsegai ZJ(2), Foote AC(3), Nguyen NH(2), Gross T(4), Pahr DH(4), Hublin JJ(2), Kivell TL(5). Author information: (1)School of Anthropology and Conservation, University of Kent, Canterbury CT2 7NR, UK. Department of Anthropology, University College London, London WC1H 0BW, UK. Department of Human Evolution, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig Germany. Evolutionary Studies Institute and Centre for Excellence in PalaeoSciences, University of the Witwatersrand, Private Bag 3, Wits 2050, South Africa. m.skinner@kent.ac.uk t.l.kivell@kent.ac.uk. (2)Department of Human Evolution, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig Germany. (3)Department of Anthropology, University College London, London WC1H 0BW, UK. (4)Institute of Lightweight Design and Structural Biomechanics, Vienna University of Technology, Gusshausstrasse 27-29, 1040 Wien, Vienna, Austria. (5)School of Anthropology and Conservation, University of Kent, Canterbury CT2 7NR, UK. Department of Human Evolution, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig Germany. Evolutionary Studies Institute and Centre for Excellence in PalaeoSciences, University of the Witwatersrand, Private Bag 3, Wits 2050, South Africa. m.skinner@kent.ac.uk t.l.kivell@kent.ac.uk. The distinctly human ability for forceful precision and power "squeeze" gripping is linked to two key evolutionary transitions in hand use: a reduction in arboreal climbing and the manufacture and use of tools. However, it is unclear when these locomotory and manipulative transitions occurred. Here we show that Australopithecus africanus (~3 to 2 million years ago) and several Pleistocene hominins, traditionally considered not to have engaged in habitual tool manufacture, have a human-like trabecular bone pattern in the metacarpals consistent with forceful opposition of the thumb and fingers typically adopted during tool use. These results support archaeological evidence for stone tool use in australopiths and provide morphological evidence that Pliocene hominins achieved human-like hand postures much earlier and more frequently than previously considered. Copyright © 2015, American Association for the Advancement of Science. PMID: 25613885 [PubMed - in process] 4. Dev Genes Evol. 2015 Jan;225(1):23-30. doi: 10.1007/s00427-015-0491-6. Epub 2015 Jan 23. The comparative study of five sex-determining proteins across insects unveils high rates of evolution at basal components of the sex determination cascade. Eirín-López JM(1), Sánchez L. Author information: (1)CHROMEVOL Group, Department of Biological Sciences, Florida International University, Marine Sciences Program, Biscayne Bay Campus, 3000 NE 151 St., Suite MSB-360, North Miami, FL, 33181, USA, jeirinlo@fiu.edu. In insects, the sex determination cascade is composed of genes that interact with each other in a strict hierarchical manner, constituting a coadapted gene complex built in reverse order from bottom to top. Accordingly, ancient elements at the bottom are expected to remain conserved ensuring the correct functionality of the cascade. In the present work, we have studied the levels of variation displayed by five key components of the sex determination cascade across 59 insect species, including Sex-lethal, transformer, transformer-2, fruitless, doublesex, and sister-of-Sex-lethal (a paralog of Sxl encompassing sex-independent functions). Surprisingly, our results reveal that basal components of the cascade (doublesex, fruitless) seem to evolve more rapidly than previously suspected. Indeed, in the case of Drosophila, these proteins evolve more rapidly than the master regulator Sex-lethal. These results agree with the notion suggesting that genes involved in early aspects of development will be more constrained due to the large deleterious pleiotropic effects of mutations, resulting in increased levels of purifying selection at top positions of the cascade. The analyses of the selective episodes involved in the recruitment of Sxl into sex-determining functions further support this idea, suggesting the presence of bursts of adaptive selection in the common ancestor of drosophilids, followed by the onset of purifying selection preserving the master regulatory role of this protein on top of the Drosophila sex determination cascade. Altogether, these results underscore the importance of the position of sex determining genes in the cascade, constituting a major constraint shaping the molecular evolution of the insect sex determination pathway. PMID: 25613749 [PubMed - in process] 5. PLoS Genet. 2015 Jan 22;11(1):e1004919. doi: 10.1371/journal.pgen.1004919. eCollection 2015. Evolutionary constraint and disease associations of post-translational modification sites in human genomes. Reimand J(1), Wagih O(1), Bader GD(1). Author information: (1)The Donnelly Centre, University of Toronto, Canada. Interpreting the impact of human genome variation on phenotype is challenging. The functional effect of protein-coding variants is often predicted using sequence conservation and population frequency data, however other factors are likely relevant. We hypothesized that variants in protein post-translational modification (PTM) sites contribute to phenotype variation and disease. We analyzed fraction of rare variants and non-synonymous to synonymous variant ratio (Ka/Ks) in 7,500 human genomes and found a significant negative selection signal in PTM regions independent of six factors, including conservation, codon usage, and GC-content, that is widely distributed across tissue-specific genes and function classes. PTM regions are also enriched in known disease mutations, suggesting that PTM variation is more likely deleterious. PTM constraint also affects flanking sequence around modified residues and increases around clustered sites, indicating presence of functionally important short linear motifs. Using target site motifs of 124 kinases, we predict that at least ∼180,000 motif-breaker amino acid residues that disrupt PTM sites when substituted, and highlight kinase motifs that show specific negative selection and enrichment of disease mutations. We provide this dataset with corresponding hypothesized mechanisms as a community resource. As an example of our integrative approach, we propose that PTPN11 variants in Noonan syndrome aberrantly activate the protein by disrupting an uncharacterized cluster of phosphorylation sites. Further, as PTMs are molecular switches that are modulated by drugs, we study mutated binding sites of PTM enzymes in disease genes and define a drug-disease network containing 413 novel predicted disease-gene links. PMCID: PMC4303425 PMID: 25611800 [PubMed - in process] 6. J Evol Biol. 2015 Jan 22. doi: 10.1111/jeb.12588. [Epub ahead of print] The evolution of reproductive isolation in the Drosophila yakuba complex of species. Turissini DA(1), Liu G, David JR, Matute DR. Author information: (1)Biology Department, University of North Carolina, Chapel Hill. In the Drosophila melanogaster subgroup, the yakuba species complex, D. yakuba, D. santomea and D. teissieri have identical mitochondrial genomes in spite of nuclear differentiation. The first two species can be readily hybridized in the laboratory, and produce fertile females and sterile males. They also form hybrids in natural conditions. Nonetheless, the third species, D. teissieri, was thought to be unable to produce hybrids with either D. yakuba or D. santomea. This in turn posed the conundrum of why the three species shared a single mitochondrial genome. In this report we show that D. teissieri can indeed hybridize with both D. yakuba and D. santomea. The resulting female hybrids from both crosses are fertile, while the hybrid males are sterile. We also characterize six isolating mechanisms that might be involved in keeping the three species apart. Our results open the possibility of studying the history of introgression in the yakuba species complex and dissecting the genetic basis of interspecific differences between these three species by genetic mapping. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved. PMID: 25611516 [PubMed - as supplied by publisher] 7. Science. 2015 Jan 16;347(6219):220-1. doi: 10.1126/science.347.6219.220. Evolution. All in the (bigger) family. Pennisi E. PMID: 25593165 [PubMed - indexed for MEDLINE] 8. Nature. 2015 Jan 15;517(7534):243. doi: 10.1038/517243a. Science and satire. [No authors listed] PMID: 25592495 [PubMed - indexed for MEDLINE] 9. Genome Res. 2015 Jan 14. pii: gr.185579.114. [Epub ahead of print] The Release 6 reference sequence of the Drosophila melanogaster genome. Hoskins RA(1), Carlson JW(1), Wan KH(1), Park S(1), Mendez I(1), Galle SE(1), Booth BW(1), Pfeiffer BD(2), George RA(2), Svirskas R(2), Krzywinski M(3), Schein J(3), Accardo MC(4), Damia E(4), Messina G(4), Méndez-Lago M(5), de Pablos B(5), Demakova OV(6), Andreyeva EN(6), Boldyreva LV(6), Marra M(3), Carvalho AB(7), Dimitri P(4), Villasante A(5), Zhimulev IF(6), Rubin GM(2), Karpen GH(1), Celniker SE(8). Author information: (1)Lawrence Berkeley National Laboratory; (2)Janelia Farm Research Campus; (3)BC Cancer Agency; (4)Sapienza Universitá di Roma; (5)Universidad Autónoma de Madrid; (6)Russian Academy of Sciences; (7)Universidade Federal do Rio de Janeiro. (8)Lawrence Berkeley National Laboratory; celniker@fruitfly.org. Drosophila melanogaster plays an important role in molecular, genetic and genomic studies of heredity, development, metabolism, behavior and human disease. The initial reference genome sequence reported more than a decade ago had a profound impact on progress in Drosophila research, and improving the accuracy and completeness of this sequence continues to be important to further progress. We previously described improvement of the 117 Mb sequence in the euchromatic portion of the genome and 21 Mb in the heterochromatic portion, using a whole genome shotgun assembly, BAC physical mapping, and clone-based finishing. Here, we report an improved reference sequence of the single-copy and middle-repetitive regions of the genome, produced using cytogenetic mapping to mitotic and polytene chromosomes, clone-based finishing and BAC fingerprint verification, ordering of scaffolds by alignment to cDNA sequences, incorporation of other map and sequence data, and validation by whole genome optical restriction mapping. These data substantially improve the accuracy and completeness of the reference sequence, and the order and orientation of sequence scaffolds into chromosome arm assemblies. Representation of the Y chromosome and other heterochromatic regions is particularly improved. The new 143.9 Mb reference sequence, designated Release 6, effectively exhausts clone-based technologies for mapping and sequencing. Highly repeat-rich regions including large satellite blocks and functional elements such as the ribosomal RNA genes and the centromeres are largely inaccessible to current sequencing and assembly methods and remain poorly represented. Further significant improvements will require sequencing technologies that do not depend on molecular cloning and that produce very long reads. Published by Cold Spring Harbor Laboratory Press. PMID: 25589440 [PubMed - as supplied by publisher] 10. Proc Natl Acad Sci U S A. 2015 Jan 27;112(4):1095-100. doi: 10.1073/pnas.1423628112. Epub 2015 Jan 12. The draft genome of Tibetan hulless barley reveals adaptive patterns to the high stressful Tibetan Plateau. Zeng X(1), Long H(2), Wang Z(3), Zhao S(3), Tang Y(1), Huang Z(3), Wang Y(1), Xu Q(1), Mao L(3), Deng G(2), Yao X(3), Li X(4), Bai L(3), Yuan H(1), Pan Z(2), Liu R(1), Chen X(2), WangMu Q(1), Chen M(3), Yu L(3), Liang J(2), DunZhu D(1), Zheng Y(3), Yu S(2), LuoBu Z(1), Guang X(3), Li J(3), Deng C(3), Hu W(3), Chen C(3), TaBa X(1), Gao L(1), Lv X(3), Abu YB(5), Fang X(3), Nevo E(6), Yu M(7), Wang J(8), Tashi N(9). Author information: (1)Tibet Academy of Agricultural and Animal Husbandry Sciences, Lhasa, Tibet 850002, China; Barley Improvement and Yak Breeding Key Laboratory of Tibet Autonomous Region, Lhasa 850002, China; (2)Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, P. R. China; (3)BGI-Tech, BGI-Shenzhen, Shenzhen 518083, China; (4)BGI-Tech, BGI-Shenzhen, Shenzhen 518083, China; College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China; (5)Projects and Physics Section, Sapir Academic College, D.N. Hof Ashkelon 79165, Israel; (6)Institute of Evolution, University of Haifa, Mount Carmel, Haifa 31905, Israel; nima_zhaxi@sina.com nevo@research.haifa.ac.il yumaoqun@cib.ac.cn wangj@genomics.org.cn. (7)Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, P. R. China; nima_zhaxi@sina.com nevo@research.haifa.ac.il yumaoqun@cib.ac.cn wangj@genomics.org.cn. (8)BGI-Shenzhen, Shenzhen 518083, China; Department of Biology, University of Copenhagen, Copenhagen 2200, Denmark; and Princess Al Jawhara Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah 21441, Saudi Arabia nima_zhaxi@sina.com nevo@research.haifa.ac.il yumaoqun@cib.ac.cn wangj@genomics.org.cn. (9)Tibet Academy of Agricultural and Animal Husbandry Sciences, Lhasa, Tibet 850002, China; Barley Improvement and Yak Breeding Key Laboratory of Tibet Autonomous Region, Lhasa 850002, China; nima_zhaxi@sina.com nevo@research.haifa.ac.il yumaoqun@cib.ac.cn wangj@genomics.org.cn. The Tibetan hulless barley (Hordeum vulgare L. var. nudum), also called "Qingke" in Chinese and "Ne" in Tibetan, is the staple food for Tibetans and an important livestock feed in the Tibetan Plateau. The diploid nature and adaptation to diverse environments of the highland give it unique resources for genetic research and crop improvement. Here we produced a 3.89-Gb draft assembly of Tibetan hulless barley with 36,151 predicted protein-coding genes. Comparative analyses revealed the divergence times and synteny between barley and other representative Poaceae genomes. The expansion of the gene family related to stress responses was found in Tibetan hulless barley. Resequencing of 10 barley accessions uncovered high levels of genetic variation in Tibetan wild barley and genetic divergence between Tibetan and non-Tibetan barley genomes. Selective sweep analyses demonstrate adaptive correlations of genes under selection with extensive environmental variables. Our results not only construct a genomic framework for crop improvement but also provide evolutionary insights of highland adaptation of Tibetan hulless barley. PMID: 25583503 [PubMed - in process] 11. Proc Natl Acad Sci U S A. 2015 Jan 8. pii: 201423275. [Epub ahead of print] Causes of natural variation in fitness: Evidence from studies of Drosophila populations. Charlesworth B(1). Author information: (1)Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JT, United Kingdom brian.charlesworth@ed.ac.uk. DNA sequencing has revealed high levels of variability within most species. Statistical methods based on population genetics theory have been applied to the resulting data and suggest that most mutations affecting functionally important sequences are deleterious but subject to very weak selection. Quantitative genetic studies have provided information on the extent of genetic variation within populations in traits related to fitness and the rate at which variability in these traits arises by mutation. This paper attempts to combine the available information from applications of the two approaches to populations of the fruitfly Drosophila in order to estimate some important parameters of genetic variation, using a simple population genetics model of mutational effects on fitness components. Analyses based on this model suggest the existence of a class of mutations with much larger fitness effects than those inferred from sequence variability and that contribute most of the standing variation in fitness within a population caused by the input of mildly deleterious mutations. However, deleterious mutations explain only part of this standing variation, and other processes such as balancing selection appear to make a large contribution to genetic variation in fitness components in Drosophila. PMID: 25572964 [PubMed - as supplied by publisher] 12. PLoS Genet. 2015 Jan 8;11(1):e1004893. doi: 10.1371/journal.pgen.1004893. eCollection 2015. Transcriptional control of an essential ribozyme in Drosophila reveals an ancient evolutionary divide in animals. Manivannan SN(1), Lai LB(2), Gopalan V(3), Simcox A(1). Author information: (1)Molecular Cellular Developmental Biology Program, Ohio State University, Columbus, Ohio, United States of America; Department of Molecular Genetics, Ohio State University, Columbus, Ohio, United States of America. (2)Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio, United States of America; Center for RNA Biology, Ohio State University, Columbus, Ohio, United States of America. (3)Molecular Cellular Developmental Biology Program, Ohio State University, Columbus, Ohio, United States of America; Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio, United States of America; Center for RNA Biology, Ohio State University, Columbus, Ohio, United States of America. Ribonuclease P (RNase P) is an essential enzyme required for 5'-maturation of tRNA. While an RNA-free, protein-based form of RNase P exists in eukaryotes, the ribonucleoprotein (RNP) form is found in all domains of life. The catalytic component of the RNP is an RNA known as RNase P RNA (RPR). Eukaryotic RPR genes are typically transcribed by RNA polymerase III (pol III). Here we showed that the RPR gene in Drosophila, which is annotated in the intron of a pol II-transcribed protein-coding gene, lacks signals for transcription by pol III. Using reporter gene constructs that include the RPR-coding intron from Drosophila, we found that the intron contains all the sequences necessary for production of mature RPR but is dependent on the promoter of the recipient gene for expression. We also demonstrated that the intron-coded RPR copurifies with RNase P and is required for its activity. Analysis of RPR genes in various animal genomes revealed a striking divide in the animal kingdom that separates insects and crustaceans into a single group in which RPR genes lack signals for independent transcription and are embedded in different protein-coding genes. Our findings provide evidence for a genetic event that occurred approximately 500 million years ago in the arthropod lineage, which switched the control of the transcription of RPR from pol III to pol II. PMCID: PMC4287351 PMID: 25569672 [PubMed - in process] 13. PLoS Genet. 2015 Jan 8;11(1):e1004911. doi: 10.1371/journal.pgen.1004911. eCollection 2015. A Re-examination of the Selection of the Sensory Organ Precursor of the Bristle Sensilla of Drosophila melanogaster. Troost T(1), Schneider M(1), Klein T(1). Author information: (1)Institut fuer Genetik, Heinrich-Heine-Universitaet Duesseldorf, Duesseldorf, Germany. The bristle sensillum of the imago of Drosophila is made of four cells that arise from a sensory organ precursor cell (SOP). This SOP is selected within proneural clusters (PNC) through a mechanism that involves Notch signalling. PNCs are defined through the expression domains of the proneural genes, whose activities enables cells to become SOPs. They encode tissue specific bHLH proteins that form functional heterodimers with the bHLH protein Daughterless (Da). In the prevailing lateral inhibition model for SOP selection, a transcriptional feedback loop that involves the Notch pathway amplifies small differences of proneural activity between cells of the PNC. As a result only one or two cells accumulate sufficient proneural activity to adopt the SOP fate. Most of the experiments that sustained the prevailing lateral inhibition model were performed a decade ago. We here re-examined the selection process using recently available reagents. Our data suggest a different picture of SOP selection. They indicate that a band-like region of proneural activity exists. In this proneural band the activity of the Notch pathway is required in combination with Emc to define the PNCs. We found a sub-group in the PNCs from which a pre-selected SOP arises. Our data indicate that most imaginal disc cells are able to adopt a proneural state from which they can progress to become SOPs. They further show that bristle formation can occur in the absence of the proneural genes if the function of emc is abolished. These results suggest that the tissue specific proneural proteins of Drosophila have a similar function as in the vertebrates, which is to determine the time of emergence and position of the SOP and to stabilise the proneural state. PMCID: PMC4287480 PMID: 25569355 [PubMed - in process] 14. PLoS Genet. 2015 Jan 8;11(1):e1004857. doi: 10.1371/journal.pgen.1004857. eCollection 2015. The Genetic and Mechanistic Basis for Variation in Gene Regulation. Pai AA(1), Pritchard JK(2), Gilad Y(3). Author information: (1)Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America. (2)Departments of Genetics and Biology, and Howard Hughes Medical Institute; Stanford University, Stanford, California, United States of America. (3)Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America. It is now well established that noncoding regulatory variants play a central role in the genetics of common diseases and in evolution. However, until recently, we have known little about the mechanisms by which most regulatory variants act. For instance, what types of functional elements in DNA, RNA, or proteins are most often affected by regulatory variants? Which stages of gene regulation are typically altered? How can we predict which variants are most likely to impact regulation in a given cell type? Recent studies, in many cases using quantitative trait loci (QTL)-mapping approaches in cell lines or tissue samples, have provided us with considerable insight into the properties of genetic loci that have regulatory roles. Such studies have uncovered novel biochemical regulatory interactions and led to the identification of previously unrecognized regulatory mechanisms. We have learned that genetic variation is often directly associated with variation in regulatory activities (namely, we can map regulatory QTLs, not just expression QTLs [eQTLs]), and we have taken the first steps towards understanding the causal order of regulatory events (for example, the role of pioneer transcription factors). Yet, in most cases, we still do not know how to interpret overlapping combinations of regulatory interactions, and we are still far from being able to predict how variation in regulatory mechanisms is propagated through a chain of interactions to eventually result in changes in gene expression profiles. PMCID: PMC4287341 PMID: 25569255 [PubMed - as supplied by publisher] 15. Mol Biol Evol. 2015 Jan 6. pii: msu404. [Epub ahead of print] Adaptive Evolution of Signaling Partners. Urano D(1), Dong T(2), Bennetzen JL(2), Jones AM(3). Author information: (1)Department of Biology and Pharmacology, University of North Carolina, Chapel Hill, NC 27599-3280, United States of America. (2)Department of Genetics, University of Georgia, Athens, Georgia, United States of America. (3)Department of Biology and Pharmacology, University of North Carolina, Chapel Hill, NC 27599-3280, United States of America Department of Biology and Pharmacology, University of North Carolina, Chapel Hill, NC 27599-3280, United States of America alan_jones@unc.edu. Proteins that interact co-evolve their structures. When mutation disrupts the interaction, compensation by the partner occurs to restore interaction otherwise counter selection occurs. We show in this study how a destabilizing mutation in one protein is compensated by a stabilizing mutation in its protein partner and their co-evolving path. The pathway in this case and likely a general principle of co-evolution is that the compensatory change must tolerate both the original and derived structures with equivalence in function and activity. Evolution of the structure of signaling elements in a network is constrained by specific protein pair interactions, by requisite conformational changes, and by catalytic activity. The heterotrimeric G protein-coupled signaling is a paragon of this protein interaction/function complexity and our deep understanding of this pathway in diverse organisms lends itself to evolutionary study. Regulators of G protein Signaling (RGS) proteins accelerate the intrinsic GTP hydrolysis rate of the Gα subunit of the heterotrimeric G protein complex. An important RGS-contact site is a hydroxyl-bearing residue on the switch I region of Gα subunits in animals and most plants such as Arabidopsis. The exception is the grasses (e.g. rice, maize, sugarcane, millets); these plants have Gα subunits that replaced the critical hydroxyl-bearing threonine with a destabilizing asparagine shown to disrupt interaction between Arabidopsis RGS protein (AtRGS1) and the grass Gα subunit. With one known exception (Setaria italica), grasses do not encode RGS genes. One parsimonious deduction is that the RGS gene was lost in the ancestor to the grasses and then recently acquired horizontally in the lineage S. italica from a non-grass monocot. Like all investigated grasses, S. italica has the Gα subunit with the destabilizing asparagine residue in the protein interface but, unlike other known grass genomes, still encodes an expressed RGS gene, SiRGS1. SiRGS1 accelerates GTP hydrolysis at similar concentration of both Gα subunits containing either the stabilizing (AtGPA1) or destabilizing (RGA1) interface residue. SiRGS1 does not use the hydroxyl-bearing residue on Gα to promote GAP activity and has a larger Gα-interface pocket fitting to the destabilizing Gα. These findings indicate that SiRGS1 adapted to a deleterious mutation on Gα using existing polymorphism in the RGS protein population. © The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. PMID: 25568345 [PubMed - as supplied by publisher] 16. Proc Natl Acad Sci U S A. 2015 Jan 20;112(3):647-8. doi: 10.1073/pnas.1423499112. Epub 2015 Jan 7. Reconstructing pathogen evolution from the ruins. Nuccio SP(1), Bäumler AJ(2). Author information: (1)Department of Microbiology and Molecular Genetics, School of Medicine, University of California, Irvine, CA 92697; and. (2)Department of Medical Microbiology and Immunology, School of Medicine, University of California, Davis, CA 95616 ajbaumler@ucdavis.edu. PMCID: PMC4311861 [Available on 2015/7/20] PMID: 25568086 [PubMed - in process] 17. Genome Res. 2015 Jan 7. pii: gr.181305.114. [Epub ahead of print] A large-scale, in vivo transcription factor screen defines bivalent chromatin as a key property of regulatory factors mediating Drosophila wing development. Schertel C(1), Albarca M(2), Rockel-Bauer C(1), Kelley NW(3), Bischof J(1), Hens K(2), van Nimwegen E(3), Basler K(1), Deplancke B(4). Author information: (1)University of Zurich; (2)EPFL; (3)University of Basel. (4)EPFL; bart.deplancke@epfl.ch. Transcription factors (TF) are key regulators of cell fate. The estimated 755 genes that encode DNA binding domain-containing proteins comprise about 5% of all Drosophila genes. However, the majority has remained uncharacterized so far due to the lack of proper genetic tools. We generated 596 site-directed transgenic Drosophila lines that contain integrations of individual UAS-TF constructs to facilitate spatio-temporally controlled misexpression in vivo. All transgenes were expressed in the developing wing and two thirds induced specific phenotypic defects. In vivo knock-down of the same genes yielded a phenotype for 50%, with both methods indicating a great potential for misexpression to characterize novel functions in wing growth, patterning and development. Thus, our UAS-TF library provides an important addition to the genetic toolbox of Drosophila research, enabling the identification of several novel wing development-related TFs. In parallel, we established the chromatin landscape of wing imaginal discs by ChIP-seq analyses of five chromatin marks and RNA pol II. Subsequent clustering revealed six distinct chromatin states with two clusters showing enrichment for both active and repressive marks. TFs that carry such `bivalent' chromatin are highly enriched for causing misexpression phenotypes in the wing, and analysis of existing expression data shows that these TFs tend to be differentially expressed across the wing disc. Thus, bivalently marked chromatin can be used as a marker for spatially regulated TFs that are functionally relevant in a developing tissue. Published by Cold Spring Harbor Laboratory Press. PMID: 25568052 [PubMed - as supplied by publisher] 18. Proc Natl Acad Sci U S A. 2015 Jan 20;112(3):785-90. doi: 10.1073/pnas.1413877112. Epub 2015 Jan 6. Shadow enhancers enable Hunchback bifunctionality in the Drosophila embryo. Staller MV(1), Vincent BJ(1), Bragdon MD(1), Lydiard-Martin T(1), Wunderlich Z(1), Estrada J(1), DePace AH(2). Author information: (1)Department of Systems Biology, Harvard Medical School, Boston, MA 02115. (2)Department of Systems Biology, Harvard Medical School, Boston, MA 02115 angela_depace@hms.harvard.edu. Hunchback (Hb) is a bifunctional transcription factor that activates and represses distinct enhancers. Here, we investigate the hypothesis that Hb can activate and repress the same enhancer. Computational models predicted that Hb bifunctionally regulates the even-skipped (eve) stripe 3+7 enhancer (eve3+7) in Drosophila blastoderm embryos. We measured and modeled eve expression at cellular resolution under multiple genetic perturbations and found that the eve3+7 enhancer could not explain endogenous eve stripe 7 behavior. Instead, we found that eve stripe 7 is controlled by two enhancers: the canonical eve3+7 and a sequence encompassing the minimal eve stripe 2 enhancer (eve2+7). Hb bifunctionally regulates eve stripe 7, but it executes these two activities on different pieces of regulatory DNA-it activates the eve2+7 enhancer and represses the eve3+7 enhancer. These two "shadow enhancers" use different regulatory logic to create the same pattern. PMCID: PMC4311800 PMID: 25564665 [PubMed - in process] 19. Mol Biol Evol. 2015 Jan 5. pii: msu407. [Epub ahead of print] Parallel functional changes in independent testis-specific duplicates of Aldehyde dehydrogenase in Drosophila. Chakraborty M(1), Fry JD(2). Author information: (1)Department of Ecology and Evolutionary Biology, University of California, Irvine, CA 92697 mchakrab@uci.edu. (2)Department of Biology, University of Rochester, Rochester, NY 14627. A large proportion of duplicates, originating from ubiquitously expressed genes, acquire testis-biased expression. Identifying the underlying cause of this observation requires determining whether the duplicates have altered functions relative to the parental genes. Typically, statistical methods are used to test for positive selection, signature of which in protein sequence of duplicates implies functional divergence. When assumptions are violated, however, such tests can lead to false inference of positive selection. More convincing evidence for naturally selected functional changes would be the occurrence of structural changes with similar functional consequences in independent duplicates of the same gene. We investigated two testis-specific duplicates of the broadly expressed enzyme gene Aldehyde dehydrogenase (Aldh) that arose in different Drosophila lineages. The duplicates show a typical pattern of accelerated amino-acid substitutions relative to their broadly expressed paralogs, with statistical evidence for positive selection in both cases. Importantly, in both duplicates, width of the entrance to the substrate binding site, known a priori to influence substrate specificity, and otherwise conserved throughout the genus Drosophila, has been reduced, resulting in narrowing of the entrance. Protein structure modeling suggests that the reduction of the size of the enzyme's substrate entry channel, which is likely to shift substrate specificity toward smaller aldehydes, is accounted for by the positively selected parallel substitutions in one duplicate but not the other. Evolution of the testis-specific duplicates was accompanied by reduction in expression of the ancestral Aldh in males, supporting the hypothesis that the duplicates may have helped resolve intralocus sexual conflict over Aldh function. © The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. PMID: 25564519 [PubMed - as supplied by publisher] 20. PLoS Biol. 2015 Jan 6;13(1):e1002033. doi: 10.1371/journal.pbio.1002033. eCollection 2015. Finding Our Way through Phenotypes. Deans AR(1), Lewis SE(2), Huala E(3), Anzaldo SS(4), Ashburner M(5), Balhoff JP(6), Blackburn DC(7), Blake JA(8), Burleigh JG(9), Chanet B(10), Cooper LD(11), Courtot M(12), Csösz S(13), Cui H(14), Dahdul W(15), Das S(16), Dececchi TA(15), Dettai A(10), Diogo R(17), Druzinsky RE(18), Dumontier M(19), Franz NM(4), Friedrich F(20), Gkoutos GV(21), Haendel M(22), Harmon LJ(23), Hayamizu TF(24), He Y(25), Hines HM(1), Ibrahim N(26), Jackson LM(15), Jaiswal P(11), James-Zorn C(27), Köhler S(28), Lecointre G(10), Lapp H(6), Lawrence CJ(29), Le Novère N(30), Lundberg JG(31), Macklin J(32), Mast AR(33), Midford PE(34), Mikó I(1), Mungall CJ(2), Oellrich A(35), Osumi-Sutherland D(35), Parkinson H(35), Ramírez MJ(36), Richter S(37), Robinson PN(38), Ruttenberg A(39), Schulz KS(40), Segerdell E(41), Seltmann KC(42), Sharkey MJ(43), Smith AD(44), Smith B(45), Specht CD(46), Squires RB(47), Thacker RW(48), Thessen A(49), Fernandez-Triana J(50), Vihinen M(51), Vize PD(52), Vogt L(53), Wall CE(54), Walls RL(55), Westerfeld M(56), Wharton RA(57), Wirkner CS(37), Woolley JB(57), Yoder MJ(58), Zorn AM(27), Mabee P(15). Author information: (1)Department of Entomology, Pennsylvania State University, University Park, Pennsylvania, United States of America. (2)Genome Division, Lawrence Berkeley National Lab, Berkeley, California, United States of America. (3)Department of Plant Biology, Carnegie Institution for Science, Stanford, California, United States of America; Phoenix Bioinformatics, Palo Alto, California, United States of America. (4)School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America. (5)Department of Genetics, University of Cambridge, Cambridge, United Kingdom. (6)National Evolutionary Synthesis Center, Durham, North Carolina, United States of America. (7)Department of Vertebrate Zoology and Anthropology, California Academy of Sciences, San Francisco, California, United States of America. (8)The Jackson Laboratory, Bar Harbor, Maine, United States of America. (9)Department of Biology, University of Florida, Gainesville, Florida, United States of America. (10)Muséum national d'Histoire naturelle, Département Systématique et Evolution, Paris, France. (11)Department of Botany and Plant Pathology, Oregon State University, Corvallis, Oregon, United States of America. (12)Molecular Biology and Biochemistry Department, Simon Fraser University, Burnaby, British Columbia, Canada. (13)MTA-ELTE-MTM, Ecology Research Group, Pázmány Péter sétány 1C, Budapest, Hungary. (14)School of Information Resources and Library Science, University of Arizona, Tucson, Arizona, United States of America. (15)Department of Biology, University of South Dakota, Vermillion, South Dakota, United States of America. (16)Department of Botany, University of Delhi, Delhi, India. (17)Department of Anatomy, Howard University College of Medicine, Washington D.C., United States of America. (18)Department of Oral Biology, College of Dentistry, University of Illinois, Chicago, Illinois, United States of America. (19)Stanford Center for Biomedical Informatics Research, Stanford, California, United States of America. (20)Biocenter Grindel and Zoological Museum, Hamburg University, Hamburg, Germany. (21)Department of Computer Science, Aberystwyth University, Aberystwyth, Ceredigion, United Kingdom. (22)Department of Medical Informatics & Epidemiology, Oregon Health & Science University, Portland, Oregon, United States of America. (23)Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America. (24)Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, Maine, United States of America. (25)Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, and Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, Michigan, United States of America. (26)Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois, United States of America. (27)Cincinnati Children's Hospital, Division of Developmental Biology, Cincinnati, Ohio, United States of America. (28)Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany. (29)Department of Genetics, Development and Cell Biology and Department of Agronomy, Iowa State University, Ames, Iowa, United States of America. (30)Signalling ISP, Babraham Institute, Babraham, Cambridgeshire, UK. (31)Department of Ichthyology, The Academy of Natural Sciences, Philadelphia, Pennsylvania, United States of America. (32)Eastern Cereal and Oilseed Research Centre, Ottawa, Ontario, Canada. (33)Department of Biological Science, Florida State University, Tallahassee, Florida, United States of America. (34)Richmond, Virginia, United States of America. (35)European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom. (36)Division of Arachnology, Museo Argentino de Ciencias Naturales - CONICET, Buenos Aires, Argentina. (37)Allgemeine & Spezielle Zoologie, Institut für Biowissenschaften, Universität Rostock, Universitätsplatz 2, Rostock, Germany. (38)Institut für Medizinische Genetik und Humangenetik Charité - Universitätsmedizin Berlin, Berlin, Germany. (39)School of Dental Medicine, University at Buffalo, Buffalo, New York, United States of America. (40)Smithsonian Institution, National Museum of Natural History, Washington, D.C., United States of America. (41)Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, United States of America. (42)Division of Invertebrate Zoology, American Museum of Natural History, New York, New York, United States of America. (43)Department of Entomology, University of Kentucky, Lexington, Kentucky, United States of America. (44)Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, United States of America. (45)Department of Philosophy, University at Buffalo, Buffalo, New York, United States of America. (46)Department of Plant and Microbial Biology, Integrative Biology, and the University and Jepson Herbaria, University of California, Berkeley, California, United States of America. (47)Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America. (48)Department of Biology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America. (49)The Data Detektiv, 1412 Stearns Hill Road, Waltham, Massachusetts, United States of America. (50)Canadian National Collection of Insects, Ottawa, Ontario, Canada. (51)Department of Experimental Medical Science, Lund University, Lund, Sweden. (52)Department of Biological Sciences, University of Calgary, Calgary, Alberta, Canada. (53)Universität Bonn, Institut für Evolutionsbiologie und Ökologie, Bonn, Germany. (54)Department of Evolutionary Anthropology, Duke University, Durham, North Carolina, United States of America. (55)iPlant Collaborative University of Arizona, Thomas J. Keating Bioresearch Building, Tucson, Arizona, United States of America. (56)Institute of Neuroscience, University of Oregon, Eugene, Oregon, United States of America. (57)Department of Entomology, Texas A & M University, College, Station, Texas, United States of America. (58)Illinois Natural History Survey, University of Illinois, Champaign, Illinois, United States of America. Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility. PMCID: PMC4285398 PMID: 25562316 [PubMed - in process] 21. Proc Natl Acad Sci U S A. 2015 Jan 5. pii: 201420037. [Epub ahead of print] Experimental replacement of an obligate insect symbiont. Moran NA(1), Yun Y(2). Author information: (1)Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712; and nancy.moran@austin.utexas.edu. (2)Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712; and Centre for Behavioural Ecology and Evolution, College of Life Sciences, Hubei University, Wuhan 430062, Hubei, China. Symbiosis, the close association of unrelated organisms, has been pivotal in biological diversification. In the obligate symbioses found in many insect hosts, organisms that were once independent are permanently and intimately associated, resulting in expanded ecological capabilities. The primary model for this kind of symbiosis is the association between the bacterium Buchnera and the pea aphid (Acyrthosiphon pisum). A longstanding obstacle to efforts to illuminate genetic changes underlying obligate symbioses has been the inability to experimentally disrupt and reconstitute symbiont-host partnerships. Our experiments show that Buchnera can be experimentally transferred between aphid matrilines and, furthermore, that Buchnera replacement has a massive effect on host fitness. Using a recipient pea aphid matriline containing Buchnera that are heat sensitive because of an allele eliminating the heat shock response of a small chaperone, we reduced native Buchnera through heat exposure and introduced a genetically distinct Buchnera from another matriline, achieving complete replacement and stable inheritance. This transfer disrupted 100 million years (∼1 billion generations) of continuous maternal transmission of Buchnera in its host aphids. Furthermore, aphids with the Buchnera replacement enjoyed a dramatic increase in heat tolerance, directly demonstrating a strong effect of symbiont genotype on host ecology. PMID: 25561531 [PubMed - as supplied by publisher] 22. Proc Natl Acad Sci U S A. 2015 Jan 20;112(3):815-20. doi: 10.1073/pnas.1418892112. Epub 2015 Jan 5. Adaptive introgression in an African malaria mosquito coincident with the increased usage of insecticide-treated bed nets. Norris LC(1), Main BJ(2), Lee Y(2), Collier TC(2), Fofana A(3), Cornel AJ(1), Lanzaro GC(4). Author information: (1)Vector Genetics Laboratory, Department of Entomology and Nematology, and. (2)Vector Genetics Laboratory, Department of Pathology, Microbiology, and Immunology, University of California, Davis, CA 95616 and. (3)Malaria Research and Training Center, University of Bamako, Bamako BP E2528, Mali. (4)Vector Genetics Laboratory, Department of Pathology, Microbiology, and Immunology, University of California, Davis, CA 95616 and gclanzaro@ucdavis.edu. Animal species adapt to changes in their environment, including man-made changes such as the introduction of insecticides, through selection for advantageous genes already present in populations or newly arisen through mutation. A possible alternative mechanism is the acquisition of adaptive genes from related species via a process known as adaptive introgression. Differing levels of insecticide resistance between two African malaria vectors, Anopheles coluzzii and Anopheles gambiae, have been attributed to assortative mating between the two species. In a previous study, we reported two bouts of hybridization observed in the town of Selinkenyi, Mali in 2002 and 2006. These hybridization events did not appear to be directly associated with insecticide-resistance genes. We demonstrate that during a brief breakdown in assortative mating in 2006, A. coluzzii inherited the entire A. gambiae-associated 2L divergence island, which includes a suite of insecticide-resistance alleles. In this case, introgression was coincident with the start of a major insecticide-treated bed net distribution campaign in Mali. This suggests that insecticide exposure altered the fitness landscape, favoring the survival of A. coluzzii/A. gambiae hybrids, and provided selection pressure that swept the 2L divergence island through A. coluzzii populations in Mali. We propose that the work described herein presents a unique description of the temporal dynamics of adaptive introgression in an animal species and represents a mechanism for the rapid evolution of insecticide resistance in this important vector of human malaria in Africa. PMCID: PMC4311837 [Available on 2015/7/20] PMID: 25561525 [PubMed - in process] 23. Genetics. 2015 Jan 2. pii: genetics.114.173716. [Epub ahead of print] An Enhanced Gene Targeting Toolkit for Drosophila: Golic+ Chen HM(1), Huang Y(2), Pfeiffer BD(3), Yao X(3), Lee T(4). Author information: (1)Howard Hughes Medical Institute; University of Massachusetts; (2)University of Oxford; Howard Hughes Medical Institute; (3)Howard Hughes Medical Institute. (4)Howard Hughes Medical Institute; University of Massachusetts; leet@janelia.hhmi.org. Ends-out gene targeting allows seamless replacement of endogenous genes with engineered DNA fragments by homologous recombination, thus creating designer "genes" in the endogenous locus. Conventional gene targeting in Drosophila involves targeting with the pre-integrated donor DNA in the larval primordial germ cells. Here we report Golic+: Gene targeting during oogenesis with lethality inhibitor and CRISPR/Cas, which improves on all major steps in such transgene-based gene targeting systems. First, donor DNA is integrated into pre-characterized attP sites for efficient flip-out. Second, FLP, I-SceI, and Cas9 are specifically expressed in cystoblasts, which arise continuously from female germline stem cells, thereby providing a continual source of independent targeting events in each offspring. Third, a repressor-based lethality selection is implemented to facilitate screening for correct targeting events. All together, Golic+ realizes high-efficiency ends-out gene targeting in ovarian cystoblasts, which can be readily scaled up to achieve high-throughput genome editing. Copyright © 2015, The Genetics Society of America. PMID: 25555988 [PubMed - as supplied by publisher] 24. PLoS One. 2015 Jan 2;10(1):e116416. doi: 10.1371/journal.pone.0116416. eCollection 2015. Heterochrony and early left-right asymmetry in the development of the cardiorespiratory system of snakes. van Soldt BJ(1), Metscher BD(2), Poelmann RE(3), Vervust B(4), Vonk FJ(5), Müller GB(2), Richardson MK(1). Author information: (1)Institute of Biology, University of Leiden, Leiden, the Netherlands. (2)Department of Theoretical Biology, University of Vienna, Vienna, Austria. (3)Department of Anatomy and Embryology, Leiden University Medical Center, Leiden, the Netherlands. (4)Department of Biology, University of Antwerp, Antwerp, Belgium. (5)Institute of Biology, University of Leiden, Leiden, the Netherlands; NCB Naturalis, Leiden, the Netherlands. Snake lungs show a remarkable diversity of organ asymmetries. The right lung is always fully developed, while the left lung is either absent, vestigial, or well-developed (but smaller than the right). A 'tracheal lung' is present in some taxa. These asymmetries are reflected in the pulmonary arteries. Lung asymmetry is known to appear at early stages of development in Thamnophis radix and Natrix natrix. Unfortunately, there is no developmental data on snakes with a well-developed or absent left lung. We examine the adult and developmental morphology of the lung and pulmonary arteries in the snakes Python curtus breitensteini, Pantherophis guttata guttata, Elaphe obsoleta spiloides, Calloselasma rhodostoma and Causus rhombeatus using gross dissection, MicroCT scanning and 3D reconstruction. We find that the right and tracheal lung develop similarly in these species. By contrast, the left lung either: (1) fails to develop; (2) elongates more slowly and aborts early without (2a) or with (2b) subsequent development of faveoli; (3) or develops normally. A right pulmonary artery always develops, but the left develops only if the left lung develops. No pulmonary artery develops in relation to the tracheal lung. We conclude that heterochrony in lung bud development contributes to lung asymmetry in several snake taxa. Secondly, the development of the pulmonary arteries is asymmetric at early stages, possibly because the splanchnic plexus fails to develop when the left lung is reduced. Finally, some changes in the topography of the pulmonary arteries are consequent on ontogenetic displacement of the heart down the body. Our findings show that the left-right asymmetry in the cardiorespiratory system of snakes is expressed early in development and may become phenotypically expressed through heterochronic shifts in growth, and changes in axial relations of organs and vessels. We propose a step-wise model for reduction of the left lung during snake evolution. PMCID: PMC4282204 PMID: 25555231 [PubMed - in process] 25. Science. 2015 Jan 2;347(6217):1258522. doi: 10.1126/science.1258522. Epub 2014 Nov 27. Mosquito genomics. Highly evolvable malaria vectors: the genomes of 16 Anopheles mosquitoes. Neafsey DE(1), Waterhouse RM(2), Abai MR(3), Aganezov SS(4), Alekseyev MA(4), Allen JE(5), Amon J(6), Arcà B(7), Arensburger P(8), Artemov G(9), Assour LA(10), Basseri H(3), Berlin A(11), Birren BW(11), Blandin SA(12), Brockman AI(13), Burkot TR(14), Burt A(15), Chan CS(16), Chauve C(17), Chiu JC(18), Christensen M(5), Costantini C(19), Davidson VL(20), Deligianni E(21), Dottorini T(13), Dritsou V(22), Gabriel SB(23), Guelbeogo WM(24), Hall AB(25), Han MV(26), Hlaing T(27), Hughes DS(28), Jenkins AM(29), Jiang X(30), Jungreis I(16), Kakani EG(31), Kamali M(32), Kemppainen P(33), Kennedy RC(34), Kirmitzoglou IK(35), Koekemoer LL(36), Laban N(37), Langridge N(5), Lawniczak MK(13), Lirakis M(38), Lobo NF(39), Lowy E(5), MacCallum RM(13), Mao C(40), Maslen G(5), Mbogo C(41), McCarthy J(8), Michel K(20), Mitchell SN(42), Moore W(43), Murphy KA(18), Naumenko AN(32), Nolan T(13), Novoa EM(16), O'Loughlin S(15), Oringanje C(43), Oshaghi MA(3), Pakpour N(44), Papathanos PA(45), Peery AN(32), Povelones M(46), Prakash A(47), Price DP(48), Rajaraman A(17), Reimer LJ(49), Rinker DC(50), Rokas A(51), Russell TL(14), Sagnon N(24), Sharakhova MV(32), Shea T(11), Simão FA(52), Simard F(19), Slotman MA(53), Somboon P(54), Stegniy V(9), Struchiner CJ(55), Thomas GW(56), Tojo M(57), Topalis P(21), Tubio JM(58), Unger MF(39), Vontas J(38), Walton C(33), Wilding CS(59), Willis JH(60), Wu YC(61), Yan G(62), Zdobnov EM(52), Zhou X(63), Catteruccia F(31), Christophides GK(13), Collins FH(39), Cornman RS(60), Crisanti A(45), Donnelly MJ(64), Emrich SJ(10), Fontaine MC(65), Gelbart W(66), Hahn MW(67), Hansen IA(48), Howell PI(68), Kafatos FC(13), Kellis M(16), Lawson D(5), Louis C(69), Luckhart S(44), Muskavitch MA(70), Ribeiro JM(71), Riehle MA(43), Sharakhov IV(72), Tu Z(73), Zwiebel LJ(74), Besansky NJ(75). Author information: (1)Genome Sequencing and Analysis Program, Broad Institute, 415 Main Street, Cambridge, MA 02142, USA. neafsey@broadinstitute.org nbesansk@nd.edu. (2)Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139, USA. The Broad Institute of Massachusetts Institute of Technology and Harvard, 415 Main Street, Cambridge, MA 02142, USA. Department of Genetic Medicine and Development, University of Geneva Medical School, Rue Michel-Servet 1, 1211 Geneva, Switzerland. Swiss Institute of Bioinformatics, Rue Michel-Servet 1, 1211 Geneva, Switzerland. (3)Department of Medical Entomology and Vector Control, School of Public Health and Institute of Health Researches, Tehran University of Medical Sciences, Tehran, Iran. (4)George Washington University, Department of Mathematics and Computational Biology Institute, 45085 University Drive, Ashburn, VA 20147, USA. (5)European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. (6)National Vector Borne Disease Control Programme, Ministry of Health, Tafea Province, Vanuatu. (7)Department of Public Health and Infectious Diseases, Division of Parasitology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy. (8)Department of Biological Sciences, California State Polytechnic-Pomona, 3801 West Temple Avenue, Pomona, CA 91768, USA. (9)Tomsk State University, 36 Lenina Avenue, Tomsk, Russia. (10)Department of Computer Science and Engineering, Eck Institute for Global Health, 211B Cushing Hall, University of Notre Dame, Notre Dame, IN 46556, USA. (11)Genome Sequencing and Analysis Program, Broad Institute, 415 Main Street, Cambridge, MA 02142, USA. (12)Inserm, U963, F-67084 Strasbourg, France. CNRS, UPR9022, IBMC, F-67084 Strasbourg, France. (13)Department of Life Sciences, Imperial College London, South Kensington Campus, London SW7 2AZ, UK. (14)Faculty of Medicine, Health and Molecular Science, Australian Institute of Tropical Health Medicine, James Cook University, Cairns 4870, Australia. (15)Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot SL5 7PY, UK. (16)Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139, USA. The Broad Institute of Massachusetts Institute of Technology and Harvard, 415 Main Street, Cambridge, MA 02142, USA. (17)Department of Mathematics, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada. (18)Department of Entomology and Nematology, One Shields Avenue, University of California-Davis, Davis, CA 95616, USA. (19)Institut de Recherche pour le Développement, Unités Mixtes de Recherche Maladies Infectieuses et Vecteurs Écologie, Génétique, Évolution et Contrôle, 911, Avenue Agropolis, BP 64501 Montpellier, France. (20)Division of Biology, Kansas State University, 271 Chalmers Hall, Manhattan, KS 66506, USA. (21)Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology, Hellas, Nikolaou Plastira 100 GR-70013, Heraklion, Crete, Greece. (22)Centre of Functional Genomics, University of Perugia, Perugia, Italy. (23)Genomics Platform, Broad Institute, 415 Main Street, Cambridge, MA 02142, USA. (24)Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou 01 BP 2208, Burkina Faso. (25)Program of Genetics, Bioinformatics, and Computational Biology, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. (26)School of Life Sciences, University of Nevada, Las Vegas, NV 89154, USA. (27)Department of Medical Research, No. 5 Ziwaka Road, Dagon Township, Yangon 11191, Myanmar. (28)European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA. (29)Boston College, 140 Commonwealth Avenue, Chestnut Hill, MA 02467, USA. (30)Department of Biochemistry, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. Program of Genetics, Bioinformatics, and Computational Biology, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. (31)Harvard School of Public Health, Department of Immunology and Infectious Diseases, Boston, MA 02115, USA. Dipartimento di Medicina Sperimentale e Scienze Biochimiche, Università degli Studi di Perugia, Perugia, Italy. (32)Department of Entomology, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. (33)Computational Evolutionary Biology Group, Faculty of Life Sciences, University of Manchester, Oxford Road, Manchester M13 9PT, UK. (34)Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94143, USA. (35)Department of Life Sciences, Imperial College London, South Kensington Campus, London SW7 2AZ, UK. Bioinformatics Research Laboratory, Department of Biological Sciences, New Campus, University of Cyprus, CY 1678 Nicosia, Cyprus. (36)Wits Research Institute for Malaria, Faculty of Health Sciences, and Vector Control Reference Unit, National Institute for Communicable Diseases of the National Health Laboratory Service, Sandringham 2131, Johannesburg, South Africa. (37)National Museums of Kenya, P.O. Box 40658-00100, Nairobi, Kenya. (38)Department of Biology, University of Crete, 700 13 Heraklion, Greece. (39)Eck Institute for Global Health and Department of Biological Sciences, University of Notre Dame, 317 Galvin Life Sciences Building, Notre Dame, IN 46556, USA. (40)Virginia Bioinformatics Institute, 1015 Life Science Circle, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. (41)Kenya Medical Research Institute-Wellcome Trust Research Programme, Centre for Geographic Medicine Research - Coast, P.O. Box 230-80108, Kilifi, Kenya. (42)Harvard School of Public Health, Department of Immunology and Infectious Diseases, Boston, MA 02115, USA. (43)Department of Entomology, 1140 East South Campus Drive, Forbes 410, University of Arizona, Tucson, AZ 85721, USA. (44)Department of Medical Microbiology and Immunology, School of Medicine, University of California Davis, One Shields Avenue, Davis, CA 95616, USA. (45)Department of Life Sciences, Imperial College London, South Kensington Campus, London SW7 2AZ, UK. Centre of Functional Genomics, University of Perugia, Perugia, Italy. (46)Department of Pathobiology, University of Pennsylvania School of Veterinary Medicine, 3800 Spruce Street, Philadelphia, PA 19104, USA. (47)Regional Medical Research Centre NE, Indian Council of Medical Research, P.O. Box 105, Dibrugarh-786 001, Assam, India. (48)Department of Biology, New Mexico State University, Las Cruces, NM 88003, USA. Molecular Biology Program, New Mexico State University, Las Cruces, NM 88003, USA. (49)Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK. (50)Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, TN 37235, USA. (51)Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, TN 37235, USA. Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA. (52)Department of Genetic Medicine and Development, University of Geneva Medical School, Rue Michel-Servet 1, 1211 Geneva, Switzerland. Swiss Institute of Bioinformatics, Rue Michel-Servet 1, 1211 Geneva, Switzerland. (53)Department of Entomology, Texas A&M University, College Station, TX 77807, USA. (54)Department of Parasitology, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand. (55)Fundação Oswaldo Cruz, Avenida Brasil 4365, RJ Brazil. Instituto de Medicina Social, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil. (56)School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA. (57)Department of Physiology, School of Medicine, Center for Research in Molecular Medicine and Chronic Diseases, Instituto de Investigaciones Sanitarias, University of Santiago de Compostela, Santiago de Compostela, A Coruña, Spain. (58)Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, UK. (59)School of Natural Sciences and Psychology, Liverpool John Moores University, Liverpool L3 3AF, UK. (60)Department of Cellular Biology, University of Georgia, Athens, GA 30602, USA. (61)Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139, USA. The Broad Institute of Massachusetts Institute of Technology and Harvard, 415 Main Street, Cambridge, MA 02142, USA. Department of Computer Science, Harvey Mudd College, Claremont, CA 91711, USA. (62)Program in Public Health, College of Health Sciences, University of California, Irvine, Hewitt Hall, Irvine, CA 92697, USA. (63)Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA. (64)Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK. Malaria Programme, Wellcome Trust Sanger Institute, Cambridge CB10 1SJ, UK. (65)Eck Institute for Global Health and Department of Biological Sciences, University of Notre Dame, 317 Galvin Life Sciences Building, Notre Dame, IN 46556, USA. Centre of Evolutionary and Ecological Studies (Marine Evolution and Conservation group), University of Groningen, Nijenborgh 7, NL-9747 AG Groningen, Netherlands. (66)Department of Molecular and Cellular Biology, Harvard University, 16 Divinity Avenue, Cambridge, MA 02138, USA. (67)Department of Biology, Indiana University, Bloomington, IN 47405, USA. School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA. (68)Centers for Disease Control and Prevention, 1600 Clifton Road NE MSG49, Atlanta, GA 30329, USA. (69)Department of Biology, University of Crete, 700 13 Heraklion, Greece. Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology, Hellas, Nikolaou Plastira 100 GR-70013, Heraklion, Crete, Greece. Centre of Functional Genomics, University of Perugia, Perugia, Italy. (70)Boston College, 140 Commonwealth Avenue, Chestnut Hill, MA 02467, USA. Biogen Idec, 14 Cambridge Center, Cambridge, MA 02142, USA. (71)Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, 12735 Twinbrook Parkway, Rockville, MD 20852, USA. (72)Department of Entomology, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. Program of Genetics, Bioinformatics, and Computational Biology, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. (73)Program of Genetics, Bioinformatics, and Computational Biology, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. Department of Biochemistry, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. (74)Departments of Biological Sciences and Pharmacology, Institutes for Chemical Biology, Genetics and Global Health, Vanderbilt University and Medical Center, Nashville, TN 37235, USA. (75)Eck Institute for Global Health and Department of Biological Sciences, University of Notre Dame, 317 Galvin Life Sciences Building, Notre Dame, IN 46556, USA. neafsey@broadinstitute.org nbesansk@nd.edu. Comment in Science. 2015 Jan 2;347(6217):27-8. Variation in vectorial capacity for human malaria among Anopheles mosquito species is determined by many factors, including behavior, immunity, and life history. To investigate the genomic basis of vectorial capacity and explore new avenues for vector control, we sequenced the genomes of 16 anopheline mosquito species from diverse locations spanning ~100 million years of evolution. Comparative analyses show faster rates of gene gain and loss, elevated gene shuffling on the X chromosome, and more intron losses, relative to Drosophila. Some determinants of vectorial capacity, such as chemosensory genes, do not show elevated turnover but instead diversify through protein-sequence changes. This dynamism of anopheline genes and genomes may contribute to their flexible capacity to take advantage of new ecological niches, including adapting to humans as primary hosts. Copyright © 2015, American Association for the Advancement of Science. PMID: 25554792 [PubMed - indexed for MEDLINE] 26. Science. 2015 Jan 2;347(6217):63-7. doi: 10.1126/science.1260064. Bacterial evolution. The type VI secretion system of Vibrio cholerae fosters horizontal gene transfer. Borgeaud S(1), Metzger LC(1), Scrignari T(1), Blokesch M(2). Author information: (1)Laboratory of Molecular Microbiology, Global Health Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland. (2)Laboratory of Molecular Microbiology, Global Health Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland. melanie.blokesch@epfl.ch. Natural competence for transformation is a common mode of horizontal gene transfer and contributes to bacterial evolution. Transformation occurs through the uptake of external DNA and its integration into the genome. Here we show that the type VI secretion system (T6SS), which serves as a predatory killing device, is part of the competence regulon in the naturally transformable pathogen Vibrio cholerae. The T6SS-encoding gene cluster is under the positive control of the competence regulators TfoX and QstR and is induced by growth on chitinous surfaces. Live-cell imaging revealed that deliberate killing of nonimmune cells via competence-mediated induction of T6SS releases DNA and makes it accessible for horizontal gene transfer in V. cholerae. Copyright © 2015, American Association for the Advancement of Science. PMID: 25554784 [PubMed - indexed for MEDLINE] 27. Science. 2015 Jan 2;347(6217):27-8. doi: 10.1126/science.aaa3600. Evolutionary genomics. Conundrum of jumbled mosquito genomes. Clark AG(1), Messer PW(2). Author information: (1)Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA. Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA. ac347@cornell.edu. (2)Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA. Comment on Science. 2015 Jan 2;347(6217):1258524. Science. 2015 Jan 2;347(6217):1258522. PMID: 25554775 [PubMed - indexed for MEDLINE] 28. Proc Natl Acad Sci U S A. 2015 Jan 20;112(3):809-14. doi: 10.1073/pnas.1418979112. Epub 2014 Dec 29. Adaptive simplification and the evolution of gecko locomotion: Morphological and biomechanical consequences of losing adhesion. Higham TE(1), Birn-Jeffery AV(2), Collins CE(2), Hulsey CD(3), Russell AP(4). Author information: (1)Department of Biology, University of California, Riverside, CA 92521; thigham@ucr.edu. (2)Department of Biology, University of California, Riverside, CA 92521; (3)Department of Biological Sciences, University of New Orleans, New Orleans, LA 70148; and. (4)Department of Biological Sciences, University of Calgary, Calgary, AB, Canada T2N 1N4. Innovations permit the diversification of lineages, but they may also impose functional constraints on behaviors such as locomotion. Thus, it is not surprising that secondary simplification of novel locomotory traits has occurred several times among vertebrates and could potentially lead to exceptional divergence when constraints are relaxed. For example, the gecko adhesive system is a remarkable innovation that permits locomotion on surfaces unavailable to other animals, but has been lost or simplified in species that have reverted to a terrestrial lifestyle. We examined the functional and morphological consequences of this adaptive simplification in the Pachydactylus radiation of geckos, which exhibits multiple unambiguous losses or bouts of simplification of the adhesive system. We found that the rates of morphological and 3D locomotor kinematic evolution are elevated in those species that have simplified or lost adhesive capabilities. This finding suggests that the constraints associated with adhesion have been circumvented, permitting these species to either run faster or burrow. The association between a terrestrial lifestyle and the loss/reduction of adhesion suggests a direct link between morphology, biomechanics, and ecology. PMCID: PMC4311805 [Available on 2015/7/20] PMID: 25548182 [PubMed - in process] 29. Proc Natl Acad Sci U S A. 2015 Jan 13;112(2):406-11. doi: 10.1073/pnas.1421138111. Epub 2014 Dec 29. Bacterial growth laws reflect the evolutionary importance of energy efficiency. Maitra A(1), Dill KA(1). Author information: (1)Laufer Center for Physical and Quantitative Biology and the Departments of Chemistry and Physics, Stony Brook University, Stony Brook, NY 11794 arijit.maitra@stonybrook.edu dill@laufercenter.org. We are interested in the balance of energy and protein synthesis in bacterial growth. How has evolution optimized this balance? We describe an analytical model that leverages extensive literature data on growth laws to infer the underlying fitness landscape and to draw inferences about what evolution has optimized in Escherichia coli. Is E. coli optimized for growth speed, energy efficiency, or some other property? Experimental data show that at its replication speed limit, E. coli produces about four mass equivalents of nonribosomal proteins for every mass equivalent of ribosomes. This ratio can be explained if the cell's fitness function is the the energy efficiency of cells under fast growth conditions, indicating a tradeoff between the high energy costs of ribosomes under fast growth and the high energy costs of turning over nonribosomal proteins under slow growth. This model gives insight into some of the complex nonlinear relationships between energy utilization and ribosomal and nonribosomal production as a function of cell growth conditions. PMCID: PMC4299221 PMID: 25548180 [PubMed - in process] 30. Proc Natl Acad Sci U S A. 2015 Jan 20;112(3):905-10. doi: 10.1073/pnas.1422242112. Epub 2014 Dec 29. Natural diversity in daily rhythms of gene expression contributes to phenotypic variation. de Montaigu A(1), Giakountis A(1), Rubin M(2), Tóth R(1), Cremer F(1), Sokolova V(1), Porri A(1), Reymond M(1), Weinig C(2), Coupland G(3). Author information: (1)Max Planck Institute for Plant Breeding Research, D-50829 Cologne, Germany; and. (2)Department of Botany, University of Wyoming, Laramie, WY 82071. (3)Max Planck Institute for Plant Breeding Research, D-50829 Cologne, Germany; and coupland@mpipz.mpg.de. Daily rhythms of gene expression provide a benefit to most organisms by ensuring that biological processes are activated at the optimal time of day. Although temporal patterns of expression control plant traits of agricultural importance, how natural genetic variation modifies these patterns during the day and how precisely these patterns influence phenotypes is poorly understood. The circadian clock regulates the timing of gene expression, and natural variation in circadian rhythms has been described, but circadian rhythms are measured in artificial continuous conditions that do not reflect the complexity of biologically relevant day/night cycles. By studying transcriptional rhythms of the evening-expressed gene GIGANTEA (GI) at high temporal resolution and during day/night cycles, we show that natural variation in the timing of GI expression occurs mostly under long days in 77 Arabidopsis accessions. This variation is explained by natural alleles that alter light sensitivity of GI, specifically in the evening, and that act at least partly independent of circadian rhythms. Natural alleles induce precise changes in the temporal waveform of GI expression, and these changes have detectable effects on PHYTOCHROME INTERACTING FACTOR 4 expression and growth. Our findings provide a paradigm for how natural alleles act within day/night cycles to precisely modify temporal gene expression waveforms and cause phenotypic diversity. Such alleles could confer an advantage by adjusting the activity of temporally regulated processes without severely disrupting the circadian system. PMCID: PMC4311856 PMID: 25548158 [PubMed - in process] 31. Proc Natl Acad Sci U S A. 2015 Jan 13;112(2):470-5. doi: 10.1073/pnas.1322632112. Epub 2014 Dec 29. Directional selection can drive the evolution of modularity in complex traits. Melo D(1), Marroig G(2). Author information: (1)Departamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de São Paulo, Sao Paulo, SP 05508-090, Brazil diogro@usp.br. (2)Departamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de São Paulo, Sao Paulo, SP 05508-090, Brazil. Modularity is a central concept in modern biology, providing a powerful framework for the study of living organisms on many organizational levels. Two central and related questions can be posed in regard to modularity: How does modularity appear in the first place, and what forces are responsible for keeping and/or changing modular patterns? We approached these questions using a quantitative genetics simulation framework, building on previous results obtained with bivariate systems and extending them to multivariate systems. We developed an individual-based model capable of simulating many traits controlled by many loci with variable pleiotropic relations between them, expressed in populations subject to mutation, recombination, drift, and selection. We used this model to study the problem of the emergence of modularity, and hereby show that drift and stabilizing selection are inefficient at creating modular variational structures. We also demonstrate that directional selection can have marked effects on the modular structure between traits, actively promoting a restructuring of genetic variation in the selected population and potentially facilitating the response to selection. Furthermore, we give examples of complex covariation created by simple regimes of combined directional and stabilizing selection and show that stabilizing selection is important in the maintenance of established covariation patterns. Our results are in full agreement with previous results for two-trait systems and further extend them to include scenarios of greater complexity. Finally, we discuss the evolutionary consequences of modular patterns being molded by directional selection. PMCID: PMC4299217 PMID: 25548154 [PubMed - in process] 32. Proc Natl Acad Sci U S A. 2015 Jan 20;112(3):803-8. doi: 10.1073/pnas.1420208112. Epub 2014 Dec 22. Deep conservation of wrist and digit enhancers in fish. Gehrke AR(1), Schneider I(2), de la Calle-Mustienes E(3), Tena JJ(3), Gomez-Marin C(3), Chandran M(1), Nakamura T(1), Braasch I(4), Postlethwait JH(4), Gómez-Skarmeta JL(3), Shubin NH(5). Author information: (1)Department of Organismal Biology and Anatomy, The University of Chicago, Chicago, IL 60637; (2)Instituto de Ciencias Biologicas, Universidade Federal do Para, 66075, Belem, Brazil; (3)Centro Andaluz de Biología del Desarrollo, Consejo Superior de Investigaciones Científicas/Universidad Pablo de Olavide, Sevilla 41013, Spain; and. (4)Institute of Neuroscience, University of Oregon, Eugene, OR 97403-1254. (5)Department of Organismal Biology and Anatomy, The University of Chicago, Chicago, IL 60637; nshubin@uchicago.edu. There is no obvious morphological counterpart of the autopod (wrist/ankle and digits) in living fishes. Comparative molecular data may provide insight into understanding both the homology of elements and the evolutionary developmental mechanisms behind the fin to limb transition. In mouse limbs the autopod is built by a "late" phase of Hoxd and Hoxa gene expression, orchestrated by a set of enhancers located at the 5' end of each cluster. Despite a detailed mechanistic understanding of mouse limb development, interpretation of Hox expression patterns and their regulation in fish has spawned multiple hypotheses as to the origin and function of "autopod" enhancers throughout evolution. Using phylogenetic footprinting, epigenetic profiling, and transgenic reporters, we have identified and functionally characterized hoxD and hoxA enhancers in the genomes of zebrafish and the spotted gar, Lepisosteus oculatus, a fish lacking the whole genome duplication of teleosts. Gar and zebrafish "autopod" enhancers drive expression in the distal portion of developing zebrafish pectoral fins, and respond to the same functional cues as their murine orthologs. Moreover, gar enhancers drive reporter gene expression in both the wrist and digits of mouse embryos in patterns that are nearly indistinguishable from their murine counterparts. These functional genomic data support the hypothesis that the distal radials of bony fish are homologous to the wrist and/or digits of tetrapods. PMCID: PMC4311833 PMID: 25535365 [PubMed - in process] 33. Proc Natl Acad Sci U S A. 2015 Jan 13;112(2):458-63. doi: 10.1073/pnas.1404167111. Epub 2014 Dec 1. Hominids adapted to metabolize ethanol long before human-directed fermentation. Carrigan MA(1), Uryasev O(2), Frye CB(2), Eckman BL(2), Myers CR(3), Hurley TD(3), Benner SA(2). Author information: (1)Department of Natural Sciences, Santa Fe College, Gainesville, FL 32606; Foundation for Applied Molecular Evolution, Gainesville, FL 32604; and matthew.carrigan@sfcollege.edu. (2)Foundation for Applied Molecular Evolution, Gainesville, FL 32604; and. (3)Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN 46202. Paleogenetics is an emerging field that resurrects ancestral proteins from now-extinct organisms to test, in the laboratory, models of protein function based on natural history and Darwinian evolution. Here, we resurrect digestive alcohol dehydrogenases (ADH4) from our primate ancestors to explore the history of primate-ethanol interactions. The evolving catalytic properties of these resurrected enzymes show that our ape ancestors gained a digestive dehydrogenase enzyme capable of metabolizing ethanol near the time that they began using the forest floor, about 10 million y ago. The ADH4 enzyme in our more ancient and arboreal ancestors did not efficiently oxidize ethanol. This change suggests that exposure to dietary sources of ethanol increased in hominids during the early stages of our adaptation to a terrestrial lifestyle. Because fruit collected from the forest floor is expected to contain higher concentrations of fermenting yeast and ethanol than similar fruits hanging on trees, this transition may also be the first time our ancestors were exposed to (and adapted to) substantial amounts of dietary ethanol. PMCID: PMC4299227 [Available on 2015/7/13] PMID: 25453080 [PubMed - in process] 34. Nucleic Acids Res. 2015 Jan 9;43(1):84-94. doi: 10.1093/nar/gku1269. Epub 2014 Nov 28. Estimating binding properties of transcription factors from genome-wide binding profiles. Zabet NR(1), Adryan B(2). Author information: (1)Cambridge Systems Biology Centre, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK n.r.zabet@gen.cam.ac.uk. (2)Cambridge Systems Biology Centre, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK ba255@cam.ac.uk. The binding of transcription factors (TFs) is essential for gene expression. One important characteristic is the actual occupancy of a putative binding site in the genome. In this study, we propose an analytical model to predict genomic occupancy that incorporates the preferred target sequence of a TF in the form of a position weight matrix (PWM), DNA accessibility data (in the case of eukaryotes), the number of TF molecules expected to be bound specifically to the DNA and a parameter that modulates the specificity of the TF. Given actual occupancy data in the form of ChIP-seq profiles, we backwards inferred copy number and specificity for five Drosophila TFs during early embryonic development: Bicoid, Caudal, Giant, Hunchback and Kruppel. Our results suggest that these TFs display thousands of molecules that are specifically bound to the DNA and that whilst Bicoid and Caudal display a higher specificity, the other three TFs (Giant, Hunchback and Kruppel) display lower specificity in their binding (despite having PWMs with higher information content). This study gives further weight to earlier investigations into TF copy numbers that suggest a significant proportion of molecules are not bound specifically to the DNA. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research. PMID: 25432957 [PubMed - in process] 35. Science. 2015 Jan 2;347(6217):1258524. doi: 10.1126/science.1258524. Epub 2014 Nov 27. Mosquito genomics. Extensive introgression in a malaria vector species complex revealed by phylogenomics. Fontaine MC(1), Pease JB(2), Steele A(3), Waterhouse RM(4), Neafsey DE(5), Sharakhov IV(6), Jiang X(7), Hall AB(7), Catteruccia F(8), Kakani E(8), Mitchell SN(9), Wu YC(10), Smith HA(1), Love RR(1), Lawniczak MK(11), Slotman MA(12), Emrich SJ(13), Hahn MW(14), Besansky NJ(15). Author information: (1)Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA. Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, USA. (2)Department of Biology, Indiana University, Bloomington, IN 47405, USA. (3)Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA. (4)Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139, USA. The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA. Department of Genetic Medicine and Development, University of Geneva Medical School, rue Michel-Servet 1, 1211 Geneva, Switzerland. Swiss Institute of Bioinformatics, rue Michel-Servet 1, 1211 Geneva, Switzerland. (5)The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA. (6)Department of Entomology, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. The Interdisciplinary PhD Program in Genetics, Bioinformatics, and Computational Biology, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. (7)The Interdisciplinary PhD Program in Genetics, Bioinformatics, and Computational Biology, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. (8)Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, MA 02115, USA. Dipartimento di Medicina Sperimentale e Scienze Biochimiche, Università degli Studi di Perugia, Perugia, Italy. (9)Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, MA 02115, USA. (10)Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139, USA. (11)Department of Life Sciences, Imperial College London, South Kensington Campus, London SW7 2AZ, UK. (12)Department of Entomology, Texas A&M University, College Station, TX 77843, USA. (13)Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, USA. Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA. (14)Department of Biology, Indiana University, Bloomington, IN 47405, USA. School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA. mwh@indiana.edu nbesansk@nd.edu. (15)Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA. Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, USA. mwh@indiana.edu nbesansk@nd.edu. Comment in Science. 2015 Jan 2;347(6217):27-8. Introgressive hybridization is now recognized as a widespread phenomenon, but its role in evolution remains contested. Here, we use newly available reference genome assemblies to investigate phylogenetic relationships and introgression in a medically important group of Afrotropical mosquito sibling species. We have identified the correct species branching order to resolve a contentious phylogeny and show that lineages leading to the principal vectors of human malaria were among the first to split. Pervasive autosomal introgression between these malaria vectors means that only a small fraction of the genome, mainly on the X chromosome, has not crossed species boundaries. Our results suggest that traits enhancing vectorial capacity may be gained through interspecific gene flow, including between nonsister species. Copyright © 2015, American Association for the Advancement of Science. PMID: 25431491 [PubMed - indexed for MEDLINE] 36. J Hered. 2015 Jan-Feb;106(1):67-79. doi: 10.1093/jhered/esu076. Epub 2014 Nov 25. Characterizing Male-Female Interactions Using Natural Genetic Variation in Drosophila melanogaster. Reinhart M(1), Carney T(1), Clark AG(1), Fiumera AC(2). Author information: (1)From the Department of Biological Sciences, Binghamton University, Binghamton, NY (Reinhart, Carney, and Fiumera); and the Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY(Clark). (2)From the Department of Biological Sciences, Binghamton University, Binghamton, NY (Reinhart, Carney, and Fiumera); and the Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY(Clark). afiumera@binghamton.edu. Drosophila melanogaster females commonly mate with multiple males establishing the opportunity for pre- and postcopulatory sexual selection. Traits impacting sexual selection can be affected by a complex interplay of the genotypes of the competing males, the genotype of the female, and compatibilities between the males and females. We scored males from 96 2nd and 94 3rd chromosome substitution lines for traits affecting reproductive success when mated with females from 3 different genetic backgrounds. The traits included male-induced female refractoriness, male remating ability, the proportion of offspring sired under competitive conditions and male-induced female fecundity. We observed significant effects of male line, female genetic background, and strong male by female interactions. Some males appeared to be "generalists" and performed consistently across the different females; other males appeared to be "specialists" and performed very well with a particular female and poorly with others. "Specialist" males did not, however, prefer to court those females with whom they had the highest reproductive fitness. Using 143 polymorphisms in male reproductive genes, we mapped several genes that had consistent effects across the different females including a derived, high fitness allele in Acp26Aa that may be the target of adaptive evolution. We also identified a polymorphism upstream of PebII that may interact with the female genetic background to affect male-induced refractoriness to remating. These results suggest that natural variation in PebII might contribute to the observed male-female interactions. © The American Genetic Association 2014. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. PMCID: PMC4261835 [Available on 2016/1/1] PMID: 25425680 [PubMed - in process] 37. Proc Natl Acad Sci U S A. 2015 Jan 6;112(1):184-9. doi: 10.1073/pnas.1408589111. Epub 2014 Nov 24. Evolutionary tipping points in the capacity to adapt to environmental change. Botero CA(1), Weissing FJ(2), Wright J(3), Rubenstein DR(4). Author information: (1)Initiative for Biological Complexity and the Department of the Interior Southeast Climate Science Center, North Carolina State University, Raleigh, NC 27695; Department of Biology, Washington University in St. Louis, St. Louis, MO 63130; c.a.botero@email.wustl.edu. (2)Centre for Ecological and Evolutionary Studies, University of Groningen, 9747 AG Groningen, The Netherlands; (3)Centre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway; and. (4)Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY 10027. In an era of rapid climate change, there is a pressing need to understand how organisms will cope with faster and less predictable variation in environmental conditions. Here we develop a unifying model that predicts evolutionary responses to environmentally driven fluctuating selection and use this theoretical framework to explore the potential consequences of altered environmental cycles. We first show that the parameter space determined by different combinations of predictability and timescale of environmental variation is partitioned into distinct regions where a single mode of response (reversible phenotypic plasticity, irreversible phenotypic plasticity, bet-hedging, or adaptive tracking) has a clear selective advantage over all others. We then demonstrate that, although significant environmental changes within these regions can be accommodated by evolution, most changes that involve transitions between regions result in rapid population collapse and often extinction. Thus, the boundaries between response mode regions in our model correspond to evolutionary tipping points, where even minor changes in environmental parameters can have dramatic and disproportionate consequences on population viability. Finally, we discuss how different life histories and genetic architectures may influence the location of tipping points in parameter space and the likelihood of extinction during such transitions. These insights can help identify and address some of the cryptic threats to natural populations that are likely to result from any natural or human-induced change in environmental conditions. They also demonstrate the potential value of evolutionary thinking in the study of global climate change. PMCID: PMC4291647 PMID: 25422451 [PubMed - in process] 38. Mol Biol Evol. 2015 Feb;32(2):495-509. doi: 10.1093/molbev/msu320. Epub 2014 Nov 17. Patterns of Linkage Disequilibrium and Long Range Hitchhiking in Evolving Experimental Drosophila melanogaster Populations. Franssen SU(1), Nolte V(1), Tobler R(1), Schlötterer C(2). Author information: (1)Institut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria. (2)Institut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria christian.schloetterer@vetmeduni.ac.at. Whole-genome resequencing of experimental populations evolving under a specific selection regime has become a popular approach to determine genotype-phenotype maps and understand adaptation to new environments. Despite its conceptual appeal and success in identifying some causative genes, it has become apparent that many studies suffer from an excess of candidate loci. Several explanations have been proposed for this phenomenon, but it is clear that information about the linkage structure during such experiments is needed. Until now only Pool-Seq (whole-genome sequencing of pools of individuals) data were available, which do not provide sufficient information about the correlation between linked sites. We address this problem in two complementary analyses of three replicate Drosophila melanogaster populations evolving to a new hot temperature environment for almost 70 generations. In the first analysis, we sequenced 58 haploid genomes from the founder population and evolved flies at generation 67. We show that during the experiment linkage disequilibrium (LD) increased almost uniformly over much greater distances than typically seen in Drosophila. In the second analysis, Pool-Seq time series data of the three replicates were combined with haplotype information from the founder population to follow blocks of initial haplotypes over time. We identified 17 selected haplotype-blocks that started at low frequencies in the base population and increased in frequency during the experiment. The size of these haplotype-blocks ranged from 0.082 to 4.01 Mb. Moreover, between 42% and 46% of the top candidate single nucleotide polymorphisms from the comparison of founder and evolved populations fell into the genomic region covered by the haplotype-blocks. We conclude that LD in such rising haplotype-blocks results in long range hitchhiking over multiple kilobase-sized regions. LD in such haplotype-blocks is therefore a major factor contributing to an excess of candidate loci. Although modifications of the experimental design may help to reduce the hitchhiking effect and allow for more precise mapping of causative variants, we also note that such haplotype-blocks might be well suited to study the dynamics of selected genomic regions during experimental evolution studies. © The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. PMID: 25415966 [PubMed - in process] 39. Genetics. 2015 Jan;199(1):73-83. doi: 10.1534/genetics.114.172072. Epub 2014 Nov 17. Normal segregation of a foreign-species chromosome during Drosophila female meiosis despite extensive heterochromatin divergence. Gilliland WD(1), Colwell EM(2), Osiecki DM(2), Park S(3), Lin D(3), Rathnam C(3), Barbash DA(4). Author information: (1)Department of Biological Sciences, DePaul University, Chicago, Illinois 60614 barbash@cornell.edu wgillila@depaul.edu. (2)Department of Biological Sciences, DePaul University, Chicago, Illinois 60614. (3)Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853. (4)Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853 barbash@cornell.edu wgillila@depaul.edu. The abundance and composition of heterochromatin changes rapidly between species and contributes to hybrid incompatibility and reproductive isolation. Heterochromatin differences may also destabilize chromosome segregation and cause meiotic drive, the non-Mendelian segregation of homologous chromosomes. Here we use a range of genetic and cytological assays to examine the meiotic properties of a Drosophila simulans chromosome 4 (sim-IV) introgressed into D. melanogaster. These two species differ by ∼12-13% at synonymous sites and several genes essential for chromosome segregation have experienced recurrent adaptive evolution since their divergence. Furthermore, their chromosome 4s are visibly different due to heterochromatin divergence, including in the AATAT pericentromeric satellite DNA. We find a visible imbalance in the positioning of the two chromosome 4s in sim-IV/mel-IV heterozygote and also replicate this finding with a D. melanogaster 4 containing a heterochromatic deletion. These results demonstrate that heterochromatin abundance can have a visible effect on chromosome positioning during meiosis. Despite this effect, however, we find that sim-IV segregates normally in both diplo and triplo 4 D. melanogaster females and does not experience elevated nondisjunction. We conclude that segregation abnormalities and a high level of meiotic drive are not inevitable byproducts of extensive heterochromatin divergence. Animal chromosomes typically contain large amounts of noncoding repetitive DNA that nevertheless varies widely between species. This variation may potentially induce non-Mendelian transmission of chromosomes. We have examined the meiotic properties and transmission of a highly diverged chromosome 4 from a foreign species within the fruitfly Drosophila melanogaster. This chromosome has substantially less of a simple sequence repeat than does D. melanogaster 4, and we find that this difference results in altered positioning when chromosomes align during meiosis. Yet this foreign chromosome segregates at normal frequencies, demonstrating that chromosome segregation can be robust to major differences in repetitive DNA abundance. Copyright © 2015 by the Genetics Society of America. PMCID: PMC4286694 [Available on 2016/1/1] PMID: 25406466 [PubMed - in process] 40. Methods Mol Biol. 2015;1253:47-70. doi: 10.1007/978-1-4939-2155-3_4. Epistasis for quantitative traits in Drosophila. Mackay TF(1). Author information: (1)Department of Biological Sciences, North Carolina State University, Campus Box 7614, Raleigh, NC, 27695-7614, USA, trudy_mackay@ncsu.edu. The role of gene-gene interactions in the genetic architecture of quantitative traits is controversial, despite the biological plausibility of nonlinear molecular interactions underpinning variation in quantitative traits. In strictly outbreeding populations, genetic architecture is inferred indirectly by estimating variance components; however, failure to detect epistatic variance does not mean lack of epistatic gene action and is even consistent with pervasive epistasis. In Drosophila, more focused approaches to detecting epistatic gene action are possible, based on the ability to create de novo mutations and perform crosses among them; to construct inbred lines, artificial selection lines, and chromosome substitution lines; to map quantitative trait loci affecting complex traits by linkage and association; and to evaluate effects of induced mutations on multiple wild-derived backgrounds. Here, I review evidence for epistasis in Drosophila from the application of these methods, and conclude that additivity is an emergent property of underlying epistatic gene action for Drosophila quantitative traits. Such studies can be used to infer novel, highly interconnected genetic networks that are enriched for gene ontology categories and metabolic and cellular pathways. The consequence of epistasis is that the main effects of each of the interacting loci depend on allele frequency, which negatively impacts the predictive ability of additive models. Finally, epistasis results in hidden quantitative genetic variation in natural populations (genetic canalization) and the potential for rapid evolution of Dobzhansky-Muller incompatibilities (speciation). PMID: 25403527 [PubMed - in process] 41. Mol Biol Evol. 2015 Jan;32(1):13-22. doi: 10.1093/molbev/msu305. Epub 2014 Nov 4. Toward more accurate ancestral protein genotype-phenotype reconstructions with the use of species tree-aware gene trees. Groussin M(1), Hobbs JK(2), Szöllősi GJ(3), Gribaldo S(4), Arcus VL(2), Gouy M(5). Author information: (1)Laboratoire de Biométrie et Biologie Evolutive, Université de Lyon, Université Lyon 1, CNRS, UMR5558, Villeurbanne, France mgroussi@mit.edu. (2)Department of Biological Sciences, University of Waikato, Hamilton, New Zealand. (3)Laboratoire de Biométrie et Biologie Evolutive, Université de Lyon, Université Lyon 1, CNRS, UMR5558, Villeurbanne, France ELTE-MTA "Lendület" Biophysics Research Group, Pázmány, Budapest, Hungary. (4)Unité de Biologie Moléculaire du Gène chez les Extrêmophiles, Département de Microbiologie, Institut Pasteur, Paris cedex, France. (5)Laboratoire de Biométrie et Biologie Evolutive, Université de Lyon, Université Lyon 1, CNRS, UMR5558, Villeurbanne, France. The resurrection of ancestral proteins provides direct insight into how natural selection has shaped proteins found in nature. By tracing substitutions along a gene phylogeny, ancestral proteins can be reconstructed in silico and subsequently synthesized in vitro. This elegant strategy reveals the complex mechanisms responsible for the evolution of protein functions and structures. However, to date, all protein resurrection studies have used simplistic approaches for ancestral sequence reconstruction (ASR), including the assumption that a single sequence alignment alone is sufficient to accurately reconstruct the history of the gene family. The impact of such shortcuts on conclusions about ancestral functions has not been investigated. Here, we show with simulations that utilizing information on species history using a model that accounts for the duplication, horizontal transfer, and loss (DTL) of genes statistically increases ASR accuracy. This underscores the importance of the tree topology in the inference of putative ancestors. We validate our in silico predictions using in vitro resurrection of the LeuB enzyme for the ancestor of the Firmicutes, a major and ancient bacterial phylum. With this particular protein, our experimental results demonstrate that information on the species phylogeny results in a biochemically more realistic and kinetically more stable ancestral protein. Additional resurrection experiments with different proteins are necessary to statistically quantify the impact of using species tree-aware gene trees on ancestral protein phenotypes. Nonetheless, our results suggest the need for incorporating both sequence and DTL information in future studies of protein resurrections to accurately define the genotype-phenotype space in which proteins diversify. © The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. PMCID: PMC4271536 PMID: 25371435 [PubMed - in process] 42. Nature. 2015 Jan 15;517(7534):369-72. doi: 10.1038/nature13827. Epub 2014 Oct 26. Long-term phenotypic evolution of bacteria. Plata G(1), Henry CS(2), Vitkup D(3). Author information: (1)1] Department of Systems Biology, Center for Computational Biology and Bioinformatics, Columbia University, New York, New York 10032, USA [2] Integrated Program in Cellular, Molecular, Structural and Genetic Studies, Columbia University, New York, New York 10032, USA. (2)Mathemathics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois 60439, USA. (3)1] Department of Systems Biology, Center for Computational Biology and Bioinformatics, Columbia University, New York, New York 10032, USA [2] Department of Biomedical Informatics, Columbia University, New York, New York 10032, USA. For many decades comparative analyses of protein sequences and structures have been used to investigate fundamental principles of molecular evolution. In contrast, relatively little is known about the long-term evolution of species' phenotypic and genetic properties. This represents an important gap in our understanding of evolution, as exactly these proprieties play key roles in natural selection and adaptation to diverse environments. Here we perform a comparative analysis of bacterial growth and gene deletion phenotypes using hundreds of genome-scale metabolic models. Overall, bacterial phenotypic evolution can be described by a two-stage process with a rapid initial phenotypic diversification followed by a slow long-term exponential divergence. The observed average divergence trend, with approximately similar fractions of phenotypic properties changing per unit time, continues for billions of years. We experimentally confirm the predicted divergence trend using the phenotypic profiles of 40 diverse bacterial species across more than 60 growth conditions. Our analysis suggests that, at long evolutionary distances, gene essentiality is significantly more conserved than the ability to utilize different nutrients, while synthetic lethality is significantly less conserved. We also find that although a rapid phenotypic evolution is sometimes observed within the same species, a transition from high to low phenotypic similarity occurs primarily at the genus level. PMID: 25363780 [PubMed - indexed for MEDLINE] 43. Genetics. 2015 Jan;199(1):39-59. doi: 10.1534/genetics.114.171850. Epub 2014 Oct 31. Positional information, positional error, and readout precision in morphogenesis: a mathematical framework. Tkačik G(1), Dubuis JO(2), Petkova MD(3), Gregor T(2). Author information: (1)Institute of Science and Technology Austria, A-3400 Klosterneuburg, Austria gasper.tkacik@ist.ac.at. (2)Joseph Henry Laboratories of Physics, Princeton University, Princeton, New Jersey 08544 Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544. (3)Joseph Henry Laboratories of Physics, Princeton University, Princeton, New Jersey 08544. The concept of positional information is central to our understanding of how cells determine their location in a multicellular structure and thereby their developmental fates. Nevertheless, positional information has neither been defined mathematically nor quantified in a principled way. Here we provide an information-theoretic definition in the context of developmental gene expression patterns and examine the features of expression patterns that affect positional information quantitatively. We connect positional information with the concept of positional error and develop tools to directly measure information and error from experimental data. We illustrate our framework for the case of gap gene expression patterns in the early Drosophila embryo and show how information that is distributed among only four genes is sufficient to determine developmental fates with nearly single-cell resolution. Our approach can be generalized to a variety of different model systems; procedures and examples are discussed in detail. Copyright © 2015 by the Genetics Society of America. PMCID: PMC4286692 [Available on 2016/1/1] PMID: 25361898 [PubMed - in process] 44. Genetics. 2015 Jan;199(1):85-93. doi: 10.1534/genetics.114.170837. Epub 2014 Oct 21. Gene Expression Variation in Drosophila melanogaster Due to Rare Transposable Element Insertion Alleles of Large Effect. Cridland JM(1), Thornton KR(2), Long AD(2). Author information: (1)Department of Evolution and Ecology, University of California, Davis, California 95616 jmcridland@ucdavis.edu. (2)Department of Ecology, Evolution, and Physiology, University of California, Irvine, California 92697. Transposable elements are a common source of genetic variation that may play a substantial role in contributing to gene expression variation. However, the contribution of transposable elements to expression variation thus far consists of a handful of examples. We used previously published gene expression data from 37 inbred Drosophila melanogaster lines from the Drosophila Genetic Reference Panel to perform a genome-wide assessment of the effects of transposable elements on gene expression. We found thousands of transcripts with transposable element insertions in or near the transcript and that the presence of a transposable element in or near a transcript is significantly associated with reductions in expression. We estimate that within this example population, ∼2.2% of transcripts have a transposable element insertion, which significantly reduces expression in the line containing the transposable element. We also find that transcripts with insertions within 500 bp of the transcript show on average a 0.67 standard deviation decrease in expression level. These large decreases in expression level are most pronounced for transposable element insertions close to transcripts and the effect diminishes for more distant insertions. This work represents the first genome-wide analysis of gene expression variation due to transposable elements and suggests that transposable elements are an important class of mutation underlying expression variation in Drosophila and likely in other systems, given the ubiquity of these mobile elements in eukaryotic genomes. Copyright © 2015 by the Genetics Society of America. PMCID: PMC4286695 PMID: 25335504 [PubMed - in process] 45. Nature. 2015 Jan 8;517(7533):196-9. doi: 10.1038/nature13825. Epub 2014 Oct 19. Copulation in antiarch placoderms and the origin of gnathostome internal fertilization. Long JA(1), Mark-Kurik E(2), Johanson Z(3), Lee MS(4), Young GC(5), Min Z(6), Ahlberg PE(7), Newman M(8), Jones R(9), den Blaauwen J(10), Choo B(11), Trinajstic K(12). Author information: (1)1] School of Biological Sciences, Flinders University, 2100, Adelaide, South Australia 5001, Australia [2] Natural History Museum of Los Angeles County, 900 Exposition Boulevard, Los Angeles, California 9007, USA [3] Museum Victoria, PO Box 666, Melbourne, Victoria 3001, Australia. (2)Institute of Geology at Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia. (3)Department of Earth Sciences, Natural History Museum, London SW7 5BD, UK. (4)1] South Australian Museum, North Terrace, Adelaide, South Australia 5000, Australia [2] School of Earth and Environmental Sciences, The University of Adelaide, South Australia 5005, Australia. (5)Research School of Earth Sciences, The Australian National University, Canberra, Australian Capital Territory 0200, Australia. (6)Key Laboratory of Evolutionary Systematics of Vertebrates, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, PO Box 643, Beijing 100044, China. (7)Department of Organismal Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18A, 752 36 Uppsala, Sweden. (8)Vine Lodge, Vine Road, Johnston, Haverfordwest, Pembrokeshire SA62 3NZ, UK. (9)6 Burghley Road, Wimbledon, London SW19 5BH, UK. (10)University of Amsterdam, Science Park 904, 1098XH, Amsterdam, The Netherlands. (11)School of Biological Sciences, Flinders University, 2100, Adelaide, South Australia 5001, Australia. (12)1] Western Australian Organic and Isotope Geochemistry Centre, Department of Chemistry, Curtin University, Perth, Western Australia 6102, Australia [2] Earth and Planetary Sciences, Western Australian Museum, Perth, Western Australia 6000, Australia. Reproduction in jawed vertebrates (gnathostomes) involves either external or internal fertilization. It is commonly argued that internal fertilization can evolve from external, but not the reverse. Male copulatory claspers are present in certain placoderms, fossil jawed vertebrates retrieved as a paraphyletic segment of the gnathostome stem group in recent studies. This suggests that internal fertilization could be primitive for gnathostomes, but such a conclusion depends on demonstrating that copulation was not just a specialized feature of certain placoderm subgroups. The reproductive biology of antiarchs, consistently identified as the least crownward placoderms and thus of great interest in this context, has until now remained unknown. Here we show that certain antiarchs possessed dermal claspers in the males, while females bore paired dermal plates inferred to have facilitated copulation. These structures are not associated with pelvic fins. The clasper morphology resembles that of ptyctodonts, a more crownward placoderm group, suggesting that all placoderm claspers are homologous and that internal fertilization characterized all placoderms. This implies that external fertilization and spawning, which characterize most extant aquatic gnathostomes, must be derived from internal fertilization, even though this transformation has been thought implausible. Alternatively, the substantial morphological evidence for placoderm paraphyly must be rejected. PMID: 25327249 [PubMed - in process] 46. Nature. 2015 Jan 1;517(7532):77-80. doi: 10.1038/nature13805. Epub 2014 Oct 15. Origins of major archaeal clades correspond to gene acquisitions from bacteria. Nelson-Sathi S(1), Sousa FL(1), Roettger M(1), Lozada-Chávez N(1), Thiergart T(1), Janssen A(2), Bryant D(3), Landan G(4), Schönheit P(5), Siebers B(6), McInerney JO(7), Martin WF(8). Author information: (1)Institute of Molecular Evolution, Heinrich-Heine University, 40225 Düsseldorf, Germany. (2)Mathematisches Institut, Heinrich-Heine University, 40225 Düsseldorf, Germany. (3)Department of Mathematics and Statistics, University of Otago, Dunedin 9054, New Zealand. (4)Genomic Microbiology Group, Institute of Microbiology, Christian-Albrechts-Universität Kiel, 24118 Kiel, Germany. (5)Institut für Allgemeine Mikrobiologie, Christian-Albrechts-Universität Kiel, 24118 Kiel, Germany. (6)Faculty of Chemistry, Biofilm Centre, Molecular Enzyme Technology and Biochemistry, University of Duisburg-Essen, 45117 Essen, Germany. (7)Department of Biology, National University of Ireland, Maynooth, County Kildare, Ireland. (8)1] Institute of Molecular Evolution, Heinrich-Heine University, 40225 Düsseldorf, Germany [2] Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa, 2780-157 Oeiras, Portugal. The mechanisms that underlie the origin of major prokaryotic groups are poorly understood. In principle, the origin of both species and higher taxa among prokaryotes should entail similar mechanisms--ecological interactions with the environment paired with natural genetic variation involving lineage-specific gene innovations and lineage-specific gene acquisitions. To investigate the origin of higher taxa in archaea, we have determined gene distributions and gene phylogenies for the 267,568 protein-coding genes of 134 sequenced archaeal genomes in the context of their homologues from 1,847 reference bacterial genomes. Archaeal-specific gene families define 13 traditionally recognized archaeal higher taxa in our sample. Here we report that the origins of these 13 groups unexpectedly correspond to 2,264 group-specific gene acquisitions from bacteria. Interdomain gene transfer is highly asymmetric, transfers from bacteria to archaea are more than fivefold more frequent than vice versa. Gene transfers identified at major evolutionary transitions among prokaryotes specifically implicate gene acquisitions for metabolic functions from bacteria as key innovations in the origin of higher archaeal taxa. PMCID: PMC4285555 [Available on 2015/7/1] PMID: 25317564 [PubMed - indexed for MEDLINE]