Publications of Bork Group in the journal "Nature".
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2024 1 publication(s).
50.
Functional and evolutionary significance of unknown genes from uncultivated taxa.
Rodríguez Del Río Á, Giner-Lamia J, Cantalapiedra CP, Botas J, Deng Z, Hernández-Plaza A, Munar-Palmer M, Santamaría-Hernando S, Rodríguez-Herva JJ, Ruscheweyh HJ, Paoli L, Schmidt TSB, Sunagawa S, Bork P, López-Solanilla E, Coelho LP, Huerta-Cepas J
Nature.
2024 Feb 18; 626(7998): 377-384. PubMed: 38109938.
AbstractMany of Earth's microbes remain uncultured and under-studied, limiting our understanding of the functional and evolutionary aspects of their genetic material, which remain largely overlooked in most metagenomic studies. Here, we analyzed 149,842 environmental genomes from multiple habitats and compiled a curated catalog of 404,085 functionally and evolutionarily significant novel (FESNov) gene families exclusive to uncultivated prokaryotic taxa. All FESNov families span multiple species, exhibit strong signals of purifying selection, and qualify as new orthologous groups, thus nearly tripling the number of bacterial and archaeal gene families described to date. The FESNov catalog is enriched in clade-specific traits, including 1,034 novel families that can distinguish entire uncultivated phyla, classes, and orders, likely representing synapomorphies that facilitated their evolutionary divergence. Using genomic context analysis and structural alignments, we predicted functional associations for 32.4% of FESNov families, including 4,349 high-confidence associations with important biological processes. These predictions provide a valuable hypothesis-driven framework, which we employed to experimentally validate a new gene family involved in cell motility and a novel set of antimicrobial peptides. We also demonstrate that the relative abundance profiles of novel families can discriminate between environments and clinical conditions, leading to the discovery of potentially new biomarkers associated with colorectal cancer. We expect this work to enhance future metagenomics studies and expand our knowledge of the genetic repertory of uncultivated organisms.
2023 1 publication(s).
49.
Questioning the fetal microbiome illustrates pitfalls of low-biomass microbial studies.
Kennedy KM, de Goffau MC, Perez-Muñoz ME, Arrieta MC, Bäckhed F, Bork P, Braun T, Bushman FD, Dore J, de Vos WM, Earl AM, Eisen JA, Elovitz MA, Ganal-Vonarburg SC, Gänzle MG, Garrett WS, Hall LJ, Hornef MW, Huttenhower C, Konnikova L, Lebeer S, Macpherson AJ, Massey RC, McHardy AC, Koren O, Lawley TD, Ley RE, O\'Mahony L, O\'Toole PW, Pamer EG, Parkhill J, Raes J, Rattei T, Salonen A, Segal E, Segata N, Shanahan F, Sloboda DM, Smith GCS, Sokol H, Spector TD, Surette MG, Tannock GW, Walker AW, Yassour M, Walter J
Nature.
2023 Jan; 613(7945): 639-649. PubMed: 36697862.AbstractWhether the human fetus and the prenatal intrauterine environment (amniotic fluid and placenta) are stably colonized by microbial communities in a healthy pregnancy remains a subject of debate. Here we evaluate recent studies that characterized microbial populations in human fetuses from the perspectives of reproductive biology, microbial ecology, bioinformatics, immunology, clinical microbiology and gnotobiology, and assess possible mechanisms by which the fetus might interact with microorganisms. Our analysis indicates that the detected microbial signals are likely the result of contamination during the clinical procedures to obtain fetal samples or during DNA extraction and DNA sequencing. Furthermore, the existence of live and replicating microbial populations in healthy fetal tissues is not compatible with fundamental concepts of immunology, clinical microbiology and the derivation of germ-free mammals. These conclusions are important to our understanding of human immune development and illustrate common pitfalls in the microbial analyses of many other low-biomass environments. The pursuit of a fetal microbiome serves as a cautionary example of the challenges of sequence-based microbiome studies when biomass is low or absent, and emphasizes the need for a trans-disciplinary approach that goes beyond contamination controls by also incorporating biological, ecological and mechanistic concepts.
2022 4 publication(s).
48.
Towards the biogeography of prokaryotic genes.
Coelho LP, Alves RJ, Del Río ÁR, Myers PN, Cantalapiedra CP, Giner-Lamia J, Schmidt TSB, Mende DR, Orakov A, Letunic I, Hildebrand F, Van Rossum T, Forslund SK, Khedkar S, Maistrenko OM, Pan S, Jia L, Ferretti P, Sunagawa S, Zhao XM, Nielsen HB, Huerta-Cepas J, Bork P
Nature.
2022 Jan 15; 601(7892): 252-256. PubMed: 34912116.Abstract + PDFMicrobial genes encode the majority of the functional repertoire of life on earth. However, despite increasing efforts in metagenomic sequencing of various habitats, little is known about the distribution of genes across the global biosphere, with implications for human and planetary health. Here we constructed a non-redundant gene catalogue of 303 million species-level genes (clustered at 95% nucleotide identity) from 13,174 publicly available metagenomes across 14 major habitats and use it to show that most genes are specific to a single habitat. The small fraction of genes found in multiple habitats is enriched in antibiotic-resistance genes and markers for mobile genetic elements. By further clustering these species-level genes into 32 million protein families, we observed that a small fraction of these families contain the majority of the genes (0.6% of families account for 50% of the genes). The majority of species-level genes and protein families are rare. Furthermore, species-level genes, and in particular the rare ones, show low rates of positive (adaptive) selection, supporting a model in which most genetic variability observed within each protein family is neutral or nearly neutral.
47.
Biosynthetic potential of the global ocean microbiome.
Paoli L, Ruscheweyh HJ, Forneris CC, Hubrich F, Kautsar S, Bhushan A, Lotti A, Clayssen Q, Salazar G, Milanese A, Carlström CI, Papadopoulou C, Gehrig D, Karasikov M, Mustafa H, Larralde M, Carroll LM, Sánchez P, Zayed AA, Cronin DR, Acinas SG, Bork P, Bowler C, Delmont TO, Gasol JM, Gossert AD, Kahles A, Sullivan MB, Wincker P, Zeller G, Robinson SL, Piel J, Sunagawa S
Nature.
2022 Jul 22; 607(7917): 111-118. PubMed: 35732736.Abstract + PDFNatural microbial communities are phylogenetically and metabolically diverse. In addition to underexplored organismal groups, this diversity encompasses a rich discovery potential for ecologically and biotechnologically relevant enzymes and biochemical compounds. However, studying this diversity to identify genomic pathways for the synthesis of such compounds and assigning them to their respective hosts remains challenging. The biosynthetic potential of microorganisms in the open ocean remains largely uncharted owing to limitations in the analysis of genome-resolved data at the global scale. Here we investigated the diversity and novelty of biosynthetic gene clusters in the ocean by integrating around 10,000 microbial genomes from cultivated and single cells with more than 25,000 newly reconstructed draft genomes from more than 1,000 seawater samples. These efforts revealed approximately 40,000 putative mostly new biosynthetic gene clusters, several of which were found in previously unsuspected phylogenetic groups. Among these groups, we identified a lineage rich in biosynthetic gene clusters ('Candidatus Eudoremicrobiaceae') that belongs to an uncultivated bacterial phylum and includes some of the most biosynthetically diverse microorganisms in this environment. From these, we characterized the phospeptin and pythonamide pathways, revealing cases of unusual bioactive compound structure and enzymology, respectively. Together, this research demonstrates how microbiomics-driven strategies can enable the investigation of previously undescribed enzymes and natural products in underexplored microbial groups and environments.
46.
Publisher Correction: Visualizing translation dynamics at atomic detail inside a bacterial cell.
Xue L, Lenz S, Zimmermann-Kogadeeva M, Tegunov D, Cramer P, Bork P, Rappsilber J, Mahamid J
Nature.
2022 Nov 10; 611(7937): E13. PubMed: 36357800.PDF45.
Visualizing translation dynamics at atomic detail inside a bacterial cell.
Xue L, Lenz S, Zimmermann-Kogadeeva M, Tegunov D, Cramer P, Bork P, Rappsilber J, Mahamid J
Nature.
2022 Oct 28; 610(7930): 205-211. PubMed: 36171285.Abstract + PDFTranslation is the fundamental process of protein synthesis and is catalysed by the ribosome in all living cells. Here we use advances in cryo-electron tomography and sub-tomogram analysis to visualize the structural dynamics of translation inside the bacterium Mycoplasma pneumoniae. To interpret the functional states in detail, we first obtain a high-resolution in-cell average map of all translating ribosomes and build an atomic model for the M. pneumoniae ribosome that reveals distinct extensions of ribosomal proteins. Classification then resolves 13 ribosome states that differ in their conformation and composition. These recapitulate major states that were previously resolved in vitro, and reflect intermediates during active translation. On the basis of these states, we animate translation elongation inside native cells and show how antibiotics reshape the cellular translation landscapes. During translation elongation, ribosomes often assemble in defined three-dimensional arrangements to form polysomes. By mapping the intracellular organization of translating ribosomes, we show that their association into polysomes involves a local coordination mechanism that is mediated by the ribosomal protein L9. We propose that an extended conformation of L9 within polysomes mitigates collisions to facilitate translation fidelity. Our work thus demonstrates the feasibility of visualizing molecular processes at atomic detail inside cells.
2021 4 publication(s).
44.
Combinatorial, additive and dose-dependent drug-microbiome associations.
Forslund SK, Chakaroun R, Zimmermann-Kogadeeva M, Markó L, Aron-Wisnewsky J, Nielsen T, Moitinho-Silva L, Schmidt TSB, Falony G, Vieira-Silva S, Adriouch S, Alves RJ, Assmann K, Bastard JP, Birkner T, Caesar R, Chilloux J, Coelho LP, Fezeu L, Galleron N, Helft G, Isnard R, Ji B, Kuhn M, Le Chatelier E, Myridakis A, Olsson L, Pons N, Prifti E, Quinquis B, Roume H, Salem JE, Sokolovska N, Tremaroli V, Valles-Colomer M, Lewinter C, Søndertoft NB, Pedersen HK, Hansen TH, MetaCardis Consortium , Gøtze JP, Køber L, Vestergaard H, Hansen T, Zucker JD, Hercberg S, Oppert JM, Letunic I, Nielsen J, Bäckhed F, Ehrlich SD, Dumas ME, Raes J, Pedersen O, Clément K, Stumvoll M, Bork P
Nature.
2021 Dec 8; 600(7889): 500-505. PubMed: 34880489.Abstract + PDFDuring the transition from a healthy state to cardiometabolic disease, patients become heavily medicated, which leads to an increasingly aberrant gut microbiome and serum metabolome, and complicates biomarker discovery. Here, through integrated multi-omics analyses of 2,173 European residents from the MetaCardis cohort, we show that the explanatory power of drugs for the variability in both host and gut microbiome features exceeds that of disease. We quantify inferred effects of single medications, their combinations as well as additive effects, and show that the latter shift the metabolome and microbiome towards a healthier state, exemplified in synergistic reduction in serum atherogenic lipoproteins by statins combined with aspirin, or enrichment of intestinal Roseburia by diuretic agents combined with beta-blockers. Several antibiotics exhibit a quantitative relationship between the number of courses prescribed and progression towards a microbiome state that is associated with the severity of cardiometabolic disease. We also report a relationship between cardiometabolic drug dosage, improvement in clinical markers and microbiome composition, supporting direct drug effects. Taken together, our computational framework and resulting resources enable the disentanglement of the effects of drugs and disease on host and microbiome features in multimedicated individuals. Furthermore, the robust signatures identified using our framework provide new hypotheses for drug-host-microbiome interactions in cardiometabolic disease.
43.
Bioaccumulation of therapeutic drugs by human gut bacteria.
Klünemann M, Andrejev S, Blasche S, Mateus A, Phapale P, Devendran S, Vappiani J, Simon B, Scott TA, Kafkia E, Konstantinidis D, Zirngibl K, Mastrorilli E, Banzhaf M, Mackmull MT, Hövelmann F, Nesme L, Brochado AR, Maier L, Bock T, Periwal V, Kumar M, Kim Y, Tramontano M, Schultz C, Beck M, Hennig J, Zimmermann M, Sévin DC, Cabreiro F, Savitski MM, Bork P, Typas A, Patil KR
Nature.
2021 Sep 8; 597(7877): 533-538. PubMed: 34497420.Abstract + PDFBacteria in the gut can modulate the availability and efficacy of therapeutic drugs. However, the systematic mapping of the interactions between drugs and bacteria has only started recently and the main underlying mechanism proposed is the chemical transformation of drugs by microorganisms (biotransformation). Here we investigated the depletion of 15 structurally diverse drugs by 25 representative strains of gut bacteria. This revealed 70 bacteria-drug interactions, 29 of which had not to our knowledge been reported before. Over half of the new interactions can be ascribed to bioaccumulation; that is, bacteria storing the drug intracellularly without chemically modifying it, and in most cases without the growth of the bacteria being affected. As a case in point, we studied the molecular basis of bioaccumulation of the widely used antidepressant duloxetine by using click chemistry, thermal proteome profiling and metabolomics. We find that duloxetine binds to several metabolic enzymes and changes the metabolite secretion of the respective bacteria. When tested in a defined microbial community of accumulators and non-accumulators, duloxetine markedly altered the composition of the community through metabolic cross-feeding. We further validated our findings in an animal model, showing that bioaccumulating bacteria attenuate the behavioural response of Caenorhabditis elegans to duloxetine. Together, our results show that bioaccumulation by gut bacteria may be a common mechanism that alters drug availability and bacterial metabolism, with implications for microbiota composition, pharmacokinetics, side effects and drug responses, probably in an individual manner.
42.
Unravelling the collateral damage of antibiotics on gut bacteria.
Maier L, Goemans CV, Wirbel J, Kuhn M, Eberl C, Pruteanu M, Müller P, Garcia-Santamarina S, Cacace E, Zhang B, Gekeler C, Banerjee T, Anderson EE, Milanese A, Löber U, Forslund SK, Patil KR, Zimmermann M, Stecher B, Zeller G, Bork P, Typas A
Nature.
2021 Nov 13; 599(7883): 120-124. PubMed: 34646011.Abstract + PDFAntibiotics are used to fight pathogens but also target commensal bacteria, disturbing the composition of gut microbiota and causing dysbiosis and disease. Despite this well-known collateral damage, the activity spectrum of different antibiotic classes on gut bacteria remains poorly characterized. Here we characterize further 144 antibiotics from a previous screen of more than 1,000 drugs on 38 representative human gut microbiome species. Antibiotic classes exhibited distinct inhibition spectra, including generation dependence for quinolones and phylogeny independence for β-lactams. Macrolides and tetracyclines, both prototypic bacteriostatic protein synthesis inhibitors, inhibited nearly all commensals tested but also killed several species. Killed bacteria were more readily eliminated from in vitro communities than those inhibited. This species-specific killing activity challenges the long-standing distinction between bactericidal and bacteriostatic antibiotic classes and provides a possible explanation for the strong effect of macrolides on animal and human gut microbiomes. To mitigate this collateral damage of macrolides and tetracyclines, we screened for drugs that specifically antagonized the antibiotic activity against abundant Bacteroides species but not against relevant pathogens. Such antidotes selectively protected Bacteroides species from erythromycin treatment in human-stool-derived communities and gnotobiotic mice. These findings illluminate the activity spectra of antibiotics in commensal bacteria and suggest strategies to circumvent their adverse effects on the gut microbiota.
41.
Swarm Learning for decentralized and confidential clinical machine learning.
Warnat-Herresthal S, Schultze H, Shastry KL, Manamohan S, Mukherjee S, Garg V, Sarveswara R, Händler K, Pickkers P, Aziz NA, Ktena S, Tran F, Bitzer M, Ossowski S, Casadei N, Herr C, Petersheim D, Behrends U, Kern F, Fehlmann T, Schommers P, Lehmann C, Augustin M, Rybniker J, Altmüller J, Mishra N, Bernardes JP, Krämer B, Bonaguro L, Schulte-Schrepping J, De Domenico E, Siever C, Kraut M, Desai M, Monnet B, Saridaki M, Siegel CM, Drews A, Nuesch-Germano M, Theis H, Heyckendorf J, Schreiber S, Kim-Hellmuth S, COVID-19 Aachen Study (COVAS) , Nattermann J, Skowasch D, Kurth I, Keller A, Bals R, Nürnberg P, Rieß O, Rosenstiel P, Netea MG, Theis F, Mukherjee S, Backes M, Aschenbrenner AC, Ulas T, Deutsche COVID-19 OMICS Initiative (DeCOI) , Breteler MMB, Giamarellos-Bourboulis EJ, Kox M, Becker M, Cheran S, Woodacre MS, Goh EL, Schultze JL, Balfanz P, Eggermann T, Boor P, Hausmann R, Kuhn H, Isfort S, Stingl JC, Schmalzing G, Kuhl CK, Röhrig R, Marx G, Uhlig S, Dahl E, Müller-Wieland D, Dreher M, Marx N, Angelov A, Bartholomäus A, Becker A, Bezdan D, Blumert C, Bonifacio E, Bork P, Boyke B, Blum H, Clavel T, Colome-Tatche M, Cornberg M, Velázquez IADL, Diefenbach A, Dilthey A, Fischer N, Förstner K, Franzenburg S, Frick JS, Gabernet G, Gagneur J, Ganzenmueller T, Gauder M, Geißert J, Goesmann A, Göpel S, Grundhoff A, Grundmann H, Hain T, Hanses F, Hehr U, Heimbach A, Hoeper M, Horn F, Hübschmann D, Hummel M, Iftner T, Iftner A, Illig T, Janssen S, Kalinowski J, Kallies R, Kehr B, Keppler OT, Klein C, Knop M, Kohlbacher O, Köhrer K, Korbel J, Kremsner PG, Kühnert D, Landthaler M, Li Y, Ludwig KU, Makarewicz O, Marz M, McHardy AC, Mertes C, Münchhoff M, Nahnsen S, Nöthen M, Ntoumi F, Overmann J, Peter S, Pfeffer K, Pink I, Poetsch AR, Protzer U, Pühler A, Rajewsky N, Ralser M, Reiche K, Ripke S, da Rocha UN, Saliba AE, Sander LE, Sawitzki B, Scheithauer S, Schiffer P, Schmid-Burgk J, Schneider W, Schulte EC, Sczyrba A, Sharaf ML, Singh Y, Sonnabend M, Stegle O, Stoye J, Vehreschild J, Velavan TP, Vogel J, Volland S, von Kleist M, Walker A, Walter J, Wieczorek D, Winkler S, Ziebuhr J
Nature.
2021 06; 594(7862): 265-270. PubMed: 34040261.Abstract + PDFFast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning-a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.
2020 1 publication(s).
40.
Statin therapy is associated with lower prevalence of gut microbiota dysbiosis.
Vieira-Silva S, Falony G, Belda E, Nielsen T, Aron-Wisnewsky J, Chakaroun R, Forslund SK, Assmann K, Valles-Colomer M, Nguyen TTD, Proost S, Prifti E, Tremaroli V, Pons N, Le Chatelier E, Andreelli F, Bastard JP, Coelho LP, Galleron N, Hansen TH, Hulot JS, Lewinter C, Pedersen HK, Quinquis B, Rouault C, Roume H, Salem JE, Søndertoft NB, Touch S, MetaCardis Consortium: , Dumas E-M , Ehrlich SD, Galan P, Gøtze JP, Hansen T, Holst JJ, Køber L, Letunic I, Nielsen J, Oppert J-M , Stumvoll M, Vestergaard H, Zucker J-D , Bork P, Pedersen O, Bäckhed F, Clément K, Raes J
Nature.
2020 May; 581(7808): 310-315. PubMed: 32433607.Abstract + PDFMicrobiome community typing analyses have recently identified the Bacteroides2 (Bact2) enterotype, an intestinal microbiota configuration that is associated with systemic inflammation and has a high prevalence in loose stools in humans. Bact2 is characterized by a high proportion of Bacteroides, a low proportion of Faecalibacterium and low microbial cell densities, and its prevalence varies from 13% in a general population cohort to as high as 78% in patients with inflammatory bowel disease. Reported changes in stool consistency and inflammation status during the progression towards obesity and metabolic comorbidities led us to propose that these developments might similarly correlate with an increased prevalence of the potentially dysbiotic Bact2 enterotype. Here, by exploring obesity-associated microbiota alterations in the quantitative faecal metagenomes of the cross-sectional MetaCardis Body Mass Index Spectrum cohort (n = 888), we identify statin therapy as a key covariate of microbiome diversification. By focusing on a subcohort of participants that are not medicated with statins, we find that the prevalence of Bact2 correlates with body mass index, increasing from 3.90% in lean or overweight participants to 17.73% in obese participants. Systemic inflammation levels in Bact2-enterotyped individuals are higher than predicted on the basis of their obesity status, indicative of Bact2 as a dysbiotic microbiome constellation. We also observe that obesity-associated microbiota dysbiosis is negatively associated with statin treatment, resulting in a lower Bact2 prevalence of 5.88% in statin-medicated obese participants. This finding is validated in both the accompanying MetaCardis cardiovascular disease dataset (n = 282) and the independent Flemish Gut Flora Project population cohort (n = 2,345). The potential benefits of statins in this context will require further evaluation in a prospective clinical trial to ascertain whether the effect is reproducible in a randomized population and before considering their application as microbiota-modulating therapeutics.
2018 3 publication(s).
39.
Structure and function of the global topsoil microbiome.
Bahram M, Hildebrand F, Forslund SK, Anderson JL, Soudzilovskaia NA, Bodegom PM, Bengtsson-Palme J, Anslan S, Coelho LP, Harend H, Huerta-Cepas J, Medema MH, Maltz MR, Mundra S, Olsson PA, Pent M, Põlme S, Sunagawa S, Ryberg M, Tedersoo L, Bork P
Nature.
2018 Aug 1; 560(7717): 233-237. PubMed: 30069051.Abstract + PDFSoils harbour some of the most diverse microbiomes on Earth and are essential for both nutrient cycling and carbon storage. To understand soil functioning, it is necessary to model the global distribution patterns and functional gene repertoires of soil microorganisms, as well as the biotic and environmental associations between the diversity and structure of both bacterial and fungal soil communities. Here we show, by leveraging metagenomics and metabarcoding of global topsoil samples (189 sites, 7,560 subsamples), that bacterial, but not fungal, genetic diversity is highest in temperate habitats and that microbial gene composition varies more strongly with environmental variables than with geographic distance. We demonstrate that fungi and bacteria show global niche differentiation that is associated with contrasting diversity responses to precipitation and soil pH. Furthermore, we provide evidence for strong bacterial-fungal antagonism, inferred from antibiotic-resistance genes, in topsoil and ocean habitats, indicating the substantial role of biotic interactions in shaping microbial communities. Our results suggest that both competition and environmental filtering affect the abundance, composition and encoded gene functions of bacterial and fungal communities, indicating that the relative contributions of these microorganisms to global nutrient cycling varies spatially.
38.
Species-specific activity of antibacterial drug combinations.
Brochado AR, Telzerow A, Bobonis J, Banzhaf M, Mateus A, Selkrig J, Huth E, Bassler S, Zamarreño Beas J, Zietek M, Ng N, Foerster S, Ezraty B, Py B, Barras F, Savitski MM, Bork P, Göttig S, Typas A
Nature.
2018 Jul 4; 559(7713): 259-263. PubMed: 29973719.Abstract + PDFThe spread of antimicrobial resistance has become a serious public health concern, making once-treatable diseases deadly again and undermining the achievements of modern medicine. Drug combinations can help to fight multi-drug-resistant bacterial infections, yet they are largely unexplored and rarely used in clinics. Here we profile almost 3,000 dose-resolved combinations of antibiotics, human-targeted drugs and food additives in six strains from three Gram-negative pathogens-Escherichia coli, Salmonella enterica serovar Typhimurium and Pseudomonas aeruginosa-to identify general principles for antibacterial drug combinations and understand their potential. Despite the phylogenetic relatedness of the three species, more than 70% of the drug-drug interactions that we detected are species-specific and 20% display strain specificity, revealing a large potential for narrow-spectrum therapies. Overall, antagonisms are more common than synergies and occur almost exclusively between drugs that target different cellular processes, whereas synergies are more conserved and are enriched in drugs that target the same process. We provide mechanistic insights into this dichotomy and further dissect the interactions of the food additive vanillin. Finally, we demonstrate that several synergies are effective against multi-drug-resistant clinical isolates in vitro and during infections of the larvae of the greater wax moth Galleria mellonella, with one reverting resistance to the last-resort antibiotic colistin.
37.
Extensive impact of non-antibiotic drugs on human gut bacteria.
Maier L, Pruteanu M, Kuhn M, Zeller G, Telzerow A, Anderson EE, Brochado AR, Fernandez KC, Dose H, Mori H, Patil KR, Bork P, Typas A
Nature.
2018 Mar 29; 555(7698): 623-628. PubMed: 29555994.Abstract + PDFA few commonly used non-antibiotic drugs have recently been associated with changes in gut microbiome composition, but the extent of this phenomenon is unknown. Here, we screened more than 1,000 marketed drugs against 40 representative gut bacterial strains, and found that 24% of the drugs with human targets, including members of all therapeutic classes, inhibited the growth of at least one strain in vitro. Particular classes, such as the chemically diverse antipsychotics, were overrepresented in this group. The effects of human-targeted drugs on gut bacteria are reflected on their antibiotic-like side effects in humans and are concordant with existing human cohort studies. Susceptibility to antibiotics and human-targeted drugs correlates across bacterial species, suggesting common resistance mechanisms, which we verified for some drugs. The potential risk of non-antibiotics promoting antibiotic resistance warrants further exploration. Our results provide a resource for future research on drug-microbiome interactions, opening new paths for side effect control and drug repurposing, and broadening our view of antibiotic resistance.
2017 2 publication(s).
36.
Corrigendum: Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota.
Forslund K, Hildebrand F, Nielsen T, Falony G, Le Chatelier E, Sunagawa S, Prifti E, Vieira-Silva S, Gudmundsdottir V, Pedersen HK, Arumugam M, Kristiansen K, Voigt AY, Vestergaard H, Hercog R, Costea PI, Kultima JR, Li J, Jørgensen T, Levenez F, Dore J, MetaHit-Consortium , Nielsen HB, Brunak S, Raes J, Hansen T, Wang J, Ehrlich SD, Bork P, Pedersen O
Nature.
2017 May 3; 545(7652): 116. PubMed: 28470190.PDF35.
Salt-responsive gut commensal modulates TH17 axis and disease.
Wilck N, Matus MG, Kearney SM, Olesen SW, Forslund K, Bartolomaeus H, Haase S, Mähler A, Balogh A, Markó L, Vvedenskaya O, Kleiner FH, Tsvetkov D, Klug L, Costea PI, Sunagawa S, Maier L, Rakova N, Schatz V, Neubert P, Frätzer C, Krannich A, Gollasch M, Grohme DA, Côrte-Real BF, Gerlach RG, Basic M, Typas A, Wu C, Titze JM, Jantsch J, Boschmann M, Dechend R, Kleinewietfeld M, Kempa S, Bork P, Linker RA, Alm EJ, Müller DN
Nature.
2017 Nov 30; 551(7682): 585-589. PubMed: 29143823.Abstract + PDFA Western lifestyle with high salt consumption can lead to hypertension and cardiovascular disease. High salt may additionally drive autoimmunity by inducing T helper 17 (TH17) cells, which can also contribute to hypertension. Induction of TH17 cells depends on gut microbiota; however, the effect of salt on the gut microbiome is unknown. Here we show that high salt intake affects the gut microbiome in mice, particularly by depleting Lactobacillus murinus. Consequently, treatment of mice with L. murinus prevented salt-induced aggravation of actively induced experimental autoimmune encephalomyelitis and salt-sensitive hypertension by modulating TH17 cells. In line with these findings, a moderate high-salt challenge in a pilot study in humans reduced intestinal survival of Lactobacillus spp., increased TH17 cells and increased blood pressure. Our results connect high salt intake to the gut-immune axis and highlight the gut microbiome as a potential therapeutic target to counteract salt-sensitive conditions.
2016 3 publication(s).
34.
Plankton networks driving carbon export in the oligotrophic ocean.
Guidi L, Chaffron S, Bittner L, Eveillard D, Larhlimi A, Roux S, Darzi Y, Audic S, Berline L, Brum J, Coelho LP, Espinoza JC, Malviya S, Sunagawa S, Dimier C, Kandels-Lewis S, Picheral M, Poulain J, Searson S, Stemmann L, Not F, Hingamp P, Speich S, Follows M, Karp-Boss L, Boss E, Ogata H, Pesant S, Weissenbach J, Wincker P, Acinas SG, Bork P, De Vargas C, Iudicone D, Sullivan MB, Raes J, Karsenti E, Bowler C, Gorsky G.
Nature.
2016 Apr 28; 532(7600): 465-470. Epub 2016 Feb 10; PubMed: 26863193.Abstract + PDFThe biological carbon pump is the process by which CO2 is transformed to organic carbon via photosynthesis, exported through sinking particles, and finally sequestered in the deep ocean. While the intensity of the pump correlates with plankton community composition, the underlying ecosystem structure driving the process remains largely uncharacterized. Here we use environmental and metagenomic data gathered during the Tara Oceans expedition to improve our understanding of carbon export in the oligotrophic ocean. We show that specific plankton communities, from the surface and deep chlorophyll maximum, correlate with carbon export at 150 m and highlight unexpected taxa such as Radiolaria and alveolate parasites, as well as Synechococcus and their phages, as lineages most strongly associated with carbon export in the subtropical, nutrient-depleted, oligotrophic ocean. Additionally, we show that the relative abundance of a few bacterial and viral genes can predict a significant fraction of the variability in carbon export in these regions.
33.
Human gut microbes impact host serum metabolome and insulin sensitivity.
Pedersen HK, Gudmundsdottir V, Nielsen HB, Hyotylainen T, Nielsen T, Jensen BA, Forslund K, Hildebrand F, Prifti E, Falony G, Le Chatelier E, Levenez F, Doré J, Mattila I, Plichta DR, Pöhö P, Hellgren LI, Arumugam M, Sunagawa S, Vieira-Silva S, Jørgensen T, Holm JB, Trošt K, Consortium M, Kristiansen K, Brix S, Raes J, Wang J, Hansen T, Bork P, Brunak S, Oresic M, Ehrlich SD, Pedersen O
Nature.
2016 Jul 21; 535(7612): 376-81. Epub 2016 Jul 13; PubMed: 27409811.Abstract + PDFInsulin resistance is a forerunner state of ischaemic cardiovascular disease and type 2 diabetes. Here we show how the human gut microbiome impacts the serum metabolome and associates with insulin resistance in 277 non-diabetic Danish individuals. The serum metabolome of insulin-resistant individuals is characterized by increased levels of branched-chain amino acids (BCAAs), which correlate with a gut microbiome that has an enriched biosynthetic potential for BCAAs and is deprived of genes encoding bacterial inward transporters for these amino acids. Prevotella copri and Bacteroides vulgatus are identified as the main species driving the association between biosynthesis of BCAAs and insulin resistance, and in mice we demonstrate that P. copri can induce insulin resistance, aggravate glucose intolerance and augment circulating levels of BCAAs. Our findings suggest that microbial targets may have the potential to diminish insulin resistance and reduce the incidence of common metabolic and cardiovascular disorders.
32.
Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses.
Roux S, Brum JR, Dutilh BE, Sunagawa S, Duhaime MB, Loy A, Poulos BT, Solonenko N, Lara E, Poulain J, Pesant S, Kandels-Lewis S, Dimier C, Picheral M, Searson S, Cruaud C, Alberti A, Duarte CM, Gasol JM, Vaqué D, Tara-Oceans-Coordinators , Bork P, Acinas SG, Wincker P, Sullivan MB
Nature.
2016 Sep 29; 537(7622): 689-693. Epub 2016 Sep 21; PubMed: 27654921.Abstract + PDFOcean microbes drive biogeochemical cycling on a global scale. However, this cycling is constrained by viruses that affect community composition, metabolic activity, and evolutionary trajectories. Owing to challenges with the sampling and cultivation of viruses, genome-level viral diversity remains poorly described and grossly understudied, with less than 1% of observed surface-ocean viruses known. Here we assemble complete genomes and large genomic fragments from both surface- and deep-ocean viruses sampled during the Tara Oceans and Malaspina research expeditions, and analyse the resulting 'global ocean virome' dataset to present a global map of abundant, double-stranded DNA viruses complete with genomic and ecological contexts. A total of 15,222 epipelagic and mesopelagic viral populations were identified, comprising 867 viral clusters (defined as approximately genus-level groups). This roughly triples the number of known ocean viral populations and doubles the number of candidate bacterial and archaeal virus genera, providing a near-complete sampling of epipelagic communities at both the population and viral-cluster level. We found that 38 of the 867 viral clusters were locally or globally abundant, together accounting for nearly half of the viral populations in any global ocean virome sample. While two-thirds of these clusters represent newly described viruses lacking any cultivated representative, most could be computationally linked to dominant, ecologically relevant microbial hosts. Moreover, we identified 243 viral-encoded auxiliary metabolic genes, of which only 95 were previously known. Deeper analyses of four of these auxiliary metabolic genes (dsrC, soxYZ, P-II (also known as glnB) and amoC) revealed that abundant viruses may directly manipulate sulfur and nitrogen cycling throughout the epipelagic ocean. This viral catalog and functional analyses provide a necessary foundation for the meaningful integration of viruses into ecosystem models where they act as key players in nutrient cycling and trophic networks.
2015 2 publication(s).
31.
Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota.
Forslund K, Hildebrand F, Nielsen T, Falony G, Le Chatelier E, Sunagawa S, Prifti E, Vieira-Silva S, Gudmundsdottir V, Krogh Pedersen H, Arumugam M, Kristiansen K, Voigt AY, Vestergaard H, Hercog R, Costea PI, Kultima JR, Li J, Jørgensen T, Levenez F, Dore J, MetaHIT C, Nielsen HB, Brunak S, Raes J, Hansen T, Wang J, Ehrlich SD, Bork P, Pedersen O
Nature.
2015 Dec 10; 528(7581): 262-266. Epub 2015 Dec 2; PubMed: 26633628.Abstract + PDFIn recent years, several associations between common chronic human disorders and altered gut microbiome composition and function have been reported. In most of these reports, treatment regimens were not controlled for and conclusions could thus be confounded by the effects of various drugs on the microbiota, which may obscure microbial causes, protective factors or diagnostically relevant signals. Our study addresses disease and drug signatures in the human gut microbiome of type 2 diabetes mellitus (T2D). Two previous quantitative gut metagenomics studies of T2D patients that were unstratified for treatment yielded divergent conclusions regarding its associated gut microbial dysbiosis. Here we show, using 784 available human gut metagenomes, how antidiabetic medication confounds these results, and analyse in detail the effects of the most widely used antidiabetic drug metformin. We provide support for microbial mediation of the therapeutic effects of metformin through short-chain fatty acid production, as well as for potential microbiota-mediated mechanisms behind known intestinal adverse effects in the form of a relative increase in abundance of Escherichia species. Controlling for metformin treatment, we report a unified signature of gut microbiome shifts in T2D with a depletion of butyrate-producing taxa. These in turn cause functional microbiome shifts, in part alleviated by metformin-induced changes. Overall, the present study emphasizes the need to disentangle gut microbiota signatures of specific human diseases from those of medication.
30.
In situ structural analysis of the human nuclear pore complex.
von Appen A, Kosinski J, Sparks L, Ori A, DiGuilio AL, Vollmer B, Mackmull MT, Banterle N, Parca L, Kastritis P, Buczak K, Mosalaganti S, Hagen W, Andres-Pons A, Lemke EA, Bork P, Antonin W, Glavy JS, Bui KH, Beck M
Nature.
2015 Oct 1; 526(7571): 140-143. Epub 2015 Sep 23; PubMed: 26416747.Abstract + PDFNuclear pore complexes are fundamental components of all eukaryotic cells that mediate nucleocytoplasmic exchange. Determining their 110-megadalton structure imposes a formidable challenge and requires in situ structural biology approaches. Of approximately 30 nucleoporins (Nups), 15 are structured and form the Y and inner-ring complexes. These two major scaffolding modules assemble in multiple copies into an eight-fold rotationally symmetric structure that fuses the inner and outer nuclear membranes to form a central channel of ~60 nm in diameter. The scaffold is decorated with transport-channel Nups that often contain phenylalanine-repeat sequences and mediate the interaction with cargo complexes. Although the architectural arrangement of parts of the Y complex has been elucidated, it is unclear how exactly it oligomerizes in situ. Here we combine cryo-electron tomography with mass spectrometry, biochemical analysis, perturbation experiments and structural modelling to generate, to our knowledge, the most comprehensive architectural model of the human nuclear pore complex to date. Our data suggest previously unknown protein interfaces across Y complexes and to inner-ring complex members. We show that the transport-channel Nup358 (also known as Ranbp2) has a previously unanticipated role in Y-complex oligomerization. Our findings blur the established boundaries between scaffold and transport-channel Nups. We conclude that, similar to coated vesicles, several copies of the same structural building block-although compositionally identical-engage in different local sets of interactions and conformations.
2014 2 publication(s).
29.
Addendum: Enterotypes of the human gut microbiome.
Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, Mende DR, Fernandes GR, Tap J, Bruls T, Batto JM, Bertalan M, Borruel N, Casellas F, Fernandez L, Gautier L, Hansen T, Hattori M, Hayashi T, Kleerebezem M, Kurokawa K, Leclerc M, Levenez F, Manichanh C, Nielsen HB, Nielsen T, Pons N, Poulain J, Qin J, Sicheritz-Ponten T, Tims S, Torrents D, Ugarte E, Zoetendal EG, Wang J, Guarner F, Pedersen O, de Vos WM, Brunak S, Doré J, MetaHIT C, Weissenbach J, Ehrlich SD, Bork P
Nature.
2014 Feb 27; 506(7489): 516. PubMed: 24572424.Abstract + PDFIt has been drawn to our attention that the methods described in the main text and the Supplementary Information of this Article have been considered by some researchers to be insufficient to enable them to identify enterotypes in their own data sets. Enterotypes were originally defined in this Article (page 177) as “densely populated areas in a multi-dimensional space of community composition? and should not be seen as discrete clusters, but as a way of stratifying samples to reduce complexity. Additionally, the Fig. 2 legend should not imply that between-class analysis is simply a method of visualizing principal component analysis (PCA); rather, it is a supervised rather than an unsupervised analysis of data because it incorporates the outcome of clustering of data. To simplify enterotype identification in the original and other data sets, we have developed a comprehensive tutorial at http://enterotype.embl.de—which is a website on enterotypes that will be updated as methods improve. We thank Ivica Letunic and Paul Costea from EMBL for setting up the tutorial.
28.
Luminal signalling links cell communication to tissue architecture during organogenesis.
Nature.
2014 Nov 6; 515(7525): 120-4. Epub 2014 Oct 22; PubMed: 25337877.Abstract + PDFMorphogenesis is the process whereby cell collectives are shaped into differentiated tissues and organs. The self-organizing nature of morphogenesis has been recently demonstrated by studies showing that stem cells in three-dimensional culture can generate complex organoids, such as mini-guts, optic-cups and even mini-brains. To achieve this, cell collectives must regulate the activity of secreted signalling molecules that control cell differentiation, presumably through the self-assembly of microenvironments or niches. However, mechanisms that allow changes in tissue architecture to feedback directly on the activity of extracellular signals have not been described. Here we investigate how the process of tissue assembly controls signalling activity during organogenesis in vivo, using the migrating zebrafish lateral line primordium. We show that fibroblast growth factor (FGF) activity within the tissue controls the frequency at which it deposits rosette-like mechanosensory organs. Live imaging reveals that FGF becomes specifically concentrated in microluminal structures that assemble at the centre of these organs and spatially constrain its signalling activity. Genetic inhibition of microlumen assembly and laser micropuncture experiments demonstrate that microlumina increase signalling responses in participating cells, thus allowing FGF to coordinate the migratory behaviour of cell groups at the tissue rear. As the formation of a central lumen is a self-organizing property of many cell types, such as epithelia and embryonic stem cells, luminal signalling provides a potentially general mechanism to locally restrict, coordinate and enhance cell communication within tissues.
2013 2 publication(s).
27.
Richness of human gut microbiome correlates with metabolic markers.
Le Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, Falony G, Almeida M, Arumugam M, Batto JM, Kennedy S, Leonard P, Li J, Burgdorf K, Grarup N, Jørgensen T, Brandslund I, Nielsen HB, Juncker AS, Bertalan M, Levenez F, Pons N, Rasmussen S, Sunagawa S, Tap J, Tims S, Zoetendal EG, Brunak S, Clément K, Doré J, Kleerebezem M, Kristiansen K, Renault P, Sicheritz-Ponten T, de Vos WM, Zucker JD, Raes J, Hansen T, MetaHIT Consortium , Bork P, Wang J, Ehrlich SD, Pedersen O
Nature.
2013 Aug 29; 500(7464): 541-6. PubMed: 23985870.Abstract + PDFWe are facing a global metabolic health crisis provoked by an obesity epidemic. Here we report the human gut microbial composition in a population sample of 123 non-obese and 169 obese Danish individuals. We find two groups of individuals that differ by the number of gut microbial genes and thus gut bacterial richness. They contain known and previously unknown bacterial species at different proportions; individuals with a low bacterial richness (23% of the population) are characterized by more marked overall adiposity, insulin resistance and dyslipidaemia and a more pronounced inflammatory phenotype when compared with high bacterial richness individuals. The obese individuals among the lower bacterial richness group also gain more weight over time. Only a few bacterial species are sufficient to distinguish between individuals with high and low bacterial richness, and even between lean and obese participants. Our classifications based on variation in the gut microbiome identify subsets of individuals in the general white adult population who may be at increased risk of progressing to adiposity-associated co-morbidities.
26.
Genomic variation landscape of the human gut microbiome.
Schloissnig S, Arumugam M, Sunagawa S, Mitreva M, Tap J, Zhu A, Waller AS, Mende DR, Kultima JR, Martin J, Kota K, Sunyaev SR, Weinstock GM, Bork P
Nature.
2013 Jan 3; 493(7430): 45-50. Epub 2012 Dec 5; PubMed: 23222524.Abstract + PDFWhereas large-scale efforts have rapidly advanced the understanding and practical impact of human genomic variation, the practical impact of variation is largely unexplored in the human microbiome. We therefore developed a framework for metagenomic variation analysis and applied it to 252 faecal metagenomes of 207 individuals from Europe and North America. Using 7.4 billion reads aligned to 101 reference species, we detected 10.3 million single nucleotide polymorphisms (SNPs), 107,991 short insertions/deletions, and 1,051 structural variants. The average ratio of non-synonymous to synonymous polymorphism rates of 0.11 was more variable between gut microbial species than across human hosts. Subjects sampled at varying time intervals exhibited individuality and temporal stability of SNP variation patterns, despite considerable composition changes of their gut microbiota. This indicates that individual-specific strains are not easily replaced and that an individual might have a unique metagenomic genotype, which may be exploitable for personalized diet or drug intake.
2011 1 publication(s).
25.
Enterotypes of the human gut microbiome.
Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, Mende DR, Fernandes GR, Tap J, Bruls T, Batto JM, Bertalan M, Borruel N, Casellas F, Fernandez L, Gautier L, Hansen T, Hattori M, Hayashi T, Kleerebezem M, Kurokawa K, Leclerc M, Levenez F, Manichanh C, Nielsen HB, Nielsen T, Pons N, Poulain J, Qin J, Sicheritz-Ponten T, Tims S, Torrents D, Ugarte E, Zoetendal EG, Wang J, Guarner F, Pedersen O, de Vos WM, Brunak S, Doré J, Consortium M, Weissenbach J, Ehrlich SD, Bork P
Nature.
2011 May 12; 473(7346): 174-80. Epub 2011 Apr 20; PubMed: 21508958.Abstract + PDFOur knowledge of species and functional composition of the human gut microbiome is rapidly increasing, but it is still based on very few cohorts and little is known about variation across the world. By combining 22 newly sequenced faecal metagenomes of individuals from four countries with previously published data sets, here we identify three robust clusters (referred to as enterotypes hereafter) that are not nation or continent specific. We also confirmed the enterotypes in two published, larger cohorts, indicating that intestinal microbiota variation is generally stratified, not continuous. This indicates further the existence of a limited number of well-balanced host-microbial symbiotic states that might respond differently to diet and drug intake. The enterotypes are mostly driven by species composition, but abundant molecular functions are not necessarily provided by abundant species, highlighting the importance of a functional analysis to understand microbial communities. Although individual host properties such as body mass index, age, or gender cannot explain the observed enterotypes, data-driven marker genes or functional modules can be identified for each of these host properties. For example, twelve genes significantly correlate with age and three functional modules with the body mass index, hinting at a diagnostic potential of microbial markers.
2010 2 publication(s).
24.
Ancient animal microRNAs and the evolution of tissue identity.
Christodoulou F, Raible F, Tomer R, Simakov O, Trachana K, Klaus S, Snyman H, Hannon GJ, Bork P, Arendt D
Nature.
2010 Feb 25; 463(7284): 1084-8. Epub 2010 Jan 31; PubMed: 20118916.Abstract + PDFThe spectacular escalation in complexity in early bilaterian evolution correlates with a strong increase in the number of microRNAs. To explore the link between the birth of ancient microRNAs and body plan evolution, we set out to determine the ancient sites of activity of conserved bilaterian microRNA families in a comparative approach. We reason that any specific localization shared between protostomes and deuterostomes (the two major superphyla of bilaterian animals) should probably reflect an ancient specificity of that microRNA in their last common ancestor. Here, we investigate the expression of conserved bilaterian microRNAs in Platynereis dumerilii, a protostome retaining ancestral bilaterian features, in Capitella, another marine annelid, in the sea urchin Strongylocentrotus, a deuterostome, and in sea anemone Nematostella, representing an outgroup to the bilaterians. Our comparative data indicate that the oldest known animal microRNA, miR-100, and the related miR-125 and let-7 were initially active in neurosecretory cells located around the mouth. Other sets of ancient microRNAs were first present in locomotor ciliated cells, specific brain centres, or, more broadly, one of four major organ systems: central nervous system, sensory tissue, musculature and gut. These findings reveal that microRNA evolution and the establishment of tissue identities were closely coupled in bilaterian evolution. Also, they outline a minimum set of cell types and tissues that existed in the protostome-deuterostome ancestor.
23.
A human gut microbial gene catalogue established by metagenomic sequencing.
Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T, Mende DR, Li J, Xu J, Li S, Li D, Cao J, Wang B, Liang H, Zheng H, Xie Y, Tap J, Lepage P, Bertalan M, Batto JM, Hansen T, Le Paslier D, Linneberg A, Nielsen HB, Pelletier E, Renault P, Sicheritz-Ponten T, Turner K, Zhu H, Yu C, Li S, Jian M, Zhou Y, Li Y, Zhang X, Li S, Qin N, Yang H, Wang J, Brunak S, Doré J, Guarner F, Kristiansen K, Pedersen O, Parkhill J, Weissenbach J, Bork P, Ehrlich SD, Wang J, MetaHIT Consortium
Nature.
2010 Mar 4; 464(7285): 59-65. PubMed: 20203603.Abstract + PDFTo understand the impact of gut microbes on human health and well-being it is crucial to assess their genetic potential. Here we describe the Illumina-based metagenomic sequencing, assembly and characterization of 3.3 million non-redundant microbial genes, derived from 576.7 gigabases of sequence, from faecal samples of 124 European individuals. The gene set, approximately 150 times larger than the human gene complement, contains an overwhelming majority of the prevalent (more frequent) microbial genes of the cohort and probably includes a large proportion of the prevalent human intestinal microbial genes. The genes are largely shared among individuals of the cohort. Over 99% of the genes are bacterial, indicating that the entire cohort harbours between 1,000 and 1,150 prevalent bacterial species and each individual at least 160 such species, which are also largely shared. We define and describe the minimal gut metagenome and the minimal gut bacterial genome in terms of functions present in all individuals and most bacteria, respectively.
2008 2 publication(s).
22.
The genome of the choanoflagellate Monosiga brevicollis and the origin of metazoans.
King N, Westbrook MJ, Young SL, Kuo A, Abedin M, Chapman J, Fairclough S, Hellsten U, Isogai Y, Letunic I, Marr M, Pincus D, Putnam N, Rokas A, Wright KJ, Zuzow R, Dirks W, Good M, Goodstein D, Lemons D, Li W, Lyons JB, Morris A, Nichols S, Richter DJ, Salamov A, Sequencing JG, Bork P, Lim WA, Manning G, Miller WT, McGinnis W, Shapiro H, Tjian R, Grigoriev IV, Rokhsar D
Abstract + PDFChoanoflagellates are the closest known relatives of metazoans. To discover potential molecular mechanisms underlying the evolution of metazoan multicellularity, we sequenced and analysed the genome of the unicellular choanoflagellate Monosiga brevicollis. The genome contains approximately 9,200 intron-rich genes, including a number that encode cell adhesion and signalling protein domains that are otherwise restricted to metazoans. Here we show that the physical linkages among protein domains often differ between M. brevicollis and metazoans, suggesting that abundant domain shuffling followed the separation of the choanoflagellate and metazoan lineages. The completion of the M. brevicollis genome allows us to reconstruct with increasing resolution the genomic changes that accompanied the origin of metazoans.
21.
The genome of the model beetle and pest Tribolium castaneum.
Tribolium Genome Sequencing Consortium , Project leader , Richards S, Principal investigators , Gibbs RA, Weinstock GM, White paper , Brown SJ, Denell R, Beeman RW, Gibbs R, Analysis leaders , Beeman RW, Brown SJ, Bucher G, Friedrich M, Grimmelikhuijzen CJ, Klingler M, Lorenzen M, Richards S, Roth S, Schröder R, Tautz D, Zdobnov EM, DNA sequence and global analysis: DNA sequencing , Muzny D, Gibbs RA, Weinstock GM, Attaway T, Bell S, Buhay CJ, Chandrabose MN, Chavez D, Clerk-Blankenburg KP, Cree A, Dao M, Davis C, Chacko J, Dinh H, Dugan-Rocha S, Fowler G, Garner TT, Garnes J, Gnirke A, Hawes A, Hernandez J, Hines S, Holder M, Hume J, Jhangiani SN, Joshi V, Khan ZM, Jackson L, Kovar C, Kowis A, Lee S, Lewis LR, Margolis J, Morgan M, Nazareth LV, Nguyen N, Okwuonu G, Parker D, Richards S, Ruiz SJ, Santibanez J, Savard J, Scherer SE, Schneider B, Sodergren E, Tautz D, Vattahil S, Villasana D, White CS, Wright R, EST sequencing , Park Y, Beeman RW, Lord J, Oppert B, Lorenzen M, Brown S, Wang L, Savard J, Tautz D, Richards S, Weinstock G, Gibbs RA, genome assembly , Liu Y, Worley K, Weinstock G, G+C content , Elsik CG, Reese JT, Elhaik E, Landan G, Graur D, repetitive DNA, transposons and telomeres , Arensburger P, Atkinson P, Beeman RW, Beidler J, Brown SJ, Demuth JP, Drury DW, Du YZ, Fujiwara H, Lorenzen M, Maselli V, Osanai M, Park Y, Robertson HM, Tu Z, Wang JJ, Wang S, gene prediction and consensus gene set , Richards S, Song H, Zhang L, Sodergren E, Werner D, Stanke M, Morgenstern B, Solovyev V, Kosarev P, Brown G, Chen HC, Ermolaeva O, Hlavina W, Kapustin Y, Kiryutin B, Kitts P, Maglott D, Pruitt K, Sapojnikov V, Souvorov A, Mackey AJ, Waterhouse RM, Wyder S, Zdobnov EM, global gene content analysis , Zdobnov EM, Wyder S, Kriventseva EV, Kadowaki T, Bork P, Developmental processes and signalling pathways , Aranda M, Bao R, Beermann A, Berns N, Bolognesi R, Bonneton F, Bopp D, Brown SJ, Bucher G, Butts T, Chaumot A, Denell RE, Ferrier DE, Friedrich M, Gordon CM, Jindra M, Klingler M, Lan Q, Lattorff HM, Laudet V, von Levetsow C, Liu Z, Lutz R, Lynch JA, da Fonseca RN, Posnien N, Reuter R, Roth S, Savard J, Schinko JB, Schmitt C, Schoppmeier M, Schröder R, Shippy TD, Simonnet F, Marques-Souza H, Tautz D, Tomoyasu Y, Trauner J, Van der Zee M, Vervoort M, Wittkopp N, Wimmer EA, Yang X, Pest biology, senses, Medea and RNAi: ligand gated ion channels , Jones AK, Sattelle DB, oxidative phosphorylation , Ebert PR, P450 genes , Nelson D, Scott JG, Beeman RW, chitin and cuticular proteins , Muthukrishnan S, Kramer KJ, Arakane Y, Beeman RW, Zhu Q, Hogenkamp D, Dixit R, digestive proteinases , Oppert B, Jiang H, Zou Z, Marshall J, Elpidina E, Vinokurov K, Oppert C, immunity , Zou Z, Evans J, Lu Z, Zhao P, Sumathipala N, Altincicek B, Vilcinskas A, Williams M, Hultmark D, Hetru C, Jiang H, neurohormones and GPCRs , Grimmelikhuijzen CJ, Hauser F, Cazzamali G, Williamson M, Park Y, Li B, Tanaka Y, Predel R, Neupert S, Schachtner J, Verleyen P, neuropeptide processing enzymes , Raible F, Bork P, opsins , Friedrich M, odorant receptors and gustatory receptors , Walden KK, Robertson HM, odorant binding and chemosensory proteins , Angeli S, Forêt S, Bucher G, Schuetz S, Maleszka R, Wimmer EA, Medea , Beeman RW, Lorenzen M, systemic RNAi , Tomoyasu Y, Miller SC, Grossmann D, Bucher G
Nature.
2008 Apr 24; 452(7190): 949-55. Epub 2008 Mar 23; PubMed: 18362917.Abstract + PDFTribolium castaneum is a member of the most species-rich eukaryotic order, a powerful model organism for the study of generalized insect development, and an important pest of stored agricultural products. We describe its genome sequence here. This omnivorous beetle has evolved the ability to interact with a diverse chemical environment, as shown by large expansions in odorant and gustatory receptors, as well as P450 and other detoxification enzymes. Development in Tribolium is more representative of other insects than is Drosophila, a fact reflected in gene content and function. For example, Tribolium has retained more ancestral genes involved in cell-cell communication than Drosophila, some being expressed in the growth zone crucial for axial elongation in short-germ development. Systemic RNA interference in T. castaneum functions differently from that in Caenorhabditis elegans, but nevertheless offers similar power for the elucidation of gene function and identification of targets for selective insect control.
2006 4 publication(s).
20.
A global initiative on sharing avian flu data.
Bogner P, Capua I, Cox NJ, Lipman DJ, Aly MM, Auewarakul P, Baltimore D, Bork P, Brown I, Buchy P, Cameron G, Chen HL, Colwell R, Porceddu MC, Couacy-Hymann E, Czifra G, Ganguly NK, Gust ID, Doherty PC, Dulbecco R, Fejzic N, Garcia A, Gojobori T, Hargreaves S, Hashash EBS, Hay A, Joannis T, Johnson M, Jørgensen PH, Kida H, Koch G, Krnjaic D, Kurth R, Lamb RA, Lederberg J, Lim W, Lomlamthong Y, Maaetah H, Ali ASM, Marzuki S, Mettenleiter TC, Minta Z, Mishra AC, Muzio F, Naeem K, Osterhaus A, Pasick J, Peiris M, Perk S, Anh BQ, Ramishvili L, Ramzi ZS, Rojs OZ, Salzberg SL, Sánchez AS, Savic V, Senne D, Sulston J, Swayne DE, Tashiro M, Tufan M, Uzakbaev T, Van den Berg T, Varmus HE, Vitasek J, Webster RG, Wilbanks J, Yilmaz N, Zamora PA
Nature.
2006 Aug 31; 442: 981. PID: 413.Abstract + PDFCorrespondence.
The global spread of the H5N1 avian influenza virus has already extensively damaged economies worldwide and food safety in developing countries. The spread of infection to new ecosystems results in adaptation of the virus to new hosts, including humans, which amplifies the potential for a flu pandemic. Because it is recognized that avian influenza viruses may be the progenitors of the next human pandemic virus, their genetic evolution should be tracked in detail and promptly investigated.
19.
Proteome survey reveals modularity of the yeast cell machinery.
Gavin AC, Aloy P, Grandi P, Krause R, Boesche M, Marzioch M, Rau C, Jensen LJ, Bastuck S, Dümpelfeld B, Edelmann A, Heurtier MA, Hoffman V, Hoefert C, Klein K, Hudak M, Michon AM, Schelder M, Schirle M, Remor M, Rudi T, Hooper SD, Bauer A, Bouwmeester T, Casari G, Drewes G, Neubauer G, Rick JM, Kuster B, Bork P, Russell RB, Superti-Furga G
Abstract + PDFProtein complexes are key molecular entities that integrate multiple gene products to perform cellular functions. Here we report the first genome-wide screen for complexes in an organism, budding yeast, using affinity purification and mass spectrometry. Through systematic tagging of open reading frames (ORFs), the majority of complexes were purified several times, suggesting screen saturation. The richness of the data set enabled a de novo characterization of the composition and organization of the cellular machinery. The ensemble of cellular proteins partitions into 491 complexes, of which 257 are novel, that differentially combine with additional attachment proteins or protein modules to enable a diversification of potential functions. Support for this modular organization of the proteome comes from integration with available data on expression, localization, function, evolutionary conservation, protein structure and binary interactions. This study provides the largest collection of physically determined eukaryotic cellular machines so far and a platform for biological data integration and modelling.
18.
Insights into social insects from the genome of the honeybee Apis mellifera.
Honeybee Genome Sequencing Consortium , Weinstock GM, Robinson GE, Gibbs RA, Worley KC, Evans JD, Maleszka R, Robertson HM, Weaver DB, Beye M, Bork P, Elsik CG, Hartfelder K, Hunt GJ, Zdobnov EM, Amdam GV, Bitondi MMG, Collins AM, Cristino AS, Lattorff HMG, Lobo CH, Moritz RFA, Nunes FMF, Page RE, Simoes ZLP, Wheeler D, Carninci P, Fukuda S, Hayashizaki Y, Kai C, Kawai J, Sakazume N, Sasaki D, Tagami M, Albert S, Baggerman G, Beggs KT, Bloch G, Cazzamali G, Cohen M, Drapeau MD, Eisenhardt D, Emore C, Ewing MA, Fahrbach SE, Foret S, Grimmelikhuijzen CJP, Hauser F, Hummon AB, Huybrechts J, Jones AK, Kadowaki T, Kaplan N, Kucharski R, Leboulle G, Linial M, Littleton JT, Mercer AR, Richmond TA, Rodriguez-Zas SL, Rubin EB, Sattelle DB, Schlipalius D, Schoofs L, Shemesh Y, Sweedler JV, Velarde R, Verleyen P, Vierstraete E, Williamson MR, Ament SA, Brown SJ, Corona M, Dearden PK, Dunn WA, Elekonich MM, Fujiyuki T, Gattermeier I, Gempe T, Hasselmann M, Kadowaki T, Kage E, Kamikouchi A, Kubo T, Kucharski R, Kunieda T, Lorenzen M, Milshina NV, Morioka M, Ohashi K, Overbeek R, Ross CA, Schioett M, Shippy T, Takeuchi H, Toth AL, Willis JH, Wilson MJ, Gordon KHJ, Letunic I, Hackett K, Peterson J, Felsenfeld A, Guyer M, Solignac M, Agarwala R, Cornuet JM, Monnerot M, Mougel F, Reese JT, Vautrin D, Gillespie JJ, Cannone JJ, Gutell RR, Johnston JS, Eisen MB, Iyer VN, Iyer V, Kosarev P, Mackey AJ, Solovyev V, Souvorov A, Aronstein KA, Bilikova K, Chen YP, Clark AG, Decanini LI, Gelbart WM, Hetru C, Hultmark D, Imler JL, Jiang HB, Kanost M, Kimura K, Lazzaro BP, Lopez DL, Simuth J, Thompson GJ, Zou Z, de Jong P, Sodergren E, Csuros M, Milosavljevic A, Osoegawa K, Richards S, Shu CL, Duret L, Elhaik E, Graur D, Anzola JM, Campbell KS, Childs KL, Collinge D, Crosby MA, Dickens CM, Grametes LS, Grozinger CM, Jones PL, Jorda M, Ling X, Matthews BB, Miller J, Mizzen C, Peinado MA, Reid JG, Russo SM, Schroeder AJ, St Pierre SE, Wang Y, Zhou PL, Jiang HY, Kitts P, Ruef B, Venkatraman A, Zhang L, Aquino-Perez G, Whitfield CW, Behura SK, Berlocher SH, Sheppard WS, Smith DR, Suarez AV, Tsutsui ND, Wei XH, Wheeler D, Havlak P, Li BS, Liu Y, Sodergren E, Jolivet A, Lee S, Nazareth LV, Pu LL, Thorn R, Stolc V, Newman T, Samanta M, Tongprasit WA, Claudianos C, Berenbaum MR, Biswas S, de Graaf DC, Feyereisen R, Johnson RM, Oakeshott JG, Ranson H, Schuler MA, Muzny D, Chacko J, Davis C, Dinh H, Gill R, Hernandez J, Hines S, Hume J, Jackson L, Kovar C, Lewis L, Miner G, Morgan M, Nguyen N, Okwuonu G, Paul H, Santibanez J, Savery G, Svatek A, Villasana D, Wright R, Honeybee Genome Sequencing Consortium
Abstract + PDFHere we report the genome sequence of the honeybee Apis mellifera, a key model for social behaviour and essential to global ecology through pollination. Compared with other sequenced insect genomes, the A. mellifera genome has high A+T and CpG contents, lacks major transposon families, evolves more slowly, and is more similar to vertebrates for circadian rhythm, RNA interference and DNA methylation genes, among others. Furthermore, A. mellifera has fewer genes for innate immunity, detoxification enzymes, cuticle-forming proteins and gustatory receptors, more genes for odorant receptors, and novel genes for nectar and pollen utilization, consistent with its ecology and social organization. Compared to Drosophila, genes in early developmental pathways differ in Apis, whereas similarities exist for functions that differ markedly, such as sex determination, brain function and behaviour. Population genetics suggests a novel African origin for the species A. mellifera and insights into whether Africanized bees spread throughout the New World via hybridization or displacement.
17.
Co-evolution of transcriptional and post-translational cell-cycle regulation.
Abstract + PDFDNA microarray studies have shown that hundreds of genes are transcribed periodically during the mitotic cell cycle of humans, budding yeast, fission yeast and the plant Arabidopsis thaliana. Here we show that despite the fact the protein complexes involved in this process are largely the same among all eukaryotes, their regulation has evolved considerably. Our comparative analysis of several large-scale data sets reveals that although the regulated subunits of each protein complex are expressed just before its time of action, the identity of the periodically expressed proteins differs significantly between organisms. Moreover, we show that these changes in transcriptional regulation have co-evolved with post-translational control independently in several lineages; loss or gain of cell-cycle-regulated transcription of specific genes is often mirrored by changes in phosphorylation of the proteins that they encode. Our results indicate that many different solutions have evolved for assembling the same molecular machines at the right time during the cell cycle, involving both transcriptional and post-translational layers that jointly control the dynamics of biological systems.
2005 2 publication(s).
16.
Generation and annotation of the DNA sequences of human chromosomes 2 and 4.
Hillier LW, Graves TA, Fulton RS, Fulton LA, Pepin KH, Minx P, Wagner-McPherson C, Layman D, Wylie K, Sekhon M, Becker MC, Fewell GA, Delehaunty KD, Miner TL, Nash WE, Kremitzki C, Oddy L, Du H, Sun H, Bradshaw-Cordum H, Ali J, Carter J, Cordes M, Harris A, Isak A, van Brunt A, Nguyen C, Du F, Courtney L, Kalicki J, Ozersky P, Abbott S, Armstrong J, Belter EA, Caruso L, Cedroni M, Cotton M, Davidson T, Desai A, Elliott G, Erb T, Fronick C, Gaige T, Haakenson W, Haglund K, Holmes A, Harkins R, Kim K, Kruchowski SS, Strong CM, Grewal N, Goyea E, Hou S, Levy A, Martinka S, Mead K, McLellan MD, Meyer R, Randall-Maher J, Tomlinson C, Dauphin-Kohlberg S, Kozlowicz-Reilly A, Shah N, Swearengen-Shahid S, Snider J, Strong JT, Thompson J, Yoakum M, Leonard S, Pearman C, Trani L, Radionenko M, Waligorski JE, Wang C, Rock SM, Tin-Wollam AM, Maupin R, Latreille P, Wendl MC, Yang SP, Pohl C, Wallis JW, Spieth J, Bieri TA, Berkowicz N, Nelson JO, Osborne J, Ding L, Meyer R, Sabo A, Shotland Y, Sinha P, Wohldmann PE, Cook LL, Hickenbotham MT, Eldred J, Williams D, Jones TA, She X, Ciccarelli FD, Izaurralde E, Taylor J, Schmutz J, Myers RM, Cox DR, Huang X, McPherson JD, Mardis ER, Clifton SW, Warren WC, Chinwalla AT, Eddy SR, Marra MA, Ovcharenko I, Furey TS, Miller W, Eichler EE, Bork P, Suyama M, Torrents D, Waterston RH, Wilson RK
Nature.
2005 Apr 7; 434(7034): 724-31. PubMed: 15815621.Abstract + PDFHuman chromosome 2 is unique to the human lineage in being the product of a head-to-head fusion of two intermediate-sized ancestral chromosomes. Chromosome 4 has received attention primarily related to the search for the Huntington's disease gene, but also for genes associated with Wolf-Hirschhorn syndrome, polycystic kidney disease and a form of muscular dystrophy. Here we present approximately 237 million base pairs of sequence for chromosome 2, and 186 million base pairs for chromosome 4, representing more than 99.6% of their euchromatic sequences. Our initial analyses have identified 1,346 protein-coding genes and 1,239 pseudogenes on chromosome 2, and 796 protein-coding genes and 778 pseudogenes on chromosome 4. Extensive analyses confirm the underlying construction of the sequence, and expand our understanding of the structure and evolution of mammalian chromosomes, including gene deserts, segmental duplications and highly variant regions.
15.
Initial sequence of the chimpanzee genome and comparison with the human genome
Mikkelsen TS, Hillier LW, Eichler EE, Zody MC, Jaffe DB, Yang SP, Enard W, Hellmann I, Lindblad-Toh K, Altheide TK, Archidiacono N, Bork P, Butler J, Chang JL, Cheng Z, Chinwalla AT, deJong P, Delehaunty KD, Fronick CC, Fulton LL, Gilad Y, Glusman G, Gnerre S, Graves TA, Hayakawa T, Hayden KE, Huang X, Ji HK, Kent WJ, King MC, Kulbokas III EJ, Lee MK, Liu G, Lopez-Otin C, Makova KD, Man O, Mardis ER, Mauceli E, Miner TL, Nash WE, Nelson JO, Pääbo S, Patterson NJ, Pohl CS, Pollard KS, Prüfer K, Puente XS, Reich D, Rocchi M, Rosenbloom K, Ruvolo M, Richter DJ, Schaffner SF, Smit AFA, Smith SM, Suyama M, Taylor J, Torrents D, Tuzun E, Varki A, Velasco G, Ventura M, Wallis JW, Wendl MC, Wilson RK, Lander ES, Waterston RH
Abstract + PDFHere we present a draft genome sequence of the common chimpanzee (Pan troglodytes). Through comparison with the human genome, we have generated a largely complete catalogue of the genetic differences that have accumulated since the human and chimpanzee species diverged from our common ancestor, constituting approximately thirty-five million single-nucleotide changes, five million insertion/deletion events, and various chromosomal rearrangements. We use this catalogue to explore the magnitude and regional variation of mutational forces shaping these two genomes, and the strength of positive and negative selection acting on their genes. In particular, we find that the patterns of evolution in human and chimpanzee protein-coding genes are highly correlated and dominated by the fixation of neutral and slightly deleterious alleles. We also use the chimpanzee genome as an outgroup to investigate human population genetics and identify signatures of selective sweeps in recent human evolution.
2004 2 publication(s).
14.
Genome sequence of the Brown Norway rat yields insights into mammalian evolution.
Gibbs RA, Weinstock GM, Metzker ML, Muzny DM, Sodergren EJ, Scherer S, Scott G, Steffen D, Worley KC, Burch PE, Okwuonu G, Hines S, Lewis L, DeRamo C, Delgado O, Dugan-Rocha S, Miner G, Morgan M, Hawes A, Gill R, Celera , Holt RA, Adams MD, Amanatides PG, Baden-Tillson H, Barnstead M, Chin S, Evans CA, Ferriera S, Fosler C, Glodek A, Gu Z, Jennings D, Kraft CL, Nguyen T, Pfannkoch CM, Sitter C, Sutton GG, Venter JC, Woodage T, Smith D, Lee HM, Gustafson E, Cahill P, Kana A, Doucette-Stamm L, Weinstock K, Fechtel K, Weiss RB, Dunn DM, Green ED, Blakesley RW, Bouffard GG, De Jong PJ, Osoegawa K, Zhu B, Marra M, Schein J, Bosdet I, Fjell C, Jones S, Krzywinski M, Mathewson C, Siddiqui A, Wye N, McPherson J, Zhao S, Fraser CM, Shetty J, Shatsman S, Geer K, Chen Y, Abramzon S, Nierman WC, Havlak PH, Chen R, Durbin KJ, Egan A, Ren Y, Song XZ, Li B, Liu Y, Qin X, Cawley S, Worley KC, Cooney AJ, D'Souza LM, Martin K, Wu JQ, Gonzalez-Garay ML, Jackson AR, Kalafus KJ, McLeod MP, Milosavljevic A, Virk D, Volkov A, Wheeler DA, Zhang Z, Bailey JA, Eichler EE, Tuzun E, Birney E, Mongin E, Ureta-Vidal A, Woodwark C, Zdobnov EM, Bork P, Suyama M, Torrents D, Alexandersson M, Trask BJ, Young JM, Huang H, Wang H, Xing H, Daniels S, Gietzen D, Schmidt J, Stevens K, Vitt U, Wingrove J, Camara F, Mar Albà M, Abril JF, Guigo R, Smit A, Dubchak I, Rubin EM, Couronne O, Poliakov A, Hübner N, Ganten D, Goesele C, Hummel O, Kreitler T, Lee YA, Monti J, Schulz H, Zimdahl H, Himmelbauer H, Lehrach H, Jacob HJ, Bromberg S, Gullings-Handley J, Jensen-Seaman MI, Kwitek AE, Lazar J, Pasko D, Tonellato PJ, Twigger S, Ponting CP, Duarte JM, Rice S, Goodstadt L, Beatson SA, Emes RD, Winter EE, Webber C, Brandt P, Nyakatura G, Adetobi M, Chiaromonte F, Elnitski L, Eswara P, Hardison RC, Hou M, Kolbe D, Makova K, Miller W, Nekrutenko A, Riemer C, Schwartz S, Taylor J, Yang S, Zhang Y, Lindpaintner K, Andrews TD, Caccamo M, Clamp M, Clarke L, Curwen V, Durbin R, Eyras E, Searle SM, Cooper GM, Batzoglou S, Brudno M, Sidow A, Stone EA, Venter JC, Payseur BA, Bourque G, López-Otín C, Puente XS, Chakrabarti K, Chatterji S, Dewey C, Pachter L, Bray N, Yap VB, Caspi A, Tesler G, Pevzner PA, Haussler D, Roskin KM, Baertsch R, Clawson H, Furey TS, Hinrichs AS, Karolchik D, Kent WJ, Rosenbloom KR, Trumbower H, Weirauch M, Cooper DN, Stenson PD, Ma B, Brent M, Arumugam M, Shteynberg D, Copley RR, Taylor MS, Riethman H, Mudunuri U, Peterson J, Guyer M, Felsenfeld A, Old S, Mockrin S, Collins F, Rat Genome Sequencing Project Consortium
Abstract + PDFThe laboratory rat (Rattus norvegicus) is an indispensable tool in experimental medicine and drug development, having made inestimable contributions to human health. We report here the genome sequence of the Brown Norway (BN) rat strain. The sequence represents a high-quality 'draft' covering over 90% of the genome. The BN rat sequence is the third complete mammalian genome to be deciphered, and three-way comparisons with the human and mouse genomes resolve details of mammalian evolution. This first comprehensive analysis includes genes and proteins and their relation to human disease, repeated sequences, comparative genome-wide studies of mammalian orthologous chromosomal regions and rearrangement breakpoints, reconstruction of ancestral karyotypes and the events leading to existing species, rates of variation, and lineage-specific and lineage-independent evolutionary events such as expansion of gene families, orthology relations and protein evolution.
13.
Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution.
Hillier LW, Miller W, Birney E, Warren W, Hardison RC, Ponting CP, Bork P, Burt DW, Groenen MA, Delany ME, Dodgson JB, Chinwalla AT, Cliften PF, Clifton SW, Delehaunty KD, Fronick C, Fulton RS, Graves TA, Kremitzki C, Layman D, Magrini V, McPherson JD, Miner TL, Minx P, Nash WE, Nhan MN, Nelson JO, Oddy LG, Pohl CS, Randall-Maher J, Smith SM, Wallis JW, Yang SP, Romanov MN, Rondelli CM, Paton B, Smith J, Morrice D, Daniels L, Tempest HG, Robertson L, Masabanda JS, Griffin DK, Vignal A, Fillon V, Jacobbson L, Kerje S, Andersson L, Crooijmans RP, Aerts J, van der Poel JJ, Ellegren H, Caldwell RB, Hubbard SJ, Grafham DV, Kierzek AM, McLaren SR, Overton IM, Arakawa H, Beattie KJ, Bezzubov Y, Boardman PE, Bonfield JK, Croning MD, Davies RM, Francis MD, Humphray SJ, Scott CE, Taylor RG, Tickle C, Brown WR, Rogers J, Buerstedde JM, Wilson SA, Stubbs L, Ovcharenko I, Gordon L, Lucas S, Miller MM, Inoko H, Shiina T, Kaufman J, Salomonsen J, Skjoedt K, Wong GK, Wang J, Liu B, Wang J, Yu J, Yang H, Nefedov M, Koriabine M, Dejong PJ, Goodstadt L, Webber C, Dickens NJ, Letunic I, Suyama M, Torrents D, von Mering C, Zdobnov EM, Makova K, Nekrutenko A, Elnitski L, Eswara P, King DC, Yang S, Tyekucheva S, Radakrishnan A, Harris RS, Chiaromonte F, Taylor J, He J, Rijnkels M, Griffiths-Jones S, Ureta-Vidal A, Hoffman MM, Severin J, Searle SM, Law AS, Speed D, Waddington D, Cheng Z, Tuzun E, Eichler E, Bao Z, Flicek P, Shteynberg DD, Brent MR, Bye JM, Huckle EJ, Chatterji S, Dewey C, Pachter L, Kouranov A, Mourelatos Z, Hatzigeorgiou AG, Paterson AH, Ivarie R, Brandstrom M, Axelsson E, Backstrom N, Berlin S, Webster MT, Pourquie O, Reymond A, Ucla C, Antonarakis SE, Long M, Emerson JJ, Betrán E, Dupanloup I, Kaessmann H, Hinrichs AS, Bejerano G, Furey TS, Harte RA, Raney B, Siepel A, Kent WJ, Haussler D, Eyras E, Castelo R, Abril JF, Castellano S, Camara F, Parra G, Guigo R, Bourque G, Tesler G, Pevzner PA, Smit A, Fulton LA, Mardis ER, Wilson RK, International Chicken Genome Sequencing Consortium
Abstract + PDFWe present here a draft genome sequence of the red jungle fowl, Gallus gallus. Because the chicken is a modern descendant of the dinosaurs and the first non-mammalian amniote to have its genome sequenced, the draft sequence of its genome--composed of approximately one billion base pairs of sequence and an estimated 20,000-23,000 genes--provides a new perspective on vertebrate genome evolution, while also improving the annotation of mammalian genomes. For example, the evolutionary distance between chicken and human provides high specificity in detecting functional elements, both non-coding and coding. Notably, many conserved non-coding sequences are far from genes and cannot be assigned to defined functional classes. In coding regions the evolutionary dynamics of protein domains and orthologous groups illustrate processes that distinguish the lineages leading to birds and mammals. The distinctive properties of avian microchromosomes, together with the inferred patterns of conserved synteny, provide additional insights into vertebrate chromosome architecture.
2003 1 publication(s).
12.
The DNA sequence of human chromosome 7.
Hillier LW, Fulton RS, Fulton LA, Graves TA, Pepin KH, Wagner-McPherson C, Layman D, Maas J, Jaeger S, Walker R, Wylie K, Sekhon M, Becker MC, O'Laughlin MD, Schaller ME, Fewell GA, Delehaunty KD, Miner TL, Nash WE, Cordes M, Du H, Sun H, Edwards J, Bradshaw-Cordum H, Ali J, Andrews S, Isak A, Vanbrunt A, Nguyen C, Du F, Lamar B, Courtney L, Kalicki J, Ozersky P, Bielicki L, Scott K, Holmes A, Harkins R, Harris A, Strong CM, Hou S, Tomlinson C, Dauphin-Kohlberg S, Kozlowicz-Reilly A, Leonard S, Rohlfing T, Rock SM, Tin-Wollam AM, Abbott A, Minx P, Maupin R, Strowmatt C, Latreille P, Miller N, Johnson D, Murray J, Woessner JP, Wendl MC, Yang SP, Schultz BR, Wallis JW, Spieth J, Bieri TA, Nelson JO, Berkowicz N, Wohldmann PE, Cook LL, Hickenbotham MT, Eldred J, Williams D, Bedell JA, Mardis ER, Clifton SW, Chissoe SL, Marra MA, Raymond C, Haugen E, Gillett W, Zhou Y, James R, Phelps K, Iadanoto S, Bubb K, Simms E, Levy R, Clendenning J, Kaul R, Kent WJ, Furey TS, Baertsch RA, Brent MR, Keibler E, Flicek P, Bork P, Suyama M, Bailey JA, Portnoy ME, Torrents D, Chinwalla AT, Gish WR, Eddy SR, McPherson JD, Olson MV, Eichler EE, Green ED, Waterston RH, Wilson RK
Abstract + PDFHuman chromosome 7 has historically received prominent attention in the human genetics community, primarily related to the search for the cystic fibrosis gene and the frequent cytogenetic changes associated with various forms of cancer. Here we present more than 153 million base pairs representing 99.4% of the euchromatic sequence of chromosome 7, the first metacentric chromosome completed so far. The sequence has excellent concordance with previously established physical and genetic maps, and it exhibits an unusual amount of segmentally duplicated sequence (8.2%), with marked differences between the two arms. Our initial analyses have identified 1,150 protein-coding genes, 605 of which have been confirmed by complementary DNA sequences, and an additional 941 pseudogenes. Of genes confirmed by transcript sequences, some are polymorphic for mutations that disrupt the reading frame.
2002 4 publication(s).
11.
Functional organization of the yeast proteome by systematic analysis of protein complexes.
Gavin AC, Bösche M, Krause R, Grandi P, Marzioch M, Bauer A, Schultz J, Rick JM, Michon AM, Cruciat CM, Remor M, Höfert C, Schelder M, Brajenovic M, Ruffner H, Merino A, Klein K, Hudak M, Dickson D, Rudi T, Gnau V, Bauch A, Bastuck S, Huhse B, Leutwein C, Heurtier MA, Copley RR, Edelmann A, Querfurth E, Rybin V, Drewes G, Raida M, Bouwmeester T, Bork P, Seraphin B, Kuster B, Neubauer G, Superti-Furga G
Abstract + PDFMost cellular processes are carried out by multiprotein complexes. The identification and analysis of their components provides insight into how the ensemble of expressed proteins (proteome) is organized into functional units. We used tandem-affinity purification (TAP) and mass spectrometry in a large-scale approach to characterize multiprotein complexes in Saccharomyces cerevisiae. We processed 1,739 genes, including 1,143 human orthologues of relevance to human biology, and purified 589 protein assemblies. Bioinformatic analysis of these assemblies defined 232 distinct multiprotein complexes and proposed new cellular roles for 344 proteins, including 231 proteins with no previous functional annotation. Comparison of yeast and human complexes showed that conservation across species extends from single proteins to their molecular environment. Our analysis provides an outline of the eukaryotic proteome as a network of protein complexes at a level of organization beyond binary interactions. This higher-order map contains fundamental biological information and offers the context for a more reasoned and informed approach to drug discovery.
9.
Comparative assessment of large-scale data sets of protein-protein interactions.
Nature.
2002 May 23; 417(6887): 399-403. Epub 2002 May 8; PubMed: 12000970.Abstract + PDFComprehensive protein protein interaction maps promise to reveal many aspects of the complex regulatory network underlying cellular function. Recently, large-scale approaches have predicted many new protein interactions in yeast. To measure their accuracy and potential as well as to identify biases, strengths and weaknesses, we compare the methods with each other and with a reference set of previously reported protein interactions.
8.
Initial sequencing and comparative analysis of the mouse genome.
Waterston RH, Lindblad-Toh K, Birney E, Rogers J, Abril JF, Agarwal P, Agarwala R, Ainscough R, Alexandersson M, An P, Antonarakis SE, Attwood J, Baertsch R, Bailey J, Barlow K, Beck S, Berry E, Birren B, Bloom T, Bork P, Botcherby M, Bray N, Brent MR, Brown DG, Brown SD, Bult C, Burton J, Butler J, Campbell RD, Carninci P, Cawley S, Chiaromonte F, Chinwalla AT, Church DM, Clamp M, Clee C, Collins FS, Cook LL, Copley RR, Coulson A, Couronne O, Cuff J, Curwen V, Cutts T, Daly M, David R, Davies J, Delehaunty KD, Deri J, Dermitzakis ET, Dewey C, Dickens NJ, Diekhans M, Dodge S, Dubchak I, Dunn DM, Eddy SR, Elnitski L, Emes RD, Eswara P, Eyras E, Felsenfeld A, Fewell GA, Flicek P, Foley K, Frankel WN, Fulton LA, Fulton RS, Furey TS, Gage D, Gibbs RA, Glusman G, Gnerre S, Goldman N, Goodstadt L, Grafham D, Graves TA, Green ED, Gregory S, Guigó R, Guyer M, Hardison RC, Haussler D, Hayashizaki Y, Hillier LW, Hinrichs A, Hlavina W, Holzer T, Hsu F, Hua A, Hubbard T, Hunt A, Jackson I, Jaffe DB, Johnson LS, Jones M, Jones TA, Joy A, Kamal M, Karlsson EK, Karolchik D, Kasprzyk A, Kawai J, Keibler E, Kells C, Kent WJ, Kirby A, Kolbe DL, Korf I, Kucherlapati RS, Kulbokas EJ, Kulp D, Landers T, Leger JP, Leonard S, Letunic I, Levine R, Li J, Li M, Lloyd C, Lucas S, Ma B, Maglott DR, Mardis ER, Matthews L, Mauceli E, Mayer JH, McCarthy M, McCombie WR, McLaren S, McLay K, McPherson JD, Meldrim J, Meredith B, Mesirov JP, Miller W, Miner TL, Mongin E, Montgomery KT, Morgan M, Mott R, Mullikin JC, Muzny DM, Nash WE, Nelson JO, Nhan MN, Nicol R, Ning Z, Nusbaum C, O'Connor MJ, Okazaki Y, Oliver K, Overton-Larty E, Pachter L, Parra G, Pepin KH, Peterson J, Pevzner P, Plumb R, Pohl CS, Poliakov A, Ponce TC, Ponting CP, Potter S, Quail M, Reymond A, Roe BA, Roskin KM, Rubin EM, Rust AG, Santos R, Sapojnikov V, Schultz B, Schultz J, Schwartz MS, Schwartz S, Scott C, Seaman S, Searle S, Sharpe T, Sheridan A, Shownkeen R, Sims S, Singer JB, Slater G, Smit A, Smith DR, Spencer B, Stabenau A, Stange-Thomann N, Sugnet C, Suyama M, Tesler G, Thompson J, Torrents D, Trevaskis E, Tromp J, Ucla C, Ureta-Vidal A, Vinson JP, Von Niederhausern AC, Wade CM, Wall M, Weber RJ, Weiss RB, Wendl MC, West AP, Wetterstrand K, Wheeler R, Whelan S, Wierzbowski J, Willey D, Williams S, Wilson RK, Winter E, Worley KC, Wyman D, Yang S, Yang SP, Zdobnov EM, Zody MC, Lander ES, Mouse Genome Sequencing Consortium
Abstract + PDFThe sequence of the mouse genome is a key informational tool for understanding the contents of the human genome and a key experimental tool for biomedical research. Here, we report the results of an international collaboration to produce a high-quality draft sequence of the mouse genome. We also present an initial comparative analysis of the mouse and human genomes, describing some of the insights that can be gleaned from the two sequences. We discuss topics including the analysis of the evolutionary forces shaping the size, structure and sequence of the genomes; the conservation of large-scale synteny across most of the genomes; the much lower extent of sequence orthology covering less than half of the genomes; the proportions of the genomes under selection; the number of protein-coding genes; the expansion of gene families related to reproduction and immunity; the evolution of proteins; and the identification of intraspecies polymorphism.
2001 3 publication(s).
7.
The draft sequences. Filling in the gaps.
Bork P, Copley R
Nature.
2001 Feb 15; 409(6822): 818-20. PubMed: 11236994.PDF5.
Initial sequencing and analysis of the human genome.
Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W, Funke R, Gage D, Harris K, Heaford A, Howland J, Kann L, Lehoczky J, Levine R, McEwan P, McKernan K, Meldrim J, Mesirov JP, Miranda C, Morris W, Naylor J, Raymond C, Rosetti M, Santos R, Sheridan A, Sougnez C, Stange-Thomann N, Stojanovic N, Subramanian A, Wyman D, Rogers J, Sulston J, Ainscough R, Beck S, Bentley D, Burton J, Clee C, Carter N, Coulson A, Deadman R, Deloukas P, Dunham A, Dunham I, Durbin R, French L, Grafham D, Gregory S, Hubbard T, Humphray S, Hunt A, Jones M, Lloyd C, McMurray A, Matthews L, Mercer S, Milne S, Mullikin JC, Mungall A, Plumb R, Ross M, Shownkeen R, Sims S, Waterston RH, Wilson RK, Hillier LW, McPherson JD, Marra MA, Mardis ER, Fulton LA, Chinwalla AT, Pepin KH, Gish WR, Chissoe SL, Wendl MC, Delehaunty KD, Miner TL, Delehaunty A, Kramer JB, Cook LL, Fulton RS, Johnson DL, Minx PJ, Clifton SW, Hawkins T, Branscomb E, Predki P, Richardson P, Wenning S, Slezak T, Doggett N, Cheng JF, Olsen A, Lucas S, Elkin C, Uberbacher E, Frazier M, Gibbs RA, Muzny DM, Scherer SE, Bouck JB, Sodergren EJ, Worley KC, Rives CM, Gorrell JH, Metzker ML, Naylor SL, Kucherlapati RS, Nelson DL, Weinstock GM, Sakaki Y, Fujiyama A, Hattori M, Yada T, Toyoda A, Itoh T, Kawagoe C, Watanabe H, Totoki Y, Taylor T, Weissenbach J, Heilig R, Saurin W, Artiguenave F, Brottier P, Bruls T, Pelletier E, Robert C, Wincker P, Smith DR, Doucette-Stamm L, Rubenfield M, Weinstock K, Lee HM, Dubois J, Rosenthal A, Platzer M, Nyakatura G, Taudien S, Rump A, Yang H, Yu J, Wang J, Huang G, Gu J, Hood L, Rowen L, Madan A, Qin S, Davis RW, Federspiel NA, Abola AP, Proctor MJ, Myers RM, Schmutz J, Dickson M, Grimwood J, Cox DR, Olson MV, Kaul R, Raymond C, Shimizu N, Kawasaki K, Minoshima S, Evans GA, Athanasiou M, Schultz R, Roe BA, Chen F, Pan H, Ramser J, Lehrach H, Reinhardt R, McCombie WR, de la Bastide M, Dedhia N, Blöcker H, Hornischer K, Nordsiek G, Agarwala R, Aravind L, Bailey JA, Bateman A, Batzoglou S, Birney E, Bork P, Brown DG, Burge CB, Cerutti L, Chen HC, Church D, Clamp M, Copley RR, Doerks T, Eddy SR, Eichler EE, Furey TS, Galagan J, Gilbert JG, Harmon C, Hayashizaki Y, Haussler D, Hermjakob H, Hokamp K, Jang W, Johnson LS, Jones TA, Kasif S, Kaspryzk A, Kennedy S, Kent WJ, Kitts P, Koonin EV, Korf I, Kulp D, Lancet D, Lowe TM, McLysaght A, Mikkelsen T, Moran JV, Mulder N, Pollara VJ, Ponting CP, Schuler G, Schultz J, Slater G, Smit AF, Stupka E, Szustakowski J, Thierry-Mieg D, Thierry-Mieg J, Wagner L, Wallis J, Wheeler R, Williams A, Wolf YI, Wolfe KH, Yang SP, Yeh RF, Collins F, Guyer MS, Peterson J, Felsenfeld A, Wetterstrand KA, Patrinos A, Morgan MJ, de Jong P, Catanese JJ, Osoegawa K, Shizuya H, Choi S, Chen YJ, Szustakowki J, International Human Genome Sequencing Consortium
Abstract + PDFThe human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.
2000 1 publication(s).
4.
Functional genomic analysis of cell division in C. elegans using RNAi of genes on chromosome III.
Gönczy P, Echeverri C, Oegema K, Coulson A, Jones SJ, Copley RR, Duperon J, Oegema J, Brehm M, Cassin E, Hannak E, Kirkham M, Pichler S, Flohrs K, Goessen A, Leidel S, Alleaume AM, Martin C, Ozlü N, Bork P, Hyman AA
Nature.
2000 Nov 16; 408(6810): 331-6. PubMed: 11099034.Abstract + PDFGenome sequencing projects generate a wealth of information; however, the ultimate goal of such projects is to accelerate the identification of the biological function of genes. This creates a need for comprehensive studies to fill the gap between sequence and function. Here we report the results of a functional genomic screen to identify genes required for cell division in Caenorhabditis elegans. We inhibited the expression of approximately 96% of the approximately 2,300 predicted open reading frames on chromosome III using RNA-mediated interference (RNAi). By using an in vivo time-lapse differential interference contrast microscopy assay, we identified 133 genes (approximately 6%) necessary for distinct cellular processes in early embryos. Our results indicate that these genes represent most of the genes on chromosome III that are required for proper cell division in C. elegans embryos. The complete data set, including sample time-lapse recordings, has been deposited in an open access database. We found that approximately 47% of the genes associated with a differential interference contrast phenotype have clear orthologues in other eukaryotes, indicating that this screen provides putative gene functions for other species as well.
1999 1 publication(s).
3.
The mahogany protein is a receptor involved in suppression of obesity.
Nagle DL, McGrail SH, Vitale J, Woolf EA, Dussault BJ, DiRocco L, Holmgren L, Montagno J, Bork P, Huszar D, Fairchild-Huntress V, Ge P, Keilty J, Ebeling C, Baldini L, Gilchrist J, Burn P, Carlson GA, Moore KJ
Nature.
1999 Mar 11; 398(6723): 148-52. PubMed: 10086355.Abstract + PDFGenetic studies have shown that mutations within the mahogany locus suppress the pleiotropic phenotypes, including obesity, of the agouti-lethal-yellow mutant. Here we identify the mahogany gene and its product; this study, to our knowledge, represents the first positional cloning of a suppressor gene in the mouse. Expression of the mahogany gene is broad; however, in situ hybridization analysis emphasizes the importance of its expression in the ventromedial hypothalamic nucleus, a region that is intimately involved in the regulation of body weight and feeding. We present new genetic studies that indicate that the mahogany locus does not suppress the obese phenotype of the melanocortin-4-receptor null allele or those of the monogenic obese models (Lep(db), tub and Cpe(fat)). However, mahogany can suppress diet-induced obesity, the mechanism of which is likely to have implications for therapeutic intervention in common human obesity. The amino-acid sequence of the mahogany protein suggests that it is a large, single-transmembrane-domain receptor-like molecule, with a short cytoplasmic tail containing a site that is conserved between Caenorhabditis elegans and mammals. We propose two potential, alternative modes of action for mahogany: one draws parallels with the mechanism of action of low-affinity proteoglycan receptors such as fibroblast growth factor and transforming growth factor-beta, and the other suggests that mahogany itself is a signalling receptor.