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J Bacteriol. 1996, 178: 5897-5903.\nCastelle CJ, Hug LA, Wrighton KC, Thomas BC, Williams KH, Wu D, Tringe SG, Singer SW, Eisen JA, Banfield JF: Extraordinary phylogenetic diversity and metabolic versatility in aquifer sediment. Nat Commun. 2013, In press\nVignais PM: Hydrogenases and H(+)-reduction in primary energy conservation. Results Probl Cell Differ. 2008, 45: 223-252. 10.1007\u002F400_2006_027.\nRosier C, Leys N, Henoumont C, Mergeay M, Wattiez R: Purification and characterization of the acetone carboxylase of Cupriavidus metallidurans strain CH34. Appl Environ Microbiol. 2012, 78: 4516-4518. 10.1128\u002FAEM.07974-11.\nYamada T, Sekiguchi Y, Imachi H, Kamagata Y, Ohashi A, Harada H: Diversity, localization, and physiological properties of filamentous microbes belonging to Chloroflexi subphylum I in mesophilic and thermophilic methanogenic sludge granules. Appl Environ Microbiol. 2005, 71: 7493-7503. 10.1128\u002FAEM.71.11.7493-7503.2005.\nSekiguchi Y: Anaerolinea thermophila gen. nov., sp. nov. and Caldilinea aerophila gen. nov., sp. nov., novel filamentous thermophiles that represent a previously uncultured lineage of the domain Bacteria at the subphylum level. Int J Syst Evol Micr. 2003, 53: 1843-1851. 10.1099\u002Fijs.0.02699-0.\nLin X, Kennedy D, Fredrickson J, Bjornstad B, Konopka A: Vertical stratification of subsurface microbial community composition across geological formations at the Hanford Site. Environ Microbiol. 2012, 14: 414-425. 10.1111\u002Fj.1462-2920.2011.02659.x.\nSinger E, Heidelberg JF, Dhillon A, Edwards KJ: Metagenomic insights into the dominant Fe(II) oxidizing Zetaproteobacteria from an iron mat at Lō´ihi, Hawai´l. Front Microbiol. 2013, 4: 52-\nHinsley AP, Berks BC: Specificity of respiratory pathways involved in the reduction of sulfur compounds by Salmonella enterica. Microbiology. 2002, 148: 3631-3638.\nRalebits TK, Senior E, Van Verseveld HW: Microbial aspects of atrazine degradation in natural environments. Biodegradation. 2002, 13: 11-19. 10.1023\u002FA:1016329628618.\nKindaichi T, Yuri S, Ozaki N, Ohashi A: Ecophysiological role and function of uncultured Chloroflexi in an anammox reactor. Water Sci Technol. 2012, 66: 2556-2561. 10.2166\u002Fwst.2012.479.\nSutcliffe IC: Cell envelope architecture in the Chloroflexi: a shifting frontline in a phylogenetic turf war. Environ Microbiol. 2011, 13: 279-282. 10.1111\u002Fj.1462-2920.2010.02339.x.\nWhite DC, Geyer R, Peacock AD, Hedrick DB, Koenigsberg SS, Sung Y, He J, Löffler FE: Phospholipid furan fatty acids and ubiquinone-8: lipid biomarkers that may protect Dehalococcoides strains from free radicals. Appl Environ Microbiol. 2005, 71: 8426-8433. 10.1128\u002FAEM.71.12.8426-8433.2005.\nSorokin DY, Lücker S, Vejmelkova D, Kostrikina NA, Kleerebezem R, Rijpstra WIC, Damsté JSS, Le Paslier D, Muyzer G, Wagner M, Van Loosdrecht MCM, Daims H: Nitrification expanded: discovery, physiology and genomics of a nitrite-oxidizing bacterium from the phylum Chloroflexi. ISME J. 2012, 6: 2245-2256. 10.1038\u002Fismej.2012.70.\nPati A, Labutti K, Pukall R, Nolan M, Glavina Del Rio T, Tice H, Cheng J-F, Lucas S, Chen F, Copeland A, Ivanova N, Mavromatis K, Mikhailova N, Pitluck S, Bruce D, Goodwin L, Land M, Hauser L, Chang Y-J, Jeffries CD, Chen A, Palaniappan K, Chain P, Brettin T, Sikorski J, Rohde M, Göker M, Bristow J, Eisen JA, Markowitz V, et al: Complete genome sequence of Sphaerobacter thermophilus type strain (S 6022). Stand Genomic Sci. 2010, 2: 49-56. 10.4056\u002Fsigs.601105.\nKiss H, Cleland D, Lapidus A, Lucas S, Del Rio TG, Nolan M, Tice H, Han C, Goodwin L, Pitluck S, Liolios K, Ivanova N, Mavromatis K, Ovchinnikova G, Pati A, Chen A, Palaniappan K, Land M, Hauser L, Chang Y-J, Jeffries CD, Lu M, Brettin T, Detter JC, Göker M, Tindall BJ, Beck B, McDermott TR, Woyke T, Bristow J, et al: Complete genome sequence of “Thermobaculum terrenum” type strain (YNP1). Stand Genomic Sci. 2010, 3: 153-162. 10.4056\u002Fsigs.1153107.\nKiss H, Nett M, Domin N, Martin K, Maresca JA, Copeland A, Lapidus A, Lucas S, Berry KW, Glavina Del Rio T, Dalin E, Tice H, Pitluck S, Richardson P, Bruce D, Goodwin L, Han C, Detter JC, Schmutz J, Brettin T, Land M, Hauser L, Kyrpides NC, Ivanova N, Göker M, Woyke T, Klenk H-P, Bryant DA: Complete genome sequence of the filamentous gliding predatory bacterium Herpetosiphon aurantiacus type strain (114-95(T)). Stand Genomic Sci. 2011, 5: 356-370. 10.4056\u002Fsigs.2194987.\nJarrell KF, McBride MJ: The surprisingly diverse ways that prokaryotes move. Nat Rev Microbiol. 2008, 6: 466-476. 10.1038\u002Fnrmicro1900.\nKrasotkina J, Walters T, Maruya KA, Ragsdale SW: Characterization of the B12- and iron-sulfur-containing reductive dehalogenase from Desulfitobacterium chlororespirans. J Biol Chem. 2001, 276: 40991-40997. 10.1074\u002Fjbc.M106217200.\nNi S, Fredrickson JK, Xun L: Purification and characterization of a novel 3-chlorobenzoate-reductive dehalogenase from the cytoplasmic membrane of Desulfomonile tiedjei DCB-1. J Bacteriol. 1995, 177: 5135-5139.\nAdrian L, Rahnenführer J, Gobom J, Hölscher T: Identification of a chlorobenzene reductive dehalogenase in Dehalococcoides sp. strain CBDB1. Appl Environ Microbiol. 2007, 73: 7717-7724. 10.1128\u002FAEM.01649-07.\nVan de Pas BA, Gerritse J, De Vos WM, Schraa G, Stams AJ: Two distinct enzyme systems are responsible for tetrachloroethene and chlorophenol reductive dehalogenation in Desulfitobacterium strain PCE1. Arch Microbiol. 2001, 176: 165-169. 10.1007\u002Fs002030100316.\nHesseler M, Bogdanović X, Hidalgo A, Berenguer J, Palm GJ, Hinrichs W, Bornscheuer UT: Cloning, functional expression, biochemical characterization, and structural analysis of a haloalkane dehalogenase from Plesiocystis pacifica SIR-1. Appl Microbiol Biotechnol. 2011, 91: 1049-1060. 10.1007\u002Fs00253-011-3328-x.\nChan WY, Wong M, Guthrie J, Savchenko AV, Yakunin AF, Pai EF, Edwards EA: Sequence- and activity-based screening of microbial genomes for novel dehalogenases. Microb Biotechnol. 2010, 3: 107-120. 10.1111\u002Fj.1751-7915.2009.00155.x.\nSmidt H, De Vos WM: Anaerobic microbial dehalogenation. Annu Rev Microbiol. 2004, 58: 43-73. 10.1146\u002Fannurev.micro.58.030603.123600.\nKrzmarzick MJ, Crary BB, Harding JJ, Oyerinde OO, Leri AC, Myneni SCB, Novak PJ: Natural niche for organohalide-respiring Chloroflexi. Appl Environ Microbiol. 2012, 78: 393-401. 10.1128\u002FAEM.06510-11.",{"EN":120},"Sediments are massive reservoirs of carbon compounds and host a large fraction of microbial life. Microorganisms within terrestrial aquifer sediments control buried organic carbon turnover, degrade organic contaminants, and impact drinking water quality. Recent 16S rRNA gene profiling indicates that members of the bacterial phylum Chloroflexi are common in sediment. Only the role of the class Dehalococcoidia, which degrade halogenated solvents, is well understood. Genomic sampling is available for only six of the approximate 30 Chloroflexi classes, so little is known about the phylogenetic distribution of reductive dehalogenation or about the broader metabolic characteristics of Chloroflexi in sediment. We used metagenomics to directly evaluate the metabolic potential and diversity of Chloroflexi in aquifer sediments. We sampled genomic sequence from 86 Chloroflexi representing 15 distinct lineages, including members of eight classes previously characterized only by 16S rRNA sequences. Unlike in the Dehalococcoidia, genes for organohalide respiration are rare within the Chloroflexi genomes sampled here. Near-complete genomes were reconstructed for three Chloroflexi. One, a member of an unsequenced lineage in the Anaerolinea, is an aerobe with the potential for respiring diverse carbon compounds. The others represent two genomically unsampled classes sibling to the Dehalococcoidia, and are anaerobes likely involved in sugar and plant-derived-compound degradation to acetate. Both fix CO2 via the Wood-Ljungdahl pathway, a pathway not previously documented in Chloroflexi. The genomes each encode unique traits apparently acquired from Archaea, including mechanisms of motility and ATP synthesis. Chloroflexi in the aquifer sediments are abundant and highly diverse. Genomic analyses provide new evolutionary boundaries for obligate organohalide respiration. We expand the potential roles of Chloroflexi in sediment carbon cycling beyond organohalide respiration to include respiration of sugars, fermentation, CO2 fixation, and acetogenesis with ATP formation by substrate-level phosphorylation.",{"EN":122},"Community genomic analyses constrain the distribution of metabolic traits across the Chloroflexi phylum and indicate roles in sediment carbon 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Wrighton",{"id":172,"sortIndex":103,"researcher":19,"roles":173,"affiliations":174,"properties":180},"87cf035a-46bd-46c7-91cb-e8d35740fecd",[132],[175],{"id":19,"sortIndex":20,"affiliation":176,"properties":19},{"id":136,"createTime":137,"updateTime":137,"relativeEntities":177,"slug":19,"properties":178,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},[],{"title":179},{"VI":141},{"title":181},{"VI":182},"Cindy J Castelle",{"id":184,"sortIndex":185,"researcher":19,"roles":186,"affiliations":187,"properties":196},"eb083791-d787-457d-995e-88360fff6fa3",6,[132],[188],{"id":19,"sortIndex":20,"affiliation":189,"properties":19},{"id":190,"createTime":191,"updateTime":191,"relativeEntities":192,"slug":19,"properties":193,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},"74027905-c2d3-4547-8d58-89737f58d276","2024-01-17T23:59:15.675+00:00",[],{"title":194},{"VI":195},"Geophysics Department, Earth Sciences Division, Lawrence Berkeley National Lab, Berkeley, USA",{"title":197},{"VI":198},"Kenneth H Williams",{"id":200,"sortIndex":201,"researcher":19,"roles":202,"affiliations":203,"properties":209},"64bb31f7-616d-4b86-a13f-f2941273326f",4,[132],[204],{"id":19,"sortIndex":20,"affiliation":205,"properties":19},{"id":136,"createTime":137,"updateTime":137,"relativeEntities":206,"slug":19,"properties":207,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},[],{"title":208},{"VI":141},{"title":210},{"VI":211},"Itai 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EA, Savidge T, Shulman RJ. 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Gut. 2018;67:255–62.\nPosserud I, Syrous A, Lindstrom L, Tack J, Abrahamsson H, Simren M. Altered rectal perception in irritable bowel syndrome is associated with symptom severity. Gastroenterology. 2007;133:1113–23.\nTornblom H, Van Oudenhove L, Sadik R, Abrahamsson H, Tack J, Simren M. Colonic transit time and IBS symptoms: what's the link? Am J Gastroenterol. 2012;107:754–60.\nCollins SM. A role for the gut microbiota in IBS. Nat Rev Gastroenterol Hepatol. 2014;11:497–505.\nOhman L, Simren M. Intestinal microbiota and its role in irritable bowel syndrome (IBS). Curr Gastroenterol Rep. 2013;15:323.\nTap J, Derrien M, Tornblom H, Brazeilles R, Cools-Portier S, Dore J, Storsrud S, Le Neve B, Ohman L, Simren M. Identification of an intestinal microbiota signature associated with severity of irritable bowel syndrome. Gastroenterology. 2017;152:111–123 e118.\nLabus JS, Hollister EB, Jacobs J, Kirbach K, Oezguen N, Gupta A, Acosta J, Luna RA, Aagaard K, Versalovic J, et al. Differences in gut microbial composition correlate with regional brain volumes in irritable bowel syndrome. Microbiome. 2017;5:49.\nYano JM, Yu K, Donaldson GP, Shastri GG, Ann P, Ma L, Nagler CR, Ismagilov RF, Mazmanian SK, Hsiao EY. Indigenous bacteria from the gut microbiota regulate host serotonin biosynthesis. Cell. 2015;161:264–76.\nMawe GM, Hoffman JM. Serotonin signalling in the gut--functions, dysfunctions and therapeutic targets. Nat Rev Gastroenterol Hepatol. 2013;10:473–86.\nSikander A, Rana SV, Prasad KK. Role of serotonin in gastrointestinal motility and irritable bowel syndrome. Clin Chim Acta. 2009;403:47–55.\nHoughton LA, Atkinson W, Lockhart C, Whorwell PJ, Keevil B. Sigmoid-colonic motility in health and irritable bowel syndrome: a role for 5-hydroxytryptamine. Neurogastroenterol Motil. 2007;19:724–31.\nHoughton LA, Atkinson W, Whitaker RP, Whorwell PJ, Rimmer MJ. Increased platelet depleted plasma 5-hydroxytryptamine concentration following meal ingestion in symptomatic female subjects with diarrhoea predominant irritable bowel syndrome. Gut. 2003;52:663–70.\nMartin CR, Osadchiy V, Kalani A, Mayer EA. The brain-gut-microbiome axis. Cell Mol Gastroenterol Hepatol. 2018;6:133–48.\nHalmos EP, Power VA, Shepherd SJ, Gibson PR, Muir JG. A diet low in FODMAPs reduces symptoms of irritable bowel syndrome. Gastroenterology. 2014;146:67–75 e65.\nAltobelli E, Del Negro V, Angeletti PM, Latella G. Low-FODMAP diet improves irritable bowel syndrome symptoms: a meta-analysis. Nutrients. 2017;9\nCarabotti M, Scirocco A, Maselli MA, Severi C. The gut-brain axis: interactions between enteric microbiota, central and enteric nervous systems. Ann Gastroenterol. 2015;28:203–9.\nOsadchiy V, Labus JS, Gupta A, Jacobs J, Ashe-McNalley C, Hsiao EY, Mayer EA. Correlation of tryptophan metabolites with connectivity of extended central reward network in healthy subjects. PLoS One. 2018;13:e0201772.\nSporns O. From simple graphs to the connectome: networks in neuroimaging. Neuroimage. 2012;62:881–6.\nBullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009;10:186–98.\nIrimia A, Chambers MC, Torgerson CM, Van Horn JD. Circular representation of human cortical networks for subject and population-level connectomic visualization. Neuroimage. 2012;60:1340–51.\nRubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52:1059–69.\nLongstreth GF, Thompson WG, Chey WD, Houghton LA, Mearin F, Spiller RC. Functional bowel disorders. Gastroenterology. 2006;130:1480–91.\nJeffery IB, O'Toole PW, Ohman L, Claesson MJ, Deane J, Quigley EM, Simren M. An irritable bowel syndrome subtype defined by species-specific alterations in faecal microbiota. Gut. 2012;61:997–1006.\nFrancis CY, Morris J, Whorwell PJ. The irritable bowel severity scoring system: a simple method of monitoring irritable bowel syndrome and its progress. Aliment Pharmacol Ther. 1997;11:395–402.\nCremonini F, Houghton LA, Camilleri M, Ferber I, Fell C, Cox V, Castillo EJ, Alpers DH, Dewit OE, Gray E, et al. Barostat testing of rectal sensation and compliance in humans: comparison of results across two centres and overall reproducibility. Neurogastroenterol Motil. 2005;17:810–20.\nLe Neve B, Posserud I, Bohn L, Guyonnet D, Rondeau P, Tillisch K, Naliboff B, Mayer EA, Simren M. A combined nutrient and lactulose challenge test allows symptom-based clustering of patients with irritable bowel syndrome. Am J Gastroenterol. 2013;108:786–95.\nMatsuki T, Watanabe K, Fujimoto J, Takada T, Tanaka R. Use of 16S rRNA gene-targeted group-specific primers for real-time PCR analysis of predominant bacteria in human feces. Appl Environ Microbiol. 2004;70:7220–8.\nGodon JJ, Zumstein E, Dabert P, Habouzit F, Moletta R. Molecular microbial diversity of an anaerobic digestor as determined by small-subunit rDNA sequence analysis. Appl Environ Microbiol. 1997;63:2802–13.\nWhitfield-Gabrieli S, Nieto-Castanon A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2012;2:125–41.\nSpielberg JM, Miller GA, Heller W, Banich MT. Flexible brain network reconfiguration supporting inhibitory control. Proc Natl Acad Sci U S A. 2015;112:10020–5.\nIcenhour A, Witt ST, Elsenbruch S, Lowen M, Engstrom M, Tillisch K, Mayer EA, Walter S. Brain functional connectivity is associated with visceral sensitivity in women with irritable bowel syndrome. Neuroimage Clin. 2017;15:449–57.\nHong JY, Naliboff B, Labus JS, Gupta A, Kilpatrick LA, Ashe-McNalley C, Stains J, Heendeniya N, Smith SR, Tillisch K, Mayer EA. Altered brain responses in subjects with irritable bowel syndrome during cued and uncued pain expectation. Neurogastroenterol Motil. 2016;28:127–38.\nMayer EA, Labus JS, Tillisch K, Cole SW, Baldi P. Towards a systems view of IBS. Nat Rev Gastroenterol Hepatol. 2015;12:592–605.\nCohen J. Statistical power analysis for the behavioral sciences. 2. Edn. Hillsdale, N.J: Lawrence Erlbaum; 1988.\nOgino Y, Nemoto H, Goto F. Somatotopy in human primary somatosensory cortex in pain system. Anesthesiology. 2005;103:821–7.\nChen TL, Babiloni C, Ferretti A, Perrucci MG, Romani GL, Rossini PM, Tartaro A, Del Gratta C. Human secondary somatosensory cortex is involved in the processing of somatosensory rare stimuli: an fMRI study. Neuroimage. 2008;40:1765–71.\nBushnell MC, Duncan GH, Hofbauer RK, Ha B, Chen JI, Carrier B. Pain perception: is there a role for primary somatosensory cortex? Proc Natl Acad Sci U S A. 1999;96:7705–9.\nMayer EA, Aziz Q, Coen S, Kern M, Labus JS, Lane R, Kuo B, Naliboff B, Tracey I. Brain imaging approaches to the study of functional GI disorders: a Rome working team report. Neurogastroenterol Motil. 2009;21:579–96.\nKrogius-Kurikka L, Lyra A, Malinen E, Aarnikunnas J, Tuimala J, Paulin L, Makivuokko H, Kajander K, Palva A. Microbial community analysis reveals high level phylogenetic alterations in the overall gastrointestinal microbiota of diarrhoea-predominant irritable bowel syndrome sufferers. BMC Gastroenterol. 2009;9:95.\nAtkinson W, Lockhart S, Whorwell PJ, Keevil B, Houghton LA. Altered 5-hydroxytryptamine signaling in patients with constipation- and diarrhea-predominant irritable bowel syndrome. Gastroenterology. 2006;130:34–43.\nBingel U, Quante M, Knab R, Bromm B, Weiller C, Buchel C. Subcortical structures involved in pain processing: evidence from single-trial fMRI. Pain. 2002;99:313–21.\nChudler EH. Response properties of neurons in the caudate-putamen and globus pallidus to noxious and non-noxious thermal stimulation in anesthetized rats. Brain Res. 1998;812:283–8.\nSong GH, Venkatraman V, Ho KY, Chee MW, Yeoh KG, Wilder-Smith CH. Cortical effects of anticipation and endogenous modulation of visceral pain assessed by functional brain MRI in irritable bowel syndrome patients and healthy controls. Pain. 2006;126:79–90.\nEllingson BM, Mayer E, Harris RJ, Ashe-McNally C, Naliboff BD, Labus JS, Tillisch K. Diffusion tensor imaging detects microstructural reorganization in the brain associated with chronic irritable bowel syndrome. Pain. 2013;154:1528–41.\nLangguth B, Sturm K, Wetter TC, Lange M, Gabriels L, Mayer EA, Schlaier J. Deep brain stimulation for obsessive compulsive disorder reduces symptoms of irritable bowel syndrome in a single patient. Clin Gastroenterol Hepatol. 2015;13:1371–1374 e1373.\nMayer EA, Knight R, Mazmanian SK, Cryan JF, Tillisch K. Gut microbes and the brain: paradigm shift in neuroscience. J Neurosci. 2014;34:15490–6.",{"EN":299},"Evidence from preclinical and clinical studies suggests that interactions among the brain, gut, and microbiota may affect the pathophysiology of irritable bowel syndrome (IBS). As disruptions in central and peripheral serotonergic signaling pathways have been found in patients with IBS, we explored the hypothesis that the abundance of serotonin-modulating microbes of the order Clostridiales is associated with functional connectivity of somatosensory brain regions and gastrointestinal (GI) sensorimotor function. We performed a prospective study of 65 patients with IBS and 21 healthy individuals (controls) recruited from 2011 through 2013 at a secondary\u002Ftertiary care outpatient clinic in Sweden. Study participants underwent functional brain imaging, rectal balloon distension, a nutrient and lactulose challenge test, and assessment of oroanal transit time within a month. They also submitted stool samples, which were analyzed by 16S ribosomal RNA gene sequencing. A tripartite network analysis based on graph theory was used to investigate the interactions among bacteria in the order Clostridiales, connectivity of brain regions in the somatosensory network, and GI sensorimotor function. We found associations between GI sensorimotor function and gut microbes in stool samples from controls, but not in samples from IBS patients. The largest differences between controls and patients with IBS were observed in the Lachnospiraceae incertae sedis, Clostridium XIVa, and Coprococcus subnetworks. We found connectivity of subcortical (thalamus, caudate, and putamen) and cortical (primary and secondary somatosensory cortices) regions to be involved in mediating interactions among these networks. In a comparison of patients with IBS and controls, we observed disruptions in the interactions between the brain, gut, and gut microbial metabolites in patients with IBS—these involve mainly subcortical but also cortical regions of brain. These disruptions may contribute to altered perception of pain in patients with IBS and may be mediated by microbial modulation of the gut serotonergic system.",{"EN":301},"Evidence for an association of gut microbial Clostridia with brain functional connectivity and gastrointestinal sensorimotor function in patients with irritable bowel syndrome, based on tripartite network analysis",{"VOID":303},"10.1186\u002Fs40168-019-0656-z","https:\u002F\u002Fmicrobiomejournal.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs40168-019-0656-z",[306,321,336,349,366,383,395,407,429,441,453,475,488,511],{"id":307,"sortIndex":20,"researcher":19,"roles":308,"affiliations":309,"properties":318},"ec91f7af-0f3e-405e-9032-853554c9bdc8",[132],[310],{"id":19,"sortIndex":20,"affiliation":311,"properties":19},{"id":312,"createTime":313,"updateTime":313,"relativeEntities":314,"slug":19,"properties":315,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},"a34784d2-a723-4f58-89e0-7958e2351a1a","2024-02-21T23:58:51.035+00:00",[],{"title":316},{"VI":317},"G. Oppenheimer Center for Neurobiology of Stress & Resilience, UCLA Vatche and Tamar Manoukian Division of Digestive Diseases, Los Angeles, USA",{"title":319},{"VI":320},"Jennifer S. 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Nutrients. 2021;13(7):2238. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fnu13072238. [published Online First: 20210629].\nMaier L, Pruteanu M, Kuhn M, Zeller G, Telzerow A, Anderson EE, Brochado AR, Fernandez KC, Dose H, Mori H, et al. Extensive impact of non-antibiotic drugs on human gut bacteria. Nature. 2018;555:623–8.\nJavdan B, Lopez JG, Chankhamjon P, Lee YJ, Hull R, Wu Q, Wang X, Chatterjee S, Donia MS. Personalized mapping of drug metabolism by the human gut microbiome. Cell. 2020;181(7):1661-1679.e22. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cell.2020.05.001. Epub 2020 Jun 10. PMID: 32526207; PMCID: PMC8591631.\nMarais S, Du Preez JL, Du Plessis LH, et al. Determination of lovastatin, mevastatin, rosuvastatin and simvastatin with HPLC by means of gradient elution. Pharmazie. 2019;74(11):658–60. https:\u002F\u002Fdoi.org\u002F10.1691\u002Fph.2019.8192.\nTan L, Ju H, Li J. Extraction and determination of short-chain fatty acids in biological samples. Se Pu. 2006;24(1):81–7.\nKobus R, Abuín JM, Müller A, et al. A big data approach to metagenomics for all-food-sequencing. BMC Bioinformatics. 2020;21(1):102. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12859-020-3429-6. [publishedOnlineFirst:20200312].\nDobin A, Davis CA, Schlesinger F, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21. https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbioinformatics\u002Fbts635. [publishedOnlineFirst:20121025].\nLiao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(7):923–30. https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbioinformatics\u002Fbtt656. [publishedOnlineFirst:20131113].\nZapala MA, Schork NJ. Multivariate regression analysis of distance matrices for testing associations between gene expression patterns and related variables. Proc Natl Acad Sci U S A. 2006;103(51):19430–5. https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.0609333103. [publishedOnlineFirst:20061204].\nJiang S, Chen D, Ma C, et al. Establishing a novel inflammatory bowel disease prediction model based on gene markers identified from single nucleotide variants of the intestinal microbiota. iMeta 2022:e40.\nMantell G. Lipid lowering drugs in atherosclerosis–the HMG-CoA reductase inhibitors. Clin Exp Hypertens A. 1989;11(5–6):927–41. https:\u002F\u002Fdoi.org\u002F10.3109\u002F10641968909035383.\nKrukemyer JJ, Talbert RL. Lovastatin: a new cholesterol-lowering agent. Pharmacotherapy. 1987;7(6):198–210. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fj.1875-9114.1987.tb03524.x.\nKwiterovich PO Jr. Safety and efficacy of treatment of children and adolescents with elevated low density lipoprotein levels with a step two diet or with lovastatin. Nutr Metab Cardiovasc Dis. 2001;11(Suppl 5):30–4.\nJabir MS, Hopkins L, Ritchie ND, et al. Mitochondrial damage contributes to Pseudomonas aeruginosa activation of the inflammasome and is downregulated by autophagy. Autophagy. 2015;11(1):166–82. https:\u002F\u002Fdoi.org\u002F10.4161\u002F15548627.2014.981915.\nCignarella F, Cantoni C, Ghezzi L, et al. Intermittent Fasting Confers Protection in CNS Autoimmunity by Altering the Gut Microbiota. Cell Metab. 2018;27(6):1222-35.e6. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cmet.2018.05.006.\nUral S, Gul Yurtsever S, Ormen B, et al. Gemella morbillorum Endocarditis. Case Rep Infect Dis. 2014;2014:456471. https:\u002F\u002Fdoi.org\u002F10.1155\u002F2014\u002F456471. [published Online First: 20141207].\nSheng S, Chen J, Zhang Y, et al. Structural and functional alterations of gut microbiota in males with hyperuricemia and high levels of liver enzymes. Front Med (Lausanne). 2021;8:779994. https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffmed.2021.779994. [published Online First: 20211119].\nSelma MV, Beltrán D, Luna MC, et al. Isolation of human intestinal bacteria capable of producing the bioactive metabolite isourolithin a from ellagic acid. Front Microbiol. 2017;8:1521. https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffmicb.2017.01521. [publishedOnlineFirst:20170807].\nNie C, He T, Zhang W, Zhang G, Ma X. Branched chain amino acids: beyond nutrition metabolism. Int J Mol Sci. 2018;19(4):954. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fijms19040954.\nEricksen RE, Lim SL, McDonnell E, et al. Loss of BCAA catabolism during carcinogenesis enhances mTORC1 activity and promotes tumor development and progression. Cell Metab. 2019;29(5):1151-65.e6. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cmet.2018.12.020. [publishedOnlineFirst:20190117].\nJing ZT, Liu W, Xue CR, et al. AKT activator SC79 protects hepatocytes from TNF-α-mediated apoptosis and alleviates d-Gal\u002FLPS-induced liver injury. Am J Physiol Gastrointest Liver Physiol. 2019;316(3):G387–96. https:\u002F\u002Fdoi.org\u002F10.1152\u002Fajpgi.00350.2018. [publishedOnlineFirst:20190110].",{"EN":569},"The existence of the gut microbiota produces an “individual drug reaction.” As members of the intestinal microbiota, probiotics, although they have prebiotic functions, may accelerate the degradation of drugs, thereby affecting drug efficacy. Lovastatin is one of the well-recognized lipid-lowering drugs. Its main action site is the liver. Therefore, if it is degraded in advance by gastrointestinal probiotics, its efficacy may be reduced. Here, we designed a two-stage experiment in vitro and in vivo to explore the degradation of lovastatin by probiotics. In vitro, the degradation of lovastatin by 83 strains of Lactiplantibacillus plantarum and the “star strain” Lacticaseibacillus paracasei strain Shirota was investigated by high-performance liquid chromatography (HPLC). The results showed that probiotics could degrade lovastatin to varying degrees. Subsequently, we selected Lactiplantibacillus plantarum A5 (16.87%) with the strongest ability to degrade lovastatin, Lactiplantibacillus plantarum C3 (4.61%) with the weakest ability to degrade lovastatin and Lacticaseibacillus paracasei strain Shirota (17.6%) as representative probiotics for in vivo experiments. In vivo, the therapeutic effect of lovastatin combined with probiotics on golden hamsters with mixed hyperlipidemia was evaluated by measuring blood indicators, intestinal microbiota metagenomic sequencing, and the liver transcriptome. The results showed that the intake of probiotics did not affect the efficacy of lovastatin and could slow the inflammatory reaction of the liver. The supplementation of probiotics produced beneficial metabolites in the intestine by promoting beneficial microbes. Intestinal metabolites affected the expression of the liver genes through the gut-liver axis, increased the relative content of the essential amino acids, and finally improved the liver inflammatory response of the host. This study aims to reveal the impact of probiotics on the human body from a unique perspective, suggesting the impact of taking probiotics while taking drugs. \n\n                  \n                    \n                  \n                ",{"EN":571},"Understanding the “individual drug reaction” from the perspective of the interaction between probiotics and lovastatin in vitro and in vivo",{"VOID":573},"10.1186\u002Fs40168-023-01658-z","VERIFIED","2024-12-12T23:56:19.946+00:00","Auto 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China",{},{"id":594,"sortIndex":103,"affiliation":595,"properties":602},"843f212f-8ab5-4129-ac64-30ed1c377148",{"id":596,"createTime":597,"updateTime":597,"relativeEntities":598,"slug":19,"properties":599,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},"5bc80277-5098-4f67-8690-a6e496386a5a","2024-01-13T07:36:00.066+00:00",[],{"title":600},{"VI":601},"One Health Institute, Hainan University, Haikou, China",{},{"id":19,"sortIndex":20,"affiliation":604,"properties":19},{"id":605,"createTime":606,"updateTime":606,"relativeEntities":607,"slug":19,"properties":608,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},"282b7b68-fb9e-4db0-8452-6eade192fc48","2023-12-23T05:38:45.975+00:00",[],{"title":609},{"VI":610},"School of Food Science and Engineering, Hainan University, Haikou, China",{"title":612},{"VI":613},"Jun 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Shen",{"url":577,"publisher":745,"properties":773},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":746,"slug":10,"properties":747,"entityType":17,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20,"subjectFields":751,"manageAffiliations":752,"indexDatabases":753,"url":19,"thumbnailPath":19,"statistic":768,"gsStatistic":19,"type":107,"analyzePriority":19},[],{"issn":748,"title":749,"url":750},{"VOID":13},{"EN":10},{"VOID":16},[],[],[754,761],{"id":82,"indexDatabase":755,"url":95,"indexYears":96,"academicFieldIds":760,"indexDatabaseRanking":100},{"id":84,"createTime":85,"updateTime":86,"relativeEntities":756,"label":757,"description":758,"key":92,"publicationTags":759,"standard":19},[],{"EN":89,"VI":89},{"EN":89,"VI":91},[94],[98,99],{"id":63,"indexDatabase":762,"url":78,"indexYears":19,"academicFieldIds":767,"indexDatabaseRanking":19},{"id":65,"createTime":66,"updateTime":67,"relativeEntities":763,"label":764,"description":765,"key":74,"publicationTags":766,"standard":19},[],{"EN":70,"VI":70},{"VI":72,"EN":73},[76,77],[80],{"impactFactor":20,"impactFactorByYear":769,"i10Index":20,"i10IndexLast5Year":20,"totalPublication":103,"totalPublicationByYear":770,"totalCitation":20,"totalCitationByYear":771,"totalCitationPerPublication":20,"totalCitationPerPublicationByYear":772,"hindexLast5Year":20,"hindex":20},{},{"2016":103},{},{},{"volume":774,"pages":776},{"VOID":775},"11",{"VOID":777},"1-16","2023-09-25",2023,{"id":781,"createTime":782,"updateTime":783,"relativeEntities":784,"slug":785,"properties":786,"entityType":125,"verifyStatus":574,"verifyTime":783,"verifyNote":576,"syncStatus":18,"languages":798,"translateLanguages":19,"viewCount":20,"primaryUrl":800,"fullTextUrl":19,"authors":801,"publicationType":253,"publisherRelationship":936,"citationCount":20,"citationInfo":969,"publishDate":19,"publishYear":19,"citationAnalyzeStatus":18,"lastCitationAnalyze":19,"indexDatabases":19,"openAccess":19,"references":971,"isForceReanalyzing":290},"257f7aae-bbfa-45a5-950a-cd9101ebc498","2024-04-16T09:29:52.357+00:00","2025-01-03T23:52:01.467+00:00",[],"Placental-TLR-recognition-of-salivary-and-subgingival-microbiota-is-associated-with-pregnancy-complications",{"keywords":787,"openalex":788,"abstract":790,"title":792,"pm":794,"doi":796},{},{"VOID":789},"W4393196056",{"EN":791},"\u003Cjats:title>Abstract\u003C\u002Fjats:title>\u003Cjats:sec>\n                \u003Cjats:title>Background\u003C\u002Fjats:title>\n                \u003Cjats:p>Pre-term birth, the leading cause of neonatal mortality, has been associated with maternal periodontal disease and the presence of oral pathogens in the placenta. However, the mechanisms that underpin this link are not known. This investigation aimed to identify the origins of placental microbiota and to interrogate the association between parturition complications and immune recognition of placental microbial motifs.\u003C\u002Fjats:p>\n                \n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Methods\u003C\u002Fjats:title>\n                \u003Cjats:p>Saliva, plaque, serum, and placenta were collected during 130 full-term (FT), pre-term (PT), or pre-term complicated by pre-eclampsia (PTPE) deliveries and subjected to whole-genome shotgun sequencing. Real-time quantitative PCR was used to measure toll-like receptors (TLR) 1–10 expression in placental samples. Source tracking was employed to trace the origins of the placental microbiota.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Results\u003C\u002Fjats:title>\n                \u003Cjats:p>We discovered 10,007 functionally annotated genes representing 420 taxa in the placenta that could not be attributed to contamination. Placental microbial composition was the biggest discriminator of pregnancy complications, outweighing hypertension, BMI, smoking, and maternal age. A machine-learning algorithm trained on this microbial dataset predicted PTPE and PT with error rates of 4.05% and 8.6% (taxonomy) and 6.21% and 7.38% (function). Logistic regression revealed 32% higher odds of parturition complication (95% CI 2.8%, 81%) for every IQR increase in the Shannon diversity index after adjusting for maternal smoking status, maternal age, and gravida. We also discovered distinct expression patterns of TLRs that detect RNA- and DNA-containing antigens in the three groups, with significant upregulation of TLR9, and concomitant downregulation of TLR7  in PTPE and PT groups, and dense correlation networks between microbial genes and these TLRs. 70–82% of placental microbiota were traced to serum and thence to the salivary and subgingival microbiomes. The oral and serum microbiomes of PTPE and PT groups displayed significant enrichment of genes encoding iron transport, exosome, adhesion, quorum sensing, lipopolysaccharide, biofilm, and steroid degradation.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Conclusions\u003C\u002Fjats:title>\n                \u003Cjats:p>Within the limits of cross-sectional analysis, we find evidence to suggest that oral bacteria might translocate to the placenta via serum and trigger immune signaling pathways capable of inducing placental vascular pathology. This might explain, in part, the higher incidence of obstetric syndromes in women with periodontal disease.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>",{"EN":793},"Placental TLR recognition of salivary and subgingival microbiota is associated with pregnancy complications",{"VOID":795},"38532461",{"VOID":797},"10.1186\u002Fs40168-024-01761-9",[799],"EN","https:\u002F\u002Fmicrobiomejournal.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs40168-024-01761-9",[802,823,844,861,876,896,915],{"id":803,"sortIndex":201,"researcher":19,"roles":804,"affiliations":805,"properties":816},"fef99dd9-ef5f-411b-9fc8-c0143da921ee",[],[806],{"id":807,"sortIndex":20,"affiliation":808,"properties":19},"de13ea11-56ca-47f1-b58f-88d3413fffbc",{"id":809,"createTime":810,"updateTime":810,"relativeEntities":811,"slug":812,"properties":813,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},"80d50d30-edf8-4d6a-a0d9-0f1d7beceadf","2024-04-16T09:29:52.402+00:00",[],"Faculty-of-Clinical-Sciences-Department-of-Periodontology-Ege-University-%C4%B0zmir-Turkey",{"title":814},{"EN":815},"Faculty of Clinical Sciences, Department of Periodontology, Ege University, İzmir, 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citation_author=G Santini, N Biondi, L Rodolfi, MR Tredici; citation_volume=10; citation_publication_date=2021; citation_pages=643; citation_doi=10.3390\u002Fplants10040643; citation_id=CR101\ncitation_journal_title=Front Cell Infect Microbiol.; citation_title=Planctomycetes as host-associated bacteria: a perspective that holds promise for their future isolations, by mimicking their native environmental niches in clinical microbiology laboratories; citation_author=OD Kaboré, S Godreuil, M Drancourt; citation_volume=10; citation_publication_date=2020; citation_pages=729; citation_doi=10.3389\u002Ffcimb.2020.519301; citation_id=CR102\ncitation_journal_title=ISME J.; citation_title=Taxonomical and functional microbial community selection in soybean rhizosphere; citation_author=LW Mendes, EE Kuramae, AA Navarrete, JA Veen, SM Tsai; citation_volume=8; citation_publication_date=2014; citation_pages=1577-87; citation_doi=10.1038\u002Fismej.2014.17; citation_id=CR103\ncitation_journal_title=Appl Soil Ecol.; citation_title=Bacterial functional prediction tools detect but underestimate metabolic diversity compared to shotgun metagenomics in southwest Florida soils; citation_author=DR Toole, J Zhao, W Martens-Habbena, SL Strauss; citation_volume=168; citation_publication_date=2021; citation_doi=10.1016\u002Fj.apsoil.2021.104129; citation_id=CR104\ncitation_journal_title=Microbiome.; citation_title=Inference-based accuracy of metagenome prediction tools varies across sample types and functional categories; citation_author=S Sun, RB Jones, AA Fodor; citation_volume=8; citation_publication_date=2020; citation_pages=46; citation_doi=10.1186\u002Fs40168-020-00815-y; citation_id=CR105",{"EN":1281},"While the rootstock genotype (belowground part of a plant) can impact rhizosphere microbial communities, few studies have examined the relationships between rootstock genotype-based recruitment of active rhizosphere bacterial communities and the availability of root nutrients for plant uptake. Rootstocks are developed to provide resistance to disease or tolerance of abiotic stresses, and compost application is a common practice to also control biotic and abiotic stresses in crops. In this field study, we examined: (i) the effect of four citrus rootstocks and\u002For compost application on the abundance, diversity, composition, and predicted functionality of active rhizosphere bacterial communities, and (ii) the relationships between active rhizosphere bacterial communities and root nutrient concentrations, with identification of bacterial taxa significantly correlated with changes in root nutrients in the rhizosphere. The rootstock genotype determined differences in the diversity of active rhizosphere bacterial communities and also impacted how compost altered the abundance, diversity, composition, and predicted functions of these active communities. Variations in the active bacterial rhizobiome were strongly linked to root nutrient cycling, and these interactions were root-nutrient- and rootstock-specific. Direct positive relationships between enriched taxa in treated soils and specific root nutrients were detected, and potentially important taxa for root nutrient uptake were identified. Significant differences in specific predicted functions were related to soil nutrient cycling (carbon, nitrogen, and tryptophan metabolisms) in the active bacterial rhizobiome among rootstocks, particularly in soils treated with compost. This study illustrates that interactions between citrus rootstocks and compost can influence active rhizosphere bacterial communities, which impact root nutrient concentrations. In particular, the response of the rhizobiome bacterial abundance, diversity, and community composition to compost was determined by the rootstock. Specific bacterial taxa therefore appear to be driving changes in root nutrient concentrations in the active rhizobiome of different citrus rootstocks. Several potential functions of active bacterial rhizobiomes recruited by different citrus rootstocks did not appear to be redundant but rather rootstock-specific. Together, these findings have important agronomic implications as they indicate the potential for agricultural production systems to maximize benefits from rhizobiomes through the choice of selected rootstocks and the application of compost.",{"EN":1283},"Interactions between rootstocks and compost influence the active rhizosphere bacterial communities in citrus",{"VOID":1285},"10.1186\u002Fs40168-023-01524-y","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1186\u002Fs40168-023-01524-y","https:\u002F\u002Fmicrobiomejournal.biomedcentral.com\u002Fcounter\u002Fpdf\u002F10.1186\u002Fs40168-023-01524-y",[1289,1304,1319],{"id":1290,"sortIndex":20,"researcher":19,"roles":1291,"affiliations":1292,"properties":1301},"93774c69-0f9e-49f2-b6bf-3a7cce65af82",[132],[1293],{"id":19,"sortIndex":20,"affiliation":1294,"properties":19},{"id":1295,"createTime":1296,"updateTime":1296,"relativeEntities":1297,"slug":19,"properties":1298,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},"82a32d0d-cd40-4994-aaa0-6a4eea0ed36a","2023-12-08T14:34:54.058+00:00",[],{"title":1299},{"VI":1300},"Department of Soil, Water, and Ecosystem Sciences, Southwest Florida Research and Education Center, Institute of Food and Agricultural Sciences, University of Florida, Immokalee, USA",{"title":1302},{"VI":1303},"Castellano-Hinojosa, Antonio",{"id":1305,"sortIndex":103,"researcher":19,"roles":1306,"affiliations":1307,"properties":1316},"eb7c7900-4ef5-4512-8684-2f4b779960cb",[132],[1308],{"id":19,"sortIndex":20,"affiliation":1309,"properties":19},{"id":1310,"createTime":1311,"updateTime":1311,"relativeEntities":1312,"slug":19,"properties":1313,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},"1d5c808b-52fd-4592-bd76-1c0010c85c24","2023-12-08T14:34:54.011+00:00",[],{"title":1314},{"VI":1315},"Department of Horticultural Sciences, Southwest Florida Research and Education Center, Institute of Food and Agricultural Sciences, University of Florida, Immokalee, USA",{"title":1317},{"VI":1318},"Albrecht, Ute",{"id":1320,"sortIndex":160,"researcher":19,"roles":1321,"affiliations":1322,"properties":1328},"b86e830d-03a3-4f6a-b2c5-659321549ecf",[132],[1323],{"id":19,"sortIndex":20,"affiliation":1324,"properties":19},{"id":1295,"createTime":1296,"updateTime":1296,"relativeEntities":1325,"slug":19,"properties":1326,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},[],{"title":1327},{"VI":1300},{"title":1329},{"VI":1330},"Strauss, Sarah L.",{"url":1286,"publisher":1332,"properties":1360},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1333,"slug":10,"properties":1334,"entityType":17,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20,"subjectFields":1338,"manageAffiliations":1339,"indexDatabases":1340,"url":19,"thumbnailPath":19,"statistic":1355,"gsStatistic":19,"type":107,"analyzePriority":19},[],{"issn":1335,"title":1336,"url":1337},{"VOID":13},{"EN":10},{"VOID":16},[],[],[1341,1348],{"id":82,"indexDatabase":1342,"url":95,"indexYears":96,"academicFieldIds":1347,"indexDatabaseRanking":100},{"id":84,"createTime":85,"updateTime":86,"relativeEntities":1343,"label":1344,"description":1345,"key":92,"publicationTags":1346,"standard":19},[],{"EN":89,"VI":89},{"EN":89,"VI":91},[94],[98,99],{"id":63,"indexDatabase":1349,"url":78,"indexYears":19,"academicFieldIds":1354,"indexDatabaseRanking":19},{"id":65,"createTime":66,"updateTime":67,"relativeEntities":1350,"label":1351,"description":1352,"key":74,"publicationTags":1353,"standard":19},[],{"EN":70,"VI":70},{"VI":72,"EN":73},[76,77],[80],{"impactFactor":20,"impactFactorByYear":1356,"i10Index":20,"i10IndexLast5Year":20,"totalPublication":103,"totalPublicationByYear":1357,"totalCitation":20,"totalCitationByYear":1358,"totalCitationPerPublication":20,"totalCitationPerPublicationByYear":1359,"hindexLast5Year":20,"hindex":20},{},{"2016":103},{},{},{"volume":1361,"pages":1362,"issue":1363},{"VOID":775},{"VOID":777},{"VOID":285},"2023-12-01",{"id":1366,"createTime":1367,"updateTime":1368,"relativeEntities":1369,"slug":1370,"properties":1371,"entityType":125,"verifyStatus":574,"verifyTime":1368,"verifyNote":576,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20,"primaryUrl":1380,"fullTextUrl":19,"authors":1381,"publicationType":253,"publisherRelationship":1485,"citationCount":19,"citationInfo":19,"publishDate":1518,"publishYear":1519,"citationAnalyzeStatus":18,"lastCitationAnalyze":19,"indexDatabases":19,"openAccess":19,"references":19,"isForceReanalyzing":290},"3ef7648a-0a0c-4fda-85da-c352d9f204c7","2024-01-15T09:15:04.009+00:00","2024-12-09T23:50:27.199+00:00",[],"BioMiCo-a-supervised-Bayesian-model-for-inference-of-microbial-community-structure",{"references":1372,"abstract":1374,"title":1376,"doi":1378},{"VOID":1373},"Caporaso JG, Lauber CL, Costello EK, Berg-Lyons D, Gonzalez A, Stombaugh J, et al. Moving pictures of the human microbiome. Genome Biol. 2011;12:R50.\nGilbert JA, Steele JA, Caporaso JG, Steinbrück L, Reeder J, Temperton B, et al. Defining seasonal marine microbial community dynamics. ISME J. 2012;6:298–308.\nBoon E, Meehan CJ, Whidden C, Wong DH, Langille MG, Beiko RG. Interactions in the microbiome: communities of organisms and communities of genes. FEMS Microbiol Rev. 2014;38:90–118.\nFaith JJ, Guruge JK, Charbonneau M, Subramanian S, Seedorf H, Goodman AL, et al. The long-term stability of the human gut microbiota. Science. 2013;341:1237439.\nBritton RA, Young VB. Role of the intestinal microbiota in resistance to colonization by Clostridium difficile. Gastroenterology. 2014;146:1547–53.\nHalm H, Lam P, Ferdelman TG, Lavik G, Dittmar T, LaRoche J, et al. Heterotrophic organisms dominate nitrogen fixation in the South Pacific Gyre. ISME J. 2012;6:1238–49.\nGevers D, Kugathasan S, Denson LA, Vázquez-Baeza Y, Van Treuren W, Ren B, et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe. 2014;15:382–92.\nKorpela K, Flint HJ, Johnstone AM, Lappi J, Poutanen K, Dewulf E, et al. Gut microbiota signatures predict host and microbiota responses to dietary interventions in obese individuals. PLoS One. 2014;9:e90702.\nKnights D, Kuczynski J, Charlson ES, Zaneveld J, Mozer MC, Collman RG, et al. Bayesian community-wide culture-independent microbial source tracking. Nat Methods. 2011;8:761–3.\nZarraonaindia I, Smith DP, Gilbert JA. Beyond the genome: community-level analysis of the microbial world. Biol Philos. 2013;28:261–82.\nHastie T, Tibshiriani R, Freidman J. The elements of statistical learning. Data mining, inference, and prediction. Springer series in statistics: Springer, New York; 2001.\nKnights D, Costello EK, Knight R. Supervised classification of human microbiota. FEMS Microbiol Rev. 2011;35:343–59.\nHolmes I, Harris K, Quince C. Dirichlet multinomial mixtures: generative models for microbial metagenomics. PLoS One. 2012;7:e30126.\nBlei DM, McAuliffe JD. Supervised topic models. Adv Neural Inf Process Syst. 2007;21:1–8.\nLeibold MA, Holyoak M, Mouquet N, Amarasekare P, Chase JM, Hoopes MF, et al. The metacommunity concept: a framework for multi- scale community ecology. Ecol Lett. 2004;7:601–13.\nBurke C, Steinberg P, Rusch D, Kjelleberg S, Thomas T. Bacterial community assembly based on functional genes rather than species. Proc Natl Acad Sci. 2011;108:14288–93.\nPatel PV, Gianoulis TA, Bjornson RD, Yip KY, Engelman DM, Gerstein MB. Analysis of membrane proteins in metagenomics: networks of correlated environmental features and protein families. Genome Res. 2010;20:960–71.\nKoropatkin NM, Cameron EA, Martens EC. How glycan metabolism shapes the human gut microbiota. Nat Rev Microbiol. 2012;10:323–35.\nGiovannoni SJ, Vergin KL. Seasonality in ocean microbial communities. Science. 2012;335:671–6.\nLiu JS. The collapsed Gibbs sampler in Bayesian computations with applications to a gene regulation problem. J Am Stat Assoc. 1994;89:958–66.\nMartínez I, Muller CE, Walter J. Long-term temporal analysis of the human fecal microbiota revealed a stable core of dominant bacterial species. PLoS One. 2013;8:e69621.\nSchloissnig S, Arumugam M, Sunagawa S, Mitreva M, Tap J, Zhu A, et al. Genomic variation landscape of the human gut microbiome. Nature. 2013;493:45–50.\nNardis C, Mosca L, Mastromarino P. Vaginal microbiota and viral sexually transmitted diseases. Ann Ig. 2013;25:443–56.\nDonati L, Di Vico A, Nucci M, Quagliozzi L, Spagnuolo T, Labianca A, et al. Vaginal microbial flora and outcome of pregnancy. Arch Gynecol Obstet. 2010;281:589–600.\nNugent RP, Krohn MA, Hillier SL. Reliability of diagnosing bacterial vaginosis is improved by a standardized method of gram stain interpretation. J Clin Microbiol. 1991;29:297–301.\nGajer P, Brotman RM, Bai G, Sakamoto J, Schütte UM, Zhong X, et al. 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PLoS Comput Biol. 2014;10:e1003918.",{"EN":1375},"Microbiome samples often represent mixtures of communities, where each community is composed of overlapping assemblages of species. Such mixtures are complex, the number of species is huge and abundance information for many species is often sparse. Classical methods have a limited value for identifying complex features within such data. Here, we describe a novel hierarchical model for Bayesian inference of microbial communities (BioMiCo). The model takes abundance data derived from environmental DNA, and models the composition of each sample by a two-level hierarchy of mixture distributions constrained by Dirichlet priors. BioMiCo is supervised, using known features for samples and appropriate prior constraints to overcome the challenges posed by many variables, sparse data, and large numbers of rare species. The model is trained on a portion of the data, where it learns how assemblages of species are mixed to form communities and how assemblages are related to the known features of each sample. Training yields a model that can predict the features of new samples. We used BioMiCo to build models for three serially sampled datasets and tested their predictive accuracy across different time points. The first model was trained to predict both body site (hand, mouth, and gut) and individual human host. It was able to reliably distinguish these features across different time points. The second was trained on vaginal microbiomes to predict both the Nugent score and individual human host. We found that women having normal and elevated Nugent scores had distinct microbiome structures that persisted over time, with additional structure within women having elevated scores. The third was trained for the purpose of assessing seasonal transitions in a coastal bacterial community. Application of this model to a high-resolution time series permitted us to track the rate and time of community succession and accurately predict known ecosystem-level events. BioMiCo provides a framework for learning the structure of microbial communities and for making predictions based on microbial assemblages. By training on carefully chosen features (abiotic or biotic), BioMiCo can be used to understand and predict transitions between complex communities composed of hundreds of microbial species.",{"EN":1377},"BioMiCo: a supervised Bayesian model for inference of microbial community structure",{"VOID":1379},"10.1186\u002Fs40168-015-0073-x","https:\u002F\u002Fmicrobiomejournal.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs40168-015-0073-x",[1382,1399,1422,1434,1446,1458,1473],{"id":1383,"sortIndex":160,"researcher":19,"roles":1384,"affiliations":1385,"properties":1396},"cce6cf87-495c-4a4c-831f-6d9a0b3199d4",[132],[1386],{"id":19,"sortIndex":20,"affiliation":1387,"properties":19},{"id":1388,"createTime":1389,"updateTime":1390,"relativeEntities":1391,"slug":1392,"properties":1393,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},"95aa11d7-855d-414e-9726-8327aa035da3","2023-12-06T10:05:52.502+00:00","2024-10-14T14:54:08.690+00:00",[],"Department-of-Biology-Dalhousie-University-Halifax-Canada",{"title":1394},{"VI":1395},"Department of Biology, Dalhousie University, Halifax, Canada",{"title":1397},{"VI":1398},"Eva Boon",{"id":1400,"sortIndex":185,"researcher":19,"roles":1401,"affiliations":1402,"properties":1419},"082cdba4-48e0-4681-9675-0cefdafaad89",[132],[1403,1412],{"id":19,"sortIndex":20,"affiliation":1404,"properties":19},{"id":1405,"createTime":1406,"updateTime":1406,"relativeEntities":1407,"slug":1408,"properties":1409,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},"972d5c9f-a8a9-4af2-a19c-3b951f5acf3d","2024-04-20T14:10:49.374+00:00",[],"Department-of-Mathematics-and-Statistics-Dalhousie-University-Halifax-Canada",{"title":1410},{"EN":1411},"Department of Mathematics and Statistics, Dalhousie University, Halifax, Canada",{"id":1413,"sortIndex":103,"affiliation":1414,"properties":1418},"59eb1782-992b-454d-8c35-f844ea7c9780",{"id":1388,"createTime":1389,"updateTime":1390,"relativeEntities":1415,"slug":1392,"properties":1416,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},[],{"title":1417},{"VI":1395},{},{"title":1420},{"VI":1421},"Joseph P Bielawski",{"id":1423,"sortIndex":20,"researcher":19,"roles":1424,"affiliations":1425,"properties":1431},"1640b531-5402-4a0a-8c4e-7e605641eb1a",[132],[1426],{"id":19,"sortIndex":20,"affiliation":1427,"properties":19},{"id":1405,"createTime":1406,"updateTime":1406,"relativeEntities":1428,"slug":1408,"properties":1429,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},[],{"title":1430},{"EN":1411},{"title":1432},{"VI":1433},"Mahdi 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DJ, Sacre JW, Harding JL, Gregg EW, Zimmet PZ, Shaw JE. 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WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.\nLangfelder P, Horvath S. Fast R functions for robust correlations and hierarchical clustering. J Stat Softw. 2012;46:i11.",{"EN":1530},"Hyperglycaemia in pregnancy (HIP) is a common metabolic disorder that not only poses risks to maternal health but also associates with an increased risk of diabetes among offspring. Vertical transmission of microbiota may influence the offspring microbiome and subsequent glucose metabolism. However, the mechanism by which maternal gut microbiota may influence glucose metabolism of the offspring remains unclear and whether intervening microbiota vertical transmission could be used as a strategy to prevent diabetes in the offspring of mothers with HIP has not been investigated. So we blocked vertical transmission to investigate its effect on glucose metabolism in the offspring. We established a murine HIP model with a high-fat diet (HFD) and investigated the importance of vertical transmission of gut microbiota on the glucose metabolism of offspring via birth and nursing by blocking these events through caesarean section (C-section) and cross-fostering. After weaning, all offspring were fed a normal diet. Based on multi-omics analysis, biochemical and transcriptional assays, we found that the glucometabolic deficits in the mothers were subsequently ‘transmitted’ to the offspring. Meanwhile, the partial change in mothers’ gut microbial community induced by HIP could be transmitted to offspring, supported by the closed clustering of the microbial structure and composition between the offspring and their mothers. Further study showed that the microbiota vertical transmission was blocked by C-section and cross-fostering, which resulted in improved insulin sensitivity and islet function of the offspring of the mothers with HIP. These effects were correlated with changes in the relative abundances of specific bacteria and their metabolites, such as increased relative abundances of Bifidobacterium and short-chain fatty acids. In particular, gut microbial communities of offspring were closely related to those of their foster mothers but not their biological mothers, and the effect of cross-fostering on the offspring’s gut microbiota was more profound than that of C-section. Our study demonstrates that the gut microbiota transmitted via birth and nursing are important contributors to the glucose metabolism phenotype in offspring. \n                  \n                    \n                      \n                    \n                  \n                ",{"EN":1532},"Vertical transmission of the gut microbiota influences glucose metabolism in offspring of mice with hyperglycaemia in 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R, Abnet CC, White O, Knight R, Huttenhower C. The microbiome quality control project: baseline study design and future directions. Genome Biol. 2015;16:276.\nSinha R, Abu-Ali G, Vogtmann E, Fodor AA, Ren B, Amir A, Schwager E, Crabtree J, Ma S. Microbiome quality control project C, et al: Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium. Nat Biotechnol. 2017;35:1077–86.\nGoh WWB, Wang W, Wong L. Why batch effects matter in omics data, and how to avoid them. Trends Biotechnol. 2017;35:498–507.\nLeek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B, Johnson WE, Geman D, Baggerly K, Irizarry RA. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet. 2010;11:733–9.\nSalter SJ, Cox MJ, Turek EM, Calus ST, Cookson WO, Moffatt MF, Turner P, Parkhill J, Loman NJ, Walker AW. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 2014;12:87.\nDahlberg J, Sun L, Persson Waller K, Ostensson K, McGuire M, Agenas S, Dicksved J. Microbiota data from low biomass milk samples is markedly affected by laboratory and reagent contamination. PLoS One. 2019;14:e0218257.\nVitek J, Kalibera T. Repeatability, reproducibility, and rigor in systems research. In: Proceedings of the Ninth ACM International Conference on Embedded Software; 2011.\nKanwal S, Khan FZ, Lonie A, Sinnott RO. Investigating reproducibility and tracking provenance - A genomic workflow case study. BMC Bioinformatics. 2017;18:337.\nSubbarao P, Anand SS, Becker AB, Befus AD, Brauer M, Brook JR, Denburg JA, HayGlass KT, Kobor MS, Kollmann TR, et al. The Canadian Healthy Infant Longitudinal Development (CHILD) Study: examining developmental origins of allergy and asthma. Thorax. 2015;70:998–1000.\nMoossavi S, Sepehri S, Robertson B, Bode L, Goruk S, Field CJ, Lix LM, de Souza RJ, Becker AB, Mandhane PJ, et al. Composition and variation of the human milk microbiome is influenced by maternal and early Life factors. Cell Host Microbe. 2019;25:324–35.\nFukushima M, Kakinuma K, Kawaguchi R. Phylogenetic analysis of Salmonella, Shigella, and Escherichia coli strains on the basis of the gyrB gene sequence. J Clin Microbiol. 2002;40:2779–85.\nKarstens L, Asquith M, Davin S, Fair D, Gregory WT, Wolfe AJ, Braun J, McWeeney S. Controlling for contaminants in low biomass 16S rRNA gene sequencing experiments. mSystems. 2019;4:e00290–19.\nDavis NM, Proctor D, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome. 2018;6:226.\nde Goffau MC, Lager S, Salter SJ, Wagner J, Kronbichler A, Charnock-Jones DS, Peacock SJ, Smith GCS, Parkhill J. Recognizing the reagent microbiome. Nat Microbiol. 2018;3:851–3.\nGibbons SM, Duvallet C, Alm EJ. Correcting for batch effects in case-control microbiome studies. PLoS Comput Biol. 2018;14:e1006102.\nDai Z, Wong SH, Yu J, Wei Y. Batch effects correction for microbiome data with Dirichlet-multinomial regression. Bioinformatics. 2019;35:807–14.\nWeiss S, Amir A, Hyde ER, Metcalf JL, Song SJ, Knight R. Tracking down the sources of experimental contamination in microbiome studies. Genome Biol. 2014;15:564.\nEisenhofer R, Minich JJ, Marotz C, Cooper A, Knight R, Weyrich LS. Contamination in low microbial biomass microbiome studies: Issues and recommendations. Trends Microbiol. 2019;27:105–17.\nPoussin C, Sierro N, Boue S, Battey J, Scotti E, Belcastro V, Peitsch MC, Ivanov NV, Hoeng J. Interrogating the microbiome: experimental and computational considerations in support of study reproducibility. Drug Discov Today. 2018;23:1644–57.\nWillis AD. Rigorous statistical methods for rigorous microbiome science. mSystems. 2019;4:e00117–9.\nCaporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer M, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6:1621–4.\nDerakhshani H, Tun HM, Khafipour E. An extended single-index multiplexed 16S rRNA sequencing for microbial community analysis on MiSeq illumina platforms. J Basic Microbiol. 2016;56:321–6.\nCallahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.\nCaporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.\nDeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–72.\nR Core Team: R. A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2017.\nMcMurdie PJ. Holmes S: phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8:e61217.\nGamer M, Lemon J, Fellows I, Singh P: irr: Various coefficients of interrater reliability and agreement. R package version 0.84.1. https:\u002F\u002FCRAN.R-project.org\u002Fpackage=irr. 2019.",{"EN":1735},"Quality control including assessment of batch variabilities and confirmation of repeatability and reproducibility are integral component of high throughput omics studies including microbiome research. Batch effects can mask true biological results and\u002For result in irreproducible conclusions and interpretations. Low biomass samples in microbiome research are prone to reagent contamination; yet, quality control procedures for low biomass samples in large-scale microbiome studies are not well established. In this study, we have proposed a framework for an in-depth step-by-step approach to address this gap. The framework consists of three independent stages: (1) verification of sequencing accuracy by assessing technical repeatability and reproducibility of the results using mock communities and biological controls; (2) contaminant removal and batch variability correction by applying a two-tier strategy using statistical algorithms (e.g. decontam) followed by comparison of the data structure between batches; and (3) corroborating the repeatability and reproducibility of microbiome composition and downstream statistical analysis. Using this approach on the milk microbiota data from the CHILD Cohort generated in two batches (extracted and sequenced in 2016 and 2019), we were able to identify potential reagent contaminants that were missed with standard algorithms and substantially reduce contaminant-induced batch variability. Additionally, we confirmed the repeatability and reproducibility of our results in each batch before merging them for downstream analysis. This study provides important insight to advance quality control efforts in low biomass microbiome research. Within-study quality control that takes advantage of the data structure (i.e. differential prevalence of contaminants between batches) would enhance the overall reliability and reproducibility of research in this field. \n                  \n                    \n                      \n                    \n                  \n                ",{"EN":1737},"Repeatability and reproducibility assessment in a large-scale population-based microbiota study: case study on human milk microbiota",{"VOID":1739},"10.1186\u002Fs40168-020-00998-4","https:\u002F\u002Fmicrobiomejournal.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs40168-020-00998-4",[1742,1767,1796,1851],{"id":1743,"sortIndex":160,"researcher":19,"roles":1744,"affiliations":1745,"properties":1764},"df498a6d-5965-4fd6-891f-b5c3dca596b3",[132],[1746,1754],{"id":19,"sortIndex":20,"affiliation":1747,"properties":19},{"id":1748,"createTime":1749,"updateTime":1749,"relativeEntities":1750,"slug":19,"properties":1751,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},"9637f94a-154c-4926-8546-c6221973e240","2024-01-03T06:43:39.179+00:00",[],{"title":1752},{"VI":1753},"Department of Animal Science, University of Manitoba, Winnipeg, 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Endosymbionts serve essential biochemical and ecological functions, but the prokaryotic viruses (phages) that determine their fate are unknown. We conducted metagenomic analysis of a deep-sea vent snail. We assembled four genome bins for Caudovirales phages that had developed dual endosymbiosis with sulphur-oxidising bacteria (SOB) and methane-oxidising bacteria (MOB). Clustered regularly interspaced short palindromic repeat (CRISPR) spacer mapping, genome comparison, and transcriptomic profiling revealed that phages Bin1, Bin2, and Bin4 infected SOB and MOB. The observation of prophages in the snail endosymbionts and expression of the phage integrase gene suggested the presence of lysogenic infection, and the expression of phage structural protein and lysozyme genes indicated active lytic infection. Furthermore, SOB and MOB appear to employ adaptive CRISPR–Cas systems to target phage DNA. Additional expressed defence systems, such as innate restriction–modification systems and dormancy-inducing toxin–antitoxin systems, may co-function and form multiple lines for anti-viral defence. To counter host defence, phages Bin1, Bin2, and Bin3 appear to have evolved anti-restriction mechanisms and expressed methyltransferase genes that potentially counterbalance host restriction activity. In addition, the high-level expression of the auxiliary metabolic genes narGH, which encode nitrate reductase subunits, may promote ATP production, thereby benefiting phage DNA packaging for replication. This study provides new insights into phage–bacteria interplay in intracellular environments of a deep-sea vent snail. \n                  \n                    \n                      \n                    \n                  \n                ",{"EN":1925},"Arms race in a cell: genomic, transcriptomic, and proteomic insights into intracellular phage–bacteria interplay in deep-sea snail holobionts",{"VOID":1927},"10.1186\u002Fs40168-021-01099-6","https:\u002F\u002Fmicrobiomejournal.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs40168-021-01099-6",[1930,1957,1972,1999],{"id":1931,"sortIndex":160,"researcher":19,"roles":1932,"affiliations":1933,"properties":1954},"210abbcf-6e00-45e1-80b0-ccaa4edb3ebb",[132],[1934,1942],{"id":19,"sortIndex":20,"affiliation":1935,"properties":19},{"id":1936,"createTime":1937,"updateTime":1937,"relativeEntities":1938,"slug":19,"properties":1939,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},"7ee7a43b-53b2-4041-88ab-780c2c059b9b","2023-12-11T19:43:06.212+00:00",[],{"title":1940},{"VI":1941},"State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, Xiamen University (Xiang’an), Xiamen, China",{"id":1943,"sortIndex":103,"affiliation":1944,"properties":1953},"b174ef91-07d3-4938-8f7e-3063cff24fd8",{"id":1945,"createTime":1946,"updateTime":1947,"relativeEntities":1948,"slug":1949,"properties":1950,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},"e3104438-cc68-441b-b030-fd8dd9f04cc4","2024-01-27T13:52:47.190+00:00","2024-11-26T14:27:26.232+00:00",[],"Southern-Marine-Science-and-Engineering-Guangdong-Laboratory-Zhuhai-Zhuhai-China",{"title":1951},{"VI":1952},"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China",{},{"title":1955},{"VI":1956},"Rui Zhang",{"id":1958,"sortIndex":242,"researcher":19,"roles":1959,"affiliations":1960,"properties":1969},"f6ac3eea-b29f-4dde-a6b5-1991369ebe32",[132],[1961],{"id":19,"sortIndex":20,"affiliation":1962,"properties":19},{"id":1963,"createTime":1964,"updateTime":1964,"relativeEntities":1965,"slug":19,"properties":1966,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},"8ddeb847-16cb-4346-a41a-d27e899149e5","2023-12-11T19:43:06.226+00:00",[],{"title":1967},{"VI":1968},"Department of Ocean Science and Hong Kong Branch of the Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Hong Kong University of Science and Technology, Hong Kong, China",{"title":1970},{"VI":1971},"Pei-Yuan Qian",{"id":1973,"sortIndex":103,"researcher":19,"roles":1974,"affiliations":1975,"properties":1996},"499304a5-7723-4cd6-8fa9-84ed5a0fcb2a",[132],[1976,1988],{"id":1977,"sortIndex":103,"affiliation":1978,"properties":1987},"6c87a339-c9fe-44bf-a58f-15b7779c1bd4",{"id":1979,"createTime":1980,"updateTime":1981,"relativeEntities":1982,"slug":1983,"properties":1984,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},"065da231-dcd4-4537-a2a6-ce70aaa7bb87","2023-12-11T19:15:18.479+00:00","2025-06-11T19:27:04.509+00:00",[],"Shenzhen-Key-Laboratory-of-Marine-Bioresource-and-Eco-Environmental-Science-College-of-Life-Sciences-and-Oceanography-Shenzhen-University-Shenzhen-China",{"title":1985},{"VI":1986},"Shenzhen Key Laboratory of Marine Bioresource and Eco-Environmental Science, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, 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