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Buendia P, Fernandez K, Raley C, Rahnavard A, Crandall KA, Castro JG. Hospital antimicrobial stewardship: profiling the oral microbiome after exposure to COVID-19 and antibiotics. Front Microbiol 2024; 15:1346762. [PMID: 38476940 PMCID: PMC10927822 DOI: 10.3389/fmicb.2024.1346762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 01/22/2024] [Indexed: 03/14/2024] Open
Abstract
Introduction During the COVID-19 Delta variant surge, the CLAIRE cross-sectional study sampled saliva from 120 hospitalized patients, 116 of whom had a positive COVID-19 PCR test. Patients received antibiotics upon admission due to possible secondary bacterial infections, with patients at risk of sepsis receiving broad-spectrum antibiotics (BSA). Methods The saliva samples were analyzed with shotgun DNA metagenomics and respiratory RNA virome sequencing. Medical records for the period of hospitalization were obtained for all patients. Once hospitalization outcomes were known, patients were classified based on their COVID-19 disease severity and the antibiotics they received. Results Our study reveals that BSA regimens differentially impacted the human salivary microbiome and disease progression. 12 patients died and all of them received BSA. Significant associations were found between the composition of the COVID-19 saliva microbiome and BSA use, between SARS-CoV-2 genome coverage and severity of disease. We also found significant associations between the non-bacterial microbiome and severity of disease, with Candida albicans detected most frequently in critical patients. For patients who did not receive BSA before saliva sampling, our study suggests Staphylococcus aureus as a potential risk factor for sepsis. Discussion Our results indicate that the course of the infection may be explained by both monitoring antibiotic treatment and profiling a patient's salivary microbiome, establishing a compelling link between microbiome and the specific antibiotic type and timing of treatment. This approach can aid with emergency room triage and inpatient management but also requires a better understanding of and access to narrow-spectrum agents that target pathogenic bacteria.
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Affiliation(s)
| | | | - Castle Raley
- The George Washington University Genomics Core, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States
| | - Ali Rahnavard
- Department of Biostatistics and Bioinformatics, Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States
| | - Keith A. Crandall
- The George Washington University Genomics Core, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States
- Department of Biostatistics and Bioinformatics, Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States
| | - Jose Guillermo Castro
- Division of Infectious Diseases, Leonard M. Miller School of Medicine, University of Miami, Miami, FL, United States
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Buendia P, Bradley RM, Taylor TJ, Schymanski EL, Patti GJ, Kabuka MR. Ontology-based metabolomics data integration with quality control. Bioanalysis 2019; 11:1139-1155. [PMID: 31179719 PMCID: PMC6661928 DOI: 10.4155/bio-2018-0303] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Accepted: 05/01/2019] [Indexed: 12/12/2022] Open
Abstract
Aim: The complications that arise when performing meta-analysis of datasets from multiple metabolomics studies are addressed with computational methods that ensure data quality, completeness of metadata and accurate interpretation across studies. Results & methodology: This paper presents an integrated system of quality control (QC) methods to assess metabolomics results by evaluating the data acquisition strategies and metabolite identification process when integrating datasets for meta-analysis. An ontology knowledge base and a rule-based system representing the experiment and chemical background information direct the processes involved in data integration and QC verification. A diabetes meta-analysis study using these QC methods finds putative biomarkers that differ between cohorts. Conclusion: The methods presented here ensure the validity of meta-analysis when integrating data from different metabolic profiling studies.
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Affiliation(s)
- Patricia Buendia
- INFOTECH Soft, Inc., 1201 Brickell Ave. Suite 220, Miami, FL 33131, USA
| | - Ray M Bradley
- INFOTECH Soft, Inc., 1201 Brickell Ave. Suite 220, Miami, FL 33131, USA
| | - Thomas J Taylor
- INFOTECH Soft, Inc., 1201 Brickell Ave. Suite 220, Miami, FL 33131, USA
| | - Emma L Schymanski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, 6 Avenue du Swing, Belvaux L-4367, Luxembourg
- Eawag – Swiss Federal Institute of Aquatic Science & Technology, Überland Strasse 133, Dübendorf 8600, Switzerland
| | - Gary J Patti
- Departments of Chemistry, Genetics, & Medicine. Washington University, Saint Louis, MO 63110, USA
| | - Mansur R Kabuka
- INFOTECH Soft, Inc., 1201 Brickell Ave. Suite 220, Miami, FL 33131, USA
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Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R, Lanczi L, Gerstner E, Weber MA, Arbel T, Avants BB, Ayache N, Buendia P, Collins DL, Cordier N, Corso JJ, Criminisi A, Das T, Delingette H, Demiralp Ç, Durst CR, Dojat M, Doyle S, Festa J, Forbes F, Geremia E, Glocker B, Golland P, Guo X, Hamamci A, Iftekharuddin KM, Jena R, John NM, Konukoglu E, Lashkari D, Mariz JA, Meier R, Pereira S, Precup D, Price SJ, Raviv TR, Reza SMS, Ryan M, Sarikaya D, Schwartz L, Shin HC, Shotton J, Silva CA, Sousa N, Subbanna NK, Szekely G, Taylor TJ, Thomas OM, Tustison NJ, Unal G, Vasseur F, Wintermark M, Ye DH, Zhao L, Zhao B, Zikic D, Prastawa M, Reyes M, Van Leemput K. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans Med Imaging 2015; 34:1993-2024. [PMID: 25494501 PMCID: PMC4833122 DOI: 10.1109/tmi.2014.2377694] [Citation(s) in RCA: 1616] [Impact Index Per Article: 179.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.
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Lynch C, Tee N, Rouse H, Gordon A, Sati L, Zeiss C, Soygur B, Bassorgun I, Goksu E, Demir R, McGrath J, Groendahl ML, Thuesen L, Andersen AN, Loft A, Smitz J, Adriaenssens T, Vikesa J, Borup R, Mersy E, Kisters N, Macville MVE, Engelen JJM, Consortium SENN, Menheere PPCA, Geraedts JP, Coumans ABC, Frints SGM, Aledani T, Assou S, Traver S, Ait-ahmed O, Dechaud H, Hamamah S, Mizutani E, Suzumori N, Sugiyama C, Hattori Y, Sato T, Ando H, Ozaki Y, Sugiura-Ogasawara M, Wissing M, Kristensen SG, Andersen CY, Mikkelsen AL, Hoest T, Borup R, Groendahl ML, Velthut-Meikas A, Simm J, Metsis M, Salumets A, Palini S, Galluzzi L, De Stefani S, Primiterra M, Wells D, Magnani M, Bulletti C, Vogt PH, Frank-Herrmann P, Bender U, Strowitzki T, Besikoglu B, Heidemann P, Wunsch L, Bettendorf M, Jelinkova L, Vilimova S, Kosarova M, Sebek P, Volemanova E, Kruzelova M, Civisova J, Svobodova L, Sobotka V, Mardesic T, van de Werken C, Santos MA, Eleveld C, Laven JSE, Baart EB, Pylyp LY, Spinenko LA, Zukin VD, Perez-Sanz J, Matorras R, Arluzea J, Bilbao J, Gonzalez-Santiago N, Yeh N, Koff A, Barlas A, Romin Y, Manova-Todorova K, Hoz CDL, Mauri AL, Nascimento AM, Vagnini LD, Petersen CG, Ricci J, Massaro FC, Cavagna M, Pontes A, Oliveira JBA, Baruffi RLR, Franco JG, Wu EX, Ma S, Parriego M, Sole M, Boada M, Coroleu B, Veiga A, Kakourou G, Poulou M, Vrettou C, Destouni A, Traeger-Synodinos J, Kanavakis E, Yatsenko AN, Georgiadis AP, McGuire MM, Zorrilla M, Bunce KD, Peters D, Rajkovic A, Olszewska M, Kurpisz M, Gilbertson AZA, Ottolini CS, Summers MC, Sage K, Handyside AH, Thornhill AR, Griffin DK, Chung MK, Kim JW, Lee JH, Jeong HJ, Kim MH, Ryu MJ, Park SJ, Kang HY, Lee HS, Zimmermann B, Banjevic M, Hill M, Lacroute P, Dodd M, Sigurjonsson S, Lau P, Prosen D, Chopra N, Ryan A, Hall M, McAdoo S, Demko Z, Levy B, Rabinowitz M, Vereczeky A, Kosa ZS, Savay S, Csenki M, Nanassy L, Dudas B, Domotor ZS, Debreceni D, Rossi A, Alegretti JR, Cuzzi J, Bonavita M, Tanada M, Matunaga P, Fettback P, Rosa MB, Maia V, Hassun P, Motta ELA, Piccolomini M, Gomes C, Barros B, Nicoliello M, Matunaga P, Criscuolo T, Bonavita M, Alegretti JR, Miyadahira E, Cuzzi J, Hassun P, Motta ELA, Montjean D, Benkhalifa M, Berthaut I, Griveau JF, Morcel K, Bashamboo A, McElreavey K, Ravel C, Rubio C, Rodrigo L, Mateu E, Mercader A, Peinado V, Buendia P, Milan M, Delgado A, Al-Asmar N, Escrich L, Campos-Galindo I, Garcia-Herrero S, Poo ME, Mir P, Simon C, Reyes-Engel A, Cortes-Rodriguez M, Lendinez A, Perez-Nevot B, Palomares AR, Galdon MR, Ruberti A, Minasi MG, Biricik A, Colasante A, Zavaglia D, Iammarrone E, Fiorentino F, Greco E, Demir N, Ozturk S, Sozen B, Morales R, Lledo B, Ortiz JA, Ten J, Llacer J, Bernabeu R, Nagayoshi M, Tanaka A, Tanaka I, Kusunoki H, Watanabe S, Temel SG, Beyazyurek C, Ekmekci GC, Aybar F, Cinar C, Kahraman S, Nordqvist S, Karehed K, Akerud H, Ottolini CS, Griffin DK, Thornhill AR, Handyside AH, Gultomruk M, Tulay P, Findikli N, Yagmur E, Karlikaya G, Ulug U, Bahceci M, Bargallo MF, Arevalo MR, Salat MM, Barbat IV, Lopez JT, Algam ME, Boluda AB, de Oya GC, Tolmacheva EN, Kashevarova AA, Skryabin NA, Lebedev IN, Semaco E, Belo A, Riboldi M, Cuzzi J, Barros B, Luz L, Criscuolo T, Nobrega N, Matunaga P, Mazetto R, Alegretti JA, Bibancos M, Hassun P, Motta ELA, Serafini P, Neupane J, Vandewoestyne M, Heindryckx B, Deroo T, Lu Y, Ghimire S, Lierman S, Qian C, Deforce D, De Sutter P, Rodrigo L, Rubio C, Mateu E, Peinado V, Milan M, Viloria T, Al-Asmar N, Mercader A, Buendia P, Delgado A, Escrich L, Martinez-Jabaloyas JM, Simon C, Gil-Salom M, Capalbo A, Treff N, Cimadomo D, Tao X, Ferry K, Ubaldi FM, Rienzi L, Scott RT, Katzorke N, Strowitzki T, Vogt HP, Hehr A, Gassner C, Paulmann B, Kowalzyk Z, Klatt M, Krauss S, Seifert D, Seifert B, Hehr U, Minasi MG, Ruberti A, Biricik A, Lobascio M, Zavaglia D, Varricchio MT, Fiorentino F, Greco E, Rubino P, Bono S, Cotarelo RP, Spizzichino L, Biricik A, Colicchia A, Giannini P, Fiorentino F, Suhorutshenko M, Rosenstein-Tamm K, Simm J, Salumets A, Metsis M. Reproductive (epi)genetics. Hum Reprod 2013. [DOI: 10.1093/humrep/det220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Abstract
Background During alternative splicing, the inclusion of an exon in the final mRNA molecule is determined by nuclear proteins that bind cis-regulatory sequences in a target pre-mRNA molecule. A recent study suggested that the regulatory codes of individual RNA-binding proteins may be nearly immutable between very diverse species such as mammals and insects. The model system Drosophila melanogaster therefore presents an excellent opportunity for the study of alternative splicing due to the availability of quality EST annotations in FlyBase. Methods In this paper, we describe an in silico analysis pipeline to extract putative exonic splicing regulatory sequences from a multiple alignment of 15 species of insects. Our method, ESTs-to-ESRs (E2E), uses graph analysis of EST splicing graphs to identify mutually exclusive (ME) exons and combines phylogenetic measures, a sliding window approach along the multiple alignment and the Welch's t statistic to extract conserved ESR motifs. Results The most frequent 100% conserved word of length 5 bp in different insect exons was "ATGGA". We identified 799 statistically significant "spike" hexamers, 218 motifs with either a left or right FDR corrected spike magnitude p-value < 0.05 and 83 with both left and right uncorrected p < 0.01. 11 genes were identified with highly significant motifs in one ME exon but not in the other, suggesting regulation of ME exon splicing through these highly conserved hexamers. The majority of these genes have been shown to have regulated spatiotemporal expression. 10 elements were found to match three mammalian splicing regulator databases. A putative ESR motif, GATGCAG, was identified in the ME-13b but not in the ME-13a of Drosophila N-Cadherin, a gene that has been shown to have a distinct spatiotemporal expression pattern of spliced isoforms in a recent study. Conclusions Analysis of phylogenetic relationships and variability of sequence conservation as implemented in the E2E spikes method may lead to improved identification of ESRs. We found that approximately half of the putative ESRs in common between insects and mammals have a high statistical support (p < 0.01). Several Drosophila genes with spatiotemporal expression patterns were identified to contain putative ESRs located in one exon of the ME exon pairs but not in the other.
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Sertyel S, Kolankaya A, Yigit A, Cengiz F, Kunacaf G, Akman MA, Gurgan T, Yu B, DeCherney A, Segars J, Russanova V, Howard B, Serafini P, Kimati C, Hassun P, Cuzzi J, Peres M, Riboldi M, Gomes C, Fettback P, Alegretti J, motta E, Lappa C, Ottolini CS, Summers MC, Sage K, Rogers S, Griffin DK, Handyside AH, Thornhill AR, Ubaldi F, Capalbo A, Wright G, Elliott T, Maggiulli R, Rienzi L, Nagy ZP, Cinar Yapan C, Beyazyurek C, Ekmekci CG, Altin G, Yesil M, Yelke H, Kahraman S, Khalil M, Rittenberg V, Khalaf Y, El-toukhy T, Alvaro Mercadal B, Imbert R, Demeestere I, De Leener A, Englert Y, Costagliola S, Delbaere A, Zimmermann B, Ryan A, Baner J, Gemelos G, Dodd M, Rabinowitz M, Hill M, Sandalinas M, Garcia-Guixe E, Jimenez-Macedo A, Gimenez C, Hill M, Wemmer N, Potter D, Keller J, Gemelos G, Rabinowitz M, Cater E, Lynch C, Jenner L, Berrisford K, Campbell A, Keown N, Rouse H, Craig A, Fishel S, Palomares AR, Lendinez Ramirez AM, Martinez F, Ruiz Galdon M, Reyes Engel A, Mamas T, Xanthopoulou L, Heath C, Doshi A, Serhal P, SenGupta SB, Plaza S, Templin C, Saguet F, Claustres M, Girardet A, Rienzi L, Biricik A, Capalbo A, Colamaria S, Bono S, Spizzichino L, Ubaldi F, Fiorentino F, Hassun P, Alegretti JR, Kimati C, Barros B, Riboldi M, Cuzzi J, Motta ELA, Serafini P, Tulay P, Naja RP, Cascales-Roman O, Cawood S, Doshi A, Serhal P, SenGupta SB, Montjean D, Ravel C, Belloc S, Cohen-Bacrie P, Bashamboo A, McElreavey K, Benkhalifa M, Filippini G, Radovanovic J, Spalvieri S, Marabella D, Timperi P, Suter T, Jemec M, Traversa M, Marshall J, Leigh D, McArthur S, Zhang L, Yilmaz A, Zhang XY, Son WY, Holzer H, Ao A, Horcajadas JA, Munne S, Fisher J, Ketterson K, Wells D, Bisignano A, Rubio C, Mateu E, Milan M, Mercader A, Bosch E, Labarta E, Crespo J, Remohi J, Simon C, Pellicer A, Mercader A, Garrido N, Rubio C, Buendia P, Delgado A, Escrich L, Poo ME, Simon C, Held K, Baukloh V, Arps S, Wittmann ST, Petrussa L, Van de Velde H, De Rycke M, Beyazyurek C, Ekmekci CG, Ajredin N, Cinar Yapan C, Tac HA, Yelke HK, Altin G, Kahraman S, Basile N, Bronet F, Nogales MC, Ariza M, Martinez E, Linan A, Gaytan A, Meseguer M, Christopikou D, Tsorva E, Economou K, Davies S, Mastrominas M, Handyside AH, Avo Santos M, M. Lens S, C. Fauser B, S. E. Laven J, B. Baart E, Nakano T, Akamatsu Y, Sato M, Hashimoto S, Maezawa T, Himeno T, Ohnishi Y, Inoue T, Ito K, Nakaoka Y, Morimoto Y, Al Sharif J, Alhalabi M, Abou Alchamat G, Madania A, Khatib A, Kinj M, Monem F, Mahayri Z, Ajlouni A, Othman A, Chung JT, Son WY, Zhang XY, Ao A, Tan SL, Holzer H, Burnik Papler T, Fon Tacer K, Devjak R, Juvan P, Virant-Klun I, Vrtacnik Bokal E, Zheng HY, Chen SL, Chen X, Tang Y, Li L, Ye DS, Yang XH, Eichenlaub-Ritter U, Trapphoff T, Hastreiter S, Haaf T, Asada H, Maekawa R, Tamura I, Tamura H, Sugino N, Zakharova E, Zaletova V, Krivokharchenko I, Ata B, Kaplan B, Danzer H, Glassner M, Opsahl M, Tan SL, Munne S. REPRODUCTIVE (EPI) GENETICS. Hum Reprod 2012. [DOI: 10.1093/humrep/27.s2.87] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Derks-Smeets IAP, Verpoest W, Mackens S, Verdyck P, Verheyen G, Paulussen A, Dreesen J, Van Golde R, Tjan-Heijnen VCG, Meijer-Hoogeveen M, Gomez Garcia EB, De Greve J, Bonduelle M, De Die-Smulders CEM, De Rycke M, Rubio C, Rodrigo L, Bellver J, Peinado L, Buendia P, Vidal C, Giles J, Domingo J, Remohi J, Pellicer A, Simon C, Sallevelt S, Dreesen J, de Die-Smulders C, Drusedau M, Spierts S, Coonen E, van Golde R, Geraedts J, Smeets H, Mateu E, Rodrigo L, Mir P, Campos I, Escrich L, Vera M, Remohi J, Pellicer A, Simon C. SESSION 51: PGD/PGS: LOOK TO THE PAST, PREPARE THE FUTURE. Hum Reprod 2012. [DOI: 10.1093/humrep/27.s2.50] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Buendia P, Escrich L, Mercader A, Delgado A, Rodrigo L, Rubio C. A combination of day-3 And day-4 blastomere biopsy does not affect embryo implantation ability. Fertil Steril 2011. [DOI: 10.1016/j.fertnstert.2011.07.849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Acar-Perk B, Weimer J, Koch K, Salmassi A, Arnold N, Mettler L, Schmutzler AG, Ottolini CS, Griffin DK, Handyside AH, Summers MC, Thornhill AR, Montjean D, Benkhalifa M, Cohen-Bacrie P, Siffroi JP, Mandelbaum J, Berthaut I, Bashamboo A, Ravel C, McElreavey K, Ao A, Zhang XY, Yilmaz A, Chung JT, Demirtas E, Son WY, Dahan M, Buckett W, Holzer H, Tan SL, Perheentupa A, Vierula M, Jorgensen N, Skakkebaek NE, Chantot-Bastaraud S, McElreavey K, Toppari J, Muzii L, Magli MC, Gioia L, Mattioli M, Ferraretti AP, Gianaroli L, Koscinski I, Elinati E, Fossard C, Kuentz P, Kilani Z, Demirol A, Gurgan T, Schmitt F, Velez de la Calle J, Iqbal N, Louanjli N, Pasquier M, Carre-Pigeon F, Muller J, Barratt C, Viville S, Magli C, Grugnetti C, Castelletti E, Paviglianiti B, Gianaroli L, Pepas L, Braude P, Grace J, Bolton V, Khalaf Y, El-Toukhy T, Galeraud-Denis I, Bouraima H, Sibert L, Rives N, Carreau S, Janse F, de With LM, Fauser BCJM, Lambalk CB, Laven JSE, Goverde AJ, Giltay JC, De Leo V, Governini L, Quagliariello A, Margollicci MA, Piomboni P, Luddi A, Miyamura H, Nishizawa H, Ota S, Suzuki M, Inagaki A, Egusa H, Nishiyama S, Kato T, Nakanishi I, Fujita T, Imayoshi Y, Markoff A, Yanagihara I, Udagawa Y, Kurahashi H, Alvaro Mercadal B, Imbert R, Demeestere I, De Leener A, Englert Y, Costagliola S, Delbaere A, Velilla E, Colomar A, Toro E, Chamosa S, Alvarez J, Lopez-Teijon M, Fernandez S, Hosoda Y, Hasegawa A, Morimoto N, Wakimoto Y, Ito Y, Komori S, Sati L, Zeiss C, Demir R, McGrath J, Ku SY, Kim YJ, Kim YY, Kim HJ, Park KE, Kim SH, Choi YM, Moon SY, Minor A, Chow V, Ma S, Martinez Mendez E, Gaytan M, Linan A, Pacheco A, San Celestino M, Nogales C, Ariza M, Cernuda D, Bronet F, Lendinez Ramirez AM, Palomares AR, Perez-Nevot B, Urraca V, Ruiz Martin A, Reche A, Ruiz Galdon M, Reyes-Engel A, Treff NR, Tao X, Taylor D, Levy B, Ferry KM, Scott Jr. RT, Vasan S, Acharya KK, Vasan B, Yalaburgi R, Ganesan KK, Darshan SC, Neelima CH, Deepa P, Akhilesh B, Sravanthi D, Sreelakshmi KS, Deepti H, van Doorninck JH, Eleveld C, van der Hoeven M, Birnie E, Steegers EAP, Galjaard RJ, Laven JSE, van den Berg IM, Fiorentino F, Spizzichino L, Bono S, Biricik A, Kokkali G, Rienzi L, Ubaldi FM, Iammarrone E, Gordon A, Pantos K, Oitmaa E, Tammiste A, Suvi S, Punab M, Remm M, Metspalu A, Salumets A, Rodrigo L, Mir P, Cervero A, Mateu E, Mercader A, Vidal C, Giles J, Remohi J, Pellicer A, Martin J, Rubio C, Mozdarani H, Moghbeli Nejad S, Behmanesh M, Alleyasin A, Ghedir H, Ibala-Romdhane S, Mamai O, Brahem S, Elghezal H, Ajina M, Gribaa M, Saad A, Mateu E, Rodrigo L, Martinez MC, Mercader A, Peinado V, Milan M, Al-Asmar N, Pellicer A, Remohi J, Rubio C, Mercader A, Buendia P, Delgado A, Escrich L, Amorocho B, Simon C, Remohi J, Pellicer A, Martin J, Rubio C, Petrussa L, Van de Velde H, De Munck N, De Rycke M, Altmae S, Martinez-Conejero JA, Esteban FJ, Ruiz-Alonso M, Stavreus-Evers A, Horcajadas JA, Salumets A, Bug B, Raabe-Meyer G, Bender U, Zimmer J, Schulze B, Vogt PH, Laisk T, Peters M, Salumets A, Grabar V, Feskov A, Zhilkova E, Sugawara N, Maeda M, Seki T, Manome T, Nagai R, Araki Y, Georgiou I, Lazaros L, Xita N, Chatzikyriakidou A, Kaponis A, Grigoriadis N, Hatzi E, Grigoriadis I, Sofikitis N, Zikopoulos K, Gunn M, Brezina PR, Benner A, Du L, Kearns WG, Shen X, Zhou C, Xu Y, Zhong Y, Zeng Y, Zhuang G, Benner A, Brezina PR, Gunn MC, Du L, Richter K, Kearns WG, Andreeva P, Dimitrov I, Konovalova M, Kyurkchiev S, Shterev A, Daser A, Day E, Turley H, Immesberger A, Haaf T, Hahn T, Dear PH, Schorsch M, Don J, Golan N, Eldar T, Yaverboim R. POSTER VIEWING SESSION - REPRODUCTIVE (EPI) GENETICS. Hum Reprod 2011. [DOI: 10.1093/humrep/26.s1.89] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Kolber MA, Buendia P, DeGruttola V, Moore RD. HIV-1 diversity after a class switch failure. AIDS Res Hum Retroviruses 2010; 26:1175-80. [PMID: 20854203 DOI: 10.1089/aid.2010.0069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The purpose of this study is to evaluate whether the choice of a PI- or an efavirenz (EFV)-based HAART initial regimen impacts on the viral diversity after failure from a second, class-switch salvage regimen. Sequential HAART failures after a class switch were identified for which the genotypes showed evidence of signature mutations at each failure. Each second failure was required to be from a viral burden <400 RNA c/ml. Thirteen cases of sequential failure from an initial EFV-containing to a PI-containing regimen (EP), and 19 sequential failures from an initial PI-containing to an EFV-containing regimen (PE) were identified. The persistence of signature mutations from the first failure were evaluated at second failure and compared between the EP and PE groups. Phylogenetic trees were constructed for a subgroup of cases from existing genetic sequence information and branch length analysis was used to determine evidence of viral diversity between groups. For EP sequential therapy, 10 of 12 cases carried forward a key non-nucleoside reverse transcriptase inhibitor (NNRTI) mutation in the second failure compared to 5 of 13 cases for PE sequential therapy (p = 0.041). Phylogenetic analysis demonstrated that there was more viral diversity in the PE group as compared to the EP group, consistent with the interpretation that mutations at the second failure added to an ancestral virus closer to baseline rather than to the dominant virus at first failure. The development of HIV viral diversity after multiple HAART failures is determined by the sequence in which the regimens are ordered.
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Affiliation(s)
- Michael A. Kolber
- Department of Medicine, University of Miami Miller School of Medicine, Miami, Florida
| | - Patricia Buendia
- Center for Computational Sciences, University of Miami, Miami, Florida
| | - Victor DeGruttola
- Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts
| | - Richard D. Moore
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
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Uum van CMJ, Stevens SJC, Dreesen JCFM, Drusedau M, Smeets HJM, Hollander-Crombach HTM, Geraedts JPM, Engelen JJM, Coonen E, Ling J, Long X, Liu J, Zhuang G, Cao B, Xu K, Mir P, Rodrigo L, Cervero A, Mercader A, Delgado A, Buendia P, Pellicer A, Rubio C, Martin J, Garcia-Quevedo L, Blanco J, Sarrate Z, Bassas L, Vidal F, Labarta E, Bosch E, Alama P, Rubio C, Remohi J, Pellicer A. Session 42: Preimplantation Genetic Diagnosis. Hum Reprod 2010. [DOI: 10.1093/humrep/de.25.s1.42] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Mir P, Rodrigo L, Mateu E, Peinado V, Milan M, Mercader A, Buendia P, Delgado A, Pellicer A, Remohi J, Rubio C. Improving FISH diagnosis for preimplantation genetic aneuploidy screening. Hum Reprod 2010; 25:1812-7. [DOI: 10.1093/humrep/deq122] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Buendia P, Cadwallader B, DeGruttola V. A phylogenetic and Markov model approach for the reconstruction of mutational pathways of drug resistance. Bioinformatics 2009; 25:2522-9. [PMID: 19654117 PMCID: PMC2752619 DOI: 10.1093/bioinformatics/btp466] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2009] [Revised: 07/24/2009] [Accepted: 07/25/2009] [Indexed: 02/05/2023] Open
Abstract
MOTIVATION Modern HIV-1, hepatitis B virus and hepatitis C virus antiviral therapies have been successful at keeping viruses suppressed for prolonged periods of time, but therapy failures attributable to the emergence of drug resistant mutations continue to be a distressing reminder that no therapy can fully eradicate these viruses from their host organisms. To better understand the emergence of drug resistance, we combined phylogenetic and statistical models of viral evolution in a 2-phase computational approach that reconstructs mutational pathways of drug resistance. RESULTS The first phase of the algorithm involved the modeling of the evolution of the virus within the human host environment. The inclusion of longitudinal clonal sequence data was a key aspect of the model due to the progressive fashion in which multiple mutations become linked in the same genome creating drug resistant genotypes. The second phase involved the development of a Markov model to calculate the transition probabilities between the different genotypes. The proposed method was applied to data from an HIV-1 Efavirenz clinical trial study. The obtained model revealed the direction of evolution over time with greater detail than previous models. Our results show that the mutational pathways facilitate the identification of fast versus slow evolutionary pathways to drug resistance. AVAILABILITY Source code for the algorithm is publicly available at http://biorg.cis.fiu.edu/vPhyloMM/
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Affiliation(s)
- Patricia Buendia
- Department of Biology and Center for Computational Science, University of Miami, Miami, USA.
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Buendia P, Narasimhan G. Serial evolutionary networks of within-patient HIV-1 sequences reveal patterns of evolution of X4 strains. BMC Syst Biol 2009; 3:62. [PMID: 19531207 PMCID: PMC2709891 DOI: 10.1186/1752-0509-3-62] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2008] [Accepted: 06/16/2009] [Indexed: 11/13/2022]
Abstract
Background The HIV virus is known for its ability to exploit numerous genetic and evolutionary mechanisms to ensure its proliferation, among them, high replication, mutation and recombination rates. Sliding MinPD, a recently introduced computational method [1], was used to investigate the patterns of evolution of serially-sampled HIV-1 sequence data from eight patients with a special focus on the emergence of X4 strains. Unlike other phylogenetic methods, Sliding MinPD combines distance-based inference with a nonparametric bootstrap procedure and automated recombination detection to reconstruct the evolutionary history of longitudinal sequence data. We present serial evolutionary networks as a longitudinal representation of the mutational pathways of a viral population in a within-host environment. The longitudinal representation of the evolutionary networks was complemented with charts of clinical markers to facilitate correlation analysis between pertinent clinical information and the evolutionary relationships. Results Analysis based on the predicted networks suggests the following:: significantly stronger recombination signals (p = 0.003) for the inferred ancestors of the X4 strains, recombination events between different lineages and recombination events between putative reservoir virus and those from a later population, an early star-like topology observed for four of the patients who died of AIDS. A significantly higher number of recombinants were predicted at sampling points that corresponded to peaks in the viral load levels (p = 0.0042). Conclusion Our results indicate that serial evolutionary networks of HIV sequences enable systematic statistical analysis of the implicit relations embedded in the topology of the structure and can greatly facilitate identification of patterns of evolution that can lead to specific hypotheses and new insights. The conclusions of applying our method to empirical HIV data support the conventional wisdom of the new generation HIV treatments, that in order to keep the virus in check, viral loads need to be suppressed to almost undetectable levels.
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Affiliation(s)
- Patricia Buendia
- Department of Biology and Center for Computational Science, University of Miami, Coral Gables, FL 33146, USA.
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Buendia P, Collins TM, Narasimhan G. The role of internal node sequences and the molecular clock in the analysis of serially-sampled data. Int J Bioinform Res Appl 2008; 4:107-121. [PMID: 18283032 DOI: 10.1504/ijbra.2008.017167] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Algorithms that infer phylogenetic relationships between serially-sampled sequences have been developed in recent years to assist in the analysis of rapidly-evolving human pathogens. Our study consisted of evaluating seven relevant methods using empirical as well as simulated data sets. In particular, we investigated how the molecular clock hypothesis affected their relative performance, as three of the algorithms that accept serially-sampled data as input assume a molecular clock. Our results show that the standard phylogenetic methods and MinPD had a better overall performance. Surprisingly, when all internal node sequences were included in the data, the topological performance measure of all the methods, with the exception of MinPD, dropped significantly.
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Affiliation(s)
- Patricia Buendia
- Department of Biology, Center for Computational Science, University of Miami, USA.
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Buendia P, Narasimhan G. Sliding MinPD: building evolutionary networks of serial samples via an automated recombination detection approach. Bioinformatics 2007; 23:2993-3000. [PMID: 17717035 PMCID: PMC3187926 DOI: 10.1093/bioinformatics/btm413] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Traditional phylogenetic methods assume tree-like evolutionary models and are likely to perform poorly when provided with sequence data from fast-evolving, recombining viruses. Furthermore, these methods assume that all the sequence data are from contemporaneous taxa, which is not valid for serially-sampled data. A more general approach is proposed here, referred to as the Sliding MinPD method, that reconstructs evolutionary networks for serially-sampled sequences in the presence of recombination. RESULTS Sliding MinPD combines distance-based phylogenetic methods with automated recombination detection based on the best-known sliding window approaches to reconstruct serial evolutionary networks. Its performance was evaluated through comprehensive simulation studies and was also applied to a set of serially-sampled HIV sequences from a single patient. The resulting network organizations reveal unique patterns of viral evolution and may help explain the emergence of disease-associated mutants and drug-resistant strains with implications for patient prognosis and treatment strategies.
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Abstract
UNLABELLED Serial NetEvolve is a flexible simulation program that generates DNA sequences evolved along a tree or recombinant network. It offers a user-friendly Windows graphical interface and a Windows or Linux simulator with a diverse selection of parameters to control the evolutionary model. Serial NetEvolve is a modification of the Treevolve program with the following additional features: simulation of serially-sampled data, the choice of either a clock-like or a variable rate model of sequence evolution, sampling from the internal nodes and the output of the randomly generated tree or network in our newly proposed NeTwick format. AVAILABILITY From website http://biorg.cis.fiu.edu/SNE Contacts: giri@cis.fiu.edu SUPPLEMENTARY INFORMATION Manual and examples available from http://biorg.cis.fiu.edu/SNE.
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Affiliation(s)
- Patricia Buendia
- Bioinformatics Research Group (BioRG), School of Computing and Information Science, Florida International University Miami, FL 33199, USA
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Buendia P, Narasimhan G. MinPD: distance-based phylogenetic analysis and recombination detection of serially-sampled HIV quasispecies. Proc IEEE Comput Syst Bioinform Conf 2004:110-9. [PMID: 16448005 PMCID: PMC3195421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
A new computational method to study within-host viral evolution is explored to better understand the evolution and pathogenesis of viruses. Traditional phylogenetic tree methods are better suited to study relationships between contemporaneous species, which appear as leaves of a phylogenetic tree. However, viral sequences are often sampled serially from a single host. Consequently, data may be available at the leaves as well as the internal nodes of a phylogenetic tree. Recombination may further complicate the analysis. Such relationships are not easily expressed by traditional phylogenetic methods. We propose a new algorithm, called MinPD, based on minimum pairwise distances. Our algorithm uses multiple distance matrices and correlation rules to output a MinPD tree or network. We test our algorithm using extensive simmulations and apply it to a set of HIV sequence data isolated from one patient over a period of ten years. The proposed visualization of the phylogenetic tree\network further enhances the benefits of our methods.
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