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Heimisdottir LH, Lin BM, Cho H, Orlenko A, Ribeiro AA, Simon-Soro A, Roach J, Shungin D, Ginnis J, Simancas-Pallares MA, Spangler HD, Zandoná AGF, Wright JT, Ramamoorthy P, Moore JH, Koo H, Wu D, Divaris K. Metabolomics Insights in Early Childhood Caries. J Dent Res 2021; 100:615-622. [PMID: 33423574 DOI: 10.1177/0022034520982963] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Dental caries is characterized by a dysbiotic shift at the biofilm-tooth surface interface, yet comprehensive biochemical characterizations of the biofilm are scant. We used metabolomics to identify biochemical features of the supragingival biofilm associated with early childhood caries (ECC) prevalence and severity. The study's analytical sample comprised 289 children ages 3 to 5 (51% with ECC) who attended public preschools in North Carolina and were enrolled in a community-based cross-sectional study of early childhood oral health. Clinical examinations were conducted by calibrated examiners in community locations using International Caries Detection and Classification System (ICDAS) criteria. Supragingival plaque collected from the facial/buccal surfaces of all primary teeth in the upper-left quadrant was analyzed using ultra-performance liquid chromatography-tandem mass spectrometry. Associations between individual metabolites and 18 clinical traits (based on different ECC definitions and sets of tooth surfaces) were quantified using Brownian distance correlations (dCor) and linear regression modeling of log2-transformed values, applying a false discovery rate multiple testing correction. A tree-based pipeline optimization tool (TPOT)-machine learning process was used to identify the best-fitting ECC classification metabolite model. There were 503 named metabolites identified, including microbial, host, and exogenous biochemicals. Most significant ECC-metabolite associations were positive (i.e., upregulations/enrichments). The localized ECC case definition (ICDAS ≥1 caries experience within the surfaces from which plaque was collected) had the strongest correlation with the metabolome (dCor P = 8 × 10-3). Sixteen metabolites were significantly associated with ECC after multiple testing correction, including fucose (P = 3.0 × 10-6) and N-acetylneuraminate (p = 6.8 × 10-6) with higher ECC prevalence, as well as catechin (P = 4.7 × 10-6) and epicatechin (P = 2.9 × 10-6) with lower. Catechin, epicatechin, imidazole propionate, fucose, 9,10-DiHOME, and N-acetylneuraminate were among the top 15 metabolites in terms of ECC classification importance in the automated TPOT model. These supragingival biofilm metabolite findings provide novel insights in ECC biology and can serve as the basis for the development of measures of disease activity or risk assessment.
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Affiliation(s)
- L H Heimisdottir
- Division of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, USA
| | - B M Lin
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - H Cho
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - A Orlenko
- Department of Biostatistics, Epidemiology and Informatics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - A A Ribeiro
- Division of Diagnostic Sciences, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, USA
| | - A Simon-Soro
- Biofilm Research Labs, Center for Innovation and Precision Dentistry, School of Dental Medicine and School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA.,Department of Orthodontics and Divisions of Pediatric Dentistry and Community Oral Health, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Stomatology, School of Dentistry, University of Sevilla, Sevilla, Spain
| | - J Roach
- Research Computing, University of North Carolina, Chapel Hill, NC, USA
| | - D Shungin
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Odontology, Umeå University, Umeå, Sweden
| | - J Ginnis
- Division of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, USA
| | - M A Simancas-Pallares
- Division of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, USA
| | - H D Spangler
- Division of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, USA
| | - A G Ferreira Zandoná
- Department of Comprehensive Care, School of Dental Medicine, Tufts University, Boston, MA, USA
| | - J T Wright
- Division of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, USA
| | | | - J H Moore
- Department of Biostatistics, Epidemiology and Informatics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - H Koo
- Biofilm Research Labs, Center for Innovation and Precision Dentistry, School of Dental Medicine and School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA.,Department of Orthodontics and Divisions of Pediatric Dentistry and Community Oral Health, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - D Wu
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.,Division of Oral & Craniofacial Health Sciences, School of Dentistry, University of North Carolina, Chapel Hill, NC, USA
| | - K Divaris
- Division of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, USA.,Department of Epidemiology, Gillings School of Public Health, University of North Carolina, Chapel Hill, NC, USA
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2
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Arvey A, Rowe M, Legutki JB, An G, Gollapudi A, Lei A, Colston B, Putterman C, Smith D, Stiles J, Tarasow T, Ramamoorthy P. Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity. Immun Ageing 2020; 17:28. [PMID: 33042204 PMCID: PMC7539520 DOI: 10.1186/s12979-020-00193-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 07/24/2020] [Indexed: 12/26/2022]
Abstract
Background The immune system undergoes a myriad of changes with age. While it is known that antibody-secreting plasma and long-lived memory B cells change with age, it remains unclear how the binding profile of the circulating antibody repertoire is impacted. Results To understand humoral immunity changes with respect to age, we characterized serum antibody binding to high density peptide microarrays in a diverse cohort of 1675 donors. We discovered thousands of peptides that bind antibodies in age-dependent fashion, many of which contain di-serine motifs. Peptide binding profiles were aggregated into an “immune age” by a machine learning regression model that was highly correlated with chronological age. Applying this regression model to previously-unobserved donors, we found that a donor’s predicted immune age is longitudinally consistent over years, suggesting it could be a robust long-term biomarker of humoral immune ageing. Finally, we assayed serum from donors with autoimmune disease and found a significant association between “accelerated immune ageing” and autoimmune disease activity. Conclusions The circulating antibody repertoire has increased binding to thousands of di-serine peptide containing peptides in older donors, which can be represented as an immune age. Increased immune age is associated with autoimmune disease, acute inflammatory disease severity, and may be a broadly relevant biomarker of immune function in health, disease, and therapeutic intervention.
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Affiliation(s)
- Aaron Arvey
- iCarbonX 2424 Camino Ramon, Suite 125, San Ramon, CA 94583 USA
| | - Michael Rowe
- iCarbonX 2424 Camino Ramon, Suite 125, San Ramon, CA 94583 USA
| | | | - Gang An
- iCarbonX 2424 Camino Ramon, Suite 125, San Ramon, CA 94583 USA
| | | | - Anna Lei
- HealthTell, 145 S. 79th St., Chandler, AZ 85226 USA
| | - Bill Colston
- iCarbonX 2424 Camino Ramon, Suite 125, San Ramon, CA 94583 USA
| | - Chaim Putterman
- Albert Einstein College of Medicine, Division of Rheumatology, Forchheimer 701N, 1300 Morris Park Ave, Bronx, NY 10461 USA.,Azrieli Faculty of Medicine, Bar-Ilan University, Zefat, Israel.,Research Institute, Galilee Medical Center, Nahariya, Israel
| | - David Smith
- HealthTell, 145 S. 79th St., Chandler, AZ 85226 USA
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Hunsberger J, Simon C, Zylberberg C, Ramamoorthy P, Tubon T, Bedi R, Gielen K, Hansen C, Fischer L, Johnson J, Baraniak P, Mahdavi B, Pereira T, Hadjisavas M, Eaker S, Miller C. Improving patient outcomes with regenerative medicine: How the Regenerative Medicine Manufacturing Society plans to move the needle forward in cell manufacturing, standards, 3D bioprinting, artificial intelligence-enabled automation, education, and training. Stem Cells Transl Med 2020; 9:728-733. [PMID: 32222115 PMCID: PMC7308637 DOI: 10.1002/sctm.19-0389] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 02/12/2020] [Accepted: 02/24/2020] [Indexed: 02/06/2023] Open
Abstract
The Regenerative Medicine Manufacturing Society (RMMS) is the first and only professional society dedicated toward advancing manufacturing solutions for the field of regenerative medicine. RMMS's vision is to provide greater patient access to regenerative medicine therapies through innovative manufacturing solutions. Our mission is to identify unmet needs and gaps in regenerative medicine manufacturing and catalyze the generation of new ideas and solutions by working with private and public stakeholders. We aim to accomplish our mission through outreach and education programs and securing grants for public-private collaborations in regenerative medicine manufacturing. This perspective will cover four impact areas that the society's leadership team has identified as critical: (a) cell manufacturing and scale-up/out, respectively, for allogeneic and autologous cell therapies, (b) standards for regenerative medicine, (c) 3D bioprinting, and (d) artificial intelligence-enabled automation. In addition to covering these areas and ways in which the society intends to advance the field in a collaborative nature, we will also discuss education and training. Education and training is an area that is critical for communicating the current challenges, developing solutions to accelerate the commercialization of the latest technological advances, and growing the workforce in the rapidly expanding sector of regenerative medicine.
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Affiliation(s)
- Joshua Hunsberger
- Regenerative Medicine Manufacturing SocietyWinston‐SalemNorth CarolinaUSA
| | - Carl Simon
- National Institute of Standards and TechnologyGaithersburgMarylandUSA
| | | | | | | | - Ram Bedi
- University of WashingtonSeattleWashingtonUSA
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4
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Abstract
A new dry-mixing process for producing iodine- and iron-fortified salt on a large scale (20 30 metric tons per shift) was developed in salt factories at Valinokkam and Hyderabad, India. Common salt is mixed with 1% sodium hexametaphosphate, 0.5% ferrous sulphate heptahydrate, and 0.0055% potassium iodide or 0.007% potassium iodate in a ribbon blender. Dry mixing is superior to spray mixing and is associated with no operational problems. The fortified salt produced by this method retains the original colour of the unfortified salt, and the distribution of iodine and iron is uniform. The acceptability of the fortified salt is satisfactory, as various food preparations using the product are indistinguishable in colour, taste, and flavour from those containing unfortified salt
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5
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Sen M, Katragadda S, Ravichandran A, Deshpande G, Parulekar M, Nayanala S, Vittal V, Shen W, Phooi Nee Yong M, Jacob J, Parchuru S, Dhanuskodi K, Eyring K, Agrawal P, Agarwal S, Shanmugam A, Gupta S, Vishwanath D, Kumari K, Hariharan AK, Balaji SA, Liang Q, Robolledo B, Gauribidanur Raghavendrachar V, Oomer Farooque M, Buresh CJ, Ramamoorthy P, Bahadur U, Subramanian K, Hariharan R, Veeramachaneni V, Sankaran S, Gupta V. StrandAdvantage test for early-line and advanced-stage treatment decisions in solid tumors. Cancer Med 2017; 6:883-901. [PMID: 28371134 PMCID: PMC5430095 DOI: 10.1002/cam4.1037] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2016] [Revised: 01/10/2017] [Accepted: 01/19/2017] [Indexed: 12/11/2022] Open
Abstract
Comprehensive genetic profiling of tumors using next‐generation sequencing (NGS) is gaining acceptance for guiding treatment decisions in cancer care. We designed a cancer profiling test combining both deep sequencing and immunohistochemistry (IHC) of relevant cancer targets to aid therapy choices in both standard‐of‐care (SOC) and advanced‐stage treatments for solid tumors. The SOC report is provided in a short turnaround time for four tumors, namely lung, breast, colon, and melanoma, followed by an investigational report. For other tumor types, an investigational report is provided. The NGS assay reports single‐nucleotide variants (SNVs), copy number variations (CNVs), and translocations in 152 cancer‐related genes. The tissue‐specific IHC tests include routine and less common markers associated with drugs used in SOC settings. We describe the standardization, validation, and clinical utility of the StrandAdvantage test (SA test) using more than 250 solid tumor formalin‐fixed paraffin‐embedded (FFPE) samples and control cell line samples. The NGS test showed high reproducibility and accuracy of >99%. The test provided relevant clinical information for SOC treatment as well as more information related to investigational options and clinical trials for >95% of advanced‐stage patients. In conclusion, the SA test comprising a robust and accurate NGS assay combined with clinically relevant IHC tests can detect somatic changes of clinical significance for strategic cancer management in all the stages.
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Affiliation(s)
- Manimala Sen
- From Strand Life Sciences, 5th Floor, Kirloskar Business Park, Bangalore, India.,Mazumdar-Shaw Center for Translational Research (MSCTR), Mazumdar-Shaw Medical Foundation, A-Block, 8th Floor #258/A, NHHealth City, Bangalore, India
| | - Shanmukh Katragadda
- From Strand Life Sciences, 5th Floor, Kirloskar Business Park, Bangalore, India
| | - Aarthi Ravichandran
- From Strand Life Sciences, 5th Floor, Kirloskar Business Park, Bangalore, India
| | - Gouri Deshpande
- From Strand Life Sciences, 5th Floor, Kirloskar Business Park, Bangalore, India
| | - Minothi Parulekar
- From Strand Life Sciences, 5th Floor, Kirloskar Business Park, Bangalore, India
| | - Swetha Nayanala
- From Strand Life Sciences, 5th Floor, Kirloskar Business Park, Bangalore, India
| | - Vikram Vittal
- From Strand Life Sciences, 5th Floor, Kirloskar Business Park, Bangalore, India
| | - Weiming Shen
- Strand Life Sciences, 12635 E. Montview Blvd., Suite 360, Aurora, Colorado, 80045
| | | | - Jemima Jacob
- From Strand Life Sciences, 5th Floor, Kirloskar Business Park, Bangalore, India
| | - Sravanthi Parchuru
- From Strand Life Sciences, 5th Floor, Kirloskar Business Park, Bangalore, India.,Mazumdar-Shaw Center for Translational Research (MSCTR), Mazumdar-Shaw Medical Foundation, A-Block, 8th Floor #258/A, NHHealth City, Bangalore, India
| | - Kalpana Dhanuskodi
- From Strand Life Sciences, 5th Floor, Kirloskar Business Park, Bangalore, India.,Mazumdar-Shaw Center for Translational Research (MSCTR), Mazumdar-Shaw Medical Foundation, A-Block, 8th Floor #258/A, NHHealth City, Bangalore, India
| | - Kenneth Eyring
- Strand Life Sciences, 12635 E. Montview Blvd., Suite 360, Aurora, Colorado, 80045
| | - Pooja Agrawal
- From Strand Life Sciences, 5th Floor, Kirloskar Business Park, Bangalore, India
| | - Smita Agarwal
- From Strand Life Sciences, 5th Floor, Kirloskar Business Park, Bangalore, India
| | - Ashwini Shanmugam
- From Strand Life Sciences, 5th Floor, Kirloskar Business Park, Bangalore, India
| | - Satish Gupta
- From Strand Life Sciences, 5th Floor, Kirloskar Business Park, Bangalore, India
| | - Divya Vishwanath
- From Strand Life Sciences, 5th Floor, Kirloskar Business Park, Bangalore, India.,Mazumdar-Shaw Center for Translational Research (MSCTR), Mazumdar-Shaw Medical Foundation, A-Block, 8th Floor #258/A, NHHealth City, Bangalore, India
| | - Kiran Kumari
- From Strand Life Sciences, 5th Floor, Kirloskar Business Park, Bangalore, India.,Mazumdar-Shaw Center for Translational Research (MSCTR), Mazumdar-Shaw Medical Foundation, A-Block, 8th Floor #258/A, NHHealth City, Bangalore, India
| | - Arun K Hariharan
- From Strand Life Sciences, 5th Floor, Kirloskar Business Park, Bangalore, India.,Mazumdar-Shaw Center for Translational Research (MSCTR), Mazumdar-Shaw Medical Foundation, A-Block, 8th Floor #258/A, NHHealth City, Bangalore, India
| | - Sai A Balaji
- From Strand Life Sciences, 5th Floor, Kirloskar Business Park, Bangalore, India.,Mazumdar-Shaw Center for Translational Research (MSCTR), Mazumdar-Shaw Medical Foundation, A-Block, 8th Floor #258/A, NHHealth City, Bangalore, India
| | - Qiaoling Liang
- Strand Life Sciences, 12635 E. Montview Blvd., Suite 360, Aurora, Colorado, 80045
| | - Belen Robolledo
- Strand Life Sciences, 12635 E. Montview Blvd., Suite 360, Aurora, Colorado, 80045
| | | | | | | | - Preveen Ramamoorthy
- Strand Life Sciences, 12635 E. Montview Blvd., Suite 360, Aurora, Colorado, 80045
| | - Urvashi Bahadur
- From Strand Life Sciences, 5th Floor, Kirloskar Business Park, Bangalore, India
| | | | - Ramesh Hariharan
- From Strand Life Sciences, 5th Floor, Kirloskar Business Park, Bangalore, India
| | | | - Satish Sankaran
- From Strand Life Sciences, 5th Floor, Kirloskar Business Park, Bangalore, India
| | - Vaijayanti Gupta
- From Strand Life Sciences, 5th Floor, Kirloskar Business Park, Bangalore, India.,Mazumdar-Shaw Center for Translational Research (MSCTR), Mazumdar-Shaw Medical Foundation, A-Block, 8th Floor #258/A, NHHealth City, Bangalore, India
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6
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Sen M, Agrawal P, Vittal P V, Ghosh M, Sheela M, Vishwanath D, Kumari K, N.S.N S, Pathak V, Deshpande G, Mannan A, Gadkari R, Kapoor S, Yadhav J, Yousuff M, Sankaran S, Hariharan R, Ramamoorthy P, Subramanian K, Gupta V. Abstract 4878: Analytical and technical validation of a cost-effective diagnostic test for BRCA1, BRCA2 and TP53. Cancer Res 2015. [DOI: 10.1158/1538-7445.am2015-4878] [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: 11/16/2022]
Abstract
Abstract
BRCA1, BRCA2 and TP53 encode tumor suppressor proteins in humans that help repair damaged DNA and play critical roles in ensuring genome stability. Several inherited mutations in any of the above 3 genes substantially increase the risk of cancer. Together, BRCA1 and BRCA2 mutations account for about 20 to 25 percent of hereditary breast and ovarian cancers. Germline mutations in TP53 are the most common cause of Li-Fraumeni syndrome, a rare disorder that increases the risk of developing multiple tumors such as breast, soft-tissue and leukemias, in children and young adults. In India, where the incidence of cancer has seen a steep rise in the last decade, there is a pressing need to develop cost-effective screening tests that can identify known and novel mutations in commonly associated genes.
We have developed and offer a 3-Gene panel (Strand® - 3 gene) covering all known HGMD/ClinVar mutations and all coding exons of BRCA1, BRCA2 and TP53 genes. Current Sanger based methods query for restricted loci across these genes. Our test is based on an NGS enrichment protocol using xGen lockdown probes that allows parallel sequencing of upto 32 - 96 samples. The test would be offered at a tenth of the cost of current Sanger based tests anywhere in the world. In this study we present the technical and clinical validation data obtained from this assay. For technical validation, we included “gold standard” HAPMAP characterized as part of 1000 Genome Project, seven cell lines with known BRCA and TP53 mutations. For clinical validation, we enrolled thirty seven (37) patients who were consented on an IRB-approved study at HCG hospital for collecting saliva / blood. These patients were stratified / selected based on their family history, known risk of hereditary cancers and availability of previously characterized clinical samples. The overall sensitivity and specificity of this panel is 99.78% and 99.74% respectively with a reproducibility of 100%. On an average, 99.75% and 97% of the bases are covered at 0.2x and 0.5x mean coverage and the average gap (<20 reads per base) is 0.0056% in the validation study. We have identified 3 separate cases of Li-Fraumeni from the cohort of thirty seven patients. We further present clinical validation data from these 3 case studies in which we have identified both known and novel mutations. Further clinical validation of panel is ongoing.
In summary this panel will provide a cost effective screening method for early detection of pathogenic variants in pre-symptomatic individuals and in families with known risk of hereditary cancer.
Citation Format: Manimala Sen, Pooja Agrawal, Vikram Vittal P, Mithua Ghosh, M.L Sheela, Divya Vishwanath, Kiran Kumari, Swetha N.S.N, Vaibhavi Pathak, Gouri Deshpande, Ashraf Mannan, Rupali Gadkari, Suman Kapoor, Jamuna Yadhav, Mohammed Yousuff, Satish Sankaran, Ramesh Hariharan, Preveen Ramamoorthy, Kalyanasundaram Subramanian, Vaijayanti Gupta. Analytical and technical validation of a cost-effective diagnostic test for BRCA1, BRCA2 and TP53. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4878. doi:10.1158/1538-7445.AM2015-4878
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Affiliation(s)
- Manimala Sen
- 1Strand Life Sciences Pvt. Ltd., Bangalore, India
| | | | | | | | - M.L Sheela
- 2HCG-Health Care Global, Bangalore, India
| | | | - Kiran Kumari
- 1Strand Life Sciences Pvt. Ltd., Bangalore, India
| | - Swetha N.S.N
- 1Strand Life Sciences Pvt. Ltd., Bangalore, India
| | | | | | | | | | - Suman Kapoor
- 1Strand Life Sciences Pvt. Ltd., Bangalore, India
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7
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Kadam RS, Ramamoorthy P, LaFlamme DJ, McKinsey TA, Kompella UB. Retraction of “Hypoxia Alters Ocular Drug Transporter Expression and Activity in Rat and Calf Models: Implications for Drug Delivery”. Mol Pharm 2015; 12:2559. [DOI: 10.1021/acs.molpharmaceut.5b00367] [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: 11/28/2022]
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8
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Veeramachaneni V, Katragadda S, Parulekar M, Sankaran S, Gupta V, Sen M, B.P. M, Swetha N, Agarwal S, HV G, Yadhav J, Kapoor S, Deshpande G, Vitthal V, Shen W, Phooi Nee Yong M, Bahadur U, Ramamoorthy P, Subramanian K, Hariharan R. Analytical validation of StrandAdvantage solid tumor NGS test. J Clin Oncol 2015. [DOI: 10.1200/jco.2015.33.15_suppl.e12539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
| | | | - Minothi Parulekar
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Satish Sankaran
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Vaijayanti Gupta
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Manimala Sen
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Manasa B.P.
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - N.S.N Swetha
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Smita Agarwal
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Goutham HV
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Jamuna Yadhav
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Suman Kapoor
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Gouri Deshpande
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Vikram Vitthal
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | | | | | - Urvashi Bahadur
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | | | | | - Ramesh Hariharan
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
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9
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Ghosh M, Bahadur U, ML S, Patil S, Nayak R, Rao NK, Murugan K, Bandemegal M, PS S, Gupta V, Sankaran S, Thungappa SC, Krishnamoorthy N, C.S S, Ravichandran A, HV G, Hariharan R, Ramamoorthy P, Subramanian K, Ajaikumar BS. Clinical utility of PI3K/AKT/mTOR pathway activation in breast cancer: A study in Indian population. J Clin Oncol 2015. [DOI: 10.1200/jco.2015.33.15_suppl.e12542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
| | - Urvashi Bahadur
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Sheela ML
- Triesta Sciences, HCG, Bangalore, India
| | - Shekar Patil
- HCG Bangalore Institute of Oncology, Bangalore, India
| | | | - Nalini K Rao
- HCG Bangalore Institute of Oncology, Bangalore, India
| | | | | | - Sridhar PS
- Health Care Global Enterprises Limited, Bangalore, India
| | - Vaijayanti Gupta
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Satish Sankaran
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | | | | | | | | | - Goutham HV
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Ramesh Hariharan
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
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10
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Mannan AU, Singh J, Gadkari R, Lakshmikeshava R, Manek P, Ramalingam R, B.P. M, Kapoor S, Yadhav J, Sankaran S, Katragadda S, Veeramachaneni V, Ramamoorthy P, Hariharan R, Subramanian K. Screening of an Indian cohort with breast and/or ovarian cancer by a next-generation sequencing-based panel to detect a high frequency of mutations. J Clin Oncol 2015. [DOI: 10.1200/jco.2015.33.15_suppl.e12505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Ashraf U Mannan
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Jaya Singh
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Rupali Gadkari
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | | | - Payal Manek
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Ravi Ramalingam
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Manasa B.P.
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Suman Kapoor
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Jamuna Yadhav
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Satish Sankaran
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | | | | | | | - Ramesh Hariharan
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
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11
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Bahadur U, Ravichandran A, Banerjee S, Paliwal S, Sripathi RR, Javaray R, NJ A, Chahal G, Pandey N, Varghese H, Sankaran S, Awasthy D, B.P. M, Agarwal S, Ghosh M, ML S, Hariharan R, Ramamoorthy P, Veeramachaneni V, Subramanian K. Clinical utility of profiling somatic alterations in Indian cancer patients using a multi-gene next generation sequencing (NGS) test. J Clin Oncol 2015. [DOI: 10.1200/jco.2015.33.15_suppl.e22127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Urvashi Bahadur
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | | | | | - Shreya Paliwal
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | | | - Ruthika Javaray
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Anusha NJ
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Gulrez Chahal
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Nanda Pandey
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Hima Varghese
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Satish Sankaran
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Disha Awasthy
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Manasa B.P.
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | - Smita Agarwal
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
| | | | - Sheela ML
- Triesta Sciences, HCG, Bangalore, India
| | - Ramesh Hariharan
- Strand Center for Genomics and Personalized Medicine, Bangalore, India
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12
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Kim J, Vasu VT, Mishra R, Singleton KR, Yoo M, Leach SM, Farias-Hesson E, Mason RJ, Kang J, Ramamoorthy P, Kern JA, Heasley LE, Finigan JH, Tan AC. Bioinformatics-driven discovery of rational combination for overcoming EGFR-mutant lung cancer resistance to EGFR therapy. Bioinformatics 2014; 30:2393-8. [PMID: 24812339 DOI: 10.1093/bioinformatics/btu323] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
MOTIVATION Non-small-cell lung cancer (NSCLC) is the leading cause of cancer death in the United States. Targeted tyrosine kinase inhibitors (TKIs) directed against the epidermal growth factor receptor (EGFR) have been widely and successfully used in treating NSCLC patients with activating EGFR mutations. Unfortunately, the duration of response is short-lived, and all patients eventually relapse by acquiring resistance mechanisms. RESULT We performed an integrative systems biology approach to determine essential kinases that drive EGFR-TKI resistance in cancer cell lines. We used a series of bioinformatics methods to analyze and integrate the functional genetics screen and RNA-seq data to identify a set of kinases that are critical in survival and proliferation in these TKI-resistant lines. By connecting the essential kinases to compounds using a novel kinase connectivity map (K-Map), we identified and validated bosutinib as an effective compound that could inhibit proliferation and induce apoptosis in TKI-resistant lines. A rational combination of bosutinib and gefitinib showed additive and synergistic effects in cancer cell lines resistant to EGFR TKI alone. CONCLUSIONS We have demonstrated a bioinformatics-driven discovery roadmap for drug repurposing and development in overcoming resistance in EGFR-mutant NSCLC, which could be generalized to other cancer types in the era of personalized medicine. AVAILABILITY AND IMPLEMENTATION K-Map can be accessible at: http://tanlab.ucdenver.edu/kMap. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jihye Kim
- Division of Medical Oncology, Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, University of Colorado Anschutz Medical Campus, 80045 Aurora, Department of Medicine, National Jewish Health, 80206 Denver, Department of Craniofacial Biology, School of Dental Medicine, Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, 80045 Aurora, CO, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, Korea
| | - Vihas T Vasu
- Division of Medical Oncology, Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, University of Colorado Anschutz Medical Campus, 80045 Aurora, Department of Medicine, National Jewish Health, 80206 Denver, Department of Craniofacial Biology, School of Dental Medicine, Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, 80045 Aurora, CO, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, Korea
| | - Rangnath Mishra
- Division of Medical Oncology, Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, University of Colorado Anschutz Medical Campus, 80045 Aurora, Department of Medicine, National Jewish Health, 80206 Denver, Department of Craniofacial Biology, School of Dental Medicine, Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, 80045 Aurora, CO, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, Korea
| | - Katherine R Singleton
- Division of Medical Oncology, Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, University of Colorado Anschutz Medical Campus, 80045 Aurora, Department of Medicine, National Jewish Health, 80206 Denver, Department of Craniofacial Biology, School of Dental Medicine, Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, 80045 Aurora, CO, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, Korea
| | - Minjae Yoo
- Division of Medical Oncology, Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, University of Colorado Anschutz Medical Campus, 80045 Aurora, Department of Medicine, National Jewish Health, 80206 Denver, Department of Craniofacial Biology, School of Dental Medicine, Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, 80045 Aurora, CO, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, Korea
| | - Sonia M Leach
- Division of Medical Oncology, Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, University of Colorado Anschutz Medical Campus, 80045 Aurora, Department of Medicine, National Jewish Health, 80206 Denver, Department of Craniofacial Biology, School of Dental Medicine, Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, 80045 Aurora, CO, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, Korea
| | - Eveline Farias-Hesson
- Division of Medical Oncology, Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, University of Colorado Anschutz Medical Campus, 80045 Aurora, Department of Medicine, National Jewish Health, 80206 Denver, Department of Craniofacial Biology, School of Dental Medicine, Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, 80045 Aurora, CO, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, Korea
| | - Robert J Mason
- Division of Medical Oncology, Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, University of Colorado Anschutz Medical Campus, 80045 Aurora, Department of Medicine, National Jewish Health, 80206 Denver, Department of Craniofacial Biology, School of Dental Medicine, Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, 80045 Aurora, CO, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, Korea Division of Medical Oncology, Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, University of Colorado Anschutz Medical Campus, 80045 Aurora, Department of Medicine, National Jewish Health, 80206 Denver, Department of Craniofacial Biology, School of Dental Medicine, Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, 80045 Aurora, CO, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, Korea
| | - Jaewoo Kang
- Division of Medical Oncology, Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, University of Colorado Anschutz Medical Campus, 80045 Aurora, Department of Medicine, National Jewish Health, 80206 Denver, Department of Craniofacial Biology, School of Dental Medicine, Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, 80045 Aurora, CO, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, Korea
| | - Preveen Ramamoorthy
- Division of Medical Oncology, Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, University of Colorado Anschutz Medical Campus, 80045 Aurora, Department of Medicine, National Jewish Health, 80206 Denver, Department of Craniofacial Biology, School of Dental Medicine, Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, 80045 Aurora, CO, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, Korea
| | - Jeffrey A Kern
- Division of Medical Oncology, Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, University of Colorado Anschutz Medical Campus, 80045 Aurora, Department of Medicine, National Jewish Health, 80206 Denver, Department of Craniofacial Biology, School of Dental Medicine, Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, 80045 Aurora, CO, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, Korea Division of Medical Oncology, Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, University of Colorado Anschutz Medical Campus, 80045 Aurora, Department of Medicine, National Jewish Health, 80206 Denver, Department of Craniofacial Biology, School of Dental Medicine, Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, 80045 Aurora, CO, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, Korea
| | - Lynn E Heasley
- Division of Medical Oncology, Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, University of Colorado Anschutz Medical Campus, 80045 Aurora, Department of Medicine, National Jewish Health, 80206 Denver, Department of Craniofacial Biology, School of Dental Medicine, Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, 80045 Aurora, CO, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, Korea
| | - James H Finigan
- Division of Medical Oncology, Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, University of Colorado Anschutz Medical Campus, 80045 Aurora, Department of Medicine, National Jewish Health, 80206 Denver, Department of Craniofacial Biology, School of Dental Medicine, Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, 80045 Aurora, CO, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, Korea Division of Medical Oncology, Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, University of Colorado Anschutz Medical Campus, 80045 Aurora, Department of Medicine, National Jewish Health, 80206 Denver, Department of Craniofacial Biology, School of Dental Medicine, Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, 80045 Aurora, CO, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, Korea
| | - Aik Choon Tan
- Division of Medical Oncology, Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, University of Colorado Anschutz Medical Campus, 80045 Aurora, Department of Medicine, National Jewish Health, 80206 Denver, Department of Craniofacial Biology, School of Dental Medicine, Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, 80045 Aurora, CO, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, Korea Division of Medical Oncology, Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, University of Colorado Anschutz Medical Campus, 80045 Aurora, Department of Medicine, National Jewish Health, 80206 Denver, Department of Craniofacial Biology, School of Dental Medicine, Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, 80045 Aurora, CO, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, Korea
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13
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Kolker E, Özdemir V, Martens L, Hancock W, Anderson G, Anderson N, Aynacioglu S, Baranova A, Campagna SR, Chen R, Choiniere J, Dearth SP, Feng WC, Ferguson L, Fox G, Frishman D, Grossman R, Heath A, Higdon R, Hutz MH, Janko I, Jiang L, Joshi S, Kel A, Kemnitz JW, Kohane IS, Kolker N, Lancet D, Lee E, Li W, Lisitsa A, Llerena A, MacNealy-Koch C, Marshall JC, Masuzzo P, May A, Mias G, Monroe M, Montague E, Mooney S, Nesvizhskii A, Noronha S, Omenn G, Rajasimha H, Ramamoorthy P, Sheehan J, Smarr L, Smith CV, Smith T, Snyder M, Rapole S, Srivastava S, Stanberry L, Stewart E, Toppo S, Uetz P, Verheggen K, Voy BH, Warnich L, Wilhelm SW, Yandl G. Toward more transparent and reproducible omics studies through a common metadata checklist and data publications. OMICS 2014; 18:10-4. [PMID: 24456465 PMCID: PMC3903324 DOI: 10.1089/omi.2013.0149] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.
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Affiliation(s)
- Eugene Kolker
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Vural Özdemir
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Office of the President, Gaziantep University, International Affairs and Global Development Strategy
- Faculty of Communications, Universite Bulvarı, Kilis Yolu, Turkey
| | - Lennart Martens
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Medical Protein Research, Vlaams Instituut voor Biotechnologie, Ghent, Belgium
- Department of Biochemistry, Ghent University; Ghent, Belgium
| | - William Hancock
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Chemistry, Barnett Institute, Northeastern University, Boston, Massachusetts
| | - Gordon Anderson
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington
| | - Nathaniel Anderson
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Sukru Aynacioglu
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Pharmacology, Gaziantep University, Gaziantep, Turkey
| | - Ancha Baranova
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- School of Systems Biology, George Mason University, Manassas, Virginia
| | - Shawn R. Campagna
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Chemistry, University of Tennessee Knoxville, Knoxville, Tennessee
| | - Rui Chen
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Genetics, Stanford University, Stanford, California
| | - John Choiniere
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Stephen P. Dearth
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Chemistry, University of Tennessee Knoxville, Knoxville, Tennessee
| | - Wu-Chun Feng
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia
- Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, Virginia
- Department of SyNeRGy Laboratory, Virginia Tech, Blacksburg, Virginia
| | - Lynnette Ferguson
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Nutrition, Auckland Cancer Society Research Centre, University of Auckland, Auckland, New Zealand
| | - Geoffrey Fox
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- School of Informatics and Computing, Indiana University, Bloomington, Indiana
| | - Dmitrij Frishman
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Technische Universitat Munchen, Wissenshaftzentrum Weihenstephan, Freising, Germany
| | - Robert Grossman
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois
- Department of Medicine, University of Chicago, Chicago, Illinois
| | - Allison Heath
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois
- Knapp Center for Biomedical Discovery, University of Chicago, Chicago, Illinois
| | - Roger Higdon
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Mara H. Hutz
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Departamento de Genetica, Instituto de Biociencias, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Imre Janko
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- High-Throughput Analysis Core, Seattle Children's Research Institute, Seattle, Washington
| | - Lihua Jiang
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Genetics, Stanford University, Stanford, California
| | - Sanjay Joshi
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Life Sciences, EMC, Hopkinton, Massachusetts
| | - Alexander Kel
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- GeneXplain GmbH, Wolfenbüttel, Germany
| | - Joseph W. Kemnitz
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Cell and Regenerative Biology, University of Wisconsin-Madison, Madison, Wisconsin
- Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, Wisconsin
| | - Isaac S. Kohane
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Pediatrics and Health Sciences Technology, Children's Hospital and Harvard Medical School, Boston, Massachusetts
- HMS Center for Biomedical Informatics, Countway Library of Medicine, Boston, Massachusetts
| | - Natali Kolker
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- High-Throughput Analysis Core, Seattle Children's Research Institute, Seattle, Washington
| | - Doron Lancet
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Molecular Genetics, Crown Human Genome Center, Weizmann Institute of Science, Rehovot, Israel
| | - Elaine Lee
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- High-Throughput Analysis Core, Seattle Children's Research Institute, Seattle, Washington
| | - Weizhong Li
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Center for Research in Biological Systems, University of California, San Diego, La Jolla, California
| | - Andrey Lisitsa
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Russian Human Proteome Organization (RHUPO), Moscow, Russia
- Institute of Biomedical Chemistry, Moscow, Russia
| | - Adrian Llerena
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Clinical Research Center, Extremadura University Hospital and Medical School, Badajoz, Spain
| | - Courtney MacNealy-Koch
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Jean-Claude Marshall
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Center for Translational Research, Catholic Health Initiatives, Towson, Maryland
| | - Paola Masuzzo
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Medical Protein Research, Vlaams Instituut voor Biotechnologie, Ghent, Belgium
- Department of Biochemistry, Ghent University; Ghent, Belgium
| | - Amanda May
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Chemistry, University of Tennessee Knoxville, Knoxville, Tennessee
| | - George Mias
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Genetics, Stanford University, Stanford, California
| | - Matthew Monroe
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington
| | - Elizabeth Montague
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Sean Mooney
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- The Buck Institute for Research on Aging, Novato, California
| | - Alexey Nesvizhskii
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Pathology, University of Michigan, Ann Arbor, Michigan
- Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan
| | - Santosh Noronha
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, India
| | - Gilbert Omenn
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor Michigan
- Department of Molecular Medicine & Genetics and Human Genetics, University of Michigan, Ann Arbor Michigan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor Michigan
- School of Public Health, University of Michigan, Ann Arbor Michigan
| | - Harsha Rajasimha
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Jeeva Informatics Solutions LLC, Derwood, Maryland
| | - Preveen Ramamoorthy
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Molecular Diagnostics Department, National Jewish Health, Denver, Colorado
| | - Jerry Sheehan
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- California Institute for Telecommunications and Information Technology, University of California-San Diego, La Jolla, California
| | - Larry Smarr
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- California Institute for Telecommunications and Information Technology, University of California-San Diego, La Jolla, California
| | - Charles V. Smith
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Center for Developmental Therapeutics, Seattle Children's Research Institute, Seattle, Washington
| | - Todd Smith
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Digital World Biology, Seattle, Washington
| | - Michael Snyder
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Genetics, Stanford University, Stanford, California
- Stanford Center for Genomics and Personalized Medicine, Stanford University, Stanford, California
| | - Srikanth Rapole
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Proteomics Laboratory, National Centre for Cell Science, University of Pune, Pune, India
| | - Sanjeeva Srivastava
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Proteomics Laboratory, Indian Institute of Technology Bombay, Mumbai, India
| | - Larissa Stanberry
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Elizabeth Stewart
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Stefano Toppo
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | - Peter Uetz
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Center for the Study of Biological Complexity (CSBC), Virginia Commonwealth University, Richmond, Virginia
| | - Kenneth Verheggen
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Medical Protein Research, Vlaams Instituut voor Biotechnologie, Ghent, Belgium
- Department of Biochemistry, Ghent University; Ghent, Belgium
| | - Brynn H. Voy
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Animal Science, University of Tennessee Institute of Agriculture, Knoxville, Tennessee
| | - Louise Warnich
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Genetics, Faculty of AgriSciences, University of Stellenbosch, Stellenbosch, South Africa
| | - Steven W. Wilhelm
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Microbiology, University of Tennessee-Knoxville, Knoxville, Tennessee
| | - Gregory Yandl
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
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14
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Kim BE, Bin L, Ye YM, Ramamoorthy P, Leung DYM. IL-25 enhances HSV-1 replication by inhibiting filaggrin expression, and acts synergistically with Th2 cytokines to enhance HSV-1 replication. J Invest Dermatol 2013; 133:2678-2685. [PMID: 23657503 PMCID: PMC3785566 DOI: 10.1038/jid.2013.223] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2012] [Revised: 04/11/2013] [Accepted: 04/12/2013] [Indexed: 11/30/2022]
Abstract
Atopic dermatitis (AD) is characterized by epidermal barrier defects and recurrent microbial skin infections. AD patients with a history of eczema herpeticum (ADEH+) have more severe skin disease and more highly T helper type 2 (Th2)-polarized immune responses as compared with uncomplicated AD (ADEH-). However, the mechanisms linking epidermal barrier defects and viral skin infection are not well understood. Recently, it has been reported that interleukin-25 may play a role in augmenting Th2 responses. We examined protein expression of IL-25 in the skin biopsies from normal subjects (n=10), ADEH- (n=18), ADEH+ (n=7), and psoriasis (n=9). IL-25 expression was increased in the skin from ADEH-, ADEH+, and psoriasis as compared with normal skin, and was significantly greater in lesional ADEH+ skin than in lesional ADEH- skin. Importantly, we demonstrated that IL-25 enhances herpes simplex virus (HSV)-1 and vaccinia virus replication by inhibiting filaggrin expression, and IL-25 acts synergistically with IL-4 and IL-13 to enhance HSV-1 replication in vitro. In contrast, IFN-γ inhibited HSV-1 replication in vitro. In addition, we demonstrate that filaggrin is a critical protein to inhibit HSV-1 replication because filaggrin small interfering RNA knockdown enhances HSV-1 replication in vitro. Filaggrin breakdown products, however, inhibited HSV-1 replication in vitro.
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Affiliation(s)
- Byung Eui Kim
- Department of Pediatrics, National Jewish Health, Denver, Colorado, USA
| | - Lianghua Bin
- Department of Pediatrics, National Jewish Health, Denver, Colorado, USA
| | - Young-Min Ye
- Department of Pediatrics, National Jewish Health, Denver, Colorado, USA; Department of Allergy and Clinical Immunology, Ajou University School of Medicine, Suwon, Korea
| | | | - Donald Y M Leung
- Department of Pediatrics, National Jewish Health, Denver, Colorado, USA; Department of Pediatrics, University of Colorado Denver, Aurora, Colorado, USA.
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15
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Kolker E, Özdemir V, Martens L, Hancock W, Anderson G, Anderson N, Aynacioglu S, Baranova A, Campagna SR, Chen R, Choiniere J, Dearth SP, Feng WC, Ferguson L, Fox G, Frishman D, Grossman R, Heath A, Higdon R, Hutz MH, Janko I, Jiang L, Joshi S, Kel A, Kemnitz JW, Kohane IS, Kolker N, Lancet D, Lee E, Li W, Lisitsa A, Llerena A, MacNealy-Koch C, Marshall JC, Masuzzo P, May A, Mias G, Monroe M, Montague E, Mooney S, Nesvizhskii A, Noronha S, Omenn G, Rajasimha H, Ramamoorthy P, Sheehan J, Smarr L, Smith CV, Smith T, Snyder M, Rapole S, Srivastava S, Stanberry L, Stewart E, Toppo S, Uetz P, Verheggen K, Voy BH, Warnich L, Wilhelm SW, Yandl G. Toward More Transparent and Reproducible Omics Studies Through a Common Metadata Checklist and Data Publications. Big Data 2013; 1:196-201. [PMID: 27447251 DOI: 10.1089/big.2013.0039] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.
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Affiliation(s)
- Eugene Kolker
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 2 Predictive Analytics , Seattle Children's, Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Vural Özdemir
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 4 Office of the President, Gaziantep University , International Affairs and Global Development Strategy
- 5 Faculty of Communications, Universite Bulvarı , Kilis Yolu, Turkey
| | - Lennart Martens
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 6 Department of Medical Protein Research, Vlaams Instituut voor Biotechnologie , Ghent, Belgium
- 7 Department of Biochemistry, Ghent University, Ghent , Belgium
| | - William Hancock
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 8 Department of Chemistry, Barnett Institute, Northeastern University , Boston, Massachusetts
| | - Gordon Anderson
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 9 Fundamental & Computational Sciences Directorate, Pacific Northwest National Laboratory , Richland, Washington
| | - Nathaniel Anderson
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Sukru Aynacioglu
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 10 Department of Pharmacology, Gaziantep University , Gaziantep, Turkey
| | - Ancha Baranova
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 11 School of Systems Biology, George Mason University , Manassas, Virginia
| | - Shawn R Campagna
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 12 Department of Chemistry, University of Tennessee Knoxville , Knoxville, Tennessee
| | - Rui Chen
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 13 Department of Genetics, Stanford University , Stanford, California
| | - John Choiniere
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Stephen P Dearth
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 12 Department of Chemistry, University of Tennessee Knoxville , Knoxville, Tennessee
| | - Wu-Chun Feng
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 14 Department of Computer Science, Virginia Tech, Blacksburg Virginia
- 15 Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg Virginia
- 16 SyNeRGy Laboratory, Virginia Tech, Blacksburg, Virginia
| | - Lynnette Ferguson
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 17 Department of Nutrition, Auckland Cancer Society Research Centre, University of Auckland , Auckland, New Zealand
| | - Geoffrey Fox
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 18 School of Informatics and Computing, Indiana University , Bloomington, Indiana
| | - Dmitrij Frishman
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 19 Technische Universitat Munchen , Wissenshaftzentrum Weihenstephan, Freising, Germany
| | - Robert Grossman
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 20 Institute for Genomics and Systems Biology, University of Chicago , Chicago Illinois
- 21 Department of Medicine, University of Chicago , Chicago, Illinois
| | - Allison Heath
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 20 Institute for Genomics and Systems Biology, University of Chicago , Chicago Illinois
- 22 Knapp Center for Biomedical Discovery, University of Chicago , Chicago, Illinois
| | - Roger Higdon
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 2 Predictive Analytics , Seattle Children's, Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Mara H Hutz
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 23 Departamento de Genetica, Instituto de Biociencias, Federal University of Rio Grande do Sul , Porto Alegre, Brazil
| | - Imre Janko
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 24 High-Throughput Analysis Core, Seattle Children's Research Institute , Seattle, Washington
| | - Lihua Jiang
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 13 Department of Genetics, Stanford University , Stanford, California
| | - Sanjay Joshi
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 25 Life Sciences , EMC, Hopkinton, Massachusetts
| | - Alexander Kel
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 26 GeneXplain GmbH , Wolfenbüttel, Germany
| | - Joseph W Kemnitz
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 27 Department of Cell and Regenerative Biology, University of Wisconsin-Madison , Madison, Wisconsin
- 28 Wisconsin National Primate Research Center, University of Wisconsin-Madison , Madison, Wisconsin
| | - Isaac S Kohane
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 29 Pediatrics and Health Sciences Technology, Children's Hospital and Harvard Medical School , Boston, Massachusetts
- 30 HMS Center for Biomedical Informatics, Countway Library of Medicine , Boston, Massachusetts
| | - Natali Kolker
- 2 Predictive Analytics , Seattle Children's, Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 24 High-Throughput Analysis Core, Seattle Children's Research Institute , Seattle, Washington
| | - Doron Lancet
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 31 Department of Molecular Genetics, Crown Human Genome Center , Weizmann Institute of Science, Rehovot, Israel
| | - Elaine Lee
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 24 High-Throughput Analysis Core, Seattle Children's Research Institute , Seattle, Washington
| | - Weizhong Li
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 32 Center for Research in Biological Systems, University of California , San Diego, La Jolla, California
| | - Andrey Lisitsa
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 33 Russian Human Proteome Organization (RHUPO) , Moscow, Russia
- 34 Institute of Biomedical Chemistry , Moscow, Russia
| | - Adrian Llerena
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 35 Clinical Research Center, Extremadura University Hospital and Medical School , Badajoz, Spain
| | - Courtney MacNealy-Koch
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Jean-Claude Marshall
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 36 Center for Translational Research, Catholic Health Initiatives , Towson, Maryland
| | - Paola Masuzzo
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 6 Department of Medical Protein Research, Vlaams Instituut voor Biotechnologie , Ghent, Belgium
- 7 Department of Biochemistry, Ghent University, Ghent , Belgium
| | - Amanda May
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 12 Department of Chemistry, University of Tennessee Knoxville , Knoxville, Tennessee
| | - George Mias
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 13 Department of Genetics, Stanford University , Stanford, California
| | - Matthew Monroe
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 37 Biological Sciences Division, Pacific Northwest National Laboratory , Richland, Washington
| | - Elizabeth Montague
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 2 Predictive Analytics , Seattle Children's, Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Sean Mooney
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 38 The Buck Institute for Research on Aging , Novato, California
| | - Alexey Nesvizhskii
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 39 Department of Pathology, University of Michigan , Ann Arbor, Michigan
- 40 Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, Michigan
| | - Santosh Noronha
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 41 Department of Chemical Engineering, Indian Institute of Technology Bombay , Powai, Mumbai, India
| | - Gilbert Omenn
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 42 Center for Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, Michigan
- 43 Departments of Molecular Medicine & Genetics and Human Genetics, University of Michigan , Ann Arbor Michigan
- 44 Department of Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, Michigan
- 45 School of Public Health, University of Michigan , Ann Arbor, Michigan
| | - Harsha Rajasimha
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 46 J eeva Informatics Solutions LLC , Derwood, Maryland
| | - Preveen Ramamoorthy
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 47 Molecular Diagnostics Department, National Jewish Health , Denver Colorado
| | - Jerry Sheehan
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 48 California Institute for Telecommunications and Information Technology, University of California-San Diego , La Jolla, California
| | - Larry Smarr
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 48 California Institute for Telecommunications and Information Technology, University of California-San Diego , La Jolla, California
| | - Charles V Smith
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 49 Center for Developmental Therapeutics, Seattle Children's Research Institute , Seattle, Washington
| | - Todd Smith
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 50 Digital World Biology , Seattle, Washington
| | - Michael Snyder
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 13 Department of Genetics, Stanford University , Stanford, California
- 51 Stanford Center for Genomics and Personalized Medicine, Stanford University , Stanford, California
| | - Srikanth Rapole
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 52 Proteomics Laboratory, National Centre for Cell Science, University of Pune , Pune, India
| | - Sanjeeva Srivastava
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 53 Proteomics Laboratory, Indian Institute of Technology Bombay , Mumbai, India
| | - Larissa Stanberry
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 2 Predictive Analytics , Seattle Children's, Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Elizabeth Stewart
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Stefano Toppo
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 54 Department of Molecular Medicine, University of Padova , Padova, Italy
| | - Peter Uetz
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 55 Center for the Study of Biological Complexity (CSBC), Virginia Commonwealth University , Richmond, Virginia
| | - Kenneth Verheggen
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 6 Department of Medical Protein Research, Vlaams Instituut voor Biotechnologie , Ghent, Belgium
- 7 Department of Biochemistry, Ghent University, Ghent , Belgium
| | - Brynn H Voy
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 56 Department of Animal Science, University of Tennessee Institute of Agriculture , Knoxville, Tennessee
| | - Louise Warnich
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 57 Department of Genetics, Faculty of AgriSciences, University of Stellenbosch , Stellenbosch, South Africa
| | - Steven W Wilhelm
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 58 Department of Microbiology, University of Tennessee-Knoxville , Knoxville, Tennessee
| | - Gregory Yandl
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
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Mohiuddin MS, Ramamoorthy P, Reynolds PR, Curran-Everett D, Leung DYM. Increased compound heterozygous filaggrin mutations in severe atopic dermatitis in the United States. J Allergy Clin Immunol Pract 2013; 1:534-6. [PMID: 24565632 DOI: 10.1016/j.jaip.2013.06.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2013] [Revised: 06/10/2013] [Accepted: 06/19/2013] [Indexed: 11/30/2022]
Affiliation(s)
- Maaz S Mohiuddin
- Division of Allergy-Immunology, Department of Pediatrics, National Jewish Health, Denver, Colo
| | - Preveen Ramamoorthy
- Division of Pathology, Department of Medicine, National Jewish Health, Denver, Colo
| | - Paul R Reynolds
- Division of Pathology, Department of Medicine, National Jewish Health, Denver, Colo
| | - Douglas Curran-Everett
- Division of Biostatistics and Bioinformatics, National Jewish Health, Denver, Colo; Department of Biostatistics and Bioinformatics, Colorado School of Public Health, University of Colorado Denver, Denver, Colo
| | - Donald Y M Leung
- Division of Allergy-Immunology, Department of Pediatrics, National Jewish Health, Denver, Colo.
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17
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Kadam RS, Ramamoorthy P, LaFlamme DJ, McKinsey TA, Kompella UB. Hypoxia alters ocular drug transporter expression and activity in rat and calf models: implications for drug delivery. Mol Pharm 2013; 10:2350-61. [PMID: 23607566 DOI: 10.1021/mp3007133] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Chronic hypoxia, a key stimulus for neovascularization, has been implicated in the pathology of proliferative diabetic retinopathy, retinopathy of prematurity, and wet age related macular degeneration. The aim of the present study was to determine the effect of chronic hypoxia on drug transporter mRNA expression and activity in ocular barriers. Sprague-Dawley rats were exposed to hypobaric hypoxia (PB = 380 mmHg) for 6 weeks, and neonatal calves were maintained under hypobaric hypoxia (PB = 445 mmHg) for 2 weeks. Age matched controls for rats, and calves were maintained at ambient altitude and normoxia. The effect of hypoxia on transporter expression was analyzed by qRT-PCR analysis of transporter mRNA expression in hypoxic and control rat choroid-retina. The effect of hypoxia on the activity of PEPT, OCT, ATB(0+), and MCT transporters was evaluated using in vitro transport studies of model transporter substrates across calf cornea and sclera-choroid-RPE (SCRPE). Quantitative gene expression analysis of 84 transporters in rat choroid-retina showed that 29 transporter genes were up regulated or down regulated by ≥1.5-fold in hypoxia. Nine ATP binding cassette (ABC) families of efflux transporters including MRP3, MRP4, MRP5, MRP6, MRP7, Abca17, Abc2, Abc3, and RGD1562128 were up-regulated. For solute carrier family transporters, 11 transporters including SLC10a1, SLC16a3, SLC22a7, SLC22a8, SLC29a1, SLC29a2, SLC2a1, SLC3a2, SLC5a4, SLC7a11, and SLC7a4 were up regulated, while 4 transporters including SLC22a2, SLC22a9, SLC28a1, and SLC7a9 were down-regulated in hypoxia. Of the three aquaporin (Aqp) water channels, Aqp-9 was down-regulated, and Aqp-1 was up-regulated during hypoxia. Gene expression analysis showed down regulation of OCT-1, OCT-2, and ATB(0+) and up regulation of MCT-3 in hypoxic rat choroid-retina, without any effect on the expression of PEPT-1 and PEPT-2. Functional activity assays of PEPT, OCT, ATB(0+), and MCT transporters in calf ocular tissues showed that PEPT, OCT, and ATB(0+) functional activity was down-regulated, whereas MCT functional activity was up-regulated in hypoxic cornea and SCRPE. Gene expression analysis of these transporters in rat tissues was consistent with the functional transport assays except for PEPT transporters. Chronic hypoxia results in significant alterations in the mRNA expression and functional activity of solute transporters in ocular tissues.
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Affiliation(s)
- Rajendra S Kadam
- Pharmaceutical Sciences and Ophthalmology, University of Colorado, Anschutz Medical Campus, Aurora, Colorado 80045, USA
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18
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Kartalija M, Ovrutsky AR, Bryan CL, Pott GB, Fantuzzi G, Thomas J, Strand MJ, Bai X, Ramamoorthy P, Rothman MS, Nagabhushanam V, McDermott M, Levin AR, Frazer-Abel A, Giclas PC, Korner J, Iseman MD, Shapiro L, Chan ED. Patients with nontuberculous mycobacterial lung disease exhibit unique body and immune phenotypes. Am J Respir Crit Care Med 2013; 187:197-205. [PMID: 23144328 PMCID: PMC5446199 DOI: 10.1164/rccm.201206-1035oc] [Citation(s) in RCA: 153] [Impact Index Per Article: 13.9] [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: 06/08/2012] [Accepted: 10/10/2012] [Indexed: 01/15/2023] Open
Abstract
RATIONALE Among patients with nontuberculous mycobacterial lung disease is a subset of previously healthy women with a slender body morphotype, often with scoliosis and/or pectus excavatum. We hypothesize that unidentified factors predispose these individuals to pulmonary nontuberculous mycobacterial disease. OBJECTIVES To compare body morphotype, serum adipokine levels, and whole-blood cytokine responses of patients with pulmonary nontuberculous mycobacteria (pNTM) with contemporary control subjects who are well matched demographically. METHODS We enrolled 103 patients with pNTM and 101 uninfected control subjects of similar demographics. Body mass index and body fat were quantified. All patients with pNTM and a subset of control subjects were evaluated for scoliosis and pectus excavatum. Serum leptin and adiponectin were measured. Specific cytokines important to host-defense against mycobacteria were measured in whole blood before and after stimulation. MEASUREMENTS AND MAIN RESULTS Patients with pNTM and control subjects were well matched for age, gender, and race. Patients with pNTM had significantly lower body mass index and body fat and were significantly taller than control subjects. Scoliosis and pectus excavatum were significantly more prevalent in patients with pNTM. The normal relationships between the adipokines and body fat were lost in the patients with pNTM, a novel finding. IFN-γ and IL-10 levels were significantly suppressed in stimulated whole blood of patients with pNTM. CONCLUSIONS This is the first study to comprehensively compare body morphotype, adipokines, and cytokine responses between patients with NTM lung disease and demographically matched controls. Our findings suggest a novel, predisposing immunophenotype that should be mechanistically defined.
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Affiliation(s)
| | | | - Courtney L. Bryan
- Division of Infectious Diseases
- Denver Veterans Affairs Medical Center, Denver, Colorado
| | - Gregory B. Pott
- Division of Infectious Diseases
- Denver Veterans Affairs Medical Center, Denver, Colorado
| | - Giamila Fantuzzi
- Department of Kinesiology and Nutrition, University of Illinois at Chicago, Chicago, Illinois; and
| | | | | | - Xiyuan Bai
- Division of Pulmonary Sciences and Critical Care Medicine, and
- Departments of Medicine and Academic Affairs
| | | | - Micol S. Rothman
- Division of Endocrinology, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado
| | | | - Michael McDermott
- Division of Endocrinology, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado
| | | | | | | | - Judith Korner
- Department of Medicine, Columbia University College of Physicians and Surgeons, New York, New York
| | - Michael D. Iseman
- Division of Infectious Diseases
- Division of Pulmonary Sciences and Critical Care Medicine, and
- Departments of Medicine and Academic Affairs
| | - Leland Shapiro
- Division of Infectious Diseases
- Denver Veterans Affairs Medical Center, Denver, Colorado
| | - Edward D. Chan
- Division of Pulmonary Sciences and Critical Care Medicine, and
- Departments of Medicine and Academic Affairs
- Denver Veterans Affairs Medical Center, Denver, Colorado
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Van Dyke MV, Martyny JW, Marola J, Ramamoorthy P, Ridder A, Harbeck RJ, Rose CS. Efficacy of occupant-collected dust samples in the evaluation of residential allergen and fungal levels. J Occup Environ Hyg 2012; 9:14-24. [PMID: 22150472 DOI: 10.1080/15459624.2012.633069] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
This study evaluated the ability of a resident to evaluate their home for allergens and mold using a settled dust test kit compared with evaluation and collection of settled dust by an industrial hygienist. Forty-three home residents were provided with a kit containing written instructions and a vacuum cleaner attachment for collecting a settled dust sample. Within 2 weeks of receiving the occupant-collected sample, an industrial hygienist evaluated these homes, including a visual inspection, collection of settled dust, and collection of spore trap samples. Settled dust samples were analyzed for major dog, cat, dust mite, and cockroach allergens using immunoassay methods, and for mold spore equivalents using quantitative polymerase chain reaction methods for the 13 mold species or species groups comprising the American Relative Moldiness Index (ARMI). Allergen concentrations and ARMIs were compared between the resident- and industrial hygienist-collected samples. Linear regression between the two sets of samples showed strong correlations for dog allergen (r(2) = 0.92) and cat allergen (r(2) = 0.90). Correlations for dust mite (r(2) = 0.57) and cockroach allergens (r(2) = 0.22) were lower, likely due to most samples being near the limit of detection. ARMIs were highly correlated (r(2) = 0.68) and were in categorical (high, medium, or low) agreement for 76% of residences. These results show that residents can reliably follow directions and collect settled dust samples, providing an efficient method to remotely screen homes for elevated allergen levels and to identify homes with a potential mold or moisture problem that may need further evaluation.
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Affiliation(s)
- M V Van Dyke
- National Jewish Health, Denver, Colorado 80206, USA.
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20
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Dubois CJ, Ramamoorthy P, Whim MD, Liu SJ. Activation of NPY type 5 receptors induces a long-lasting increase in spontaneous GABA release from cerebellar inhibitory interneurons. J Neurophysiol 2011; 107:1655-65. [PMID: 22190627 DOI: 10.1152/jn.00755.2011] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Neuropeptide Y (NPY), a widely distributed neuropeptide in the central nervous system, can transiently suppress inhibitory synaptic transmission and alter membrane excitability via Y2 and Y1 receptors (Y2rs and Y1rs), respectively. Although many GABAergic neurons express Y5rs, the functional role of these receptors in inhibitory neurons is not known. Here, we investigated whether activation of Y5rs can modulate inhibitory transmission in cerebellar slices. Unexpectedly, application of NPY triggered a long-lasting increase in the frequency of miniature inhibitory postsynaptic currents in stellate cells. NPY also induced a sustained increase in spontaneous GABA release in cultured cerebellar neurons. When cerebellar cultures were examined for Y5r immunoreactivity, the staining colocalized with that of VGAT, a presynaptic marker for GABAergic cells, suggesting that Y5rs are located in the presynaptic terminals of inhibitory neurons. RT-PCR experiments confirmed the presence of Y5r mRNA in the cerebellum. The NPY-induced potentiation of GABA release was blocked by Y5r antagonists and mimicked by application of a selective peptide agonist for Y5r. Thus Y5r activation is necessary and sufficient to trigger an increase in GABA release. Finally, the potentiation of inhibitory transmission could not be reversed by a Y5r antagonist once it was initiated, consistent with the development of a long-term potentiation. These results indicate that activation of presynaptic Y5rs induces a sustained increase in spontaneous GABA release from inhibitory neurons in contrast to the transient suppression of inhibitory transmission that is characteristic of Y1r and Y2r activation. Our findings thus reveal a novel role of presynaptic Y5rs in inhibitory interneurons in regulating GABA release and suggest that these receptors could play a role in shaping neuronal network activity in the cerebellum.
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Affiliation(s)
- C J Dubois
- Department of Cell Biology and Anatomy, Louisiana State University Health Sciences Center, New Orleans, Louisiana 70112, USA
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Malka J, Fish J, Pickering P, Ramamoorthy P, Gleason M, Spahn J. Is Exhaled Nitric Oxide (FeNO) Elevated in Children with Acute Asthma? J Allergy Clin Immunol 2011. [DOI: 10.1016/j.jaci.2010.12.530] [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: 11/26/2022]
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22
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Sambol AR, Iwen PC, Pieretti M, Basu S, Levi MH, Gilonske KD, Moses KD, Marola JL, Ramamoorthy P. Validation of the Cepheid Xpert Flu A real time RT-PCR detection panel for emergency use authorization. J Clin Virol 2010; 48:234-8. [PMID: 20580600 DOI: 10.1016/j.jcv.2010.06.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2010] [Revised: 05/26/2010] [Accepted: 06/02/2010] [Indexed: 10/19/2022]
Abstract
BACKGROUND In April 2009, the United States Secretary of the Department of Health and Human Services declared a public health emergency concerning the 2009 influenza H1N1 outbreak. This declaration allowed the FDA to issue Emergency Use Authorization (EUA) of approved in vitro diagnostics to detect the 2009 influenza H1N1 in clinical specimens. OBJECTIVES This report outlines the validation testing of the Cepheid Xpert Flu A Panel for the qualitative detection of 2009 H1N1 viral RNA. STUDY DESIGN This study was a multi-site, dual-method clinical evaluation comparing the results of testing between the Xpert Panel assay to the FDA-cleared Luminex Molecular Diagnostics xTAG Respiratory Viral Panel (Luminex RVP) assay and the EUA-granted Focus Diagnostics Influenza A/H1N1 (2009) Real Time RT-PCR (Focus H1N1) assay. RESULTS When compared to Luminex RVP (n=300) for influenza A detection, the Xpert Panel had a sensitivity of 91.2% (95% CI: 85.1-95.4), specificity of 99.4% (95% CI: 96.7-100), positive predictive value (PPV) of 99.2% (95% CI: 95.6-100), and a negative predictive value (NPV) of 93.1% (95% CI: 88.3-96.4). When compared to the Focus H1N1 (n=258) for detection of H1N1, the Xpert Panel had a sensitivity of 92.1% (95% CI: 82.4-97.4), specificity of 100% (95% CI: 98.5-100), PPV of 100% (95% CI: 95.0-100), and a NPV of 97.5% (95% CI: 94.3-99.2). CONCLUSIONS The results show the Cepheid Xpert Flu A Panel to be comparable to both the Luminex RVP and the Focus H1N1 assays. The Cepheid Xpert Panel was granted an EUA on 24 Dec 2009.
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Affiliation(s)
- Anthony R Sambol
- Department of Pathology & Microbiology, University of Nebraska Medical Center, 42nd and Emile Str., Omaha, NE 68198-5900, USA.
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Das R, Hammamieh R, Neill R, Ludwig GV, Eker S, Lincoln P, Ramamoorthy P, Dhokalia A, Mani S, Mendis C, Cummings C, Kearney B, Royaee A, Huang XZ, Paranavitana C, Smith L, Peel S, Kanesa-Thasan N, Hoover D, Lindler LE, Yang D, Henchal E, Jett M. Early indicators of exposure to biological threat agents using host gene profiles in peripheral blood mononuclear cells. BMC Infect Dis 2008; 8:104. [PMID: 18667072 PMCID: PMC2542375 DOI: 10.1186/1471-2334-8-104] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2008] [Accepted: 07/30/2008] [Indexed: 12/03/2022] Open
Abstract
Background Effective prophylaxis and treatment for infections caused by biological threat agents (BTA) rely upon early diagnosis and rapid initiation of therapy. Most methods for identifying pathogens in body fluids and tissues require that the pathogen proliferate to detectable and dangerous levels, thereby delaying diagnosis and treatment, especially during the prelatent stages when symptoms for most BTA are indistinguishable flu-like signs. Methods To detect exposures to the various pathogens more rapidly, especially during these early stages, we evaluated a suite of host responses to biological threat agents using global gene expression profiling on complementary DNA arrays. Results We found that certain gene expression patterns were unique to each pathogen and that other gene changes occurred in response to multiple agents, perhaps relating to the eventual course of illness. Nonhuman primates were exposed to some pathogens and the in vitro and in vivo findings were compared. We found major gene expression changes at the earliest times tested post exposure to aerosolized B. anthracis spores and 30 min post exposure to a bacterial toxin. Conclusion Host gene expression patterns have the potential to serve as diagnostic markers or predict the course of impending illness and may lead to new stage-appropriate therapeutic strategies to ameliorate the devastating effects of exposure to biothreat agents.
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Affiliation(s)
- Rina Das
- Division of Pathology, Walter Reed Army Institute of Research, Silver Spring, MD, USA.
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Abstract
Human prolactin (hPRL) has been shown to be one of the important survival/growth factors that promotes the proliferation of breast cancer cells in an autocrine/paracrine manner. In our recent studies, we demonstrated that a hPRL antagonist with a single amino acid substitution mutation (hPRL-G129R) was able to inhibit breast cancer cell proliferation via induction of apoptosis (1). In this study three independent yet related experiments were carried out regarding the effects of hPRL-G129R in breast cancer cells. We investigated the possible mechanism(s) of hPRL-G129R induced apoptosis in breast cancer cells. It is well documented that transforming growth factors (TGF) in conjunction with hormones such as estrogen and PRL play a major role in modulating the proliferation and apoptosis of mammary cells. We first investigated the relationships between hPRL/hPRL-G129R and TGFs. We show that hPRL is able to down-regulate TGF beta 1 (apoptotic factor) secretion and up-regulate TGF alpha (survival factor) secretion in a dose-dependent manner in T-47D cells. More importantly the hPRL antagonist up-regulates TGF beta 1 and down-regulates TGF alpha secretion. When hPRL-G129R was applied together with hPRL, it blocked the effects of hPRL. Secondly, we tested the possible involvement of caspases in hPRL-G129R induced apoptosis. We have shown that caspase-3 is activated by hPRL-G129R at a concentration of 250 ng/ml in T-47D breast cancer cells. Thirdly, we explored the additive effects of an anti-neoplastic drug, cisplatin, with the hPRL-G129R in T47D breast cancer cells. We show that cisplatin and hPRL-G129R when applied together resulted in about 40% growth inhibition in T-47D cells.
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Affiliation(s)
- P Ramamoorthy
- Department of Microbiology and Molecular Medicine, Clemson University, Clemson, SC 29681, USA
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Cataldo L, Chen NY, Yuan Q, Li W, Ramamoorthy P, Wagner TE, Sticca RP, Chen WY. Inhibition of oncogene STAT3 phosphorylation by a prolactin antagonist, hPRL-G129R, in T-47D human breast cancer cells. Int J Oncol 2000; 17:1179-85. [PMID: 11078803 DOI: 10.3892/ijo.17.6.1179] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
We have previously demonstrated that a hPRL antagonist (hPRL-G129R) was able to inhibit PRL induced breast cancer cell proliferation through induction of apoptosis. In the present study, we test the hypothesis that the inhibitory effect of hPRL-G129R in breast cancer cells occurs, at least in part, through the inhibition of oncogene STAT3 activation. We first demonstrated that STAT5 and STAT3 could be activated by either hGH or hPRL in T-47D breast cancer cells. Although the patterns of STAT5 activation by hGH and hPRL are similar, we observed a nearly 10-fold greater efficacy of hPRL in STAT3 activation as compared to that of hGH. More importantly, we have demonstrated that activation of STAT3 by hPRL could be inhibited by hPRL-G129R. Since T-47D cells coexpress GHR and PRLR, an attempt was made to dissect the molecular events mediated through hGHR or hPRLR using mouse L-cells expressing a single population of receptors (hGHR or hPRLR). To our surprise, only STAT5, not STAT3 phosphorylation was observed in these L-cells. In conclusion, our results suggest that: a) STAT3 is preferably activated through hPRLR in T-47D cells; b) hPRL-G129R is effective in inhibiting STAT3 phosphorylation; and c) the mechanism of STAT3 activation is different from that of STAT5.
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Affiliation(s)
- L Cataldo
- Oncology Research Institute, Greenville Hospital System, Greenville, SC 29605, USA
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Chen WY, Ramamoorthy P, Chen N, Sticca R, Wagner TE. A human prolactin antagonist, hPRL-G129R, inhibits breast cancer cell proliferation through induction of apoptosis. Clin Cancer Res 1999; 5:3583-93. [PMID: 10589775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/14/2023]
Abstract
Human breast cancer is the predominant malignancy and the leading cause of cancer death in women from Western societies. The cause of breast cancer is still unknown. Recently, the association between human prolactin (hPRL) activity and breast cancer has been reemphasized. Biologically active hPRL has been found to be produced locally by breast cancer cells that contain high levels of PRL receptor. A high incidence of mammary tumor growth has also been found in transgenic mice overexpressing lactogenic hormones. More importantly, it has been demonstrated that the receptors for sex steroids and PRL are coexpressed and cross-regulated. In this study, we report that we have designed and produced a hPRL antagonist, hPRL-G129R. By using cell proliferation assays, we have demonstrated that: (a) hPRL and E2 exhibited an additive stimulatory effect on human breast cancer cell (T-47D) proliferation; (b) hPRL-G129R possessed an inhibitory effect on T-47D cell proliferation; and (c) when antiestrogen (4-OH-tamoxifen) and anti-PRL (hPRL-G129R) agents were added together, an additive inhibitory effect was observed. We further investigated the mechanism of the inhibitory effects of hPRL-G129R in four hPRLR positive breast cancer cell lines. We report that hPRL-G129R is able to induce apoptosis in all four cell lines in a dose-dependent manner as determined by the Terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling assay. The apoptosis is induced within 2 h of treatment at a dose as low as 50 ng/ml. We hope that the hPRL antagonist could be used to improve the outcome of human breast cancer therapy in the near future.
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Affiliation(s)
- W Y Chen
- Oncology Research Institute, Cancer Center, Greenville Hospital System, South Carolina 29605, USA.
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Ramamoorthy P, Johnson BJ, Wilkinson AR, Galloway JM, McCollum PT. Vascular surgical society of great britain and ireland: limb salvage in the octogenarian. Br J Surg 1999; 86:706. [PMID: 10361343 DOI: 10.1046/j.1365-2168.1999.0706b.x] [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: 11/20/2022]
Abstract
BACKGROUND: Critical limb ischaemia (CLI) presents a unique set of problems in the elderly patient. Foremost among these is the much greater likelihood of loss of independence and reduced quality of life if major amputation is required. For this reason it has been this unit's policy to attempt reconstructive vascular surgery in almost all cases of CLI. The outcome of this policy was examined. METHODS: All patients had surgery performed under one consultant and data were entered prospectively on to a database. RESULTS: Risk factors included diabetes (17 per cent), smoking (78 per cent) and ischaemic heart disease (31 per cent). Some 127 patients had either femoropopliteal (59), femorodistal (64) or popliteal-distal grafts (four) performed for limb-threatening ischaemia. Follow-up was performed at 3, 6 and 12 months and then at annual intervals until death. Seventeen of these patients required a subsequent major amputation, 12 at the below-knee and five at the above-knee level. Mean follow-up was 2 years. The perioperative mortality rate was 15 per cent, although eight of these patients were admitted with acute or chronic ischaemia. Cumulative graft secondary patency rate was 68 per cent at 4 years for vein grafts. Some 95 per cent of patients with patent grafts were independently mobile. CONCLUSION: Excellent results can be achieved for limb salvage with a relatively low morbidity in this group. Elderly patients with CLI do not live long and avoidance of amputation is particularly desirable in order to maximize the quality of their remaining life.
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Raghavan KR, Shenoi RP, Zaer F, Aiyer R, Ramamoorthy P, Mehta MN. Melioidosis in India. Indian Pediatr 1991; 28:184-8. [PMID: 2055637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- K R Raghavan
- Department of Pediatrics, LTMMC and LTMGH, Sion, Bombay
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