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Reed BD, Meyer MJ, Abramzon V, Ad O, Ad O, Adcock P, Ahmad FR, Alppay G, Ball JA, Beach J, Belhachemi D, Bellofiore A, Bellos M, Beltrán JF, Betts A, Bhuiya MW, Blacklock K, Boer R, Boisvert D, Brault ND, Buxbaum A, Caprio S, Choi C, Christian TD, Clancy R, Clark J, Connolly T, Croce KF, Cullen R, Davey M, Davidson J, Elshenawy MM, Ferrigno M, Frier D, Gudipati S, Hamill S, He Z, Hosali S, Huang H, Huang L, Kabiri A, Kriger G, Lathrop B, Li A, Lim P, Liu S, Luo F, Lv C, Ma X, McCormack E, Millham M, Nani R, Pandey M, Parillo J, Patel G, Pike DH, Preston K, Pichard-Kostuch A, Rearick K, Rearick T, Ribezzi-Crivellari M, Schmid G, Schultz J, Shi X, Singh B, Srivastava N, Stewman SF, Thurston TR, Thurston TR, Trioli P, Tullman J, Wang X, Wang YC, Webster EAG, Zhang Z, Zuniga J, Patel SS, Griffiths AD, van Oijen AM, McKenna M, Dyer MD, Rothberg JM. Real-time dynamic single-molecule protein sequencing on an integrated semiconductor device. Science 2022; 378:186-192. [PMID: 36227977 DOI: 10.1126/science.abo7651] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Studies of the proteome would benefit greatly from methods to directly sequence and digitally quantify proteins and detect posttranslational modifications with single-molecule sensitivity. Here, we demonstrate single-molecule protein sequencing using a dynamic approach in which single peptides are probed in real time by a mixture of dye-labeled N-terminal amino acid recognizers and simultaneously cleaved by aminopeptidases. We annotate amino acids and identify the peptide sequence by measuring fluorescence intensity, lifetime, and binding kinetics on an integrated semiconductor chip. Our results demonstrate the kinetic principles that allow recognizers to identify multiple amino acids in an information-rich manner that enables discrimination of single amino acid substitutions and posttranslational modifications. With further development, we anticipate that this approach will offer a sensitive, scalable, and accessible platform for single-molecule proteomic studies and applications.
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Affiliation(s)
| | | | | | - Omer Ad
- Quantum-Si, Inc., Guilford, CT 06437, USA
| | - Omer Ad
- Quantum-Si, Inc., Guilford, CT 06437, USA
| | - Pat Adcock
- Quantum-Si, Inc., Guilford, CT 06437, USA
| | | | - Gün Alppay
- Quantum-Si, Inc., Guilford, CT 06437, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Mel Davey
- Quantum-Si, Inc., Guilford, CT 06437, USA
| | | | | | | | | | | | | | - Zhaoyu He
- Quantum-Si, Inc., Guilford, CT 06437, USA
| | | | | | - Le Huang
- Quantum-Si, Inc., Guilford, CT 06437, USA
| | - Ali Kabiri
- Quantum-Si, Inc., Guilford, CT 06437, USA
| | | | | | - An Li
- Quantum-Si, Inc., Guilford, CT 06437, USA
| | - Peter Lim
- Quantum-Si, Inc., Guilford, CT 06437, USA
| | | | | | - Caixia Lv
- Quantum-Si, Inc., Guilford, CT 06437, USA
| | | | | | | | - Roger Nani
- Quantum-Si, Inc., Guilford, CT 06437, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Xin Wang
- Quantum-Si, Inc., Guilford, CT 06437, USA
| | | | | | | | | | - Smita S Patel
- Department of Biochemistry and Molecular Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Andrew D Griffiths
- Laboratoire de Biochimie, ESPCI Paris, Université PSL, CNRS UMR 8231, Paris, France
| | - Antoine M van Oijen
- Molecular Horizons, University of Wollongong, Wollongong, NSW 2522, Australia
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Gardner SN, Frey KG, Redden CL, Thissen JB, Allen JE, Allred AF, Dyer MD, Mokashi VP, Slezak TR. Targeted amplification for enhanced detection of biothreat agents by next-generation sequencing. BMC Res Notes 2015; 8:682. [PMID: 26572552 PMCID: PMC4647626 DOI: 10.1186/s13104-015-1530-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2015] [Accepted: 09/28/2015] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Historically, identification of causal agents of disease has relied heavily on the ability to culture the organism in the laboratory and/or the use of pathogen-specific antibodies or sequence-based probes. However, these methods can be limiting: Even highly sensitive PCR-based assays must be continually updated due to signature degradation as new target strains and near neighbors are sequenced. Thus, there has been a need for assays that do not suffer as greatly from these limitations and/or biases. Recent advances in library preparation technologies for Next-Generation Sequencing (NGS) are focusing on the use of targeted amplification and targeted enrichment/capture to ensure that the most highly discriminating regions of the genomes of known targets (organism-unique regions and/or regions containing functionally important genes or phylogenetically-discriminating SNPs) will be sequenced, regardless of the complex sample background. RESULTS In the present study, we have assessed the feasibility of targeted sequence enhancement via amplification to facilitate detection of a bacterial pathogen present in low copy numbers in a background of human genomic material. Our results indicate that the targeted amplification of signature regions can effectively identify pathogen genomic material present in as little as 10 copies per ml in a complex sample. Importantly, the correct species and strain calls could be made in amplified samples, while this was not possible in unamplified samples. CONCLUSIONS The results presented here demonstrate the efficacy of a targeted amplification approach to biothreat detection, using multiple highly-discriminative amplicons per biothreat organism that provide redundancy in case of variation in some primer regions. Importantly, strain level discrimination was possible at levels of 10 genome equivalents. Similar results could be obtained through use of panels focused on the identification of amplicons targeted for specific genes or SNPs instead of, or in addition to, those targeted for specific organisms (ongoing gene-targeting work to be reported later). Note that without some form of targeted enhancement, the enormous background present in complex clinical and environmental samples makes it highly unlikely that sufficient coverage of key pathogen(s) present in the sample will be achieved with current NGS technology to guarantee that the most highly discriminating regions will be sequenced.
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Affiliation(s)
- Shea N Gardner
- Bioinformatics, Global Security Program, Lawrence Livermore National Laboratory, 7000 East Avenue, L-174, Livermore, CA, 94550, USA.
| | - Kenneth G Frey
- Naval Medical Research Center, NMRC-Frederick, 8400 Research Plaza, Fort Detrick, MD, 21702, USA. .,Henry M. Jackson Foundation, 6720-A Rockledge Drive, Suite 100, Bethesda, MD, 20817, USA.
| | - Cassie L Redden
- Naval Medical Research Center, NMRC-Frederick, 8400 Research Plaza, Fort Detrick, MD, 21702, USA. .,Henry M. Jackson Foundation, 6720-A Rockledge Drive, Suite 100, Bethesda, MD, 20817, USA.
| | - James B Thissen
- Bioinformatics, Global Security Program, Lawrence Livermore National Laboratory, 7000 East Avenue, L-174, Livermore, CA, 94550, USA.
| | - Jonathan E Allen
- Bioinformatics, Global Security Program, Lawrence Livermore National Laboratory, 7000 East Avenue, L-174, Livermore, CA, 94550, USA.
| | - Adam F Allred
- Thermo Fisher Scientific, 180 Oyster Point Boulevard, Building 200, South San Francisco, CA, 94080, USA.
| | - Matthew D Dyer
- Thermo Fisher Scientific, 180 Oyster Point Boulevard, Building 200, South San Francisco, CA, 94080, USA.
| | - Vishwesh P Mokashi
- Naval Medical Research Center, NMRC-Frederick, 8400 Research Plaza, Fort Detrick, MD, 21702, USA.
| | - Tom R Slezak
- Bioinformatics, Global Security Program, Lawrence Livermore National Laboratory, 7000 East Avenue, L-174, Livermore, CA, 94550, USA.
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Kim YH, Liang H, Liu X, Izzo J, Lemos R, Lee JS, Cho JY, Cheong JH, Kim H, Li M, Downey TJ, Dyer MD, Sun Y, Sun J, Beasley EM, Chung HC, Noh SH, Weinstein JN, Liu CG, Powis G. Abstract 970: Multi-layer and integrative analysis of the whole transcriptome in Asian gastric cancer: AMPKβ modulation in cancer progression. Cancer Res 2012. [DOI: 10.1158/1538-7445.am2012-970] [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
We performed whole-transcriptome profiling of gastric cancer_the most common cancer in developing countries and the second leading cause of cancer deaths in the world. Applying a innovative SOLiD RNA-Seq approach to 24 gastric tumor samples and 6 noncancerous samples, we generated 3.0 billion short reads to quantitatively measure the abundance of mRNAs and small non-coding RNAs. We then developed a multi-layer analysis to identify differentially expressed mRNAs and microRNAs, and novel single-nucleotide mutations candidates associated with gastric cancer. With the insights from the multi-layer analysis, we experimentally demonstrated a critical role for AMPKβ modulation in gastric cancer progression. This work provides a multi-faceted portrait of the Asian gastric cancer transcriptome, which facilitates the elucidation of molecular mechanisms of gastric carcinogenesis and the development of targeted therapies. By integrating data obtained on the expression of both mRNa and miRNa molecules, we were able to identify certain gene signatures uniquely expressed in gastric cancer. This gene signatures along with gastric cancer-related therapeutic target gene signature, showed molecular changes as robust biological markers. Integration of differentially expressed mRNAs and microRNA profiles allowed us to identify potential miRNA drivers of disease progression. We demonstrated the translational relevance of AMPKβ as a potential therapeutic target for treatment and for prediction of early stage gastric cancer. This work lays a critical foundation for the identification of molecular mechanisms of gastric carcinogenesis and the development of related targeted therapies.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 970. doi:1538-7445.AM2012-970
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Affiliation(s)
| | - Han Liang
- 1MD Anderson Cancer Center, Houston, TX
| | | | | | | | | | - Jae Yong Cho
- 2Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jae Ho Cheong
- 2Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hoguen Kim
- 2Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Min Li
- 3Partek Inc., St. Louis, MO
| | | | | | | | | | | | - Hyun Cheol Chung
- 2Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sung Hoon Noh
- 2Yonsei University College of Medicine, Seoul, Republic of Korea
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Kim YH, Liang H, Liu X, Lee JS, Cho JY, Cheong JH, Kim H, Li M, Downey TJ, Dyer MD, Sun Y, Sun J, Beasley EM, Chung HC, Noh SH, Weinstein JN, Liu CG, Powis G. AMPKα modulation in cancer progression: multilayer integrative analysis of the whole transcriptome in Asian gastric cancer. Cancer Res 2012; 72:2512-21. [PMID: 22434430 DOI: 10.1158/0008-5472.can-11-3870] [Citation(s) in RCA: 83] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Gastric cancer is the most common cancer in Asia and most developing countries. Despite the use of multimodality therapeutics, it remains the second leading cause of cancer death in the world. To identify the molecular underpinnings of gastric cancer in the Asian population, we applied an RNA-sequencing approach to gastric tumor and noncancerous specimens, generating 680 million informative short reads to quantitatively characterize the entire transcriptome of gastric cancer (including mRNAs and miRNAs). A multilayer analysis was then developed to identify multiple types of transcriptional aberrations associated with different stages of gastric cancer, including differentially expressed mRNAs, recurrent somatic mutations, and key differentially expressed miRNAs. Through this approach, we identified the central metabolic regulator AMP-activated protein kinase (AMPK)α as a potential functional target in Asian gastric cancer. Furthermore, we experimentally showed the translational relevance of this gene as a potential therapeutic target for early-stage gastric cancer in Asian patients. Together, our findings not only provide a valuable information resource for identifying and elucidating the molecular mechanisms of Asian gastric cancer, but also represent a general integrative framework to develop more effective therapeutic targets.
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Affiliation(s)
- Yon Hui Kim
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030,USA
| | - Han Liang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Xiuping Liu
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030,USA
| | - Ju-Seog Lee
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jae Yong Cho
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul 120-749, Korea
| | - Jae-Ho Cheong
- Department of Surgery, Yonsei University College of Medicine, Seoul 120-749, Korea
| | - Hoguen Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul 120-749, Korea
| | - Min Li
- Partek Inc., St. Louis, MO 63141, USA
| | | | | | | | - Jingtao Sun
- Life Technologies, Foster City, CA 94404, USA
| | | | - Hyun Cheol Chung
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul 120-749, Korea
| | - Sung Hoon Noh
- Department of Surgery, Yonsei University College of Medicine, Seoul 120-749, Korea
| | - John N Weinstein
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Chang-Gong Liu
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030,USA
| | - Garth Powis
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030,USA
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Murali TM, Dyer MD, Badger D, Tyler BM, Katze MG. Network-based prediction and analysis of HIV dependency factors. PLoS Comput Biol 2011; 7:e1002164. [PMID: 21966263 PMCID: PMC3178628 DOI: 10.1371/journal.pcbi.1002164] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.2] [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: 12/20/2010] [Accepted: 06/30/2011] [Indexed: 01/27/2023] Open
Abstract
HIV Dependency Factors (HDFs) are a class of human proteins that are essential for HIV replication, but are not lethal to the host cell when silenced. Three previous genome-wide RNAi experiments identified HDF sets with little overlap. We combine data from these three studies with a human protein interaction network to predict new HDFs, using an intuitive algorithm called SinkSource and four other algorithms published in the literature. Our algorithm achieves high precision and recall upon cross validation, as do the other methods. A number of HDFs that we predict are known to interact with HIV proteins. They belong to multiple protein complexes and biological processes that are known to be manipulated by HIV. We also demonstrate that many predicted HDF genes show significantly different programs of expression in early response to SIV infection in two non-human primate species that differ in AIDS progression. Our results suggest that many HDFs are yet to be discovered and that they have potential value as prognostic markers to determine pathological outcome and the likelihood of AIDS development. More generally, if multiple genome-wide gene-level studies have been performed at independent labs to study the same biological system or phenomenon, our methodology is applicable to interpret these studies simultaneously in the context of molecular interaction networks and to ask if they reinforce or contradict each other. Medicines to cure infectious diseases usually target proteins in the pathogens. Since pathogens have short life cycles, the targeted proteins can rapidly evolve and make the medicines ineffective, especially in viruses such as HIV. However, since viruses have very small genomes, they must exploit the cellular machinery of the host to propagate. Therefore, disrupting the activity of selected host proteins may impede viruses. Three recent experiments have discovered hundreds of such proteins in human cells that HIV depends upon. Surprisingly, these three sets have very little overlap. In this work, we demonstrate that this discrepancy can be explained by considering physical interactions between the human proteins in these studies. Moreover, we exploit these interactions to predict new dependency factors for HIV. Our predictions show very significant overlaps with human proteins that are known to interact with HIV proteins and with human cellular processes that are known to be subverted by the virus. Most importantly, we show that proteins predicted by us may play a prominent role in affecting HIV-related disease progression in lymph nodes. Therefore, our predictions constitute a powerful resource for experimentalists who desire to discover new human proteins that can control the spread of HIV.
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Affiliation(s)
- T. M. Murali
- Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
- * E-mail: (TMM) (TM); (MGK) (MK)
| | - Matthew D. Dyer
- Applied Biosystems, Foster City, California, United States of America
| | - David Badger
- Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
| | - Brett M. Tyler
- Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
| | - Michael G. Katze
- Department of Microbiology, University of Washington, Seattle, Washington, United States of America
- * E-mail: (TMM) (TM); (MGK) (MK)
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6
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Dyer MD, Murali TM, Sobral BW. Supervised learning and prediction of physical interactions between human and HIV proteins. Infect Genet Evol 2011; 11:917-23. [PMID: 21382517 DOI: 10.1016/j.meegid.2011.02.022] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2010] [Revised: 02/22/2011] [Accepted: 02/24/2011] [Indexed: 02/08/2023]
Abstract
BACKGROUND Infectious diseases result in millions of deaths each year. Physical interactions between pathogen and host proteins often form the basis of such infections. While a number of methods have been proposed for predicting protein-protein interactions (PPIs), they have primarily focused on intra-species protein-protein interactions. METHODOLOGY We present an application of a supervised learning method for predicting physical interactions between host and pathogen proteins, using the human-HIV system. Using a Support Vector Machine with a linear kernel, we explore the use of a number of features including domain profiles, protein sequence k-mers, and properties of human proteins in a human PPI network. We achieve the best cross-validation performance when we use a combination of all three of these features. At a precision value of 70% we obtain recall values greater than 40%, depending on the ratio of positive examples to negative examples used during training. We use a classifier trained using these features to predict new PPIs between human and HIV proteins. We focus our discussion on those predicted interactions that involve human proteins known to be critical for HIV replication and propagation. Examples of predicted interactions with support in the literature include those necessary for viral attachment to the host membrane and subsequent invasion of the host cell. SIGNIFICANCE Unlike intra-species PPIs, host-pathogen PPIs have not yet been experimentally detected on a large scale, though they are likely to play important roles in pathogenesis and disease outcomes. Computational methods that can robustly and accurately predict host-pathogen PPIs hold the promise of guiding future experiments and gaining insights into potential mechanisms of pathogenesis.
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Affiliation(s)
- Matthew D Dyer
- Virginia Bioinformatics Institute, Virginia Tech, 1 Washington St, Blacksburg, VA 24061, USA
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7
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Dyer MD, Neff C, Dufford M, Rivera CG, Shattuck D, Bassaganya-Riera J, Murali TM, Sobral BW. The human-bacterial pathogen protein interaction networks of Bacillus anthracis, Francisella tularensis, and Yersinia pestis. PLoS One 2010; 5:e12089. [PMID: 20711500 PMCID: PMC2918508 DOI: 10.1371/journal.pone.0012089] [Citation(s) in RCA: 107] [Impact Index Per Article: 7.6] [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: 04/13/2010] [Accepted: 07/17/2010] [Indexed: 01/01/2023] Open
Abstract
Background Bacillus anthracis, Francisella tularensis, and Yersinia pestis are bacterial pathogens that can cause anthrax, lethal acute pneumonic disease, and bubonic plague, respectively, and are listed as NIAID Category A priority pathogens for possible use as biological weapons. However, the interactions between human proteins and proteins in these bacteria remain poorly characterized leading to an incomplete understanding of their pathogenesis and mechanisms of immune evasion. Methodology In this study, we used a high-throughput yeast two-hybrid assay to identify physical interactions between human proteins and proteins from each of these three pathogens. From more than 250,000 screens performed, we identified 3,073 human-B. anthracis, 1,383 human-F. tularensis, and 4,059 human-Y. pestis protein-protein interactions including interactions involving 304 B. anthracis, 52 F. tularensis, and 330 Y. pestis proteins that are uncharacterized. Computational analysis revealed that pathogen proteins preferentially interact with human proteins that are hubs and bottlenecks in the human PPI network. In addition, we computed modules of human-pathogen PPIs that are conserved amongst the three networks. Functionally, such conserved modules reveal commonalities between how the different pathogens interact with crucial host pathways involved in inflammation and immunity. Significance These data constitute the first extensive protein interaction networks constructed for bacterial pathogens and their human hosts. This study provides novel insights into host-pathogen interactions.
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Affiliation(s)
- Matthew D. Dyer
- Virginia Bioinformatics Institute, Blacksburg, Virginia, United States of America
| | - Chris Neff
- Myriad Genetics, Salt Lake City, Utah, United States of America
| | - Max Dufford
- Myriad Genetics, Salt Lake City, Utah, United States of America
| | - Corban G. Rivera
- Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
| | - Donna Shattuck
- Myriad Genetics, Salt Lake City, Utah, United States of America
| | | | - T. M. Murali
- Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
- * E-mail: (TMM); (BWS)
| | - Bruno W. Sobral
- Virginia Bioinformatics Institute, Blacksburg, Virginia, United States of America
- * E-mail: (TMM); (BWS)
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8
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Goodman AG, Fornek JL, Medigeshi GR, Perrone LA, Peng X, Dyer MD, Proll SC, Knoblaugh SE, Carter VS, Korth MJ, Nelson JA, Tumpey TM, Katze MG. P58(IPK): a novel "CIHD" member of the host innate defense response against pathogenic virus infection. PLoS Pathog 2009; 5:e1000438. [PMID: 19461876 PMCID: PMC2677460 DOI: 10.1371/journal.ppat.1000438] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.3] [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: 02/12/2009] [Accepted: 04/21/2009] [Indexed: 12/26/2022] Open
Abstract
To support their replication, viruses take advantage of numerous cellular factors and processes. Recent large-scale screens have identified hundreds of such factors, yet little is known about how viruses exploit any of these. Influenza virus infection post-translationally activates P58(IPK), a cellular inhibitor of the interferon-induced, dsRNA-activated eIF2alpha kinase, PKR. Here, we report that infection of P58(IPK) knockout mice with influenza virus resulted in increased lung pathology, immune cell apoptosis, PKR activation, and mortality. Analysis of lung transcriptional profiles, including those induced by the reconstructed 1918 pandemic virus, revealed increased expression of genes associated with the cell death, immune, and inflammatory responses. These experiments represent the first use of a mammalian infection model to demonstrate the role of P58(IPK) in the antiviral response. Our results suggest that P58(IPK) represents a new class of molecule, a cellular inhibitor of the host defense (CIHD), as P58(IPK) is activated during virus infection to inhibit virus-induced apoptosis and inflammation to prolong host survival, even while prolonging viral replication.
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Affiliation(s)
- Alan G. Goodman
- Department of Microbiology, University of Washington, Seattle, Washington, United States of America
- Graduate Program in Bioengineering, University of Washington, Seattle, Washington, United States of America
| | - Jamie L. Fornek
- Department of Microbiology, University of Washington, Seattle, Washington, United States of America
| | - Guruprasad R. Medigeshi
- Vaccine and Gene Therapy Institute, Oregon Health and Science University, Beaverton, Oregon, United States of America
| | - Lucy A. Perrone
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Xinxia Peng
- Department of Microbiology, University of Washington, Seattle, Washington, United States of America
| | - Matthew D. Dyer
- Department of Microbiology, University of Washington, Seattle, Washington, United States of America
| | - Sean C. Proll
- Department of Microbiology, University of Washington, Seattle, Washington, United States of America
| | - Sue E. Knoblaugh
- Animal Health Shared Resources, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Victoria S. Carter
- Department of Microbiology, University of Washington, Seattle, Washington, United States of America
| | - Marcus J. Korth
- Department of Microbiology, University of Washington, Seattle, Washington, United States of America
| | - Jay A. Nelson
- Vaccine and Gene Therapy Institute, Oregon Health and Science University, Beaverton, Oregon, United States of America
| | - Terrence M. Tumpey
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Michael G. Katze
- Department of Microbiology, University of Washington, Seattle, Washington, United States of America
- Washington National Primate Research Center, Seattle, Washington, United States of America
- * E-mail:
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Abstract
Protein-protein interactions (PPIs) play a vital role in initiating infection in a number of pathogens. Identifying which interactions allow a pathogen to infect its host can help us to understand methods of pathogenesis and provide potential targets for therapeutics. Public resources for studying host-pathogen systems, in particular PPIs, are scarce. To facilitate the study of host-pathogen PPIs, we have collected and integrated host-pathogen PPI (HP-PPI) data from a number of public resources to create the Pathogen Interaction Gateway (PIG). PIG provides a text based search and a BLAST interface for searching the HP-PPI data. Each entry in PIG includes information such as the functional annotations and the domains present in the interacting proteins. PIG provides links to external databases to allow for easy navigation among the various websites. Additionally, PIG includes a tool for visualizing a single HP-PPI network or two HP-PPI networks. PIG can be accessed at http://pig.vbi.vt.edu.
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Affiliation(s)
- Tim Driscoll
- Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
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10
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Abstract
Infectious diseases result in millions of deaths each year. Mechanisms of infection have been studied in detail for many pathogens. However, many questions are relatively unexplored. What are the properties of human proteins that interact with pathogens? Do pathogens interact with certain functional classes of human proteins? Which infection mechanisms and pathways are commonly triggered by multiple pathogens? In this paper, to our knowledge, we provide the first study of the landscape of human proteins interacting with pathogens. We integrate human-pathogen protein-protein interactions (PPIs) for 190 pathogen strains from seven public databases. Nearly all of the 10,477 human-pathogen PPIs are for viral systems (98.3%), with the majority belonging to the human-HIV system (77.9%). We find that both viral and bacterial pathogens tend to interact with hubs (proteins with many interacting partners) and bottlenecks (proteins that are central to many paths in the network) in the human PPI network. We construct separate sets of human proteins interacting with bacterial pathogens, viral pathogens, and those interacting with multiple bacteria and with multiple viruses. Gene Ontology functions enriched in these sets reveal a number of processes, such as cell cycle regulation, nuclear transport, and immune response that participate in interactions with different pathogens. Our results provide the first global view of strategies used by pathogens to subvert human cellular processes and infect human cells. Supplementary data accompanying this paper is available at http://staff.vbi.vt.edu/dyermd/publications/dyer2008a.html.
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Affiliation(s)
- Matthew D Dyer
- Genetics, Bioinformatics, and Computational Biology Program, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
- Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
| | - T. M Murali
- Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
- * To whom correspondence should be addressed. E-mail: (TMM), (BWS)
| | - Bruno W Sobral
- Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
- * To whom correspondence should be addressed. E-mail: (TMM), (BWS)
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Abstract
MOTIVATION Infectious diseases such as malaria result in millions of deaths each year. An important aspect of any host-pathogen system is the mechanism by which a pathogen can infect its host. One method of infection is via protein-protein interactions (PPIs) where pathogen proteins target host proteins. Developing computational methods that identify which PPIs enable a pathogen to infect a host has great implications in identifying potential targets for therapeutics. RESULTS We present a method that integrates known intra-species PPIs with protein-domain profiles to predict PPIs between host and pathogen proteins. Given a set of intra-species PPIs, we identify the functional domains in each of the interacting proteins. For every pair of functional domains, we use Bayesian statistics to assess the probability that two proteins with that pair of domains will interact. We apply our method to the Homo sapiens-Plasmodium falciparum host-pathogen system. Our system predicts 516 PPIs between proteins from these two organisms. We show that pairs of human proteins we predict to interact with the same Plasmodium protein are close to each other in the human PPI network and that Plasmodium pairs predicted to interact with same human protein are co-expressed in DNA microarray datasets measured during various stages of the Plasmodium life cycle. Finally, we identify functionally enriched sub-networks spanned by the predicted interactions and discuss the plausibility of our predictions. AVAILABILITY Supplementary data are available at http://staff.vbi.vt.edu/dyermd/publications/dyer2007a.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Matthew D Dyer
- Genetics, Bioinformatics and Computational Biology Program, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA.
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Zhou CE, Smith J, Lam M, Zemla A, Dyer MD, Slezak T. MvirDB--a microbial database of protein toxins, virulence factors and antibiotic resistance genes for bio-defence applications. Nucleic Acids Res 2006; 35:D391-4. [PMID: 17090593 PMCID: PMC1669772 DOI: 10.1093/nar/gkl791] [Citation(s) in RCA: 172] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Knowledge of toxins, virulence factors and antibiotic resistance genes is essential for bio-defense applications aimed at identifying ‘functional’ signatures for characterizing emerging or engineered pathogens. Whereas genetic signatures identify a pathogen, functional signatures identify what a pathogen is capable of. To facilitate rapid identification of sequences and characterization of genes for signature discovery, we have collected all publicly available (as of this writing), organized sequences representing known toxins, virulence factors, and antibiotic resistance genes in one convenient database, which we believe will be of use to the bio-defense research community. MvirDB integrates DNA and protein sequence information from Tox-Prot, SCORPION, the PRINTS virulence factors, VFDB, TVFac, Islander, ARGO and a subset of VIDA. Entries in MvirDB are hyperlinked back to their original sources. A blast tool allows the user to blast against all DNA or protein sequences in MvirDB, and a browser tool allows the user to search the database to retrieve virulence factor descriptions, sequences, and classifications, and to download sequences of interest. MvirDB has an automated weekly update mechanism. Each protein sequence in MvirDB is annotated using our fully automated protein annotation system and is linked to that system's browser tool. MvirDB can be accessed at .
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Affiliation(s)
- C E Zhou
- Pathogen Bioinformatics, Mailstop L-174 Energy, Environment, and Biology Division, Computations Directorate, Lawrence Livermore National Laboratory, 70000 East Avenue, Livermore, CA 94550, USA.
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Zhou CLE, Lam MW, Smith JR, Zemla AT, Dyer MD, Kuczmarski TA, Vitalis EA, Slezak TR. MannDB - a microbial database of automated protein sequence analyses and evidence integration for protein characterization. BMC Bioinformatics 2006; 7:459. [PMID: 17044936 PMCID: PMC1622758 DOI: 10.1186/1471-2105-7-459] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2006] [Accepted: 10/17/2006] [Indexed: 11/16/2022] Open
Abstract
Background MannDB was created to meet a need for rapid, comprehensive automated protein sequence analyses to support selection of proteins suitable as targets for driving the development of reagents for pathogen or protein toxin detection. Because a large number of open-source tools were needed, it was necessary to produce a software system to scale the computations for whole-proteome analysis. Thus, we built a fully automated system for executing software tools and for storage, integration, and display of automated protein sequence analysis and annotation data. Description MannDB is a relational database that organizes data resulting from fully automated, high-throughput protein-sequence analyses using open-source tools. Types of analyses provided include predictions of cleavage, chemical properties, classification, features, functional assignment, post-translational modifications, motifs, antigenicity, and secondary structure. Proteomes (lists of hypothetical and known proteins) are downloaded and parsed from Genbank and then inserted into MannDB, and annotations from SwissProt are downloaded when identifiers are found in the Genbank entry or when identical sequences are identified. Currently 36 open-source tools are run against MannDB protein sequences either on local systems or by means of batch submission to external servers. In addition, BLAST against protein entries in MvirDB, our database of microbial virulence factors, is performed. A web client browser enables viewing of computational results and downloaded annotations, and a query tool enables structured and free-text search capabilities. When available, links to external databases, including MvirDB, are provided. MannDB contains whole-proteome analyses for at least one representative organism from each category of biological threat organism listed by APHIS, CDC, HHS, NIAID, USDA, USFDA, and WHO. Conclusion MannDB comprises a large number of genomes and comprehensive protein sequence analyses representing organisms listed as high-priority agents on the websites of several governmental organizations concerned with bio-terrorism. MannDB provides the user with a BLAST interface for comparison of native and non-native sequences and a query tool for conveniently selecting proteins of interest. In addition, the user has access to a web-based browser that compiles comprehensive and extensive reports. Access to MannDB is freely available at .
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Affiliation(s)
- Carol L Ecale Zhou
- Lawrence Livermore National Laboratory, Pathogen Bio-informatics, Livermore, CA, USA
| | - Marisa W Lam
- Lawrence Livermore National Laboratory, Pathogen Bio-informatics, Livermore, CA, USA
| | - Jason R Smith
- Lawrence Livermore National Laboratory, Pathogen Bio-informatics, Livermore, CA, USA
| | - Adam T Zemla
- Lawrence Livermore National Laboratory, Pathogen Bio-informatics, Livermore, CA, USA
| | - Matthew D Dyer
- Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Thomas A Kuczmarski
- Lawrence Livermore National Laboratory, Pathogen Bio-informatics, Livermore, CA, USA
| | - Elizabeth A Vitalis
- Lawrence Livermore National Laboratory, Pathogen Bio-informatics, Livermore, CA, USA
| | - Thomas R Slezak
- Lawrence Livermore National Laboratory, Pathogen Bio-informatics, Livermore, CA, USA
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