Early indicators of exposure to biological threat agents using host gene profiles in peripheral blood mononuclear cells

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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 <it>in vitro</it> and <it>in vivo</it> findings were compared. We found major gene expression changes at the earliest times tested post exposure to aerosolized <it>B. anthracis </it>spores and 30 min post exposure to a bacterial toxin.</p> <p>Conclusion</p> <p>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.</p

Detection of high frequency of mutations in a breast and/or ovarian cancer cohort: implications of embracing a multi-gene panel in molecular diagnosis in India

Breast and/or ovarian cancer (BOC) are among the most frequently diagnosed forms of hereditary cancers and leading cause of death in India. This emphasizes on the need for a cost-effective method for early detection of these cancers. We sequenced 141 unrelated patients and families with BOC using the TruSight Cancer panel, which includes 13 genes strongly associated with risk of inherited BOC. Multi-gene sequencing was done on the Illumina MiSeq platform. Genetic variations were identified using the Strand NGS software and interpreted using the StrandOmics platform. We were able to detect pathogenic mutations in 51 (36.2%) cases, out of which 19 were novel mutations. When we considered familial breast cancer cases only, the detection rate increased to 52%. When cases were stratified based on age of diagnosis into three categories,. 40 years, 40-50 years and >50 years, the detection rates were higher in the first two categories (44.4% and 53.4%, respectively) as compared with the third category, in which it was 26.9%. Our study suggests that next-generation sequencing-based multi-gene panels increase the sensitivity of mutation detection and help in identifying patients with a high risk of developing cancer as compared with sequential tests of individual genes

Toward more transparent and reproducible omics studies through a common metadata checklist and data publications

none61siBiological 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.noneKolker, Eugene; Özdemir, Vural; Martens, Lennart; Hancock, William; Anderson, Gordon; Anderson, Nathaniel; Aynacioglu, Sukru; Baranova, Ancha; Campagna, Shawn R.; Chen, Rui; Choiniere, John; Dearth, Stephen P.; Feng, Wu-Chun; Ferguson, Lynnette; Fox, Geoffrey; Frishman, Dmitrij; Grossman, Robert; Heath, Allison; Higdon, Roger; Hutz, Mara H.; Janko, Imre; Jiang, Lihua; Joshi, Sanjay; Kel, Alexander; Kemnitz, Joseph W.; Kohane, Isaac S.; Kolker, Natali; Lancet, Doron; Lee, Elaine; Li, Weizhong; Lisitsa, Andrey; Llerena, Adrian; MacNealy-Koch, Courtney; Marshall, Jean-Claude; Masuzzo, Paola; May, Amanda; Mias, George; Monroe, Matthew; Montague, Elizabeth; Mooney, Sean; Nesvizhskii, Alexey; Noronha, Santosh; Omenn, Gilbert; Rajasimha, Harsha; Ramamoorthy, Preveen; Sheehan, Jerry; Smarr, Larry; Smith, Charles V.; Smith, Todd; Snyder, Michael; Rapole, Srikanth; Srivastava, Sanjeeva; Stanberry, Larissa; Stewart, Elizabeth; Toppo, Stefano; Uetz, Peter; Verheggen, Kenneth; Voy, Brynn H.; Warnich, Louise; Wilhelm, Steven W.; Yandl, GregoryKolker, Eugene; Özdemir, Vural; Martens, Lennart; Hancock, William; Anderson, Gordon; Anderson, Nathaniel; Aynacioglu, Sukru; Baranova, Ancha; Campagna, Shawn R.; Chen, Rui; Choiniere, John; Dearth, Stephen P.; Feng, Wu Chun; Ferguson, Lynnette; Fox, Geoffrey; Frishman, Dmitrij; Grossman, Robert; Heath, Allison; Higdon, Roger; Hutz, Mara H.; Janko, Imre; Jiang, Lihua; Joshi, Sanjay; Kel, Alexander; Kemnitz, Joseph W.; Kohane, Isaac S.; Kolker, Natali; Lancet, Doron; Lee, Elaine; Li, Weizhong; Lisitsa, Andrey; Llerena, Adrian; MacNealy Koch, Courtney; Marshall, Jean Claude; Masuzzo, Paola; May, Amanda; Mias, George; Monroe, Matthew; Montague, Elizabeth; Mooney, Sean; Nesvizhskii, Alexey; Noronha, Santosh; Omenn, Gilbert; Rajasimha, Harsha; Ramamoorthy, Preveen; Sheehan, Jerry; Smarr, Larry; Smith, Charles V.; Smith, Todd; Snyder, Michael; Rapole, Srikanth; Srivastava, Sanjeeva; Stanberry, Larissa; Stewart, Elizabeth; Toppo, Stefano; Uetz, Peter; Verheggen, Kenneth; Voy, Brynn H.; Warnich, Louise; Wilhelm, Steven W.; Yandl, Gregor

Toward More Transparent and Reproducible Omics Studies Through a Common Metadata Checklist and Data Publications

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