An integrated transcriptome and expressed variant analysis of sepsis survival and death.
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2014
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BACKGROUND: Sepsis, a leading cause of morbidity and mortality, is not a homogeneous disease but rather a syndrome encompassing many heterogeneous pathophysiologies. Patient factors including genetics predispose to poor outcomes, though current clinical characterizations fail to identify those at greatest risk of progression and mortality. METHODS: The Community Acquired Pneumonia and Sepsis Outcome Diagnostic study enrolled 1,152 subjects with suspected sepsis. We sequenced peripheral blood RNA of 129 representative subjects with systemic inflammatory response syndrome (SIRS) or sepsis (SIRS due to infection), including 78 sepsis survivors and 28 sepsis non-survivors who had previously undergone plasma proteomic and metabolomic profiling. Gene expression differences were identified between sepsis survivors, sepsis non-survivors, and SIRS followed by gene enrichment pathway analysis. Expressed sequence variants were identified followed by testing for association with sepsis outcomes. RESULTS: The expression of 338 genes differed between subjects with SIRS and those with sepsis, primarily reflecting immune activation in sepsis. Expression of 1,238 genes differed with sepsis outcome: non-survivors had lower expression of many immune function-related genes. Functional genetic variants associated with sepsis mortality were sought based on a common disease-rare variant hypothesis. VPS9D1, whose expression was increased in sepsis survivors, had a higher burden of missense variants in sepsis survivors. The presence of variants was associated with altered expression of 3,799 genes, primarily reflecting Golgi and endosome biology. CONCLUSIONS: The activation of immune response-related genes seen in sepsis survivors was muted in sepsis non-survivors. The association of sepsis survival with a robust immune response and the presence of missense variants in VPS9D1 warrants replication and further functional studies. TRIAL REGISTRATION: ClinicalTrials.gov NCT00258869. Registered on 23 November 2005.
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Tsalik, Ephraim L, Raymond J Langley, Darrell L Dinwiddie, Neil A Miller, Byunggil Yoo, Jennifer C van Velkinburgh, Laurie D Smith, Isabella Thiffault, et al. (2014). An integrated transcriptome and expressed variant analysis of sepsis survival and death. Genome Med, 6(11). p. 111. 10.1186/s13073-014-0111-5 Retrieved from https://hdl.handle.net/10161/13120.
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Ephraim Tsalik
My research at Duke has focused on understanding the dynamic between host and pathogen so as to discover and develop host-response markers that can diagnose and predict health and disease. This new and evolving approach to diagnosing illness has the potential to significantly impact individual as well as public health considering the rise of antibiotic resistance.
With any potential infectious disease diagnosis, it is difficult, if not impossible, to determine at the time of presentation what the underlying cause of illness is. For example, acute respiratory illness is among the most frequent reasons for patients to seek care. These symptoms, such as cough, sore throat, and fever may be due to a bacterial infection, viral infection, both, or a non-infectious condition such as asthma or allergies. Given the difficulties in making the diagnosis, most patients are inappropriately given antibacterials. However, each of these etiologies (bacteria, virus, or something else entirely) leaves a fingerprint embedded in the host’s response. We are very interested in finding those fingerprints and exploiting them to generate new approaches to understand, diagnose, and manage disease.
These principles also apply to sepsis, defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Just as with acute respiratory illness, it is often difficult to identify whether infection is responsible for a patient’s critical illness. We have embarked on a number of research programs that aim to better identify sepsis; define sepsis subtypes that can be used to guide future clinical research; and to better predict sepsis outcomes. These efforts have focused on many systems biology modalities including transcriptomics, miRNA, metabolomics, and proteomics. Consequently, our Data Science team has utilized these highly complex data to develop new statistical methods, furthering both the clinical and statistical research communities.
These examples are just a small sampling of the breadth of research Dr. Tsalik and his colleagues have conducted.
In April 2022, Dr. Tsalik has joined Danaher Diagnostics as the VP and Chief Scientific Officer for Infectious Disease, where he is applying this experience in biomarkers and diagnostics to shape the future of diagnostics in ID.
Ricardo Henao
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