Inflammation and genomics in the critical care unit

Quality Control Measures and Validation in Gene Association Studies: Lessons for Acute Illness

Acute illness is a complex constellation of responses involving dysregulated inflammatory and immune responses, which are ultimately associated with multiple organ dysfunction. Gene association studies have associated single-nucleotide polymorphisms (SNPs) with clinical and pharmacological outcomes in a variety of disease states, including acute illness. With approximately 4 to 5 million SNPs in the human genome and recent studies suggesting that a large portion of SNP studies are not reproducible, we suggest that the ultimate clinical utility of SNPs in acute illness depends on validation and quality control measures. To investigate this issue, in December 2018 and January 2019 we searched the literature for peer-reviewed studies reporting data on associations between SNPs and clinical outcomes and between SNPs and pharmaceuticals (i.e., pharmacogenomics) published between January 2011 to February 2019. We review key methodologies and results from a variety of clinical and pharmacological gene association studies, including trauma and sepsis studies, as illustrative examples on current SNP association studies. In this review article, we have found three key points which strengthen the potential accuracy of SNP association studies in acute illness and other diseases: providing evidence of following a protocol quality control method such as the one in Nature Protocols or the OncoArray QC Guidelines; enrolling enough patients to have large cohort groups; and validating the SNPs using an independent technique such as a second study using the same SNPs with new patient cohorts. Our survey suggests the need to standardize validation methods and SNP quality control measures in medicine in general, and specifically in the context of complex disease states such as acute illness.

From data patterns to mechanistic models in acute critical illness

The complexity of the physiologic and inflammatory response in acute critical illness has stymied the accurate diagnosis and development of therapies. The Society for Complex Acute Illness was formed a decade ago with the goal of leveraging multiple complex systems approaches to address this unmet need. Two main paths of development have characterized the society's approach: (i) data pattern analysis, either defining the diagnostic/prognostic utility of complexity metrics of physiologic signals or multivariate analyses of molecular and genetic data and (ii) mechanistic mathematical and computational modeling, all being performed with an explicit translational goal. Here, we summarize the progress to date on each of these approaches, along with pitfalls inherent in the use of each approach alone. We suggest that the next decade holds the potential to merge these approaches, connecting patient diagnosis to treatment via mechanism-based dynamical system modeling and feedback control and allowing extrapolation from physiologic signals to biomarkers to novel drug candidates. As a predicate example, we focus on the role of data-driven and mechanistic models in neuroscience and the impact that merging these modeling approaches can have on general anesthesia.

Sepsis: from pattern to mechanism and back

Sepsis is a clinical entity in which complex inflammatory and physiological processes are mobilized, not only across a range of cellular and molecular interactions, but also in clinically relevant physiological signals accessible at the bedside. There is a need for a mechanistic understanding that links the clinical phenomenon of physiologic variability with the underlying patterns of the biology of inflammation, and we assert that this can be facilitated through the use of dynamic mathematical and computational modeling. An iterative approach of laboratory experimentation and mathematical/computational modeling has the potential to integrate cellular biology, physiology, control theory, and systems engineering across biological scales, yielding insights into the control structures that govern mechanisms by which phenomena, detected as biological patterns, are produced. This approach can represent hypotheses in the formal language of mathematics and computation, and link behaviors that cross scales and domains, thereby offering the opportunity to better explain, diagnose, and intervene in the care of the septic patient.

Differential proteomics of the plasma of individuals with sepsis caused by Acinetobacter baumannii

This study examines alterations in the plasma proteome in ten adults affected by sepsis caused by Acinetobacter baumannii as compared to paired healthy controls. 2-DE profiles of plasma from patients and paired healthy donors, depleted of the six most abundant proteins, were analysed by the DIGE technique. Protein spot detection and quantification were performed with the Differential In-gel Analysis and Biological Variation Analysis modules of the DeCyder() software. Differentially expressed proteins were identified by mass spectrometry (MALDI-TOF/TOF) after colloidal Coomassie blue staining. Almost 900 spots were detected on a unique 2-D gel by the DIGE technique. A total of 269 protein spots of differential abundance were shown to be statistically significant (2.5-fold) with p values of p< or =0.01 (135 spots) and p< or =0.05 (134 spots) as determined by the t test. Seventy-one spots were submitted to mass spectrometry and about 30% could be successfully identified. This multiplex approach significantly reduced experimental variability, allowing for the confident detection of small differences in protein levels. Results include differentially expressed lipoproteins as well as proteins belonging to inflammatory/coagulation pathways and the kallikrein-kinin system. These data improves the knowledge for future developments in sepsis diagnosis, staging and therapy.

Genetic influences in common respiratory disorders

Respiratory disorders are common problems for adults and children in North America and generally represent the outcome of gene-environment interactions. Some problems are considered genetic in origin, such as cystic fibrosis, and others are considered environmental in origin, such as respiratory infections. Emerging information indicates that even genetic-based disorders are influenced by the environment and that environmental-based disorders are modified by personal genetic factors in individual physiologic responses. An understanding of an individual's personal risk factors for disease or health problem development can allow health care professionals to tailor health promotion strategies and treatment plans with appropriate environmental manipulation. This article explores the genetic influences that may affect the individual's physiologic responses and the consequences of environmental stimuli.