Omics and Critical Care



Omics and Critical Care


Mary K. Dahmer

Michael Quasney





Humans are complex and dynamic organisms. Their biology is often difficult to predict, especially in times of severe stress that commonly occur with critical illness and injury. Biomedical research has taken the reductionist approach to understanding the individual parts of organisms to better predict the behavior of the organism in entirety. Over the last several years, the development of high-throughput techniques has permitted characterization of intracellular and extracellular environments at the molecular and biochemical levels. Analysis of high-throughput data relies on bioinformatics and computational modeling and allows for a system-wide approach that focuses on complex interactions, which are particularly relevant to the field of critical care. These advances have rapidly expanded our understanding of complex disease processes and altered the vision of how medicine will be practiced in the future. Furthermore, these technologies have helped to foster the idea of personalized or individualized medicine, whereby patients will be treated with medical therapies customized to the individual rather than with standard therapies specific to the disease. These technological advances are making inroads into the pediatric intensive care unit. They provide a better understanding of the pathophysiology of diseases, such as sepsis and acute respiratory distress syndrome (ARDS), and may potentially be used to stratify patients and to discover new biomarkers that will help determine risk factors and prognosis. This chapter will describe these new techniques and how they are used to study critical illness and injury.


OMICS

Novel large-scale and data-rich technologies have been developed to examine various fields of biology including genomics, transcriptomics, proteomics, and metabolomics. These new approaches have had a dramatic impact on our understanding of various biological processes and have revealed complex interactions among them. Integration of the analyses of these datasets to gain insights that may not be revealed by analyzing them individually has gained widespread interest and is beginning to impact a number of medical disciplines including critical illness.

Omics refers to approaches that globally assess a specific class of biological molecules in a cell, tissue, or biological fluid. For example, genomics refers to the study of the structure and function of the entire genome, which is composed of all DNAs within a cell. Likewise, study of the transcriptome (RNA transcripts in cells or tissues), proteome (proteins in cells or tissues), and metabolome (small metabolites in cells, tissues or bodily fluids) give rise to the fields of transcriptomics, proteomics, and metabolomics. In addition, epigenomics is the study of
global epigenetic changes in cells or tissues. High-throughput, high-dimensional study of the genome, transcriptome, proteome, metabolome, and epigenome has produced a massive amount of interconnecting data from many biological systems and processes. However, the sheer volume and complexity of these data make analyses and interpretation extremely challenging. Increasingly sophisticated bioinformatics tools and computational analyses are required to perform studies using image these technologies. These “omic” approaches may provide better insight into highly complex diseases, such as sepsis, trauma, and other illnesses with multiorgan dysfunction that could result in improved diagnostic tests and novel therapies. In the following sections, these various “omics” disciplines will be discussed in relation to critical illness and injury.


GENOMICS

image Genomics is the study of the structure and function of DNA within cells. Genomics includes efforts to determine nucleotide sequences, to conduct fine-scale genetic mapping, and to analyze interactions between loci that occur within the genome. The key technology driving genomics is high-throughput DNA sequencing combined with bioinformatics to analyze the large volumes of data. High-throughput DNA sequencing technology facilitated the sequencing of the entire human genome, in addition to the genomes of many other organisms. These data revealed that many sites in the human genome are variable and there are numerous differences in DNA sequences between individuals.

The most frequent type of genetic variation is a single nucleotide polymorphism (SNP) that results from a nucleotide substitution. Variations may also arise from insertions or deletions of DNA fragments or from the presence of a variable number of tandem repeats (VNTRs) of short, repetitive DNA sequences. In some individuals, the differences in sequence are large (>1 kilobase), resulting in alterations in DNA copy number. Such variants are called copy number variants (CNVs). CNVs are relatively common in human genomes and contribute significantly to human genetic variation. Given that individuals have two copies of each gene, an individual is either heterozygous or homozygous for one or the other variant whether it is an SNP, VNTR, or CNV.

Variants do not necessarily affect the expression or the function of the gene product, particularly when they occur in a noncoding region of the gene that is not involved in regulating messenger RNA (mRNA) transcription from a DNA template or in mRNA processing or stability. Variants resulting from large changes in coding regions are likely to affect the protein product; however, many SNPs in the coding region do not affect gene function or stability if the encoded amino acid remains the same (a silent substitution) or if the amino acid substitution does not affect protein stability or function. There are instances where genetic variants, including SNPs, affect protein expression (by altering noncoding regulatory regions of the gene) or function (by altering the amino acid sequence), but not all such changes are necessarily deleterious. In many instances, genetic variants explain the variation in protein levels observed in the general population. Variants that alter protein levels or function are partially responsible image for genetically determined variation in our physical characteristics, physiology, and personality traits. Genetic variability also explains some of the variability in disease susceptibility, disease severity, and response to treatment that is observed in patient populations.

Genotyping to identify specific variants in a particular gene is commonplace for diagnosing genetic disorders in the clinical setting. Nearly all genotyping techniques utilize polymerase chain reaction (PCR) to amplify a DNA fragment that contains the site of interest. For amplification, PCR uses small pieces of DNA, termed primers, which are complementary to the regions that flank the site of interest. Early techniques identified genotypes based on the size of the PCR product (insertions or deletions, VNTRs, SNPs present in restriction enzyme recognition sites) or by using allele-specific hybridization approaches, such as allele-specific PCR or hybridization with labeled allele-specific oligonucleotide probes. Genotyping is now routinely performed as a single-site assay or by genotyping hundreds to millions of SNPs using custom-made arrays or arrays that probe for SNPs from across the entire genome (genome-wide SNP arrays). These techniques are more amenable to high-throughput technology. Genome-wide SNP arrays are used for genome-wide association studies (GWASs), which examine millions of SNPs simultaneously to determine whether any are associated with specific diseases.

Next-generation sequencing technologies are commonplace (1), partly due to a reduction in the cost of DNA sequencing. Using this technique, DNA is randomly fragmented and ligated to common adaptor sequences to form a library. The library is hybridized to an array platform of millions of spatially fixed PCR fragments that are complementary to the DNA fragments. Given that the array contains multiple PCR fragments that are complementary to a DNA library fragment, millions of short DNA reads are produced in a highly efficient manner. Following enzyme-driven biochemistry and imaging-based data processing, short reads are mapped to a source genome to generate a sequence read. Recent advances in DNA sequencing technologies, combined with reduced cost, make sequencing the human genome much less expensive and make it realistic to use this technology to identify novel genetic variants.

The technologies described above have been used to examine the influence of genetic variation on the susceptibility to, and outcomes of, various diseases relevant to critical care. This information may allow physicians to identify children who are at greatest risk for poor outcomes, allowing for modified monitoring strategies in the intensive care unit or novel therapies. Several genes that harbor genetic variations associated with the severity of sepsis or acute lung injury (ALI) in critically ill populations have been described (Tables 18.1 and 18.2). One example is the cystic fibrosis transmembrane conductance regulator (CFTR) gene, which codes for a chloride channel protein expressed on epithelial cells in bronchi, bronchioles, and alveoli (2,3,4,5). Influx of fluid into the alveoli following increased permeability of the alveolar-capillary barrier is one of the hallmarks of ARDS (6), and the ability to clear fluid rapidly is associated with improved outcome (7). The clearance of alveolar fluid occurs through active ion transport (8), and CFTR has a role in both cyclic adenosine monophosphate-stimulated fluid absorption and modulation of the epithelial sodium channel (2,3,9). CFTR contains 27 exons that are spliced together to give mature CFTR mRNA. Alternatively spliced transcripts are relatively common, and levels of CFTR protein and activity vary between individuals (10). Mutations in the CFTR gene cause cystic fibrosis (CF), a disease characterized by progressive injury to the lungs (11). Interestingly, in vitro and ex vivo studies suggest that CFTR deficiency results in a dysregulated inflammatory response (12,13,14,15,16,17) and promotes lipopolysaccharide (LPS)-induced lung injury in mice (15,18), suggesting that CFTR has immune modulatory activity.

In addition to the relatively rare variants that affect CFTR function, two common polymorphisms affect the function of CFTR. One polymorphism is the (TG)mTn variable repeat region located in intron 8. Both in vitro and in vivo studies show an association between either a higher number of TG repeats and/or a lower number of Ts with an increased proportion of mRNA transcripts deficient in exon 9 (19,20,21,22,23). Mechanistic studies also reveal that different alleles at the (TG)mTn


site affect exon 9 skipping owing to differences in the binding affinity of splicing regulatory proteins (24,25). Exon 9 is essential for CFTR function given that, together with exons 10-12, it encodes the first nucleotide-binding domain, and mRNA transcripts without exon 9 do not produce functional CFTR (26,27,28). In healthy individuals, 5%-90% of CFTR transcripts are missing exon 9 (29), suggesting that CFTR activity in healthy individuals varies greatly. Although CFTR activity may be reduced to <5% of normal in CF, other variants that have less profound effects on CFTR may still increase the risk of other lung diseases (11). We examined the (TG)mTn alleles in a cohort of children with community-acquired pneumonia (CAP). African American children with CAP who have (TG)mTn alleles associated with increased exon 9 skipping are more likely to require mechanical ventilation and to develop ARDS (30). These data suggest that less functional CFTR may contribute to more severe lung injury and that the genetic makeup of the host may contribute to an increased risk for ARDS.








TABLE 18.1 GENETIC POLYMORPHISMS ASSOCIATED WITH SEPSIS

























































































































GENE


POLYMORPHISMa


CONSEQUENCE OF POLYMORPHISM


REFERENCE


ACE


Insertion/deletion (I/D)


DD associated with increased serum and tissue levels; associated with more severe meningococcal disease; DD associated with decreased risk of sepsis


144,145


BPI


+545 G/C


Increased risk of gram – sepsis and mortality


146



rs4358188 (+645 A/G, Lys216Glu)


CD14


-1145 G/A


Association with CD14 expression, MODS, and sepsis


147-152



rs7524551 (-159 C/T)


Association with CD14 levels, MODS, sepsis, mortality, and gram – infections


FCγRIIa


rs1801274 (H131R)


Associated with decreased affinity to IgG2 and opsonization; associated with increased risk of meningococcal and septic shock


153-157


HSPA1L


rs2227956 (-2437 C/T)


Associated with increased cytokine levels and liver failure but not sepsis-related morbidity


158-160


HSPA1B (-1538 G/A)


Associated with increased cytokine levels and liver failure but not sepsis-related morbidity


158-160


HSP70-2


rs1061581 (+1267 G/A)


An allele associated with septic shock in adults with CAP


161


IL-1β and IL-1RA


rs16944 (-511 T/C) Variable 86-bp repeat -1470 G/C


rs1143627 (-31 C/T)


-511 allele associated with increased survival of meningococcemia; combination of IL-1β and IL-1RA alleles associated with decreased survival


162-165


IL-6


rs4800795 (-174 G/C)


rs1800796 (-572 G/C)


Associated with increased IL-6 levels and risk of sepsis and severity of sepsis


166


IL-10


rs1800896 (-1082 G/A)


rs1800871 (-819 C/T)


rs1800872 (-592 C/A)


GCC haplotype associated with increased levels; associated with sepsis and some variations associated with mortality


167-170


LBP


rs1780616 (-1978 C/T)


rs5741812 (-921 A/T)


rs2232571 (-836 C/T)


rs1780617 (-763 A/G)


rs2232618 (26877 T/C Phe436Leu)


Severe sepsis for four SNP haplotype CATA; serum LBP increased and mortality for -836 C/T; Phe436Leu associated with sepsis in trauma cohort


171-173


TNF-β and LT-α


rs1800629 (-308 G/A)


rs361525 (-238 G/A)


Many others


rs909253 (LT-α+252 G/A)


Associated with increased TNF-α levels; associated with increased mortality in sepsis and bacteremia


174-201


MBL


rs11003125 (-550 G/C)


rs7096206 (-221 G/C)


rs5030737 (Arg52Cys C/T)


rs1800450 (Gly54Asp G/A)


rs1800451 (Gly57Glu G/A)


-221 associated with MBL levels, sepsis, but not to mortality; structural variants associated with decreased levels and activity and increased risk of infection and severity of disease


149,202-207


ND1


rs1599988 (m4216 T/C)


Associated with decreased NADH dehydrogenase 1 activity


208,209


PAI-1


4G/5G


4G associated with increased levels; associated with septic shock and DIC in meningococcal disease


210-215


Protein C


rs1799808 (-1654 C/T)


rs1799809 (-1641 G/A)


rs2069912 (673 T/C)


CA haplotype (-1654 and -1641) and C allele associated with increased mortality in Asian populations; CG haplotype associated with more severe meningococcal disease in Caucasians


216-218


TIMP-1


rs4898 (372 T/C)


Associated with higher levels of TIMP-1 and higher 30-day mortality


219


TLR1


rs5743551 (-7202 A/G)


Associated with greater immune response, higher mortality, and worse organ function


220,221


TLR2


-16933 A/T


rs1895830 (-15607 A/G)


rs3804099 (597 C/T)


2029 C/T (Arg677Trp)


rs5743708 (2257 A/G, Arg753Gln)


Associated with gram + sepsis but not survival


Associated with cytokine expression


Associated with cytokine expression, MODS, and morbidity


Associated with mycobacterial infections


Associated with increased severe bacterial infections, increased sepsis in African Americans


149,198, 222-227


TLR4


-2242 T/C


rs4986790 (Asp299Gly)


Thr399Ile


Associated with increased cytokine expression, sepsisrelated morbidity, and MODS; gram – bacteremia; associated with increased risk of sepsis and mortality


150,190,226, 228,229


VEGF


rs3025039 (936 C/T)


Associated with development of AKI in patients with severe sepsis


230


a Terminology used for the various polymorphisms are the ones most commonly used in the literature and may refer to the nucleotide position, amino acid position, or name of the allele. This table is representative of polymorphisms examined in sepsis, but does not include all such polymorphisms. ACE, angiotensin-converting enzyme; BPI, bactericidal permeability increasing protein; MODS, multiorgan dysfunction syndrome; Ig, immunoglobulin; HSP, heat shock protein; CAP, community-acquired pneumonia; IL-1RA, interleukin 1 receptor antagonist (GCC haplotype of the IL-10 promoter is defined by three single-site polymorphisms at -1082, -819, and -592); LBP, lipopolysaccharide-binding protein; TNF, tumor necrosis factor; LT, lymphotoxin; MBL, mannose-binding lectin; PAI, plasminogen activator inhibitor; DIC, disseminated intravascular coagulation; TIMP

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Jun 4, 2016 | Posted by in CRITICAL CARE | Comments Off on Omics and Critical Care

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