Immune Phenotype and Postoperative Complications After Elective Surgery.

OBJECTIVES: To characterize and quantify accumulating immunologic alterations, pre and postoperatively in patients undergoing elective surgical procedures. BACKGROUND: Elective surgery is an anticipatable, controlled human injury. Although the human response to injury is generally stereotyped, individual variability exists. This makes surgical outcomes less predictable, even after standardized procedures, and may provoke complications in patients unable to compensate for their injury. One potential source of variation is found in immune cell maturation, with phenotypic changes dependent on an individual's unique, lifelong response to environmental antigens. METHODS: We enrolled 248 patients in a prospective trial facilitating comprehensive biospecimen and clinical data collection in patients scheduled to undergo elective surgery. Peripheral blood was collected preoperatively, and immediately on return to the postanesthesia care unit. Postoperative complications that occurred within 30 days after surgery were captured. RESULTS: As this was an elective surgical cohort, outcomes were generally favorable. With a median follow-up of 6 months, the overall survival at 30 days was 100%. However, 20.5% of the cohort experienced a postoperative complication (infection, readmission, or system dysfunction). We identified substantial heterogeneity of immune senescence and terminal differentiation phenotypes in surgical patients. More importantly, phenotypes indicating increased T-cell maturation and senescence were associated with postoperative complications and were evident preoperatively. CONCLUSIONS: The baseline immune repertoire may define an immune signature of resilience to surgical injury and help predict risk for surgical complications.

Multidimensional machine learning models predicting outcomes after trauma.

BACKGROUND: An emerging body of literature supports the role of individualized prognostic tools to guide the management of patients after trauma. The aim of this study was to develop advanced modeling tools from multidimensional data sources, including immunological analytes and clinical and administrative data, to predict outcomes in trauma patients. METHODS: This was a prospective study of trauma patients at Level 1 centers from 2015 to 2019. Clinical, flow cytometry, and serum cytokine data were collected within 48 hours of admission. Sparse logistic regression models were developed, jointly selecting predictors and estimating the risk of ventilator-associated pneumonia, acute kidney injury, complicated disposition (death, rehabilitation, or nursing facility), and return to the operating room. Model parameters (regularization controlling model sparsity) and performance estimation were obtained via nested leave-one-out cross-validation. RESULTS: A total of 179 patients were included. The incidences of ventilator-associated pneumonia, acute kidney injury, complicated disposition, and return to the operating room were 17.7%, 28.8%, 22.5%, and 12.3%, respectively. Regarding extensive resource use, 30.7% of patients had prolonged intensive care unit stay, 73.2% had prolonged length of stay, and 23.5% had need for prolonged ventilatory support. The models were developed and cross-validated for ventilator-associated pneumonia, acute kidney injury, complicated dispositions, and return to the operating room, yielding predictive areas under the curve from 0.70 to 0.91. Each model derived its optimal predictive value by combining clinical, administrative, and immunological analyte data. CONCLUSION: Clinical, immunological, and administrative data can be combined to predict post-traumatic outcomes and resource use. Multidimensional machine learning modeling can identify trauma patients with complicated clinical trajectories and high resource needs.

A Multidimensional Bioinformatic Platform for the Study of Human Response to Surgery.

OBJECTIVE: To design and establish a prospective biospecimen repository that integrates multi-omics assays with clinical data to study mechanisms of controlled injury and healing. BACKGROUND: Elective surgery is an opportunity to understand both the systemic and focal responses accompanying controlled and well-characterized injury to the human body. The overarching goal of this ongoing project is to define stereotypical responses to surgical injury, with the translational purpose of identifying targetable pathways involved in healing and resilience, and variations indicative of aberrant peri-operative outcomes. METHODS: Clinical data from the electronic medical record combined with large-scale biological data sets derived from blood, urine, fecal matter, and tissue samples are collected prospectively through the peri-operative period on patients undergoing 14 surgeries chosen to represent a range of injury locations and intensities. Specimens are subjected to genomic, transcriptomic, proteomic, and metabolomic assays to describe their genetic, metabolic, immunologic, and microbiome profiles, providing a multidimensional landscape of the human response to injury. RESULTS: The highly multiplexed data generated includes changes in over 28,000 mRNA transcripts, 100 plasma metabolites, 200 urine metabolites, and 400 proteins over the longitudinal course of surgery and recovery. In our initial pilot dataset, we demonstrate the feasibility of collecting high quality multi-omic data at pre- and postoperative time points and are already seeing evidence of physiologic perturbation between timepoints. CONCLUSIONS: This repository allows for longitudinal, state-of-the-art geno-mic, transcriptomic, proteomic, metabolomic, immunologic, and clinical data collection and provides a rich and stable infrastructure on which to fuel further biomedical discovery.

Can Genetic Analysis of Putative Blood Alzheimer's Disease Biomarkers Lead to Identification of Susceptibility Loci?

Although 24 Alzheimer's disease (AD) risk loci have been reliably identified, a large portion of the predicted heritability for AD remains unexplained. It is expected that additional loci of small effect will be identified with an increased sample size. However, the cost of a significant increase in Case-Control sample size is prohibitive. The current study tests whether exploring the genetic basis of endophenotypes, in this case based on putative blood biomarkers for AD, can accelerate the identification of susceptibility loci using modest sample sizes. Each endophenotype was used as the outcome variable in an independent GWAS. Endophenotypes were based on circulating concentrations of proteins that contributed significantly to a published blood-based predictive algorithm for AD. Endophenotypes included Monocyte Chemoattractant Protein 1 (MCP1), Vascular Cell Adhesion Molecule 1 (VCAM1), Pancreatic Polypeptide (PP), Beta2 Microglobulin (B2M), Factor VII (F7), Adiponectin (ADN) and Tenascin C (TN-C). Across the seven endophenotypes, 47 SNPs were associated with outcome with a p-value ≤1x10(-7). Each signal was further characterized with respect to known genetic loci associated with AD. Signals for several endophenotypes were observed in the vicinity of CR1, MS4A6A/MS4A4E, PICALM, CLU, and PTK2B. The strongest signal was observed in association with Factor VII levels and was located within the F7 gene. Additional signals were observed in MAP3K13, ZNF320, ATP9B and TREM1. Conditional regression analyses suggested that the SNPs contributed to variation in protein concentration independent of AD status. The identification of two putatively novel AD loci (in the Factor VII and ATP9B genes), which have not been located in previous studies despite massive sample sizes, highlights the benefits of an endophenotypic approach for resolving the genetic basis for complex diseases. The coincidence of several of the endophenotypic signals with known AD loci may point to novel genetic interactions and should be further investigated.

Variant calling in low-coverage whole genome sequencing of a Native American population sample.

BACKGROUND: The reduction in the cost of sequencing a human genome has led to the use of genotype sampling strategies in order to impute and infer the presence of sequence variants that can then be tested for associations with traits of interest. Low-coverage Whole Genome Sequencing (WGS) is a sampling strategy that overcomes some of the deficiencies seen in fixed content SNP array studies. Linkage-disequilibrium (LD) aware variant callers, such as the program Thunder, may provide a calling rate and accuracy that makes a low-coverage sequencing strategy viable. RESULTS: We examined the performance of an LD-aware variant calling strategy in a population of 708 low-coverage whole genome sequences from a community sample of Native Americans. We assessed variant calling through a comparison of the sequencing results to genotypes measured in 641 of the same subjects using a fixed content first generation exome array. The comparison was made using the variant calling routines GATK Unified Genotyper program and the LD-aware variant caller Thunder. Thunder was found to improve concordance in a coverage dependent fashion, while correctly calling nearly all of the common variants as well as a high percentage of the rare variants present in the sample. CONCLUSIONS: Low-coverage WGS is a strategy that appears to collect genetic information intermediate in scope between fixed content genotyping arrays and deep-coverage WGS. Our data suggests that low-coverage WGS is a viable strategy with a greater chance of discovering novel variants and associations than fixed content arrays for large sample association analyses.

Association of a single nucleotide polymorphism in neuronal acetylcholine receptor subunit alpha 5 (CHRNA5) with smoking status and with 'pleasurable buzz' during early experimentation with smoking.

AIMS: To extend the previously identified association between a single nucleotide polymorphism (SNP) in neuronal acetylcholine receptor subunit alpha-5 (CHRNA5) and nicotine dependence to current smoking and initial smoking-experience phenotypes. DESIGN, SETTING, PARTICIPANTS: Case-control association study with a community-based sample, comprising 363 Caucasians and 72 African Americans (203 cases, 232 controls). MEASUREMENTS: Cases had smoked > or = five cigarettes/day for > or = 5 years and had smoked at their current rate for the past 6 months. Controls had smoked between one and 100 cigarettes in their life-time, but never regularly. Participants also rated, retrospectively, pleasurable and displeasurable sensations experienced when they first smoked. We tested for associations between smoking phenotypes and the top 25 SNPs tested for association with nicotine dependence in a previous study. FINDINGS: A non-synonymous coding SNP in CHRNA5, rs16969968, was associated with case status [odds ratio (OR) = 1.5, P = 0.01] and, in Caucasians, with experiencing a pleasurable rush or buzz during the first cigarette (OR = 1.6, P = 0.01); these sensations were associated highly with current smoking (OR = 8.2, P = 0.0001). CONCLUSIONS: We replicated the observation that the minor allele of rs16969968 affects smoking behavior, and extended these findings to sensitivity to smoking effects upon experimentation. While the ability to test genetic associations was limited by sample size, the polymorphism in the CHRNA5 subunit was shown to be associated significantly with enhanced pleasurable responses to initial cigarettes in regular smokers in an a priori test. The findings suggest that phenotypes related to subjective experiences upon smoking experimentation may mediate the development of nicotine dependence.

Genome-scale analysis reveals Sst2 as the principal regulator of mating pheromone signaling in the yeast Saccharomyces cerevisiae.

A common property of G protein-coupled receptors is that they become less responsive with prolonged stimulation. Regulators of G protein signaling (RGS proteins) are well known to accelerate G protein GTPase activity and do so by stabilizing the transition state conformation of the G protein alpha subunit. In the yeast Saccharomyces cerevisiae there are four RGS-homologous proteins (Sst2, Rgs2, Rax1, and Mdm1) and two Galpha proteins (Gpa1 and Gpa2). We show that Sst2 is the only RGS protein that binds selectively to the transition state conformation of Gpa1. The other RGS proteins also bind Gpa1 and modulate pheromone signaling, but to a lesser extent and in a manner clearly distinct from Sst2. To identify other candidate pathway regulators, we compared pheromone responses in 4,349 gene deletion mutants representing nearly all nonessential genes in yeast. A number of mutants produced an increase (sst2, bar1, asc1, and ygl024w) or decrease (cla4) in pheromone sensitivity or resulted in pheromone-independent signaling (sst2, pbs2, gas1, and ygl024w). These findings suggest that Sst2 is the principal regulator of Gpa1-mediated signaling in vivo but that other proteins also contribute in distinct ways to pathway regulation.

Identification of yeast pheromone pathway modulators by high-throughput agonist response profiling of a yeast gene knockout strain collection.

Gene deletion analysis is a powerful tool for resolving the contributions of individual open reading frames to the physiology of cells. Analysis of deletion phenotypes in conjunction with a specific pathway reporter can identify constituents of a physiological pathway and reveal potential effectors that regulate the pathway by quantifying the phenotypic responses of the mutant cells. This article describes a high-throughput method of analyzing a yeast gene deletion library for novel G-protein signaling modulators using a yeast pheromone pathway-specific reporter.

A seven-transmembrane RGS protein that modulates plant cell proliferation.

G protein-coupled receptors (GPCRs) at the cell surface activate heterotrimeric G proteins by inducing the G protein alpha (Galpha) subunit to exchange guanosine diphosphate for guanosine triphosphate. Regulators of G protein signaling (RGS) proteins accelerate the deactivation of Galpha subunits to reduce GPCR signaling. Here we identified an RGS protein (AtRGS1) in Arabidopsis that has a predicted structure similar to a GPCR as well as an RGS box with GTPase accelerating activity. Expression of AtRGS1 complemented the pheromone supersensitivity phenotype of a yeast RGS mutant, sst2Delta. Loss of AtRGS1 increased the activity of the Arabidopsis Galpha subunit, resulting in increased cell elongation in hypocotyls in darkness and increased cell production in roots grown in light. These findings suggest that AtRGS1 is a critical modulator of plant cell proliferation.

RGS proteins: G protein-coupled receptors meet their match.

Many drugs act on receptors coupled to heterotrimeric G proteins. Historically, drug discovery has focused on agents that bind to the receptors and either stimulate or inhibit the receptor-initiated signal. This is an approach that is both direct and logical, and has proven extremely fruitful in the past. However, as our understanding of G-protein signaling has increased, novel opportunities for drug development have emerged. RGS proteins are multifunctional GTPase-accelerating proteins that inactivate G-protein signaling pathways. GTPase-accelerating protein activity is a general feature of RGS proteins, and serves to facilitate the inactivation of the G protein rather than the receptor. Thus, agents that bind and inhibit RGS proteins could modulate endogenous neurotransmitter and hormone signaling, in a manner analogous to neurotransmitter uptake inhibitors. Here we discuss the potential of RGS proteins as drug targets.