Integrative genomics identifies 7p11.2 as a novel locus for fever and clinical stress response in humans

Inflammatory response to bacterial lipopolysaccharide drives iron accumulation in human adipocytes

The association among increased inflammation, disrupted iron homeostasis, and adipose tissue dysfunction in obesity has been widely recognized. However, the specific impact of inflammation on iron homeostasis during human adipogenesis and in adipocytes remains poorly understood. In this study, we investigated the effects of bacterial lipopolysaccharide (LPS) on iron homeostasis during human adipocyte differentiation, in fully differentiated adipocytes, and in human adipose tissue. We found that LPS-induced inflammation hindered adipogenesis and led to a gene expression profile indicative of intracellular iron accumulation. This was accompanied by increased expression of iron importers (TFRC and SLC11A2), markers of intracellular iron accumulation (FTH, CYBA, FTL, and LCN2), and decreased expression of iron exporter-related genes (SLC40A1), concomitant with elevated intracellular iron levels. Mechanistically, RNA-seq analysis and gene knockdown experiments revealed the significant involvement of iron importers SLC39A14, SLC39A8, and STEAP4 in LPS-induced intracellular iron accumulation in human adipocytes. Notably, markers of LPS signaling pathway-related inflammation were also associated with a gene expression pattern indicative of intracellular iron accumulation in human adipose tissue, corroborating the link between LPS-induced inflammation and iron accumulation at the tissue level. In conclusion, our findings demonstrate that induction of adipocyte inflammation disrupts iron homeostasis, resulting in adipocyte iron overload.

Periodontitis and cardiometabolic disorders: The role of lipopolysaccharide and endotoxemia

Lipopolysaccharide is a virulence factor of gram-negative bacteria with a crucial importance to the bacterial surface integrity. From the host's perspective, lipopolysaccharide plays a role in both local and systemic inflammation, activates both innate and adaptive immunity, and can trigger inflammation either directly (as a microbe-associated molecular pattern) or indirectly (by inducing the generation of nonmicrobial, danger-associated molecular patterns). Translocation of lipopolysaccharide into the circulation causes endotoxemia, which is typically measured as the biological activity of lipopolysaccharide to induce coagulation of an aqueous extract of blood cells of the assay. Apparently healthy subjects have a low circulating lipopolysaccharide activity, since it is neutralized and cleared rapidly. However, chronic endotoxemia is involved in the pathogenesis of many inflammation-driven conditions, especially cardiometabolic disorders. These include atherosclerotic cardiovascular diseases, obesity, liver diseases, diabetes, and metabolic syndrome, where endotoxemia has been recognized as a risk factor. The main source of endotoxemia is thought to be the gut microbiota. However, the oral dysbiosis in periodontitis, which is typically enriched with gram-negative bacterial species, may also contribute to endotoxemia. As endotoxemia is associated with an increased risk of cardiometabolic disorders, lipopolysaccharide could be considered as a molecular link between periodontal microbiota and cardiometabolic diseases.

Genetic Profile of Endotoxemia Reveals an Association With Thromboembolism and Stroke

Background Translocation of lipopolysaccharide from gram-negative bacteria into the systemic circulation results in endotoxemia. In addition to acute infections, endotoxemia is detected in cardiometabolic disorders, such as cardiovascular diseases and obesity. Methods and Results We performed a genome-wide association study of serum lipopolysaccharide activity in 11 296 individuals from 6 different Finnish study cohorts. Endotoxemia was measured by limulus amebocyte lysate assay in the whole population and by 2 other techniques (Endolisa and high-performance liquid chromatography/tandem mass spectrometry) in subpopulations. The associations of the composed genetic risk score of endotoxemia and thrombosis-related clinical end points for 195 170 participants were analyzed in FinnGen. Lipopolysaccharide activity had a genome-wide significant association with 741 single-nucleotide polymorphisms in 5 independent loci, which were mainly located at genes affecting the contact activation of the coagulation cascade and lipoprotein metabolism and explained 1.5% to 9.2% of the variability in lipopolysaccharide activity levels. The closest genes included KNG1, KLKB1, F12, SLC34A1, YPEL4, CLP1, ZDHHC5, SERPING1, CBX5, and LIPC. The genetic risk score of endotoxemia was associated with deep vein thrombosis, pulmonary embolism, pulmonary heart disease, and venous thromboembolism. Conclusions The biological activity of lipopolysaccharide in the circulation (ie, endotoxemia) has a small but highly significant genetic component. Endotoxemia is associated with genetic variation in the contact activation pathway, vasoactivity, and lipoprotein metabolism, which play important roles in host defense, lipopolysaccharide neutralization, and thrombosis, and thereby thromboembolism and stroke.

Genetics of Acute Respiratory Distress Syndrome: Pathways to Precision

Clinical risk factors alone fail to fully explain acute respiratory distress syndrome (ARDS) risk or ARDS death, suggesting that individual risk factors contribute. The goals of genomic ARDS studies include better mechanistic understanding, identifying dysregulated pathways that may be amenable to pharmacologic targeting, using genomic causal inference techniques to find measurable traits with meaning, and deconvoluting ARDS heterogeneity by proving reproducible subpopulations that may share a unique biology. This article discusses the latest advances in ARDS genomics, provides historical perspective, and highlights some of the ways that the coronavirus disease 2019 (COVID-19) pandemic is accelerating genomic ARDS research.

NIPSNAP protein family emerges as a sensor of mitochondrial health

Since their discovery over two decades ago, the molecular and cellular functions of the NIPSNAP family of proteins (NIPSNAPs) have remained elusive until recently. NIPSNAPs interact with a variety of mitochondrial and cytoplasmic proteins. They have been implicated in multiple cellular processes and associated with different physiologic and pathologic conditions, including pain transmission, Parkinson's disease, and cancer. Recent evidence demonstrated a direct role for NIPSNAP1 and NIPSNAP2 proteins in regulation of mitophagy, a process that is critical for cellular health and maintenance. Importantly, NIPSNAPs contain a 110 amino acid domain that is evolutionary conserved from mammals to bacteria. However, the molecular function of the conserved NIPSNAP domain and its potential role in mitophagy have not been explored. It stands to reason that the highly conserved NIPSNAP domain interacts with a substrate that is ubiquitously present across all species and can perhaps act as a sensor for mitochondrial health.

Sepsis in the era of data-driven medicine: personalizing risks, diagnoses, treatments and prognoses

Sepsis is a series of clinical syndromes caused by the immunological response to infection. The clinical evidence for sepsis could typically attribute to bacterial infection or bacterial endotoxins, but infections due to viruses, fungi or parasites could also lead to sepsis. Regardless of the etiology, rapid clinical deterioration, prolonged stay in intensive care units and high risk for mortality correlate with the incidence of sepsis. Despite its prevalence and morbidity, improvement in sepsis outcomes has remained limited. In this comprehensive review, we summarize the current landscape of risk estimation, diagnosis, treatment and prognosis strategies in the setting of sepsis and discuss future challenges. We argue that the advent of modern technologies such as in-depth molecular profiling, biomedical big data and machine intelligence methods will augment the treatment and prevention of sepsis. The volume, variety, veracity and velocity of heterogeneous data generated as part of healthcare delivery and recent advances in biotechnology-driven therapeutics and companion diagnostics may provide a new wave of approaches to identify the most at-risk sepsis patients and reduce the symptom burden in patients within shorter turnaround times. Developing novel therapies by leveraging modern drug discovery strategies including computational drug repositioning, cell and gene-therapy, clustered regularly interspaced short palindromic repeats -based genetic editing systems, immunotherapy, microbiome restoration, nanomaterial-based therapy and phage therapy may help to develop treatments to target sepsis. We also provide empirical evidence for potential new sepsis targets including FER and STARD3NL. Implementing data-driven methods that use real-time collection and analysis of clinical variables to trace, track and treat sepsis-related adverse outcomes will be key. Understanding the root and route of sepsis and its comorbid conditions that complicate treatment outcomes and lead to organ dysfunction may help to facilitate identification of most at-risk patients and prevent further deterioration. To conclude, leveraging the advances in precision medicine, biomedical data science and translational bioinformatics approaches may help to develop better strategies to diagnose and treat sepsis in the next decade.

Non-random sampling leads to biased estimates of transcriptome association

Integration of independent data resources across -omics platforms offers transformative opportunity for novel clinical and biological discoveries. However, application of emerging analytic methods in the context of selection bias represents a noteworthy and pervasive challenge. We hypothesize that combining differentially selected samples for integrated transcriptome analysis will lead to bias in the estimated association between predicted expression and the trait. Our results are based on in silico investigations and a case example focused on body mass index across four well-described cohorts apparently derived from markedly different populations. Our findings suggest that integrative analysis can lead to substantial relative bias in the estimate of association between predicted expression and the trait. The average estimate of association ranged from 51.3% less than to 96.7% greater than the true value for the biased sampling scenarios considered, while the average error was - 2.7% for the unbiased scenario. The corresponding 95% confidence interval coverage rate ranged from 46.4% to 69.5% under biased sampling, and was equal to 75% for the unbiased scenario. Inverse probability weighting with observed and estimated weights is applied as one corrective measure and appears to reduce the bias and improve coverage. These results highlight a critical need to address selection bias in integrative analysis and to use caution in interpreting findings in the presence of different sampling mechanisms between groups.

Convergence of human cellular models and genetics to study neural stem cell signaling to enhance central nervous system regeneration and repair

Human central nervous system (CNS) regeneration is considered the holy grail of neuroscience research, and is one of the most pressing and difficult questions in biology and science. Despite more than 20 years of work in the field of neural stem cells (NSCs), the area remains in its infancy as our understanding of the fundamental mechanisms that can be leveraged to improve CNS regeneration in neurological diseases is still growing. Here, we focus on the recent lessons from lower organism CNS regeneration genetics and how such findings are starting to illuminate our understanding of NSC signaling pathways in humans. These findings will allow us to improve upon our knowledge of endogenous NSC function, the utility of exogenous NSCs, and the limitations of NSCs as therapeutic vehicles for providing relief from devastating human neurological diseases. We also discuss the limitations of activating NSC signaling for CNS repair in humans, especially the potential for tumor formation. Finally, we will review the recent advances in new culture techniques, including patient-derived cells and cerebral organoids to model the genetic regulation of signaling pathways controlling the function of NSCs during injury and disease states.

Stochastic imputation for integrated transcriptome association analysis of a longitudinally measured trait

The mechanistic pathways linking genetic polymorphisms and complex disease traits remain largely uncharacterized. At the same time, expansive new transcriptome data resources offer unprecedented opportunity to unravel the mechanistic underpinnings of complex disease associations. Two-stage strategies involving conditioning on a single, penalized regression imputation for transcriptome association analysis have been described for cross-sectional traits. In this manuscript, we propose an alternative two-stage approach based on stochastic regression imputation that additionally incorporates error in the predictive model. Application of a bootstrap procedure offers flexibility when a closed form predictive distribution is not available. The two-stage strategy is also generalized to longitudinally measured traits, using a linear mixed effects modeling framework and a composite test statistic to evaluate whether the genetic component of gene-level expression modifies the biomarker trajectory over time. Simulations studies are performed to evaluate relative performance with respect to type-1 error rates, coverage, estimation error, and power under a range of conditions. A case study is presented to investigate the association between whole blood expression for each of five inflammasome genes with inflammatory response over time after endotoxin challenge.

A likelihood-based approach to transcriptome association analysis

Elucidating the mechanistic underpinnings of genetic associations with complex traits requires formally characterizing and testing associated cell and tissue-specific expression profiles. New opportunities exist to bolster this investigation with the growing numbers of large publicly available omics level data resources. Herein, we describe a fully likelihood-based strategy to leveraging external resources in the setting that expression profiles are partially or fully unobserved in a genetic association study. A general framework is presented to accommodate multiple data types, and strategies for implementation using existing software packages are described. The method is applied to an investigation of the genetics of evoked inflammatory response in cardiovascular disease research. Simulation studies suggest appropriate type-1 error control and power gains compared to single regression imputation, the most commonly applied practice in this setting.

Plasma angiopoietin-2 as a potential causal marker in sepsis-associated ARDS development: evidence from Mendelian randomization and mediation analysis

PURPOSE

A causal biomarker for acute respiratory distress syndrome (ARDS) could fuel precision therapy options. Plasma angiopoietin-2 (ANG2), a vascular permeability marker, is a strong candidate on the basis of experimental and observational evidence. We used genetic causal inference methods-Mendelian randomization and mediation-to infer potential effects of plasma ANG2.

METHODS

We genotyped 703 septic subjects, measured ICU admission plasma ANG2, and performed a quantitative trait loci (QTL) analysis to determine variants in the ANGPT2 gene associated with plasma ANG2 (p < 0.005). We then used linear regression and post-estimation analysis to genetically predict plasma ANG2 and tested genetically predicted ANG2 for ARDS association using logistic regression. We estimated the proportion of the genetic effect explained by plasma ANG2 using mediation analysis.

RESULTS

Plasma ANG2 was strongly associated with ARDS (OR 1.59 (95% CI 1.35, 1.88) per log). Five ANGPT2 variants were associated with ANG2 in European ancestry subjects (n = 404). Rs2442608C, the most extreme cis QTL (coefficient 0.22, 95% CI 0.09-0.36, p = 0.001), was associated with higher ARDS risk: adjusted OR 1.38 (95% CI 1.01, 1.87), p = 0.042. No significant QTL were identified in African ancestry subjects. Genetically predicted plasma ANG2 was associated with ARDS risk: adjusted OR 2.25 (95% CI 1.06-4.78), p = 0.035. Plasma ANG2 mediated 34% of the rs2442608C-related ARDS risk.

CONCLUSIONS

In septic European ancestry subjects, the strongest ANG2-determining ANGPT2 genetic variant is associated with higher ARDS risk. Plasma ANG2 may be a causal factor in ARDS development. Strategies to reduce plasma ANG2 warrant testing to prevent or treat sepsis-associated ARDS.

A score test for genetic class-level association with nonlinear biomarker trajectories

Emerging data suggest that the genetic regulation of the biological response to inflammatory stress may be fundamentally different to the genetic underpinning of the homeostatic control (resting state) of the same biological measures. In this paper, we interrogate this hypothesis using a single-SNP score test and a novel class-level testing strategy to characterize protein-coding gene and regulatory element-level associations with longitudinal biomarker trajectories in response to stimulus. Using the proposed class-level association score statistic for longitudinal data, which accounts for correlations induced by linkage disequilibrium, the genetic underpinnings of evoked dynamic changes in repeatedly measured biomarkers are investigated. The proposed method is applied to data on two biomarkers arising from the Genetics of Evoked Responses to Niacin and Endotoxemia study, a National Institutes of Health-sponsored investigation of the genomics of inflammatory and metabolic responses during low-grade endotoxemia. Our results suggest that the genetic basis of evoked inflammatory response is different than the genetic contributors to resting state, and several potentially novel loci are identified. A simulation study demonstrates appropriate control of type-1 error rates, relative computational efficiency, and power. Copyright © 2017 John Wiley & Sons, Ltd.

Low Plasma Levels of Adiponectin Do Not Explain Acute Respiratory Distress Syndrome Risk: a Prospective Cohort Study of Patients with Severe Sepsis

BACKGROUND

Obesity is associated with the development of acute respiratory distress syndrome (ARDS) in at-risk patients. Low plasma levels of adiponectin, a circulating hormone-like molecule, have been implicated as a possible mechanism for this association. The objective of this study was to determine the association of plasma adiponectin level at ICU admission with ARDS and 30-day mortality in patients with severe sepsis and septic shock.

METHODS

This is a prospective cohort study of patients admitted to the medical ICU at the Hospital of the University of Pennsylvania. Plasma adiponectin was measured at the time of ICU admission. ARDS was defined by Berlin criteria. Multivariable logistic regression was used to determine the association of plasma adiponectin with the development of ARDS and mortality at 30 days.

RESULTS

The study included 164 patients. The incidence of ARDS within 5 days of admission was 45%. The median initial plasma adiponectin level was 7.62 mcg/ml (IQR: 3.87, 14.90) in those without ARDS compared to 8.93 mcg/ml (IQR: 4.60, 18.85) in those developing ARDS. The adjusted odds ratio for ARDS associated with each 5 mcg increase in adiponectin was 1.12 (95% CI 1.01, 1.25), p-value 0.025). A total of 82 patients (51%) of the cohort died within 30 days of ICU admission. There was a statistically significant association between adiponectin and mortality in the unadjusted model (OR 1.11, 95% CI 1.00, 1.23, p-value 0.04) that was no longer significant after adjusting for potential confounders.

CONCLUSIONS

In this study, low levels of adiponectin were not associated with an increased risk of ARDS in patients with severe sepsis and septic shock. This argues against low levels of adiponectin as a mechanism explaining the association of obesity with ARDS. At present, it is unclear whether circulating adiponectin is involved in the pathogenesis of ARDS or simply represents an epiphenomenon of other unknown functions of adipose tissue or metabolic alterations in sepsis.

Downregulation of SUMF2 gene in ovalbumin-induced rat model of allergic inflammation

Sulfate-modifying factor 2 (SUMF2), a member of the formylglycine-generating enzyme family, was earlier found to play a role in the regulation of interleukin (IL)-13 expression and secretion in airway smooth muscle cells. IL-13 is a T helper 2 cytokine that plays important roles in the pathogenesis of asthma. However, there is little evidence of the potential role of SUMF2 in the cellular inflammatory responses in asthma. Here, using an ovalbumin-induced asthma rat model, we show that SUMF2 gene expression is significantly decreased in allergic asthma rats. Moreover, several pathological changes were observed in the lung tissue and IL-13 was found to be overexpressed in the ovalbumin-induced asthma model. Additional studies on the lung bronchial epithelial tissues, peripheral blood lymphocytes and bronchoalveolar lavage fluid of the OVA-induced asthma rats showed that SUMF2 mRNA and protein expression were attenuated. However, there was only a little significant correlation was found between SUMF2 and IL-13 expression. These results indicate that SUMF2 may mediate airway inflammation in allergic asthma by modulating the expression of IL-13. More data from in vivo experiments are needed to clearly understand the role of SUMF2 in asthma.

Human experimental endotoxemia in modeling the pathophysiology, genomics, and therapeutics of innate immunity in complex cardiometabolic diseases

Inflammation is a fundamental feature of several complex cardiometabolic diseases. Indeed, obesity, insulin resistance, metabolic dyslipidemia, and atherosclerosis are all closely linked inflammatory states. Increasing evidence suggests that the infectious, biome-related, or endogenous activation of the innate immune system may contribute to the development of metabolic syndrome and cardiovascular disease. Here, we describe the human experimental endotoxemia model for the specific study of innate immunity in understanding further the pathogenesis of cardiometabolic disease. In a controlled, experimental setting, administration of an intravenous bolus of purified Escherichia coli endotoxin activates innate immunity in healthy human volunteers. During endotoxemia, changes emerge in glucose metabolism, lipoprotein composition, and lipoprotein functions that closely resemble those observed chronically in inflammatory cardiovascular disease risk states. In this review, we describe the transient systemic inflammation and specific metabolic consequences that develop during human endotoxemia. Such a model provides a controlled induction of systemic inflammation, eliminates confounding, undermines reverse causation, and possesses unique potential as a starting point for genomic screening and testing of novel therapeutics for treatment of the inflammatory underpinning of cardiometabolic disease.