scholarly journals Impact of Inherited Genetic Variants on Critically Ill Septic Children

Pathogens ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 96
Author(s):  
Mariana Miranda ◽  
Simon Nadel

Sepsis remains an important source of morbidity and mortality in children, despite the development of standardized care. In the last decades, there has been an increased interest in genetic and genomic approaches to early recognition and development of treatments to manipulate the host inflammatory response. This review will present a summary of the normal host response to infection and progression to sepsis, followed by highlighting studies with a focus on gene association studies, epigenetics, and genome-wide expression profiling. The susceptibility (or outcome) of sepsis in children has been associated with several polymorphisms of genes broadly involved in inflammation, immunity, and coagulation. More recently, gene expression profiling has been focused on identifying novel biomarkers, pathways and therapeutic targets, and gene expression-based subclassification. Knowledge of a patient’s individual genotype may, in the not-too-remote future, be used to guide tailored treatment for sepsis. However, at present, the impact of genomics remains far from the bedside of critically ill children.

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S43-S44
Author(s):  
Kevin J Downes ◽  
Athena F Zuppa ◽  
Anna Sharova ◽  
Lauren Gianchetti ◽  
Emily Duffey ◽  
...  

Abstract Background There are a paucity of robust population PK (popPK) models to inform vancomycin (VAN) dosing in critically ill children. The majority of published models incorporate peak/trough data and rely on flawed estimates of renal function. We sought to develop a popPK model for IV VAN in critically ill children utilizing novel plasma and urinary biomarkers. Methods We conducted a prospective observational study of critically ill children prescribed VAN for a suspected infection in the CHOP pediatric ICU. Children < 1 year of age and those receiving ECMO or CRRT were excluded. Five VAN samples were collected from a single dosing interval for each subject. Plasma biomarkers (creatinine [Cr], cystatin C [CysC], NGAL) and urinary biomarkers (CysC, NGAL, KIM-1, osteopontin) were collected the morning of PK sampling; urinary biomarkers were corrected for urine creatinine. Nonparametric popPK modeling was performed using Pmetrics. The impact of renal function (GFR) on VAN clearance (CL) was estimated first, comparing model performance with each biomarker (Cr and plasma CysC). The influence of age, sex, additional biomarkers, PIM3 score, and receipt of vasopressors as covariates was then assessed for relevant PK parameters. Results 30 subjects completed the study. Median age was 10 years (range 1-17); 76% were male. The majority (90%) of children received VAN for suspected sepsis. PK sampling occurred at a median of 37.7 hours (range 24.6-94.8) into VAN treatment; 136 VAN samples were included. A 2-compartment model with fixed allometric scaling of 0.75 on clearances and 1 on volumes best described the data. CysC-based GFR as a covariate on VAN CL using the HOEK formula (GFR = -4.32 + (80.35/CysC)) resulted in the best model fit. Age and plasma NGAL were also informative on VAN CL in the final model (Figure 1). During model building, urinary NGAL was also associated with VAN CL (comparable to plasma NGAL) and outperformed Cr, although it was not retained in the final model. Figure 1. Final population PK model and parameter estimates. Conclusion Plasma CysC is a better renal function estimate than Cr to inform VAN clearance in critically ill children. Urinary and plasma NGAL also improved estimation of VAN CL during popPK modeling. Novel biomarkers can better describe VAN exposures in critically ill children than reliance on Cr alone. Disclosures Kevin J. Downes, MD, Merck (Individual(s) Involved: Self): Grant/Research Support Stuart L. Goldstein, MD, Bioporto (Consultant, Grant/Research Support)


Metabolites ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 513
Author(s):  
Grace H. Yang ◽  
Danielle A. Fontaine ◽  
Sukanya Lodh ◽  
Joseph T. Blumer ◽  
Avtar Roopra ◽  
...  

Transcription factor 19 (TCF19) is a gene associated with type 1 diabetes (T1DM) and type 2 diabetes (T2DM) in genome-wide association studies. Prior studies have demonstrated that Tcf19 knockdown impairs β-cell proliferation and increases apoptosis. However, little is known about its role in diabetes pathogenesis or the effects of TCF19 gain-of-function. The aim of this study was to examine the impact of TCF19 overexpression in INS-1 β-cells and human islets on proliferation and gene expression. With TCF19 overexpression, there was an increase in nucleotide incorporation without any change in cell cycle gene expression, alluding to an alternate process of nucleotide incorporation. Analysis of RNA-seq of TCF19 overexpressing cells revealed increased expression of several DNA damage response (DDR) genes, as well as a tightly linked set of genes involved in viral responses, immune system processes, and inflammation. This connectivity between DNA damage and inflammatory gene expression has not been well studied in the β-cell and suggests a novel role for TCF19 in regulating these pathways. Future studies determining how TCF19 may modulate these pathways can provide potential targets for improving β-cell survival.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jamie W. Robinson ◽  
Richard M. Martin ◽  
Spiridon Tsavachidis ◽  
Amy E. Howell ◽  
Caroline L. Relton ◽  
...  

AbstractGenome-wide association studies (GWAS) have discovered 27 loci associated with glioma risk. Whether these loci are causally implicated in glioma risk, and how risk differs across tissues, has yet to be systematically explored. We integrated multi-tissue expression quantitative trait loci (eQTLs) and glioma GWAS data using a combined Mendelian randomisation (MR) and colocalisation approach. We investigated how genetically predicted gene expression affects risk across tissue type (brain, estimated effective n = 1194 and whole blood, n = 31,684) and glioma subtype (all glioma (7400 cases, 8257 controls) glioblastoma (GBM, 3112 cases) and non-GBM gliomas (2411 cases)). We also leveraged tissue-specific eQTLs collected from 13 brain tissues (n = 114 to 209). The MR and colocalisation results suggested that genetically predicted increased gene expression of 12 genes were associated with glioma, GBM and/or non-GBM risk, three of which are novel glioma susceptibility genes (RETREG2/FAM134A, FAM178B and MVB12B/FAM125B). The effect of gene expression appears to be relatively consistent across glioma subtype diagnoses. Examining how risk differed across 13 brain tissues highlighted five candidate tissues (cerebellum, cortex, and the putamen, nucleus accumbens and caudate basal ganglia) and four previously implicated genes (JAK1, STMN3, PICK1 and EGFR). These analyses identified robust causal evidence for 12 genes and glioma risk, three of which are novel. The correlation of MR estimates in brain and blood are consistently low which suggested that tissue specificity needs to be carefully considered for glioma. Our results have implicated genes yet to be associated with glioma susceptibility and provided insight into putatively causal pathways for glioma risk.


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