Identification of novel biomarkers for prediction of neurological prognosis following cardiac arrest

Multi-omics Study of Hypoxic-Ischemic Brain Injury After Cardiopulmonary Resuscitation in Swine

BACKGROUND

Hypoxic-ischemic brain injury is a common cause of mortality after cardiac arrest (CA) and cardiopulmonary resuscitation; however, the specific underlying mechanisms are unclear. This study aimed to explore postresuscitation changes based on multi-omics profiling.

METHODS

A CA swine model was established, and the neurological function was assessed at 24 h after resuscitation, followed by euthanizing animals. Their fecal, blood, and hippocampus samples were collected to analyze gut microbiota, metabolomics, and transcriptomics.

RESULTS

The 16S ribosomal DNA sequencing showed that the microbiota composition and diversity changed after resuscitation, in which the abundance of Akkermansia and Muribaculaceae_unclassified increased while the abundance of Bifidobacterium and Romboutsia decreased. A relationship was observed between CA-related microbes and metabolites via integrated analysis of gut microbiota and metabolomics, in which Escherichia-Shigella was positively correlated with glycine. Combined metabolomics and transcriptomics analysis showed that glycine was positively correlated with genes involved in apoptosis, interleukin-17, mitogen-activated protein kinases, nuclear factor kappa B, and Toll-like receptor signal pathways.

CONCLUSIONS

Our results provided novel insight into the mechanism of hypoxic-ischemic brain injury after resuscitation, which is envisaged to help identify potential diagnostic and therapeutic markers.

Developments in Post-Resuscitation Care for Out-of-Hospital Cardiac Arrests in Adults-A Narrative Review

This review focuses on current developments in post-resuscitation care for adults with an out-of-hospital cardiac arrest (OHCA). As the incidence of OHCA is high and with a low percentage of survival, it remains a challenge to treat those who survive the initial phase and regain spontaneous circulation. Early titration of oxygen in the out-of-hospital phase is not associated with increased survival and should be avoided. Once the patient is admitted, the oxygen fraction can be reduced. To maintain an adequate blood pressure and urine output, noradrenaline is the preferred agent over adrenaline. A higher blood pressure target is not associated with higher rates of good neurological survival. Early neuro-prognostication remains a challenge, and prognostication bundles should be used. Established bundles could be extended by novel biomarkers and methods in the upcoming years. Whole blood transcriptome analysis has shown to reliably predict neurological survival in two feasibility studies. This needs further investigation in larger cohorts.

Identification of predictors for neurological outcome after cardiac arrest in peripheral blood mononuclear cells through integrated bioinformatics analysis and machine learning

Neurological prognostication after cardiac arrest (CA) is important to avoid pursuing futile treatments for poor outcome and inappropriate withdrawal of life-sustaining treatment for good outcome. To predict neurological outcome after CA through biomarkers in peripheral blood mononuclear cells, four datasets were downloaded from the Gene Expression Omnibus database. GSE29546 and GSE74198 were used as training datasets, while GSE92696 and GSE34643 were used as verification datasets. The intersection of differentially expressed genes and hub genes from multiscale embedded gene co-expression network analysis (MEGENA) was utilized in the machine learning screening. Key genes were identified using support vector machine recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF). The results were validated using receiver operating characteristic curve analysis. An mRNA-miRNA network was constructed. The distribution of immune cells was evaluated using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). Five biomarkers were identified as predictors for neurological outcome after CA, with an area under the curve (AUC) greater than 0.7: CASP8 and FADD-like apoptosis regulator (CFLAR), human protein kinase X (PRKX), miR-483-5p, let-7a-5p, and let-7c-5p. Interestingly, the combination of CFLAR minus PRKX showed an even higher AUC of 0.814. The mRNA-miRNA network consisted of 30 nodes and 76 edges. Statistical differences were found in immune cell distribution, including neutrophils, NK cells active, NK cells resting, T cells CD4 memory activated, T cells CD4 memory resting, T cells CD8, B cells memory, and mast cells resting between individuals with good and poor neurological outcome after CA. In conclusion, our study identified novel predictors for neurological outcome after CA. Further clinical and laboratory studies are needed to validate our findings.

Serum proteomic analysis of novel predictive serum proteins for neurological prognosis following cardiac arrest

Early prognostication of neurological outcome in comatose patients after cardiac arrest (CA) is vital for clinicians when assessing the survival time of sufferers and formulating appropriate treatment strategies to avoid the withdrawal of life‐sustaining treatment (WLST) from patients. However, there is still a lack of sensitive and specific serum biomarkers for early and accurate identification of these patients. Using an isobaric tag for relative and absolute quantitation (iTRAQ)‐based proteomic approach, we discovered 55 differentially expressed proteins, with 39 up‐regulated secreted serum proteins and 16 down‐regulated secreted serum proteins between three comatose CA survivors with good versus poor neurological recovery. Then, four proteins were selected and were validated via an enzyme‐linked immunosorbent assay (ELISA) approach in a larger‐scale sample containing 32 good neurological outcome patients and 46 poor neurological outcome patients, and it was confirmed that serum angiotensinogen (AGT) and alpha‐1‐antitrypsin (SERPINA1) were associated with neurological function and prognosis in CA survivors. A prognostic risk score was developed and calculated using a linear and logistic regression model based on a combination of AGT, SERPINA1 and neuron‐specific enolase (NSE) with an area under the curve of 0.865 (P < .001), and the prognostic risk score was positively correlated with the CPC value (R = 0.708, P < .001). We propose that the results of the risk score assessment not only reveal changes in biomarkers during neurological recovery but also assist in enhancing current therapeutic strategies for comatose CA survivors.

Identification of the Key Genes of Autism Spectrum Disorder Through Protein-Protein Interaction Network

Background:

Currently, the prevalence of autism spectrum disorder (ASD) is increasing, which widely spurs the interest in the molecular investigation. Thereby, a better understanding of the given disorder mechanisms is likely to be achieved. Bioinformatics suiting protein-protein interactions analysis via the application of high-throughput studies, such as protein array, is one of these achievements.

Materials and Methods:

The gene expression data from Gene Expression Omnibus (GEO) database were downloaded, and the expression profile of patients with developmental delay and autistic features were analyzed via Cytoscape and its relevant plug-ins.

Results:

Our findings indicated that EGFR, ACTB, RHOA, CALM1, MAPK1, and JUN genes as the hub-bottlenecks and their related terms could be important in ASD risk. In other words, any expression modification in these genes could trigger dysfunctions in the corresponding biological processes.

Conclusion:

We suggest that differentially expressed genes could be used as suitable targets for ASD after being validated.

Early blood transcriptomic signature predicts patients' outcome after out-of-hospital cardiac arrest

BACKGROUND

Early prognostication is a major challenge after out-of-hospital cardiac arrest (OHCA).

AIMS

We hypothesized that a genome-wide analysis of blood gene expression could offer new prognostic tools and lines of research.

METHODS

Sixty-nine patients were enrolled from an ancillary study of the clinical trial NCT00999583 that tested the effect of erythropoietin (EPO) after OHCA. Blood samples were collected in comatose survivors of OHCA at hospital admission and 1 and 3 days after resuscitation. Gene expression profiles were analyzed (Illumina HumanHT-12 V4 BeadChip; >34,000 genes). Patients were classified into two categories representing neurological favorable outcome (cerebral performance category [CPC] = 1-2) vs unfavorable outcome (CPC > 2) at Day 60 after OHCA. Differential and functional enrichment analyses were performed to compare transcriptomic profiles between these two categories.

RESULTS

Among the 69 enrolled patients, 33 and 36 patients were treated or not by EPO, respectively. Among them, 42% had a favorable neurological outcome in both groups. EPO did not affect the transcriptomic response at Day-0 and 1 after OHCA. In contrast, 76 transcripts differed at Day-0 between patients with unfavorable vs favorable neurological outcome. This signature persisted at Day-1 after OHCA. Functional enrichment analysis revealed a down-regulation of adaptive immunity with concomitant up-regulation of innate immunity and inflammation in patients with unfavorable vs favorable neurological outcome. The transcription of many genes of the HLA family was decreased in patients with unfavorable vs favorable neurological outcome. Concomitantly, neutrophil activation and inflammation were observed. Up-stream regulators analysis showed the implication of numerous factors involved in cell cycle and damages. A logistic regression including a set of genes allowed a reliable prediction of the clinical outcomes (specificity = 88%; Hit Rate = 83%).

CONCLUSIONS

A transcriptomic signature involving a counterbalance between adaptive and innate immune responses is able to predict neurological outcome very early after hospital admission after OHCA. This deserves confirmation in a larger population.

Compound-Protein Interaction Analysis in Condition Following Cardiac Arrest

BACKGROUND

Cardiac arrest (CA) and differentially expressed genes (DEGs) relative to postCA have attracted the attention of scientist to prevent damages, which threaten patients. In the present study, metabolites relevant to DEGs of post-CA condition investigated via protein-compound interaction to understand the pathological mechanisms in the human body.

MATERIALS AND METHODS

STITCH plug-in integrated into Cytoscape V.3.6.1 was used to detect the most significant interacting compounds relative to DEGs of pig's brain after 5 minutes' CA. The genes were obtained from the Gene Expression Omnibus database. The identified elements were considered for further evaluation and validation by literature survey.

RESULT

Findings indicate that biochemical compounds including magnesium, calcium, glucose, glycerol, hydrogen, chloride, sulfate, and estradiol interact with DEGs in the two up- and down-regulated networks.

CONCLUSION

The compounds interacting with DEGs are suitable subjects to analysis for re-regulation of the body after CA.