Network analysis of inflammatory responses to sepsis by neutrophils and peripheral blood mononuclear cells

Impaired Mitochondrial Respiration in REM-Sleep Behavior Disorder: A Biomarker of Parkinson's Disease?

BACKGROUND

Idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) is associated with prodromal Parkinson's disease (PD), but the mechanisms linking phenoconversion of iRBD to PD have not yet been clarified. Considering the association between mitochondrial dysfunction and sleep disturbances in PD, we explored mitochondrial activity in fibroblasts derived from iRBD patients to identify a biochemical profile that could mark the presence of impending neurodegeneration.

METHODS

The study involved 28 participants, divided into three groups: patients diagnosed with iRBD, PD patients converted from iRBD (RBD-PD), and healthy controls. We performed a comprehensive assessment of mitochondrial function, including an examination of mitochondrial morphology, analysis of mitochondrial protein expression levels by western blot, and measurement of mitochondrial respiration using the Seahorse XFe24 analyzer.

RESULTS

In basal conditions, mitochondrial respiration did not differ between iRBD and control fibroblasts, but when cells were challenged with a higher energy demand, iRBD fibroblasts exhibited a significant (P = 0.006) drop in maximal and spare respiration compared to controls. Interestingly, RBD-PD patients showed the same alterations with a further significant reduction in oxygen consumption linked to adenosine triphosphate production (P = 0.032). Moreover, RBD-PD patients exhibited a significant decrease in protein levels of complexes III (P = 0.02) and V (P = 0.002) compared to controls, along with fragmentation of the mitochondrial network. iRBD patients showed similar, but milder alterations.

CONCLUSIONS

Altogether, these findings suggest that mitochondrial dysfunctions in individuals with iRBD might predispose to worsening of the bioenergetic profile observed in RBD-PD patients, highlighting these alterations as potential predictors of phenoconversion to PD. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Identifying immune signatures of sepsis to increase diagnostic accuracy in very preterm babies

Bacterial infections are a major cause of mortality in preterm babies, yet our understanding of early-life disease-associated immune dysregulation remains limited. Here, we combine multi-parameter flow cytometry, single-cell RNA sequencing and plasma analysis to longitudinally profile blood from very preterm babies (<32 weeks gestation) across episodes of invasive bacterial infection (sepsis). We identify a dynamically changing blood immune signature of sepsis, including lymphopenia, reduced dendritic cell frequencies and myeloid cell HLA-DR expression, which characterizes sepsis even when the common clinical marker of inflammation, C-reactive protein, is not elevated. Furthermore, single-cell RNA sequencing identifies upregulation of amphiregulin in leukocyte populations during sepsis, which we validate as a plasma analyte that correlates with clinical signs of disease, even when C-reactive protein is normal. This study provides insights into immune pathways associated with early-life sepsis and identifies immune analytes as potential diagnostic adjuncts to standard tests to guide targeted antibiotic prescribing.

Immune-Related Molecules CD3G and FERMT3: Novel Biomarkers Associated with Sepsis

Sepsis ranks among the most common health problems worldwide, characterized by organ dysfunction resulting from infection. Excessive inflammatory responses, cytokine storms, and immune-induced microthrombosis are pivotal factors influencing the progression of sepsis. Our objective was to identify novel immune-related hub genes for sepsis through bioinformatic analysis, subsequently validating their specificity and potential as diagnostic and prognostic biomarkers in an animal experiment involving a sepsis mice model. Gene expression profiles of healthy controls and patients with sepsis were obtained from the Gene Expression Omnibus (GEO) and analysis of differentially expressed genes (DEGs) was conducted. Subsequently, weighted gene co-expression network analysis (WGCNA) was used to analyze genes within crucial modules. The functional annotated DEGs which related to the immune signal pathways were used for constructing protein-protein interaction (PPI) analysis. Following this, two hub genes, FERMT3 and CD3G, were identified through correlation analyses associated with sequential organ failure assessment (SOFA) scores. These two hub genes were associated with cell adhesion, migration, thrombosis, and T-cell activation. Furthermore, immune infiltration analysis was conducted to investigate the inflammation microenvironment influenced by the hub genes. The efficacy and specificity of the two hub genes were validated through a mice sepsis model study. Concurrently, we observed a significant negative correlation between the expression of CD3G and IL-1β and GRO/KC. These findings suggest that these two genes probably play important roles in the pathogenesis and progression of sepsis, presenting the potential to serve as more stable biomarkers for sepsis diagnosis and prognosis, deserving further study.

LEUKOCYTE PHENOTYPING IN SEPSIS USING OMICS, FUNCTIONAL ANALYSIS, AND IN SILICO MODELING

ABSTRACT

Sepsis is a major health issue and a leading cause of death in hospitals globally. The treatment of sepsis is largely supportive, and there are no therapeutics available that target the underlying pathophysiology of the disease. The development of therapeutics for the treatment of sepsis is hindered by the heterogeneous nature of the disease. The presence of multiple, distinct immune phenotypes ranging from hyperimmune to immunosuppressed can significantly impact the host response to infection. Recently, omics, biomarkers, cell surface protein expression, and immune cell profiles have been used to classify immune status of sepsis patients. However, there has been limited studies of immune cell function during sepsis and even fewer correlating omics and biomarker alterations to functional consequences. In this review, we will discuss how the heterogeneity of sepsis and associated immune cell phenotypes result from changes in the omic makeup of cells and its correlation with leukocyte dysfunction. We will also discuss how emerging techniques such as in silico modeling and machine learning can help in phenotyping sepsis patients leading to precision medicine.

Diagnostic model constructed by nine inflammation-related genes for diagnosing ischemic stroke and reflecting the condition of immune-related cells

Background

Ischemic cerebral infarction is the most common type of stroke with high rates of mortality, disability, and recurrence. However, the known diagnostic biomarkers and therapeutic targets for ischemic stroke (IS) are limited. In the current study, we aimed to identify novel inflammation-related biomarkers for IS using machine learning analysis and to explore their relationship with the levels of immune-related cells in whole blood samples.

Methods

Gene expression profiles of healthy controls and patients with IS were download from the Gene Expression Omnibus. Analysis of differentially expressed genes (DEGs) was performed in healthy controls and patients with IS. Single-sample gene set enrichment analysis was performed to calculate inflammation scores, and weighted gene co-expression network analysis was used to analyze genes in significant modules associated with inflammation scores. Key DEGs in significant modules were then analyzed using LASSO regression analysis for constructing a diagnostic model. The effectiveness and specificity of the diagnostic model was verified in healthy controls and patients with IS and with cerebral hemorrhage (CH) using qRT-PCR. The relationship between diagnostic score and the levels of immune-related cells in whole blood were analyzed using Pearson correlations.

Results

A total of 831 DEGs were identified. Both chronic and acute inflammation scores were higher in patients with IS, while 54 DEGs were also clustered in the gene modules associated with chronic and acute inflammation scores. Among them, a total of 9 genes were selected to construct a diagnostic model. Interestingly, RT-qPCR showed that the diagnostic model had better diagnostic value for IS but not for CH. The levels of lymphocytes were lower in blood of patients with IS, while the levels of monocytes and neutrophils were increased. The diagnostic score of the model was negatively associated with the levels of lymphocytes and positively associated with levels of monocytes and neutrophils.

Conclusions

Taken together, the diagnostic model constructed using the inflammation-related genes TNFSF10, ID1, PAQR8, OSR2, PDK4, PEX11B, TNIP1, FFAR2, and JUN exhibited high and specific diagnostic value for IS and reflected the condition of lymphocytes, monocytes, and neutrophils in the blood. The diagnostic model may contribute to the diagnosis of IS.

Adenosine A1R/A3R agonist AST-004 reduces brain infarction in mouse and rat models of acute ischemic stroke

Acute ischemic stroke (AIS) is the second leading cause of death globally. No Food and Drug Administration (FDA) approved therapies exist that target cerebroprotection following stroke. Our group recently reported significant cerebroprotection with the adenosine A1/A3 receptor agonist, AST-004, in a transient stroke model in non-human primates (NHP) and in a preclinical mouse model of traumatic brain injury (TBI). However, the specific receptor pathway activated was only inferred based on in vitro binding studies. The current study investigated the underlying mechanism of AST-004 cerebroprotection in two independent models of AIS: permanent photothrombotic stroke in mice and transient middle cerebral artery occlusion (MCAO) in rats. AST-004 treatments across a range of doses were cerebroprotective and efficacy could be blocked by A3R antagonism, indicating a mechanism of action that does not require A1R agonism. The high affinity A3R agonist MRS5698 was also cerebroprotective following stroke, but not the A3R agonist Cl-IB-MECA under our experimental conditions. AST-004 efficacy was blocked by the astrocyte specific mitochondrial toxin fluoroacetate, confirming an underlying mechanism of cerebroprotection that was dependent on astrocyte mitochondrial metabolism. An increase in A3R mRNA levels following stroke suggested an intrinsic cerebroprotective response that was mediated by A3R signaling. Together, these studies confirm that certain A3R agonists, such as AST-004, may be exciting new therapeutic avenues to develop for AIS.

Immune Cell Plasticity in Inflammation: Insights into Description and Regulation of Immune Cell Phenotypes

Inflammation is a life-saving immune reaction occurring in response to invading pathogens. Nonetheless, inflammation can also occur in an uncontrolled, unrestricted manner, leading to chronic disease and organ damage. Mechanisms triggering an inflammatory response, hindering such a response, or leading to its resolution are well-studied but so far insufficiently elucidated with regard to precise therapeutic interventions. Notably, as an immune reaction evolves, requirements and environments for immune cells change, and thus cellular phenotypes adapt and shift, leading to the appearance of distinct cellular subpopulations with new functional features. In this article, we aim to highlight properties of, and overarching regulatory factors involved in, the occurrence of immune cell phenotypes with a special focus on neutrophils, macrophages and platelets. Additionally, we point out implications for both diagnostics and therapeutics in inflammation research.

Identification of the TF-miRNA-mRNA co-regulatory networks involved in sepsis

Sepsis is a life-threatening medical condition caused by a dysregulated host response to infection. Recent studies have found that the expression of miRNAs is associated with the pathogenesis of sepsis and septic shock. Our study aimed to reveal which miRNAs may be involved in the dysregulated immune response in sepsis and how these miRNAs interact with transcription factors (TFs) using a computational approach with in vitro validation studies. To determine the network of TFs, miRNAs, and target genes involved in sepsis, GEO datasets GSE94717 and GSE131761 were used to identify differentially expressed miRNAs and DEGs. TargetScan and miRWalk databases were used to predict biological targets that overlap with the identified DEGs of differentially expressed miRNAs. The TransmiR database was used to predict the differential miRNA TFs that overlap with the identified DEGs. The TF-miRNA-mRNA network was constructed and visualized. Finally, qRT-PCR was used to verify the expression of TFs and miRNA in HUVECs. Between the healthy and sepsis groups, there were 146 upregulated and 98 downregulated DEGs in the GSE131761 dataset, and there were 1 upregulated and 183 downregulated DEMs in the GSE94717 dataset. A regulatory network of the TF-miRna target genes was established. According to the experimental results, RUNX3 was found to be downregulated while MAPK14 was upregulated, which corroborates the result of the computational expression analysis. In a HUVECs model, miR-19b-1-5p and miR-5009-5p were found to be significantly downregulated. Other TFs and miRNAs did not correlate with our bioinformatics expression analysis. We constructed a TF-miRNA-target gene regulatory network and identified potential treatment targets RUNX3, MAPK14, miR-19b-1-5p, and miR-5009-5p. This information provides an initial basis for understanding the complex sepsis regulatory mechanisms.

Identification of a novel four-gene diagnostic signature for patients with sepsis by integrating weighted gene co-expression network analysis and support vector machine algorithm

Sepsis is a life-threatening condition in which the immune response is directed towards the host tissues, causing organ failure. Since sepsis does not present with specific symptoms, its diagnosis is often delayed. The lack of diagnostic accuracy results in a non-specific diagnosis, and to date, a standard diagnostic test to detect sepsis in patients remains lacking. Therefore, it is vital to identify sepsis-related diagnostic genes. This study aimed to conduct an integrated analysis to assess the immune scores of samples from patients diagnosed with sepsis and normal samples, followed by weighted gene co-expression network analysis (WGCNA) to identify immune infiltration-related genes and potential transcriptome markers in sepsis. Furthermore, gene regulatory networks were established to screen diagnostic markers for sepsis based on the protein-protein interaction networks involving these immune infiltration-related genes. Moreover, we integrated WGCNA with the support vector machine (SVM) algorithm to build a diagnostic model for sepsis. Results showed that the immune score was significantly lower in the samples from patients with sepsis than in normal samples. A total of 328 and 333 genes were positively and negatively correlated with the immune score, respectively. Using the MCODE plugin in Cytoscape, we identified four modules, and through functional annotation, we found that these modules were related to the immune response. Gene Ontology functional enrichment analysis showed that the identified genes were associated with functions such as neutrophil degranulation, neutrophil activation in the immune response, neutrophil activation, and neutrophil-mediated immunity. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed the enrichment of pathways such as primary immunodeficiency, Th1- and Th2-cell differentiation, T-cell receptor signaling pathway, and natural killer cell-mediated cytotoxicity. Finally, we identified a four-gene signature, containing the hub genes LCK, CCL5, ITGAM, and MMP9, and established a model that could be used to diagnose patients with sepsis.

Short-Chain Fatty Acid and FFAR2 Activation - A New Option for Treating Infections?

The human innate immune system is equipped with multiple mechanisms to detect microbe-associated molecular patterns (MAMPs) to fight bacterial infections. The metabolite short-chain fatty acids (SCFAs) acetate, propionate and butyrate are released by multiple bacteria or are food ingredients. SCFA production, especially acetate production, is usually essential for bacteria, and knockout of pathways involved in acetate production strongly impairs bacterial fitness. Because host organisms use SCFAs as MAMPs and alter immune reactions in response to SCFAs, interventions that modulate SCFA levels can be a new strategy for infection control. The interaction between SCFAs and host cells has been primarily investigated in the intestinal lumen because of the high local levels of SCFAs released by bacterial microbiome members. However, members of not only the intestinal microbiome but also the skin microbiome produce SCFAs, which are known ligands of the seven-transmembrane G-protein-coupled receptor FFAR2. In addition to enterocytes, FFAR2 is expressed on other human cell types, including leukocytes, especially neutrophils. This finding is in line with other research that determined that targeted activation of FFAR2 diminishes susceptibility toward various types of infection by bacteria such as Klebsiella pneumonia, Citrobacter rodentium, and Staphylococcus aureus but also by viruses such as respiratory syncytial and influenza viruses. Thus, our immune system appears to be able to use FFAR2-dependent detection of SCFAs for perceiving and even averting severe infections. We summarize recent advances in understanding the role of SCFAs and FFAR2 in various infection types and propose the manipulation of this receptor as an additional therapeutic strategy to fight infections.

Identification of key immune genes for sepsis-induced ARDS based on bioinformatics analysis

Regarding the extremely high mortality caused by sepsis-induced acute respiratory distress syndrome (ARDS), it is urgent to develop new biomarkers of sepsis-induced ARDS for treatment. Here, 532 differential expression genes (DEGs) related to sepsis and 433 DEGs related to sepsis-induced ARDS were screened in the GSE32707 dataset. Compared with sepsis samples, sepsis ARDS samples showed a higher infiltration of activated memory CD4 T cells and naive B cells, but a relatively lower infiltration of CD8 T cells. The pink and green modules which are significantly associated with sepsis-induced ARDS were then screened through co-expression network analysis. Differentially up-regulated GYPE and aberrantly down-regulated HSPB1, were subsequently found in the pink module of ARDS. CD81 and RPL22, two differentially low-expressed genes peculiar to ARDS, were identified in the green module. The function of CD81 was verified at the cellular level, and it was found that the up-regulation of CD81 in A549 could alleviate the LPS-induced injury of A549 cells. More importantly, the overexpressed CD81 can also increase the content of CD4⁺ CD25⁺ Foxp3⁺ Treg in Jurkat cells, and after the co-culture of overexpressed CD81 Jurkat cells with LPS treatment A549 cells, the LPS-induced lung epithelial cell damage can be improved. Overall, four new plasma biomarker candidates were found in sepsis-induced ARDS, and we verified that CD81 may play critical roles in the biological and immunological processes of sepsis-induced ARDS.

Biochemical Analysis of Leukocytes after In Vitro and In Vivo Activation with Bacterial and Fungal Pathogens Using Raman Spectroscopy

Biochemical information from activated leukocytes provide valuable diagnostic information. In this study, Raman spectroscopy was applied as a label-free analytical technique to characterize the activation pattern of leukocyte subpopulations in an in vitro infection model. Neutrophils, monocytes, and lymphocytes were isolated from healthy volunteers and stimulated with heat-inactivated clinical isolates of Candida albicans, Staphylococcus aureus, and Klebsiella pneumoniae. Binary classification models could identify the presence of infection for monocytes and lymphocytes, classify the type of infection as bacterial or fungal for neutrophils, monocytes, and lymphocytes and distinguish the cause of infection as Gram-negative or Gram-positive bacteria in the monocyte subpopulation. Changes in single-cell Raman spectra, upon leukocyte stimulation, can be explained with biochemical changes due to the leukocyte’s specific reaction to each type of pathogen. Raman spectra of leukocytes from the in vitro infection model were compared with spectra from leukocytes of patients with infection (DRKS-ID: DRKS00006265) with the same pathogen groups, and a good agreement was revealed. Our study elucidates the potential of Raman spectroscopy-based single-cell analysis for the differentiation of circulating leukocyte subtypes and identification of the infection by probing the molecular phenotype of those cells.

Association of Circulating, Inflammatory-Response Exosomal mRNAs With Acute Myocardial Infarction

Background: Although many cardiovascular disease studies have focused on the microRNAs of circulating exosomes, the profile and the potential clinical diagnostic value of plasma exosomal long RNAs (exoLRs) are unknown for acute myocardial infarction (AMI).

Methods: In this study, the exoLR profile of 10 AMI patients, eight stable coronary artery disease (CAD) patients, and 10 healthy individuals was assessed by RNA sequencing. Bioinformatic approaches were used to investigate the characteristics and potential clinical value of exoLRs.

Results: Exosomal mRNAs comprised the majority of total exoLRs. Immune cell types analyzed by CIBERSORT showed that neutrophils and monocytes were significantly enriched in AMI patients, consistent with clinical baseline values. Biological process enrichment analysis and co-expression network analysis demonstrated neutrophil activation processes to be enriched in AMI patients. Furthermore, two exosomal mRNAs, ALPL and CXCR2, were identified as AMI biomarkers that may be useful for evaluation of the acute inflammatory response mediated by neutrophils.

Conclusions: ExoLRs were assessed in AMI patients and found to be associated with the acute inflammatory response mediated by neutrophils. Exosomal mRNAs, ALPL and CXCR2, were identified as potentially useful biomarkers for the study of AMI.

SDF4 Is a Prognostic Factor for 28-Days Mortality in Patients With Sepsis via Negatively Regulating ER Stress

Sepsis is a heterogeneous syndrome induced by infection and results in high mortality. Even though more than 100 biomarkers for sepsis prognosis were evaluated, prediction of patient outcomes in sepsis continues to be driven by clinical signs because of unsatisfactory specificity and sensitivity of these biomarkers. This study aimed to elucidate the key candidate genes involved in sepsis response and explore their downstream effects based on weighted gene co-expression network analysis (WGCNA). The dataset GSE63042 with sepsis outcome information was obtained from the Gene Expression Omnibus (GEO) database and then consensus WGCNA was conducted. We identified the hub gene SDF4 (stromal cell derived factor 4) from the M6 module, which was significantly associated with mortality. Subsequently, two datasets (GSE54514 and E-MTAB-4421) and cohort validation (n=89) were performed. Logistic regression analysis was used to build a prediction model and the combined score resulting in a satisfactory prognosis value (area under the ROC curve=0.908). The model was subsequently tested by another sepsis cohort (n=70, ROC= 0.925). We next demonstrated that endoplasmic reticulum (ER) stress tended to be more severe in patients PBMCs with negative outcomes compared to those with positive outcomes and SDF4 was related to this phenomenon. In addition, our results indicated that adenovirus-mediated Sdf4 overexpression attenuated ER stress in cecal ligation and puncture (CLP) mice lung. In summary, our study indicates that incorporation of SDF4 can improve clinical parameters predictive value for the prognosis of sepsis, and decreased expression levels of SDF4 contributes to excessive ER stress, which is associated with worsened outcomes, whereas overexpression of SDF4 attenuated such activation.

Network Analysis of Postoperative Surgical Complications in a Cohort of Children Reported to the National Surgical Quality Improvement Program: Pediatric

OBJECTIVE

To understand the temporal relationships of postoperative complications in children and determine if they are related to each other in a predictable manner.

SUMMARY OF BACKGROUND DATA

Children with multiple postoperative complications have increased suffering and higher risk for mortality. Rigorous analysis of the temporal relations between complications, how complications might cluster, and the implications of such clusters for children have not been published. Herein, we analyze the relationships between postoperative complications in children.

METHODS

Data source: Surgical operations included in the National Surgical Quality Improvement Program Pediatric Participant Use Data File from 2013 to 2017. The main outcomes measure was presence of 1 or more postoperative complications within 30 days of surgery. Operations followed by multiple complications were analyzed using network analysis to study prevalence, timing, and co-occurrences of clusters of complications.

RESULTS

This study cohort consisted of 432,090 operations; 388,738 (89.97%) had no postoperative complications identified, 36,105 (8.35%) operations resulted in 1 postoperative complication and 7247 (1.68%) operations resulted in 2 or more complications. Patients with multiple complications were more likely to be younger, male, African American, with a higher American Society of Anesthesiologists score, and to undergo nonelective operations (P < 0.001). More patients died with 2 complication versus 1 complication vs no complication (5.3% vs 1.5% vs 0.14%, P < 0.001). Network analysis identified 4 Louvain clusters of complications with dense intracluster relationships.

CONCLUSIONS

Children with multiple postoperative complications are at higher risk of death, than patients with no complication, or a single complication. Multiple complications are grouped into defined clusters and are not independent.

Gene correlation network analysis to identify regulatory factors in sepsis

BACKGROUND AND OBJECTIVES

Sepsis is a leading cause of mortality and morbidity in the intensive care unit. Regulatory mechanisms underlying the disease progression and prognosis are largely unknown. The study aimed to identify master regulators of mortality-related modules, providing potential therapeutic target for further translational experiments.

METHODS

The dataset GSE65682 from the Gene Expression Omnibus (GEO) database was utilized for bioinformatic analysis. Consensus weighted gene co-expression netwoek analysis (WGCNA) was performed to identify modules of sepsis. The module most significantly associated with mortality were further analyzed for the identification of master regulators of transcription factors and miRNA.

RESULTS

A total number of 682 subjects with various causes of sepsis were included for consensus WGCNA analysis, which identified 27 modules. The network was well preserved among different causes of sepsis. Two modules designated as black and light yellow module were found to be associated with mortality outcome. Key regulators of the black and light yellow modules were the transcription factor CEBPB (normalized enrichment score = 5.53) and ETV6 (NES = 6), respectively. The top 5 miRNA regulated the most number of genes were hsa-miR-335-5p (n = 59), hsa-miR-26b-5p (n = 57), hsa-miR-16-5p (n = 44), hsa-miR-17-5p (n = 42), and hsa-miR-124-3p (n = 38). Clustering analysis in 2-dimension space derived from manifold learning identified two subclasses of sepsis, which showed significant association with survival in Cox proportional hazard model (p = 0.018).

CONCLUSIONS

The present study showed that the black and light-yellow modules were significantly associated with mortality outcome. Master regulators of the module included transcription factor CEBPB and ETV6. miRNA-target interactions identified significantly enriched miRNA.

Screening and identification of key gene in sepsis development: Evidence from bioinformatics analysis

Sepsis is one of the leading causes of mortality in intensive care units (ICU). The growing incidence rate of sepsis and its high mortality rate result are very important sociosanitary problems. Sepsis is a result of infection which can cause systemic inflammatory and organ failure. But the pathogenesis and the molecular mechanisms of sepsis is still not well understood. The aim of the present study was to identify the candidate key genes in the progression of sepsis.Microarray datasets GSE28750, GSE64457, and GSE95233 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and function enrichment analyses were performed. The protein-protein interaction network (PPI) was constructed and the module analysis was performed using STRING and Cytoscape. Furthermore, to verify the results of the bioinformatics analyses, the expression levels of selected DEGs were quantified by Reverse Transcription-Polymerase Chain Reaction (RT-PCR) in libobolysaccharide (LPS)-induced Human Umbilical Vein Endothelial Cells (HUVECs) to support the result of bioinformatics analysis.Thirteen hub genes were identified and biological process analysis revealed that these genes were mainly enriched in apoptotic process, inflammatory response, innate immune response. Hub genes with high degrees, including MAPK14, SLC2A3, STOM, and MMP8, were demonstrated to have an association with sepsis. Furthermore, RT-PCR results showed that SLC2A3 and MAPK14 were significantly upregulated in the HUVECs induced by LPS compared with controls.In conclusion, DEGs and hub genes identified in the present study help us understand the molecular mechanisms of sepsis, and provide candidate targets for diagnosis and treatment of sepsis.

Virus-induced genetics revealed by multidimensional precision medicine transcriptional workflow applicable to COVID-19

Precision medicine requires the translation of basic biological understanding to medical insights, mainly applied to characterization of each unique patient. In many clinical settings, this requires tools that can be broadly used to identify pathology and risks. Patients often present to the intensive care unit with broad phenotypes, including multiple organ dysfunction syndrome (MODS) resulting from infection, trauma, or other disease processes. Etiology and outcomes are unique to individuals, making it difficult to cohort patients with MODS, but presenting a prime target for testing/developing tools for precision medicine. Using multitime point whole blood (cellular/acellular) total transcriptomics in 27 patients, we highlight the promise of simultaneously mapping viral/bacterial load, cell composition, tissue damage biomarkers, balance between syndromic biology versus environmental response, and unique biological insights in each patient using a single platform measurement. Integration of a transcriptome workflow yielded unexpected insights into the complex interplay between host genetics and viral/bacterial specific mechanisms, highlighted by a unique case of virally induced genetics (VIG) within one of these 27 patients. The power of RNA-Seq to study unique patient biology while investigating environmental contributions can be a critical tool moving forward for translational sciences applied to precision medicine.

Identification and evaluation of hub mRNAs and long non-coding RNAs in neutrophils during sepsis

OBJECTIVE

To reveal the systematic response of neutrophils to sepsis and to study the hub lncRNAs in sepsis.

MATERIALS AND METHODS

Neutrophils taken from the femur and tibia of male C57 BL/6 mice were used in this study. And neutrophils were treated for 0 h, 0.5 h, 1 h, and 4 h with or without 1 µg/mL lipopolysaccharide (LPS) for further chip detection. In addition, cecal ligation and perforation were used to simulate sepsis. Here, we used different bioinformatics analyses, including differential expression analysis, weighted gene co-expression network analysis (WGCNA), and gene regulatory network analysis, to analyze the systemic response of neutrophils to sepsis.

RESULTS

We identified nine modules and found hub lncRNAs in each module. The blue and pink modules were closely related to the inflammatory state of sepsis. Some hub lncRNAs (NONMMUT005259, KnowTID_00004196, and NR_003507) may have functions related to the inflammatory state in sepsis.

CONCLUSIONS

Based on a new biological approach, our research results revealed the systemic-level response of neutrophils to sepsis and identified several hub lncRNAs with potential regulatory effects on this condition.