Development of a Bioinformatics Framework for Identification and Validation of Genomic Biomarkers and Key Immunopathology Processes and Controllers in Infectious and Non-infectious Severe Inflammatory Response Syndrome

The 'analysis of gene expression and biomarkers for point-of-care decision support in Sepsis' study; temporal clinical parameter analysis and validation of early diagnostic biomarker signatures for severe inflammation andsepsis-SIRS discrimination

Introduction

Early diagnosis of sepsis and discrimination from SIRS is crucial for clinicians to provide appropriate care, management and treatment to critically ill patients. We describe identification of mRNA biomarkers from peripheral blood leukocytes, able to identify severe, systemic inflammation (irrespective of origin) and differentiate Sepsis from SIRS, in adult patients within a multi-center clinical study.

Methods

Participants were recruited in Intensive Care Units (ICUs) from multiple UK hospitals, including fifty-nine patients with abdominal sepsis, eighty-four patients with pulmonary sepsis, forty-two SIRS patients with Out-of-Hospital Cardiac Arrest (OOHCA), sampled at four time points, in addition to thirty healthy control donors. Multiple clinical parameters were measured, including SOFA score, with many differences observed between SIRS and sepsis groups. Differential gene expression analyses were performed using microarray hybridization and data analyzed using a combination of parametric and non-parametric statistical tools.

Results

Nineteen high-performance, differentially expressed mRNA biomarkers were identified between control and combined SIRS/Sepsis groups (FC>20.0, p<0.05), termed 'indicators of inflammation' (I°I), including CD177, FAM20A and OLAH. Best-performing minimal signatures e.g. FAM20A/OLAH showed good accuracy for determination of severe, systemic inflammation (AUC>0.99). Twenty entities, termed 'SIRS or Sepsis' (S°S) biomarkers, were differentially expressed between sepsis and SIRS (FC>2·0, p-value<0.05).

Discussion

The best performing signature for discriminating sepsis from SIRS was CMTM5/CETP/PLA2G7/MIA/MPP3 (AUC=0.9758). The I°I and S°S signatures performed variably in other independent gene expression datasets, this may be due to technical variation in the study/assay platform.

ADVANCING THE UNDERSTANDING OF CLINICAL SEPSIS USING GENE EXPRESSION-DRIVEN MACHINE LEARNING TO IMPROVE PATIENT OUTCOMES

ABSTRACT

Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of machine learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. Machine learning has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management.

INVESTIGATION INTO P2Y RECEPTOR FUNCTION IN PLATELETS FROM PATIENTS WITH SEPSIS

ABSTRACT

Key underlying pathological mechanisms contributing to sepsis are hemostatic dysfunction and overwhelming inflammation. Platelet aggregation is required for hemostasis, and platelets are also separately involved in inflammatory responses that require different functional attributes. Nevertheless, P2Y receptor activation of platelets is required for this dichotomy of function. The aim of this study was to elucidate whether P2YR-dependent hemostatic and inflammatory functions were altered in platelets isolated from sepsis patients, compared with patients with mild sterile inflammation. Platelets from patients undergoing elective cardiac surgery (20 patients, 3 female) or experiencing sepsis after community-acquired pneumonia (10 patients, 4 female) were obtained through the IMMunE dysfunction and Recovery from SEpsis-related critical illness in adults (IMMERSE) Observational Clinical Trial. In vitro aggregation and chemotaxis assays were performed with platelets after stimulation with ADP and compared with platelets isolated from healthy control subjects (7 donors, 5 female). Cardiac surgery and sepsis both induced a robust inflammatory response with increases in circulating neutrophil counts with a trend toward decreased circulating platelet counts being observed. The ability of platelets to aggregate in response to ex vivo ADP stimulation was preserved in all groups. However, platelets isolated from patients with sepsis lost the ability to undergo chemotaxis toward N -formylmethionyl-leucyl-phenylalanine, and this suppression was evident at admission through to and including discharge from hospital. Our results suggest that P2Y 1 -dependent inflammatory function in platelets is lost in patients with sepsis resulting from community-acquired pneumonia. Further studies will need to be undertaken to determine whether this is due to localized recruitment to the lungs of a platelet responsive population or loss of function as a result of dysregulation of the immune response.

Artificial intelligence-driven drug development against autoimmune diseases

Artificial intelligence (AI)-based predictive models are being used to foster a precision medicine approach to treat complex chronic diseases such as autoimmune and autoinflammatory disorders (AIIDs). In the past few years the first models of systemic lupus erythematosus (SLE), primary Sjögren syndrome (pSS), and rheumatoid arthritis (RA) have been produced by molecular profiling of patients using omic technologies and integrating the data with AI. These advances have confirmed a complex pathophysiology involving multiple proinflammatory pathways and also provide evidence for shared molecular dysregulation across different AIIDs. I discuss how models are used to stratify patients, assess causality in pathophysiology, design drug candidates in silico, and predict drug efficacy in virtual patients. By relating individual patient characteristics to the predicted properties of millions of drug candidates, these models can improve the management of AIIDs through more personalized treatments.

Virtual patients, digital twins and causal disease models: paving the ground for in silico clinical trials

Computational models are being explored to simulate in silico the efficacy and safety of drug candidates and medical devices. Disease models that are based on patients' profiling data are being produced to represent interactomes of genes or proteins and to infer causality in the pathophysiology {AuQ: Edit OK?}, which makes it possible to mimic the impact of drugs on relevant targets. Virtual patients designed from medical records as well as digital twins were generated to simulate specific organs and to predict treatment efficacy at the individual patient level {AuQ: Edit OK?}. As the acceptance of digital evidence by regulators grows, predictive artificial intelligence (AI)-based models will support the design of confirmatory trials in humans and will accelerate the development of efficient drugs and medical devices.

Procalcitonin Values Fail to Track the Presence of Secondary Bacterial Infections in COVID-19 ICU Patients

The development of secondary bacterial infections in COVID-19 patients has been associated with increased mortality and worse clinical outcomes. Consequently, many patients have received empirical antibiotic therapies with the potential to further exacerbate an ongoing antimicrobial resistance crisis. The pandemic has seen a rise in the use of procalcitonin testing to guide antimicrobial prescribing, although its value remains elusive. This single-centre retrospective study sought to analyse the efficacy of procalcitonin in identifying secondary infections in COVID-19 patients and evaluate the proportion of patients prescribed antibiotics to those with confirmed secondary infection. Inclusion criteria comprised patients admitted to the Grange University Hospital intensive care unit with SARS-CoV-2 infection throughout the second and third waves of the pandemic. Data collected included daily inflammatory biomarkers, antimicrobial prescriptions, and microbiologically proven secondary infections. There was no statistically significant difference between PCT, WBC, or CRP values in those with an infection versus those without. A total of 57.02% of patients had a confirmed secondary infection, with 80.2% prescribed antibiotics in Wave 2, compared to 44.07% with confirmed infection and 52.1% prescribed antibiotics in Wave 3. In conclusion, procalcitonin values failed to indicate the emergence of critical care-acquired infection in COVID-19 patients.

Research Progress of Respiratory Disease and Idiopathic Pulmonary Fibrosis Based on Artificial Intelligence

Machine Learning (ML) is an algorithm based on big data, which learns patterns from the previously observed data through classifying, predicting, and optimizing to accomplish specific tasks. In recent years, there has been rapid development in the field of ML in medicine, including lung imaging analysis, intensive medical monitoring, mechanical ventilation, and there is need for intubation etiology prediction evaluation, pulmonary function evaluation and prediction, obstructive sleep apnea, such as biological information monitoring and so on. ML can have good performance and is a great potential tool, especially in the imaging diagnosis of interstitial lung disease. Idiopathic pulmonary fibrosis (IPF) is a major problem in the treatment of respiratory diseases, due to the abnormal proliferation of fibroblasts, leading to lung tissue destruction. The diagnosis mainly depends on the early detection of imaging and early treatment, which can effectively prolong the life of patients. If the computer can be used to assist the examination results related to the effects of fibrosis, a timely diagnosis of such diseases will be of great value to both doctors and patients. We also previously proposed a machine learning algorithm model that can play a good clinical guiding role in early imaging prediction of idiopathic pulmonary fibrosis. At present, AI and machine learning have great potential and ability to transform many aspects of respiratory medicine and are the focus and hotspot of research. AI needs to become an invisible, seamless, and impartial auxiliary tool to help patients and doctors make better decisions in an efficient, effective, and acceptable way. The purpose of this paper is to review the current application of machine learning in various aspects of respiratory diseases, with the hope to provide some help and guidance for clinicians when applying algorithm models.

Leveraging big data bioinformatics approaches to extract knowledge from Staphylococcus aureus public omics data

Staphylococcus aureus is a notorious pathogen posing challenges in the medical industry due to drug resistance and biofilm formation. The horizon of knowledge on S. aureus pathogenesis has expanded with the advancement of data-driven bioinformatics techniques. Mining information from sequenced genomes and their expression data is an economic approach that alleviates wastage of resources and redundancy in experiments. The current review covers how big data bioinformatics has been used in the analysis of S. aureus from publicly available -omics data to uncover mechanisms of infection and inhibition. Particularly, advances in the past two decades in biomarker discovery, host responses, phenotype identification, consolidation of information, and drug development are discussed highlighting the challenges and shortcomings. Overall, the review summarizes the diverse aspects of scrupulous re-analysis of S. aureus proteomic and transcriptomic expression datasets retrieved from public repositories in terms of the efforts taken, benefits offered, and follow-up actions. The detailed review thus serves as a reference and aid for (i) Computational biologists by briefing the approaches utilized for bacterial omics re-analysis concerning S. aureus and (ii) Experimental biologists by elucidating the potential of bioinformatics in biological research to generate reliable postulates in a prompt and economical manner.

The Use of Different Sepsis Risk Stratification Tools on the Wards and in Emergency Departments Uncovers Different Mortality Risks: Results of the Three Welsh National Multicenter Point-Prevalence Studies

Supplemental Digital Content is available in the text.

To compare the performance of Sequential Organ Failure Assessment, systemic inflammatory response syndrome, Red Flag Sepsis, and National Institute of Clinical Excellence sepsis risk stratification tools in the identification of patients at greatest risk of mortality from sepsis in nonintensive care environments.

The Optimization and Biological Significance of a 29-Host-Immune-mRNA Panel for the Diagnosis of Acute Infections and Sepsis

In response to the unmet need for timely accurate diagnosis and prognosis of acute infections and sepsis, host-immune-response-based tests are being developed to help clinicians make more informed decisions including prescribing antimicrobials, ordering additional diagnostics, and assigning level of care. One such test (InSep™, Inflammatix, Inc.) uses a 29-mRNA panel to determine the likelihood of bacterial infection, the separate likelihood of viral infection, and the risk of physiologic decompensation (severity of illness). The test, being implemented in a rapid point-of-care platform with a turnaround time of 30 min, enables accurate and rapid diagnostic use at the point of impact. In this report, we provide details on how the 29-biomarker signature was chosen and optimized, together with its molecular, immunological, and medical significance to better understand the pathophysiological relevance of altered gene expression in disease. We synthesize key results obtained from gene-level functional annotations, geneset-level enrichment analysis, pathway-level analysis, and gene-network-level upstream regulator analysis. Emerging findings are summarized as hallmarks on immune cell interaction, inflammatory mediators, cellular metabolism and homeostasis, immune receptors, intracellular signaling and antiviral response; and converging themes on neutrophil degranulation and activation involved in immune response, interferon, and other signaling pathways.

Multi-dimensional and longitudinal systems profiling reveals predictive pattern of severe COVID-19

COVID-19 is a respiratory tract infection that can affect multiple organ systems. Predicting the severity and clinical outcome of individual patients is a major unmet clinical need that remains challenging due to intra- and inter-patient variability. Here, we longitudinally profiled and integrated more than 150 clinical, laboratory, and immunological parameters of 173 patients with mild to fatal COVID-19. Using systems biology, we detected progressive dysregulation of multiple parameters indicative of organ damage that correlated with disease severity, particularly affecting kidneys, hepatobiliary system, and immune landscape. By performing unsupervised clustering and trajectory analysis, we identified T and B cell depletion as early indicators of a complicated disease course. In addition, markers of hepatobiliary damage emerged as robust predictor of lethal outcome in critically ill patients. This allowed us to propose a novel clinical COVID-19 SeveriTy (COST) score that distinguishes complicated disease trajectories and predicts lethal outcome in critically ill patients.

Artificial Intelligence for Clinical Decision Support in Sepsis

Sepsis is one of the main causes of death in critically ill patients. Despite the continuous development of medical technology in recent years, its morbidity and mortality are still high. This is mainly related to the delay in starting treatment and non-adherence of clinical guidelines. Artificial intelligence (AI) is an evolving field in medicine, which has been used to develop a variety of innovative Clinical Decision Support Systems. It has shown great potential in predicting the clinical condition of patients and assisting in clinical decision-making. AI-derived algorithms can be applied to multiple stages of sepsis, such as early prediction, prognosis assessment, mortality prediction, and optimal management. This review describes the latest literature on AI for clinical decision support in sepsis, and outlines the application of AI in the prediction, diagnosis, subphenotyping, prognosis assessment, and clinical management of sepsis. In addition, we discussed the challenges of implementing and accepting this non-traditional methodology for clinical purposes.

Signatures of Dermal Fibroblasts from RDEB Pediatric Patients

The recessive form of dystrophic epidermolysis bullosa (RDEB) is a debilitating disease caused by impairments in the junctions of the dermis and the basement membrane of the epidermis. Mutations in the COL7A1 gene induce multiple abnormalities, including chronic inflammation and profibrotic changes in the skin. However, the correlations between the specific mutations in COL7A1 and their phenotypic output remain largely unexplored. The mutations in the COL7A1 gene, described here, were found in the DEB register. Among them, two homozygous mutations and two cases of compound heterozygous mutations were identified. We created the panel of primary patient-specific RDEB fibroblast lines (FEB) and compared it with control fibroblasts from healthy donors (FHC). The set of morphological features and the contraction capacity of the cells distinguished FEB from FHC. We also report the relationships between the mutations and several phenotypic traits of the FEB. Based on the analysis of the available RNA-seq data of RDEB fibroblasts, we performed an RT-qPCR gene expression analysis of our cell lines, confirming the differential status of multiple genes while uncovering the new ones. We anticipate that our panels of cell lines will be useful not only for studying RDEB signatures but also for investigating the overall mechanisms involved in disease progression.

Differential genes expression analysis of invasive aspergillosis: a bioinformatics study based on mRNA/microRNA

Invasive aspergillosis is a severe opportunistic infection with high mortality in immunocompromised patients. Recently, the roles of microRNAs have been taken into consideration in the immune system and inflammatory responses. Using bioinformatics approaches, we aimed to study the microRNAs related to invasive aspergillosis to understand the molecular pathways involved in the disease pathogenesis. Data were extracted from the gene expression omnibus (GEO) database. We proposed 3 differentially expressed genes; S100B, TDRD9 and TMTC1 related to pathogenesis of invasive aspergillosis. Using miRWalk 2.0 predictive tool, microRNAs that targeted the selected genes were identified. The roles of microRNAs were investigated by microRNA target prediction and molecular pathways analysis. The significance of combined expression changes in selected genes was analyzed by ROC curves study. Thirty-three microRNAs were identified as the common regulator of S100B, TDRD9 and TMTC1 genes. Several of them were previously reported in the pathogenesis of fungal infections including miR-132. Predicted microRNAs were involved in innate immune response as well as toll-like receptor signaling. Most of the microRNAs were also linked to platelet activation. The ROC chart in the combination mode of S100B/TMTC1, showed the sensitivity of 95.65 percent and the specificity of 69.23 percent. New approaches are needed for rapid and accurate detection of invasive aspergillosis. Given the pivotal signaling pathways involved, predicted microRNAs can be considered as the potential candidates of the disease diagnosis. Further investigation of the microRNAs expression changes and related pathways would lead to identifying the effective biomarkers for IA detection.

Clinical Risk Prediction Scores in Coronavirus Disease 2019: Beware of Low Validity and Clinical Utility

Several risk stratification tools were developed to predict disease progression in coronavirus disease 2019, with no external validation to date. We attempted to validate three previously published risk-stratification tools in a multicenter study. Primary outcome was a composite outcome of development of severe coronavirus disease 2019 disease leading to ICU admission or death censored at hospital discharge or 30 days. We collected data from 169 patients. Patients were 73 years old (59–82 yr old), 66 of 169 (39.1%) were female, 57 (33.7%) had one comorbidity, and 80 (47.3%) had two or more comorbidities. Area under the receiver operating characteristic curve (95% CI) for the COVID-GRAM score was 0.636 (0.550–0.722), for the CALL score 0.500 (0.411–0.589), and for the nomogram 0.628 (0.543–0.714).