The host response as a tool for infectious disease diagnosis and management

Protein and transcriptional biomarker profiling may inform treatment strategies in lower respiratory tract infections by indicating bacterial-viral differentiation

Lower respiratory tract infections (LRTIs) remain a significant global cause of infectious disease-related mortality. Accurate discrimination between acute bacterial and viral LRTIs is crucial for optimal patient care, prevention of unnecessary antibiotic prescriptions, and resource allocation. Plasma samples from LRTI patients with bacterial (n = 36), viral (n = 27; excluding SARS-CoV-2), SARS-CoV-2 (n = 22), and mixed bacterial-viral (n = 38) etiology were analyzed for protein profiling. Whole-blood RNA samples from a subset of patients (bacterial, n = 8; viral, n = 8; and SARS-CoV-2, n = 8) were analyzed for transcriptional profiling. Lasso regression modeling identified a seven-protein signature (CRP, IL4, IL9, IP10, MIP1α, MIP1β, and TNFα) that discriminated between patients with bacterial (n = 36) vs viral (n = 27) infections with an area under the curve (AUC) of 0.98. When comparing patients with bacterial and mixed bacterial-viral infections (antibiotics clinically justified; n = 74) vs patients with viral and SARS-CoV-2 infections (antibiotics clinically not justified; n = 49), a 10-protein signature (CRP, bFGF, eotaxin, IFNγ, IL1β, IL7, IP10, MIP1α, MIP1β, and TNFα) with an AUC of 0.94 was identified. The transcriptional profiling analysis identified 232 differentially expressed genes distinguishing bacterial (n = 8) from viral and SARS-CoV-2 (n = 16) etiology. Protein-protein interaction enrichment analysis identified 20 genes that could be useful in the differentiation between bacterial and viral infections. Finally, we examined the performance of selected published gene signatures for bacterial-viral differentiation in our gene set, yielding promising results. Further validation of both protein and gene signatures in diverse clinical settings is warranted to establish their potential to guide the treatment of acute LRTIs.

IMPORTANCE

Accurate differentiation between bacterial and viral lower respiratory tract infections (LRTIs) is vital for effective patient care and resource allocation. This study investigated specific protein signatures and gene expression patterns in plasma and blood samples from LRTI patients that distinguished bacterial and viral infections. The identified signatures can inform the design of point-of-care tests that can aid healthcare providers in making informed decisions about antibiotic prescriptions in order to reduce unnecessary use, thereby contributing to reduced side effects and antibiotic resistance. Furthermore, the potential for faster and more accurate diagnoses for improved patient management in acute LRTIs is compelling.

Deep learning model to discriminate diverse infection types based on pairwise analysis of host gene expression

Accurate detection of pathogens, particularly distinguishing between Gram-positive and Gram-negative bacteria, could improve disease treatment. Host gene expression can capture the immune system's response to infections caused by various pathogens. Here, we present a deep neural network model, bvnGPS2, which incorporates the attention mechanism based on a large-scale integrated host transcriptome dataset to precisely identify Gram-positive and Gram-negative bacterial infections as well as viral infections. We performed analysis of 4,949 blood samples across 40 cohorts from 10 countries using our previously designed omics data integration method, iPAGE, to select discriminant gene pairs and train the bvnGPS2. The performance of the model was evaluated on six independent cohorts comprising 374 samples. Overall, our deep neural network model shows robust capability to accurately identify specific infections, paving the way for precise medicine strategies in infection treatment and potentially also for identifying subtypes of other diseases.

CLINICAL, MOLECULAR, AND EXOSOMAL MECHANISMS OF CARDIAC AND BRAIN DYSFUNCTION IN SEPSIS

ABSTRACT

Sepsis is a complex disease resulting from a dysregulated inflammatory response to an infection. Initiation of sepsis occurs from a localized infection that disseminates to the bloodstream placing all organ systems at risk. Septic shock is classically observed to manifest itself as systemic hypotension with hyporesponsiveness to vasopressor agents. Myocardial dysfunction occurs resulting in an inability to perfuse major organ systems throughout the body. Most importantly, the brain is hypoperfused creating an ischemic and inflammatory state resulting in the clinical observation of acute mental status changes and cognitive dysfunction commonly known as sepsis-associated encephalopathy. This short review describes the inflammatory molecular mechanisms of myocardial dysfunction, discusses the evidence of the dual roles of the microglia resulting in blood-brain barrier disruption, and suggests that septic-derived exosomes, endosome-derived lipid bilayer spheroids released from living cells, influence cardiac and neurological cellular function.

The potential of digital molecular diagnostics for infectious diseases in sub-Saharan Africa

There is a large gap between diagnostic needs and diagnostic access across much of sub-Saharan Africa (SSA), particularly for infectious diseases that inflict a substantial burden of morbidity and mortality. Accurate diagnostics are essential for the correct treatment of individuals and provide vital information underpinning disease surveillance, prevention, and control strategies. Digital molecular diagnostics combine the high sensitivity and specificity of molecular detection with point-of-care format and mobile connectivity. Recent developments in these technologies create an opportunity for a radical transformation of the diagnostic ecosystem. Rather than trying to emulate diagnostic laboratory models in resource-rich settings, African countries have the potential to pioneer new models of healthcare designed around digital diagnostics. This article describes the need for new diagnostic approaches, highlights advances in digital molecular diagnostic technology, and outlines their potential for tackling infectious diseases in SSA. It then addresses the steps that will be necessary for the development and implementation of digital molecular diagnostics. Although the focus is on infectious diseases in SSA, many of the principles apply to other resource-limited settings and to noncommunicable diseases.

Advancing Diagnostic Stewardship for Healthcare Associated Infections, Antibiotic Resistance, and Sepsis

Diagnostic stewardship means ordering the right tests, for the right patient at the right time to inform optimal clinical care. Diagnostic stewardship is an integral part of antibiotic stewardship efforts to optimize antibiotic use and improve patient outcomes, including reductions in antibiotic resistance, and treatment of sepsis. CDC's Division of Healthcare Quality Promotion (DHQP) hosted a meeting on improving patient safety through diagnostic stewardship with a focus on the use of the laboratory. The meeting identified emerging issues in the field of diagnostic stewardship, raised awareness of these issues among stakeholders, and discussed strategies and interventions to address the issues-all with an emphasis on improved outcomes and patient safety. This white paper summarizes the key takeaways of the meeting including needs for diagnostic stewardship implementation, promising future avenues for diagnostic stewardship implementation, and areas of needed research.

VB10, a new blood biomarker for differential diagnosis and recovery monitoring of acute viral and bacterial infections

BACKGROUND

Precise differential diagnosis between acute viral and bacterial infections is important to enable appropriate therapy, avoid unnecessary antibiotic prescriptions and optimize the use of hospital resources. A systems view of host response to infections provides opportunities for discovering sensitive and robust molecular diagnostics.

METHODS

We combine blood transcriptomes from six independent datasets (n = 756) with a knowledge-based human protein-protein interaction network, identifies subnetworks capturing host response to each infection class, and derives common response cores separately for viral and bacterial infections. We subject the subnetworks to a series of computational filters to identify a parsimonious gene panel and a standalone diagnostic score that can be applied to individual samples. We rigorously validate the panel and the diagnostic score in a wide range of publicly available datasets and in a newly developed Bangalore-Viral Bacterial (BL-VB) cohort.

FINDING

We discover a 10-gene blood-based biomarker panel (Panel-VB) that demonstrates high predictive performance to distinguish viral from bacterial infections, with a weighted mean AUROC of 0.97 (95% CI: 0.96-0.99) in eleven independent datasets (n = 898). We devise a new stand-alone patient-wise score (VB10) based on the panel, which shows high diagnostic accuracy with a weighted mean AUROC of 0.94 (95% CI 0.91-0.98) in 2996 patient samples from 56 public datasets from 19 different countries. Further, we evaluate VB10 in a newly generated South Indian (BL-VB, n = 56) cohort and find 97% accuracy in the confirmed cases of viral and bacterial infections. We find that VB10 is (a) capable of accurately identifying the infection class in culture-negative indeterminate cases, (b) reflects recovery status, and (c) is applicable across different age groups, covering a wide spectrum of acute bacterial and viral infections, including uncharacterized pathogens. We tested our VB10 score on publicly available COVID-19 data and find that our score detected viral infection in patient samples.

INTERPRETATION

Our results point to the promise of VB10 as a diagnostic test for precise diagnosis of acute infections and monitoring recovery status. We expect that it will provide clinical decision support for antibiotic prescriptions and thereby aid in antibiotic stewardship efforts.

FUNDING

Grand Challenges India, Biotechnology Industry Research Assistance Council (BIRAC), Department of Biotechnology, Govt. of India.

Immune Profiling Panel: A Proof-of-Concept Study of a New Multiplex Molecular Tool to Assess the Immune Status of Critically Ill Patients

BACKGROUND

Critical illness such as sepsis is a life-threatening syndrome defined as a dysregulated host response to infection and is characterized by patients exhibiting impaired immune response. In the field of diagnosis, a gap still remains in identifying the immune profile of critically ill patients in the intensive care unit (ICU).

METHODS

A new multiplex immune profiling panel (IPP) prototype was assessed for its ability to semiquantify messenger RNA immune-related markers directly from blood, using the FilmArray System, in less than an hour. Samples from 30 healthy volunteers were used for the technical assessment of the IPP tool. Then the tool was clinically assessed using samples from 10 healthy volunteers and 20 septic shock patients stratified using human leukocyte antigen-DR expression on monocytes (mHLA-DR).

RESULTS

The IPP prototype consists of 16 biomarkers that target the immune response. The majority of the assays had a linear expression with different RNA inputs and a coefficient of determination (R2) > 0.8. Results from the IPP pouch were comparable to standard quantitative polymerase chain reaction and the assays were within the limits of agreement in Bland-Altman analysis. Quantification cycle values of the target genes were normalized against reference genes and confirmed to account for the different cell count and technical variability. The clinical assessment of the IPP markers demonstrated various gene modulations that could distinctly differentiate 3 profiles: healthy volunteers, intermediate mHLA-DR septic shock patients, and low mHLA-DR septic shock patients.

CONCLUSIONS

The use of IPP showed great potential for the development of a fully automated, rapid, and easy-to-use immune profiling tool. The IPP tool may be used in the future to stratify critically ill patients in the ICU according to their immune status. Such stratification will enable personalized management of patients and guide treatments to avoid secondary infections and lower mortality.

Validation of a host gene expression test for bacterial/viral discrimination in immunocompromised hosts

BACKGROUND

Host gene expression has emerged as a complementary strategy to pathogen detection tests for the discrimination of bacterial and viral infection. The impact of immunocompromise on host response tests remains unknown. We evaluated a host response test discriminating bacterial, viral, and non-infectious conditions in immunocompromised subjects.

METHODS

An 81-gene signature was measured using RT-PCR in subjects with immunocompromise (chemotherapy, solid organ transplant, immunomodulatory agents, AIDS) with bacterial infection, viral infection, or noninfectious illness. A regularized logistic regression model trained in immunocompetent subjects was used to estimate the likelihood of each class in immunocompromised subjects.

RESULTS

Accuracy in the 136-subject immunocompetent training cohort was 84.6% for bacterial vs. non-bacterial discrimination and 80.8% for viral vs. non-viral discrimination. Model validation in 134 immunocompromised subjects showed overall accuracy of 73.9% for bacterial infection (p=0.04 relative to immunocompetent subjects) and 75.4% for viral infection (p=0.30). A scheme reporting results by quartile improved test utility. The highest probability quartile ruled-in bacterial and viral infection with 91.4% and 84.0% specificity, respectively. The lowest probability quartile ruled-out infection with 90.1% and 96.4% sensitivity for bacterial and viral infection, respectively. Performance was independent of the type or number of immunocompromising conditions.

CONCLUSION

A host gene expression test discriminated bacterial, viral, and non-infectious etiologies at a lower overall accuracy in immunocompromised patients compared to immunocompetent patients, though this difference was only significant for bacterial infection classification. With modified interpretive criteria, a host response strategy may offer clinically useful diagnostic information for patients with immunocompromise.

Integrative Transcriptome and Proteome Analyses Provide New Insights Into the Interaction Between Live Borrelia burgdorferi and Frontal Cortex Explants of the Rhesus Brain

Borrelia burgdorferi (Bb), which is neurotropic, can attack the central nervous system (CNS), leading to the development of various neurologic symptoms. The pathogenesis of Lyme neuroborreliosis (LNB) remains poorly understood. Presently, there is a lack of knowledge of the changes in mRNA and proteins in the CNS following early disseminated Lyme disease. Explants from the frontal cortex of 3 rhesus brains were incubated with medium alone or with medium containing live Bb for 6, 12, or 24 hours. Then, we analyzed identified mRNA and proteins in the frontal cortex tissues, allowing for an in-depth view of the transcriptome and proteome for a macroscopic and unbiased understanding of early disseminated Lyme disease in the brain. Through bioinformatics analysis, a complex network of enriched pathways that were mobilized during the progression of Lyme spirochete infection was described. Furthermore, based on the analysis of omics data, translational regulation, glycosaminoglycan/proteoglycan-binding activity in colonization and dissemination to tissues, disease-associated genes, and synaptic function were enriched, which potentially play a role in pathogenesis during the interaction between frontal cortex tissues and spirochetes. These integrated omics results provide unbiased and comprehensive information for the further understanding of the molecular mechanisms of LNB.

Molecular and non-molecular approaches to etiologic diagnosis of gastroenteritis

Gastroenteritis is a major cause of mortality and morbidity globally and rapid identification of the causative pathogen is important for appropriate treatment and patient management, implementation of effective infection control measures, reducing hospital length of stay, and reducing overall medical costs. Although stool culture and microscopic examination of diarrheal stool has been the primary method for laboratory diagnosis, culture-independent proteomic and genomic tests are receiving increased attention. Antigen tests for stool pathogens are routinely implemented as rapid and simple analytics whereas molecular tests are now available in various formats from high complexity to waived point-of-care tests. In addition, metagenomic next-generation sequencing stands poised for use as a method for both diagnosis and routine characterization of the gut microbiome in the very near future. Analysis of host biomarkers as indicators of infection status and pathogenesis may also become important for prediction, diagnosis, and monitoring of gastrointestinal infection. Here we review current methods and emerging technologies for the etiologic diagnosis of gastroenteritis in the clinical laboratory. Benefits and limitations of these evolving methods are highlighted.

Proteomic Profiles of Exosomes of Septic Patients Presenting to the Emergency Department Compared to Healthy Controls

BACKGROUND

Septic Emergency Department (ED) patients provide a unique opportunity to investigate early sepsis. Recent work focuses on exosomes, nanoparticle-sized lipid vesicles (30-130 nm) that are released into the bloodstream to transfer its contents (RNA, miRNA, DNA, protein) to other cells. Little is known about how early changes related to exosomes may contribute to the dysregulated inflammatory septic response that leads to multi-organ dysfunction. We aimed to evaluate proteomic profiles of plasma derived exosomes obtained from septic ED patients and healthy controls.

METHODS

This is a prospective observational pilot study evaluating a plasma proteomic exosome profile at an urban tertiary care hospital ED using a single venipuncture blood draw, collecting 40 cc Ethylenediaminetetraacetic acid (EDTA) blood.

MEASUREMENTS

We recruited seven patients in the ED within 6 h of their presentation and five healthy controls. Plasma exosomes were isolated using the Invitrogen Total Exosome Isolation Kit. Exosome proteomic profiles were analyzed using fusion mass spectroscopy and Proteome Discoverer. Principal component analysis (PCA) and differential expression analysis (DEA) for sepsis versus control was performed.

RESULTS

PCA of 261 proteins demonstrated septic patients and healthy controls were distributed in two groups. DEA revealed that 62 (23.8%) proteins differed between the exosomes of septic patients and healthy controls, p-value < 0.05. Adjustments using the False Discovery Rate (FDR) showed 23 proteins remained significantly different (FDR < 0.05) between sepsis and controls. Septic patients and controls were classified into two distinct groups by hierarchical clustering using the 62 nominally DE proteins. After adjustment multiple comparisons, three acute phase proteins remained significantly different between patients and controls: Serum amyloid A-1, C-reactive protein and Serum Amyloid A-2. Inflammatory response proteins immunoglobulin heavy constant Δ and Fc-fragment of IgG binding protein were increased.

CONCLUSION

Exosome proteomic profiles of septic ED patients differ from their healthy counterparts with regard to acute phase response and inflammation.

A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections

Improved identification of bacterial and viral infections would reduce morbidity from sepsis, reduce antibiotic overuse, and lower healthcare costs. Here, we develop a generalizable host-gene-expression-based classifier for acute bacterial and viral infections. We use training data (N = 1069) from 18 retrospective transcriptomic studies. Using only 29 preselected host mRNAs, we train a neural-network classifier with a bacterial-vs-other area under the receiver-operating characteristic curve (AUROC) 0.92 (95% CI 0.90–0.93) and a viral-vs-other AUROC 0.92 (95% CI 0.90–0.93). We then apply this classifier, inflammatix-bacterial-viral-noninfected-version 1 (IMX-BVN-1), without retraining, to an independent cohort (N = 163). In this cohort, IMX-BVN-1 AUROCs are: bacterial-vs.-other 0.86 (95% CI 0.77–0.93), and viral-vs.-other 0.85 (95% CI 0.76–0.93). In patients enrolled within 36 h of hospital admission (N = 70), IMX-BVN-1 AUROCs are: bacterial-vs.-other 0.92 (95% CI 0.83–0.99), and viral-vs.-other 0.91 (95% CI 0.82–0.98). With further study, IMX-BVN-1 could provide a tool for assessing patients with suspected infection and sepsis at hospital admission.

Diagnosing acute infections based on transcriptional host response shows promise, but generalizability is wanting. Here, the authors use a co-normalization framework to train a classifier to diagnose acute infections and apply it to independent data on a targeted diagnostic platform.

Companion and complementary diagnostics for infectious diseases

INTRODUCTION

Companion diagnostics (CDx) are important in oncology therapeutic decision-making, but specific regulatory-approved CDx for infectious disease treatment are officially lacking. While not approved as CDx, several ID diagnostics are used as CDx. The diagnostics community, manufacturers, and regulatory agencies have made major efforts to ensure that diagnostics for new antimicrobials are available at or near release of new agents.

AREAS COVERED

This review highlights the status of Complementary and companion diagnostic (c/CDx) in the infectious disease literature, with a focus on genotypic antimicrobial resistance testing against pathogens as a class of diagnostic tests.

EXPERT OPINION

CRISPR, sepsis markers, and narrow spectrum antimicrobials, in addition to current and emerging technologies, present opportunities for infectious disease c/CDx. Challenges include slow guideline revision, high costs for regulatory approval, lengthy buy in by agencies, discordant pharmaceutical/diagnostic partnerships, and higher treatment costs. The number of patients and available medications used to treat different infectious diseases is well suited to support competing diagnostic tests. However, newer approaches to treatment (for example, narrow spectrum antibiotics), may be well suited for a small number of patients, i.e. a niche market in support of a CDx. The current emphasis is rapid and point-of-care (POC) diagnostic platforms as well as changes in treatment.

Sepsis: Personalized Medicine Utilizing 'Omic' Technologies-A Paradigm Shift?

Sepsis has over the years proven a considerable challenge to physicians and researchers. Numerous pharmacological and non-pharmacological interventions have been tested in trials, but have unfortunately failed to improve the general prognosis. This has led to the speculation that the sepsis population may be too heterogeneous to be targeted with the traditional one treatment suits all’ approach. Recent advances in genetic and biochemical analyses now allow genotyping and biochemical characterisation of large groups of patients via the ‘omics’ technologies. These new opportunities could lead to a paradigm shift in the approach to sepsis towards personalised treatments with interventions targeted towards specific pathophysiological mechanisms activated in the patient. In this article, we review the potentials and pitfalls of using new advanced technologies to deepen our understanding of the clinical syndrome of sepsis.