Discovery and validation of a three-gene signature to distinguish COVID-19 and other viral infections in emergency infectious disease presentations: a case-control and observational cohort study

Human Conjunctival Transcriptome in Acanthamoeba Keratitis: An Exploratory Study

PURPOSE

The purpose of this study was to identify conjunctival transcriptome differences in patients with Acanthamoeba keratitis compared with keratitis with no known associated pathogen.

METHODS

The host conjunctival transcriptome of 9 patients with Acanthamoeba keratitis (AK) is compared with the host conjunctival transcriptome of 13 patients with pathogen-free keratitis. Culture and/or confocal confirmed Acanthamoeba in 8 of 9 participants with AK who underwent metagenomic RNA sequencing as the likely pathogen. Cultures were negative in all 13 cases where metagenomic RNA sequencing did not identify a pathogen.

RESULTS

Transcriptome analysis identified 36 genes differently expressed between patients with AK and patients with presumed sterile, or pathogen-free, keratitis. Gene enrichment analysis revealed that some of these genes participate in several biologic pathways important for cellular signaling, ion transport and homeostasis, glucose transport, and mitochondrial metabolism. Notable relatively differentially expressed genes with potential relevance to Acanthamoeba infection included CPS1, SLC35B4, STEAP2, ATP2B2, NMNAT3, and AKAP12.

CONCLUSIONS

This research suggests that the local transcriptome in Acanthamoeba keratitis may be sufficiently robust to be detected in the conjunctiva and that corneas infected with Acanthamoeba may be distinguished from the inflamed cornea where no pathogen was identified. Given the low sensitivity for corneal cultures, identification of differentially expressed genes may serve as a suggestive transcriptional signature allowing for a complementary diagnostic technique to identify this blinding parasite. Knowledge of differentially expressed genes may also direct investigation of disease pathophysiology and suggest novel pathways for therapeutic targets.

Host gene expression signatures to identify infection type and organ dysfunction in children evaluated for sepsis: a multicentre cohort study

BACKGROUND

Sepsis is defined as dysregulated host response to infection that leads to life-threatening organ dysfunction. Biomarkers characterising the dysregulated host response in sepsis are lacking. We aimed to develop host gene expression signatures to predict organ dysfunction in children with bacterial or viral infection.

METHODS

This cohort study was done in emergency departments and intensive care units of four hospitals in Queensland, Australia, and recruited children aged 1 month to 17 years who, upon admission, underwent a diagnostic test, including blood cultures, for suspected sepsis. Whole-blood RNA sequencing of blood was performed with Illumina NovaSeq (San Diego, CA, USA). Samples with completed phenotyping, monitoring, and RNA extraction by March 31, 2020, were included in the discovery cohort; samples collected or completed thereafter and by Oct 27, 2021, constituted the Rapid Paediatric Infection Diagnosis in Sepsis (RAPIDS) internal validation cohort. An external validation cohort was assembled from RNA sequencing gene expression count data from the observational European Childhood Life-threatening Infectious Disease Study (EUCLIDS), which recruited children with severe infection in nine European countries between 2012 and 2016. Feature selection approaches were applied to derive novel gene signatures for disease class (bacterial vs viral infection) and disease severity (presence vs absence of organ dysfunction 24 h post-sampling). The primary endpoint was the presence of organ dysfunction 24 h after blood sampling in the presence of confirmed bacterial versus viral infection. Gene signature performance is reported as area under the receiver operating characteristic curves (AUCs) and 95% CI.

FINDINGS

Between Sept 25, 2017, and Oct 27, 2021, 907 patients were enrolled. Blood samples from 595 patients were included in the discovery cohort, and samples from 312 children were included in the RAPIDS validation cohort. We derived a ten-gene disease class signature that achieved an AUC of 94·1% (95% CI 90·6-97·7) in distinguishing bacterial from viral infections in the RAPIDS validation cohort. A ten-gene disease severity signature achieved an AUC of 82·2% (95% CI 76·3-88·1) in predicting organ dysfunction within 24 h of sampling in the RAPIDS validation cohort. Used in tandem, the disease class and disease severity signatures predicted organ dysfunction within 24 h of sampling with an AUC of 90·5% (95% CI 83·3-97·6) for patients with predicted bacterial infection and 94·7% (87·8-100·0) for patients with predicted viral infection. In the external EUCLIDS validation dataset (n=362), the disease class and disease severity predicted organ dysfunction at time of sampling with an AUC of 70·1% (95% CI 44·1-96·2) for patients with predicted bacterial infection and 69·6% (53·1-86·0) for patients with predicted viral infection.

INTERPRETATION

In children evaluated for sepsis, novel host transcriptomic signatures specific for bacterial and viral infection can identify dysregulated host response leading to organ dysfunction.

FUNDING

Australian Government Medical Research Future Fund Genomic Health Futures Mission, Children's Hospital Foundation Queensland, Brisbane Diamantina Health Partners, Emergency Medicine Foundation, Gold Coast Hospital Foundation, Far North Queensland Foundation, Townsville Hospital and Health Services SERTA Grant, and Australian Infectious Diseases Research Centre.

Whole-Blood Gene Expression Profile After Hypoxic-Ischemic Encephalopathy

Importance

Induced hypothermia, the standard treatment for hypoxic-ischemic encephalopathy (HIE) in high-income countries (HICs), is less effective in the low-income populations in South Asia, who have the highest disease burden.

Objective

To investigate the differences in blood genome expression profiles of neonates with HIE from an HIC vs neonates with HIE from South Asia.

Design, Setting, and Participants

This case-control study analyzed data from (1) a prospective observational study involving neonates with moderate or severe HIE who underwent whole-body hypothermia between January 2017 and June 2019 and age-matched term healthy controls in Italy and (2) a randomized clinical trial involving neonates with moderate or severe HIE in India, Sri Lanka, and Bangladesh recruited between August 2015 and February 2019. Data were analyzed between October 2020 and August 2023.

Exposure

Whole-blood RNA that underwent next-generation sequencing.

Main Outcome and Measures

The primary outcomes were whole-blood genome expression profile at birth associated with adverse outcome (death or disability at 18 months) after HIE in the HIC and South Asia cohorts and changes in whole-genome expression profile during the first 72 hours after birth in neonates with HIE and healthy controls from the HIC cohort. Blood samples for RNA extraction were collected before whole-body hypothermia at 4 time points (6, 24, 48, and 72 hours after birth) for the HIC cohort. Only 1 blood sample was drawn within 6 hours after birth for the South Asia cohort.

Results

The HIC cohort was composed of 35 neonates (21 females [60.0%]) with a median (IQR) birth weight of 3.3 (3.0-3.6) kg and gestational age of 40.0 (39.0-40.6) weeks. The South Asia cohort consisted of 99 neonates (57 males [57.6%]) with a median (IQR) birth weight of 2.9 (2.7-3.3) kg and gestational age of 39.0 (38.0-40.0) weeks. Healthy controls included 14 neonates (9 females [64.3%]) with a median (IQR) birth weight of 3.4 (3.2-3.7) kg and gestational age of 39.2 (38.9-40.4) weeks. A total of 1793 significant genes in the HIC cohort and 99 significant genes in the South Asia cohort were associated with adverse outcome (false discovery rate <0.05). Only 11 of these genes were in common, and all had opposite direction in fold change. The most significant pathways associated with adverse outcome were downregulation of eukaryotic translation initiation factor 2 signaling in the HIC cohort (z score = -4.56; P < .001) and aldosterone signaling in epithelial cells in the South Asia cohort (z score = null; P < .001). The genome expression profile of neonates with HIE (n = 35) at birth, 24 hours, 48 hours, and 72 hours remained significantly different from that of age-matched healthy controls in the HIC cohort (n = 14).

Conclusions and Relevance

This case-control study found that disease mechanisms underlying HIE were primarily associated with acute hypoxia in the HIC cohort and nonacute hypoxia in the South Asia cohort. This finding might explain the lack of hypothermic neuroprotection.

Using risk factors and markers to predict bacterial respiratory co-/superinfections in COVID-19 patients: is the antibiotic steward's toolbox full or empty?

BACKGROUND

Adequate diagnosis of bacterial respiratory tract co-/superinfection (bRTI) in coronavirus disease (COVID-19) patients is challenging, as there is insufficient knowledge about the role of risk factors and (para)clinical parameters in the identification of bacterial co-/superinfection in the COVID-19 setting. Empirical antibiotic therapy is mainly based on COVID-19 severity and expert opinion, rather than on scientific evidence generated since the start of the pandemic.

PURPOSE

We report the best available evidence regarding the predictive value of risk factors and (para)clinical markers in the diagnosis of bRTI in COVID-19 patients.

METHODS

A multidisciplinary team identified different potential risk factors and (para)clinical predictors of bRTI in COVID-19 and formulated one or two research questions per topic. After a thorough literature search, research gaps were identified, and suggestions concerning further research were formulated. The quality of this narrative review was ensured by following the Scale for the Assessment of Narrative Review Articles.

RESULTS

Taking into account the scarcity of scientific evidence for markers and risk factors of bRTI in COVID-19 patients, to date, COVID-19 severity is the only parameter which can be associated with higher risk of developing bRTI.

CONCLUSIONS

Evidence on the usefulness of risk factors and (para)clinical factors as predictors of bRTI in COVID-19 patients is scarce. Robust studies are needed to optimise antibiotic prescribing and stewardship activities in the context of COVID-19.

Virus-specific and shared gene expression signatures in immune cells after vaccination in response to influenza and vaccinia stimulation

Background

In the vaccine era, individuals receive multiple vaccines in their lifetime. Host gene expression in response to antigenic stimulation is usually virus-specific; however, identifying shared pathways of host response across a wide spectrum of vaccine pathogens can shed light on the molecular mechanisms/components which can be targeted for the development of broad/universal therapeutics and vaccines.

Method

We isolated PBMCs, monocytes, B cells, and CD8+ T cells from the peripheral blood of healthy donors, who received both seasonal influenza vaccine (within <1 year) and smallpox vaccine (within 1 - 4 years). Each of the purified cell populations was stimulated with either influenza virus or vaccinia virus. Differentially expressed genes (DEGs) relative to unstimulated controls were identified for each in vitro viral infection, as well as for both viral infections (shared DEGs). Pathway enrichment analysis was performed to associate identified DEGs with KEGG/biological pathways.

Results

We identified 2,906, 3,888, 681, and 446 DEGs in PBMCs, monocytes, B cells, and CD8+ T cells, respectively, in response to influenza stimulation. Meanwhile, 97, 120, 20, and 10 DEGs were identified as gene signatures in PBMCs, monocytes, B cells, and CD8+ T cells, respectively, upon vaccinia stimulation. The majority of DEGs identified in PBMCs were also found in monocytes after either viral stimulation. Of the virus-specific DEGs, 55, 63, and 9 DEGs occurred in common in PBMCs, monocytes, and B cells, respectively, while no DEGs were shared in infected CD8+ T cells after influenza and vaccinia. Gene set enrichment analysis demonstrated that these shared DEGs were over-represented in innate signaling pathways, including cytokine-cytokine receptor interaction, viral protein interaction with cytokine and cytokine receptor, Toll-like receptor signaling, RIG-I-like receptor signaling pathways, cytosolic DNA-sensing pathways, and natural killer cell mediated cytotoxicity.

Conclusion

Our results provide insights into virus-host interactions in different immune cells, as well as host defense mechanisms against viral stimulation. Our data also highlights the role of monocytes as a major cell population driving gene expression in ex vivo PBMCs in response to viral stimulation. The immune response signaling pathways identified in this study may provide specific targets for the development of novel virus-specific therapeutics and improved vaccines for vaccinia and influenza. Although influenza and vaccinia viruses have been selected in this study as pathogen models, this approach could be applicable to other pathogens.

Managing Viral Emerging Infectious Diseases via Current and Future Molecular Diagnostics

Emerging viral infectious diseases have been a constant threat to global public health in recent times. In managing these diseases, molecular diagnostics has played a critical role. Molecular diagnostics involves the use of various technologies to detect the genetic material of various pathogens, including viruses, in clinical samples. One of the most commonly used molecular diagnostics technologies for detecting viruses is polymerase chain reaction (PCR). PCR amplifies specific regions of the viral genetic material in a sample, making it easier to detect and identify viruses. PCR is particularly useful for detecting viruses that are present in low concentrations in clinical samples, such as blood or saliva. Another technology that is becoming increasingly popular for viral diagnostics is next-generation sequencing (NGS). NGS can sequence the entire genome of a virus present in a clinical sample, providing a wealth of information about the virus, including its genetic makeup, virulence factors, and potential to cause an outbreak. NGS can also help identify mutations and discover new pathogens that could affect the efficacy of antiviral drugs and vaccines. In addition to PCR and NGS, there are other molecular diagnostics technologies that are being developed to manage emerging viral infectious diseases. One of these is CRISPR-Cas, a genome editing technology that can be used to detect and cut specific regions of viral genetic material. CRISPR-Cas can be used to develop highly specific and sensitive viral diagnostic tests, as well as to develop new antiviral therapies. In conclusion, molecular diagnostics tools are critical for managing emerging viral infectious diseases. PCR and NGS are currently the most commonly used technologies for viral diagnostics, but new technologies such as CRISPR-Cas are emerging. These technologies can help identify viral outbreaks early, track the spread of viruses, and develop effective antiviral therapies and vaccines.

Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease

Assays detecting blood transcriptome changes are studied for infectious disease diagnosis. Blood-based RNA alternative splicing (AS) events, which have not been well characterized in pathogen infection, have potential normalization and assay platform stability advantages over gene expression for diagnosis. Here, we present a computational framework for developing AS diagnostic biomarkers. Leveraging a large prospective cohort of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and whole-blood RNA sequencing (RNA-seq) data, we identify a major functional AS program switch upon viral infection. Using an independent cohort, we demonstrate the improved accuracy of AS biomarkers for SARS-CoV-2 diagnosis compared with six reported transcriptome signatures. We then optimize a subset of AS-based biomarkers to develop microfluidic PCR diagnostic assays. This assay achieves nearly perfect test accuracy (61/62 = 98.4%) using a naive principal component classifier, significantly more accurate than a gene expression PCR assay in the same cohort. Therefore, our RNA splicing computational framework enables a promising avenue for host-response diagnosis of infection.

Longitudinal home self-collection of capillary blood using homeRNA correlates interferon and innate viral defense pathways with SARS-CoV-2 viral clearance

Blood transcriptional profiling is a powerful tool to evaluate immune responses to infection; however, blood collection via traditional phlebotomy remains a barrier to precise characterization of the immune response in dynamic infections (e.g., respiratory viruses). Here we present an at-home self-collection methodology, homeRNA, to study the host transcriptional response during acute SARS-CoV-2 infections. This method uniquely enables high frequency measurement of the host immune kinetics in non-hospitalized adults during the acute and most dynamic stage of their infection. COVID-19+ and healthy participants self-collected blood every other day for two weeks with daily nasal swabs and symptom surveys to track viral load kinetics and symptom burden, respectively. While healthy uninfected participants showed remarkably stable immune kinetics with no significant dynamic genes, COVID-19+ participants, on the contrary, depicted a robust response with over 418 dynamic genes associated with interferon and innate viral defense pathways. When stratified by vaccination status, we detected distinct response signatures between unvaccinated and breakthrough (vaccinated) infection subgroups; unvaccinated individuals portrayed a response repertoire characterized by higher innate antiviral responses, interferon signaling, and cytotoxic lymphocyte responses while breakthrough infections portrayed lower levels of interferon signaling and enhanced early cell-mediated response. Leveraging cross-platform longitudinal sampling (nasal swabs and blood), we observed that IFI27, a key viral response gene, tracked closely with SARS-CoV-2 viral clearance in individual participants. Taken together, these results demonstrate that at-home sampling can capture key host antiviral responses and facilitate frequent longitudinal sampling to detect transient host immune kinetics during dynamic immune states.

Toward a more generalizable blood RNA signature for bacterial and viral infections

Host-response profiles can discriminate different infections. A new 8-gene blood RNA signature to discriminate bacterial and viral infections extends our focus hitherto on the case mix from the US and Europe to include that of low- and middle-income countries.¹ Challenges remain.

Antiviral metabolite 3'-deoxy-3',4'-didehydro-cytidine is detectable in serum and identifies acute viral infections including COVID-19

BACKGROUND

There is a critical need for rapid viral infection diagnostics to enable prompt case identification in pandemic settings and support targeted antimicrobial prescribing.

METHODS

Using untargeted high-resolution liquid chromatography coupled with mass spectrometry, we compared the admission serum metabolome of emergency department patients with viral infections (including COVID-19), bacterial infections, inflammatory conditions, and healthy controls. Sera from an independent cohort of emergency department patients admitted with viral or bacterial infections underwent profiling to validate findings. Associations between whole-blood gene expression and the identified metabolite of interest were examined.

FINDINGS

3'-Deoxy-3',4'-didehydro-cytidine (ddhC), a free base of the only known human antiviral small molecule ddhC-triphosphate (ddhCTP), was detected for the first time in serum. When comparing 60 viral with 101 non-viral cases in the discovery cohort, ddhC was the most significantly differentially abundant metabolite, generating an area under the receiver operating characteristic curve (AUC) of 0.954 (95% CI: 0.923-0.986). In the validation cohort, ddhC was again the most significantly differentially abundant metabolite when comparing 40 viral with 40 bacterial cases, generating an AUC of 0.81 (95% CI 0.708-0.915). Transcripts of viperin and CMPK2, enzymes responsible for ddhCTP synthesis, were among the five genes most highly correlated with ddhC abundance.

CONCLUSIONS

The antiviral precursor molecule ddhC is detectable in serum and an accurate marker for acute viral infection. Interferon-inducible genes viperin and CMPK2 are implicated in ddhC production in vivo. These findings highlight a future diagnostic role for ddhC in viral diagnosis, pandemic preparedness, and acute infection management.

FUNDING

NIHR Imperial BRC; UKRI.

Implications of Using Host Response-Based Molecular Diagnostics on the Management of Bacterial and Viral Infections: A Review

Host-based diagnostics are a rapidly evolving field that may serve as an alternative to traditional pathogen-based diagnostics for infectious diseases. Understanding the exact mechanisms underlying a host-immune response and deriving specific host-response signatures, biomarkers and gene transcripts will potentially achieve improved diagnostics that will ultimately translate to better patient outcomes. Several studies have focused on novel techniques and assays focused on immunodiagnostics. In this review, we will highlight recent publications on the current use of host-based diagnostics alone or in combination with traditional microbiological assays and their potential future implications on the diagnosis and prognostic accuracy for the patient with infectious complications. Finally, we will address the cost-effectiveness implications from a healthcare and public health perspective.

Systematic comparison of published host gene expression signatures for bacterial/viral discrimination

Background

Measuring host gene expression is a promising diagnostic strategy to discriminate bacterial and viral infections. Multiple signatures of varying size, complexity, and target populations have been described. However, there is little information to indicate how the performance of various published signatures compare to one another.

Methods

This systematic comparison of host gene expression signatures evaluated the performance of 28 signatures, validating them in 4589 subjects from 51 publicly available datasets. Thirteen COVID-specific datasets with 1416 subjects were included in a separate analysis. Individual signature performance was evaluated using the area under the receiving operating characteristic curve (AUC) value. Overall signature performance was evaluated using median AUCs and accuracies.

Results

Signature performance varied widely, with median AUCs ranging from 0.55 to 0.96 for bacterial classification and 0.69–0.97 for viral classification. Signature size varied (1–398 genes), with smaller signatures generally performing more poorly (P < 0.04). Viral infection was easier to diagnose than bacterial infection (84% vs. 79% overall accuracy, respectively; P < .001). Host gene expression classifiers performed more poorly in some pediatric populations (3 months–1 year and 2–11 years) compared to the adult population for both bacterial infection (73% and 70% vs. 82%, respectively; P < .001) and viral infection (80% and 79% vs. 88%, respectively; P < .001). We did not observe classification differences based on illness severity as defined by ICU admission for bacterial or viral infections. The median AUC across all signatures for COVID-19 classification was 0.80 compared to 0.83 for viral classification in the same datasets.

Conclusions

In this systematic comparison of 28 host gene expression signatures, we observed differences based on a signature’s size and characteristics of the validation population, including age and infection type. However, populations used for signature discovery did not impact performance, underscoring the redundancy among many of these signatures. Furthermore, differential performance in specific populations may only be observable through this type of large-scale validation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01025-x.

The immunology of asymptomatic SARS-CoV-2 infection: what are the key questions?

An important challenge during the COVID-19 pandemic has been to understand asymptomatic disease and the extent to which this may be a source of transmission. As asymptomatic disease is by definition hard to screen for, there is a lack of clarity about this aspect of the COVID-19 spectrum. Studies have considered whether the prevalence of asymptomatic disease is determined by differences in age, demographics, viral load, duration of shedding, and magnitude or durability of immunity. It is clear that adaptive immunity is strongly activated during asymptomatic infection, but some features of the T cell and antibody response may differ from those in symptomatic disease. Areas that need greater clarity include the extent to which asymptomatic disease leads to persistent symptoms (long COVID), and the quality, quantity and durability of immune priming required to confer subsequent protection.