Host transcriptomics and machine learning for secondary bacterial infections in patients with COVID-19: a prospective, observational cohort study

BACKGROUND

Viral respiratory tract infections are frequently complicated by secondary bacterial infections. This study aimed to use machine learning to predict the risk of bacterial superinfection in SARS-CoV-2-positive individuals.

METHODS

In this prospective, multicentre, observational cohort study done in nine centres in six countries (Australia, Indonesia, Singapore, Italy, Czechia, and France) blood samples and RNA sequencing were used to develop a robust model of predicting secondary bacterial infections in the respiratory tract of patients with COVID-19. Eligible participants were older than 18 years, had known or suspected COVID-19, and symptoms of a recent respiratory infection. A control cohort of participants without COVID-19 who were older than 18 years and with no infection symptoms was also recruited from one Australian centre. In the pre-analysis phase, data were filtered to include only individuals with complete blood transcriptomics and patient data (ie, age, sex, location, and WHO severity score at the time of sample collection). The dataset was then divided randomly (4:1) into a training set (80%) and a test set (20%). Gene expression data in the training set and control cohort were used for differential expression analysis. Differentially expressed genes, along with WHO severity score, location, age, and sex, were used for feature selection with least absolute shrinkage and selection operator (LASSO) in the training set. For LASSO analysis, samples were excluded if gene expression data were not obtained at study admission, no longitudinal clinical information was available, a bacterial infection at the time of study admission was present, or a fungal infection in the absence of a bacterial infection was detected. LASSO regression was performed using three subsets of predictor variables: patient data alone, gene expression data alone, or a combination of patient data and gene expression data. The accuracy of the resultant models was tested on data from the test set.

FINDINGS

Between March, 2020, and October, 2021, we recruited 536 SARS-CoV-2-positive individuals and between June, 2013, and January, 2020, we recruited 74 participants into the control cohort. After prefiltering analysis and other exclusions, samples from 158 individuals were analysed in the training set and 47 in the test set. The expression of seven host genes (DAPP1, CST3, FGL2, GCH1, CIITA, UPP1, and RN7SL1) in the blood at the time of study admission was identified by LASSO as predictive of the risk of developing a secondary bacterial infection of the respiratory tract more than 24 h after study admission. Specifically, the expression of these genes in combination with a patient's WHO severity score at the time of study enrolment resulted in an area under the curve of 0·98 (95% CI 0·89-1·00), a true positive rate (sensitivity) of 1·00 (95% CI 1·00-1·00), and a true negative rate (specificity) of 0·94 (95% CI 0·89-1·00) in the test cohort. The combination of patient data and host transcriptomics at hospital admission identified all seven individuals in the training and test sets who developed a bacterial infection of the respiratory tract 5-9 days after hospital admission.

INTERPRETATION

These data raise the possibility that host transcriptomics at the time of clinical presentation, together with machine learning, can forward predict the risk of secondary bacterial infections and allow for the more targeted use of antibiotics in viral infection.

FUNDING

Snow Medical Research Foundation, the National Health and Medical Research Council, the Jack Ma Foundation, the Helmholtz-Association, the A2 Milk Company, National Institute of Allergy and Infectious Disease, and the Fondazione AIRC Associazione Italiana per la Ricerca contro il Cancro.

The effectiveness of neurofeedback on the management of academic performance in students with academic failure: experimental research

The present study aimed to study the impact of neurofeedback on the academic performance of nursing students with academic failure.

Methods

This study was an experimental one with a pretest-posttest design with a control group. The statistical population of this research was the nursing students of the Faculty of Nursing, Tehran University of Medical Sciences University of Medical Sciences. The sample of this study consisted of 60 individuals chosen by a simple random sampling method and two experiment groups (N=30) and a control group (N=30) were replaced by accident. Neurofeedback was an advanced Raven test and a researcher-made questionnaire for data collection. Thereafter, the experimental group was treated with neurofeedback for 7-10 weeks and 20 50-min therapeutic sessions as the experimental condition. In the first 130 s, the baseline was determined for the individual, and during the session, the baseline was practiced. Each session consisted of six exercises, each lasting 7 min.

Results

The results of the covariance analysis showed that students who had an educational drop and were trained in neurofeedback sessions showed a significant increase in the next half (P<0.05) compared to the control group.

Conclusion

The results of this study showed that neurofeedback is an effective method for managing the academic performance of nursing students with academic failure.

The Role of Transcriptomics in Redefining Critical Illness

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2023. Other selected articles can be found online at  https://www.biomedcentral.com/collections/annualupdate2023 . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from  https://link.springer.com/bookseries/8901 .

Pathway and Network Analyses Identify Growth Factor Signaling and MMP9 as Potential Mediators of Mitochondrial Dysfunction in Severe COVID-19

Patients with preexisting metabolic disorders such as diabetes are at a higher risk of developing severe coronavirus disease 2019 (COVID-19). Mitochondrion, the very organelle that controls cellular metabolism, holds the key to understanding disease progression at the cellular level. Our current study aimed to understand how cellular metabolism contributes to COVID-19 outcomes. Metacore pathway enrichment analyses on differentially expressed genes (encoded by both mitochondrial and nuclear deoxyribonucleic acid (DNA)) involved in cellular metabolism, regulation of mitochondrial respiration and organization, and apoptosis, was performed on RNA sequencing (RNASeq) data from blood samples collected from healthy controls and patients with mild/moderate or severe COVID-19. Genes from the enriched pathways were analyzed by network analysis to uncover interactions among them and up- or downstream genes within each pathway. Compared to the mild/moderate COVID-19, the upregulation of a myriad of growth factor and cell cycle signaling pathways, with concomitant downregulation of interferon signaling pathways, were observed in the severe group. Matrix metallopeptidase 9 (MMP9) was found in five of the top 10 upregulated pathways, indicating its potential as therapeutic target against COVID-19. In summary, our data demonstrates aberrant activation of endocrine signaling in severe COVID-19, and its implication in immune and metabolic dysfunction.

Multisite validation of a host response signature for predicting likelihood of bacterial and viral infections in patients with suspected influenza

BACKGROUND

Indiscriminate use of antimicrobials and antimicrobial resistance is a public health threat. IMX-BVN-1, a 29-host mRNA classifier, provides two separate scores that predict likelihoods of bacterial and viral infections in patients with suspected acute infections. We validated the performance of IMX-BVN-1 in adults attending acute health care settings with suspected influenza.

METHOD

We amplified 29-host response genes in RNA extracted from blood by NanoString nCounter. IMX-BVN-1 calculated two scores to predict probabilities of bacterial and viral infections. Results were compared against the infection status (no infection; highly probable/possible infection; confirmed infection) determined by clinical adjudication.

RESULTS

Amongst 602 adult patients (74.9% ED, 16.9% ICU, 8.1% outpatients), 7.6% showed in-hospital mortality and 15.5% immunosuppression. Median IMX-BVN-1 bacterial and viral scores were higher in patients with confirmed bacterial (0.27) and viral (0.62) infections than in those without bacterial (0.08) or viral (0.21) infection, respectively. The AUROC distinguishing bacterial from nonbacterial illness was 0.81 and 0.87 when distinguishing viral from nonviral illness. The bacterial top quartile's positive likelihood ratio (LR) was 4.38 with a rule-in specificity of 88%; the bacterial bottom quartile's negative LR was 0.13 with a rule-out sensitivity of 96%. Similarly, the viral top quartile showed an infinite LR with rule-in specificity of 100%; the viral bottom quartile had a LR of 0.22 and a rule-out sensitivity of 85%.

CONCLUSION

IMX-BVN-1 showed high accuracy for differentiating bacterial and viral infections from noninfectious illness in patients with suspected influenza. Clinical utility of IMX-BVN will be validated following integration into a point of care system.

Blood transcriptome responses in patients correlate with severity of COVID-19 disease

Background

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Infected individuals display a wide spectrum of disease severity, as defined by the World Health Organization (WHO). One of the main factors underlying this heterogeneity is the host immune response, with severe COVID-19 often associated with a hyperinflammatory state.

Aim

Our current study aimed to pinpoint the specific genes and pathways underlying differences in the disease spectrum and outcomes observed, through in-depth analyses of whole blood transcriptomics in a large cohort of COVID-19 participants.

Results

All WHO severity levels were well represented and mild and severe disease displaying distinct gene expression profiles. WHO severity levels 1-4 were grouped as mild disease, and signatures from these participants were different from those with WHO severity levels 6-9 classified as severe disease. Severity level 5 (moderate cases) presented a unique transitional gene signature between severity levels 2-4 (mild/moderate) and 6-9 (severe) and hence might represent the turning point for better or worse disease outcome. Gene expression changes are very distinct when comparing mild/moderate or severe cases to healthy controls. In particular, we demonstrated the hallmark down-regulation of adaptive immune response pathways and activation of neutrophil pathways in severe compared to mild/moderate cases, as well as activation of blood coagulation pathways.

Conclusions

Our data revealed discrete gene signatures associated with mild, moderate, and severe COVID-19 identifying valuable candidates for future biomarker discovery.

IFI27 transcription is an early predictor for COVID-19 outcomes, a multi-cohort observational study

Purpose

Robust biomarkers that predict disease outcomes amongst COVID-19 patients are necessary for both patient triage and resource prioritisation. Numerous candidate biomarkers have been proposed for COVID-19. However, at present, there is no consensus on the best diagnostic approach to predict outcomes in infected patients. Moreover, it is not clear whether such tools would apply to other potentially pandemic pathogens and therefore of use as stockpile for future pandemic preparedness.

Methods

We conducted a multi-cohort observational study to investigate the biology and the prognostic role of interferon alpha-inducible protein 27 (IFI27) in COVID-19 patients.

Results

We show that IFI27 is expressed in the respiratory tract of COVID-19 patients and elevated IFI27 expression in the lower respiratory tract is associated with the presence of a high viral load. We further demonstrate that the systemic host response, as measured by blood IFI27 expression, is associated with COVID-19 infection. For clinical outcome prediction (e.g., respiratory failure), IFI27 expression displays a high sensitivity (0.95) and specificity (0.83), outperforming other known predictors of COVID-19 outcomes. Furthermore, IFI27 is upregulated in the blood of infected patients in response to other respiratory viruses. For example, in the pandemic H1N1/09 influenza virus infection, IFI27-like genes were highly upregulated in the blood samples of severely infected patients.

Conclusion

These data suggest that prognostic biomarkers targeting the family of IFI27 genes could potentially supplement conventional diagnostic tools in future virus pandemics, independent of whether such pandemics are caused by a coronavirus, an influenza virus or another as yet-to-be discovered respiratory virus.

High-temporal resolution profiling reveals distinct immune trajectories following the first and second doses of COVID-19 mRNA vaccines

Knowledge of the mechanisms underpinning the development of protective immunity conferred by mRNA vaccines is fragmentary. Here, we investigated responses to coronavirus disease 2019 (COVID-19) mRNA vaccination via high-temporal resolution blood transcriptome profiling. The first vaccine dose elicited modest interferon and adaptive immune responses, which peaked on days 2 and 5, respectively. The second vaccine dose, in contrast, elicited sharp day 1 interferon, inflammation, and erythroid cell responses, followed by a day 5 plasmablast response. Both post-first and post-second dose interferon signatures were associated with the subsequent development of antibody responses. Yet, we observed distinct interferon response patterns after each of the doses that may reflect quantitative or qualitative differences in interferon induction. Distinct interferon response phenotypes were also observed in patients with COVID-19 and were associated with severity and differences in duration of intensive care. Together, this study also highlights the benefits of adopting high-frequency sampling protocols in profiling vaccine-elicited immune responses.

Neutrophil-to-Lymphocyte Ratio Cut-Off Point for COVID-19 Mortality: A Retrospective Study

Several months have passed since the onset of the COVID-19 pandemic. Multiple characteristics have been proposed as prognostic factors so far. This study aims to provide evidence on the association of neutrophil-to-lymphocyte ratio (NLR) at the hospitalization time and three desired outcomes (mortality, prolonged hospitalization, and intensive care unit [ICU] admission). We designed a single-centre retrospective observational study in Baharloo Hospital (Tehran, Iran) from 20 February to 19 April 2020. Patients with confirmed COVID-19 diagnosis via rt-PCR or chest CT imaging were included. Demographic and clinical data were obtained. The sample was divided into three groups, using tertile boundaries of initial NLR. The differences in mortality, comorbidities, hospitalization duration, drug administration, and ICU admission between these three groups were investigated. The identified confounding factors were adjusted to calculate the odds ratio of death, ICU admission, and prolonged hospitalization. Nine hundred sixty-three patients were included. In total, 151 and 212 participants experienced mortality and ICU admission, respectively. In multivariate logistic regression models, the adjusted odds ratio for mortality event in the second and third tertile of initial NLR after full adjustment were 1.89 (95% CI:1.07-3.32) and 2.57 (95% CI:1.48-4.43) and for ICU admission were 1.85 (95% CI:1.14-3.01) and 2.88 (95% CI:1.79-4.61), respectively. The optimal cut-off value of the initial NLR for predicting mortality was 4.27. Initial NLR can predict mortality and ICU admission in COVID-19 patients. Further investigations for curating the calculated cut-off can propose initial NLR as an indicator of poor prognosis for COVID-19 patients.

Predicting sepsis severity at first clinical presentation: The role of endotypes and mechanistic signatures

BACKGROUND

Inter-individual variability during sepsis limits appropriate triage of patients. Identifying, at first clinical presentation, gene expression signatures that predict subsequent severity will allow clinicians to identify the most at-risk groups of patients and enable appropriate antibiotic use.

METHODS

Blood RNA-Seq and clinical data were collected from 348 patients in four emergency rooms (ER) and one intensive-care-unit (ICU), and 44 healthy controls. Gene expression profiles were analyzed using machine learning and data mining to identify clinically relevant gene signatures reflecting disease severity, organ dysfunction, mortality, and specific endotypes/mechanisms.

FINDINGS

Gene expression signatures were obtained that predicted severity/organ dysfunction and mortality in both ER and ICU patients with accuracy/AUC of 77-80%. Network analysis revealed these signatures formed a coherent biological program, with specific but overlapping mechanisms/pathways. Given the heterogeneity of sepsis, we asked if patients could be assorted into discrete groups with distinct mechanisms (endotypes) and varying severity. Patients with early sepsis could be stratified into five distinct and novel mechanistic endotypes, named Neutrophilic-Suppressive/NPS, Inflammatory/INF, Innate-Host-Defense/IHD, Interferon/IFN, and Adaptive/ADA, each based on ∼200 unique gene expression differences, and distinct pathways/mechanisms (e.g., IL6/STAT3 in NPS). Endotypes had varying overall severity with two severe (NPS/INF) and one relatively benign (ADA) groupings, consistent with reanalysis of previous endotype studies. A 40 gene-classification tool (accuracy=96%) and several gene-pairs (accuracy=89-97%) accurately predicted endotype status in both ER and ICU validation cohorts.

INTERPRETATION

The severity and endotype signatures indicate that distinct immune signatures precede the onset of severe sepsis and lethality, providing a method to triage early sepsis patients.

Reasons for delayed spinal cord decompression in individuals with traumatic spinal cord injuries in Iran: A qualitative study from the perspective of neurosurgeons

PURPOSE

The median time from the event leading to the spinal cord injury (SCI) to the time of decompressive surgery is estimated to be 6.9 days in Iran, which is much longer than the proposed ideal time (less than 24 h) in published guidelines. The current qualitative study aimed to determine the reasons for the observed decompression surgery delay in Iran from the perspective of neurosurgeons.

METHODS

This qualitative study is designed to perform content analysis on the gathered data from face-to-face semi-structured interviews with 12 Iranian neurosurgeons.

RESULTS

The findings of the current study suggest that patient-related factors constitute more than half of the codes extracted from the interviews. Overall, the type of injury, presence of polytrauma, and surgeons' wrong attitude are the main factors causing delayed spinal cord decompression in Iranian patients from the perspective of neurosurgeons. Other notable factors include delay in transferring patients to the trauma center, delay in availability of necessary equipment, and scarce medical personnel.

CONCLUSION

In the perspective of neurosurgeons, the type of injury, presence of polytrauma, and surgeons' wrong attitude are the leading reasons for delayed decompressive surgery of individuals with SCI in Iran.

The effectiveness of different types of motorcycle helmets - A scoping review

BACKGROUND

Protective helmets may reduce the risk of death and head injury in motorcycle collisions. However, there remains a large gap in knowledge regarding the effectiveness of different types of helmets in preventing injuries.

OBJECTIVE

To explore and evaluate the effectiveness of different types of motorcycle helmets; that is the association between different helmet types and the incidence and severity of head, neck, and facial injuries among motorcyclists. Also, to explore the effect of different helmet types on riders.

METHODS

A systematic search of different scientific databases was conducted from 1965 to April 2019. A scoping review was performed on the included articles. Eligible articles were included regarding defined criteria. Study characteristics, helmet types, fixation status, retention system, the prevention of injury or reduction of its severity were extracted.

RESULTS

A total of 137 studies were included. There was very limited evidence for the better protection of full-face helmets from head and facial injury compared to open-face and half-coverage helmets. There was however scarce evidence for the superiority of a certain helmet type over others in terms of protection from neck injury. The retention system and the fixation status of helmets were two important factors affecting the risk of head and brain injury in motorcyclists. Helmets could also affect and limit the riders in terms of vision, hearing, and ventilation. Multiple solutions have been discussed to mitigate these effects.

CONCLUSION

Full-face helmets may protect head and face in motorcycle riders more than open-face and half-coverage helmets, but there is not enough evidence for better neck protection among these three helmet types. Helmets can affect the rider's vision, hearing, and ventilation. When designing a helmet, all of these factors should be taken into account.

Blood transcriptome analysis of patients with uncomplicated bacterial infection and sepsis

OBJECTIVES

Hospitalized patients who presented within the last 24 h with a bacterial infection were recruited. Participants were assigned into sepsis and uncomplicated infection groups. In addition, healthy volunteers were recruited as controls. RNA was prepared from whole blood, depleted from beta-globin mRNA and sequenced. This dataset represents a highly valuable resource to better understand the biology of sepsis and to identify biomarkers for severe sepsis in humans.

DATA DESCRIPTION

The data presented here consists of raw and processed transcriptome data obtained by next generation RNA sequencing from 105 peripheral blood samples from patients with uncomplicated infections, patients who developed sepsis, septic shock patients, and healthy controls. It is provided as raw sequenced reads and as normalized log2 transformed relative expression levels. This data will allow performing detailed analyses of gene expression changes between uncomplicated infections and sepsis patients, such as identification of differentially expressed genes, co-regulated modules as well as pathway activation studies.

A Comparison between Electron Gamma Shower, National Research Council/Easy Particle Propagation (EGSnrc/Epp) and Monte Carlo N-Particle Transport Code (MCNP) in Simulation of the INTRABEAM ® System with Spherical Applicators

BACKGROUND

Online Monte Carlo (MC) treatment planning is very crucial to increase the precision of intraoperative radiotherapy (IORT). However, the performance of MC methods depends on the geometries and energies used for the problem under study.

OBJECTIVE

This study aimed to compare the performance of MC N-Particle Transport Code version 4c (MCNP4c) and Electron Gamma Shower, National Research Council/easy particle propagation (EGSnrc/Epp) MC codes using similar geometry of an INTRABEAM^® system.

MATERIAL AND METHODS

This simulation study was done by increasing the number of particles and compared the performance of MCNP4c and EGSnrc/Epp simulations using an INTRABEAM^® system with 1.5 and 5 cm diameter spherical applicators. A comparison of these two codes was done using simulation time, statistical uncertainty, and relative depth-dose values obtained after doing the simulation by each MC code.

RESULTS

The statistical uncertainties for the MCNP4c and EGSnrc/Epp MC codes were below 2% and 0.5%, respectively. 1e9 particles were simulated in 117.89 hours using MCNP4c but a much greater number of particles (5e10 particles) were simulated in a shorter time of 90.26 hours using EGSnrc/Epp MC code. No significant deviations were found in the calculated relative depth-dose values for both in the presence and absence of an air gap between MCNP4c and EGSnrc/Epp MC codes. Nevertheless, the EGSnrc/Epp MC code was found to be speedier and more efficient to achieve accurate statistical precision than MCNP4c.

CONCLUSION

Therefore, in all comparisons criteria used, EGSnrc/Epp MC code is much better than MCNP4c MC code for simulating an INTRABEAM^® system.

Prospective validation study of prognostic biomarkers to predict adverse outcomes in patients with COVID-19: a study protocol

INTRODUCTION

Accurate triage is an important first step to effectively manage the clinical treatment of severe cases in a pandemic outbreak. In the current COVID-19 global pandemic, there is a lack of reliable clinical tools to assist clinicians to perform accurate triage. Host response biomarkers have recently shown promise in risk stratification of disease progression; however, the role of these biomarkers in predicting disease progression in patients with COVID-19 is unknown. Here, we present a protocol outlining a prospective validation study to evaluate the biomarkers' performance in predicting clinical outcomes of patients with COVID-19.

METHODS AND ANALYSIS

This prospective validation study assesses patients infected with COVID-19, in whom blood samples are prospectively collected. Recruited patients include a range of infection severity from asymptomatic to critically ill patients, recruited from the community, outpatient clinics, emergency departments and hospitals. Study samples consist of peripheral blood samples collected into RNA-preserving (PAXgene/Tempus) tubes on patient presentation or immediately on study enrolment. Real-time PCR (RT-PCR) will be performed on total RNA extracted from collected blood samples using primers specific to host response gene expression biomarkers that have been previously identified in studies of respiratory viral infections. The RT-PCR data will be analysed to assess the diagnostic performance of individual biomarkers in predicting COVID-19-related outcomes, such as viral pneumonia, acute respiratory distress syndrome or bacterial pneumonia. Biomarker performance will be evaluated using sensitivity, specificity, positive and negative predictive values, likelihood ratios and area under the receiver operating characteristic curve.

ETHICS AND DISSEMINATION

This research protocol aims to study the host response gene expression biomarkers in severe respiratory viral infections with a pandemic potential (COVID-19). It has been approved by the local ethics committee with approval number 2020/ETH00886. The results of this project will be disseminated in international peer-reviewed scientific journals.

630. A 5-mRNA host response whole-blood classifier trained using patients with non-COVID-19 viral infections accurately predicts severity of COVID-19

Background

While major progress has been made to establish diagnostic tools for the diagnosis of SARS-CoV-2 infection, determining the severity of COVID-19 remains an unmet medical need. With limited hospital resources, gauging severity would allow for some patients to safely recover in home quarantine while ensuring sicker patients get needed care. We discovered a 5 host mRNA-based classifier for the severity of influenza and other acute viral infections and validated the classifier in COVID-19 patients from Greece.

Methods

We used training data (N=705) from 21 retrospective clinical studies of influenza and other viral illnesses. Five host mRNAs from a preselected panel were applied to train a logistic regression classifier for predicting 30-day mortality in influenza and other viral illnesses. We then applied this classifier, with fixed weights, to an independent cohort of subjects with confirmed COVID-19 from Athens, Greece (N=71) using NanoString nCounter. Finally, we developed a proof-of-concept rapid, isothermal qRT-LAMP assay for the 5-mRNA host signature using the QuantStudio 6 qPCR platform.

Results

In 71 patients with COVID-19, the 5 mRNA classifier had an AUROC of 0.88 (95% CI 0.80-0.97) for identifying patients with severe respiratory failure and/or 30-day mortality (Figure 1). Applying a preset cutoff based on training data, the 5-mRNA classifier had 100% sensitivity and 46% specificity for identifying mortality, and 88% sensitivity and 68% specificity for identifying severe respiratory failure. Finally, our proof-of-concept qRT-LAMP assay showed high correlation with the reference NanoString 5-mRNA classifier (r=0.95).

Figure 1. Validation of the 5-mRNA classifier in the COVID-19 cohort. (A) Expression of the 5 genes used in the logistic regression model in patients with (red) and without (blue) mortality. (B) The 5-mRNA classifier accurately distinguishes non-severe and severe patients with COVID-19 as well as those at risk of death.

Conclusion

Our 5-mRNA classifier demonstrated very high accuracy for the prediction of COVID-19 severity and could assist in the rapid, point-of-impact assessment of patients with confirmed COVID-19 to determine level of care thereby improving patient management and healthcare burden.

Disclosures

ljubomir Buturovic, PhD, Inflammatix Inc. (Employee, Shareholder) Purvesh Khatri, PhD, Inflammatix Inc. (Shareholder) Oliver Liesenfeld, MD, Inflammatix Inc. (Employee, Shareholder) James Wacker, n/a, Inflammatix Inc. (Employee, Shareholder) Uros Midic, PhD, Inflammatix Inc. (Employee, Shareholder) Roland Luethy, PhD, Inflammatix Inc. (Employee, Shareholder) David C. Rawling, PhD, Inflammatix Inc. (Employee, Shareholder) Timothy Sweeney, MD, Inflammatix, Inc. (Employee)

Pathway mapping of leukocyte transcriptome in influenza patients reveals distinct pathogenic mechanisms associated with progression to severe infection

Background

Influenza infections produce a spectrum of disease severity, ranging from a mild respiratory illness to respiratory failure and death. The host-response pathways associated with the progression to severe influenza disease are not well understood.

Methods

To gain insight into the disease mechanisms associated with progression to severe infection, we analyzed the leukocyte transcriptome in severe and moderate influenza patients and healthy control subjects. Pathway analysis on differentially expressed genes was performed using a topology-based pathway analysis tool that takes into account the interaction between multiple cellular pathways. The pathway profiles between moderate and severe influenza were then compared to delineate the biological mechanisms underpinning the progression from moderate to severe influenza.

Results

107 patients (44 severe and 63 moderate influenza patients) and 52 healthy control subjects were included in the study. Severe influenza was associated with upregulation in several neutrophil-related pathways, including pathways involved in neutrophil differentiation, migration, degranulation and neutrophil extracellular trap (NET) formation. The degree of upregulation in neutrophil-related pathways were significantly higher in severely infected patients compared to moderately infected patients. Severe influenza was also associated with downregulation in immune response pathways, including pathways involved in antigen presentation such as CD4+ T-cell co-stimulation, CD8+ T cell and Natural Killer (NK) cells effector functions. Apoptosis pathways were also downregulated in severe influenza patients compare to moderate and healthy controls.

Conclusions

These findings showed that there are changes in gene expression profile that may highlight distinct pathogenic mechanisms associated with progression from moderate to severe influenza infection.

Neutrophils-related host factors associated with severe disease and fatality in patients with influenza infection

Severe influenza infection has no effective treatment available. One of the key barriers to developing host-directed therapy is a lack of reliable prognostic factors needed to guide such therapy. Here, we use a network analysis approach to identify host factors associated with severe influenza and fatal outcome. In influenza patients with moderate-to-severe diseases, we uncover a complex landscape of immunological pathways, with the main changes occurring in pathways related to circulating neutrophils. Patients with severe disease display excessive neutrophil extracellular traps formation, neutrophil-inflammation and delayed apoptosis, all of which have been associated with fatal outcome in animal models. Excessive neutrophil activation correlates with worsening oxygenation impairment and predicted fatal outcome (AUROC 0.817-0.898). These findings provide new evidence that neutrophil-dominated host response is associated with poor outcomes. Measuring neutrophil-related changes may improve risk stratification and patient selection, a critical first step in developing host-directed immune therapy.

A novel immune biomarker IFI27 discriminates between influenza and bacteria in patients with suspected respiratory infection

Host response biomarkers can accurately distinguish between influenza and bacterial infection. However, published biomarkers require the measurement of many genes, thereby making it difficult to implement them in clinical practice. This study aims to identify a single-gene biomarker with a high diagnostic accuracy equivalent to multi-gene biomarkers.In this study, we combined an integrated genomic analysis of 1071 individuals with in vitro experiments using well-established infection models.We identified a single-gene biomarker, IFI27, which had a high prediction accuracy (91%) equivalent to that obtained by multi-gene biomarkers. In vitro studies showed that IFI27 was upregulated by TLR7 in plasmacytoid dendritic cells, antigen-presenting cells that responded to influenza virus rather than bacteria. In vivo studies confirmed that IFI27 was expressed in influenza patients but not in bacterial infection, as demonstrated in multiple patient cohorts (n=521). In a large prospective study (n=439) of patients presented with undifferentiated respiratory illness (aetiologies included viral, bacterial and non-infectious conditions), IFI27 displayed 88% diagnostic accuracy (AUC) and 90% specificity in discriminating between influenza and bacterial infections.IFI27 represents a significant step forward in overcoming a translational barrier in applying genomic assay in clinical setting; its implementation may improve the diagnosis and management of respiratory infection.

Application of quartz crystal nanobalance and principal component analysis for detection and determination of nickel in solution

Quartz crystal nanobalance (QCN) sensors are considered as powerful mass sensitive sensors to determine materials in the sub-nanogram level. In the first part of this study, a single piezoelectric QCN modified with polypyrrole (PPy) has been tested for detection and determination of nickel ions in the solution at room temperature. The developed method was successfully applied for detection of total nickel in samples taken from several hot springs located at the Northwest of Iran. The frequency shifts were linear with respect to the concentration of nickel in solution. Using this method, nickel can be measured in the range of 3-20 mg L(-1). A lower limit of detection of 0.79 mg L(-1) and a sensitivity factor of 4.429 Hz/mg L(-1) were obtained. Some possible interference such as heavy metal ions (lead, mercury, and cadmium) was checked. No major interference was observed with the performance of the sensor except for mercury. To evaluate the ability of the PPy-modified QCN in discriminating between nickel ions and interfering mercury ions, a principal component analysis (PCA) was carried out. PCA was utilized to process the frequency response data of the single piezoelectric crystal at different times, considering different adsorption-desorption dynamics of nickel and interfering mercury ions on electrode. Using PCA, it was found that about 97.90% of the data variance could still be explained by two principal components (PC1 and PC2). The score plot of the data for the first two PCs showed that the PPy-modified QCN yields favorable identification and quantification performances for nickel ions. The accuracy of method for hot spring samples was evaluated and RSD of 4.10% was obtained.

Structure and inhibition of orotidine 5'-monophosphate decarboxylase from Plasmodium falciparum

Orotidine 5'-monophosphate (OMP) decarboxylase from Plasmodium falciparum (PfODCase, EC 4.1.1.23) has been overexpressed, purified, subjected to kinetic and biochemical analysis, and crystallized. The native enzyme is a homodimer with a subunit molecular mass of 38 kDa. The saturation curve for OMP as a substrate conformed to Michaelis-Menten kinetics with K m = 350 +/- 60 nM and V max = 2.70 +/- 0.10 micromol/min/mg protein. Inhibition patterns for nucleoside 5'-monophosphate analogues were linear competitive with respect to OMP with a decreasing potency of inhibition of PfODCase in the order: pyrazofurin 5'-monophosphate ( K i = 3.6 +/- 0.7 nM) > xanthosine 5'-monophosphate (XMP, K i = 4.4 +/- 0.7 nM) > 6-azauridine 5'-monophosphate (AzaUMP, K i = 12 +/- 3 nM) > allopurinol-3-riboside 5'-monophosphate ( K i = 240 +/- 20 nM). XMP is an approximately 150-fold more potent inhibitor of PfODCase compared with the human enzyme. The structure of PfODCase was solved in the absence of ligand and displays a classic TIM-barrel fold characteristic of the enzyme. Both the phosphate-binding loop and the betaalpha5-loop have conformational flexibility, which may be associated with substrate capture and product release along the reaction pathway.

13C and 15N isotope effects for conversion of L-dihydroorotate to N-carbamyl-L-aspartate using dihydroorotase from hamster and Bacillus caldolyticus

In the pyrimidine biosynthetic pathway, N-carbamyl-L-aspartate (CA-asp) is converted to L-dihydroorotate (DHO) by dihydroorotase (DHOase). The mechanism of this important reaction was probed using primary and secondary 15N and 13C isotope effects on the ring opening of DHO using isotope ratio mass spectrometry (IRMS). The reaction was performed at three different temperatures (25, 37, and 45 degrees C for hamster DHOase; 37, 50, and 60 degrees C for Bacillus caldolyticus), and the product CA-asp was purified for analysis. The primary and secondary kinetic isotope effects for the ring opening of the DHO were determined from analysis of the N and C of the carbamyl group after hydrolysis. In addition, the beta-carboxyl of the residual aspartate was liberated enzymatically by transamination to oxaloacetate with aspartate aminotransferase and then decarboxylation with oxaloacetate decarboxylase. The 13C/12C ratio from the released CO2 was determined by IRMS, yielding a second primary isotope effect. The primary and secondary isotope effects for the reaction catalyzed by DHOase showed little variation between enzymes or temperatures, the primary 13C and 15N isotope effects being approximately 1% on average, while the secondary 13C isotope effect is negligible or very slightly normal (>1.0000). These data indicate that the chemistry is at least partially rate-limiting while the secondary isotope effects suggest that the transition state may have lost some bending and torsional modes leading to a slight lessening of bond stiffness at the carbonyl carbon of the amide of CA-asp. The equilibrium isotope effects for DHO --> CA-asp have also been measured (secondary 13K(eq) = 1.0028 +/- 0.0002, primary 13K(eq) = 1.0053 +/- 0.0003, primary 15K(eq) = 1.0027 +/- 0.0003). Using these equilibrium isotope effects, the kinetic isotope effects for the physiological reaction (CA-asp --> DHO) have been calculated. These values indicate that the carbon of the amide group is more stiffly bonded in DHO while the slightly lesser, but still normal, values of the primary kinetic isotope effect show that the chemistry remains at least partially rate-limiting for the physiological reaction. It appears that the ring opening and closing is the slow step of the reaction.

Cloning and expression of malarial pyrimidine enzymes

We have cloned genes encoding three enzymes of the de novo pyrimidine pathway using genomic DNA from Plasmodium falciparum and sequence information from the Malarial Genome Project. Genes encoding dihydroorotase (reaction 3), orotate phosphoribosyltransferase (reaction 5), and OMP decarboxylase (reaction 6) have been cloned into the plasmid pET 3a or 3d with a thrombin cleavable 9xHis tag at the C-terminus and the enzymes were expressed in Escherichia coli. To overcome the toxicity of malarial OMP decarboxylase when expressed in E. coli, and the unusual codon usage of the malarial gene, a hybrid plasmid, pMICO, was constructed which expresses low levels of T7 lysozyme to inhibit T7 RNA polymerase used for recombinant expression, and extra copies of rare tRNAs. Catalytically-active OMP decarboxylase has been purified in tens of milligrams by chromatography on Ni-NTA. The gene encoding orotate phosphoribosyltransferase includes an extension of 66 amino acids from the N-terminus when compared with sequences for this enzyme from other organisms. We have found that other pyrimidine enzymes also contain unusual protein inserts. Milligram quantities of pure recombinant malarial enzymes from the pyrimidine pathway will provide targets for development of novel antimalarial drugs.

Molecular analysis of the bvg-repressed urease of Bordetella bronchiseptica

Bordetella bronchiseptica is a ureolytic mammalian respiratory pathogen. We have investigated the regulation of urease in B. bronchiseptica and the potential role of this enzyme in eukaryotic invasion and intracellular survival. Our results indicate urease is a bordetella virulence repressed gene. Urease activity in virulent B. bronchiseptica BB7865 is up-regulated from basal levels by 5 gl1 magnesium sulphate at 37 degrees C. At 30 degrees C, urease activity remained at basal levels, even in the presence on magnesium sulphate, suggesting a second temperature dependent mechanism of urease regulation was also operating. Urease was not inducible by 10 mM urea nor up-regulated in nitrogen limiting conditions. To evaluate the role of urease in intracellular invasion and survival urease-negative mutants of B. bronchiseptica BB7865 and B. bronchiseptica BB7866 were created by transposon mutagenesis, and compared to the urease-positive parental strains in a HeLa cell invasion assay. We demonstrate that increasing the concentration of urea in the assay increased survival of the urease-positive but not urease-negative strains after 24 h, suggesting that urease does have a role in intracellular survival. Partial DNA sequence analysis of an 11.0 kb EcoRI DNA fragment encoding urease activity revealed an open reading frame containing 50%, 45%, 45%, and 41% homology to the UreA urease subunit protein of Klebsiella aerogenes, Proteus vulgaris, Helicobacter pylori and Proteus mirabilis respectively. We also show Bordetella pertussis to contain sequences homologous with a DNA probe containing the gene encoding UreA of B. bronchiseptica indicating the possible presence of cryptic urease genes in this species.