Bioinformatics analysis identifies a key gene HLA_DPA1 in severe influenza-associated immune infiltration

BACKGROUND

Severe influenza is a serious global health issue that leads to prolonged hospitalization and mortality on a significant scale. The pathogenesis of this infectious disease is poorly understood. Therefore, this study aimed to identify the key genes associated with severe influenza patients necessitating invasive mechanical ventilation.

METHODS

The current study utilized two publicly accessible gene expression profiles (GSE111368 and GSE21802) from the Gene Expression Omnibus database. The research focused on identifying the genes exhibiting differential expression between severe and non-severe influenza patients. We employed three machine learning algorithms, namely the Least Absolute Shrinkage and Selection Operator regression model, Random Forest, and Support Vector Machine-Recursive Feature Elimination, to detect potential key genes. The key gene was further selected based on the diagnostic performance of the target genes substantiated in the dataset GSE101702. A single-sample gene set enrichment analysis algorithm was applied to evaluate the participation of immune cell infiltration and their associations with key genes.

RESULTS

A total of 44 differentially expressed genes were recognized; among them, we focused on 10 common genes, namely PCOLCE2, HLA_DPA1, LOC653061, TDRD9, MPO, HLA_DQA1, MAOA, S100P, RAP1GAP, and CA1. To ensure the robustness of our findings, we employed overlapping LASSO regression, Random Forest, and SVM-RFE algorithms. By utilizing these algorithms, we were able to pinpoint the aforementioned 10 genes as potential biomarkers for distinguishing between both cases of influenza (severe and non-severe). However, the gene HLA_DPA1 has been recognized as a crucial factor in the pathological condition of severe influenza. Notably, the validation dataset revealed that this gene exhibited the highest area under the receiver operating characteristic curve, with a value of 0.891. The use of single-sample gene set enrichment analysis has provided valuable insights into the immune responses of patients afflicted with severe influenza that have further revealed a categorical correlation between the expression of HLA_DPA1 and lymphocytes.

CONCLUSION

The findings indicated that the HLA_DPA1 gene may play a crucial role in the immune-pathological condition of severe influenza and could serve as a promising therapeutic target for patients infected with severe influenza.

Genetic analysis of cuproptosis subtypes and immunological features in severe influenza

The mechanisms regulating cuproptosis in severe influenza are still unknown. We aimed to identify the molecular subtypes of cuproptosis and immunological characteristics associated with severe influenza in patients requiring invasive mechanical ventilation (IMV). The expression of cuproptosis modulatory factors and immunological characteristics of these patients were analyzed using the public datasets (GSE101702, GSE21802, and GSE111368) from the Gene Expression Omnibus (GEO). Seven cuproptotic-associated genes (ATP7B, ATP7A, FDX1, LIAS, DLD, MTF1, DBT) related to active immune responses were identified in patients suffering from severe and non-severe influenza and two cuproptosis-associated molecular subtypes were discovered in severe influenza patients. Singe-set gene set expression analysis (SsGSEA) indicated that compared with subtype 2, subtype 1 was characterized by reduced adaptive cellular immune responses and increased neutrophil activation. Gene set variation assessment revealed that cluster-specific differentially expressed genes (DEGs) in subtype 1 were involved in autophagy, apoptosis, oxidative phosphorylation, and T cell, immune, and inflammatory responses, amongst others. The random forest (RF) model revealed the most differentiating efficiency with relatively small residual and root mean square error and an increased area under the curve value (AUC = 0.857). Lastly, a five-gene-based RF model (CD247, GADD45A, KIF1B, LIN7A, HLA_DPA1) was established, which showed satisfactory efficiency in the test datasets GSE111368 (AUC = 0.819). Nomogram calibration and decision curve analysis demonstrated its accuracy for the prediction of severe influenza. This study suggests that cuproptosis might be associated with the immunopathology of severe influenza. Additionally, an efficient model for the prediction of cuproptosis subtypes was developed which will contribute to the prevention and treatment of severe influenza patients needing IMV.

Development of a nomogram for severe influenza in previously healthy children: a retrospective cohort study

OBJECTIVE

We aimed to develop a nomogram to predict the risk of severe influenza in previously healthy children.

METHODS

In this retrospective cohort study, we reviewed the clinical data of 1135 previously healthy children infected with influenza who were hospitalized in the Children's Hospital of Soochow University between 1 January 2017 and 30 June 2021. Children were randomly assigned in a 7:3 ratio to a training or validation cohort. In the training cohort, univariate and multivariate logistic regression analyses were used to identify risk factors, and a nomogram was established. The validation cohort was used to evaluate the predictive ability of the model.

RESULT

Wheezing rales, neutrophils, procalcitonin > 0.25 ng/mL, Mycoplasma pneumoniae infection, fever, and albumin were selected as predictors. The areas under the curve were 0.725 (95% CI: 0.686-0.765) and 0.721 (95% CI: 0.659-0.784) for the training and validation cohorts, respectively. The calibration curve showed that the nomogram was well calibrated.

CONCLUSION

The nomogram may predict the risk of severe influenza in previously healthy children.

Co-expression network analysis identifies potential candidate hub genes in severe influenza patients needing invasive mechanical ventilation

BACKGROUND

Influenza is a contagious disease that affects people of all ages and is linked to considerable mortality during epidemics and occasional outbreaks. Moreover, effective immunological biomarkers are needed for elucidating aetiology and preventing and treating severe influenza. Herein, we aimed to evaluate the key genes linked with the disease severity in influenza patients needing invasive mechanical ventilation (IMV). Three gene microarray data sets (GSE101702, GSE21802, and GSE111368) from blood samples of influenza patients were made available by the Gene Expression Omnibus (GEO) database. The GSE101702 and GSE21802 data sets were combined to create the training set. Hub indicators for IMV patients with severe influenza were determined using differential expression analysis and Weighted correlation network analysis (WGCNA) from the training set. The receiver operating characteristic curve (ROC) was also used to evaluate the hub genes from the test set's diagnostic accuracy. Different immune cells' infiltration levels in the expression profile and their correlation with hub gene markers were examined using single-sample gene set enrichment analysis (ssGSEA).

RESULTS

In the present study, we evaluated a total of 447 differential genes. WGCNA identified eight co-expression modules, with the red module having the strongest correlation with IMV patients. Differential genes were combined to obtain 3 hub genes (HLA-DPA1, HLA-DRB3, and CECR1). The identified genes were investigated as potential indicators for patients with severe influenza who required IMV using the least absolute shrinkage and selection operator (LASSO) approach. The ROC showed the diagnostic value of the three hub genes in determining the severity of influenza. Using ssGSEA, it has been revealed that the expression of key genes was negatively correlated with neutrophil activation and positively associated with adaptive cellular immune response.

CONCLUSION

We evaluated three novel hub genes that could be linked to the immunopathological mechanism of severe influenza patients who require IMV treatment and could be used as potential biomarkers for severe influenza prevention and treatment.

Invasive pulmonary aspergillosis among intubated patients with SARS-CoV-2 or influenza pneumonia: a European multicenter comparative cohort study

BACKGROUND

Recent multicenter studies identified COVID-19 as a risk factor for invasive pulmonary aspergillosis (IPA). However, no large multicenter study has compared the incidence of IPA between COVID-19 and influenza patients.

OBJECTIVES

To determine the incidence of putative IPA in critically ill SARS-CoV-2 patients, compared with influenza patients.

METHODS

This study was a planned ancillary analysis of the coVAPid multicenter retrospective European cohort. Consecutive adult patients requiring invasive mechanical ventilation for > 48 h for SARS-CoV-2 pneumonia or influenza pneumonia were included. The 28-day cumulative incidence of putative IPA, based on Blot definition, was the primary outcome. IPA incidence was estimated using the Kalbfleisch and Prentice method, considering extubation (dead or alive) within 28 days as competing event.

RESULTS

A total of 1047 patients were included (566 in the SARS-CoV-2 group and 481 in the influenza group). The incidence of putative IPA was lower in SARS-CoV-2 pneumonia group (14, 2.5%) than in influenza pneumonia group (29, 6%), adjusted cause-specific hazard ratio (cHR) 3.29 (95% CI 1.53-7.02, p = 0.0006). When putative IPA and Aspergillus respiratory tract colonization were combined, the incidence was also significantly lower in the SARS-CoV-2 group, as compared to influenza group (4.1% vs. 10.2%), adjusted cHR 3.21 (95% CI 1.88-5.46, p < 0.0001). In the whole study population, putative IPA was associated with significant increase in 28-day mortality rate, and length of ICU stay, compared with colonized patients, or those with no IPA or Aspergillus colonization.

CONCLUSIONS

Overall, the incidence of putative IPA was low. Its incidence was significantly lower in patients with SARS-CoV-2 pneumonia than in those with influenza pneumonia. Clinical trial registration The study was registered at ClinicalTrials.gov, number NCT04359693 .

Evaluation of efficacy and safety of Qiangzhu-qinggan formula as an adjunctive therapy in adult patients with severe influenza: study protocol for a randomized parallel placebo-controlled double-blind multicenter trial

Background

Influenza can fall into three categories according to severity: mild influenza, severe influenza, and critical influenza. Severe influenza can result in critical illness and sometimes death particularly in patients with comorbidities, advanced age, or pregnancy. Neuraminidase inhibitors (NAIs) are the only antiviral drugs in widespread use for influenza. However, the effectiveness of NAIs against severe influenza is uncertain. New effective drugs or regimens are therefore urgently needed. Qiangzhu-qinggan (QZQG) formula has been found to be effective against influenza virus infection during long-term application in China, which lacks support of evidence-based clinical trial till now. This study is designed to assess the efficacy and safety of QZQG formula as an adjuvant therapy in adult patients with severe influenza.

Methods

This protocol is drawn up in accordance with the SPIRIT guidelines and CONSORT Extension for Chinese herbal medicine formulas. This is a randomized, placebo-controlled, double-blind, multicenter trial. Two hundred twenty-eight adults with severe influenza are randomly assigned in a 1:1 ratio to QZQG or placebo for 7 days. All participants need to receive 1 day of screening before randomization, 7 days of intervention, and 21 days of observation after randomization. The primary outcome is the proportion of clinical improvement, defined as the proportion of patients who met the criteria of 3 points or less in the seven-category ordinal scale or 2 points or less in National Early Warning Score 2 within 7 days after randomization.

Discussion

This is the first randomized, controlled, parallel, double-blind clinical trial to evaluate the efficacy and safety of traditional Chinese herbal formula granules as an adjuvant therapy in adult patients with severe influenza. This study aims to redefine the value of traditional Chinese herbal medicines in the treatment of virus-related respiratory infectious diseases and serves as an example of evidence-based clinical trials of other Chinese herbal medicines.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-021-05929-8.

Grupo de Trabajo Gripe A Grave (GETGAG) de la Sociedad Española de Medicina Intensiva Crítica y Unidades Coronarias (SEMICYUC). Spanish Influenza Score (SIS): Usefulness of machine learning in the development of an early mortality prediction score in severe influenza

OBJECTIVE

To develop a mortality prediction score (Spanish Influenza Score [SIS]) for patients with severe influenza considering only variables at ICU admission, and compare its performance respect of Random Forest (RF).

DESIGN

Sub-analysis from the GETGAG/SEMICYUC database.

SCOPE

Intensive Care Medicine.

PATIENTS

Patients admitted to 184 Spanish ICUs (2009-2018) with influenza infection Intervention: None.

VARIABLES

Demographic data, severity of illness, times from symptoms onset until hospital admission (Gap-H), hospital to ICU (Gap-ICU) or hospital to diagnosis (Gap-Dg), antiviral vaccination, number of quadrants infiltrated, acute renal failure, invasive or noninvasive ventilation, shock and comorbidities. The study variable cut-off points and importance were obtained automatically. Logistic regression analysis with cross-validation was performed to develop the SIS score using the output coefficients. Accuracy and discrimination (AUC-ROC) were applied to evaluate SIS and RF. All analyses were performed using R (CRAN-R Project).

RESULTS

A total of 3959 patients were included. The mean age was 55 years (range 43-67), 60% were men, APACHE II 16 (12-21) and SOFA 5 (4-8), with ICU mortality 21.3%. Mechanical ventilation, shock, APACHE II, SOFA, acute renal failure and Gap-ICU were included in the SIS. The latter was generated according to the ORs obtained by logistic regression, and showed an accuracy of 83% with an AUC-ROC of 82%, similar to RF (AUC-ROC 82%).

CONCLUSIONS

The SIS score is easy to apply and shows adequate capacity to stratify the risk of ICU mortality. However, further studies are needed to validate the tool prospectively.

Efficacy of convalescent plasma for the treatment of severe influenza

BACKGROUND

Convalescent plasma administration may be of clinical benefit in patients with severe influenza, but reports on the efficacy of this therapy vary.

METHODS

We conducted a systematic review and meta-analysis assessing randomized controlled trials (RCTs) involving the administration of convalescent plasma to treat severe influenza. Healthcare databases were searched in February 2020. All records were screened against eligibility criteria, and the risks of bias were assessed. The primary outcome was the fatality rate.

RESULTS

A total of 2861 studies were retrieved and screened. Five eligible RCTs were identified. Pooled analyses yielded no evidence that using convalescent plasma to treat severe influenza resulted in significant reductions in mortality (odds ratio, 1.06; 95% CI, 0.51-2·23; P = 0.87; I² = 35%), number of days in the intensive care unit, or number of days on mechanical ventilation. This treatment may have the possible benefits of increasing hemagglutination inhibition titers and reducing influenza B viral loads and cytokine levels. No serious adverse events were reported. The included studies were generally of high quality with a low risk of bias.

CONCLUSIONS

The administration of convalescent plasma appears safe but may not reduce the mortality, number of days in the intensive care unit, or number of days on mechanical ventilation in patients with severe influenza.

Seasonal influenza surveillance: Observational study on the 2017-2018 season with predominant B influenza virus circulation

INTRODUCTION

Influenza is a common respiratory infectious disease affecting population worldwide yearly. The aim of this work is to describe the 2017-2018 influenza season and how it affected elderly population in Catalonia despite moderate vaccine coverage among this age group.

METHODS

Influenza surveillance based on a primary care sentinel surveillance, virological indicators systematic sampling of ILI attended and severe influenza confirmed cases (SHLCI) admitted to hospital.Analysis of data by Chi-squared, ANOVA, multiple regression and negative control test or case to case for vaccine effectiveness assessment in primary care and SHLCI respectively.

RESULTS

Moderate-high intensity and early onset season with predominance of influenza B virus (IVB) (63%) followed by an increase of circulation of influenza A virus (IVA). A total of 419 IV from primary care samples. Vaccine effectiveness (VE) in primary care setting was 14% (95%CI: 0-47%). 1306 severe cases (adjusted cumulative incidence 18.54/100,000 inhabitants (95%CI: 17.54-19.55)). The highest proportion of severe cases were in the >64 (65.1%) (aOR 15.70; 95%CI: 12.06-20.46; p < 0.001) followed by 45-64 yo (25.4%) (aOR 6.03; 95%CI: 4.57-7.97). VE in preventing intensive care unit (ICU) admission was 35% (95%CI: 10-54%). Final outcome death while hospitalized occurred in 175 SHLCI cases with a case fatality rate of 13.4%.

CONCLUSIONS

2017-2018 influenza season was an unusual epidemic season with an early onset, great predominance of influenza B (Yamagata strain) virus with a high hospitalization rate of severe cases among elderly stressing the need to upgrade vaccine uptake in this age group.

Effect of vaccination, comorbidities and age on mortality and severe disease associated with influenza during the season 2016-2017 in a Spanish tertiary hospital

BACKGROUND

Identifying risk factors for complications or death associated with influenza remains crucial to target preventive interventions. Scores like the Charlson comorbidity index (CCI) may be of help. The aims of this study were to assess the effect of vaccination and comorbidities on severe influenza disease and influenza-related death among hospitalized patients during the season 2016/17; and to evaluate the validity of the CCI to predict death among these patients.

METHODS

Data from adult patients (≥18 years old) with influenza infection admitted to La Paz University Hospital (LPUH) were recorded during the 2016/17 epidemic. The effect of influenza vaccine to prevent severe influenza or death was evaluated using multivariate logistic regression models. The area under the curve of the CCI and the age-adjusted CCI were compared to assess the predictive effect on mortality.

RESULTS

A total of 342 adult patients with influenza infection were admitted, of which 83 developed severe influenza and 25 died during hospitalization. There were no differences between patients who survived and those who died concerning the CCI, but the age-adjusted CCI was higher in fatal cases (p-value=0.005). Influenza vaccine had no statistically significant effect on the risk of mortality (p-value=0.162) while age (OR: 1.12, p-value<0.001) and dementia (OR: 3.05, p-value=0.016) proved to be independent predictors for mortality. The seasonal vaccine was found to be protective for severe infection (OR: 0.54, p-value=0.019). The age-adjusted CCI was a better predictor of mortality than the crude CCI.

CONCLUSIONS

Age and dementia are significant independent risk factors for mortality associated with influenza among hospitalized patients. The age-adjusted CCI seems to be a better predictor of mortality than the crude CCI. Influenza vaccine has shown to be effective in preventing severe influenza in the season 2016/17 among hospitalized patients and should be promoted in population at risk.

Risk factors for nosocomial infection among hospitalised severe influenza A(H1N1)pdm09 patients

BACKGROUND AND OBJECTIVE

Nosocomial infections following influenza are important causes of death, requiring early implementation of preventive measures, but predictors for nosocomial infection in the early stage remained undetermined. We aimed to determine risk factors that can help clinicians identify patients with high risk of nosocomial infection following influenza on admission.

METHOD

Using a database prospectively collected through a Chinese national network for hospitalised severe influenza A(H1N1)pdm09 patients, we compared the characteristics on admission between patients with and without nosocomial infection.

RESULT

A total of 2146 patients were enrolled in the final analysis with a median age of 36.0 years, male patients comprising 50.2% of the sample and 232 (10.8%) patients complicated with nosocomial infection. Acinetobacter baumannii, Pseudomonas aeruginosa, Stenotrophomonas maltophilia and Staphylococcus aureus were the leading pathogens, and invasive fungal infection was found in 30 cases (12.9%). The in-hospital mortality was much higher in patients with nosocomial infection than those without (45.7% vs 11.8%, P < 0.001). Need for mechanical ventilation (OR: 3.336; 95% CI 2.362-4.712), sepsis (OR: 2.125; 95% CI 1.236-3.651), ICU admission on first day (OR: 2.074; 95% CI 1.425-3.019), lymphocytopenia (OR: 1.906; 95% CI 1.361-2.671), age > 65 years (OR: 1.83; 95% CI 1.04-3.21) and anaemia (OR: 1.39; 95% CI 1.39-2.79) were independently associated with nosocomial infection.

CONCLUSION

Need for mechanical ventilation, sepsis, ICU admission on first day, lymphocytopenia, older age and anaemia were independent risk factors that can help clinicians identify severe influenza A(H1N1)pdm09 patients at high risk of nosocomial infection.

patients, 2013-14

•

22.5% of adult patients with H1N1 developed bacterial co-infection.

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Staphylococcus aureus was the most common cause of co-infection.

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Bacterial and viral co-infections were associated with death in bivariate.

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Patients with a bacterial co-infection had greater use of resources.

Background

Influenza acts synergistically with bacterial co-pathogens. Few studies have described co-infection in a large cohort with severe influenza infection.

Objectives

To describe the spectrum and clinical impact of co-infections.

Study design

Retrospective cohort study of patients with severe influenza infection from September 2013 through April 2014 in intensive care units at 33 U.S. hospitals comparing characteristics of cases with and without co-infection in bivariable and multivariable analysis.

Results

Of 507 adult and pediatric patients, 114 (22.5%) developed bacterial co-infection and 23 (4.5%) developed viral co-infection. Staphylococcus aureus was the most common cause of co-infection, isolated in 47 (9.3%) patients. Characteristics independently associated with the development of bacterial co-infection of adult patients in a logistic regression model included the absence of cardiovascular disease (OR 0.41 [0.23–0.73], p = 0.003), leukocytosis (>11 K/μl, OR 3.7 [2.2–6.2], p < 0.001; reference: normal WBC 3.5–11 K/μl) at ICU admission and a higher ICU admission SOFA score (for each increase by 1 in SOFA score, OR 1.1 [1.0–1.2], p = 0.001). Bacterial co-infections (OR 2.2 [1.4–3.6], p = 0.001) and viral co-infections (OR 3.1 [1.3–7.4], p = 0.010) were both associated with death in bivariable analysis. Patients with a bacterial co-infection had a longer hospital stay, a longer ICU stay and were likely to have had a greater delay in the initiation of antiviral administration than patients without co-infection (p < 0.05) in bivariable analysis.

Conclusions

Bacterial co-infections were common, resulted in delay of antiviral therapy and were associated with increased resource allocation and higher mortality.

Obesity not associated with severity among hospitalized adults with seasonal influenza virus infection

We examined seasonal influenza severity [artificial ventilation, intensive care unit (ICU) admission, and radiographic-confirmed pneumonia] by weight category among adults hospitalized with laboratory-confirmed influenza. Using multivariate logistic regression models, we found no association between obesity or severe obesity and artificial ventilation or ICU admission; however, overweight and obese patients had decreased risk of pneumonia. Underweight was associated with pneumonia (adjusted odds ratio 1.31; 95 % confidence interval 1.04, 1.64).