Integrating host response and unbiased microbe detection for lower respiratory tract infection diagnosis in critically ill adults

Comparison of the Impact of tNGS with mNGS on Antimicrobial Management in Patients with LRTIs: A Multicenter Retrospective Cohort Study

Background

tNGS and mNGS are valuable tools for diagnosing pathogens in lower respiratory tract infections (LRTIs), which subsequently influence treatment strategies. However, the impact of tNGS and mNGS on antimicrobial stewardship in patients with LRTIs remains unclear.

Methods

Patients diagnosed with LRTIs who underwent tNGS or mNGS between June 2021 and January 2024 were included. Patients who underwent both tNGS and conventional microbiologic tests (CMTs) were grouped into the tNGS group, the others were divided into the mNGS group. Then, the diagnostic efficacy of tNGS and mNGS was compared, along with their impact on antimicrobial management and clinical outcomes.

Results

548 patients with an initial diagnosis of LRTIs who underwent tNGS or mNGS were evaluated. Finally, 321 patients were analyzed, with 117 patients in tNGS group and 204 patients in mNGS group. The overall pathogen detection rates for tNGS and mNGS were 89.74% and 89.71% (P=0.991). The distribution of detected pathogens was similar between tNGS and mNGS, with bacteria being the predominant microorganisms. The proportions of patients who underwent antimicrobial agent changes and received targeted therapy were not significantly different between tNGS and mNGS groups (P=0.270; P=0.893). Additionally, no significant differences were noted in the rates of antibiotic de-escalation, escalation, or changes in the opposite direction (all P>0.05). The same results was observed in the proportions of patients with addition or reductions in antiviral, antifungal, and antibacterial agents (all P>0.05). Hospital stays, improvement rate and mortality rate were also similar (all P>0.05).

Conclusion

tNGS and mNGS demonstrate comparable overall pathogen yield rates in patients with LRTIs. Furthermore, tNGS is also comparable to mNGS in terms of adjusting antimicrobial treatments and clinical outcomes, tNGS meets the clinical needs of most patients with LRTIs and can be firstly used for these patients.

Clinical and metagenomic predicted antimicrobial resistance in pediatric critically ill patients with infectious diseases in a single center of Zhejiang

BACKGROUND

Antimicrobial resistance (AMR) poses a significant threat to pediatric health; therefore, precise identification of pathogens as well as AMR is imperative. This study aimed at comprehending antibiotic resistance patterns among critically ill children with infectious diseases admitted to pediatric intensive care unit (PICU) and to clarify the impact of drug-resistant bacteria on the prognosis of children.

METHODS

This study retrospectively collected clinical data, identified pathogens and AMR from 113 children's who performed metagenomic next-generation sequencing for pathogen and antibiotic resistance genes identification, and compared the clinical characteristic difference and prognostic effects between children with and without AMR detected.

RESULTS

Based on the presence or absence of AMR test results, the 113 patients were divided into Antimicrobial resistance test positive group (AMRT+, n = 44) and Antimicrobial resistance test negative group (AMRT-, n = 69). Immunocompromised patients (50% vs. 28.99%, P = 0.0242) and patients with underlying diseases (70.45% vs. 40.58%, P = 0.0019) were more likely to develop resistance to antibiotics. Children in the AMRT + group showed significantly increased C-reaction protein, score of pediatric sequential organ failure assessment and pediatric risk of mortality of children and longer hospital stay and ICU stay in the AMRT + group compared to the AMRT+- group (P < 0.05). Detection rate of Gram-negative bacteria was significantly higher in the AMRT + group rather than Gram-positive bacteria (n = 45 vs. 31), in contrast to the AMRT- group (n = 10 vs. 36). Cephalosporins, β-lactams/β-Lactamase inhibitors, carbapenems and sulfonamides emerged as the most common types of drug resistance in children. Resistance rates to these antibiotics exhibited considerable variation across common pathogens, including Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii.

CONCLUSIONS

The development of drug resistance in bacteria will significantly affect the prognosis of patients. The significant differences in drug resistance of common pathogenic bacteria indicate that identification of drug resistance is important for the rational use of antibiotics and patient prognosis.

Diagnosis and management of invasive fungal diseases by next-generation sequencing: are we there yet?

INTRODUCTION

Invasive fungal diseases (IFDs) are a serious threat to immunocompromised patients. Routine diagnostic methods have limited performance in identifying IFDs. Next-generation sequencing (NGS), including metagenomic NGS (mNGS) and whole-genome sequencing (WGS), recently emerged as diagnostic methods that could provide more accurate and timely diagnoses and management of IFDs.

AREAS COVERED

This article describes the emergence of NGS as a diagnostic tool to address the limitations of current tests. The literature regarding its application and clinical utility in the diagnosis of IFDs is reviewed. Practical considerations, challenges, and opportunities as they relate to the development and implementation of mNGS and WGS for fungal pathogens are discussed.

EXPERT OPINION

NGS emerged over a decade ago with the potential to solve many of the challenges in diagnosing infectious diseases, including IFDs. However, published literature has yielded conflicting data about its clinical utility. The increased clinical adoption of NGS is improving our understanding of how to interpret and use its results to guide actionable decisions. Still, several gaps remain. As the cost, effort, and expertise involved in performing NGS decrease and the reporting of its results becomes standardized, NGS is poised to fill current gaps in the diagnosis of IFDs.

Diagnostic performance of metagenomic next-generation sequencing based on alveolar lavage fluid in unexplained lung shadows

BACKGROUND

Unexplained lung shadows are challenging in respiratory medicine, with both infectious and non-infectious etiologies. Lung biopsy is definitive but invasive, prompting a need for non-invasive alternatives. Metagenomic next-generation sequencing (mNGS) of bronchoalveolar lavage fluid (BALF) is emerging as a promising diagnostic tool.

METHODS

We retrospectively analyzed 105 patients with unexplained lung shadows, collecting general information, mNGS results from BALF, and clinical diagnosis. We evaluated mNGS's diagnostic performance by comparing with final diagnosis.

RESULTS

mNGS showed good diagnostic performance in differentiating infectious from non-infectious causes. The specificity and accuracy for bacteria and fungi exceeded 90%, while the sensitivity and precision for fungi were lower than for bacteria. Atypical pathogens were frequently identified, especially in mixed infections.

CONCLUSIONS

mNGS of BALF is efficient in diagnosing infectious and non-infectious causes of unexplained lung shadows. While effective for bacteria and fungi detection, the sensitivity and precision for fungi are lower.

Acute respiratory failure in adult patients with acute myeloid leukemia

INTRODUCTION

Patients with acute myeloid leukemia (AML) are at high risk of developing life-threatening complications. It is estimated that a quarter of adult patients diagnosed with AML will require admission to the intensive care unit (ICU) at least once during their disease. Acute respiratory failure (ARF) is the main reason for ICU admission and is associated with high mortality rates, depending on the etiology of ARF.

AREAS COVERED

In this population, the high prevalence of severe pulmonary infections highlights the importance of immunosuppression caused by the disease and its treatment. In the early stages of the disease, in addition to pneumonia, which should be systematically sought, leukemia-specific lung involvement (leukostasis, leukemic pulmonary infiltration, and acute lysis pneumopathy) is an important cause of ARF in this population, representing up to 60% of cases. This review aims to help understand the pathophysiology and management of leukemia-specific lung involvement, based on the most contemporary literature.

EXPERT OPINION

The number of AML patients requiring ICU care is expected to increase. AML patients admitted to the ICU for ARF have a high mortality rate, but survivors have encouraging long-term outcomes. Future research will focus on improving risk stratification, cytoreduction, oxygenation strategies, and diagnostic techniques for ARF.

Comparison of targeted next-generation sequencing and traditional microbial culture in the diagnosis of pulmonary infections

This study investigated the diagnostic potential of targeted next-generation sequencing (tNGS) for pulmonary infections. The positivity rate of tNGS was significantly higher than that of traditional microbial culture (92.6 % vs 25.2 %, χ2 = 378.272, P < 0.001). The proportion of two or more species of pathogens detected using tNGS exceeded that detected using microbial culture (χ2 = 337.283, P < 0.001). There were inconsistencies between the results of the tNGS antibiotic resistance gene and the drug susceptibility test resistance phenotype. The tNGS technique demonstrates rapid and effective capabilities in identifying bacteria, fungi, viruses, and specific pathogens, with a detection sensitivity that surpasses that of conventional culture methodologies. Microbial drug resistance genotypes detected by tNGS cannot accurately predict drug resistance phenotypes and require further improvement or integration with traditional microbial culture to establish a foundation for effective clinical treatment.

Clinical relevance of lung microbiota composition in critically ill children with acute lower respiratory tract infections: insights from a retrospective analysis of metagenomic sequencing

PURPOSE

Acute lower respiratory tract infections (ALRIs) is a leading cause of child mortality worldwide. Metagenomic next-generation sequencing (mNGS) identifies ALRIs pathogens and explores the lung microbiota's role in disease severity and clinical outcomes. This study examines the association between lung microbiota and ALRIs outcomes in children, exploring its potential as a prognostic biomarker.

METHODS

We retrospectively analyzed mNGS data from the bronchoalveolar lavage fluid (BALF) of 83 pediatric ALRIs patients from 2019 to 2023. Microbial diversity and relative abundances of specific taxa were compared between survivor and non-survivor groups, as well as between varying severity levels. LEfSe was employed to identify key biomarkers related to survival and disease severity.

RESULTS

Among the 83 patients, 68 survived and 15 died. Patients were also divided into a low severity group (n = 38) and a moderate-to-very-high severity group (n = 45) according to mPIRO score at admission. Significant differences in beta diversity were observed between the survival groups and across different severity levels. Prevotella, Haemophilus and Veillonella exhibited higher abundances in both the survivor and low severity groups, suggesting their potential as predictors of better outcomes. Conversely, Enterococcus and Acinetobacter baumannii were more prevalent in the non-survivor and moderate-to-very-high severity groups. Additionally, Streptococcus pneumoniae and Streptococcus mitis showed increased abundances in survivors. LEfSe further revealed that these microorganisms may predict outcomes and severity in ALRIs.

CONCLUSION

Our findings underscore the complex relationship between lung microbiota and ALRIs, with specific microbial profiles associated with disease severity and clinical outcomes. This underscores the need for further research to explore and validate its prognostic predictive capacity.

CLINICAL TRIAL NUMBER

Not applicable.

Microbial dynamics and pulmonary immune responses in COVID-19 secondary bacterial pneumonia

Secondary bacterial pneumonia (2°BP) is associated with significant morbidity following respiratory viral infection, yet remains incompletely understood. In a prospective cohort of 112 critically ill adults intubated for COVID-19, we comparatively assess longitudinal airway microbiome dynamics and the pulmonary transcriptome of patients who developed 2°BP versus controls who did not. We find that 2°BP is significantly associated with both mortality and corticosteroid treatment. The pulmonary microbiome in 2°BP is characterized by increased bacterial RNA mass and dominance of culture-confirmed pathogens, detectable days prior to 2°BP clinical diagnosis, and frequently also present in nasal swabs. Assessment of the pulmonary transcriptome reveals suppressed TNFα signaling in patients with 2°BP, and sensitivity analyses suggest this finding is mediated by corticosteroid treatment. Further, we find that increased bacterial RNA mass correlates with reduced expression of innate and adaptive immunity genes in both 2°BP patients and controls. Taken together, our findings provide fresh insights into the microbial dynamics and host immune features of COVID-19-associated 2°BP, and suggest that suppressed immune signaling, potentially mediated by corticosteroid treatment, permits expansion of opportunistic bacterial pathogens.

Acute respiratory distress syndrome

The understanding of acute respiratory distress syndrome (ARDS) has evolved greatly since it was first described in a 1967 case series, with several subsequent updates to the definition of the syndrome. Basic science advances and clinical trials have provided insight into the mechanisms of lung injury in ARDS and led to reduced mortality through comprehensive critical care interventions. This review summarizes the current understanding of the epidemiology, pathophysiology, and management of ARDS. Key highlights include a recommended new global definition of ARDS and updated guidelines for managing ARDS on a backbone of established interventions such as low tidal volume ventilation, prone positioning, and a conservative fluid strategy. Future priorities for investigation of ARDS are also highlighted.

The clinical application of metagenomic next-generation sequencing in immunocompromised patients with severe respiratory infections in the ICU

BACKGROUND

Early targeted antibiotic therapy is crucial for improving the prognosis of immunocompromised patients with severe respiratory infections (SRIs) in the intensive care unit (ICU). Metagenomic next-generation sequencing (mNGS) has shown significant value in pathogen detection, but research on lower respiratory tract microorganisms remains limited.

METHODS

This study enrolled 234 patients with SRIs in the ICU, and individuals were categorized into immunocompromised and immunocompetent groups. We compared the diagnostic performance of mNGS using bronchoalveolar lavage fluid (BALF) with conventional microbiological tests (CMTs) and analyzed the value of mNGS in immunocompromised patients with SRIs in the ICU.

RESULTS

Among all patients, the pathogenic microorganism detection rate of mNGS was higher than that of CMTs (94.02% vs 66.67%, P < 0.05), both in the immunocompromised group (95.0% vs 58.75%, P < 0.05) and the immunocompetent group (93.51% vs 71.43%, P < 0.05). mNGS detected more pathogens than CMTs did (167 vs 51), identifying 116 organisms that were missed by CMTs. The proportion of antibiotic regimen adjustments based on mNGS results was significantly higher compared to CMTs in both the immunocompromised (70.00% vs 17.50%, P < 0.05) and immunocompetent groups (48.70% vs 15.58%, P < 0.05). In the immunocompromised group, patients who had their antibiotic treatment adjusted on mNGS results had improved prognosis, with significantly lower ICU mortality (8.93% vs 50%, P < 0.05) and 28-day mortality rates (30.36% vs 68.75%, P < 0.05) than CMTs. In the immunocompetent group, no statistically significant differences were observed in ICU mortality or 28-day mortality (20.00% vs 33.33%, P > 0.05; 42.67% vs 45.83%, P > 0.05).

CONCLUSION

mNGS shows significant value in detecting pathogens in immunocompromised patients with SRIs in ICU. For immunocompromised patients who respond poorly to empirical treatment, mNGS can provide an etiological basis, helping adjust antibiotic regimens more precisely and thereby improving patient prognosis.

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

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

IMPORTANCE

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

Application of the second-generation sequencing technology of metagenomics in the detection of pathogens in respiratory patients

OBJECTIVE

To explore the application value of the second-generation metagenomic next-generation sequencing (mNGS) in the detection of pathogens in patients with pulmonary infection.

METHODS

We conducted a retrospective analysis of 65 pulmonary infection cases treated at our institution and the Fifth People's Hospital of Shanghai between January 2021 and May 2023. All subjects were subjected to mNGS, targeted next-generation sequencing (tNGS), and conventional microbiological culture. A comparative analysis was performed to evaluate the diversity and quantity of pathogens identified by these methodologies and to appraise their respective diagnostic capabilities in pulmonary infection diagnostics.

RESULTS

The mNGS successfully identified etiological agents in 60 of the 65 cases, compared to tNGS, which yielded positive results in 42 cases, and conventional laboratory cultures, which detected pathogens in 24 cases. At the bacterial genus level, mNGS discerned 9 genera, 11 species, and 92 isolates of pathogenic bacteria, whereas tNGS identified 8 genera, 8 species, and 71 isolates. Conventional methods were less sensitive, detecting only 6 genera, 7 species, and 33 isolates. In terms of fungal detection, mNGS identified 4 fungal species, tNGS detected 4 isolates of the Candida genus, and conventional methods identified 2 isolates of the same genus. Viral detection at the species level revealed 10 species and 46 isolates by mNGS, whereas tNGS detected only 3 species and 7 isolates. The area under the receiver operating characteristic curve (AUC) with 95% confidence intervals for diagnosing pulmonary infections was 0.818 (0.671 to 0.966) for mNGS, 0.668 (0.475 to 0.860) for tNGS, and 0.721 (0.545 to 0.897) for conventional culture.The mNGS demonstrates superior diagnostic efficacy and pathogen detection breadth in critically ill patients with respiratory infections, offering a significant advantage by reducing the time to diagnosis. The enhanced sensitivity and comprehensive pathogen profiling of mNGS underscore its potential as a leading diagnostic tool in clinical microbiology.

Deep longitudinal lower respiratory tract microbiome profiling reveals genome-resolved functional and evolutionary dynamics in critical illness

The lower respiratory tract (LRT) microbiome impacts human health, especially among critically ill patients. However, comprehensive characterizations of the LRT microbiome remain challenging due to low microbial mass and host contamination. We develop a chelex100-based low-biomass microbial-enrichment method (CMEM) that enables deep metagenomic profiling of LRT samples to recover near-complete microbial genomes. We apply the method to 453 longitudinal LRT samples from 157 intensive care unit (ICU) patients in three geographically distant hospitals. We recover 120 high-quality metagenome-assembled genomes (MAGs) and associated plasmids without culturing. We detect divergent longitudinal microbiome dynamics and hospital-specific dominant opportunistic pathogens and resistomes in pneumonia patients. Diagnosed pneumonia and the ICU stay duration were associated with the abundance of specific antibiotic-resistance genes (ARGs). Moreover, CMEM can serve as a robust tool for genome-resolved analyses. MAG-based analyses reveal strain-specific resistome and virulome among opportunistic pathogen strains. Evolutionary analyses discover increased mobilome in prevailing opportunistic pathogens, highly conserved plasmids, and new recombination hotspots associated with conjugative elements and prophages. Integrative analysis with epidemiological data reveals frequent putative inter-patient strain transmissions in ICUs. In summary, we present a genome-resolved functional, transmission, and evolutionary landscape of the LRT microbiota in critically ill patients.

Integrating respiratory microbiome and host immune response through machine learning for respiratory tract infection diagnosis

At present, the diagnosis of lower respiratory tract infections (LRTIs) is difficult, and there is an urgent need for better diagnostic methods. This study enrolled 136 patients from 2020 to 2021 and collected bronchoalveolar lavage fluid (BALF) specimens. We used metatranscriptome to analyze the lower respiratory tract microbiome (LRTM) and host immune response. The diversity of the LRTM in LRTIs significantly decreased, manifested by a decrease in the abundance of normal microbiota and an increase in the abundance of opportunistic pathogens. The upregulated differentially expressed genes (DEGs) in the LRTIs group were mainly enriched in infection immune response-related pathways. Klebsiella pneumoniae had the most significant increase in abundance in LRTIs, which was strongly correlated with host infection or inflammation genes TNFRSF1B, CSF3R, and IL6R. We combined LRTM and host transcriptome data to construct a machine-learning model with 12 screened features to discriminate LRTIs and non-LRTIs. The results showed that the model trained by Random Forest in the validate set had the best performance (ROC AUC: 0.937, 95% CI: 0.832-1). The independent external dataset showed an accuracy of 76.5% for this model. This study suggests that the model integrating LRTM and host transcriptome data can be an effective tool for LRTIs diagnosis.

Evaluating metagenomics and targeted approaches for diagnosis and surveillance of viruses

BACKGROUND

Metagenomics is a powerful approach for the detection of unknown and novel pathogens. Workflows based on Illumina short-read sequencing are becoming established in diagnostic laboratories. However, high sequencing depth requirements, long turnaround times, and limited sensitivity hinder broader adoption. We investigated whether we could overcome these limitations using protocols based on untargeted sequencing with Oxford Nanopore Technologies (ONT), which offers real-time data acquisition and analysis, or a targeted panel approach, which allows the selective sequencing of known pathogens and could improve sensitivity.

METHODS

We evaluated detection of viruses with readily available untargeted metagenomic workflows using Illumina and ONT, and an Illumina-based enrichment approach using the Twist Bioscience Comprehensive Viral Research Panel (CVRP), which targets 3153 viruses. We tested samples consisting of a dilution series of a six-virus mock community in a human DNA/RNA background, designed to resemble clinical specimens with low microbial abundance and high host content. Protocols were designed to retain the host transcriptome, since this could help confirm the absence of infectious agents. We further compared the performance of commonly used taxonomic classifiers.

RESULTS

Capture with the Twist CVRP increased sensitivity by at least 10-100-fold over untargeted sequencing, making it suitable for the detection of low viral loads (60 genome copies per ml (gc/ml)), but additional methods may be needed in a diagnostic setting to detect untargeted organisms. While untargeted ONT had good sensitivity at high viral loads (60,000 gc/ml), at lower viral loads (600-6000 gc/ml), longer and more costly sequencing runs would be required to achieve sensitivities comparable to the untargeted Illumina protocol. Untargeted ONT provided better specificity than untargeted Illumina sequencing. However, the application of robust thresholds standardized results between taxonomic classifiers. Host gene expression analysis is optimal with untargeted Illumina sequencing but possible with both the CVRP and ONT.

CONCLUSIONS

Metagenomics has the potential to become standard-of-care in diagnostics and is a powerful tool for the discovery of emerging pathogens. Untargeted Illumina and ONT metagenomics and capture with the Twist CVRP have different advantages with respect to sensitivity, specificity, turnaround time and cost, and the optimal method will depend on the clinical context.

Integrated epigenomic exposure signature discovery

Aim: The epigenome influences gene regulation and phenotypes in response to exposures. Epigenome assessment can determine exposure history aiding in diagnosis.Materials & methods: Here we developed and implemented a machine learning algorithm, the exposure signature discovery algorithm (ESDA), to identify the most important features present in multiple epigenomic and transcriptomic datasets to produce an integrated exposure signature (ES).Results: Signatures were developed for seven exposures including Staphylococcus aureus, human immunodeficiency virus, SARS-CoV-2, influenza A (H3N2) virus and Bacillus anthracis vaccinations. ESs differed in the assays and features selected and predictive value.Conclusion: Integrated ESs can potentially be utilized for diagnosis or forensic attribution. The ESDA identifies the most distinguishing features enabling diagnostic panel development for future precision health deployment.

Value of bronchoalveolar lavage fluid metagenomic next-generation sequencing in acute exacerbation of fibrosing interstitial lung disease: an individualized treatment protocol based on microbiological evidence

BACKGROUND

Acute exacerbation of fibrosing interstitial lung diseases (AE-ILD) is a serious life-threatening event per year. Methylprednisolone and/or immunosuppressive agents (ISA) are a mainstay in any regimen, under the premise that pulmonary infection has been promptly identified and controlled. We investigated the value of bronchoalveolar lavage fluid (BALF) metagenomic next-generation sequencing (mNGS) on the treatment adjustment of AE-ILD.

METHODS

We conducted a cross-sectional observational study. All data were collected prospectively and retrospectively analyzed. We included fifty-six patients with AE-ILD and nineteen stable ILD who underwent BALF mNGS at the beginning of admission.

RESULTS

Patients with a variety of ILD classification were included. Connective-tissue disease related ILD (CTD-ILD) occupy the most common underlying non-idiopathic pulmonary fibrosis (non-IPF). The infection-triggered AE accounted for 39.29%, with the majority of cases being mixed infections. The microorganisms load in the AE-ILD group was significantly higher. After adjusted by mNGS, the therapy coverage number of pathogens was significantly higher compared to the initial treatment (p < 0.001). After treatment, the GGO score and the consolidation score were significantly lower during follow up in survivors (1.57 ± 0.53 vs. 2.38 ± 0.83 with p < 0.001, 1.11 ± 0.24 vs. 1.49 ± 0.47 with p < 0.001, respectively). Some detected microorganisms, such as Tropheryma whipplei, Mycobacterium, Aspergillus, and mixed infections were difficult to be fully covered by empirical medication. BALF mNGS was also very helpful for excluding infections and early administration of methylprednisolone and/or ISA.

CONCLUSIONS

mNGS has been shown to be a useful tool to determine pathogens in patients with AE-ILD, the results should be fully analyzed. The comprehensive treatment protocol based on mNGS has been shown crucial in AE-ILD patients.

Utility of Metagenomic Next-Generation Sequencing for Diagnosis of Infectious Diseases in Critically Ill Immunocompromised Pediatric Patients

Purpose

Infections cause high rates of illness and death in children worldwide. However, studies on the clinical value of metagenomic next-generation sequencing (mNGS) for immunocompromised children are still limited.

Patients and Methods

From June 2021 to December 2023, 119 samples were collected at Pediatric Intensive Care Unit (PICU) of a single-center pediatric hospital and classified into two groups based on their immune states. We compared the diagnostic performance of mNGS and conventional microbiological test (CMT) for pathogen identification, and assessed the clinical impacts of mNGS.

Results

Among the 119 samples, 48 (40.34%) belonged to the immunocompromised children. mNGS had a higher positivity rate than CMT (76.47% vs 55.46%, P = 0.0006). The positive percent agreement (PPA) of mNGS for immunocompromised children was higher compared to immunocompetent children (95.24% vs 77.78%). The most common pathogens for immunocompromised patients were gram-negative bacteria and herpesvirus. However, immunocompetent children showed a higher detection rate for gram-positive bacteria and respiratory viruses. Furthermore, the proportions of the positive impact of mNGS results were significantly higher in immunocompromised patients compared to immunocompetent patients for both diagnosis (91.67% vs 57.75%) and treatment (95.83% vs 64.79%) (P < 0.0001). Immunocompromised state, length of hospital stays, times stay in ICU, Pediatric Risk of Mortality (PRISM) score, neutrophil percentage (NEUT%) and the ratio of arterial oxygen partial pressure to fractional inspired oxygen (PaO2/FiO2) were considered independent factors for poor prognosis in critically ill pediatric patients.

Conclusion

In patients from PICU, mNGS had a greater clinical significance in immunocompromised children compared to immunocompetent children. mNGS technology is an important auxiliary method for achieving accurate diagnosis and treatment of critically ill pediatric patients.

Efficacy of targeted next generation sequencing for pathogen detection in lower respiratory tract infections

OBJECTIVE

To investigate the diagnostic utility of targeted next-generation sequencing (tNGS) in the diagnosis of lower respiratory tract infections.

METHODS

Patients with lower respiratory tract infection in East Area of Yantai Yantaishan Hospital from December 2021 to September 2023 were retrospectively analyzed. Sputum samples were tested using both tNGS technology and conventional microbiological examination. Data were collected on general clinical features and test outcomes. The study evaluated the efficacy of tNGS by comparing its positive detection rate against traditional methods and analyzing detection differences among patients with varying clinical characteristics. Receiver operating characteristic (ROC) analysis was used to determine the diagnostic accuracy of both testing methods.

RESULTS

A total of 281 patients were included, with corresponding sputum specimens. The tNGS method showed a higher positivity rate of 90.0%, significantly outperforming the conventional method's rate of 70.82% (P<0.05). Among 199 patients with concordant positive results, 38.22% fully agreed, while 53.40% completely disagreed between the two methods. Mycobacterium tuberculosis, Candida albicans, and Pseudomonas aeruginosa were the most frequently detected pathogens, respectively. tNGS significantly reduced the time required for pathogen detection (P<0.001) and identified a higher rate of mixed infections compared to conventional methods (49.11% vs 2.85%, P<0.001). Positive tNGS detection rates significantly differed between patients with abnormal vs normal C-reactive protein or procalcitonin levels. The AUC for tNGS was 0.867, indicating superior diagnostic accuracy over the conventional method (P<0.05).

CONCLUSIONS

tNGS technology demonstrates a high positivity rate and rapid pathogen detection in lower respiratory tract infections, with notable advantages in identifying mixed infections. This method shows potential for enhancing diagnostic accuracy and treatment decisions in clinical settings.

The diagnostic value of bronchoalveolar lavage fluid metagenomic next-generation sequencing in critically ill patients with respiratory tract infections

Metagenomic next-generation sequencing (mNGS) is an unbiased and rapid method for detecting pathogens. This study enrolled 145 suspected severe pneumonia patients who were admitted to the Affiliated Hospital of Jining Medical University. This study primarily aimed to determine the diagnostic performance of mNGS and conventional microbiological tests (CMTs) using bronchoalveolar lavage fluid samples for detecting pathogens. Our findings indicated that mNGS performed significantly higher sensitivity (97.54% vs 28.68%, P < 0.001), coincidence (90.34% vs 35.17%, P < 0.001), and negative predictive value (80.00% vs 13.21%, P < 0.001) but performed lower specificity than CMTs (52.17% vs 87.5%, P < 0.001). Streptococcus pneumoniae as the most common bacterial pathogen had the largest proportion (22.90%, 30/131) in this study. In addition to bacteria, fungi, and virus, mNGS can detect a variety of atypical pathogens such as Mycobacterium tuberculosis and non-tuberculous. Mixed infections were common in patients with severe pneumonia, and bacterial-fungal-viral-atypical pathogens were the most complicated infection. After adjustments of antibiotics based on mNGS and CMTs, the clinical manifestation improved in 139 (95.86%, 139/145) patients. Our data demonstrated that mNGS had significant advantage in diagnosing respiratory tract infections, especially atypical pathogens and fungal infections. Pathogens were detected timely and comprehensively, contributing to the adjustments of antibiotic treatments timely and accurately, improving patient prognosis and decreasing mortality potentially.IMPORTANCEMetagenomic next-generation sequencing using bronchoalveolar lavage fluid can provide more comprehensive and accurate pathogens for respiratory tract infections, especially when considering the previous usage of empirical antibiotics before admission or complicated clinical presentation. This technology is expected to play an important role in the precise application of antimicrobial drugs in the future.

Application of Second-Generation Sequencing Technology in Lower Respiratory Tract Infection

BACKGROUND

Lower respiratory tract infection (LRTI) has long been an important threat to people's life and health, so the rapid diagnosis of LRTI is of great significance in clinical treatment. In recent years, the development of the sequencing technology provides a new direction for the rapid diagnosis of LRTI. In this review, the advantages and disadvantages of second-generation sequencing techniques represented by metagenomics next-generation sequencing (mNGS) and droplet digital polymerase chain reaction (ddPCR) in LRTI were reviewed. Furthermore, it offers insights into the future trajectory of this technology, highlighting its potential to revolutionise the field of respiratory infection diagnostics.

OBJECTIVE

This review summarises developments in mechanistic research of second-generation sequencing technology their relationship with clinical practice, providing insights for future research.

METHODS

Authors conducted a search on PubMed and Web of Science using the professional terms 'Lower respiratory tract infection' and 'droplet digital polymerase chain reaction' and 'metagenomics next generation sequencing'. The obtained literature was then roughly categorised based on their research content. Similar studies were grouped into the same sections, and further searches were conducted based on the keywords of each section.

RESULTS

Different studies discussed the application of second-generation sequencing technology in LRTI from different angles, including the detection of pathogens of LRTI by mNGS and ddPCR, the prediction ability of drug-resistant bacteria, and comparison with traditional methods. We try to analyse the advantages and disadvantages of the second-generation sequencing technology by combing the research results of mNGS and ddPCR. In addition, the development direction of the second-generation sequencing technology is prospected.

Investigation of acute encephalitis syndrome with implementation of metagenomic next generation sequencing in Nepal

BACKGROUND

The causative agents of Acute Encephalitis Syndrome remain unknown in 68-75% of the cases. In Nepal, the cases are tested only for Japanese encephalitis, which constitutes only about 15% of the cases. However, there could be several organisms, including vaccine-preventable etiologies that cause acute encephalitis, when identified could direct public health efforts for prevention, including addressing gaps in vaccine coverage.

OBJECTIVES

This study employs metagenomic next-generation-sequencing in the investigation of underlying causative etiologies contributing to acute encephalitis syndrome in Nepal.

METHODS

In this study, we investigated 90, Japanese-encephalitis-negative, banked cerebrospinal fluid samples that were collected as part of a national surveillance network in 2016 and 2017. Randomization was done to include three age groups (< 5-years; 5-14-years; >15-years). Only some metadata (age and gender) were available. The investigation was performed in two batches which included total nucleic-acid extraction, followed by individual library preparation (DNA and RNA) and sequencing on Illumina iSeq100. The genomic data were interpreted using Chan Zuckerberg-ID and confirmed with polymerase-chain-reaction.

RESULTS

Human-alphaherpes-virus 2 and Enterovirus-B were seen in two samples. These hits were confirmed by qPCR and semi-nested PCR respectively. Most of the other samples were marred by low abundance of pathogen, possible freeze-thaw cycles, lack of process controls and associated clinical metadata.

CONCLUSION

From this study, two documented causative agents were revealed through metagenomic next-generation-sequencing. Insufficiency of clinical metadata, process controls, low pathogen abundance and absence of standard procedures to collect and store samples in nucleic-acid protectants could have impeded the study and incorporated ambiguity while correlating the identified hits to infection. Therefore, there is need of standardized procedures for sample collection, inclusion of process controls and clinical metadata. Despite challenging conditions, this study highlights the usefulness of mNGS to investigate diseases with unknown etiologies and guide development of adequate clinical-management-algorithms and outbreak investigations in Nepal.

Optimizing treatment administration strategies using negative mNGS results in corticosteroid-sensitive diffuse parenchymal lung diseases

Timely adjustments of antibiotic and corticosteroid treatments are vital for patients with diffuse parenchymal lung diseases (DPLDs). In this study, 41 DPLD patients with negative metagenomic next-generation sequencing (mNGS) results who were responsive to corticosteroids were enrolled. Among these patients, about 26.8% suffered from drug-induced DPLD, while 9.8% presented autoimmune-related DPLD. Following the report of the negative mNGS results, in 34 patients with complete antibiotics administration profiles, 79.4% (27/34) patients discontinued antibiotics after receiving negative mNGS results. Moreover, 70.7% (29/41) patients began or increased the administration of corticosteroid upon receipt of negative mNGS results. In the microbiota analysis, Staphylococcus and Stenotrophomonas showed higher detection rates in patients with oxygenation index (OI) below 300, while Escherichia and Stenotrophomonas had higher abundance in patients with pleural effusion. In summary, our findings demonstrated the clinical significance of mNGS in assisting the antibiotic and corticosteroid treatment adjustments in corticosteroid-responsive DPLD. Lung microbiota may imply the severity of the disease.

The bacterial and fungal profiles of patients hospitalized with non-COVID-19 lower respiratory tract infections in Wuhan, China, 2019-2021

AIMS

A severe lockdown occurred in Wuhan during the COVID-19 pandemic, followed by a remission phase in the pandemic's aftermath. This study analyzed the bacterial and fungal profiles of respiratory pathogens in patients hospitalized with non-COVID-19 lower respiratory tract infections (LRTIs) during this period to determine the pathogen profile distributions in different age groups and hospital departments in Wuhan.

METHODS AND RESULTS

We collected reports of pathogen testing in the medical records of patients hospitalized with non-COVID-19 LRTI between 2019 and 2021. These cases were tested for bacterial and fungal pathogens using 16S and internal transcribed spacer sequencing methods on bronchoalveolar lavage fluid samples. The study included 1368 cases. The bacteria most commonly identified were Streptococcus pneumoniae (12.50%) and Mycoplasma pneumoniae (8.33%). The most commonly identified fungi were Aspergillus fumigatus (2.49%) and Pneumocystis jirovecii (1.75%). Compared to 2019, the S. pneumoniae detection rates increased significantly in 2021, and those of M. pneumoniae decreased. Streptococcus pneumoniae was detected mainly in children. The detection rates of almost all fungi were greater in the respiratory Intensive Care Unit compared to respiratory medicine. Streptococcus pneumoniae and M. pneumoniae were detected more frequently in the pediatric department.

CONCLUSIONS

Before and after the COVID-19 outbreak, a change in the common pathogen spectrum was detected in patients with non-COVID-19 in Wuhan, with the greatest change occurring among children. The major pathogens varied by the patient's age and the hospital department.

Integrating DNA/RNA microbe detection and host response for accurate diagnosis, treatment and prognosis of childhood infectious meningitis and encephalitis

BACKGROUND

Infectious meningitis/encephalitis (IM) is a severe neurological disease that can be caused by bacterial, viral, and fungal pathogens. IM suffers high morbidity, mortality, and sequelae in childhood. Metagenomic next-generation sequencing (mNGS) can potentially improve IM outcomes by sequencing both pathogen and host responses and increasing the diagnosis accuracy.

METHODS

Here we developed an optimized mNGS pipeline named comprehensive mNGS (c-mNGS) to monitor DNA/RNA pathogens and host responses simultaneously and applied it to 142 cerebrospinal fluid samples. According to retrospective diagnosis, these samples were classified into three categories: confirmed infectious meningitis/encephalitis (CIM), suspected infectious meningitis/encephalitis (SIM), and noninfectious controls (CTRL).

RESULTS

Our pipeline outperformed conventional methods and identified RNA viruses such as Echovirus E30 and etiologic pathogens such as HHV-7, which would not be clinically identified via conventional methods. Based on the results of the c-mNGS pipeline, we successfully detected antibiotic resistance genes related to common antibiotics for treating Escherichia coli, Acinetobacter baumannii, and Group B Streptococcus. Further, we identified differentially expressed genes in hosts of bacterial meningitis (BM) and viral meningitis/encephalitis (VM). We used these genes to build a machine-learning model to pinpoint sample contaminations. Similarly, we also built a model to predict poor prognosis in BM.

CONCLUSIONS

This study developed an mNGS-based pipeline for IM which measures both DNA/RNA pathogens and host gene expression in a single assay. The pipeline allows detecting more viruses, predicting antibiotic resistance, pinpointing contaminations, and evaluating prognosis. Given the comparable cost to conventional mNGS, our pipeline can become a routine test for IM.

Disposable Peptidoglycan-Specific Biosensor for Noninvasive Real-Time Detection of Broad-Spectrum Gram-Positive Bacteria in Exhaled Breath Condensates

Rapidly identifying and quantifying Gram-positive bacteria are crucial to diagnosing and treating bacterial lower respiratory tract infections (LRTIs). This work presents a field-deployable biosensor for detecting Gram-positive bacteria from exhaled breath condensates (EBCs) based on peptidoglycan recognition using an aptamer. Dielectrophoretic force is employed to enrich the bacteria in 10 s without additional equipment or steps. Concurrently, the measurement of the sensor's interfacial capacitance is coupled to quantify the bacteria during the enrichment process. By incorporation of a semiconductor condenser, the whole detection process, including EBC collection, takes about 3 min. This biosensor has a detection limit of 10 CFU/mL, a linear range of up to 105 CFU/mL and a selectivity of 1479:1. It is cost-effective and disposable due to its low cost. The sensor provides a nonstaining, culture-free and PCR-independent solution for noninvasive and real-time diagnosis of Gram-positive bacterial LRTIs.

Longitudinal multicompartment characterization of host-microbiota interactions in patients with acute respiratory failure

Critical illness can significantly alter the composition and function of the human microbiome, but few studies have examined these changes over time. Here, we conduct a comprehensive analysis of the oral, lung, and gut microbiota in 479 mechanically ventilated patients (223 females, 256 males) with acute respiratory failure. We use advanced DNA sequencing technologies, including Illumina amplicon sequencing (utilizing 16S and ITS rRNA genes for bacteria and fungi, respectively, in all sample types) and Nanopore metagenomics for lung microbiota. Our results reveal a progressive dysbiosis in all three body compartments, characterized by a reduction in microbial diversity, a decrease in beneficial anaerobes, and an increase in pathogens. We find that clinical factors, such as chronic obstructive pulmonary disease, immunosuppression, and antibiotic exposure, are associated with specific patterns of dysbiosis. Interestingly, unsupervised clustering of lung microbiota diversity and composition by 16S independently predicted survival and performed better than traditional clinical and host-response predictors. These observations are validated in two separate cohorts of COVID-19 patients, highlighting the potential of lung microbiota as valuable prognostic biomarkers in critical care. Understanding these microbiome changes during critical illness points to new opportunities for microbiota-targeted precision medicine interventions.

Hospital-acquired bacterial pneumonia in critically ill patients: from research to clinical practice

INTRODUCTION

Hospital-acquired pneumonia (HAP) represents a significant cause of mortality among critically ill patients admitted to Intensive Care Units (ICUs). Timely and precise diagnosis is imperative to enhance therapeutic efficacy and patient outcomes. However, the diagnostic process is challenged by test limitations and a wide-ranging list of differential diagnoses, particularly in patients exhibiting escalating oxygen requirements, leukocytosis, and increased secretions.

AREAS COVERED

This narrative review aims to update diagnostic modalities, facilitating the prompt identification of nosocomial pneumonia while guiding, developing, and assessing therapeutic interventions. A comprehensive literature review was conducted utilizing the MEDLINE/PubMed database from 2013 to April 2024.

EXPERT OPINION

An integrated approach that integrates clinical, microbiological, and imaging tools is paramount. Progress in diagnostic techniques, including novel molecular methods, the expanding utilization and accuracy of bedside ultrasound, and the emergence of Artificial Intelligence, coupled with an improved comprehension of lung microbiota and host-pathogen interactions, continues to enhance our capability to accurately and swiftly identify HAP and its causative agents. This advancement enables the refinement of treatment strategies and facilitates the implementation of precision medicine approaches.

Integrative analyses identify opportunistic pathogens of patients with lower respiratory tract infections based on metagenomic next-generation sequencing

Lower respiratory tract infections (LRTIs) represent some of the most globally prevalent and detrimental diseases. Metagenomic next-generation sequencing (mNGS) technology has effectively addressed the requirement for the diagnosis of clinical infectious diseases. This study aimed at identifying and classifying opportunistic pathogens from the respiratory tract-colonizing microflora in LRTI patients using data acquired from mNGS analyses. A retrospective study was performed employing the mNGS data pertaining to the respiratory samples derived from 394 LRTIs patients. Linear discriminant analysis effect size (LEfSe) analysis was conducted to discern the discriminant bacteria. Receiver operating characteristic curves (ROC) were established to demonstrate discriminant bacterial behavior to distinguish colonization from infection. A total of 443 discriminant bacteria were identified and segregated into three cohorts contingent upon their correlation profiles, detection frequency, and relative abundance in order to distinguish pathogens from colonizing microflora. Among them, 119 emerging opportunistic pathogens (cohort 2) occupied an average area under the curve (AUC) of 0.976 for exhibiting the most prominent predictability in distinguishing colonization from infection, 39 were colonizing bacteria (cohort 1, 0.961), and 285 were rare opportunistic pathogens (cohort 3, 0.887). The LTRIs patients appeared modular in the form of cohorts depicting complex microbial co-occurrence networks, reduced diversity, and a high degree of antagonistic interactions in the respiratory tract microbiome. The study findings indicate that therapeutic interventions should target interaction networks rather than individual microbes, providing an innovative perspective for comprehending and combating respiratory infections. Conclusively, this study reports a profile of LRTIs-associated bacterial colonization and opportunistic pathogens in a relatively large-scale cohort, which might serve as a reference panel for the interpretation of mNGS results in clinical practice.

Biological effects of corticosteroids on pneumococcal pneumonia in Mice-translational significance

BACKGROUND

Streptococcus pneumoniae is the most common bacterial cause of community acquired pneumonia and the acute respiratory distress syndrome (ARDS). Some clinical trials have demonstrated a beneficial effect of corticosteroid therapy in community acquired pneumonia, COVID-19, and ARDS, but the mechanisms of this benefit remain unclear. The primary objective of this study was to investigate the effects of corticosteroids on the pulmonary biology of pneumococcal pneumonia in a mouse model. A secondary objective was to identify shared transcriptomic features of pneumococcal pneumonia and steroid treatment in the mouse model and clinical samples.

METHODS

We carried out comprehensive physiologic, biochemical, and histological analyses in mice to identify the mechanisms of lung injury in Streptococcus pneumoniae with and without adjunctive steroid therapy. We also studied lower respiratory tract gene expression from a cohort of 15 mechanically ventilated patients (10 with Streptococcus pneumoniae and 5 controls) to compare with the transcriptional studies in the mice.

RESULTS

In mice with pneumonia, dexamethasone in combination with ceftriaxone reduced (1) pulmonary edema formation, (2) alveolar protein permeability, (3) proinflammatory cytokine release, (4) histopathologic lung injury score, and (5) hypoxemia but did not increase bacterial burden. Transcriptomic analyses identified effects of steroid therapy in mice that were also observed in the clinical samples.

CONCLUSIONS

In combination with appropriate antibiotic therapy in mice, treatment of pneumococcal pneumonia with steroid therapy reduced hypoxemia, pulmonary edema, lung permeability, and histologic criteria of lung injury, and also altered inflammatory responses at the protein and gene expression level. The transcriptional studies in patients suggest that the mouse model replicates some of the features of pneumonia in patients with Streptococcus pneumoniae and steroid treatment. Overall, these studies provide evidence for the mechanisms that may explain the beneficial effects of glucocorticoid therapy in patients with community acquired pneumonia from Streptococcus Pneumoniae.

Use of Transcriptional Signatures to Differentiate Pathogen-Specific and Treatment-Specific Host Responses in Patients With Bacterial Bloodstream Infections

BACKGROUND

Clinical outcomes in bacterial bloodstream infections (BSIs) are influenced by bacterial species, host immunity, and antibiotic therapy. The mechanisms by which such factors influence outcomes are poorly understood. We aimed to identify bacterial- and antibiotic-specific host transcriptional signatures in patients with bacterial BSI.

METHODS

RNA sequencing was performed on blood samples from patients with BSI due to gram-negative (GN) versus gram-positive (GP) pathogens: Escherichia coli (n = 30) or Klebsiella pneumoniae (n = 28) versus methicillin-susceptible Staphylococcus aureus (MSSA) (n = 24) or methicillin-resistant S. aureus (MRSA) (n = 58). Patients were matched by age, sex, and race.

RESULTS

No significant host transcriptome differences were detected in patients with E. coli versus K. pneumoniae BSI, so these were considered together as GN BSI. Relative to S. aureus BSI, patients with GN BSI had increased activation of the classic complement system. However, the most significant signal was a reduction in host transcriptional signatures involving mitochondrial energy transduction and oxidative burst in MRSA versus MSSA. This attenuated host transcriptional signature remained after controlling for antibiotic therapy.

CONCLUSIONS

Given the importance of immune cellular energetics and reactive oxygen species in eliminating hematogenous or intracellular MRSA, these findings may offer insights into its persistence relative to other bacterial BSIs.

Simultaneous detection of pathogens and antimicrobial resistance genes with the open source, cloud-based, CZ ID pipeline

Antimicrobial resistant (AMR) pathogens represent urgent threats to human health, and their surveillance is of paramount importance. Metagenomic next generation sequencing (mNGS) has revolutionized such efforts, but remains challenging due to the lack of open-access bioinformatics tools capable of simultaneously analyzing both microbial and AMR gene sequences. To address this need, we developed the CZ ID AMR module, an open-access, cloud-based workflow designed to integrate detection of both microbes and AMR genes in mNGS and whole-genome sequencing (WGS) data. It leverages the Comprehensive Antibiotic Resistance Database and associated Resistance Gene Identifier software, and works synergistically with the CZ ID short-read mNGS module to enable broad detection of both microbes and AMR genes. We highlight diverse applications of the AMR module through analysis of both publicly available and newly generated mNGS and WGS data from four clinical cohort studies and an environmental surveillance project. Through genomic investigations of bacterial sepsis and pneumonia cases, hospital outbreaks, and wastewater surveillance data, we gain a deeper understanding of infectious agents and their resistomes, highlighting the value of integrating microbial identification and AMR profiling for both research and public health. We leverage additional functionalities of the CZ ID mNGS platform to couple resistome profiling with the assessment of phylogenetic relationships between nosocomial pathogens, and further demonstrate the potential to capture the longitudinal dynamics of pathogen and AMR genes in hospital acquired bacterial infections. In sum, the new AMR module advances the capabilities of the open-access CZ ID microbial bioinformatics platform by integrating pathogen detection and AMR profiling from mNGS and WGS data. Its development represents a critical step toward democratizing pathogen genomic analysis and supporting collaborative efforts to combat the growing threat of AMR.

Clinical application of metagenomic next-generation sequencing for the diagnosis of suspected infection in adults: A cross-sectional study

Metagenomic next-generation sequencing (mNGS) has become an available method for pathogen detection. The clinical application of mNGS requires further evaluation. We conducted a cross-sectional study of 104 patients with suspected infection between May 2019 and May 2021. The risk factors associated with infection were analyzed using univariate logistic analysis. The diagnostic performance of pathogens was compared between mNGS and conventional microbiological tests. About 104 patients were assigned into 3 groups: infected group (n = 69), noninfected group (n = 20), and unknown group (n = 15). With the composite reference standard (combined results of all microbiological tests, radiological testing results, and a summary of the hospital stay of the patient) as the gold standard, the sensitivity, specificity, positive predictive value, negative predictive value of mNGS was 84.9%, 50.0%, 88.6%, and 42.1%, respectively. Compared with conventional microbiological tests, mNGS could detect more pathogens and had obvious advantages in Mycobacterium tuberculosis, Aspergillus, and virus detection. Moreover, mNGS had distinct benefits in detecting mixed infections. Bacteria-fungi-virus mixed infections were the most common in patients with severe pneumonia. mNGS had a higher sensitivity than conventional microbiological tests, especially for M. tuberculosis, Aspergillus, viruses, and mixed infections. We suggest that mNGS should be used more frequently in the early diagnosis of pathogens in critically ill patients in the future.

Simultaneous detection of pathogens and antimicrobial resistance genes with the open source, cloud-based, CZ ID pipeline

Antimicrobial resistant (AMR) pathogens represent urgent threats to human health, and their surveillance is of paramount importance. Metagenomic next generation sequencing (mNGS) has revolutionized such efforts, but remains challenging due to the lack of open-access bioinformatics tools capable of simultaneously analyzing both microbial and AMR gene sequences. To address this need, we developed the Chan Zuckerberg ID (CZ ID) AMR module, an open-access, cloud-based workflow designed to integrate detection of both microbes and AMR genes in mNGS and whole-genome sequencing (WGS) data. It leverages the Comprehensive Antibiotic Resistance Database and associated Resistance Gene Identifier software, and works synergistically with the CZ ID short-read mNGS module to enable broad detection of both microbes and AMR genes. We highlight diverse applications of the AMR module through analysis of both publicly available and newly generated mNGS and WGS data from four clinical cohort studies and an environmental surveillance project. Through genomic investigations of bacterial sepsis and pneumonia cases, hospital outbreaks, and wastewater surveillance data, we gain a deeper understanding of infectious agents and their resistomes, highlighting the value of integrating microbial identification and AMR profiling for both research and public health. We leverage additional functionalities of the CZ ID mNGS platform to couple resistome profiling with the assessment of phylogenetic relationships between nosocomial pathogens, and further demonstrate the potential to capture the longitudinal dynamics of pathogen and AMR genes in hospital acquired bacterial infections. In sum, the new AMR module advances the capabilities of the open-access CZ ID microbial bioinformatics platform by integrating pathogen detection and AMR profiling from mNGS and WGS data. Its development represents a critical step toward democratizing pathogen genomic analysis and supporting collaborative efforts to combat the growing threat of AMR.

Host-microbe multiomic profiling reveals age-dependent immune dysregulation associated with COVID-19 immunopathology

Age is a major risk factor for severe coronavirus disease 2019 (COVID-19), yet the mechanisms behind this relationship have remained incompletely understood. To address this, we evaluated the impact of aging on host immune response in the blood and the upper airway, as well as the nasal microbiome in a prospective, multicenter cohort of 1031 vaccine-naïve patients hospitalized for COVID-19 between 18 and 96 years old. We performed mass cytometry, serum protein profiling, anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody assays, and blood and nasal transcriptomics. We found that older age correlated with increased SARS-CoV-2 viral abundance upon hospital admission, delayed viral clearance, and increased type I interferon gene expression in both the blood and upper airway. We also observed age-dependent up-regulation of innate immune signaling pathways and down-regulation of adaptive immune signaling pathways. Older adults had lower naïve T and B cell populations and higher monocyte populations. Over time, older adults demonstrated a sustained induction of pro-inflammatory genes and serum chemokines compared with younger individuals, suggesting an age-dependent impairment in inflammation resolution. Transcriptional and protein biomarkers of disease severity differed with age, with the oldest adults exhibiting greater expression of pro-inflammatory genes and proteins in severe disease. Together, our study finds that aging is associated with impaired viral clearance, dysregulated immune signaling, and persistent and potentially pathologic activation of pro-inflammatory genes and proteins.

The Lung Microbiome

Although the lungs were once considered a sterile environment, advances in sequencing technology have revealed dynamic, low-biomass communities in the respiratory tract, even in health. Key features of these communities-composition, diversity, and burden-are consistently altered in lung disease, associate with host physiology and immunity, and can predict clinical outcomes. Although initial studies of the lung microbiome were descriptive, recent studies have leveraged advances in technology to identify metabolically active microbes and potential associations with their immunomodulatory by-products and lung disease. In this brief review, we discuss novel insights in airway disease and parenchymal lung disease, exploring host-microbiome interactions in disease pathogenesis. We also discuss complex interactions between gut and oropharyngeal microbiota and lung immunobiology. Our advancing knowledge of the lung microbiome will provide disease targets in acute and chronic lung disease and may facilitate the development of new therapeutic strategies.

Exploring viral aetiology in upper respiratory tract infections: insights from metagenomic next-generation sequencing in Swiss outpatients before and during the SARS-CoV-2 pandemic

AIMS OF THE STUDY

Upper respiratory tract infections are among the most common reasons for primary care consultations. They are diagnosed predominantly based on clinical assessment. Here, we investigated the benefit of viral metagenomic next-generation sequencing (mNGS) in an outpatient setting.

METHODS

This prospective cross-sectional study included immunocompetent patients with acute upper respiratory tract infections. General practitioners collected pharyngeal swabs and demographic and clinical data. Specimens were analysed using viral mNGS and conventional tests.

RESULTS

Two hundred seventy-seven patients were recruited by 21 general practitioners between 10/2019 and 12/2020, of which 91% had a suspected viral aetiology. For 138 patients (49.8%), mNGS identified one or more respiratory viruses. The mNGS showed a high overall agreement with conventional routine diagnostic tests. Rhinoviruses were the most frequently detected respiratory viruses (20.2% of patients). Viral mNGS reflected the influenza wave in early 2020 and the SARS-CoV-2 pandemic outbreak in Switzerland in March 2020. Notably, rhinoviruses continued to circulate despite non-pharmaceutical hygiene measures.

CONCLUSIONS

Viral mNGS allowed the initial diagnosis to be retrospectively re-evaluated. Assuming reduced turnaround times, mNGS has the potential to directly guide the treatment of upper respiratory tract infections. On an epidemiological level, our study highlights the utility of mNGS in respiratory infection surveillance, allowing early detection of epidemics and providing information crucial for prevention.

Clinical performance of metagenomic next-generation sequencing for diagnosis of pulmonary Aspergillus infection and colonization

Background

Investigations assessing the value of metagenomic next-generation sequencing (mNGS) for distinguish Aspergillus infection from colonization are currently insufficient.

Methods

The performance of mNGS in distinguishing Aspergillus infection from colonization, along with the differences in patients' characteristics, antibiotic adjustment, and lung microbiota, were analyzed.

Results

The abundance of Aspergillus significantly differed between patients with Aspergillus infection (n=36) and colonization (n=32) (P < 0.0001). Receiver operating characteristic (ROC) curve result for bronchoalveolar lavage fluid (BALF) mNGS indicated an area under the curve of 0.894 (95%CI: 0.811-0.976), with an optimal threshold value of 23 for discriminating between Aspergillus infection and colonization. The infection group exhibited a higher proportion of antibiotic adjustments in comparison to the colonization group (50% vs. 12.5%, P = 0.001), with antibiotic escalation being more dominant. Age, length of hospital stay, hemoglobin, cough and chest distress were significantly positively correlated with Aspergillus infection. The abundance of A. fumigatus and Epstein-Barr virus (EBV) significantly increased in the infection group, whereas the colonization group exhibited higher abundance of A. niger.

Conclusion

BALF mNGS is a valuable tool for differentiating between colonization and infection of Aspergillus. Variations in patients' age, length of hospital stay, hemoglobin, cough and chest distress are observable between patients with Aspergillus infection and colonization.

Diagnosis of a Familial Psittacosis Outbreak with Clinical Analysis and Metagenomic Next-Generation Sequencing Under COVID-19: A Case Series

Purpose

To explore the clinical characteristics, diagnosis, and treatment of family outbreak of psittacosis and to improve the success rate of treatment.

Patients and Methods

The clinical characteristics, diagnosis, treatment, and outcome of family outbreak of psittacosis, which consists three patients, diagnosed by clinical analysis and metagenomic next-generation sequencing (mNGS) in our hospital were analyzed retrospectively.

Results

We report on three instances of clustered atypical pneumonia, which were caused by Chlamydia psittaci during the COVID-19 pandemic. All patients exhibited symptoms of fever and cough, while one patient also experienced gastrointestinal symptoms such as nausea, vomiting, and diarrhea. Laboratory tests indicated no significant increase in leukocytes and neutrophils, but a mild increase in C-reactive protein was observed in all three patients. Chest computed tomography (CT) scans revealed a consolidation shadow in a unilateral lung lobe in all three patients. Both patients were treated with empirical moxifloxacin, yielding unsatisfactory outcomes. mNGS was conducted on sputum samples from one adult patient, revealing the presence of Chlamydia psittaci. Additional doxycycline was prescribed immediately, and then the patients' temperatures were stabilized, and the lesion in chest CT was absorbed. The pediatric patient exhibited less severe symptoms compared to the adult patients and exhibited a favorable response to azithromycin administration.

Conclusion

This study reports a cluster of a family outbreak of atypical pneumonia caused by C. psittaci in China. The occurrence of a family outbreak during the COVID-19 pandemic may be attributed to familial aggregation resulting from the epidemic. The three cases reported in this study did not experience severe complications, which can be attributed to the prompt medical intervention and swift diagnosis. This finding implies the need to enhance patients' awareness and vigilance towards their health. Additionally, mNGS emerges as a valuable technique for accurately identifying pathogens causing pulmonary infections.

How I manage acute respiratory failure in patients with hematological malignancies

ABSTRACT

Acute respiratory failure (ARF) is common in patients with hematological malignancies notably those with acute leukemia, myelodysplastic syndrome, or allogeneic stem cell transplantation. ARF is the leading reason for intensive care unit (ICU) admission, with a 35% case fatality rate. Failure to identify the ARF cause is associated with mortality. A prompt, well-designed diagnostic workup is crucial. The investigations are chosen according to pretest diagnostic probabilities, estimated by the DIRECT approach: D stands for delay, or time since diagnosis; I for pattern of immune deficiency; R and T for radiological evaluation; E refers to clinical experience, and C to the clinical picture. Thorough familiarity with rapid diagnostic tests helps to decrease the use of bronchoscopy with bronchoalveolar lavage, which can cause respiratory status deterioration in those patients with hypoxemia. A prompt etiological diagnosis shortens the time on unnecessary empirical treatments, decreasing iatrogenic harm and costs. High-quality collaboration between intensivists and hematologists and all crossdisciplinary health care workers is paramount. All oxygen delivery systems should be considered to minimize invasive mechanical ventilation. Treatment of the malignancy is started or continued in the ICU under the guidance of the hematologists. The goal is to use the ICU as a bridge to recovery, with the patient returning to the hematology ward in sufficiently good clinical condition to receive optimal anticancer treatment.

The microbiological diagnostic performance of metagenomic next-generation sequencing in patients with infectious diseases

OBJECTIVE

Metagenomic next-generation sequencing (mNGS) can be used to detect pathogens in clinical infectious diseases through the sequencing analysis of microbial and host nucleic acids in clinical samples. This study aimed to assess the diagnostic performance of mNGS in patients with infections.

METHODS

In this study, 641 patients with infectious diseases were enrolled. These patients simultaneously underwent pathogen detection by both mNGS and microbial culture. Through statistical analysis, we judged the diagnostic performance of mNGS and microbial culture on different pathogens.

RESULTS

Among 641 patients, 276 cases of bacteria and 95 cases of fungi were detected by mNGS, whereas 108 cases of bacteria and 41 cases of fungi were detected by traditional cultures. Among all mixed infections, combined bacterial and viral infections were the highest (51%, 87/169), followed by combined bacterial with fungal infections (16.57%, 28/169) and mixed bacterial, fungal, and viral infections (13.61%, 23/169). Among all sample types, bronchoalveolar lavage fluid (BALF) samples had the highest positive rate (87.8%, 144/164), followed by sputum (85.4%, 76/89) and blood samples (61.2%, 158/258). For the culture method, sputum samples had the highest positive rate (47.2%, 42/89), followed by BALF (37.2%, 61/164). The positive rate of mNGS was 69.89% (448/641), which was significantly higher than that of traditional cultures (22.31% [143/641]) (P < .05).

CONCLUSIONS

Our results show that mNGS is an effective tool for the rapid diagnosis of infectious diseases. Compared with traditional detection methods, mNGS also showed obvious advantages in mixed infections and infections with uncommon pathogens.

[High-throughput sequencing for infectious disease diagnoses: Example of shotgun metagenomics in central nervous system infections]

The advent of high-throughput sequencing in clinical microbiology is opening the way to new diagnostic and prognostic approaches in infectious diseases. Detection, identification and characterisation of pathogenic microorganisms are essential steps in diagnosis and implementation of appropriate antimicrobial therapy. However, standard methods of microbiological diagnosis are failing in some cases. In addition, the emergence of new infections, facilitated by international travel and global warming, requires the implementation of innovative diagnostic methods. Among the different strategies used in clinical microbiology and reviewed in this article, shotgun metagenomics is the only technique that allows today a panpathogenic and unbiased detection of all microorganisms potentially responsible for an infectious disease, including those still unknown. The aims of this article are to present the different possible strategies of high-throughput sequencing used in the microbiological diagnosis of infectious diseases and to highlight the diagnostic contribution of shotgun metagenomics in the field of central nervous system infections.

Biological Effects of Corticosteroids on Pneumococcal Pneumonia in Mice and Humans

Background

Streptococcus pneumoniae is the most common bacterial cause of community acquired pneumonia and the acute respiratory distress syndrome (ARDS). Some clinical trials have demonstrated a beneficial effect of corticosteroid therapy in community acquired pneumonia, COVID-19, and ARDS, but the mechanisms of this benefit remain unclear. The objective of this study was to investigate the effects of corticosteroids on the pulmonary biology of pneumococcal pneumonia in an observational cohort of mechanically ventilated patients and in a mouse model of bacterial pneumonia with Streptococcus pneumoniae.

Methods

We studied gene expression with lower respiratory tract transcriptomes from a cohort of mechanically ventilated patients and in mice. We also carried out comprehensive physiologic, biochemical, and histological analyses in mice to identify the mechanisms of lung injury in Streptococcus pneumoniae with and without adjunctive steroid therapy.

Results

Transcriptomic analysis identified pleiotropic effects of steroid therapy on the lower respiratory tract in critically ill patients with pneumococcal pneumonia, findings that were reproducible in mice. In mice with pneumonia, dexamethasone in combination with ceftriaxone reduced (1) pulmonary edema formation, (2) alveolar protein permeability, (3) proinflammatory cytokine release, (4) histopathologic lung injury score, and (5) hypoxemia but did not increase bacterial burden.

Conclusions

The gene expression studies in patients and in the mice support the clinical relevance of the mouse studies, which replicate several features of pneumococcal pneumonia and steroid therapy in humans. In combination with appropriate antibiotic therapy in mice, treatment of pneumococcal pneumonia with steroid therapy reduced hypoxemia, pulmonary edema, lung permeability, and histologic criteria of lung injury, and also altered inflammatory responses at the protein and gene expression level. The results from these studies provide evidence for the mechanisms that may explain the beneficial effects of glucocorticoid therapy in patients with community acquired pneumonia from Streptococcus Pneumoniae.

Host-Microbe Multiomic Profiling Reveals Age-Dependent COVID-19 Immunopathology

Age is a major risk factor for severe coronavirus disease-2019 (COVID-19), yet the mechanisms responsible for this relationship have remained incompletely understood. To address this, we evaluated the impact of aging on host and viral dynamics in a prospective, multicenter cohort of 1,031 patients hospitalized for COVID-19, ranging from 18 to 96 years of age. We performed blood transcriptomics and nasal metatranscriptomics, and measured peripheral blood immune cell populations, inflammatory protein expression, anti-SARS-CoV-2 antibodies, and anti-interferon (IFN) autoantibodies. We found that older age correlated with an increased SARS-CoV-2 viral load at the time of admission, and with delayed viral clearance over 28 days. This contributed to an age-dependent increase in type I IFN gene expression in both the respiratory tract and blood. We also observed age-dependent transcriptional increases in peripheral blood IFN-γ, neutrophil degranulation, and Toll like receptor (TLR) signaling pathways, and decreases in T cell receptor (TCR) and B cell receptor signaling pathways. Over time, older adults exhibited a remarkably sustained induction of proinflammatory genes (e.g., CXCL6) and serum chemokines (e.g., CXCL9) compared to younger individuals, highlighting a striking age-dependent impairment in inflammation resolution. Augmented inflammatory signaling also involved the upper airway, where aging was associated with upregulation of TLR, IL17, type I IFN and IL1 pathways, and downregulation TCR and PD-1 signaling pathways. Metatranscriptomics revealed that the oldest adults exhibited disproportionate reactivation of herpes simplex virus and cytomegalovirus in the upper airway following hospitalization. Mass cytometry demonstrated that aging correlated with reduced naïve T and B cell populations, and increased monocytes and exhausted natural killer cells. Transcriptional and protein biomarkers of disease severity markedly differed with age, with the oldest adults exhibiting greater expression of TLR and inflammasome signaling genes, as well as proinflammatory proteins (e.g., IL6, CXCL8), in severe COVID-19 compared to mild/moderate disease. Anti-IFN autoantibody prevalence correlated with both age and disease severity. Taken together, this work profiles both host and microbe in the blood and airway to provide fresh insights into aging-related immune changes in a large cohort of vaccine-naïve COVID-19 patients. We observed age-dependent immune dysregulation at the transcriptional, protein and cellular levels, manifesting in an imbalance of inflammatory responses over the course of hospitalization, and suggesting potential new therapeutic targets.

Diagnostic performance and clinical impacts of metagenomic sequencing after allogeneic hematopoietic stem cell transplantation

BACKGROUND

Metagenomic Next-Generation Sequencing (mNGS) is a rapid, non-culture-based, high-throughput technique for pathogen diagnosis. Despite its numerous advantages, only a few studies have investigated its use in patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT).

METHODS

We conducted a retrospective analysis of 404 mNGS tests performed on 264 patients after allo-HSCT. The tests were divided into three groups (Phase A, B, C) based on the time spent hospitalized post-transplantation, and we evaluated the analytical performance of mNGS in comparison with conventional microbiological tests (CMT), while also analyzing its clinical utility for clinical impacts.

RESULTS

Metagenomic sequencing demonstrated a significantly higher rate of positive microbiological findings as compared to CMT (334/404 (82.7 %) vs. 159/404 (39.4 %), respectively, P < 0.001). The detection rates by both mNGS and CMT varied across the three-phase (mNGS: A-60/89 (67.4 %), B-147/158 (93.0 %), C-125/157 (79.6 %), respectively, P < 0.001; CMT: A-21/89 (23.6 %), B-79/158 (50.0 %), C-59/157 (37.6 %), respectively, P < 0.001). The infection sites and types of pathogens were also different across the three phases. Compared to non-GVHD cases, mNGS detected more Aspergillus spp. and Mucorales in GVHD patients (Aspergillus: 12/102 (11.8 %) vs. 8/158 (5.1 %), respectively, P = 0.048; Mucorales: 6/102 (5.9 %) vs. 2/158 (1.3 %), respectively, P = 0.035). Forty-five (181/404) percent of mNGS tests yielded a positive impact on the clinical diagnosis, while 24.3 % (98/404) of tests benefited the patients in antimicrobial treatment.

CONCLUSION

mNGS is an indispensable diagnostic tool in identifying pathogens and optimizing antibiotic therapy for hematological patients receiving allo-HSCT.

A novel synthetic nucleic acid mixture for quantification of microbes by mNGS

Metagenomic next-generation sequencing (mNGS) provides considerable advantages in identifying emerging and re-emerging, difficult-to-detect and co-infected pathogens; however, the clinical application of mNGS remains limited primarily due to the lack of quantitative capabilities. This study introduces a novel approach, KingCreate-Quantification (KCQ) system, for quantitative analysis of microbes in clinical specimens by mNGS, which co-sequence the target DNA extracted from the specimens along with a set of synthetic dsDNA molecules used as Internal-Standard (IS). The assay facilitates the conversion of microbial reads into their copy numbers based on IS reads utilizing a mathematical model proposed in this study. The performance of KCQ was systemically evaluated using commercial mock microbes with varying IS input amounts, different proportions of human genomic DNA, and at varying amounts of sequence analysis data. Subsequently, KCQ was applied in microbial quantitation in 36 clinical specimens including blood, bronchoalveolar lavage fluid, cerebrospinal fluid and oropharyngeal swabs. A total of 477 microbe genetic fragments were screened using the bioinformatic system. Of these 83 fragments were quantitatively compared with digital droplet PCR (ddPCR), revealing a correlation coefficient of 0.97 between the quantitative results of KCQ and ddPCR. Our study demonstrated that KCQ presents a practical approach for the quantitative analysis of microbes by mNGS in clinical samples.

Emerging multianalyte biosensors for the simultaneous detection of protein and nucleic acid biomarkers

Traditionally, biosensors are designed to detect one specific analyte. Nevertheless, disease progression is regulated in a highly interactive way by different classes of biomolecules like proteins and nucleic acids. Therefore, a more comprehensive analysis of biomarkers from a single sample is of utmost importance to further improve both, the accuracy of diagnosis as well as the therapeutic success. This review summarizes fundamentals like biorecognition and sensing strategies for the simultaneous detection of proteins and nucleic acids and discusses challenges related to multianalyte biosensor development. We present an overview of the current state of biosensors for the combined detection of protein and nucleic acid biomarkers associated with widespread diseases, among them cancer and infectious diseases. Furthermore, we outline the multianalyte analysis in the rapidly evolving field of single-cell multiomics, to stress its significance for the future discovery and validation of biomarkers. Finally, we provide a critical perspective on the performance and translation potential of multianalyte biosensors for medical diagnostics.