Respiratory Microbiome Profiling for Etiologic Diagnosis of Pneumonia in Mechanically Ventilated Patients

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.

Pathogenesis and therapeutic opportunities of gut microbiome dysbiosis in critical illness

For many years, it has been hypothesized that pathological changes to the gut microbiome in critical illness is a driver of infections, organ dysfunction, and other adverse outcomes in the intensive care unit (ICU). The advent of contemporary microbiome methodologies and multi-omics tools have allowed researchers to test this hypothesis by dissecting host-microbe interactions in the gut to better define its contribution to critical illness pathogenesis. Observational studies of patients in ICUs have revealed that gut microbial communities are profoundly altered in critical illness, characterized by markedly reduced alpha diversity, loss of commensal taxa, and expansion of potential pathogens. These key features of ICU gut dysbiosis have been associated with adverse outcomes including life-threatening hospital-acquired (nosocomial) infections. Current research strives to define cellular and molecular mechanisms connecting gut dysbiosis with infections and other outcomes, and to identify opportunities for therapeutic modulation of host-microbe interactions. This review synthesizes evidence from studies of critically ill patients that have informed our understanding of intestinal dysbiosis in the ICU, mechanisms linking dysbiosis to infections and other adverse outcomes, as well as clinical trials of microbiota-modifying therapies. Additionally, we discuss novel avenues for precision microbial therapeutics to combat nosocomial infections and other life-threatening complications of critical illness.

Evaluating the diagnostic accuracy and clinical utility of 16S and 18S rRNA gene targeted next-generation sequencing based on five years of clinical experience

BACKGROUND

The use of 16S/18S rRNA targeted next-generation sequencing (tNGS) has improved microbial diagnostics, however, the use of tNGS in a routine clinical setting requires further elucidation. We retrospectively evaluated the diagnostic accuracy and clinical utility of 16S/18S tNGS, routinely used in the North Denmark Region between 2017 and 2021.

METHODS

We retrieved 544 tNGS results from 491 patients hospitalised with suspected infection (e.g. meningitis, pneumonia, intraabdominal abscess, osteomyelitis and joint infection). The tNGS assays was performed using the Illumina MiSeq desktop sequencer, and BION software for annotation. The patients' diagnosis and clinical management was evaluated by medical chart review. We calculated sensitivity and specificity, and determined the diagnostic accuracy of tNGS by defining results as true positive, true negative, false positive, and false negative.

RESULTS

Overall, tNGS had a sensitivity of 56% and a specificity of 97%. tNGS was more frequently true positive compared to culture (32% vs 18%), and tNGS detected a greater variety of bacteria and fungi, and was more frequently polymicrobial. However, the total diagnostic turnaround time was 16 days, and although 73% of tNGS results were true positive or true negative, only 4.4% of results led to changes in clinical management.

CONCLUSIONS

As a supplement to culture, tNGS improves identification of pathogenic microorganisms in a broad range of clinical specimens. However, the long turnaround time of tNGS in our setting may have contributed to a limited clinical utility. An improved turnaround time can be the key to improved clinical utility in a future setting.

Lung microbiome on admission in critically ill patients with acute bacterial and viral pneumonia

Composition of pulmonary microbiome of patients with severe pneumonia is poorly known. The aim of this work was to analyse the lung microbiome of patients admitted to the intensive care unit  (ICU) with severe community acquired pneumonia (CAP) between 2019 and 2021 in comparison with a control group of 6 patients undergoing digestive surgery. As a second objective, the diagnostic capabilities of metagenomics was also studied in a small group of selected patients. The lung microbiome of patients with viral (5 with Influenza A and 8 with SARS-CoV-2) pneumonia at admission showed a similar diversity as the control group (p = 0.140 and p = 0.213 respectively). Contrarily, the group of 12 patients with pneumococcal pneumonia showed a significant lower Simpson´s index (p = 0.002). In the control group (n = 6) Proteobacteria (36.6%), Firmicutes (24.2%) and Actinobacteria (23.0%) were the predominant phyla. In SARS-CoV-2 patients (n = 8), there was a predominance of Proteobacteria (mean 41.6%) (Moraxella and Pelomonas at the genus level), Actinobacteria (24.6%) (Microbacterium) and Firmicutes (22.8%) mainly Streptococcus, Staphylococcus and Veillonella. In patients with Influenza A pneumonia (n = 5) there was a predominance of Firmicutes (35.1%) mainly Streptococcus followed by Proteobacteria (29.2%) (Moraxella, Acinetobacter and Pelomonas). In the group of pneumococcal pneumonia (n = 12) two phyla predominated: Firmicutes (53.1%) (Streptococcus) and Proteobacteria (36.5%) (Haemophilus). In the 7 patients with non-pneumococcal bacterial pneumonia Haemophilus influenzae (n = 2), Legionella pneumophila (n = 2), Klebsiella pneumoniae, Streptococcus pyogenes and Leptospira were detected by metagenomics, confirming the diagnosis done using conventional microbiological techniques. The diversity of the respiratory microbiome in patients with severe viral pneumonia at ICU admission was similar to that of the control group. Contrarily, patients with pneumococcal pneumonia showed a lower grade of diversity. At initial stages of SARS-CoV-2 infection, no important alterations in the pulmonary microbiome were observed. The analysis of bacterial microbiome showed promising results as a diagnostic tool.

Prognostic Insights from Longitudinal Multicompartment Study of Host-Microbiota Interactions in Critically Ill Patients

Critical illness can disrupt the composition and function of the microbiome, yet comprehensive longitudinal studies are lacking. We conducted a longitudinal analysis of oral, lung, and gut microbiota in a large cohort of 479 mechanically ventilated patients with acute respiratory failure. Progressive dysbiosis emerged in all three body compartments, characterized by reduced alpha diversity, depletion of obligate anaerobe bacteria, and pathogen enrichment. Clinical variables, including chronic obstructive pulmonary disease, immunosuppression, and antibiotic exposure, shaped dysbiosis. Notably, of the three body compartments, unsupervised clusters of lung microbiota diversity and composition independently predicted survival, transcending clinical predictors, organ dysfunction severity, and host-response sub-phenotypes. These independent associations of lung microbiota may serve as valuable biomarkers for prognostication and treatment decisions in critically ill patients. Insights into the dynamics of the microbiome during critical illness highlight the potential for microbiota-targeted interventions in precision medicine.

Prognostic Insights from Longitudinal Multicompartment Study of Host-Microbiota Interactions in Critically Ill Patients

Critical illness can disrupt the composition and function of the microbiome, yet comprehensive longitudinal studies are lacking. We conducted a longitudinal analysis of oral, lung, and gut microbiota in a large cohort of 479 mechanically ventilated patients with acute respiratory failure. Progressive dysbiosis emerged in all three body compartments, characterized by reduced alpha diversity, depletion of obligate anaerobe bacteria, and pathogen enrichment. Clinical variables, including chronic obstructive pulmonary disease, immunosuppression, and antibiotic exposure, shaped dysbiosis. Notably, of the three body compartments, unsupervised clusters of lung microbiota diversity and composition independently predicted survival, transcending clinical predictors, organ dysfunction severity, and host-response sub-phenotypes. These independent associations of lung microbiota may serve as valuable biomarkers for prognostication and treatment decisions in critically ill patients. Insights into the dynamics of the microbiome during critical illness highlight the potential for microbiota-targeted interventions in precision medicine.

Lung microbiome and origins of the respiratory diseases

The studies on the composition of the human microbiomes in healthy individuals, its variability in the course of inflammation, infection, antibiotic therapy, diets and different pathological conditions have revealed their intra and inter-kingdom relationships. The lung microbiome comprises of major species members of the phylum Bacteroidetes, Firmicutes, Actinobacteria, Fusobacteria and Proteobacteria, which are distributed in ecological niches along nasal cavity, nasopharynx, oropharynx, trachea and in the lungs. Commensal and pathogenic species are maintained in equilibrium as they have strong relationships. Bacterial overgrowth after dysbiosis and/or imbalanced of CD4⁺ helper T cells, CD8⁺ cytotoxic T cells and regulatory T cells (Treg) populations can promote lung inflammatory reactions and distress, and consequently acute and chronic respiratory diseases. This review is aimed to summarize the latest advances in resident lung microbiome and its participation in most common pulmonary infections and pneumonia, community-acquired pneumonia (CAP), ventilator-associated pneumonia (VAP), immunodeficiency associated pneumonia, SARS-CoV-2-associated pneumonia, acute respiratory distress syndrome (ARDS) and chronic obstructive pulmonary disease (COPD). We briefly describe physiological and immunological mechanisms that selectively create advantages or disadvantages for relative growth of pathogenic bacterial species in the respiratory tract. At the end, we propose some directions and analytical methods that may facilitate the identification of key genera and species of resident and transient microbes involved in the respiratory diseases' initiation and progression.

Airway bacterial community composition in persons with advanced cystic fibrosis lung disease

BACKGROUND

The progression of lung disease in people with cystic fibrosis (pwCF) has been associated with a decrease in the diversity of airway bacterial communities. How often low diversity communities occur in advanced CF lung disease and how they may be associated with clinical outcomes is not clear, however.

METHODS

We sequenced a region of the bacterial 16S ribosomal RNA gene to characterize bacterial communities in sputum from 190 pwCF with advanced lung disease (FEV1≤40% predicted), with particular attention to the prevalence and relative abundance of dominant genera. We evaluated relationships between community diversity and clinical outcomes.

RESULTS

Although most of the 190 pwCF with advanced lung disease had airway bacterial communities characterized by low diversity with a dominant genus, a considerable minority (40%) did not. The absence of a dominant genus, presence of methicillin-susceptible Staphylococcus aureus, and greater bacterial richness positively correlated with lung function. Higher relative abundance of the dominant genus and greater antimicrobial use negatively correlated with lung function. PwCF with a low diversity community and dominant genus had reduced lung transplant-free survival compared to those without (median survival of 1.6 vs 2.9 years).

CONCLUSIONS

A considerable proportion of pwCF with advanced lung disease do not have airway bacterial communities characterized by low diversity and a dominant genus and these individuals had better survival. An understanding of the antecedents of low diversity airway communities- and the impact these may have on lung disease trajectory - may provide avenues for improved management strategies.

Exploring the complex relationship between the lung microbiome and ventilator-associated pneumonia

INTRODUCTION

Understanding the presence and function of a diverse lung microbiome in acute lung infections, particularly ventilator-associated pneumonia (VAP), is still limited, evidencing significant gaps in our knowledge.

AREAS COVERED

In this comprehensive narrative review, we aim to elucidate the contribution of the respiratory microbiome in the development of VAP by examining the current knowledge on the interactions among microorganisms. By exploring these intricate connections, we endeavor to enhance our understanding of the disease's pathophysiology and pave the way for novel ideas and interventions in studying the respiratory tract microbiome.

EXPERT OPINION

The conventional perception of lungs as sterile is deprecated since it is currently recognized the existence of a diverse microbial community within them. However, despite extensive research on the role of the respiratory microbiome in healthy lungs, respiratory chronic diseases and acute lung infections such as pneumonia are not fully understood. It is crucial to investigate further the relationship between the pathophysiology of VAP and the pulmonary microbiome, elucidating the mechanisms underlying the interactions between the microbiome, host immune response and mechanical ventilation for the development of VAP.

The upper and lower respiratory tract microbiome in severe aspiration pneumonia

Uncertainty persists whether anaerobic bacteria represent important pathogens in aspiration pneumonia. In a nested case-control study of mechanically ventilated patients classified as macro-aspiration pneumonia (MAsP, n = 56), non-macro-aspiration pneumonia (NonMAsP, n = 91), and uninfected controls (n = 11), we profiled upper (URT) and lower respiratory tract (LRT) microbiota with bacterial 16S rRNA gene sequencing, measured plasma host-response biomarkers, analyzed bacterial communities by diversity and oxygen requirements, and performed unsupervised clustering with Dirichlet Multinomial Models (DMM). MAsP and NonMAsP patients had indistinguishable microbiota profiles by alpha diversity and oxygen requirements with similar host-response profiles and 60-day survival. Unsupervised DMM clusters revealed distinct bacterial clusters in the URT and LRT, with low-diversity clusters enriched for facultative anaerobes and typical pathogens, associated with higher plasma levels of SPD and sCD14 and worse 60-day survival. The predictive inter-patient variability in these bacterial profiles highlights the importance of microbiome study in patient sub-phenotyping and precision medicine approaches for severe pneumonia.

16S rRNA-Based Microbiota Profiling Assists Conventional Culture Analysis of Airway Samples from Pediatric Cystic Fibrosis Patients

16S-based sequencing provides broader information on the respiratory microbial community than conventional culturing. However, it (often) lacks species- and strain-level information. To overcome this issue, we used 16S rRNA-based sequencing results from 246 nasopharyngeal samples obtained from 20 infants with cystic fibrosis (CF) and 43 healthy infants, which were all 0 to 6 months old, and compared them to both standard (blind) diagnostic culturing and a 16S-sequencing-informed "targeted" reculturing approach. Using routine culturing, we almost uniquely detected Moraxella catarrhalis, Staphylococcus aureus, and Haemophilus influenzae (42%, 38%, and 33% of samples, respectively). Using the targeted reculturing approach, we were able to reculture 47% of the top-5 operational taxonomical units (OTUs) in the sequencing profiles. In total, we identified 60 species from 30 genera with a median of 3 species per sample (range, 1 to 8). We also identified up to 10 species per identified genus. The success of reculturing the top-5 genera present from the sequencing profile depended on the genus. In the case of Corynebacterium being in the top 5, we recultured them in 79% of samples, whereas for Staphylococcus, this value was only 25%. The success of reculturing was also correlated with the relative abundance of those genera in the corresponding sequencing profile. In conclusion, revisiting samples using 16S-based sequencing profiles to guide a targeted culturing approach led to the detection of more potential pathogens per sample than conventional culturing and may therefore be useful in the identification and, consequently, treatment of bacteria considered relevant for the deterioration or exacerbation of disease in patients like those with CF. IMPORTANCE Early and effective treatment of pulmonary infections in cystic fibrosis is vital to prevent chronic lung damage. Although microbial diagnostics and treatment decisions are still based on conventional culture methods, research is gradually focusing more on microbiome and metagenomic-based approaches. This study compared the results of both methods and proposed a way to combine the best of both worlds. Many species can relatively easily be recultured based on the 16S-based sequencing profile, and it provides more in-depth information about the microbial composition of a sample than that obtained through routine (blind) diagnostic culturing. Still, well-known pathogens can be missed by both routine diagnostic culture methods as well as by targeted reculture methods, sometimes even when they are highly abundant, which may be a consequence of either sample storage conditions or antibiotic treatment at the time of sampling.

Detection of Pathogens and Antimicrobial Resistance Genes in Ventilator-Associated Pneumonia by Metagenomic Next-Generation Sequencing Approach

Background

The early identification of pathogens and their antibiotic resistance are essential for the management and treatment of patients affected by ventilator-associated pneumonia (VAP). However, microbiological culture may be time-consuming and has a limited culturability of many potential pathogens. In this study, we developed a rapid nanopore-based metagenomic next-generation sequencing (mNGS) diagnostic assay for detection of VAP pathogens and antimicrobial resistance genes (ARGs).

Patients and Methods

Endotracheal aspirate (ETA) samples from 63 patients with suspected VAP were collected between November 2021 and July 2022. Receiver operating characteristic (ROC) curves were established to compare the pathogen identification performance of the target pathogen reads, reads percent of microbes (RPM) and relative abundance (RA). The evaluation of the accuracy of mNGS was performed comparing with the gold standard and the composite standard, respectively. Then, the ARGs were analyzed by mNGS.

Results

ROC curves showed that RA has the highest diagnostic value and the corresponding threshold was 9.93%. The sensitivity and specificity of mNGS test were 91.3% and 78.3%, respectively, based on the gold standard, while the sensitivity and specificity of mNGS test were 97.4% and 100%, respectively, based on the composite standard. A total of 13 patients were virus-positive based on mNGS results, while the coinfection rate increased from 27% to 46% compared to the rate obtained based on clinical findings. The mNGS test also performed well at predicting antimicrobial resistance phenotypes. Patients with a late-onset VAP had a significantly greater proportion of ARGs in their respiratory microbiome compared to those with early-onset VAP (P = 0.041). Moreover, the median turnaround time of mNGS was 4.43 h, while routine culture was 72.00 h.

Conclusion

In this study, we developed a workflow that can accurately detect VAP pathogens and enable prediction of antimicrobial resistance phenotypes within 5 h of sample receipt by mNGS.

The Role of the Respiratory Microbiome in the Pathogenesis of Aspiration Pneumonia: Implications for Diagnosis and Potential Therapeutic Choices

Although the lungs were considered to be sterile until recently, the advent of molecular biology techniques, such as polymerase chain reaction, 16 S rRNA sequencing and metagenomics has led to our expanding knowledge of the lung microbiome. These methods may be particularly useful for the identification of the causative agent(s) in cases of aspiration pneumonia, in which there is usually prior administration of antibiotics. The most common empirical treatment of aspiration pneumonia is the administration of broad-spectrum antibiotics; however, this may result in negative cultures from specimens taken from the respiratory tract. Therefore, in such cases, polymerase chain reaction or metagenomic next-generation sequencing may be life-saving. Moreover, these modern molecular methods may assist with antimicrobial stewardship. Based upon factors such as age, altered mental consciousness and recent hospitalization, there is a shift towards the predominance of aerobes, especially Gram-negative bacteria, over anaerobes in aspiration pneumonia. Thus, the therapeutic choices should be expanded to cover multi-drug resistant Gram-negative bacteria in selected cases of aspiration pneumonia.

Species-level respiratory microbiome profiling for etiologic diagnosis of children pneumonia using full length 16S rRNA gene sequencing

PURPOSE

The study was conducted to analyze the role of respiratory microbiome composition in children pneumonia etiology diagnosis.

METHODS

The bronchoalveolar lavage fluid bacterial community between the Mycoplasma pneumoniae pneumonia (MP group, n ​= ​13) and the pathogen negative pneumonia (N group, n ​= ​20) children were compared using the full-length 16S rRNA gene sequencing.

RESULTS

Distinct bacterial communities were identified in two groups and lower α-diversity was revealed in the MP patients indicating the lower abundance microbiota composition. Dominant bacteria were Mycoplasma and Mycoplasma pneumoniae for MP patients at genus and species levels. Possible pathogens were characterized in 17 out of 20 patients in the N group by detection of higher abundance using the 16S rRNA gene sequencing.

CONCLUSIONS

The high taxonomic resolution of full-length 16S rRNA gene sequencing assists in improving characterization of potential pathogens, and more studies are necessary to further evaluate the prognostic roles of specific bacteria in the pathogenicity of pneumonia.

Oral and fecal microbiome of confiscated Bengal slow lorises in response to confinement duration

Slow lorises are small arboreal and nocturnal primates. Due to the illegal trade, a large number of slow lorises were confiscated into wildlife sanctuaries or rescue centers. The re-release has been considered a preferable approach for alleviating the captive pressure, but inappropriate and long-term confinement make it difficult to achieve this goal. In this study, we investigated and compared the fecal and oral microbiome of Bengal slow lorises (Nycticebus bengalensis) under long-term captivity (LC) and short-term captivity (SC) groups based on 16s rRNA high-throughput gene sequencing. The oral microbiome displayed higher Chao1 richness but lower Shannon and Simpson indices than the fecal microbiome. The Bengal slow lorises under long-term captivity had abundant pathogenic genera in both gut and oral microbiomes, such as Desulfovibrio, Actinomyces, Capnocytophaga, Neisseria, and Fusobacterium, while some specific bacterial taxa associated with intestinal balance were more enriched in the SC group. Due to the plant gum scarcity in the diet, both groups had a low abundance of Bifidobacterium. Function profile prediction indicated that the LC group was enriched with genetic information processing and metabolism pathways due to the stable food intake. The increased membrane transport and xenobiotic metabolism and degradation functions in the SC group could be explained by the function of the host microbiome in facilitating adaptation to changing environments and diets. The results demonstrated that the oral microbiome had the potential to be used as a regular surveillance tool. Also, current captive management should be improved to ensure reintroduction success.

Endotracheal tube microbiome in hospitalized patients defined largely by hospital environment

Background

Studies of the respiratory tract microbiome primarily focus on airway and lung microbial diversity, but it is still unclear how these microbial communities may be affected by intubation and long periods in intensive care units (ICU), an aspect that today could aid in the understanding of COVID19 progression and disease severity. This study aimed to explore and characterize the endotracheal tube (ETT) microbiome by analyzing ETT-associated microbial communities.

Methods

This descriptive study was carried out on adult patients subjected to invasive mechanical ventilation from 2 to 21 days. ETT samples were obtained from 115 patients from ICU units in two hospitals. Bacteria isolated from endotracheal tubes belonging to the ESKAPE group were analyzed for biofilm formation using crystal violet quantification. Microbial profiles were obtained using Illumina sequencing of 16S rRNA gene.

Results

The ETT microbiome was mainly composed by the phyla Proteobacteria, Firmicutes and Bacteroidetes. Microbiome composition correlated with the ICU in which patients were hospitalized, while intubation time and diagnosis of ventilator-associated pneumonia (VAP) did not show any significant association.

Conclusion

These results suggest that the ICU environment, or medical practices, could be a key to microbial colonization and have a direct influence on the ETT microbiomes of patients that require mechanical ventilation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02086-7.

Analysis of Negative Results of Metagenomics Next-Generation Sequencing in Clinical Practice

Background

Metagenomics next-generation sequencing (mNGS) has been increasingly used in the clinic, which provides a powerful tool for the etiological diagnosis of infectious diseases. Precise treatment can be carried out according to the positive mNGS results. However, the role of negative results of mNGS remains poorly defined in clinical practice.

Methods

The results of 1,021 samples from patients who received the mNGS test at Zhongshan Hospital, Fudan University, between January 2019 and December 2019 were analyzed.

Results

There were 308 samples (30.17%) of negative results included in the current study. The top 2 types of negative samples were blood (130/308) and tissue (63/308), which also accounted for the highest negative proportion in diseases. Sputum and bronchoalveolar lavage fluid (BALF) were more likely to have positive results. In false-negative results (defined as negative in mNGS test but reported positive in other sample types or assays), 118 samples were found when compared to regular microbiological assays. The negative predictive value (NPV) of mNGS was 95.79% [95%CI, 93.8%–97.8%] as compared to culture and smear. Mycobacterium, Aspergillus, and Mycoplasma ranked as the top 3 microorganisms on the undetected pathogen list.

Conclusions

The present data indicate that when the mNGS test is negative, the negative prediction accuracy rate of the original specimen is significant. However, other laboratory assays results and clinical presentations should always be carefully considered prior to drawing a diagnosis.

Essential Regression: A generalizable framework for inferring causal latent factors from multi-omic datasets

High-dimensional cellular and molecular profiling of biological samples highlights the need for analytical approaches that can integrate multi-omic datasets to generate prioritized causal inferences. Current methods are limited by high dimensionality of the combined datasets, the differences in their data distributions, and their integration to infer causal relationships. Here, we present Essential Regression (ER), a novel latent-factor-regression-based interpretable machine-learning approach that addresses these problems by identifying latent factors and their likely cause-effect relationships with system-wide outcomes/properties of interest. ER can integrate many multi-omic datasets without structural or distributional assumptions regarding the data. It outperforms a range of state-of-the-art methods in terms of prediction. ER can be coupled with probabilistic graphical modeling, thereby strengthening the causal inferences. The utility of ER is demonstrated using multi-omic system immunology datasets to generate and validate novel cellular and molecular inferences in a wide range of contexts including immunosenescence and immune dysregulation.

Multi-drug resistant gram-negative bacterial pneumonia: etiology, risk factors, and drug resistance patterns

Bacterial pneumonia is one of the most serious public health issues owing to its medical and economic costs, which result in increased morbidity and mortality in people of all ages around the world. Furthermore, antimicrobial resistance has risen over time, and the advent of multi-drug resistance in GNB complicates therapy and has a detrimental impact on patient outcomes. The current review aimed to summarize bacterial pneumonia with an emphasis on gram-negative etiology, pathogenesis, risk factors, resistance mechanisms, treatment updates, and vaccine concerns to tackle the problem before it causes a serious consequence. In conclusion, the global prevalence of GNB in CAP was reported 49.7% to 83.1%, whereas in VAP patients ranged between 76.13% to 95.3%. The most commonly reported MDR-GNB causes of pneumonia were A. baumannii, K. pneumoniae, and P. aeruginosa, with A. baumannii isolated particularly in VAP patients and the elderly. In most studies, ampicillin, tetracyclines, amoxicillin-clavulanic acid, cephalosporins, and carbapenems were shown to be highly resistant. Prior MDR-GNB infection, older age, previous use of broad-spectrum antibiotics, high frequency of local antibiotic resistance, prolonged hospital stays, ICU admission, mechanical ventilation, and immunosuppression are associated with the MDR-GNB colonization. S. maltophilia was reported as a severe cause of HAP/VAP in patients with mechanically ventilated and having hematologic malignancy due to its ability of biofilm formation, site adhesion in respiratory devices, and its intrinsic and acquired drug resistance mechanisms. Effective combination therapies targeting PDR strains and drug-resistant genes, antibiofilm agents, gene-based vaccinations, and pathogen-specific lymphocytes should be developed in the future.

Metataxonomic investigation of the microbial community in the trachea and oropharynx of healthy controls and diabetic patients using endotracheal tubes

BACKGROUND

Although the study of respiratory microbiota has been an active field of research, obtaining the appropriate respiratory samples for healthy controls remains to be a challenge. As such, this study aims to evaluate the use of endotracheal tube washing as a viable control for sputum samples.

METHODS

A total of 14 subjects, including 8 healthy respiratory controls and 6 diabetic patients without any respiratory disease, were enrolled in this study, during which the endotracheal tubes used in their scheduled routine surgery were collected. Pre-operative oral gargles were also collected from non-diabetic subjects.

RESULTS

16S amplicon sequencing revealed similar taxa composition in endotracheal tube washings and oral gargles in the healthy control subjects, although the relative abundance of 11 genus level operational taxonomic units was significantly different between the two sample sources. The diabetic subjects showed relatively lower diversity than those of non-diabetic subjects. The proportion range of the most abundant taxa detected in each endotracheal tube washings were 10.1-33.2%.

CONCLUSION

Endotracheal tube washing fluid may provide healthy control samples for upper respiratory investigations without incurring any additional risk to the subject.

Exploring the nasopharyngeal microbiota composition in infants with whooping cough: A test-negative case-control study

Purpose

The purpose of this study was to characterize the nasopharyngeal microbiota of infants with possible and confirmed pertussis compared to healthy controls.

Methods

This prospective study included all infants <1 year with microbiologically confirmed diagnosis of pertussis attended at a University Hospital over a 12-month period. For each confirmed case, up to 2 consecutive patients within the same age range and meeting the clinical case definition of pertussis but testing PCR-negative were included as possible cases. A third group of asymptomatic infants (healthy controls) were also included. Nasopharyngeal microbiota was characterized by sequencing the V3-V4 region of the 16S rRNA gene. Common respiratory DNA/RNA viral co-infection was tested by multiplex PCR.

Results

Twelve confirmed cases, 21 possible cases and 9 healthy controls were included. Confirmed whooping cough was primarily driven by detection of Bordetella with no other major changes on nasopharyngeal microbiota. Possible cases had limited abundance or absence of Bordetella and a distinctive microbiota with lower bacterial richness and diversity and higher rates of viral co-infection than both confirmed cases and healthy controls. Bordetella reads determined by 16S rRNA gene sequencing were found in all 12 confirmed cases (100%), 3 out of the 21 possible cases (14.3%) but in any healthy control.

Conclusion

This study supports the usefulness of 16S rRNA gene sequencing for improved sensitivity on pertussis diagnosis compared to real-time PCR and to understand other microbial changes occurring in the nasopharynx in children <1 year old with suspected whooping cough compared to healthy controls.

Sepsis and the Human Microbiome. Just Another Kind of Organ Failure? A Review

Next-generation sequencing (NGS) has been further optimised during the last years and has given us new insights into the human microbiome. The 16S rDNA sequencing, especially, is a cheap, fast, and reliable method that can reveal significantly more microorganisms compared to culture-based diagnostics. It might be a useful method for patients suffering from severe sepsis and at risk of organ failure because early detection and differentiation between healthy and harmful microorganisms are essential for effective therapy. In particular, the gut and lung microbiome in critically ill patients have been probed by NGS. For this review, an iterative approach was used. Current data suggest that an altered microbiome with a decreased alpha-diversity compared to healthy individuals could negatively influence the individual patient’s outcome. In the future, NGS may not only contribute to the diagnosis of complications. Patients at risk could also be identified before surgery or even during their stay in an intensive care unit. Unfortunately, there is still a lack of knowledge to make precise statements about what constitutes a healthy microbiome, which patients exactly have an increased perioperative risk, and what could be a possible therapy to strengthen the microbiome. This work is an iterative review that presents the current state of knowledge in this field.

Microbiota, probiotics and respiratory infections: the three musketeers can tip off potential management of COVID-19

Rapid infectivity of SARS-CoV2 with recent viral variants is posing a challenge in the development of robust therapeutic strategies. On the other hand, microbiota is debated for its involvement in SARS-CoV2 infection with varied opinions. Although ample data about the role of microbiota and probiotics in respiratory viral infections are available, their role in COVID-19 is limited albeit emerging rapidly. The utilization of probiotics for the management of COVID-19 is still under investigation in many clinical trials. Existing information coupled with recent COVID-19 related studies can suggest various ways to use microbiota modulation and probiotics for managing this pandemic. Present article indicates the role of microbiota modulation and probiotics in respiratory infections. In addition, scattered evidence was gathered to understand the potential of microbiota and probiotics in the management of SARS-CoV2. Gut-airway microbiota connection is already apparent in respiratory tract viral infections, including SARS-CoV2. Though few clinical trials are evaluating microbiota and probiotics for COVID-19 management, the safety evaluation must be given more serious consideration because of the possibility of opportunistic infections among COVID-19 patients. Nevertheless, the information about microbiota modulation using probiotics and prebiotics can be helpful to manage this outbreak and this review presents different aspects of this idea.

Neurological Complications Acquired During Pediatric Critical Illness: Exploratory "Mixed Graphical Modeling" Analysis Using Serum Biomarker Levels

OBJECTIVES

Neurologic complications, consisting of the acute development of a neurologic disorder, that is, not present at admission but develops during the course of illness, can be difficult to detect in the PICU due to sedation, neuromuscular blockade, and young age. We evaluated the direct relationships of serum biomarkers and clinical variables to the development of neurologic complications. Analysis was performed using mixed graphical models, a machine learning approach that allows inference of cause-effect associations from continuous and discrete data.

DESIGN

Secondary analysis of a previous prospective observational study.

SETTING

PICU, single quaternary-care center.

PATIENTS

Individuals admitted to the PICU, younger than18 years old, with intravascular access via an indwelling catheter.

INTERVENTIONS

None.

MEASUREMENTS

About 101 patients were included in this analysis. Serum (days 1-7) was analyzed for glial fibrillary acidic protein, ubiquitin C-terminal hydrolase-L1, and alpha-II spectrin breakdown product 150 utilizing enzyme-linked immunosorbent assays. Serum levels of neuron-specific enolase, myelin basic protein, and S100 calcium binding protein B used in these models were reported previously. Demographic data, use of selected clinical therapies, lengths of stay, and ancillary neurologic testing (head CT, brain MRI, and electroencephalogram) results were recorded. The Mixed Graphical Model-Fast-Causal Inference-Maximum algorithm was applied to the dataset.

MAIN RESULTS

About 13 of 101 patients developed a neurologic complication during their critical illness. The mixed graphical model identified peak levels of the neuronal biomarker neuron-specific enolase and ubiquitin C-terminal hydrolase-L1, and the astrocyte biomarker glial fibrillary acidic protein to be the direct causal determinants for the development of a neurologic complication; in contrast, clinical variables including age, sex, length of stay, and primary neurologic diagnosis were not direct causal determinants.

CONCLUSIONS

Graphical models that include biomarkers in addition to clinical data are promising methods to evaluate direct relationships in the development of neurologic complications in critically ill children. Future work is required to validate and refine these models further, to determine if they can be used to predict which patients are at risk for/or with early neurologic complications.

Respiratory microbiome disruption and risk for ventilator-associated lower respiratory tract infection

BACKGROUND

Ventilator-associated lower respiratory tract infection (VA-LRTI) is a common among critically ill patients and has been associated with increased morbidity and mortality. In acute critical illness, respiratory microbiome disruption indices (MDIs) have been shown to predict risk for VA-LRTI, but their utility beyond the first days of critical illness is unknown. We sought to characterize how MDIs previously shown to predict VA-LRTI at initiation of mechanical ventilation change with prolonged mechanical ventilation, and if they remain associated with VA-LRTI risk.

METHODS

We developed a cohort of 83 subjects admitted to a long-term acute care hospital due to their prolonged dependence on mechanical ventilation; performed dense, longitudinal sampling of the lower respiratory tract, collecting 1066 specimens; and characterized the lower respiratory microbiome by 16S rRNA sequencing as well as total bacterial abundance by 16S rRNA qPCR.

RESULTS

Cross-sectional MDIs, including low Shannon diversity and high total bacterial abundance, were associated with risk for VA-LRTI, but the associations had wide posterior credible intervals. Persistent lower respiratory microbiome disruption showed a more robust association with VA-LRTI risk, with each day of (base e) Shannon diversity < 2.0 associated with a VA-LRTI odds ratio of 1.36 (95% credible interval 1.10 to 1.72). The observed association was consistent across multiple clinical definitions of VA-LRTI.

CONCLUSIONS

Cross-sectional MDIs have limited ability to discriminate VA-LRTI risk during prolonged mechanical ventilation, but persistent lower respiratory tract microbiome disruption, best characterized by consecutive days with low Shannon diversity, may identify a population at high risk for infection and may help target infection prevention interventions.

The lung microbiome in lung transplantation

Culture-independent study of the lower respiratory tract after lung transplantation has enabled an understanding of the microbiome - that is, the collection of bacteria, fungi, and viruses, and their respective gene complement - in this niche. The lung has unique features as a microbial environment, with balanced entry from the upper respiratory tract, clearance, and local replication. There are many pressures impacting the microbiome after transplantation, including donor allograft factors, recipient host factors such as underlying disease and ongoing exposure to the microbe-rich upper respiratory tract, and transplantation-related immunosuppression, antimicrobials, and postsurgical changes. To date, we understand that the lung microbiome after transplant is dysbiotic; that is, it has higher biomass and altered composition compared to a healthy lung. Emerging data suggest that specific microbiome features may be linked to host responses, both immune and non-immune, and clinical outcomes such as chronic lung allograft dysfunction (CLAD), but many questions remain. The goal of this review is to put into context our burgeoning understanding of the lung microbiome in the postlung transplant patient, the interactions between microbiome and host, the role the microbiome may play in post-transplant complications, and critical outstanding research questions.

Plasma 1,3-β-d-glucan levels predict adverse clinical outcomes in critical illness

BACKGROUNDThe fungal cell wall constituent 1,3-β-d-glucan (BDG) is a pathogen-associated molecular pattern that can stimulate innate immunity. We hypothesized that BDG from colonizing fungi in critically ill patients may translocate into the systemic circulation and be associated with host inflammation and outcomes.METHODSWe enrolled 453 mechanically ventilated patients with acute respiratory failure (ARF) without invasive fungal infection and measured BDG, innate immunity, and epithelial permeability biomarkers in serially collected plasma samples.RESULTSCompared with healthy controls, patients with ARF had significantly higher BDG levels (median [IQR], 26 pg/mL [15-49 pg/mL], P < 0.001), whereas patients with ARF with high BDG levels (≥40 pg/mL, 31%) had higher odds for assignment to the prognostically adverse hyperinflammatory subphenotype (OR [CI], 2.88 [1.83-4.54], P < 0.001). Baseline BDG levels were predictive of fewer ventilator-free days and worse 30-day survival (adjusted P < 0.05). Integrative analyses of fungal colonization and epithelial barrier disruption suggested that BDG may translocate from either the lung or gut compartment. We validated the associations between plasma BDG and host inflammatory responses in 97 hospitalized patients with COVID-19.CONCLUSIONBDG measurements offered prognostic information in critically ill patients without fungal infections. Further research in the mechanisms of translocation and innate immunity recognition and stimulation may offer new therapeutic opportunities in critical illness.FUNDINGUniversity of Pittsburgh Clinical and Translational Science Institute, COVID-19 Pilot Award and NIH grants (K23 HL139987, U01 HL098962, P01 HL114453, R01 HL097376, K24 HL123342, U01 HL137159, R01 LM012087, K08HK144820, F32 HL142172, K23 GM122069).

An inter-laboratory study to investigate the impact of the bioinformatics component on microbiome analysis using mock communities

Despite the advent of whole genome metagenomics, targeted approaches (such as 16S rRNA gene amplicon sequencing) continue to be valuable for determining the microbial composition of samples. Amplicon microbiome sequencing can be performed on clinical samples from a normally sterile site to determine the aetiology of an infection (usually single pathogen identification) or samples from more complex niches such as human mucosa or environmental samples where multiple microorganisms need to be identified. The methodologies are frequently applied to determine both presence of micro-organisms and their quantity or relative abundance. There are a number of technical steps required to perform microbial community profiling, many of which may have appreciable precision and bias that impacts final results. In order for these methods to be applied with the greatest accuracy, comparative studies across different laboratories are warranted. In this study we explored the impact of the bioinformatic approaches taken in different laboratories on microbiome assessment using 16S rRNA gene amplicon sequencing results. Data were generated from two mock microbial community samples which were amplified using primer sets spanning five different variable regions of 16S rRNA genes. The PCR-sequencing analysis included three technical repeats of the process to determine the repeatability of their methods. Thirteen laboratories participated in the study, and each analysed the same FASTQ files using their choice of pipeline. This study captured the methods used and the resulting sequence annotation and relative abundance output from bioinformatic analyses. Results were compared to digital PCR assessment of the absolute abundance of each target representing each organism in the mock microbial community samples and also to analyses of shotgun metagenome sequence data. This ring trial demonstrates that the choice of bioinformatic analysis pipeline alone can result in different estimations of the composition of the microbiome when using 16S rRNA gene amplicon sequencing data. The study observed differences in terms of both presence and abundance of organisms and provides a resource for ensuring reproducible pipeline development and application. The observed differences were especially prevalent when using custom databases and applying high stringency operational taxonomic unit (OTU) cut-off limits. In order to apply sequencing approaches with greater accuracy, the impact of different analytical steps needs to be clearly delineated and solutions devised to harmonise microbiome analysis results.

A Pipeline for Integrated Theory and Data-Driven Modeling of Biomedical Data

Genome sequencing technologies have the potential to transform clinical decision making and biomedical research by enabling high-throughput measurements of the genome at a granular level. However, to truly understand mechanisms of disease and predict the effects of medical interventions, high-throughput data must be integrated with demographic, phenotypic, environmental, and behavioral data from individuals. Further, effective knowledge discovery methods must infer relationships between these data types. We recently proposed a pipeline (CausalMGM) to achieve this. CausalMGM uses probabilistic graphical models to infer the relationships between variables in the data; however, CausalMGM's graphical structure learning algorithm can only handle small datasets efficiently. We propose a new methodology (piPref-Div) that selects the most informative variables for CausalMGM, enabling it to scale. We validate the efficacy of piPref-Div against other feature selection methods and demonstrate how the use of the full pipeline improves breast cancer outcome prediction and provides biologically interpretable views of gene expression data.

Temporal airway microbiome changes related to ventilator-associated pneumonia in children

We sought to determine whether temporal changes in the lower airway microbiome are associated with ventilator-associated pneumonia (VAP) in children.Using a multicentre prospective study of children aged 31 days to 18 years requiring mechanical ventilation support for >72 h, daily tracheal aspirates were collected and analysed by sequencing of the 16S rRNA gene. VAP was assessed using 2008 Centers for Disease Control and Prevention paediatric criteria. The association between microbial factors and VAP was evaluated using joint longitudinal time-to-event modelling, matched case-control comparisons and unsupervised clustering.Out of 366 eligible subjects, 66 (15%) developed VAP at a median of 5 (interquartile range 3-5) days post intubation. At intubation, there was no difference in total bacterial load (TBL), but Shannon diversity and the relative abundance of Streptococcus, Lactobacillales and Prevotella were lower for VAP subjects versus non-VAP subjects. However, higher TBL on each sequential day was associated with a lower hazard (hazard ratio 0.39, 95% CI 0.23-0.64) for developing VAP, but sequential values of diversity were not associated with VAP. Similar findings were observed from the matched analysis and unsupervised clustering. The most common dominant VAP pathogens included Prevotella species (19%), Pseudomonas aeruginosa (14%) and Streptococcus mitis/pneumoniae (10%). Mycoplasma and Ureaplasma were also identified as dominant organisms in several subjects.In mechanically ventilated children, changes over time in microbial factors were marginally associated with VAP risk, although these changes were not suitable for predicting VAP in individual patients. These findings suggest that focusing exclusively on pathogen burden may not adequately inform VAP diagnosis.

Causal effects in microbiomes using interventional calculus

Causal inference in biomedical research allows us to shift the paradigm from investigating associational relationships to causal ones. Inferring causal relationships can help in understanding the inner workings of biological processes. Association patterns can be coincidental and may lead to wrong conclusions about causality in complex systems. Microbiomes are highly complex, diverse, and dynamic environments. Microbes are key players in human health and disease. Hence knowledge of critical causal relationships among the entities in a microbiome, and the impact of internal and external factors on microbial abundance and their interactions are essential for understanding disease mechanisms and making appropriate treatment recommendations. In this paper, we employ causal inference techniques to understand causal relationships between various entities in a microbiome, and to use the resulting causal network to make useful computations. We introduce a novel pipeline for microbiome analysis, which includes adding an outcome or "disease" variable, and then computing the causal network, referred to as a "disease network", with the goal of identifying disease-relevant causal factors from the microbiome. Internventional techniques are then applied to the resulting network, allowing us to compute a measure called the causal effect of one or more microbial taxa on the outcome variable or the condition of interest. Finally, we propose a measure called causal influence that quantifies the total influence exerted by a microbial taxon on the rest of the microiome. Our pipeline is robust, sensitive, different from traditional approaches, and able to predict interventional effects without any controlled experiments. The pipeline can be used to identify potential eubiotic and dysbiotic microbial taxa in a microbiome. We validate our results using synthetic data sets and using results on real data sets that were previously published.

Diagnosis of pleural empyema/parapneumonic effusion by next-generation sequencing

BACKGROUND

Although a microbiological diagnosis of pleural infection is clinically important, it is often complicated by prior antibiotic treatment and/or difficulties with culturing some bacterial species. Therefore, we aimed to identify probable causative bacteria in pleural empyema/parapneumonic effusions by combining 16S ribosomal RNA (rRNA) gene amplification and next-generation sequencing (NGS).

METHODS

Pleural fluids were collected from 19 patients with infectious effusions and nine patients with non-infectious malignant effusions. We analysed DNA extracted from the pleural fluid supernatant by NGS using the Genome Search Toolkit and GenomeSync database, either directly or after PCR amplification of the 16S rRNA gene. Infectious and non-infectious effusions were distinguished by semi-quantitative PCR of the 16S rRNA gene.

RESULTS

Only 8 (42%) effusions were culture-positive, however, NGS of the 16S rRNA gene amplicon identified 14 anaerobes and 7 aerobes/facultative anaerobes in all patients, including Streptococcus sp. (n = 6), Fusobacterium sp. (n = 5), Porphyromonas sp. (n = 5), and Prevotella sp. (n = 4), accounting for >10% of the total genomes. The culture and NGS results were discordant for 3 out of 8 patients, all of whom had previously been treated with antibiotics. Total (2^ΔCT value in semi-quantitative PCR of the 16S rRNA gene) and specific (total bacterial load multiplied by the proportion of primary bacteria in NGS) bacterial loads could efficiently distinguish empyema/parapneumonic effusion from non-infectious effusion.

CONCLUSION

Combining NGS with semi-quantitative PCR can facilitate the diagnosis of pleural empyema/parapneumonic effusion and its causal bacteria.

Detection of associated bacteria in aspiration pneumonia and lung abscesses using partial 16S rRNA gene amplicon sequencing

OBJECTIVES

Lower respiratory tract infections (LRTIs) are often caused by the patient's own oral commensal bacteria. Causative bacteria must be identified to select the appropriate antimicrobial agents; however, the pathogens are identified via routine culture methods in only approximately half of LRTI cases.

METHODS

To investigate LRTI-associated bacteria, we conducted culture testing under aerobic and anaerobic conditions using culture-independent partial 16S rRNA gene amplicon sequencing analysis using a high-throughput sequencer in cases of aspiration pneumonia and lung abscesses.

RESULTS

Culture testing of 17 aspiration pneumonia cases revealed Streptococcus spp. (n = 13), Prevotella spp. (n = 9), and Veillonella spp. (n = 8); 16S rRNA analysis of these cases yielded Streptococcus spp. (n = 16), Veillonella spp. (n = 12), Haemophilus spp. (n = 12), Prevotella spp. (n = 11), and Rothia spp. (n = 11). Culture testing of 8 lung abscess cases revealed Streptococcus spp. (n = 7) and Fusobacterium spp. (n = 4); 16S rRNA analysis of these cases yielded Fusobacterium spp. (n = 8), Prevotella spp. (n = 7), Streptococcus spp. (n = 6), and Porphyromonas spp. (n = 5). All taxa with abundance ratios of ≥50% on the 16S rRNA analysis were also detected in the cultures. However, several taxa were either undetected in the cultures despite relatively high abundance ratios on the 16S rRNA analysis or negative on the 16S rRNA analysis and isolated only by culturing.

CONCLUSION

Our data provide a comprehensive list of bacterial taxa that may be associated with aspiration pneumonia and lung abscesses. In empirically treating LRTIs, this information will help determine the best treatment against the targeted anaerobes.

Respiratory Tract Dysbiosis Is Associated with Worse Outcomes in Mechanically Ventilated Patients

Rationale: Host inflammatory responses have been strongly associated with adverse outcomes in critically ill patients, but the biologic underpinnings of such heterogeneous responses have not been defined.Objectives: We examined whether respiratory tract microbiome profiles are associated with host inflammation and clinical outcomes of acute respiratory failure.Methods: We collected oral swabs, endotracheal aspirates (ETAs), and plasma samples from mechanically ventilated patients. We performed 16S ribosomal RNA gene sequencing to characterize upper and lower respiratory tract microbiota and classified patients into host-response subphenotypes on the basis of clinical variables and plasma biomarkers of innate immunity and inflammation. We derived diversity metrics and composition clusters with Dirichlet multinomial models and examined our data for associations with subphenotypes and clinical outcomes.Measurements and Main Results: Oral and ETA microbial communities from 301 mechanically ventilated subjects had substantial heterogeneity in α and β diversity. Dirichlet multinomial models revealed a cluster with low α diversity and enrichment for pathogens (e.g., high Staphylococcus or Pseudomonadaceae relative abundance) in 35% of ETA samples, associated with a hyperinflammatory subphenotype, worse 30-day survival, and longer time to liberation from mechanical ventilation (adjusted P < 0.05), compared with patients with higher α diversity and relative abundance of typical oral microbiota. Patients with evidence of dysbiosis (low α diversity and low relative abundance of "protective" oral-origin commensal bacteria) in both oral and ETA samples (17%, combined dysbiosis) had significantly worse 30-day survival and longer time to liberation from mechanical ventilation than patients without dysbiosis (55%; adjusted P < 0.05).Conclusions: Respiratory tract dysbiosis may represent an important, modifiable contributor to patient-level heterogeneity in systemic inflammatory responses and clinical outcomes.

Diagnosis of complication in lung transplantation by TBLB + ROSE + mNGS

Lung transplantation is a potentially life-saving therapy for patients with terminal respiratory illnesses. Long-term survival is limited by the development of a variety of opportunistic infections and rejection. Optimal means of differential diagnosis of infection and rejection have not been established. With these challenges in mind, we tried to use transbronchial lung biopsy (TBLB) rapid on-site cytological evaluation (ROSE), metagenomic next-generation sequencing (mNGS), and routine histologic examination to timely distinguish infection and rejection, and accurately detect etiologic pathogens. We reviewed the medical records of all patients diagnosed with infection or rejection by these means from December 2017 to September 2018 in our center. We identified seven recipients whose clinical course was complicated by infection or rejection. Three patients were diagnosed with acute rejection, organizing pneumonia, and acute fibrinoid organizing pneumonia, respectively. Four of the seven patients were diagnosed with infections, including Pneumocystis carinii pneumonia, cytomegalovirus, Aspergillus, and bacterial pneumonia. These patients recovered after proper treatment. TBLB + ROSE + mNGS might be a good method to accurately detect etiologic pathogens, which may help us to facilitate the use of targeted and precision medicine therapy in postoperative complications and avoid unnecessary potential adverse effects of drugs.

Respiratory microbiome profiles differ by recent hospitalization and nursing home residence in patients on mechanical ventilation

Background

Healthcare-associated pneumonia (HCAP) is a heterogeneous disease. We redefined nursing-home- and hospital-associated infections (NHAI) group by revising existing HCAP risk factors. The NHAI group comprised nursing home residents with a poor functional status, or recent (past 90 days) hospitalization or recent (past 180 days) antibiotic therapy. Our aim was to determine whether respiratory microbiota profiles are related to newly defined NHAI group in critically ill patients on mechanical ventilation.

Methods

The 180 endotracheal aspirates (ETAs) from 60 mechanically ventilated ICU patients (NHAI group, n = 24; non-NHAI group, n = 36) were prospectively collected on days 1, 3 and 7 in a university hospital. The bacterial community profiles of the ETAs were explored by 16S rRNA gene sequencing. A phylogenetic-tree-based microbiome association test (TMAT), generalized linear mixed models (GLMMs), the Wilcoxon test and the reference frame method were used to analyze the association between microbiome abundance and disease phenotype.

Results

The relative abundance of the genus Corynebacterium was significantly higher in the pneumonia than in the non-pneumonia group. The microbiome analysis revealed significantly lower α-diversity in the NHAI group than in the non-NHAI group. In the analysis of β-diversity, the structure of the microbiome also differed significantly between the two groups (weighted UniFrac distance, Adonis, p < 0.001). The abundance of Corynebacterium was significantly higher, and the relative abundances of Granulicatella, Staphylococcus, Streptococcus and Veillonella were significantly lower, in the NHAI group than in the non-NHAI group.

Conclusions

The microbiota signature of the ETAs distinguished between patients with and without risk factors for NHAI. The lung microbiome may serve as a therapeutic target for NHAI group.

Optimal specimen type for accurate diagnosis of infectious peripheral pulmonary lesions by mNGS

Background

Reports on the application of metagenomic next-generation sequencing (mNGS) to the diagnosis of peripheral pulmonary lesions (PPLs) are scarce. There have been no studies investigating the optimal specimen type for mNGS.

Methods

We used mNGS to detect pathogens in matched transbronchial lung biopsy (TBLB), bronchoalveolar lavage fluid (BALF), and bronchial needle brushing (BB) specimens from 39 patients suspected of having infectious PPLs. We explored differences in microbial composition and diagnostic accuracy of mNGS for the 3 specimen types.

Results

mNGS was more sensitive than conventional culture for detection of bacteria and fungi in TBLB, BALF, and BB specimens, with no difference in the sensitivity of mNGS across the different specimen types. mNGS showed higher sensitivity for fungi or uncategorized pulmonary pathogens in TBLB+BALF+BB compared to TBLB but not BALF or BB specimens. There were no significant differences between the 3 specimen types in the relative abundance of pathogens, or between TBLB and BB specimens in the relative abundance of 6 common lower respiratory tract commensals.

Conclusions

mNGS has a higher sensitivity than the conventional culture method for detecting pathogens in TBLB, BALF, or BB specimens. mNGS of BB samples is a less invasive alternative to TBLB for the diagnosis of infectious PPLs.

Reconsidering ventilator-associated pneumonia from a new dimension of the lung microbiome

Complex microbial communities that reside in the lungs, skin and gut are now appreciated for their role in maintaining organ, tissue and immune homoeostasis. As lungs are currently seen as an ecosystem, the shift in paradigm calls for the consideration of new algorithms related to lung ecology in pulmonology. Evidence of lung microbiota does not solely challenge the traditional physiopathology of ventilator-associated pneumonia (VAP); indeed, it also reinforces the need to include molecular techniques in VAP diagnosis and accelerate the use of immunomodulatory drugs, including corticosteroids, and other supplements such as probiotics for VAP prevention and/or treatment. With that stated, both microbiome and virome, including phageome, can lead to new opportunities in further understanding the relationship between health and dysbiosis in VAP. Previous knowledge may be, however, reconsidered at a microbiome scale.

CausalMGM: an interactive web-based causal discovery tool

High-throughput sequencing and the availability of large online data repositories (e.g. The Cancer Genome Atlas and Trans-Omics for Precision Medicine) have the potential to revolutionize systems biology by enabling researchers to study interactions between data from different modalities (i.e. genetic, genomic, clinical, behavioral, etc.). Currently, data mining and statistical approaches are confined to identifying correlates in these datasets, but researchers are often interested in identifying cause-and-effect relationships. Causal discovery methods were developed to infer such cause-and-effect relationships from observational data. Though these algorithms have had demonstrated successes in several biomedical applications, they are difficult to use for non-experts. So, there is a need for web-based tools to make causal discovery methods accessible. Here, we present CausalMGM (http://causalmgm.org/), the first web-based causal discovery tool that enables researchers to find cause-and-effect relationships from observational data. Web-based CausalMGM consists of three data analysis tools: (i) feature selection and clustering; (ii) automated identification of cause-and-effect relationships via a graphical model; and (iii) interactive visualization of the learned causal (directed) graph. We demonstrate how CausalMGM enables an end-to-end exploratory analysis of biomedical datasets, giving researchers a clearer picture of its capabilities.

Infection, antibiotic exposure and development of atopic dermatitis: A nationwide case-control study

Previous studies suggest an association between atopic dermatitis (AD) and exposure to microorganisms and antibiotics. However, these studies have limitations, and the sole influence on the development of AD was elusive. We performed a nationwide population-based case-control study in a Korean population to investigate the association between AD and early-life infection or antibiotic exposure. A total of 244 805 children with AD from the 2 283 601 children born between January 2010 and December 2014 and an equal number of sex- and age-matched healthy children were enrolled. A conditional logistic regression analysis showed that the episode of infection and antibiotic exposure were associated with an increased risk of AD (odds ratio [OR], 1.60; 95% confidence interval [CI], 1.58-1.63 for infection; and OR, 1.11; 95% CI, 1.09-1.13 for antibiotic exposure). A dose-dependent relationship was observed between risk for AD, the number of infection episodes and antibiotic cycles and the duration of antibiotic exposure. On further analysis using a conditional logistic model, the risk of AD was less when the antibiotics were used during the infection episode than that without the use of antibiotics, especially if the duration of the infection was short. Although our study could not consider the effect of cause or severity of infection, class of antibiotics and genetic or environmental factors of enrolled subjects, our results suggested that infection and antibiotic exposure were associated with an increased risk of AD. In addition, the results also implied that the use of antibiotics during an infection episode can decrease the risk of AD induced by the infection and that appropriate management of infections can minimize the risk of AD induced by infection or antibiotics.

The evolution of radiographic edema in ARDS and its association with clinical outcomes: A prospective cohort study in adult patients

PURPOSE

To assess the longitudinal evolution of radiographic edema using chest X-rays (CXR) in patients with Acute Respiratory Distress Syndrome (ARDS) and to examine its association with prognostic biomarkers, ARDS subphenotypes and outcomes.

MATERIALS AND METHODS

We quantified radiographic edema on CXRs from patients with ARDS or cardiogenic pulmonary edema (controls) using the Radiographic Assessment of Lung Edema (RALE) score on day of intubation and up to 10 days after. We measured baseline plasma biomarkers and recorded clinical variables.

RESULTS

The RALE score had good inter-rater agreement (r = 0.83, p < 0.0001) applied on 488 CXRs from 129 patients, with higher RALE scores in patients with ARDS (n = 108) compared to controls (n = 21, p = 0.01). Baseline RALE scores were positively correlated with levels of the receptor for end-glycation end products (RAGE) in ARDS patients (p < 0.05). Baseline RALE scores were not predictive of 30- or 90-day survival. Persistently elevated RALE scores were associated with prolonged need for mechanical ventilation (p = 0.002).

CONCLUSIONS

The RALE score is easily implementable with high inter-rater reliability. Longitudinal RALE scoring appears to be a reproducible approach to track the evolution of radiographic edema in patients with ARDS and can potentially predict prolonged need for mechanical ventilation.

Next-generation sequencing diagnosis of severe pneumonia from fulminant psittacosis with multiple organ failure: a case report and literature review

This study includes a retrospective analysis of the diagnosis and treatment of a case of severe pneumonia from fulminant psittacosis with multiple organ failure. Next-generation sequencing (NGS) of the pathogen was conducted. The purpose of this study was to summarize the clinical, laboratory, and imaging characteristics of the case and to improve understanding of the value of NGS in the diagnosis of severe community-acquired pneumonia (CAP). Fulminant psittacosis can be manifested as severe pneumonia with rapid progression, acute respiratory distress syndrome, sepsis, and multiple organ failure. Imaging shows unilateral lung consolidation, which is difficult to differentiate from CAP caused by common pathogens. The NGS technology can early detect rare pathogens, thus reducing unnecessary use of antibiotics and shortening the course of the disease.

Methods in Lung Microbiome Research

The lung microbiome is associated with host immune response and health outcomes in experimental models and patient cohorts. Lung microbiome research is increasing in volume and scope; however, there are no established guidelines for study design, conduct, and reporting of lung microbiome studies. Standardized approaches to yield reliable and reproducible data that can be synthesized across studies will ultimately improve the scientific rigor and impact of published work and greatly benefit microbiome research. In this review, we identify and address several key elements of microbiome research: conceptual modeling and hypothesis framing; study design; experimental methodology and pitfalls; data analysis; and reporting considerations. Finally, we explore possible future directions and research opportunities. Our goal is to aid investigators who are interested in this burgeoning research area and hopefully provide the foundation for formulating consensus approaches in lung microbiome research.

The Dynamics of Respiratory Microbiota during Mechanical Ventilation in Patients with Pneumonia

Bacterial pneumonia is a major cause of mechanical ventilation in intensive care units. We hypothesized that the presence of particular microbiota in endotracheal tube aspirates during the course of intubation was associated with clinical outcomes such as extubation failure or 28-day mortality. Sixty mechanically ventilated ICU (intensive care unit) patients (41 patients with pneumonia and 19 patients without pneumonia) were included, and tracheal aspirates were obtained on days 1, 3, and 7. Gene sequencing of 16S rRNA was used to measure the composition of the respiratory microbiome. A total of 216 endotracheal aspirates were obtained from 60 patients. A total of 22 patients were successfully extubatedwithin3 weeks, and 12 patients died within 28days. Microbiota profiles differed significantly between the pneumonia group and the non-pneumonia group (Adonis, p < 0.01). While α diversity (Shannon index) significantly decreased between day 1 and day 7 in the successful extubation group, it did not decrease in the failed extubation group among intubated patients with pneumonia. There was a significant difference in the change of βdiversity between the successful extubation group and the failed extubation group for Bray-Curtis distances (p < 0.001). At the genus level, Rothia, Streptococcus, and Prevotella correlated with the change of β diversity. A low relative abundance of Streptococci at the time of intubation was strongly associated with 28-day mortality. The dynamics of respiratory microbiome were associated with clinical outcomes such as extubation failure and mortality. Further large prospective studies are needed to test the predictive value of endotracheal aspirates in intubated patients.