The most important findings of the present study are as follows: first, the genus Streptococcus spp. has the highest overall abundance; second, there are differences in bacterial composition between samples that had positive culture results and negative cultures; third, the presence of Veillonella sp., Granulicatella sp., Enterococcus sp. and Lactiplantibacillus sp. are biomarkers of negative cultures; and fourth, the presence of Rothia is a biomarker of mortality.
Rothia sp. is associated with the development of ARDS in patients with COVID-19 pneumonia (11). Han Y et al. performed a metatranscriptomic study in BAL fluid from 19 patients with COVID-19 and 23 healthy controls and found a correlation between Rothia mucilaginosa and SARS-CoV-2, suggesting that it plays a role in the host's inflammatory response. We did not find a correlation between Rothia and IL-1β; we previously described that patients with IL-1β levels < 1,365 pg/mL had increased mortality(12). The levels of only three cytokines, IFN-γ, GM-CSF and IL-17A, were correlated with the levels of some genera; however, we did not find studies on COVID-19 with similar findings.
Veillonella sp., Granulicatella sp. and other opportunistic oral pathogens have been detected in the BAL fluid of patients with COVID-19(13). Kumar D et al. reported that Veillonella was a biomarker for survival in COVID-19 pneumonia patients and improved the inflammatory response in these patients(14). Meng H et al. performed metatranscriptomic sequencing in 72 patients with severe COVID-19 pneumonia and 57 patients who had already recovered, finding that Veillonella rodentium was a marker of recovery. We found that the presence of Veillonella in the lung samples of patients was a biomarker of negative cultures. Whether this lower probability of bacterial coinfection is a possible explanation for the higher probability of recovery is a possible hypothesis. We did not find similar studies in severe COVID-19 pneumonia that correlated the genera Veillonella sp., Granulicatella sp., Enterococcus sp. and Lactiplantibacillus sp. by metataxonomy with negative cultures.
The main difficulty we had was the low concentration of the isolated DNA. The lung samples from these patients with severe COVID-19 pneumonia in the IMV were scant, bloody, and contained a large amount of mucus, so we performed pretreatment to break up and inactivate the mucus. We suggest conducting studies on how to optimize DNA and RNA extraction techniques in these patients.
Other limitations were as follows: first, we did not have control samples from ventilated patients without pneumonia or from patients with pneumonia not related to COVID-9, which could serve as a reference for the community microbiota and be able to determine whether there was dysbiosis. Second, there was variability in the number of reads obtained from the samples, some with few reads and others with many reads. Third, metataxonomy does not provide species-level resolution for comparisons with cultures and the FA-PNEU.
The strength of the study is the scarcity of literature on patients with severe pneumonia in the ICU at VMI; with less than 48 hours of hospitalization, the isolation of bacteria reflects possible bacterial coinfection. Thomsen K et al. evaluated the diagnosis of respiratory coinfections with the analysis of 16S/18S rRNA gene amplicons in 34 patients with severe COVID-19. They detected potential pathogens in four patients (12%) with the 16S gene, seven patients (21%) using conventional cultures, and one patient (3%) using a molecular respiratory panel. Fourteen patients had not received antibiotics, and four microorganisms (3 Haemophilus influenzae and 1 Fusobacterium necrophorum) were detected in the 16S gene. They concluded that metataxonomy complements conventional microbial diagnosis(7).
Lloréns-Rico V et al. studied 21 patients with V4 amplification of the 16S gene in BAL fluid samples and demonstrated that the length of stay in the ICU, IMV and the use of antibiotics explained the greatest variation within the lung microbiota(15). We did not find differences in the composition of the bacterial communities between the samples of patients who were or were not on IMV for more than 7 days or in mortality. Merenstein C et al analyzed V1-V2 of the 16S rRNA gene in endotracheal aspirates from 24 patients with COVID-19 on IMV, with a median of 4 days of hospitalization. Six patients had taxa dominated by Staphylococcus, and three were a prominent minority; only 3 patients had Staphylococcus aureus identified by culture, suggesting that sequencing was more sensitive than culture(16). In our study, Staphylococcus was detected by taxonomy in 58 of the 67 lung samples (86.56%).
Another strength is that the patients did not receive previous antibiotics, and their samples were taken in the first 12 hours of IMV, which reduces the probability of alteration of the bacterial composition. Castilhos et al. demonstrated that the use of antibiotics leads to greater dysbiosis in critically ill patients with COVID-19(17). IMV alters the microbiome, allowing the growth of opportunistic pathogens(6).
We used Oxford Nanopore technology with a GridION (ONT) device, and the articles used Illumina MiSeq technology. Sequence reads are longer with Nanopore, and its accuracy, per base, is lower than that of MiSeq (95% vs. 99.9%)(18), so we would be biased in performing comparisons. Heikema AP et al. compared both technologies in 59 nasal swab samples in which Nanopore presented problems in the detection of bacteria of the genus Corynebacterium(19). Another difference is that we sequenced the complete 16S rRNA gene and not the individual hypervariable regions, which improved the precision of the measurements of bacterial diversity(20).