Here we propose to spike BioBall® to control the processing of (mini-)BAL samples and to provide absolute quantification of detected SOI in the mNGS workflow. We selected B. subtilis as SPC because of its rare natural presence in BAL samples and the ability of mNGS to distinguish its sequence reads from those of SOIs, commensal flora and human genome (see Additional figure 1). The quantitative metagenomics assay and metrics (Figure 1) were developed using a “one system” approach. This means that all individual steps, from sample preparation to results reporting, are controlled by the SPC, thus eliminating the need for the fastidious steps required to control and quantify extracted DNA and sequencing libraries.
In a mNGS run, the detection limit of a SOI can be impacted by intrinsic factors including genome length and efficiency of its DNA extraction as well as extrinsic factors such as the accuracy of taxonomic classification and sample composition (host cell load, relative abundance of microorganisms, peculiarities of genome sequences for certain microorganisms) . To avoid reporting false negative results, the detection of a SOI should only be reported as negative when Cmin ≤ MT (Figure 1). This was especially useful for the analysis of culture-negative samples in which, after patient’s DNA removal, remaining DNA quantities were significantly below those recommended for library preparation and sequencing. While modifications of the library preparation protocol allowed sequencing of these samples, the number of reads remained very low. Thanks to the detection of SPC and calculation of Cmin for each SOI, it was possible to validate the ability of the test to detect a SOI at a concentration at least equal to the metagenomics threshold. Unfortunately, we were unable to validate 21.1 % of negative detections: in the 4 samples where SPC were not detected, we reported a probable infection by the detected SOI but the 72 negative SOI detections could not be validated. In the samples where SPC were detected, 75 negative SOI detections had an estimated Cmin > MT. High Cmin values resulted mainly from DTSOI above 1,000 RPMB. To reduce DTSOI it may be necessary to decrease the level of false taxonomic classification of reads by improving the specificity of classification algorithms and by using a curated reference sequence database. The example of E. cloacae illustrates difficulties in interpreting results due to a high detection threshold (>1,000,000 RPMB) likely caused by a contaminated reference database and, as a consequence, a high rate of misclassifications.
It is interesting to note that despite the competition effect in detecting SOIs, our mNGS assay allowed to detect co-infections by two or three SOIs with concentrations ranging over three orders of magnitude. These co-infections were not detected by the routine cultures. Our results are consistent with previous observations that mNGS assays could be more effective in characterizing polymicrobial infections .
Qualitative detection of microorganisms by mNGS can reflect resident microbiota, transient colonization, sample contamination, and/or infection. To differentiate asymptomatic presence of bacteria from probable infection, the absolute quantity of pathogens has to be determined and compared to defined clinical decision thresholds [5, 40]. For that purpose, we used the counts of reads assigned to SPC as calibrator for quantification of SOIs (Figure 2). Using S. aureus as an example, the results of absolute quantification by mNGS (Figure 2B) were comparable to those of qPCR .
Clinical microbiology laboratories have defined clinical decision thresholds for the HAP/VAP causative pathogen(s) in CFU/mL (mini-BAL: 1.0E+3 CFU/mL and BAL: 1.0E+4 CFU/mL) . We did not find obvious correlation between the number of genomes quantified by mNGS or qPCR and CFU counts from culture plates (Figure 2A). This may have different causes such as a lack of precision of the culture report that provides concentration of CFU at the nearest log level. A second cause could be the presence of viable but non-culturable (VBNC) cells in the sample from which genomic DNA remained detectable [42, 43]. Therefore, we defined the metagenomics threshold (MT) at 5.3E+3 GEq/mL to differentiate, similarly to the clinical decision thresholds, asymptomatic presence of bacteria from infection [5, 40, 44–46].
Assessment of our mNGS process and defined metrics on the HAP/VAP panel showed good diagnostic capabilities (specificity: 96.8 %; sensitivity: 100 %), albeit with a “false discovery rate” of 65.5 %. To avoid “false positive” detections that may result from a lack of accuracy in the taxonomic classification, additional control methods should be considered. In this study, we confirmed correct sequence classification to the SOI for at least 26 % of “false positive” detections using 16S/MetaPhlAn2 markers detection by BLAST. For the other “false positive” results, the quantity of reads was insufficient for sequence assembly required for BLAST-based 16S/MetaPhlAn2 markers search. Therefore, other controls should be considered such as the removal of reads that are stacked on a single location and share identity with human genome or with commensal flora, as suggested by Uprety et al. . Nevertheless, in the presented mNGS workflow, no “false positive” tests seemed to result from (k-mer based) miss-classification of sequence reads, as they were not invalidated by finding 16S/MetaPhlAn2 markers from a different species. As the DNase I treatment of samples before bacterial lysis step may remove extracellular DNA and genomic DNA from dead bacteria [6, 48], the new detections might reflect the presence of VBNC [6, 7] or antibiotic persister bacterial cells  within BAL samples. Currently, we have defined a MT that we apply to all bacterial species of our HAP/VAP panel, by analogy with cultures where a single clinical decision threshold is applied to all bacterial pathogens. It was suggested by Jahn et al.  that specific MT should be established for each bacterium depending on its pathogenicity. Presence of VBNC and antibiotic persisters could also be taken into account for the setting of specific MT. However, the evaluation of specific thresholds for each SOI would require large numbers of samples and clinical data that were not available to us at the time of this study.