Reducing delays in the genomic epidemiology of tuberculosis: a flexible and decentralized analysis of each incident case

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Short-read sequencing in reference labs ensures full population coverage but delays local data access. We assessed a case-by-case genomic approach using primary cultures from each incident case. Methods We prospectively evaluated 53 TB cases in Almeria, Spain, over two periods: 4 months (March–July 2023) for solid (LJ, 23 cases) and 9 months (April–December 2024) for liquid (MGIT, 30 cases) cultures. DNA was purified from LJ when sufficient growth was observed and from MGIT at positivity. Nanopore sequencing was performed on 1–4 cases per run, with flow-cells reused up to six times. Results In 74% of LJ, adequate growth was reached within 21 days. All LJ cultures achieved optimal genome coverage (> 90% at > 20X), 61% with runs under 2.5 hours. Nearly all MGIT cultures, analyzed at positivity (all but six within 21 days), achieved optimal coverage, despite interfering DNA. Four cases had slightly lower but acceptable coverage (73–88%, > 20X). Same-day classification as clustered or orphan was possible. Nanopore sequencing correlated with high-throughput short-read data, enabling quicker correction of epidemiological errors and identification of transmission links. Conclusions Our strategy offers a flexible, rapid decentralized alternative, accelerating cluster information contact tracing. General Microbiology Genomic epidemiology tuberculosis nanopore sequencing intervention Figures Figure 1 Figure 2 Figure 3 Introduction Genomic epidemiology has completely revolutionized our ability to track the transmission dynamics of Mycobacterium tuberculosis (MTB) identifying clusters with maximum discriminative power [ 1 ]. To extract maximum value from the high discrimination offered by genomic epidemiology, comprehensive population coverage, ensuring all tuberculosis (TB) cases in a population are included. High-throughput genomic analysis offered by equipment commonly used for standard genomic analysis allows coverage of entire populations. Considering TB incidence in many populations, samples must be sent to centralized reference laboratories to ensure a cost-effective genomic analysis and the high number of isolates required for a high-throughput analysis. Thus, in exchange for the guarantee of good analytical coverage of TB cases in a population, we must accept the built-in time delays due to i) the necessary transfer of material and data for centralized testing, and ii) the time required for sub-culturing the material sent. For populations where genomic data are used for epidemiological interventions, these time delays are a major limitation. In such situations, the corresponding data should be made available as quickly and as close as possible to the diagnosis of the incident case, while contact tracing is still in progress, so that epidemiological investigations can be refocused based on the findings extracted from genomic analysis. While direct genomic analysis on clinical specimens would be the ideal solution for speed, it presents challenges with variable performance depending on the bacterial load and the proportion of interfering non-mycobacterial DNA [ 2 ], [ 3 ]. Additionally, the procedures are cumbersome and/or expensive. Meanwhile, we need to explore alternative strategies that can provide rapid responses where intervention epidemiology is being conducted, but the number of TB cases is not enough to feed the dynamics of high-throughput sequencing. In this study, we evaluate an alternative scheme supported by i) analysis of the primary culture, either solid or liquid, from each incident case, ii) rapid nanopore sequencing, and iii) reuse of flow cells. The dynamics of this flexible and decentralized genomic analysis were evaluated on 53 incident cases in a real-life context of socio-epidemiological complexity due to the high rate of TB in migrants, which required a rapid genomic analysis to properly orientate the epidemiological investigation during contact tracing. Methods Samples In the first stage of the study (March, 2023), for every smear-positive TB case diagnosed in Almeria, a duplicate Löwenstein-Jensen (LJ) tube was inoculated; for the remainder of the LJ study period, inoculation was in triplicate. For the other part of the study on Mycobacteria Growth Indicator Tube (MGIT), they were inoculated in duplicates. Tubes were sent to our Madrid lab for incubation, allowing DNA extraction as soon as LJ cultures showed visible growth (inspected daily), or at MGIT positivity as indicated by MGIT automatic reader. The idea was to recreate the circumstances of a local laboratory by performing both culture and analysis. Inoculation protocols were done to provide a back-up for samples for further downstream analysis. DNA Purification For purification procedure, in a biosafety laboratory (BSL-3), Wizard Genomic DNA Purification kit (Promega, Wisconsin, USA) was used for LJ cultures and QIAamp DNA Mini Kit (Qiagen, Hilden, Germany) for (4–5 ml) MGIT cultures, after heat inactivation at 95°C for 15 minutes, following the manufacturer’s instructions. Nanopore sequencing Libraries were prepared from purified DNA using Rapid Sequencing DNA-PCR barcoding kit (SQK-RPB004 & SQK-RPB114) (ONT, Oxford, UK) in accordance with the manufacturer's instructions. 5% Dimethyl sulfoxide (DMSO) was added to the amplification step to improve denaturation. For libraries prepared from MGIT cultures, the amplification step was modified from 14 to 25 cycles according to manufacturer’s recommendations. One to 4 libraries were loaded in a MinION device (12–100 fmol, except in three LJ analyses where 2–5 fmol were loaded and in four MGITs in which we loaded 3–9 fmol) (R.9.4.1 FLO-MIN106 and R10.4.1 FLO-MIN114, when R.9 was discontinued). As a proportion of reads fail quality control in all nanopore sequencing runs, it was decided to stop the run at an estimated minimum value of 100X coverage in order to be sure of achieving our target values (> 90% at > 20X coverage; ) after the quality control filters. An in-house pipeline analysis ( https://github.com/MG-IiSGM/prokaION ) was applied to examine the acquired sequencing data. The pipeline workflow went through the following steps: i) preprocessing of the initial fast5/pod5 files into more accessible fastq format by basecalling and barcoding with Guppy v.6.5.7 /Dorado v.7.3; ii) quality assessment with NanoFilt v2.8.0 and NanoPlot 1.41.6 to ensure the most reliable and informative data for downstream analysis; iii) species identification, with Kraken2 2.1.3 and Mash v2.3; iv) minimap2 v2.28 was used for mapping, and Freebayes v1.3.5 for SNP calling, using as reference a hypothetical MTB ancestral genome (identical to H37Rv in terms of structure, but including ancestral nucleotide positions inferred by maximum likelihood from a virtual ancestor)[ 4 ]; v) variant annotation using the SnpEff v5.1 tool; and vi) recalibration of occasional low-coverage positions using joint variant calling. Flow cells were washed for reuse with the Flow Cell Wash Kit (EXP-WSH004). A flow cell was removed once the number of available pores had been reduced to 400 pores. Illumina sequencing Libraries were prepared using Nextera XT kit (Illumina) following manufacturer’s instructions for NextSeq device (2x151bp). Sequence analysis was performed using an in-house pipeline deposited in GitHub: https://github.com/MG-IiSGM/autosnippy . The pipeline followed the same steps described previously [ 5 ] using the hypothetical MTB ancestral genome [ 4 ] as a reference. The sequences generated for both, Illumina and Nanopore, were deposited in the ENA (project number PRJEB67993). Identification of clustered cases A case was considered as clustered when the pairwise genomic distances with another case were < 12 SNPs [ 6 ]. Alignments and SNP variants were visualized and checked with IGV (Integrative Genomics Viewer) software for both Nanopore and Illumina sequencing. MIRU-VNTR based cluster pre-assignment In the MGIT sub-study, MIRU-VNTR analysis had previously been performed either directly on the corresponding clinical specimen (when smear-positive) or at MGIT positivity in Almeria laboratory [ 7 ]. It allowed us to obtain a preliminary assignment of cases in clusters, which was used to select only candidates of clustered cases for subsequent analysis. Results Our alternative analysis scheme to minimize delays in genomic data availability applied nanopore sequencing for a rapid, one-case/one-result response, avoiding the need to pool and batch specimens as in the standard approach. Flexible nanopore sequencing on primary solid cultures We first evaluated our proposal on LJ primary solid cultures, on a non-selected study sample that included all 23 consecutive, smear-positive new incident cases diagnosed in Almeria, Spain, over a 4-month period (March 22- July 26, 2023). 82.6% of the cases in our study were immigrants. All cultures corresponded to sputum, except for one extra-respiratory specimen (psoas abscess, isolate 15). Bacterial loads were high (Table 1 , > 10 BAAR/field; 3 + or 4+) in all but 4, which showed < 10 BAAR/field (1 + or 2+). After observing in the first studied cases that the growth times for specimens with similar bacterial loads were somewhat variable, it was decided to inoculate three tubes per specimen and select the one that grew faster, which allowed us to reduce the incubation time before extraction. In 74% of cases, sufficient culture growth was obtained to prepare sequencing libraries < 21 days after inoculation (Table 1 ). The time required for DNA extraction and library preparation ranged from 4 to 8 hours. Table 1 Consecutive LJ primary cultures sequenced by nanopore technology. Isolate Bacillary load Days between inoculation and extraction Final library concentration (fmol) Run Flow cell ID Sequencing time Mean coverage (X) Genome coverage 20X (%) Final interpretation 1 4+ 20 60 1 A 1h 13min 80,88 96,49 Orphan 2 4+ 23 2,1 2 B 24h 48min 48,51 94,6 Clustered 3 4+ 30 40 3 C 1h 50min 91 95,91 Orphan 4 4+ 22 40 1h 50min 50,33 93,02 Orphan 5 4+ 15 6,5 4* C 4h 4 min 48,47 94,05 Orphan 6 4+ 20 5 5* C 2h 37 min 36,02 92,18 Orphan 7 4+ 47 75 6 C 1h 13 min 76,68 96,63 Orphan 8 4+ 18 100 7 C 1h 25min 72,73 95,64 Clustered 9 4+ 15 100 8 C 4h 06min 81,94 96,29 Orphan 10 1+ 42 100 4h 06min 54,91 95,01 Orphan 11 4+ 31 100 9 D 51 min 70,91 95,59 Orphan 12 4+ 20 40 10 D 1h 23 min 86,82 96,53 Clustered 13 4+ 20 40 2h 49 min 59,68 95,42 Orphan 14 3+ 15 100 11 D 1h 35 min 72,36 95,84 Clustered 15 1+ 21 50 12 E 1h 11min 76,84 94,69 Clustered 16 4+ 14 12 13 F 2h 28 min 74,74 96,49 Orphan 17 4+ 11 17 14 E 3h 11min 75,32 95,99 Clustered 18 4+ 15 22 15* F 3h 58min 87,58 96,46 Orphan 19 4+ 19 60 16 E 1h 24 min 78,72 97,27 Clustered 20 4+ 18 80 17 F 1h 29 min 72,59 96,76 Clustered 21 4+ 13 13 18 E 3h 4min 73,42 97,34 Clustered 22 2+ 17 60 19 F 2h 24min 73,65 97,46 Orphan 23 2+ 13 12 20 G 2h 18min 68,24 96,1 Orphan For samples 8–23 LJ were inoculated in triplicate. Bacillary loads correspond to: 1+: 100/field). *Runs including a second isolate not belonging to this study. Twenty nanopore sequencing runs were performed to analyze the 23 isolates (Table 1 ); most runs included a single isolate, while a minority included two isolates whose growth times had coincided and so were co-extracted at the same time. Seven flow cells were used in total. They were reused up to 6 times (Table 1 ). Run times with a single isolate ranged between 51 minutes and 3 hours 11 minutes (median time 1h 42min; 78% <2h 30min) and those with two isolates ranged between 1h 50min and 4h 6 min (median time 2h 9min; 56%<3h). Only one isolate deviated from the average run times (Table 1 , isolate 2). This was due to the low amount of library loaded, 2 fmol, as compared with average loads of 51.5 fmol, which allowed us to assign the former value as a minimum threshold to ensure shorter run times. In all cases, good coverage depths were achieved, with more than 90% of the genome sequenced at > 20X (Fig. 1 , Table 1 ). Fast identification and analysis of clustered cases Sequences were analyzed on the same day they were obtained, including the SNP-calling and a comparative analysis performed with data in our own genomic database obtained from standard high-throughput genomic analysis (Illumina), which included 664 retrospective cases (period 2003–2023). Of the 23 cases analyzed, we were able to anticipate all clustered cases, which involved 9 (39%) of the cases in study, who were included in 7 clusters already identified (two cases in the same cluster), and one in a new cluster (involving 2 cases), while the remaining cases were orphans (Table 1 ). Of the nine clustered cases, eight were men and one woman (median age, 37 years (26–59 years); three (33%) were autochthonous and six (67%) migrants (3 from Morocco and 3 from Sub-Saharan Africa). Of the clustered cases identified, most (six) were included in longstanding clusters (the first cases in the clusters were diagnosed in 2003–2012) and 2 were recently detected clusters (first case detected in 2020–2022). With respect to the nationalities involved, 5 were mixed clusters (involving autochthonous and migrant cases), 2 were immigrant clusters and the remaining one was an autochthonous cluster (Fig. 2 ). First epidemiological analysis of the clustered cases A general analysis of the nine cases that were genetically clustered identified reasons, in several of them, justifying their involvement in clusters. The six migrant cases had been living in Spain, after arrival, for > 5 years before diagnosis (median 12 years). In addition, drug addiction (alcohol, cannabis, cocaine), were identified in 5 of the cases, together with one with immunosuppression, one with diabetes and one with malnutrition. Nevertheless, the initial epidemiological investigation failed to assign most of these nine clustered cases to specific transmission chains. A history of contact with TB was found in only two cases (22%): one case had been identified in a previous contact tracing in 2020 and another had a family member with TB in 2014, but refused to participate at that time. In the five cases with addictions, an increased risk of participating in active transmission chains associated with this risk was considered, but, again, links to other TB cases could not be identified. Moreover, in another two of the genetically clustered cases, an orphan/imported status, instead of clustering, had initially been considered, due to national and international mobility of the cases before their diagnosis: one of the cases had returned eight months before from a trip to the country of origin (1.5 years of visiting friends and relatives stay), and the other one had moved two years before from another region of Spain. Second epidemiological analysis of the clustered cases oriented by genomic data Once determined that for most of the genetically clustered cases, involvement with transmission chains had not been suspected/found in the initial epidemiological standard investigation, we reanalyzed the cases, now considering the genomic findings, which indicated specific linkages with other clustered cases. The incorporation of genomic data in the epidemiological investigation of the cases was possible due to the short turn-around time to have these data available. For the two cases with recent national and international mobility, that has led us to consider them as independent importations from exposures in their countries of origin, genomic data confirmed the acquisition of the infection in the host territory, after arrival. Therefore, these cases were re-interviewed, now allowing us to identify risk factors shared with other members of the cluster (they had shared the same urban territory), which led us to expand the contact tracing beyond the contexts where it was initially carried out. For the only two cases with an initial epidemiological suspicion of being clustered, genomic data supported only one of the links suspected, leading to intervening again in the epidemiological context, to expand preventive treatments. On the contrary, the other case was genetically associated with a transmission chain different to the one initially suspected, which allowed contact tracing to be reoriented in relation to the members of this new cluster. For the five cases with risk factors associated with drug usage, in which relationships with other cases had not been initially suspected, genomic analysis finally incorporated them into previously known clusters, associated with shared leisure areas related to drug consumption. Given the complexity of performing contact tracing in these settings, these cases had not been previously identified for preventive treatment. After the linkage of these new cases with those clusters, contact tracing was expanded, now supported on community health agents. Finally, regarding the case that was incorporated by genomic analysis into a newly identified cluster, due to the linkage with another case diagnosed in 2020; they were re-surveyed and the link between both was identified. It led us to expand the contact tracing, once it was confirmed that the previous efforts had not been able to identify all close contacts. Performance of nanopore sequencing on primary MGIT cultures Once we evaluated the performance and epidemiological usefulness of our flexible nanopore analysis of new incident cases on LJ, we aimed to also evaluate 30 consecutive liquid (MGIT) primary cultures as analytical samples (March-November 2024), to face the challenges expected from using a sample potentially harboring interfering DNA from other bacteria and/or from the host. 44% of the samples had high bacillary load from (3 + or 4+) (Table 2 ). Table 2 MGIT primary cultures sequenced by nanopore technology Isolate bacillary load Days from sampling to positivity/ diagnosis Final library concentration (fmol) Sequencing time MTB reads (%) Human reads (%) Other bacterial reads (%) Mean coverage (X) Genome coverage > 20X (%) ONT chemistry 1 3+ 16 24 3h 91.47 8.27 0.26 93.88 97.11 R09 2 4+ 14 15 38.12 61 0.29 52.91 94.35 R09 3 4+ 17 16.9 5h 47min 89.94 3.44 6.62 35.92 73.22 R09 4 - N/A 24.6 3h 49min 99.98 0 0.02 116.14 97.88 R09 5 < 1+ 26 10.91 99.69 0.05 0.26 59.2 95.12 R09 6 1+ 17 15.7 89.43 0.06 10.51 41.1 88.75 R09 7 4+ 17 26.8 99.55 0.43 0.02 72.27 96.17 R09 8 4+ 7 59.8 1h 30min 98.98 0.75 0.27 79.75 96.32 R09 9 - 13 12.4 5h 10min 81.36 18.61 0.03 111.66 97.81 R09 10 4+ 7 20.46 86.97 10.78 2.25 71.17 94.91 R09 11 2+ 11 11.66 5h 51 min 99.75 0.19 0.05 167.14 98.07 R09 12 2+ 17 50 6h 28min 98.45 0.07 1.48 140.43 97.09 R10 13 4+ N/A 3.05 70h 34min 65.57 2.19 32.24 95.37 94.51 R10 14 - N/A 7 72h 96.19 0.07 3.74 606.78 98.51 R10 15 4+ 11 3.6 3h 17min 90.48 5.54 3.98 147.72 97.61 R10 16 4+ 10 3.3 74.03 13.88 12.09 75.77 95.09 R10 17 2+ 13 40.05 5h 50min 97.57 0.43 2 86.98 96.89 R10 18 3+ 11 39.98 98.16 0.98 0.86 75.78 94.35 R10 19 4+ 10 39.88 72 6.05 21.95 66.63 95.63 R10 20 1+ 17 50 4h 7min 99.86 0.13 0.01 237.71 97.98 R10 21 - 14 27.76 72h 79.25 0.23 20.52 54.23 80.06 R10 22 < 1+ 29 11.6 5.13 60.76 34.11 49.12 85.19 R10 23 3+ 6 15.9 24h 47min 92.23 6.48 1.29 264.57 97.97 R10 24 - 40 15.9 5h 70.09 29.15 0.76 58.49 92.68 R10 25 1+ 27 9.975 3hr 7 min 99.97 0.03 0 162.79 97.63 R10 26 4+ 16 9.99 5h 99.99 0 0.01 115.11 97.17 R10 27 - 27 19 2h 32min 90.4 0.28 9.32 86.07 95.11 R10 28 1+ 19 19 94.42 0.75 4.83 93.46 91.19 R10 29 1+ 22 20.8 5h 21min 99.82 0.15 0.03 186.32 98,74 R10 30 3+ 17 20.4 99.46 0.04 0.5 94.42 97,65 R10 Bacillary loads correspond to: 1+: 100/field). N/A represents an unknown time period from inoculation to positivity, for the reason that these cultures were received positive in our facility. Even when working with MGIT cultures, we succeeded in obtaining good coverage for all cases, with 83% of them achieving optimal values (> 90% genome coverage > 20X, Fig. 3 ); at MGIT positivity (in all but six cases, corresponding to low bacillary loads or smear-negative cases, in < 21 days after inoculation) (Table 2 ). These results were obtained despite the presence of interfering DNA (up to 60% of human or up to 32% of bacterial (other than MTB) interfering materials; Table 2 ). Comparison with the standard Illumina-based sequencing scheme Finally, the data obtained from our alternative scheme of genomic analysis were compared with those of 16 isolates obtained with the standard Illumina high-throughput approach (waiting to accumulate isolates to be run together with other isolates in a NextSeq device in two different runs, including 10 and 6 isolates from our study). Correlations of assignment as clustered or orphan were identical in all cases. Focusing on the specific SNPs called using each strategy, we identified only three SNPs with differences, all based on the differential frequencies at which the allele was called. For these three SNPs, the respective alleles were called by nanopore sequencing at frequencies of 30%, 49% and 70%, respectively, versus 92%, 100% and 100% by Illumina. Discussion One of the advantages of genomic analysis in TB is to confirm suspected links between patients, supported by the standard epidemiological investigation (to rule in potential clusters). However, it is frequent that the genomic analysis also demonstrates the lack of relationships between cases initially suspected to be linked [[ 5 ]], when we detect that they are infected by different strains (to rule out potential clusters). This is not uncommon in a complex population like ours, with, not only through obvious links (e.g. among migrants with same nationality or among autochthonous cases), but also frequently between autochthonous and migrant cases or between migrants of different nationalities [ 8 ]. In these contexts, genomic analysis helps us to reorient the epidemiological investigation; from our initial assumptions based only on epidemiological data, to final links revealed by genomic analysis. Therefore, the faster the genomic data are available, the sooner we can reorient the epidemiological investigation, when contact tracing is still running. In our study, genomic findings completely transformed the analysis, conclusions and interventions compared to those based solely on the standard epidemiological analysis. The more refined information obtained by genomic analysis led us to i) label some cases as clustered that would have been considered as independent importation, ii) understand the true links with other cases, correcting the misassignments derived from the first epidemiological analysis and iii) identify links with other preceding cases that had not been initially suspected. In addition to the most valuable information from an epidemiological perspective extracted from genomic data, namely clustered cases, we also identified that one of the cases in our sample corresponded to an M. caprae infection (case 7), associated with a longstanding zoonotic event in the region [ 5 ]. Having achieved high discriminatory power and accurate SNP calling in TB genomic analysis, the remaining challenge is speed, especially for this slow-growing bacterium. The best solution would obviously be to perform the analysis directly on clinical specimens, similar to metagenomic analysis for other microorganisms. However, unlike metagenomic diagnostics, our goal is not only to identify MTB but to ensure optimal genome-wide coverage for accurate SNP calling, enabling confident cluster assignment and establishing the correct chronology and relationships between cases. It is much more challenging to do this when the genomic data are obtained directly from specimens. Some efforts to perform MTB sequencing directly on the specimens [ 2 ], [ 3 ], [ 9 ] have shown its possibility, though not yet for all specimens. This probably depends on MTB DNA ratio to interfering human/microbial DNA in the sample. If sequencing MTB directly on specimens’ remains challenging, cumbersome and/or expensive, the only alternative to reduce the delay would be to switch to primary cultures, which eliminates the time required to grow subcultures for sequencing. In our study, we evaluated both LJ solid media and MGIT liquid media with a double aim: i) offer data useful for settings with low resources and therefore lack of automatic MGIT readers and ii) evaluate potential impact of human or other interfering DNA, most likely expected in liquid cultures, that could lead to sequencing reads being hijacked by unwanted DNA. In our study from MGIT a range between 0–60% and 0–34% of the total reads when analyzing MGIT cultures corresponded to human and other bacteria respectively. These values were lowered to 0 and 7.8% when analyzing LJ cultures. However, despite the interfering non-MTB DNA, we managed to obtain optimal results from almost all MGIT cultures, and finally correctly assign them as members of their corresponding cluster or as orphans. In addition, the use of LJ media is better suited to our strategy in low resource settings, where automated MGIT incubators are not available and the lower cost of solid media is appreciated. Despite the use of solid media, our times from inoculation to extraction were quite reasonable, and > 70% of cultures yielded sufficient load for extraction in less than 21 days after inoculation. A crucial aspect of our study is obtaining genomic results within 21 days of diagnosis. This is particularly important given the high proportion of migrant cases in our study population, many of whom are hospitalized for 21 days post-diagnosis to ensure isolation and reduce the risk of transmission within their households and communities. Due to generally overcrowded and substandard living conditions, effective isolation outside the hospital cannot be guaranteed. By acquiring genomic data within this timeframe, we can identify probable transmission links early, allowing us to re-interview patients while they are still hospitalized and accessible. These follow-up interviews, conducted by our team, enhance traditional contact tracing by incorporating genomic insights, providing a clearer understanding of transmission dynamics, and enabling targeted interventions to better control the spread of infection. Our main goal, to obtain genomic data as close as possible to the time of each new incident patient diagnosis, was incompatible with the standard high-throughput genomic analysis, in which a sufficient number of samples must first be accumulated, pooled and run together in the interests of cost-effectiveness. The nanopore sequencing option could provide the flexibility needed for the one-case/one-analysis approach we were aiming for. While nanopore sequencing is slowly being introduced into genomic analysis of MTB [ 10 ], a recent review acknowledged that the number of studies in MTB supported by this technology was still small [ 1 ]. The decision to perform a case-by-case analysis would recommend reusing the flow cell to reduce costs. While it would have been possible to load more samples into the same flow cell without greatly extending the run duration, the decision to limit the number of samples was justified by our objective of obtaining genomic data as soon as possible after diagnosis. We must acknowledge that our LJ study used nanopore R9.4.1 version, which has been recently discontinued with the release of R10.4.1 flow cells, and new V14 chemistry, as a potentially more efficient successor to R9. The company promotes R10’s dual-reader head nanopore design as offering higher base-calling accuracy. It claims this adaptation increases raw read accuracy to 95% using Dorado base-calling software. Since R10’s improvements focus on call accuracy, the parameters evaluated in our study—run time and final coverage—are not expected to differ with the new flow cells. In fact, we noticed no differences in the output of both chemistries on the MGIT sub-study (31% analyzed on R9 and the remaining on R10) in which mean coverages and run-time to reach > 20X in > 90% of the genome were comparable between R9 and R10. The initial assumption that nanopore sequencing was associated with a higher error rate in SNP calling compared to short read sequencing will be dispelled by these recent improvements. Indeed, a multinational study that exploited genomic data to assess resistances and determine epidemiological relationships found that nanopore and short-read sequencing were comparable in performance [ 11 ]. Similarly, a recent study comparing nanopore technology with Illumina demonstrated that the results are highly consistent in SNP-based outbreak investigations, further supporting the use of nanopore sequencing for accurate and rapid genomic epidemiological analysis [ 12 ]. In our study, SNP determination from nanopore data was almost identical to that from the high-throughput short-read analysis reference standard. Moreover, the average length obtained by nanopore sequencing (> 3kb, data not shown) would enable extraction of MIRU-VNTR patterns [ 13 ], which is somewhat impaired when using short reads, allowing the genomic data to be linked to those in historical MIRU-VNTR databases. Regarding comparative costs, for nanopore, the cost of loading a single sample per run and reusing a single flow cell up to 6 times was estimated at 165 euros per specimen. By loading 2 samples whenever possible, the cost was significantly reduced to 111 euros. For Illumina sequencing the cost for sequencing the 23 LJ isolates would have been 81.5 euros per sample in a MiSeq device. When comparing costs, we must also consider that competitive costs in Illumina are only obtained when accumulating as many samples as possible in the same run, meaning prolonged delays to obtain the genomic data, which would not fit the requirements of our proposal, based on the immediate analysis of every new single case. In the financial analysis we must consider that from these same sequencing efforts, the analysis of resistance mutations, in addition to the VNTR analysis previously mentioned, can be performed, which could reduce the laboratory costs dedicated to antibiograms and first-line typing. The final goal in our evaluation was to determine whether it was possible to guide the epidemiological investigation during contact tracing in Almeria based on the genomic results being available more quickly. Rapid identification of orphan cases allowed control professionals to focus interventions on minimizing new secondary cases by prioritizing the prescription of prophylaxis in infected cases identified through contact tracing. At the same time, their efforts to identify new undiagnosed cases were directed to contexts associated with new cases identified as clustered, indicators of the presence of active transmission. We identified several examples of complex transmission dynamics in populations with a high percentage of immigrants, which is the case in Almeria, which justifies the need to develop new rapid and accurate alternatives such as the one developed here. Our proposal could provide an alternative to the massive genomic data analyses performed in central reference laboratories, and is particularly well suited to local decentralized surveillance of TB transmission in settings where interventions based on rapid availability of genomic data can be performed. Declarations Acknowledgements The authors would like to thank Janet Dawson for her editing and proofreading assistance. Funding This work was supported by the Instituto de Salud Carlos III [PI21/01823, PI19/00331], a Miguel Servet Contract (CPII20/00001) to LPL, and PFIS contracts to CRG (FI20/00129) and SBS (FI21/00145)], CIBER - Consorcio Centro de Investigación Biomédica en Red (CB06/06/0058, CB21/13/00044), IiSGM 2021-II-PI-01 Intramural project to DGV, a 2022-II-PREDOCIA-01 IiSGM contract to DPU, AP-0062-2021-C2-F2 from the Junta de Andalucía, SEPAR 2023 (REF 1401/2023) and co-financed by European Regional Development Funds of the European Commission (ERDF): “A way of making Europe”. The authors don’t have any conflict of interests References Meehan CJ et al (2019) Whole genome sequencing of Mycobacterium tuberculosis: current standards and open issues. 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Dis. , vol. 13, no. 2, pp. 137–146, Feb Alonso M, Herranz M, Lirola MM, Gonzaĺez-Rivera M, Bouza E, De Viedmaa DG (2012) Real-time molecular epidemiology of tuberculosis by direct genotyping of smear-positive clinical specimens. J Clin Microbiol, 50, 5 Abascal E et al (2019) Whole genome sequencing-based analysis of tuberculosis (TB) in migrants: Rapid tools for crossborder surveillance and to distinguish between recent transmission in the host country and new importations. Eurosurveillance, 24, 4 Zhou X et al (2019) Clinical Evaluation of Diagnosis Efficacy of Active Mycobacterium tuberculosis Complex Infection via Metagenomic Next-Generation Sequencing of Direct Clinical Samples, Front. Cell. Infect. Microbiol. , vol. 9, no. October, pp. 1–9 Di Marco F, Spitaleri A, Battaglia S, Batignani V, Cabibbe AM, Cirillo DM (2023) Advantages of long- and short-reads sequencing for the hybrid investigation of the Mycobacterium tuberculosis genome, Front. Microbiol. , vol. 14, Feb Hall MB et al (2023) Evaluation of Nanopore sequencing for Mycobacterium tuberculosis drug susceptibility testing and outbreak investigation: a genomic analysis. Lancet Microbe 4(2):e84–e92 Bogaerts B et al (2024) Closing the gap: Oxford Nanopore Technologies R10 sequencing allows comparable results to Illumina sequencing for SNP-based outbreak investigation of bacterial pathogens. J Clin Microbiol, 62, 5 Tang CY, Ong RTH (2020) MIRUReader: MIRU-VNTR typing directly from long sequencing reads. Bioinformatics 36(5):1625–1626 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4729960","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":329009063,"identity":"833a780b-8cc6-4b39-90ad-f02a3dc2dd4f","order_by":0,"name":"Cristina Rodríguez-Grande","email":"","orcid":"https://orcid.org/0000-0002-2468-8810","institution":"Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM)","correspondingAuthor":false,"prefix":"","firstName":"Cristina","middleName":"","lastName":"Rodríguez-Grande","suffix":""},{"id":460913557,"identity":"9b706d05-d3b7-4dd8-baff-3d7384d0646c","order_by":1,"name":"Sheri M Saleeb","email":"","orcid":"","institution":"Servicio de Microbiología Clínica y Enfermedades Infecciosas, Hospital General Universitario Gregorio Marañón","correspondingAuthor":false,"prefix":"","firstName":"Sheri","middleName":"M","lastName":"Saleeb","suffix":""},{"id":329009062,"identity":"9ed2527f-4fe0-4f4c-890d-687eaf0f22ce","order_by":2,"name":"Amadeo Sanz-Pérez","email":"","orcid":"https://orcid.org/0009-0004-4079-982X","institution":"Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM)","correspondingAuthor":false,"prefix":"","firstName":"Amadeo","middleName":"","lastName":"Sanz-Pérez","suffix":""},{"id":460913340,"identity":"5e24a27f-d255-4996-b61a-c616b904116a","order_by":3,"name":"Silvia Vallejo-Godoy","email":"","orcid":"","institution":"Department of Preventive Medicine, Public Health and Epidemiological Surveillance, Poniente University Hospital, Almería","correspondingAuthor":false,"prefix":"","firstName":"Silvia","middleName":"","lastName":"Vallejo-Godoy","suffix":""},{"id":329009064,"identity":"0f426687-8bf6-4d15-b17c-ca0378cc6353","order_by":4,"name":"Sergio Buenestado-Serrano","email":"","orcid":"https://orcid.org/0000-0003-1739-4459","institution":"Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM)","correspondingAuthor":false,"prefix":"","firstName":"Sergio","middleName":"","lastName":"Buenestado-Serrano","suffix":""},{"id":329009065,"identity":"47294a0f-f6eb-44e8-877f-d9645699a55e","order_by":5,"name":"Miguel Martínez-Lirola","email":"","orcid":"","institution":"Unidad de Gestión de Laboratorios, UGMI, Complejo Hospitalario Torrecárdenas","correspondingAuthor":false,"prefix":"","firstName":"Miguel","middleName":"","lastName":"Martínez-Lirola","suffix":""},{"id":460914029,"identity":"ba8b18be-0a0e-4829-a0b6-099b0351ceb4","order_by":6,"name":"Teresa Cabezas","email":"","orcid":"","institution":"Microbiology 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11:35:09","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-4729960/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-4729960/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86274241,"identity":"e905cc24-94e9-46a6-a436-24fe1b58d796","added_by":"auto","created_at":"2025-07-08 18:30:39","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37780,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequency of MTB genome coverage of samples on LJ cultures. (A) \u003c/strong\u003eMTB genome coverage depth and percentage of the genome covered for LJ cultures, each dot corresponds to a culture. Cluster assignment of the cases and characteristics of the clusters.\u003cstrong\u003e (B) \u003c/strong\u003eDistribution of the results according to the percentage of genome covered \u0026gt;20X\u003c/p\u003e","description":"","filename":"f1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4729960/v2/c34962eb5a2d26bc1a9ba948.jpg"},{"id":86274242,"identity":"3644a807-0e0c-4683-89d3-3d25a8aa1e52","added_by":"auto","created_at":"2025-07-08 18:30:39","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24163,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCluster assignment of the cases from the LJ analysis and characteristics of the clusters.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"f2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4729960/v2/3c25f8d75c85649e974d9ff7.jpg"},{"id":86274243,"identity":"c8605a9f-4334-4a43-98bb-e4eb0fa0c890","added_by":"auto","created_at":"2025-07-08 18:30:39","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":16712,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of genome coverage of isolates on MGIT and LJ. \u003c/strong\u003eMTB genome coverage depth and percentage of the genome covered even by one read for MGIT cultures (LJ cultures are also represented just for reference purposes), each dot corresponds to a culture. One outlier from a MGIT culture (coverage 600X) was excluded from the representation.\u003c/p\u003e","description":"","filename":"f3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4729960/v2/3aa5906983612cf37eae1db3.jpg"},{"id":86274710,"identity":"c0a3eaac-5992-49b6-a4fd-b1f5ce3c8996","added_by":"auto","created_at":"2025-07-08 18:38:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1351839,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4729960/v2/0f095a7d-07e1-42a1-8d94-aa807cffee36.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Reducing delays in the genomic epidemiology of tuberculosis: a flexible and decentralized analysis of each incident case","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGenomic epidemiology has completely revolutionized our ability to track the transmission dynamics of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (MTB) identifying clusters with maximum discriminative power [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo extract maximum value from the high discrimination offered by genomic epidemiology, comprehensive population coverage, ensuring all tuberculosis (TB) cases in a population are included. High-throughput genomic analysis offered by equipment commonly used for standard genomic analysis allows coverage of entire populations. Considering TB incidence in many populations, samples must be sent to centralized reference laboratories to ensure a cost-effective genomic analysis and the high number of isolates required for a high-throughput analysis. Thus, in exchange for the guarantee of good analytical coverage of TB cases in a population, we must accept the built-in time delays due to i) the necessary transfer of material and data for centralized testing, and ii) the time required for sub-culturing the material sent.\u003c/p\u003e \u003cp\u003eFor populations where genomic data are used for epidemiological interventions, these time delays are a major limitation. In such situations, the corresponding data should be made available as quickly and as close as possible to the diagnosis of the incident case, while contact tracing is still in progress, so that epidemiological investigations can be refocused based on the findings extracted from genomic analysis. While direct genomic analysis on clinical specimens would be the ideal solution for speed, it presents challenges with variable performance depending on the bacterial load and the proportion of interfering non-mycobacterial DNA [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Additionally, the procedures are cumbersome and/or expensive.\u003c/p\u003e \u003cp\u003eMeanwhile, we need to explore alternative strategies that can provide rapid responses where intervention epidemiology is being conducted, but the number of TB cases is not enough to feed the dynamics of high-throughput sequencing. In this study, we evaluate an alternative scheme supported by i) analysis of the primary culture, either solid or liquid, from each incident case, ii) rapid nanopore sequencing, and iii) reuse of flow cells. The dynamics of this flexible and decentralized genomic analysis were evaluated on 53 incident cases in a real-life context of socio-epidemiological complexity due to the high rate of TB in migrants, which required a rapid genomic analysis to properly orientate the epidemiological investigation during contact tracing.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSamples\u003c/h2\u003e \u003cp\u003eIn the first stage of the study (March, 2023), for every smear-positive TB case diagnosed in Almeria, a duplicate L\u0026ouml;wenstein-Jensen (LJ) tube was inoculated; for the remainder of the LJ study period, inoculation was in triplicate. For the other part of the study on Mycobacteria Growth Indicator Tube (MGIT), they were inoculated in duplicates. Tubes were sent to our Madrid lab for incubation, allowing DNA extraction as soon as LJ cultures showed visible growth (inspected daily), or at MGIT positivity as indicated by MGIT automatic reader. The idea was to recreate the circumstances of a local laboratory by performing both culture and analysis. Inoculation protocols were done to provide a back-up for samples for further downstream analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDNA Purification\u003c/h3\u003e\n\u003cp\u003eFor purification procedure, in a biosafety laboratory (BSL-3), Wizard Genomic DNA Purification kit (Promega, Wisconsin, USA) was used for LJ cultures and QIAamp DNA Mini Kit (Qiagen, Hilden, Germany) for (4\u0026ndash;5 ml) MGIT cultures, after heat inactivation at 95\u0026deg;C for 15 minutes, following the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\n\u003ch3\u003eNanopore sequencing\u003c/h3\u003e\n\u003cp\u003eLibraries were prepared from purified DNA using Rapid Sequencing DNA-PCR barcoding kit (SQK-RPB004 \u0026amp; SQK-RPB114) (ONT, Oxford, UK) in accordance with the manufacturer's instructions. 5% Dimethyl sulfoxide (DMSO) was added to the amplification step to improve denaturation. For libraries prepared from MGIT cultures, the amplification step was modified from 14 to 25 cycles according to manufacturer\u0026rsquo;s recommendations. One to 4 libraries were loaded in a MinION device (12\u0026ndash;100 fmol, except in three LJ analyses where 2\u0026ndash;5 fmol were loaded and in four MGITs in which we loaded 3\u0026ndash;9 fmol) (R.9.4.1 FLO-MIN106 and R10.4.1 FLO-MIN114, when R.9 was discontinued). As a proportion of reads fail quality control in all nanopore sequencing runs, it was decided to stop the run at an estimated minimum value of 100X coverage in order to be sure of achieving our target values (\u0026gt;\u0026thinsp;90% at \u0026gt;\u0026thinsp;20X coverage; ) after the quality control filters.\u003c/p\u003e \u003cp\u003eAn in-house pipeline analysis (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/MG-IiSGM/prokaION\u003c/span\u003e\u003cspan address=\"https://github.com/MG-IiSGM/prokaION\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was applied to examine the acquired sequencing data. The pipeline workflow went through the following steps: i) preprocessing of the initial fast5/pod5 files into more accessible fastq format by basecalling and barcoding with Guppy v.6.5.7 /Dorado v.7.3; ii) quality assessment with NanoFilt v2.8.0 and NanoPlot 1.41.6 to ensure the most reliable and informative data for downstream analysis; iii) species identification, with Kraken2 2.1.3 and Mash v2.3; iv) minimap2 v2.28 was used for mapping, and Freebayes v1.3.5 for SNP calling, using as reference a hypothetical MTB ancestral genome (identical to H37Rv in terms of structure, but including ancestral nucleotide positions inferred by maximum likelihood from a virtual ancestor)[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; v) variant annotation using the SnpEff v5.1 tool; and vi) recalibration of occasional low-coverage positions using joint variant calling.\u003c/p\u003e \u003cp\u003eFlow cells were washed for reuse with the Flow Cell Wash Kit (EXP-WSH004). A flow cell was removed once the number of available pores had been reduced to 400 pores.\u003c/p\u003e\n\u003ch3\u003eIllumina sequencing\u003c/h3\u003e\n\u003cp\u003eLibraries were prepared using Nextera XT kit (Illumina) following manufacturer\u0026rsquo;s instructions for NextSeq device (2x151bp).\u003c/p\u003e \u003cp\u003eSequence analysis was performed using an in-house pipeline deposited in GitHub: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/MG-IiSGM/autosnippy\u003c/span\u003e\u003cspan address=\"https://github.com/MG-IiSGM/autosnippy\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The pipeline followed the same steps described previously [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] using the hypothetical MTB ancestral genome [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] as a reference.\u003c/p\u003e \u003cp\u003eThe sequences generated for both, Illumina and Nanopore, were deposited in the ENA (project number PRJEB67993).\u003c/p\u003e\n\u003ch3\u003eIdentification of clustered cases\u003c/h3\u003e\n\u003cp\u003eA case was considered as clustered when the pairwise genomic distances with another case were \u0026lt;\u0026thinsp;12 SNPs [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Alignments and SNP variants were visualized and checked with IGV (Integrative Genomics Viewer) software for both Nanopore and Illumina sequencing.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMIRU-VNTR based cluster pre-assignment\u003c/h2\u003e \u003cp\u003eIn the MGIT sub-study, MIRU-VNTR analysis had previously been performed either directly on the corresponding clinical specimen (when smear-positive) or at MGIT positivity in Almeria laboratory [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It allowed us to obtain a preliminary assignment of cases in clusters, which was used to select only candidates of clustered cases for subsequent analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOur alternative analysis scheme to minimize delays in genomic data availability applied nanopore sequencing for a rapid, one-case/one-result response, avoiding the need to pool and batch specimens as in the standard approach.\u003c/p\u003e\n\u003ch3\u003eFlexible nanopore sequencing on primary solid cultures\u003c/h3\u003e\n\u003cp\u003eWe first evaluated our proposal on LJ primary solid cultures, on a non-selected study sample that included all 23 consecutive, smear-positive new incident cases diagnosed in Almeria, Spain, over a 4-month period (March 22- July 26, 2023). 82.6% of the cases in our study were immigrants.\u003c/p\u003e \u003cp\u003eAll cultures corresponded to sputum, except for one extra-respiratory specimen (psoas abscess, isolate 15). Bacterial loads were high (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u0026gt;\u0026thinsp;10 BAAR/field; 3\u0026thinsp;+\u0026thinsp;or 4+) in all but 4, which showed\u0026thinsp;\u0026lt;\u0026thinsp;10 BAAR/field (1\u0026thinsp;+\u0026thinsp;or 2+). After observing in the first studied cases that the growth times for specimens with similar bacterial loads were somewhat variable, it was decided to inoculate three tubes per specimen and select the one that grew faster, which allowed us to reduce the incubation time before extraction. In 74% of cases, sufficient culture growth was obtained to prepare sequencing libraries\u0026thinsp;\u0026lt;\u0026thinsp;21 days after inoculation (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The time required for DNA extraction and library preparation ranged from 4 to 8 hours.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConsecutive LJ primary cultures sequenced by nanopore technology.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsolate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBacillary load\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDays between inoculation and extraction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFinal library concentration (fmol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRun\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFlow cell ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSequencing time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMean coverage (X)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGenome coverage 20X (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFinal interpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1h 13min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e80,88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e96,49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOrphan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24h 48min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e48,51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e94,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eClustered\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1h 50min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e95,91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOrphan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1h 50min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e50,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e93,02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOrphan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4h 4 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e48,47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e94,05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOrphan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2h 37 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e36,02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e92,18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOrphan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1h 13 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e76,68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e96,63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOrphan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1h 25min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e72,73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e95,64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eClustered\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4h 06min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e81,94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e96,29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOrphan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4h 06min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e54,91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e95,01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOrphan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e70,91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e95,59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOrphan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1h 23 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e86,82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e96,53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eClustered\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2h 49 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e59,68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e95,42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOrphan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1h 35 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e72,36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e95,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eClustered\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1h 11min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e76,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e94,69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eClustered\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2h 28 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e74,74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e96,49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOrphan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3h 11min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e75,32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e95,99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eClustered\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3h 58min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e87,58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e96,46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOrphan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1h 24 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e78,72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e97,27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eClustered\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1h 29 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e72,59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e96,76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eClustered\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3h 4min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e73,42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e97,34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eClustered\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2h 24min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e73,65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e97,46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOrphan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2h 18min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e68,24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e96,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOrphan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eFor samples 8\u0026ndash;23 LJ were inoculated in triplicate. Bacillary loads correspond to: 1+: \u0026lt;1/field, 2+: 1\u0026ndash;9/field, 3+: 10\u0026ndash;100/field, 4+: \u0026gt;100/field). *Runs including a second isolate not belonging to this study.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTwenty nanopore sequencing runs were performed to analyze the 23 isolates (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e); most runs included a single isolate, while a minority included two isolates whose growth times had coincided and so were co-extracted at the same time. Seven flow cells were used in total. They were reused up to 6 times (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Run times with a single isolate ranged between 51 minutes and 3 hours 11 minutes (median time 1h 42min; 78% \u0026lt;2h 30min) and those with two isolates ranged between 1h 50min and 4h 6 min (median time 2h 9min; 56%\u0026lt;3h). Only one isolate deviated from the average run times (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, isolate 2). This was due to the low amount of library loaded, 2 fmol, as compared with average loads of 51.5 fmol, which allowed us to assign the former value as a minimum threshold to ensure shorter run times.\u003c/p\u003e \u003cp\u003eIn all cases, good coverage depths were achieved, with more than 90% of the genome sequenced at \u0026gt;\u0026thinsp;20X (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFast identification and analysis of clustered cases\u003c/h2\u003e \u003cp\u003eSequences were analyzed on the same day they were obtained, including the SNP-calling and a comparative analysis performed with data in our own genomic database obtained from standard high-throughput genomic analysis (Illumina), which included 664 retrospective cases (period 2003\u0026ndash;2023).\u003c/p\u003e \u003cp\u003eOf the 23 cases analyzed, we were able to anticipate all clustered cases, which involved 9 (39%) of the cases in study, who were included in 7 clusters already identified (two cases in the same cluster), and one in a new cluster (involving 2 cases), while the remaining cases were orphans (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Of the nine clustered cases, eight were men and one woman (median age, 37 years (26\u0026ndash;59 years); three (33%) were autochthonous and six (67%) migrants (3 from Morocco and 3 from Sub-Saharan Africa). Of the clustered cases identified, most (six) were included in longstanding clusters (the first cases in the clusters were diagnosed in 2003\u0026ndash;2012) and 2 were recently detected clusters (first case detected in 2020\u0026ndash;2022). With respect to the nationalities involved, 5 were mixed clusters (involving autochthonous and migrant cases), 2 were immigrant clusters and the remaining one was an autochthonous cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFirst epidemiological analysis of the clustered cases\u003c/h2\u003e \u003cp\u003eA general analysis of the nine cases that were genetically clustered identified reasons, in several of them, justifying their involvement in clusters. The six migrant cases had been living in Spain, after arrival, for \u0026gt;\u0026thinsp;5 years before diagnosis (median 12 years). In addition, drug addiction (alcohol, cannabis, cocaine), were identified in 5 of the cases, together with one with immunosuppression, one with diabetes and one with malnutrition.\u003c/p\u003e \u003cp\u003eNevertheless, the initial epidemiological investigation failed to assign most of these nine clustered cases to specific transmission chains. A history of contact with TB was found in only two cases (22%): one case had been identified in a previous contact tracing in 2020 and another had a family member with TB in 2014, but refused to participate at that time. In the five cases with addictions, an increased risk of participating in active transmission chains associated with this risk was considered, but, again, links to other TB cases could not be identified.\u003c/p\u003e \u003cp\u003eMoreover, in another two of the genetically clustered cases, an orphan/imported status, instead of clustering, had initially been considered, due to national and international mobility of the cases before their diagnosis: one of the cases had returned eight months before from a trip to the country of origin (1.5 years of visiting friends and relatives stay), and the other one had moved two years before from another region of Spain.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSecond epidemiological analysis of the clustered cases oriented by genomic data\u003c/h2\u003e \u003cp\u003eOnce determined that for most of the genetically clustered cases, involvement with transmission chains had not been suspected/found in the initial epidemiological standard investigation, we reanalyzed the cases, now considering the genomic findings, which indicated specific linkages with other clustered cases. The incorporation of genomic data in the epidemiological investigation of the cases was possible due to the short turn-around time to have these data available.\u003c/p\u003e \u003cp\u003eFor the two cases with recent national and international mobility, that has led us to consider them as independent importations from exposures in their countries of origin, genomic data confirmed the acquisition of the infection in the host territory, after arrival. Therefore, these cases were re-interviewed, now allowing us to identify risk factors shared with other members of the cluster (they had shared the same urban territory), which led us to expand the contact tracing beyond the contexts where it was initially carried out.\u003c/p\u003e \u003cp\u003eFor the only two cases with an initial epidemiological suspicion of being clustered, genomic data supported only one of the links suspected, leading to intervening again in the epidemiological context, to expand preventive treatments. On the contrary, the other case was genetically associated with a transmission chain different to the one initially suspected, which allowed contact tracing to be reoriented in relation to the members of this new cluster.\u003c/p\u003e \u003cp\u003eFor the five cases with risk factors associated with drug usage, in which relationships with other cases had not been initially suspected, genomic analysis finally incorporated them into previously known clusters, associated with shared leisure areas related to drug consumption. Given the complexity of performing contact tracing in these settings, these cases had not been previously identified for preventive treatment. After the linkage of these new cases with those clusters, contact tracing was expanded, now supported on community health agents.\u003c/p\u003e \u003cp\u003eFinally, regarding the case that was incorporated by genomic analysis into a newly identified cluster, due to the linkage with another case diagnosed in 2020; they were re-surveyed and the link between both was identified. It led us to expand the contact tracing, once it was confirmed that the previous efforts had not been able to identify all close contacts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePerformance of nanopore sequencing on primary MGIT cultures\u003c/h2\u003e \u003cp\u003eOnce we evaluated the performance and epidemiological usefulness of our flexible nanopore analysis of new incident cases on LJ, we aimed to also evaluate 30 consecutive liquid (MGIT) primary cultures as analytical samples (March-November 2024), to face the challenges expected from using a sample potentially harboring interfering DNA from other bacteria and/or from the host. 44% of the samples had high bacillary load from (3\u0026thinsp;+\u0026thinsp;or 4+) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMGIT primary cultures sequenced by nanopore technology\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsolate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebacillary load\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDays from sampling to positivity/ diagnosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFinal library concentration (fmol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSequencing time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMTB reads (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHuman reads (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOther bacterial reads (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMean coverage (X)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGenome coverage\u0026thinsp;\u0026gt;\u0026thinsp;20X (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eONT chemistry\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e93.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e97.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e52.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e94.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5h 47min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e35.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e73.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e3h 49min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e116.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e97.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e59.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e95.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e41.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e88.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e72.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e96.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1h 30min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e79.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e96.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5h 10min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e111.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e97.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e71.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e94.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5h 51 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e167.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e98.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6h 28min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e140.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e97.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70h 34min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e95.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e94.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e606.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e98.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3h 17min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e147.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e97.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e75.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e95.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5h 50min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e86.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e96.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e75.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e94.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e66.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e95.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4h 7min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e237.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e97.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e72h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e54.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e80.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e49.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e85.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24h 47min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e264.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e97.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e58.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e92.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3hr 7 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e162.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e97.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e115.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e97.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2h 32min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e86.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e95.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e93.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e91.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5h 21min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e186.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e98,74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e94.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e97,65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eBacillary loads correspond to: 1+: \u0026lt;1/field, 2+: 1\u0026ndash;9/field, 3+: 10\u0026ndash;100/field, 4+: \u0026gt;100/field). N/A represents an unknown time period from inoculation to positivity, for the reason that these cultures were received positive in our facility.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEven when working with MGIT cultures, we succeeded in obtaining good coverage for all cases, with 83% of them achieving optimal values (\u0026gt;\u0026thinsp;90% genome coverage\u0026thinsp;\u0026gt;\u0026thinsp;20X, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e); at MGIT positivity (in all but six cases, corresponding to low bacillary loads or smear-negative cases, in \u0026lt;\u0026thinsp;21 days after inoculation) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These results were obtained despite the presence of interfering DNA (up to 60% of human or up to 32% of bacterial (other than MTB) interfering materials; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eComparison with the standard Illumina-based sequencing scheme\u003c/h2\u003e \u003cp\u003eFinally, the data obtained from our alternative scheme of genomic analysis were compared with those of 16 isolates obtained with the standard Illumina high-throughput approach (waiting to accumulate isolates to be run together with other isolates in a NextSeq device in two different runs, including 10 and 6 isolates from our study). Correlations of assignment as clustered or orphan were identical in all cases. Focusing on the specific SNPs called using each strategy, we identified only three SNPs with differences, all based on the differential frequencies at which the allele was called. For these three SNPs, the respective alleles were called by nanopore sequencing at frequencies of 30%, 49% and 70%, respectively, versus 92%, 100% and 100% by Illumina.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOne of the advantages of genomic analysis in TB is to confirm suspected links between patients, supported by the standard epidemiological investigation (to rule in potential clusters). However, it is frequent that the genomic analysis also demonstrates the lack of relationships between cases initially suspected to be linked [[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]], when we detect that they are infected by different strains (to rule out potential clusters). This is not uncommon in a complex population like ours, with, not only through obvious links (e.g. among migrants with same nationality or among autochthonous cases), but also frequently between autochthonous and migrant cases or between migrants of different nationalities [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn these contexts, genomic analysis helps us to reorient the epidemiological investigation; from our initial assumptions based only on epidemiological data, to final links revealed by genomic analysis. Therefore, the faster the genomic data are available, the sooner we can reorient the epidemiological investigation, when contact tracing is still running. In our study, genomic findings completely transformed the analysis, conclusions and interventions compared to those based solely on the standard epidemiological analysis. The more refined information obtained by genomic analysis led us to i) label some cases as clustered that would have been considered as independent importation, ii) understand the true links with other cases, correcting the misassignments derived from the first epidemiological analysis and iii) identify links with other preceding cases that had not been initially suspected.\u003c/p\u003e \u003cp\u003eIn addition to the most valuable information from an epidemiological perspective extracted from genomic data, namely clustered cases, we also identified that one of the cases in our sample corresponded to an \u003cem\u003eM. caprae\u003c/em\u003e infection (case 7), associated with a longstanding zoonotic event in the region [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHaving achieved high discriminatory power and accurate SNP calling in TB genomic analysis, the remaining challenge is speed, especially for this slow-growing bacterium. The best solution would obviously be to perform the analysis directly on clinical specimens, similar to metagenomic analysis for other microorganisms. However, unlike metagenomic diagnostics, our goal is not only to identify MTB but to ensure optimal genome-wide coverage for accurate SNP calling, enabling confident cluster assignment and establishing the correct chronology and relationships between cases. It is much more challenging to do this when the genomic data are obtained directly from specimens. Some efforts to perform MTB sequencing directly on the specimens [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] have shown its possibility, though not yet for all specimens. This probably depends on MTB DNA ratio to interfering human/microbial DNA in the sample.\u003c/p\u003e \u003cp\u003eIf sequencing MTB directly on specimens\u0026rsquo; remains challenging, cumbersome and/or expensive, the only alternative to reduce the delay would be to switch to primary cultures, which eliminates the time required to grow subcultures for sequencing. In our study, we evaluated both LJ solid media and MGIT liquid media with a double aim: i) offer data useful for settings with low resources and therefore lack of automatic MGIT readers and ii) evaluate potential impact of human or other interfering DNA, most likely expected in liquid cultures, that could lead to sequencing reads being hijacked by unwanted DNA. In our study from MGIT a range between 0\u0026ndash;60% and 0\u0026ndash;34% of the total reads when analyzing MGIT cultures corresponded to human and other bacteria respectively. These values were lowered to 0 and 7.8% when analyzing LJ cultures. However, despite the interfering non-MTB DNA, we managed to obtain optimal results from almost all MGIT cultures, and finally correctly assign them as members of their corresponding cluster or as orphans.\u003c/p\u003e \u003cp\u003eIn addition, the use of LJ media is better suited to our strategy in low resource settings, where automated MGIT incubators are not available and the lower cost of solid media is appreciated. Despite the use of solid media, our times from inoculation to extraction were quite reasonable, and \u0026gt;\u0026thinsp;70% of cultures yielded sufficient load for extraction in less than 21 days after inoculation.\u003c/p\u003e \u003cp\u003eA crucial aspect of our study is obtaining genomic results within 21 days of diagnosis. This is particularly important given the high proportion of migrant cases in our study population, many of whom are hospitalized for 21 days post-diagnosis to ensure isolation and reduce the risk of transmission within their households and communities. Due to generally overcrowded and substandard living conditions, effective isolation outside the hospital cannot be guaranteed. By acquiring genomic data within this timeframe, we can identify probable transmission links early, allowing us to re-interview patients while they are still hospitalized and accessible. These follow-up interviews, conducted by our team, enhance traditional contact tracing by incorporating genomic insights, providing a clearer understanding of transmission dynamics, and enabling targeted interventions to better control the spread of infection.\u003c/p\u003e \u003cp\u003eOur main goal, to obtain genomic data as close as possible to the time of each new incident patient diagnosis, was incompatible with the standard high-throughput genomic analysis, in which a sufficient number of samples must first be accumulated, pooled and run together in the interests of cost-effectiveness. The nanopore sequencing option could provide the flexibility needed for the one-case/one-analysis approach we were aiming for. While nanopore sequencing is slowly being introduced into genomic analysis of MTB [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], a recent review acknowledged that the number of studies in MTB supported by this technology was still small [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe decision to perform a case-by-case analysis would recommend reusing the flow cell to reduce costs. While it would have been possible to load more samples into the same flow cell without greatly extending the run duration, the decision to limit the number of samples was justified by our objective of obtaining genomic data as soon as possible after diagnosis.\u003c/p\u003e \u003cp\u003eWe must acknowledge that our LJ study used nanopore R9.4.1 version, which has been recently discontinued with the release of R10.4.1 flow cells, and new V14 chemistry, as a potentially more efficient successor to R9. The company promotes R10\u0026rsquo;s dual-reader head nanopore design as offering higher base-calling accuracy. It claims this adaptation increases raw read accuracy to 95% using Dorado base-calling software. Since R10\u0026rsquo;s improvements focus on call accuracy, the parameters evaluated in our study\u0026mdash;run time and final coverage\u0026mdash;are not expected to differ with the new flow cells. In fact, we noticed no differences in the output of both chemistries on the MGIT sub-study (31% analyzed on R9 and the remaining on R10) in which mean coverages and run-time to reach\u0026thinsp;\u0026gt;\u0026thinsp;20X in \u0026gt;\u0026thinsp;90% of the genome were comparable between R9 and R10.\u003c/p\u003e \u003cp\u003eThe initial assumption that nanopore sequencing was associated with a higher error rate in SNP calling compared to short read sequencing will be dispelled by these recent improvements. Indeed, a multinational study that exploited genomic data to assess resistances and determine epidemiological relationships found that nanopore and short-read sequencing were comparable in performance [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Similarly, a recent study comparing nanopore technology with Illumina demonstrated that the results are highly consistent in SNP-based outbreak investigations, further supporting the use of nanopore sequencing for accurate and rapid genomic epidemiological analysis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In our study, SNP determination from nanopore data was almost identical to that from the high-throughput short-read analysis reference standard. Moreover, the average length obtained by nanopore sequencing (\u0026gt;\u0026thinsp;3kb, data not shown) would enable extraction of MIRU-VNTR patterns [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], which is somewhat impaired when using short reads, allowing the genomic data to be linked to those in historical MIRU-VNTR databases.\u003c/p\u003e \u003cp\u003eRegarding comparative costs, for nanopore, the cost of loading a single sample per run and reusing a single flow cell up to 6 times was estimated at 165 euros per specimen. By loading 2 samples whenever possible, the cost was significantly reduced to 111 euros. For Illumina sequencing the cost for sequencing the 23 LJ isolates would have been 81.5 euros per sample in a MiSeq device. When comparing costs, we must also consider that competitive costs in Illumina are only obtained when accumulating as many samples as possible in the same run, meaning prolonged delays to obtain the genomic data, which would not fit the requirements of our proposal, based on the immediate analysis of every new single case. In the financial analysis we must consider that from these same sequencing efforts, the analysis of resistance mutations, in addition to the VNTR analysis previously mentioned, can be performed, which could reduce the laboratory costs dedicated to antibiograms and first-line typing.\u003c/p\u003e \u003cp\u003eThe final goal in our evaluation was to determine whether it was possible to guide the epidemiological investigation during contact tracing in Almeria based on the genomic results being available more quickly. Rapid identification of orphan cases allowed control professionals to focus interventions on minimizing new secondary cases by prioritizing the prescription of prophylaxis in infected cases identified through contact tracing. At the same time, their efforts to identify new undiagnosed cases were directed to contexts associated with new cases identified as clustered, indicators of the presence of active transmission. We identified several examples of complex transmission dynamics in populations with a high percentage of immigrants, which is the case in Almeria, which justifies the need to develop new rapid and accurate alternatives such as the one developed here. Our proposal could provide an alternative to the massive genomic data analyses performed in central reference laboratories, and is particularly well suited to local decentralized surveillance of TB transmission in settings where interventions based on rapid availability of genomic data can be performed.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Janet Dawson for her editing and proofreading assistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Instituto de Salud Carlos III [PI21/01823, PI19/00331], a Miguel Servet Contract (CPII20/00001) to LPL, and PFIS contracts to CRG (FI20/00129) and SBS (FI21/00145)], CIBER - Consorcio Centro de Investigaci\u0026oacute;n Biom\u0026eacute;dica en Red (CB06/06/0058, CB21/13/00044), IiSGM 2021-II-PI-01 Intramural project to DGV, a 2022-II-PREDOCIA-01 IiSGM contract to DPU, AP-0062-2021-C2-F2 from the Junta de Andaluc\u0026iacute;a, SEPAR 2023 (REF 1401/2023) and co-financed by European Regional Development Funds of the European Commission (ERDF): \u0026ldquo;A way of making Europe\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe authors don\u0026rsquo;t have any conflict of interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMeehan CJ et al (2019) Whole genome sequencing of Mycobacterium tuberculosis: current standards and open issues. Nat Rev Microbiol 17(9):533\u0026ndash;545\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoig GA et al (Aug. 2020) Whole-genome sequencing of Mycobacterium tuberculosis directly from clinical samples for high-resolution genomic epidemiology and drug resistance surveillance: an observational study. Lancet Microbe 1(4):e175\u0026ndash;e183\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNilgiriwala K et al (2023) Genomic Sequencing from Sputum for Tuberculosis Disease Diagnosis, Lineage Determination, and Drug Susceptibility Prediction, \u003cem\u003eJ. Clin. Microbiol.\u003c/em\u003e, vol. 61, no. 3, p. e0157822, Mar\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eComas Ĩ et al (2010) Human T cell epitopes of Mycobacterium tuberculosis are evolutionarily hyperconserved. Nat Genet 42(6):498\u0026ndash;503\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMart\u0026iacute;nez-Lirola M et al (2023) A One Health approach revealed the long-term role of Mycobacterium caprae as the hidden cause of human tuberculosis in a region of Spain, 2003 to 2022, \u003cem\u003eEurosurveillance\u003c/em\u003e, vol. 28, no. 12, pp. 1\u0026ndash;11\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalker TM et al (2013) Whole-genome sequencing to delineate Mycobacterium tuberculosis outbreaks: A retrospective observational study, \u003cem\u003eLancet Infect. Dis.\u003c/em\u003e, vol. 13, no. 2, pp. 137\u0026ndash;146, Feb\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlonso M, Herranz M, Lirola MM, Gonzaĺez-Rivera M, Bouza E, De Viedmaa DG (2012) Real-time molecular epidemiology of tuberculosis by direct genotyping of smear-positive clinical specimens. J Clin Microbiol, 50, 5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbascal E et al (2019) Whole genome sequencing-based analysis of tuberculosis (TB) in migrants: Rapid tools for crossborder surveillance and to distinguish between recent transmission in the host country and new importations. Eurosurveillance, 24, 4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou X et al (2019) Clinical Evaluation of Diagnosis Efficacy of Active Mycobacterium tuberculosis Complex Infection via Metagenomic Next-Generation Sequencing of Direct Clinical Samples, \u003cem\u003eFront. Cell. Infect. Microbiol.\u003c/em\u003e, vol. 9, no. October, pp. 1\u0026ndash;9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Marco F, Spitaleri A, Battaglia S, Batignani V, Cabibbe AM, Cirillo DM (2023) Advantages of long- and short-reads sequencing for the hybrid investigation of the Mycobacterium tuberculosis genome, \u003cem\u003eFront. Microbiol.\u003c/em\u003e, vol. 14, Feb\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHall MB et al (2023) Evaluation of Nanopore sequencing for Mycobacterium tuberculosis drug susceptibility testing and outbreak investigation: a genomic analysis. Lancet Microbe 4(2):e84\u0026ndash;e92\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBogaerts B et al (2024) Closing the gap: Oxford Nanopore Technologies R10 sequencing allows comparable results to Illumina sequencing for SNP-based outbreak investigation of bacterial pathogens. J Clin Microbiol, 62, 5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang CY, Ong RTH (2020) MIRUReader: MIRU-VNTR typing directly from long sequencing reads. Bioinformatics 36(5):1625\u0026ndash;1626\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"599e15c1-0bcc-4505-adcd-e22b909836fd","identifier":"10.13039/501100004587","name":"Instituto de Salud Carlos III","awardNumber":"PI21/01823","order_by":0},{"identity":"8afdacde-43f6-4b9d-bd09-b340c9df3c00","identifier":"10.13039/501100004587","name":"Instituto de Salud Carlos III","awardNumber":"PI19/00331","order_by":1},{"identity":"03aa0953-5c7b-4ea3-b791-d8383986c186","identifier":"10.13039/501100004587","name":"Instituto de Salud Carlos III","awardNumber":"CPII20/00001","order_by":2},{"identity":"4e7ff8b2-1f95-4e3a-9165-b46d1d309169","identifier":"10.13039/501100004587","name":"Instituto de Salud Carlos III","awardNumber":"FI20/00129","order_by":3},{"identity":"013a9f27-0950-4316-990b-ea6c7890d6ee","identifier":"10.13039/501100004587","name":"Instituto de Salud Carlos III","awardNumber":"FI21/00145","order_by":4},{"identity":"bf513afd-3742-4c1d-903f-326fdecfecc2","identifier":"10.13039/501100014365","name":"Instituto de Investigación Sanitaria Gregorio Marañón","awardNumber":"2021-II-PI-01","order_by":5},{"identity":"0e7d5a55-f135-4e71-9629-1438b564df40","identifier":"10.13039/501100014365","name":"Instituto de Investigación Sanitaria Gregorio Marañón","awardNumber":"2022-II-PREDOC-IA-01","order_by":6},{"identity":"34bfc07e-6aa9-4e36-bde4-3721b96bfe5e","identifier":"10.13039/501100011011","name":"Junta de Andalucía","awardNumber":"AP-0062-2021-C2-F2","order_by":7},{"identity":"3a69ec70-2a70-46c4-bdf3-79fc1d6c1d6a","identifier":"10.13039/501100007509","name":"Sociedad Española de Neumología y Cirugía Torácica","awardNumber":"REF 1401/2023","order_by":8},{"identity":"5ef22cda-da00-4a42-bf5d-373799770373","identifier":"10.13039/501100008530","name":"European Regional Development Fund","awardNumber":"A way of making Europe","order_by":9}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Hospital General Universitario Gregorio Marañón","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Genomic epidemiology, tuberculosis, nanopore sequencing, intervention","lastPublishedDoi":"10.21203/rs.3.rs-4729960/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4729960/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGenomic analysis has enhanced our understanding of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (MTB) transmission. Short-read sequencing in reference labs ensures full population coverage but delays local data access. We assessed a case-by-case genomic approach using primary cultures from each incident case.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe prospectively evaluated 53 TB cases in Almeria, Spain, over two periods: 4 months (March\u0026ndash;July 2023) for solid (LJ, 23 cases) and 9 months (April\u0026ndash;December 2024) for liquid (MGIT, 30 cases) cultures. DNA was purified from LJ when sufficient growth was observed and from MGIT at positivity. Nanopore sequencing was performed on 1\u0026ndash;4 cases per run, with flow-cells reused up to six times.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn 74% of LJ, adequate growth was reached within 21 days. All LJ cultures achieved optimal genome coverage (\u0026gt;\u0026thinsp;90% at \u0026gt;\u0026thinsp;20X), 61% with runs under 2.5 hours. Nearly all MGIT cultures, analyzed at positivity (all but six within 21 days), achieved optimal coverage, despite interfering DNA. Four cases had slightly lower but acceptable coverage (73\u0026ndash;88%, \u0026gt;\u0026thinsp;20X). Same-day classification as clustered or orphan was possible. Nanopore sequencing correlated with high-throughput short-read data, enabling quicker correction of epidemiological errors and identification of transmission links.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur strategy offers a flexible, rapid decentralized alternative, accelerating cluster information contact tracing.\u003c/p\u003e","manuscriptTitle":"Reducing delays in the genomic epidemiology of tuberculosis: a flexible and decentralized analysis of each incident case","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2025-07-08 18:30:34","doi":"10.21203/rs.3.rs-4729960/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2024-07-19 06:21:25","doi":"10.21203/rs.3.rs-4729960/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"08a00333-8d16-4b6a-8111-7f88b745b207","owner":[],"postedDate":"July 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34826559,"name":"General Microbiology"}],"tags":[],"updatedAt":"2024-07-19T06:21:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-08 18:30:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-4729960","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4729960","identity":"rs-4729960","version":["v2"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}