Comparative Study of Host DNA Depletion Methods for Microbial Metagenomics

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Farraj, Sherif A. El-Kafrawy, Taha A. Kumosani, Jehad M. Yousef, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5991450/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract One of the biggest obstacles in applying metagenomics in the clinical field is the increase of human genome reads in the resulting sequences. In this study, three depletion methods were compared that utilize the methylation of cytosine in human DNA as a mechanism of action. Two commercial kits NEBNext Microbiome DNA Enrichment Kit and MethylMiner™ Methylated DNA Enrichment Kit bind methylated cytosines to specific antibodies, along with MspJI restriction enzyme that digests methylated cytosines. The depletion methods were applied to a HeLa cell suspension (as a source for the human genome) spiked with well-characterized bacterial isolates of Staphylococcus aureus, ‎‎Klebsiella pneumoniae and Cryptococcus neoformans . The products of the depletion methods were run in parallel with undepleted samples as controls on Miseq. The outcome of the NEBNext Microbiome DNA Enrichment Kit showed the most significant decrease in human genome (p = 0.005) compared to undepleted samples and a significant increase in both K. pneumoniae (p = 0.005) and S. aureus reads (p = 0.014). While MethylMiner™ Methylated DNA Enrichment kit showed a significant increase for C. neoformans (p = 0.007) besides a significant increase in the percent coverage of the references for C. neoformans and K. pneumoniae (p = 0.046 and p = 0.004, respectively). Results showed that both kits could help in depleting human DNA and enriching spiked pathogens in the study sample. The findings of this study are recommended to be validated on a large number of clinical samples. Molecular Biology Clinical metagenomics host DNA depletion microbiome DNA enrichment infectious diseases Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. INTRODUCTION Infectious diseases remain the leading cause of human morbidity and mortality worldwide [ 1 ]. Based on World Health Organization (WHO), lower respiratory infections ranked 4th among the top 10 causes of death with an estimated 2.6 million deaths in 2019 (WHO, 2020). The critical step in preventing and minimizing the burden of infectious disease is to have a rapid and accurate laboratory identification of the etiological agent [ 2 ]. Up to 70% of the clinical samples are from patients diagnosed with infectious diseases such as encephalitis, meningitis and pneumonia [ 3 – 5 ]. Clinical metagenomics (CMg) is a promising application that utilizes the ability of next-generation sequencing (NGS) to sequence all the genome fragments that exist in clinical specimens [ 6 ] [ 7 ]. This ability of NGS allowed for the identification of SARS-CoV-2 which reflects the importance of this field in the identification of emerging and re-emerging infections [ 8 ]. The type of clinical samples plays a role in determining the extent of host genome abundance in the generated sequences which might be up to 90% [ 9 – 11 ]. Current methods for host DNA depletion utilize the methylation phenomenon of host DNA with 4–6% of its cytosines are methylated, whereas this is a rare phenomenon in microbes. Theoretically, the removal of methylated DNA from a clinical specimen should be expected to enrich the pathogen’s nucleic acid [ 12 ]. Several methods for the depletion of human DNA and enrichment of microbial DNA either before or after extraction were employed. The use of combinations of nucleases and selective detergents before extraction for the selective lysis of blood cells to release human DNA (due to their fragile nature compared to microbial cells), followed by using nuclease to degrade the released host DNA was reported by several studies[ 13 – 17 ]. One drawback of this approach is the possible degradation of the microbial DNA if their cell wall is lysed by the used reagents [ 15 ]. Others have subjected samples to multiple freeze/thaw cycles in order to disrupt human cells but this approach might lead to the disruption of both human cells and pathogens releasing both nucleic acids exposing them to enzymatic digestion in the following steps [ 18 – 20 ]. There are two approaches to depleting host DNA after extraction: the first enriches the microbes’ genome by using either a protein to bind non-methylated CpG motifs in bacterial genomes or DNA probes complementary to the pathogen’s nucleic acid. Both binding options lead to a bias in the distribution of microbial species because of their differential binding to various microbes [ 12 , 21 ]. The other approach is to enrich the microbial nucleic acids by targeting human DNA through methylated sites where the human genome is rich in methylated cytosine than microbial DNA [ 12 ]. Targeting methylated sites is achieved through 2 procedures: 1) digesting methyl-CpGs by methylation-dependent endonucleases (MspJI in this study) and 2) separating DNA containing methylated CpGs from non-methylated DNA by binding to human MBD2 protein conjugated to magnetic beads (NEBNext Microbiome DNA Enrichment Kit and the MethylMiner™ Methylated DNA Enrichment Kit in this study). Although both kits depend on the same principle, the main difference is the size of the human genome used as a template, whereas the first kit requires an intact human genome with at least ≥ 15 kb, the second kit requires a fragmented DNA with an average size of < 1000 bp. This study was designed to evaluate the effect of different human genome depletion methods on the outcome of NGS data for bacterial DNA detection from mock samples. 2. MATERIALS AND METHODS 2.1. Mock Sample Preparation The mock sample was composed of: Gram-positive bacteria Staphylococcus aureus‎‎ ( S. aureus ) ‎‎ (8.6×10 7 cfu/ml) (National Collection of Type Cultures, England, NCTC#8325) and Gram-negative bacteria Klebsiella pneumoniae ( K. pneumoniae‎‎ ) ‎ (9.4×10 6 cfu/ml) isolated and characterized from a clinical sample in the microbiology lab at King Abdulaziz University Hospital (KAUH) with encapsulated yeast Cryptococcus neoformans ( C. neoformans ) (ATCC, USA, ATCC® 14116) (1.05×10 6 cfu/ml). An equal cell number of each pathogen was spiked in the same cell number of HeLa cell suspension (2.7×10 6 cells/ml) to mimic a human clinical sample. 2.2. MagNA Pure Extraction Nucleic acid extraction of the mock sample (200 µl) was performed using a MagNA Pure Compact NA Isolation Kit I on a MagNA Pure Compact Instrument (Roche, Japan). Samples were pretreated with MagNA Pure Bacteria Lysis Buffer (Roche, Germany), lysozyme, and proteinase K prior to automated extraction as recommended by the manufacturer to ensure complete lysis of the bacteria. Extracted nucleic acid was eluted in 50µl of elution buffer. 2.3. Host DNA Depletion Methods Host DNA depletion methods were divided into two groups (Figure I): (1) Non-fragmented Group: for procedures that do not require fragmentation of nucleic acid; (2) Fragmented Group: for procedures that require fragmentation of nucleic acid. In all depletion processes, 100 ng of the extracted nucleic acid were used for depletion and the depleted nucleic acid was eluted in 30 µl of elution buffer. Each depletion group had a control sample tested in duplicate which was 100 ng of the extracted sample then followed the same procedure of the test samples without the depletion process. Non-fragmented controls were designated Unprocessed1 and Unprocessed2 and fragmented controls were designated UnprocessedF1 and UnprocessedF2. 2.3.1. Non-fragmented Group: this group included the following two depletion protocols: 2.3.1.1. NEBNext Microbiome DNA Enrichment Kit The kit utilizes MBD2-Fc protein bound to magnetic beads. This protein binds to CpG-methylated DNA from the host DNA, which is then removed by applying a magnetic field to separate the CpG-methylated DNA of the host and leave the non-CpG-methylated DNA. The procedure was performed according to the manufacturer’s instructions, followed by purifying the depleted DNA using Agencourt AMPure XP (Beckman Coulter, USA) protocol. The duplicated samples for this protocol were designated NEB1 and NEB2. 2.3.1.2. MspJI Digestion Following Oyola et al. [ 12 ], MspJI, which is a methylation-dependent restriction endonuclease, was used to digest the methylated CpG DNA. Briefly, digestion was performed by adding extracted DNA, 1x of CutSmart Buffer, 1x of Enzyme Activator Solution with 2.5 units of MspJI, the mixture was then completed to 30 µl using nuclease-free water. The mixture was incubated for 4 h at 37°C, then inactivated for 20 min at 65°C and cooled to 4°C in a Veriti thermal cycler (Applied Biosystems, USA). The digestion product was run on a 1% agarose gel (Figure II), and the high molecular-weight undigested bands were excised and purified using GFX PCR DNA and a Gel Band Purification Kit (Amersham Biosciences, USA) following the manufacturer’s instructions. The duplicated samples resulting from this assay were designated MSPJI 1 and MSPJI 2. 2.3.2. Fragmented Group Some of the host DNA depletion methods required DNA fragmentation. Fragmentation was performed using NEBNext dsDNA Fragmentase (New England BioLabs, England). The fragmentation master mix was composed of 100ng of extracted DNA, 1x Fragmentase Reaction Buffer v2, and 2 µl dsDNA Fragmentase in a reaction volume of 20 µl by nuclease-free water. The mixture was incubated in a Veriti thermal cycler at 37°C for 20 min; followed by the addition of 5 µl of 0.5 M EDTA to stop the reaction. The fragmented product was purified with 1.8x of AMPure XP beads and eluted in 25 µl of nuclease-free water. Next, the purified fragmented DNA was subjected to one of the two following depletion methods: 2.3.2.1. MethylMiner™ Methylated DNA Enrichment Kit This kit utilized an MBD2 protein coupled with paramagnetic Dynabeads® M-280 of streptavidin via a biotin linker to capture the methylated DNA. The fragmented DNA was subjected to DNA depletion according to the manufacturer’s recommendations. The magnetic beads captured the methylated genomic DNA, and the resulting wash buffer contained the non-methylated microbial DNA. The resulting wash buffers were purified by 1.8x of AMPure XP beads according to the manufacturer’s instructions. The eluted products were concentrated into a final volume of 30 µl through evaporation. The above steps were performed in duplicate for each sample and labeled as Methyl1 and Methyl2. 2.3.2.2. MspJI Digestion Fragmented DNA was digested by MspJI, as described previously, following the protocol of the Oyola group [ 12 ]. Then, the digested DNA was purified by an equal volume of AMPure XP beads. The duplicated samples were designated as MspJI F1 and MspJI F2. 2.4. Assessment of depletion efficacy using Real-time PCR Real-time PCR was performed to assess the efficiency of DNA depletion using primers and probes (Table I) targeting C-myc gene as a representative for human DNA, and C. neoformans using QuantiFast-Probe-RT-PCR Kit (Qiagen, Germany). The amplification mix consisted of 1x QuantiFast PCR Master Mix (w/o ROX), 0.8µM of each primer, 0.2µM of the specific probe, 0.2µl of 50X ROX dye solution, 0.2µl of QuantiFast RT Mix, 5µl of extracted nucleic acid and the mix was completed to 20µl by nuclease-free water. Amplification was performed on Applied Biosystems® 7500 fast (Applied Biosystems, Singapore) with a thermal profile: 10min at 50°C, 5min at 95°C followed by 40cycles of 10s at 95°C and 30s at 60°C where the fluorescence data were collected. S. aureus and K. pneumonia were amplified using FTD Respiratory pathogens 33 kit (Fast Track Diagnostics, Luxembourg), according to manufacturer instructions. 2.5. Nucleic acid quantification Quantification of the nucleic acid from the original samples and depleted host DNA obtained in this study using different protocols were quantified by Qubit 2.0 (Life Technologies, USA) using a Qubit™ dsDNA HS assay kit (Life Technologies, USA) following the manufacturer’s instructions. 2.6. Library preparation Nextera XT DNA library prep (Illumina, USA) was used following the manufacturer’s instructions, with the normalization step performed manually according to the manufacturer’s instructions for samples with low concentrations. 2.7. Pooling libraries and loading into MiSeq Qubit™ dsDNA HS assay kit was used to quantify each library, and the average size of each library was estimated using a 2100 Bioanalyzer System (Agilent, Germany) using Agilent High Sensitivity DNA Kit (Agilent, USA). Each library was diluted to 4 nM using elution buffer; then, 5 µl of each library was pooled together and diluted to 10 pM following the Denature and Dilute Libraries Guide. Sample data including indexes were loaded onto the Illumina Experimental Manager 1.15.1 software. The final library mix was added to the MiSeq Reagent Kit v2 (300-cycles) (Illumina, Singapore), and the run was performed on a MiSeqDX platform (Illumina, USA). 2.8. Analysis of NGS Data The MiSeq generated two demultiplexing FASTQ read files for each sample (R1 and R2). Data analysis was performed using GENEIOUS 11.1.5 software. First, duplicate reads were removed using Dedupe version 37.64 then each sample’s R1 and R2 read files were merged with BBMerge from the BBtools suite. The bad quality sequences from both ends were trimmed by BBDuk algorithm from the BBTools with error probability = 0.05. The remaining sequence reads were mapped with standard Geneious mapper [ 22 ] to the reference sequences for each microorganism used in the mock sample (Table II). The mapping process was sequentially performed to avoid overlap between common reads hence avoiding calculation bias starting with S. aureus then the remaining unmapped reads were mapped to K. pneumonia ‎‎ then the unmapped reads were mapped to C. neoformans and finally, the unmapped reads were mapped to Homo sapiens. 2.9. Statistical Analysis The results of real-time PCR where evaluated in Fold changes (FC) and were calculated based on Ct values using the equation FC = 2^ΔCt, where ΔCt = (Ct unprocessed - Ct processed). The DNA percentage of different targets in the depleted samples was calculated from fold changes of targets in the unprocessed specimens [ 18 ]. The NGS yield of the test samples was compared by both the percentage of reads mapped to the reference sequence following the equation: [number of reads mapped/total number of reads mapped (pathogen + host) × 100] [ 21 ] and the percentage of reference coverage of each microorganism (coverage %). One-way ANOVA was used to check the statistical significance of the differences between different assays, and a p-value of ≤ 0.05 was considered as statistically significant [ 23 ]. 2.10. Applying the selected depletion method to clinical samples A clinical throat swab sample was received for respiratory pathogen detection. Ethical approval (ref#290 − 17) was obtained from the unit of Biomedical Ethics in KAUH, the sample was run in duplicate, with and without human DNA depletion, in NGS. The generated reads were analyzed using metagenomics online apps: Illumina® DRAGEN Metagenomics app using Kraken2 algorithm ( https://basespace.illumina.com/apps/9929920/DRAGEN-Metagenomics-Pipeline?preferredversion ) and CosmosID’s bioinformatics platform ( https://www.cosmosid.com/platform ) for identifying the microbes. The enrichment factors for the identified microbes were calculated using the following equation: Enrichment Factor for a microbe = percentage of reads for this microbe in the depletion assay (processed) / percentage of reads for this microbe in non-depleted samples (unprocessed) [ 24 ]. 3. RESULTS In this study, host DNA depletion methods were divided into two groups: (1) Non-fragmented Group: where nucleic acids were used by NEBNext Microbiome DNA Enrichment Kit or MspJI restriction enzyme without fragmentation, or (2) Fragmented Group: where extracted nucleic acids were fragmented prior to use in the two methods: MethylMiner™ Methylated DNA Enrichment Kit or MspJI restriction enzyme. Real-time PCR was performed to assess the yield of C. neoformans, S . aureus, K. pneumonia , ‎‎and the human genome (as represented by C-myc) in processed samples by comparison to unprocessed samples. K. pneumonia‎‎ was not detected by real-time PCR after 40 PCR amplification cycles in the unprocessed samples in both fragmented and non-fragmented‎ protocols and was not included in Real-time PCR comparison. The fold changes calculated from Ct values are shown in Figure III. No significant increase in the targets’ enrichment was observed by real-time PCR. In contrast, a significant depletion of the targets together with the C-myc was observed in the MspJI fragmentation protocol ( C. neoformans p = 0.012 and S . aureus ‎‎ p = 0.008) as well as the Methyl protocol for S . aureus ‎‎(p = 0.052). The FASTQ files R1 and R2 for each sample were merged to generate raw sequence reads with an average number of reads of 2,113,159 reads per sample, and a range of 1,006,432 to 3,103,410. Removing duplicate reads resulted in a reduction in the number of reads for non-fragmented group samples by ≈ 30% whereas in the fragmented group samples, the number of reads decreased by ≈ 47% of the original number of reads. The unprocessed samples in each group showed the least percentage of decrease after duplicate removal (23% vs 34% for the non-fragmented and fragmented groups, respectively). After trimming low-quality sequences from both sides, the unique reads in the mock sample were mapped to reference sequences for each pathogen and the human genome. After duplicate read removal and trimming of low-quality sequences, samples showed consistency in target reads percentage among duplicate samples. This consistency between duplicate samples was highest in the unprocessed samples of both groups. The MspJI F samples from the Fragmented Group showed the lowest consistency between duplicate samples (Figure IV). Comparing the percentage of reads (depleted vs non-depleted) for each target (Table III), we found the NEB and Methyl protocols to show a significant decrease in human reads (p = 0.005 and p = 0.044, respectively). For the S. aureus , MspJI protocol showed a significant increase in target reads (p = 0.012) as well as the Methyl protocol (p = 0.021) and the NEB protocol (p = 0.014). The only protocol that showed a significant increase in target reads for K. pneumoniae was the NEB protocol (p = 0.005) while the Methyl protocol was the only protocol to show a significant increase in C. neoformans reads (p = 0.007). The Methyl protocol showed a significant increase in coverage% of C. neoformans (p = 0.046) and K. pneumoniae (p = 0.004) (Table III). While the best increase of S. aureus coverage was accomplished by MspJI assay (p = 037). The coverage % for each pathogen was found to be reflected in the depth of its coverage and is inversely proportional to the size of the pathogen genome. For example, the mean depth of S. aureus , with the highest coverage among the spiked pathogens and has the smallest genome, was the highest in MspJI (2.25) (Figure V) compared to the other pathogens that showed negligible depth. The NEB protocol was applied to a patient's throat swab and the result was compared to the result of the same sample without depletion. Candida albicans was found in the sample beside a number of pathogen bacteria which were: Haemophilus influenzae , Haemophilus parainfluenzae and Streptococcus pneumoniae (Table IV). The depletion protocol showed an improvement in the microbes' results against the un-processed for both number of assembled reads and % coverage against their references (figure VI shows bacteria distribution as an example). That has clearly appeared in the enrichment factor where Candida albicans increased 120 fold, Haemophilus influenzae increased 186 fold, Haemophilus parainfluenzae increased 167 fold, and finally, Streptococcus pneumoniae increased 171 fold. That was a consequence of a 6% reduction of human reads in the processed sample against the un-processed one. 4. DISCUSSION Host genome depletion plays an important role in improving the identification of microbial genomes directly from clinical samples using NGS by increasing the number of reads for the microbial genomes. In this study, we compared four protocols for depleting the human genome (Fig. 1). As a general observation in this study, the percentage of duplicate reads generated from NGS showed that the fragmentation step caused an increase in duplicate reads of about 17%. Moreover, the depletion step increased the duplicate reads by about 10% compared to the undepleted controls. Two depletion protocols showed a significant depletion of the human genome or significant enrichment for two of the three pathogens used in this study namely the NEBNext Microbiome DNA Enrichment Kit and the MethylMiner™ Methylated DNA Enrichment Kit where both kits utilize the binding of MBD2 protein to methylated DNA. The NEBNext Microbiome DNA Enrichment Kit was used effectively for DNA depletion in previous studies. Thoendel et al. showed 5% enrichment in S. aureus DNA by spiking the bacterium into uninfected sonicate fluids [ 24 ]. The same improvement was observed in this study, where an overall enrichment of 11% for all spiked pathogens compared to unprocessed samples was found. Another study reported an enrichment in E. coli reads of about 75% compared to unenriched samples using the NEBNext Microbiome DNA Enrichment Kit. The same study evaluated the kit on a mock sample containing 90% human and 10% P. falciparum DNA and found an 8-fold increase in the Plasmodium DNA reads [ 21 ]. From 120 to 186 folds increase was found when we applied this protocol to a clinical throat sample, this difference could be due to the varying levels of CpG methylation density between the human genome in the clinical sample and the Hela cells used in the mock sample. Although the main purpose of the MethylMiner™ Methylated DNA Enrichment Kit is the enrichment of methylated DNA for downstream analysis, it was the only kit in this study that showed a significant increase in coverage % for C. neoformans and K. pneumoniae . The protocol for this kit involves the collection of the supernatant resulting from the methylated DNA binding step and the two wash solutions (with a total volume of 500µl) followed by isolating the unmethylated DNA from these solutions. The main drawback of this method is the large final volume which leads to the dilution of the microbial DNA resulting in lower yield after purification. An improvement in this step might make this protocol superior to the other protocols used in this study. For the MspJI assays, both the fragmented and non-fragmented protocols didn’t show any significant increase in reads % or coverage % compared to the unprocessed samples except with S. aureus in both reads and coverage %. In contrast, Oyola et al. [ 12 ] investigated human genome depletion in their malaria clinical samples using MspJI and found significant enrichment in malarial DNA of up to ≈ 9-fold compared to undepleted samples; this could be due to the difference in sample type and pathogen type between the two studies. The limitation of this study includes the low reads of the pathogenic yeast C. neoformans (average ≈ 0.266 reads percentage) in the unprocessed samples although the starting concentration in the mock sample was high and the genome size is 19 Mb. This could be due to the hard and resistant capsule which can be improved in the extraction step by using a bead-beating step in the extraction protocol [ 25 ]. We avoided using this step to keep the nucleic acid intact as much as possible not to affect the depletion methods that require a non-fragmented genome [ 23 ]. Another limitation of the study is the higher number of reads for K. pneumoniae among the reads of the other pathogens which could be because K. pneumoniae is a Gram-negative bacteria making it easier to extract than Gram-positive bacteria ( S. aureus) [ 26 ]. Finally, the MspJI F protocol showed consistency between duplicate samples in PCR but not in NGS results which was contrary to the findings of Oyola et al [ 12 ], this could be due to the difference in library preparation protocols. In Oyola et al study, the authors performed the fragmentation using Covaris before MspJI digestion and then proceeded directly to library preparation which did not involve further fragmentation. While in our study, we used NEB fragmentase followed by MspJI digestion then followed the procedure for the Nextera XT DNA Library Prep Kit which involved further fragmentation using transposome. In conclusion, among the methods used in this study, both NEBNext Microbiome DNA Enrichment Kit and MethylMiner™ Methylated DNA Enrichment Kit were the most effective at depleting human DNA and enriching the spiked pathogen in the mock sample. The results from this study need to be validated on more clinical samples with known pathogens to validate the usefulness of this approach. Declarations Funding This research work was funded by Institutional Fund Projects under grant no (IFPRC-021-140-2020). Therefore, authors gratefully acknowledge technical and financial support from the Ministry of Education and King Abdulaziz University, Jeddah, Saudi Arabia Acknowledgments The authors are thankful to Dr. Muhammad Yasir Norwali for providing consultations on bacterial isolation and Hessa Al-Sharif, Randa Ba-Abdulah, Ahmed Hassan, Mohamed Al-Saadi and Norah Uthman for technical assistance. 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Mol Cell Probes 24(2):110–113 Tables Table I Primers and probes for Real time PCR targeting C. neoformans and C-myc (for cellular DNA) Target Name Sequence Ref C. neoformans CneoFwd 5′-GCCGCGACCTGCAAAG-3′ [27] CneoRev 5′-GGTAATCACCTTCCCACTAACACAT-3′ CneoProbe 5′-FAM-ACGTCGGCTCGCC-BHQ1-3′ C-myc (for cellular DNA) C-myc F 5′- GCCAGAGGAGGAACGAGCT -3′ [28] C-myc R 5′- GGGCCTTTTCATTGTTTTCCA -3′ C-myc TM 5′-NED-ATGCCCTGCGTGACCAGATCC-BHQ2-3′ Table II . Genome Sequences Used for Reference Mapping Microorganism Reference Name Gene Bank Accession # Human Homo sapiens GRCh38.p12 Primary Assembly CM000663.2 to CM000686.2 C. neoformans ‎ Cryptococcus neoformans ‎ var. neoformans ‎ JEC21 AE017341-356 K. pneumoniae Klebsiella pneumoniae‎‎ subsp. pneumoniae ‎‎ HS11286 CP003223 - 228 + CP003200 S. aureus‎‎ Staphylococcus aureus‎‎ subsp. aureus ‎‎ NCTC 8325 CP000253 Table III . The Average Reads Percent, and Coverage Percent for the Pathogens Included in the Study Sample Non-fragmented Group Fragmented Group Un-processed NEB MspJI Un-processed F Methyl MspJI F Target No. No. P value No. P value No. No. P value No. P value Reads % Average Human 88.09 77.33 0.005 89.73 0.222 90.63 86.87 0.044 73.24 0.268 C. neoformans 0.0084 0.0075 0.666 0.2149 0.412 0.0109 0.9374 0.007 0.4186 0.324 K. pneumoniae 10.00 19.93 0.005 3.11 0.002 7.53 9.70 0.160 19.52 0.258 S. aureus‎‎ 1.90 2.73 0.014 6.95 0.012 1.83 2.49 0.021 6.82 0.347 Coverage % Average C. neoformans 0 0 ---- 0.2 0.423 0 0.78 0.046 0.38 0.426 K. pneumoniae‎‎ 10.54 10.45 0.849 9.79 0.300 10.9 16.88 0.004 13.34 0.589 S. aureus‎‎ 52.7 57.05 0.285 81.45 0.037 57.9 32.1 0.064 55.15 0.814 Table IV The Reads Per Million (RPM), and Coverage Percent of some Microbes Included in the Throat Swab Sample Treated with NEBNext Microbiome DNA Enrichment Kit TS Proceeded TS Un-Proceeded No. of Reads RPM Coverage% No. of Reads RPM Coverage% # Reads 2,334,282 2,946,142 No. of Reads Mapped to Homo 1,826,322 782,391 2,472,890 839,366 No. of Reads Mapped to Candida albicans 28,839 12,355 3.80 30,403 10,320 0.59 No. of Reads Mapped to Haemophilus influenzae 24,919 10,675 43.50 16,884 5,731 38.00 No. of Reads Mapped to Haemophilus parainfluenzae 47,539 20,366 81.00 36,034 12,231 77.00 No. of Reads Mapped to Streptococcus pneumoniae 28,913 12,386 55.20 21,329 7,240 51.40 Additional Declarations The authors declare no competing interests. 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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-5991450","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":413151267,"identity":"b287df0a-cdf0-4f11-aafc-8944a62c6500","order_by":0,"name":"Suha A. Farraj","email":"","orcid":"","institution":"Special Infectious Agents Unit, King Fahd Medical Research Center, King Abdulaziz University, Saudi Arabia","correspondingAuthor":false,"prefix":"","firstName":"Suha","middleName":"A.","lastName":"Farraj","suffix":""},{"id":413168754,"identity":"428ad500-6734-42b7-8911-728d11b004c8","order_by":1,"name":"Sherif A. El-Kafrawy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIie2PvwrCMBCHrxSc/DdaFPsKka6Kr3Il4FQnwdVO6aK46lsUBFcbOrgU50xiEZwEFRcFBVMEx1A3wXyQOwL3cb8D0Gh+E4P77ayb8fsf5XC438tagX6tFJ18ih3EEZ/h1ibCu5I7g2ZZoHm+KRSS9JCHOGiFor9wJwwcS2DBGqsU8AhPb2gspRKVGLihVKCoCjY9nfkOsbsU3p4/GYykYl4eqmOEBzIYulIxqdyCRCDUVVuIOBB5PtJVcnCcxqbWmicpqzeUwWiajhE784DureOwbZfXNL4cVcE+VDGrNfkMP5cAUIlyDmo0Gs3f8QI7KFqw8QgFoAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-3667-7529","institution":"Special Infectious Agents Unit, King Fahd Medical Research Center, King Abdulaziz University, Saudi Arabia","correspondingAuthor":true,"prefix":"","firstName":"Sherif","middleName":"A.","lastName":"El-Kafrawy","suffix":""},{"id":413168755,"identity":"23418be8-67e6-4a02-8298-285f01692bf3","order_by":2,"name":"Taha A. Kumosani","email":"","orcid":"","institution":"Biochemistry Department, Faculty of Sciences, King Abdulaziz University, Saudi Arabia","correspondingAuthor":false,"prefix":"","firstName":"Taha","middleName":"A.","lastName":"Kumosani","suffix":""},{"id":413168756,"identity":"d2c024d9-a31d-4aff-b515-71efe77929c9","order_by":3,"name":"Jehad M. Yousef","email":"","orcid":"","institution":"Department of Biochemistry, College of Sciences, University of Jeddah, Jeddah, Saudi Arabia","correspondingAuthor":false,"prefix":"","firstName":"Jehad","middleName":"M.","lastName":"Yousef","suffix":""},{"id":413168757,"identity":"270e15aa-6ba3-4857-bf97-4ff051a27635","order_by":4,"name":"Tagreed L. Alsubhi","email":"","orcid":"","institution":"Special Infectious Agents Unit, King Fahd Medical Research Center, King Abdulaziz University, Saudi Arabia","correspondingAuthor":false,"prefix":"","firstName":"Tagreed","middleName":"L.","lastName":"Alsubhi","suffix":""},{"id":413168758,"identity":"49199271-14cc-4d25-b893-89e3b875a560","order_by":5,"name":"Thamir A. Alandijany","email":"","orcid":"","institution":"Special Infectious Agents Unit, King Fahd Medical Research Center, King Abdulaziz University, Saudi Arabia","correspondingAuthor":false,"prefix":"","firstName":"Thamir","middleName":"A.","lastName":"Alandijany","suffix":""},{"id":413168759,"identity":"801f64ce-c9ab-48f0-9bba-7b292c5c82b1","order_by":6,"name":"Leena H. Bajrai","email":"","orcid":"","institution":"Special Infectious Agents Unit, King Fahd Medical Research Center, King Abdulaziz University, Saudi Arabia","correspondingAuthor":false,"prefix":"","firstName":"Leena","middleName":"H.","lastName":"Bajrai","suffix":""},{"id":413168760,"identity":"263a0e7a-b5db-4eb6-9a83-405d38014c1b","order_by":7,"name":"Samar A. Badreddine","email":"","orcid":"","institution":"Infection Control Department, Dr. Soliman Fakeeh Hospital, Jeddah, Saudi Arabia","correspondingAuthor":false,"prefix":"","firstName":"Samar","middleName":"A.","lastName":"Badreddine","suffix":""},{"id":413168761,"identity":"c828abdf-a0d7-4169-ac00-765196332e99","order_by":8,"name":"Ahmed Al Atrouni","email":"","orcid":"","institution":"Microbiology Department, Dr. Soliman Fakeeh Hospital, Jeddah, Saudi Arabia","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"Al","lastName":"Atrouni","suffix":""},{"id":413168762,"identity":"81600198-8eee-48db-8b59-f47902d67b60","order_by":9,"name":"Esam I. Azhar","email":"","orcid":"https://orcid.org/0000-0002-1736-181X","institution":"Special Infectious Agents Unit, King Fahd Medical Research Center, King Abdulaziz University, Saudi Arabia","correspondingAuthor":false,"prefix":"","firstName":"Esam","middleName":"I.","lastName":"Azhar","suffix":""}],"badges":[],"createdAt":"2025-02-09 09:03:34","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5991450/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5991450/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76034359,"identity":"6cf3d1bb-84be-47a9-923b-f24dd7c38bc8","added_by":"auto","created_at":"2025-02-11 15:48:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":133722,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic Presentation of the Host DNA Depletion Protocols Used for Comparison. The depletion methods were divided into two groups after extraction: (1) Non-fragmented Group, which included the use of either NEBNext Microbiome DNA Enrichment Kit or using the restriction enzyme MspJI where both require non-fragmented‎ nucleic acid as a starting material; and (2) Fragmented Group, which included the use of either MethylMiner\u003csup\u003eTM\u003c/sup\u003e Methylated DNA Enrichment Kit or using the restriction enzyme MspJI where both methods require nucleic acid fragmentation of the samples prior used.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5991450/v1/877ec202b26b42a315a4d8be.png"},{"id":76034362,"identity":"80b9b54b-9454-497c-904f-44a3b6f2130d","added_by":"auto","created_at":"2025-02-11 15:48:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":162649,"visible":true,"origin":"","legend":"\u003cp\u003eAgarose gel electrophoresis showing the DNA digestion using MspJI. The DNA was run on a 1% agarose gel. The figure shows undigested DNA with higher molecular weight and two replicates of the digestion products at the arrow for a replicate sample (MspJI1 \u0026amp; MspJI2).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5991450/v1/5d609eab6fccc9dd7f2f9f06.png"},{"id":76034361,"identity":"4dae6a2d-7d0d-4da9-8ead-152df821714f","added_by":"auto","created_at":"2025-02-11 15:48:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":39800,"visible":true,"origin":"","legend":"\u003cp\u003eDNA percentage for each target in mock sample based on real-time PCR Ct. The percentage of DNA was calculated from fold changes of Ct values of the targets relative to unprocessed sample of each group. Error bars represent the deviation observed in two independent replicates.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5991450/v1/6278064753a67a4d653c064e.png"},{"id":76034365,"identity":"736493fa-1ba9-41da-bbee-e5afe92edd7d","added_by":"auto","created_at":"2025-02-11 15:48:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":71934,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of target reads from the total mapped reads. The percentage of reads generated from NGS for each target (Human, \u003cem\u003eC. neoformans,\u003c/em\u003e \u003cem\u003eK. pneumoniae \u003c/em\u003eand\u003cem\u003e \u0026nbsp;S. aureus)\u003c/em\u003ecalculated relative to the total number of mapped reads\u003cem\u003e.\u003c/em\u003e Error bars represent the standard deviation observed in 2 independent replicates.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5991450/v1/25999cd446d5dd80b9f361b5.png"},{"id":76034373,"identity":"f646c186-7327-42fe-892a-e4de27bb2c4f","added_by":"auto","created_at":"2025-02-11 15:48:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":254803,"visible":true,"origin":"","legend":"\u003cp\u003eThe depth coverage of \u003cem\u003eS. aureu\u003c/em\u003es for the sample with the highest depth between all samples in this study (MspJI1) and the lowest one (Methyl2).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5991450/v1/9556376b91d6a5c8399d8381.png"},{"id":76034367,"identity":"87e327c8-c99e-4aff-85f4-df10e0b0b0ec","added_by":"auto","created_at":"2025-02-11 15:48:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":545320,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical pie plots show bacteria species and hierarchy abundances between (A) un-processed sample and (B) processed sample. The krona classification chart generated by Illumina® DRAGEN Metagenomics app\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5991450/v1/3d3fad58170471b7dad844a1.png"},{"id":76035812,"identity":"f4c1a28a-491d-4646-8e27-d113109ac152","added_by":"auto","created_at":"2025-02-11 16:04:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2165432,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5991450/v1/5daacdc5-f289-45ae-8426-ba32b211c823.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eComparative Study of Host DNA Depletion Methods for Microbial Metagenomics\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1.\tINTRODUCTION","content":"\u003cp\u003eInfectious diseases remain the leading cause of human morbidity and mortality worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Based on World Health Organization (WHO), lower respiratory infections ranked 4th among the top 10 causes of death with an estimated 2.6\u0026nbsp;million deaths in 2019 (WHO, 2020). The critical step in preventing and minimizing the burden of infectious disease is to have a rapid and accurate laboratory identification of the etiological agent [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Up to 70% of the clinical samples are from patients diagnosed with infectious diseases such as encephalitis, meningitis and pneumonia [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Clinical metagenomics (CMg) is a promising application that utilizes the ability of next-generation sequencing (NGS) to sequence all the genome fragments that exist in clinical specimens [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This ability of NGS allowed for the identification of SARS-CoV-2 which reflects the importance of this field in the identification of emerging and re-emerging infections [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe type of clinical samples plays a role in determining the extent of host genome abundance in the generated sequences which might be up to 90% [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Current methods for host DNA depletion utilize the methylation phenomenon of host DNA with 4\u0026ndash;6% of its cytosines are methylated, whereas this is a rare phenomenon in microbes. Theoretically, the removal of methylated DNA from a clinical specimen should be expected to enrich the pathogen\u0026rsquo;s nucleic acid [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral methods for the depletion of human DNA and enrichment of microbial DNA either before or after extraction were employed. The use of combinations of nucleases and selective detergents before extraction for the selective lysis of blood cells to release human DNA (due to their fragile nature compared to microbial cells), followed by using nuclease to degrade the released host DNA was reported by several studies[\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. One drawback of this approach is the possible degradation of the microbial DNA if their cell wall is lysed by the used reagents [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Others have subjected samples to multiple freeze/thaw cycles in order to disrupt human cells but this approach might lead to the disruption of both human cells and pathogens releasing both nucleic acids exposing them to enzymatic digestion in the following steps [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThere are two approaches to depleting host DNA after extraction: the first enriches the microbes\u0026rsquo; genome by using either a protein to bind non-methylated CpG motifs in bacterial genomes or DNA probes complementary to the pathogen\u0026rsquo;s nucleic acid. Both binding options lead to a bias in the distribution of microbial species because of their differential binding to various microbes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The other approach is to enrich the microbial nucleic acids by targeting human DNA through methylated sites where the human genome is rich in methylated cytosine than microbial DNA [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Targeting methylated sites is achieved through 2 procedures: 1) digesting methyl-CpGs by methylation-dependent endonucleases (MspJI in this study) and 2) separating DNA containing methylated CpGs from non-methylated DNA by binding to human MBD2 protein conjugated to magnetic beads (NEBNext Microbiome DNA Enrichment Kit and the MethylMiner\u0026trade; Methylated DNA Enrichment Kit in this study). Although both kits depend on the same principle, the main difference is the size of the human genome used as a template, whereas the first kit requires an intact human genome with at least\u0026thinsp;\u0026ge;\u0026thinsp;15 kb, the second kit requires a fragmented DNA with an average size of \u0026lt;\u0026thinsp;1000 bp.\u003c/p\u003e\u003cp\u003eThis study was designed to evaluate the effect of different human genome depletion methods on the outcome of NGS data for bacterial DNA detection from mock samples.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2.\tMATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Mock Sample Preparation\u003c/h2\u003e \u003cp\u003eThe mock sample was composed of: Gram-positive bacteria \u003cem\u003eStaphylococcus aureus\u0026lrm;\u0026lrm;\u003c/em\u003e (\u003cem\u003eS. aureus\u003c/em\u003e) \u003cem\u003e\u0026lrm;\u0026lrm;\u003c/em\u003e(8.6\u0026times;10\u003csup\u003e7\u003c/sup\u003e cfu/ml) (National Collection of Type Cultures, England, NCTC#8325) and Gram-negative bacteria \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (\u003cem\u003eK. pneumoniae\u0026lrm;\u0026lrm;\u003c/em\u003e) \u003cem\u003e\u0026lrm;\u003c/em\u003e(9.4\u0026times;10\u003csup\u003e6\u003c/sup\u003e cfu/ml) isolated and characterized from a clinical sample in the microbiology lab at King Abdulaziz University Hospital (KAUH) with encapsulated yeast \u003cem\u003eCryptococcus neoformans\u003c/em\u003e (\u003cem\u003eC. neoformans\u003c/em\u003e) (ATCC, USA, ATCC\u0026reg; 14116) (1.05\u0026times;10\u003csup\u003e6\u003c/sup\u003e cfu/ml). An equal cell number of each pathogen was spiked in the same cell number of HeLa cell suspension (2.7\u0026times;10\u003csup\u003e6\u003c/sup\u003e cells/ml) to mimic a human clinical sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. MagNA Pure Extraction\u003c/h2\u003e \u003cp\u003eNucleic acid extraction of the mock sample (200 \u0026micro;l) was performed using a MagNA Pure Compact NA Isolation Kit I on a MagNA Pure Compact Instrument (Roche, Japan). Samples were pretreated with MagNA Pure Bacteria Lysis Buffer (Roche, Germany), lysozyme, and proteinase K prior to automated extraction as recommended by the manufacturer to ensure complete lysis of the bacteria. Extracted nucleic acid was eluted in 50\u0026micro;l of elution buffer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Host DNA Depletion Methods\u003c/h2\u003e \u003cp\u003eHost DNA depletion methods were divided into two groups (Figure I):\u003c/p\u003e \u003cp\u003e(1) Non-fragmented Group: for procedures that do not require fragmentation of nucleic acid;\u003c/p\u003e \u003cp\u003e(2) Fragmented Group: for procedures that require fragmentation of nucleic acid.\u003c/p\u003e \u003cp\u003eIn all depletion processes, 100 ng of the extracted nucleic acid were used for depletion and the depleted nucleic acid was eluted in 30 \u0026micro;l of elution buffer. Each depletion group had a control sample tested in duplicate which was 100 ng of the extracted sample then followed the same procedure of the test samples without the depletion process. Non-fragmented controls were designated Unprocessed1 and Unprocessed2 and fragmented controls were designated UnprocessedF1 and UnprocessedF2.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Non-fragmented Group: this group included the following two depletion protocols:\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section4\"\u003e \u003ch2\u003e2.3.1.1. NEBNext Microbiome DNA Enrichment Kit\u003c/h2\u003e \u003cp\u003eThe kit utilizes MBD2-Fc protein bound to magnetic beads. This protein binds to CpG-methylated DNA from the host DNA, which is then removed by applying a magnetic field to separate the CpG-methylated DNA of the host and leave the non-CpG-methylated DNA. The procedure was performed according to the manufacturer\u0026rsquo;s instructions, followed by purifying the depleted DNA using Agencourt AMPure XP (Beckman Coulter, USA) protocol. The duplicated samples for this protocol were designated NEB1 and NEB2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section4\"\u003e \u003ch2\u003e2.3.1.2. \u003cem\u003eMspJI\u003c/em\u003e Digestion\u003c/h2\u003e \u003cp\u003eFollowing Oyola \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], MspJI, which is a methylation-dependent restriction endonuclease, was used to digest the methylated CpG DNA. Briefly, digestion was performed by adding extracted DNA, 1x of CutSmart Buffer, 1x of Enzyme Activator Solution with 2.5 units of MspJI, the mixture was then completed to 30 \u0026micro;l using nuclease-free water. The mixture was incubated for 4 h at 37\u0026deg;C, then inactivated for 20 min at 65\u0026deg;C and cooled to 4\u0026deg;C in a Veriti thermal cycler (Applied Biosystems, USA). The digestion product was run on a 1% agarose gel (Figure II), and the high molecular-weight undigested bands were excised and purified using GFX PCR DNA and a Gel Band Purification Kit (Amersham Biosciences, USA) following the manufacturer\u0026rsquo;s instructions. The duplicated samples resulting from this assay were designated MSPJI 1 and MSPJI 2.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Fragmented Group\u003c/h2\u003e \u003cp\u003eSome of the host DNA depletion methods required DNA fragmentation. Fragmentation was performed using NEBNext dsDNA Fragmentase (New England BioLabs, England). The fragmentation master mix was composed of 100ng of extracted DNA, 1x Fragmentase Reaction Buffer v2, and 2 \u0026micro;l dsDNA Fragmentase in a reaction volume of 20 \u0026micro;l by nuclease-free water. The mixture was incubated in a Veriti thermal cycler at 37\u0026deg;C for 20 min; followed by the addition of 5 \u0026micro;l of 0.5 M EDTA to stop the reaction. The fragmented product was purified with 1.8x of AMPure XP beads and eluted in 25 \u0026micro;l of nuclease-free water. Next, the purified fragmented DNA was subjected to one of the two following depletion methods:\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section4\"\u003e \u003ch2\u003e2.3.2.1. MethylMiner\u0026trade; Methylated DNA Enrichment Kit\u003c/h2\u003e \u003cp\u003eThis kit utilized an MBD2 protein coupled with paramagnetic Dynabeads\u0026reg; M-280 of streptavidin via a biotin linker to capture the methylated DNA. The fragmented DNA was subjected to DNA depletion according to the manufacturer\u0026rsquo;s recommendations. The magnetic beads captured the methylated genomic DNA, and the resulting wash buffer contained the non-methylated microbial DNA. The resulting wash buffers were purified by 1.8x of AMPure XP beads according to the manufacturer\u0026rsquo;s instructions. The eluted products were concentrated into a final volume of 30 \u0026micro;l through evaporation. The above steps were performed in duplicate for each sample and labeled as Methyl1 and Methyl2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section4\"\u003e \u003ch2\u003e2.3.2.2. \u003cem\u003eMspJI\u003c/em\u003e Digestion\u003c/h2\u003e \u003cp\u003eFragmented DNA was digested by MspJI, as described previously, following the protocol of the Oyola group [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Then, the digested DNA was purified by an equal volume of AMPure XP beads. The duplicated samples were designated as MspJI F1 and MspJI F2.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Assessment of depletion efficacy using Real-time PCR\u003c/h2\u003e \u003cp\u003eReal-time PCR was performed to assess the efficiency of DNA depletion using primers and probes (Table I) targeting C-myc gene as a representative for human DNA, and \u003cem\u003eC. neoformans\u003c/em\u003e using QuantiFast-Probe-RT-PCR Kit (Qiagen, Germany). The amplification mix consisted of 1x QuantiFast PCR Master Mix (w/o ROX), 0.8\u0026micro;M of each primer, 0.2\u0026micro;M of the specific probe, 0.2\u0026micro;l of 50X ROX dye solution, 0.2\u0026micro;l of QuantiFast RT Mix, 5\u0026micro;l of extracted nucleic acid and the mix was completed to 20\u0026micro;l by nuclease-free water. Amplification was performed on Applied Biosystems\u0026reg; 7500 fast (Applied Biosystems, Singapore) with a thermal profile: 10min at 50\u0026deg;C, 5min at 95\u0026deg;C followed by 40cycles of 10s at 95\u0026deg;C and 30s at 60\u0026deg;C where the fluorescence data were collected. \u003cem\u003eS. aureus\u003c/em\u003e and \u003cem\u003eK. pneumonia\u003c/em\u003e were amplified using FTD Respiratory pathogens 33 kit (Fast Track Diagnostics, Luxembourg), according to manufacturer instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Nucleic acid quantification\u003c/h2\u003e \u003cp\u003eQuantification of the nucleic acid from the original samples and depleted host DNA obtained in this study using different protocols were quantified by Qubit 2.0 (Life Technologies, USA) using a Qubit\u0026trade; dsDNA HS assay kit (Life Technologies, USA) following the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Library preparation\u003c/h2\u003e \u003cp\u003eNextera XT DNA library prep (Illumina, USA) was used following the manufacturer\u0026rsquo;s instructions, with the normalization step performed manually according to the manufacturer\u0026rsquo;s instructions for samples with low concentrations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Pooling libraries and loading into MiSeq\u003c/h2\u003e \u003cp\u003eQubit\u0026trade; dsDNA HS assay kit was used to quantify each library, and the average size of each library was estimated using a 2100 Bioanalyzer System (Agilent, Germany) using Agilent High Sensitivity DNA Kit (Agilent, USA). Each library was diluted to 4 nM using elution buffer; then, 5 \u0026micro;l of each library was pooled together and diluted to 10 pM following the Denature and Dilute Libraries Guide. Sample data including indexes were loaded onto the Illumina Experimental Manager 1.15.1 software. The final library mix was added to the MiSeq Reagent Kit v2 (300-cycles) (Illumina, Singapore), and the run was performed on a MiSeqDX platform (Illumina, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Analysis of NGS Data\u003c/h2\u003e \u003cp\u003eThe MiSeq generated two demultiplexing FASTQ read files for each sample (R1 and R2). Data analysis was performed using GENEIOUS 11.1.5 software. First, duplicate reads were removed using Dedupe version 37.64 then each sample\u0026rsquo;s R1 and R2 read files were merged with BBMerge from the BBtools suite. The bad quality sequences from both ends were trimmed by BBDuk algorithm from the BBTools with error probability\u0026thinsp;=\u0026thinsp;0.05. The remaining sequence reads were mapped with standard Geneious mapper [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] to the reference sequences for each microorganism used in the mock sample (Table II). The mapping process was sequentially performed to avoid overlap between common reads hence avoiding calculation bias starting with \u003cem\u003eS. aureus\u003c/em\u003e then the remaining unmapped reads were mapped to \u003cem\u003eK. pneumonia\u003c/em\u003e\u0026lrm;\u0026lrm; then the unmapped reads were mapped to \u003cem\u003eC. neoformans\u003c/em\u003e and finally, the unmapped reads were mapped to Homo sapiens.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Statistical Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe results of real-time PCR where evaluated in Fold changes (FC) and were calculated based on Ct values using the equation FC\u0026thinsp;=\u0026thinsp;2^ΔCt, where ΔCt = (Ct unprocessed - Ct processed). The DNA percentage of different targets in the depleted samples was calculated from fold changes of targets in the unprocessed specimens [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe NGS yield of the test samples was compared by both the percentage of reads mapped to the reference sequence following the equation: [number of reads mapped/total number of reads mapped (pathogen\u0026thinsp;+\u0026thinsp;host) \u0026times; 100] [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and the percentage of reference coverage of each microorganism (coverage %).\u003c/p\u003e \u003cp\u003eOne-way ANOVA was used to check the statistical significance of the differences between different assays, and a p-value of \u0026le;\u0026thinsp;0.05 was considered as statistically significant [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Applying the selected depletion method to clinical samples\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA clinical throat swab sample was received for respiratory pathogen detection. Ethical approval (ref#290\u0026thinsp;\u0026minus;\u0026thinsp;17) was obtained from the unit of Biomedical Ethics in KAUH, the sample was run in duplicate, with and without human DNA depletion, in NGS. The generated reads were analyzed using metagenomics online apps: Illumina\u0026reg; DRAGEN Metagenomics app using Kraken2 algorithm (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://basespace.illumina.com/apps/9929920/DRAGEN-Metagenomics-Pipeline?preferredversion\u003c/span\u003e\u003cspan address=\"https://basespace.illumina.com/apps/9929920/DRAGEN-Metagenomics-Pipeline?preferredversion\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and CosmosID\u0026rsquo;s bioinformatics platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cosmosid.com/platform\u003c/span\u003e\u003cspan address=\"https://www.cosmosid.com/platform\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for identifying the microbes.\u003c/p\u003e \u003cp\u003eThe enrichment factors for the identified microbes were calculated using the following equation: Enrichment Factor for a microbe\u0026thinsp;=\u0026thinsp;percentage of reads for this microbe in the depletion assay (processed) / percentage of reads for this microbe in non-depleted samples (unprocessed) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003eIn this study, host DNA depletion methods were divided into two groups:\u003c/p\u003e \u003cp\u003e(1) Non-fragmented Group: where nucleic acids were used by NEBNext Microbiome DNA Enrichment Kit or MspJI restriction enzyme without fragmentation, or\u003c/p\u003e \u003cp\u003e(2) Fragmented Group: where extracted nucleic acids were fragmented prior to use in the two methods: MethylMiner\u0026trade; Methylated DNA Enrichment Kit or MspJI restriction enzyme.\u003c/p\u003e \u003cp\u003eReal-time PCR was performed to assess the yield of \u003cem\u003eC. neoformans, S\u003c/em\u003e. \u003cem\u003eaureus, K. pneumonia\u003c/em\u003e, \u0026lrm;\u0026lrm;and the human genome (as represented by C-myc) in processed samples by comparison to unprocessed samples. \u003cem\u003eK. pneumonia\u0026lrm;\u0026lrm;\u003c/em\u003e was not detected by real-time PCR after 40 PCR amplification cycles in the unprocessed samples in both fragmented and non-fragmented\u0026lrm; protocols and was not included in Real-time PCR comparison. The fold changes calculated from Ct values are shown in Figure III. No significant increase in the targets\u0026rsquo; enrichment was observed by real-time PCR. In contrast, a significant depletion of the targets together with the C-myc was observed in the MspJI fragmentation protocol (\u003cem\u003eC. neoformans\u003c/em\u003e p\u0026thinsp;=\u0026thinsp;0.012 and \u003cem\u003eS\u003c/em\u003e. \u003cem\u003eaureus\u003c/em\u003e\u0026lrm;\u0026lrm; p\u0026thinsp;=\u0026thinsp;0.008) as well as the Methyl protocol for \u003cem\u003eS\u003c/em\u003e. \u003cem\u003eaureus\u003c/em\u003e \u0026lrm;\u0026lrm;(p\u0026thinsp;=\u0026thinsp;0.052).\u003c/p\u003e \u003cp\u003eThe FASTQ files R1 and R2 for each sample were merged to generate raw sequence reads with an average number of reads of 2,113,159 reads per sample, and a range of 1,006,432 to 3,103,410. Removing duplicate reads resulted in a reduction in the number of reads for non-fragmented group samples by \u0026asymp;\u0026thinsp;30% whereas in the fragmented group samples, the number of reads decreased by \u0026asymp;\u0026thinsp;47% of the original number of reads. The unprocessed samples in each group showed the least percentage of decrease after duplicate removal (23% vs 34% for the non-fragmented and fragmented groups, respectively). After trimming low-quality sequences from both sides, the unique reads in the mock sample were mapped to reference sequences for each pathogen and the human genome. After duplicate read removal and trimming of low-quality sequences, samples showed consistency in target reads percentage among duplicate samples. This consistency between duplicate samples was highest in the unprocessed samples of both groups. The MspJI F samples from the Fragmented Group showed the lowest consistency between duplicate samples (Figure IV).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eComparing the percentage of reads (depleted vs non-depleted) for each target (Table III), we found the NEB and Methyl protocols to show a significant decrease in human reads (p\u0026thinsp;=\u0026thinsp;0.005 and p\u0026thinsp;=\u0026thinsp;0.044, respectively). For the \u003cem\u003eS. aureus\u003c/em\u003e, MspJI protocol showed a significant increase in target reads (p\u0026thinsp;=\u0026thinsp;0.012) as well as the Methyl protocol (p\u0026thinsp;=\u0026thinsp;0.021) and the NEB protocol (p\u0026thinsp;=\u0026thinsp;0.014). The only protocol that showed a significant increase in target reads for \u003cem\u003eK. pneumoniae\u003c/em\u003e was the NEB protocol (p\u0026thinsp;=\u0026thinsp;0.005) while the Methyl protocol was the only protocol to show a significant increase in \u003cem\u003eC. neoformans\u003c/em\u003e reads (p\u0026thinsp;=\u0026thinsp;0.007).\u003c/p\u003e\u003cp\u003eThe Methyl protocol showed a significant increase in coverage% of \u003cem\u003eC. neoformans\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.046) and \u003cem\u003eK. pneumoniae\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.004) (Table III). While the best increase of \u003cem\u003eS. aureus\u003c/em\u003e coverage was accomplished by MspJI assay (p\u0026thinsp;=\u0026thinsp;037). The coverage % for each pathogen was found to be reflected in the depth of its coverage and is inversely proportional to the size of the pathogen genome. For example, the mean depth of \u003cem\u003eS. aureus\u003c/em\u003e, with the highest coverage among the spiked pathogens and has the smallest genome, was the highest in MspJI (2.25) (Figure V) compared to the other pathogens that showed negligible depth.\u003c/p\u003e\u003cp\u003eThe NEB protocol was applied to a patient's throat swab and the result was compared to the result of the same sample without depletion. \u003cem\u003eCandida albicans\u003c/em\u003e was found in the sample beside a number of pathogen bacteria which were: \u003cem\u003eHaemophilus influenzae\u003c/em\u003e, \u003cem\u003eHaemophilus parainfluenzae\u003c/em\u003e and \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e (Table IV). The depletion protocol showed an improvement in the microbes' results against the un-processed for both number of assembled reads and % coverage against their references (figure VI shows bacteria distribution as an example). That has clearly appeared in the enrichment factor where \u003cem\u003eCandida albicans\u003c/em\u003e increased 120 fold, \u003cem\u003eHaemophilus influenzae\u003c/em\u003e increased 186 fold, \u003cem\u003eHaemophilus parainfluenzae\u003c/em\u003e increased 167 fold, and finally, \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e increased 171 fold. That was a consequence of a 6% reduction of human reads in the processed sample against the un-processed one.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"4.\tDISCUSSION","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eHost genome depletion plays an important role in improving the identification of microbial genomes directly from clinical samples using NGS by increasing the number of reads for the microbial genomes. In this study, we compared four protocols for depleting the human genome (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eAs a general observation in this study, the percentage of duplicate reads generated from NGS showed that the fragmentation step caused an increase in duplicate reads of about 17%. Moreover, the depletion step increased the duplicate reads by about 10% compared to the undepleted controls.\u003c/p\u003e \u003cp\u003eTwo depletion protocols showed a significant depletion of the human genome or significant enrichment for two of the three pathogens used in this study namely the NEBNext Microbiome DNA Enrichment Kit and the MethylMiner\u0026trade; Methylated DNA Enrichment Kit where both kits utilize the binding of MBD2 protein to methylated DNA. The NEBNext Microbiome DNA Enrichment Kit was used effectively for DNA depletion in previous studies. Thoendel \u003cem\u003eet al.\u003c/em\u003e showed 5% enrichment in \u003cem\u003eS. aureus\u003c/em\u003e DNA by spiking the bacterium into uninfected sonicate fluids [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The same improvement was observed in this study, where an overall enrichment of 11% for all spiked pathogens compared to unprocessed samples was found. Another study reported an enrichment in \u003cem\u003eE. coli\u003c/em\u003e reads of about 75% compared to unenriched samples using the NEBNext Microbiome DNA Enrichment Kit. The same study evaluated the kit on a mock sample containing 90% human and 10% \u003cem\u003eP. falciparum\u003c/em\u003e DNA and found an 8-fold increase in the Plasmodium DNA reads [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. From 120 to 186 folds increase was found when we applied this protocol to a clinical throat sample, this difference could be due to the varying levels of CpG methylation density between the human genome in the clinical sample and the Hela cells used in the mock sample.\u003c/p\u003e \u003cp\u003eAlthough the main purpose of the MethylMiner\u0026trade; Methylated DNA Enrichment Kit is the enrichment of methylated DNA for downstream analysis, it was the only kit in this study that showed a significant increase in coverage % for \u003cem\u003eC. neoformans\u003c/em\u003e and \u003cem\u003eK. pneumoniae\u003c/em\u003e. The protocol for this kit involves the collection of the supernatant resulting from the methylated DNA binding step and the two wash solutions (with a total volume of 500\u0026micro;l) followed by isolating the unmethylated DNA from these solutions. The main drawback of this method is the large final volume which leads to the dilution of the microbial DNA resulting in lower yield after purification. An improvement in this step might make this protocol superior to the other protocols used in this study.\u003c/p\u003e \u003cp\u003eFor the MspJI assays, both the fragmented and non-fragmented protocols didn\u0026rsquo;t show any significant increase in reads % or coverage % compared to the unprocessed samples except with \u003cem\u003eS. aureus\u003c/em\u003e in both reads and coverage %. In contrast, Oyola \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] investigated human genome depletion in their malaria clinical samples using MspJI and found significant enrichment in malarial DNA of up to \u0026asymp;\u0026thinsp;9-fold compared to undepleted samples; this could be due to the difference in sample type and pathogen type between the two studies.\u003c/p\u003e \u003cp\u003eThe limitation of this study includes the low reads of the pathogenic yeast \u003cem\u003eC. neoformans\u003c/em\u003e (average\u0026thinsp;\u0026asymp;\u0026thinsp;0.266 reads percentage) in the unprocessed samples although the starting concentration in the mock sample was high and the genome size is 19 Mb. This could be due to the hard and resistant capsule which can be improved in the extraction step by using a bead-beating step in the extraction protocol [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. We avoided using this step to keep the nucleic acid intact as much as possible not to affect the depletion methods that require a non-fragmented genome [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnother limitation of the study is the higher number of reads for \u003cem\u003eK. pneumoniae\u003c/em\u003e among the reads of the other pathogens which could be because \u003cem\u003eK. pneumoniae\u003c/em\u003e is a Gram-negative bacteria making it easier to extract than Gram-positive bacteria (\u003cem\u003eS.\u003c/em\u003e aureus) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Finally, the MspJI F protocol showed consistency between duplicate samples in PCR but not in NGS results which was contrary to the findings of Oyola \u003cem\u003eet al\u003c/em\u003e [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], this could be due to the difference in library preparation protocols. In Oyola \u003cem\u003eet al\u003c/em\u003e study, the authors performed the fragmentation using Covaris before MspJI digestion and then proceeded directly to library preparation which did not involve further fragmentation. While in our study, we used NEB fragmentase followed by MspJI digestion then followed the procedure for the Nextera XT DNA Library Prep Kit which involved further fragmentation using transposome.\u003c/p\u003e \u003cp\u003eIn conclusion, among the methods used in this study, both NEBNext Microbiome DNA Enrichment Kit and MethylMiner\u0026trade; Methylated DNA Enrichment Kit were the most effective at depleting human DNA and enriching the spiked pathogen in the mock sample. The results from this study need to be validated on more clinical samples with known pathogens to validate the usefulness of this approach.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research work was funded by Institutional Fund Projects under grant no (IFPRC-021-140-2020). Therefore, authors gratefully acknowledge technical and financial support from the Ministry of Education and King Abdulaziz University, Jeddah, Saudi Arabia\u003c/p\u003e\u003ch2\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eAcknowledgments\u003c/span\u003e \u003c/h2\u003e \u003cp\u003eThe authors are thankful to Dr. Muhammad Yasir Norwali for providing consultations on bacterial isolation and Hessa Al-Sharif, Randa Ba-Abdulah, Ahmed Hassan, Mohamed Al-Saadi and Norah Uthman for technical assistance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBullman S, Meyerson M, Kostic AD (2017) Emerging Concepts and Technologies for the Discovery of Microorganisms Involved in Human Disease. Annu Rev Pathol 12:217\u0026ndash;244\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaliendo AM, Gilbert DN, Ginocchio CC, Hanson KE, May L, Quinn TC, Tenover FC, Alland D, Blaschke AJ, Bonomo RA, Carroll KC, Ferraro MJ, Hirschhorn LR, Joseph WP, Karchmer T, MacIntyre AT, Reller LB, Jackson AF (2013) Infectious Diseases Society of, \u003cem\u003eBetter tests, better care: improved diagnostics for infectious diseases\u003c/em\u003e. Clin Infect Dis 57:S139\u0026ndash;S170\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBousbia S, Raoult D, La Scola B (2013) Pneumonia pathogen detection and microbial interactions in polymicrobial episodes. Future Microbiol 8(5):633\u0026ndash;660\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. The top 10 causes of death\u0026lrm; (2014) [cited 2016 November 04\u0026lrm;]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePolage CR, Cohen SH (2016) State-of-the-Art Microbiologic Testing for Community-Acquired Meningitis and Encephalitis. J Clin Microbiol 54(5):1197\u0026ndash;1202\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForbes JD, Knox NC, Peterson CL, Reimer AR (2018) Highlighting Clinical Metagenomics for Enhanced Diagnostic Decision-making: A Step Towards Wider Implementation. Comput Struct Biotechnol J 16:108\u0026ndash;120\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDatta S, Budhauliya R, Das B, Chatterjee S, Vanlalhmuaka, Veer V (2015) Next-generation sequencing in clinical virology: Discovery of new viruses. World J Virol 4(3):265\u0026ndash;276\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiller RR, Montoya V, Gardy JL, Patrick DM, Tang P (2013) Metagenomics for pathogen detection in public health. Genome Med 5(9):81\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLecuit M, Eloit M (2014) The diagnosis of infectious diseases by whole genome next generation sequencing: a new era is opening. Front Cell Infect Microbiol 4:25\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrey KG, Bishop-Lilly KA (2015) Next-Generation Sequencing for Pathogen Detection and Identification\u0026lrm;. Methods Microbiol 42:\u0026ndash;525\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBikel S, Valdez-Lara A, Cornejo-Granados F, Rico K, Canizales-Quinteros S, Soberon X, Del Pozo-Yauner L, Ochoa-Leyva A (2015) Combining metagenomics, metatranscriptomics and viromics to explore novel microbial interactions: towards a systems-level understanding of human microbiome. Comput Struct Biotechnol J 13:390\u0026ndash;401\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOyola SO, Gu Y, Manske M, Otto TD, O'Brien J, Alcock D, Macinnis B, Berriman M, Newbold CI, Kwiatkowski DP, Swerdlow HP, Quail MA (2013) Efficient depletion of host DNA contamination in malaria clinical sequencing. J Clin Microbiol 51(3):745\u0026ndash;751\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou L, Pollard AJ (2012) A novel method of selective removal of human DNA improves PCR sensitivity for detection of Salmonella Typhi in blood samples. BMC Infect Dis 12:164\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorz HP, Scheer S, Huenger F, Vianna ME, Conrads G (2008) Selective isolation of bacterial DNA from human clinical specimens. J Microbiol Methods 72(1):98\u0026ndash;102\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorz HP, Scheer S, Vianna ME, Conrads G (2010) New methods for selective isolation of bacterial DNA from human clinical specimens. Anaerobe 16(1):47\u0026ndash;53\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHandschur M, Karlic H, Hertel C, Pfeilstocker M, Haslberger AG (2009) Preanalytic removal of human DNA eliminates false signals in general 16S rDNA PCR monitoring of bacterial pathogens in blood. Comp Immunol Microbiol Infect Dis 32(3):207\u0026ndash;219\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHansen WL, Bruggeman CA, Wolffs PF (2009) Evaluation of new preanalysis sample treatment tools and DNA isolation protocols to improve bacterial pathogen detection in whole blood. J Clin Microbiol 47(8):2629\u0026ndash;2631\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHasan MR, Rawat A, Tang P, Jithesh PV, Thomas E, Tan R, Tilley P (2016) Depletion of Human DNA in Spiked Clinical Specimens for Improvement of Sensitivity of Pathogen Detection by Next-Generation Sequencing. J Clin Microbiol 54(4):919\u0026ndash;927\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLewandowska DW, Zagordi O, Geissberger FD, Kufner V, Schmutz S, Boni J, Metzner KJ, Trkola A, Huber M (2017) Optimization and validation of sample preparation for metagenomic sequencing of viruses in clinical samples. Microbiome 5(1):94\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaly GM, Bexfield N, Heaney J, Stubbs S, Mayer AP, Palser A, Kellam P, Drou N, Caccamo M, Tiley L, Alexander GJ, Bernal W, Heeney JL (2011) A viral discovery methodology for clinical biopsy samples utilising massively parallel next generation sequencing. PLoS ONE 6(12):e28879\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeehery GR, Yigit E, Oyola SO, Langhorst BW, Schmidt VT, Stewart FJ, Dimalanta ET, Amaral-Zettler LA, Davis T, Quail MA, Pradhan S (2013) A method for selectively enriching microbial DNA from contaminating vertebrate host DNA. PLoS ONE 8(10):e76096\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, Buxton S, Cooper A, Markowitz S, Duran C, Thierer T, Ashton B, Meintjes P, Drummond A (2012) \u003cem\u003eGeneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data\u003c/em\u003e, in \u003cem\u003eBioinformatics\u003c/em\u003e. pp. 1647-9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeravi FS, Zakrzewski M, Vickery K, Hu H (2020) Host DNA depletion efficiency of microbiome DNA enrichment methods in infected tissue samples. J Microbiol Methods 170:105856\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThoendel M, Jeraldo PR, Greenwood-Quaintance KE, Yao JZ, Chia N, Hanssen AD, Abdel MP, Patel R (2016) Comparison of microbial DNA enrichment tools for metagenomic whole genome sequencing. J Microbiol Methods 127:141\u0026ndash;145\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBolano A, Stinchi S, Preziosi R, Bistoni F, Allegrucci M, Baldelli F, Martini A, Cardinali G (2001) Rapid methods to extract DNA and RNA from Cryptococcus neoformans. FEMS Yeast Res 1(3):221\u0026ndash;224\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKetchum RN, Smith EG, Vaughan GO, Phippen BL, McParland D, Al-Mansoori N, Carrier TJ, Burt JA, Reitzel AM (2018) DNA Extraction Method Plays a Significant Role When Defining Bacterial Community Composition in the Marine Invertebrate Echinometra mathaei. Front Mar Sci, 5(255)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVeron V, Simon S, Blanchet D, Aznar C (2009) Real-time polymerase chain reaction detection of Cryptococcus neoformans and Cryptococcus gattii in human samples. Diagn Microbiol Infect Dis 65(1):69\u0026ndash;72\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchroeder K, Nitsche A (2010) Multicolour, multiplex real-time PCR assay for the detection of human-pathogenic poxviruses. Mol Cell Probes 24(2):110\u0026ndash;113\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eI\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Primers and probes for Real time PCR targeting C. neoformans and C-myc (for cellular DNA)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eTarget\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eName\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 367px;\"\u003e\n \u003cp\u003eSequence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cem\u003eC.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eneoformans\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eCneoFwd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 367px;\"\u003e\n \u003cp\u003e5\u0026prime;-GCCGCGACCTGCAAAG-3\u0026prime;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 94px;\"\u003e\n \u003cp\u003e[27]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eCneoRev\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 367px;\"\u003e\n \u003cp\u003e5\u0026prime;-GGTAATCACCTTCCCACTAACACAT-3\u0026prime;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eCneoProbe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 367px;\"\u003e\n \u003cp\u003e5\u0026prime;-FAM-ACGTCGGCTCGCC-BHQ1-3\u0026prime;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 99px;\"\u003e\n \u003cp\u003eC-myc\u003c/p\u003e\n \u003cp\u003e(for cellular DNA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eC-myc\u0026nbsp;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 367px;\"\u003e\n \u003cp\u003e5\u0026prime;- GCCAGAGGAGGAACGAGCT -3\u0026prime;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 94px;\"\u003e\n \u003cp\u003e[28]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eC-myc\u0026nbsp;R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 367px;\"\u003e\n \u003cp\u003e5\u0026prime;- GGGCCTTTTCATTGTTTTCCA -3\u0026prime;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eC-myc\u0026nbsp;TM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 367px;\"\u003e\n \u003cp\u003e5\u0026prime;-NED-ATGCCCTGCGTGACCAGATCC-BHQ2-3\u0026prime;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eII\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eGenome Sequences Used for Reference Mapping\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMicroorganism\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene Bank Accession #\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eHuman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003eHomo sapiens GRCh38.p12 Primary Assembly\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eCM000663.2 to CM000686.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003eC. neoformans \u0026lrm;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003e\u003cem\u003eCryptococcus neoformans \u0026lrm;\u0026nbsp;\u003c/em\u003evar. \u003cem\u003eneoformans\u0026nbsp;\u003c/em\u003e\u0026lrm; JEC21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eAE017341-356\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003eK. pneumoniae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;Klebsiella pneumoniae\u0026lrm;\u0026lrm;\u003c/em\u003e subsp. \u003cem\u003epneumoniae\u003c/em\u003e\u0026lrm;\u0026lrm; HS11286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eCP003223 - 228 + CP003200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003eS. aureus\u0026lrm;\u0026lrm;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus aureus\u0026lrm;\u0026lrm;\u003c/em\u003e subsp. \u003cem\u003eaureus\u003c/em\u003e\u0026lrm;\u0026lrm; NCTC 8325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eCP000253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eIII\u003c/strong\u003e\u003cstrong\u003e. The Average Reads Percent, and Coverage Percent for the Pathogens Included in the Study Sample\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"766\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-fragmented Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 285px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFragmented Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eUn-processed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 131px;\"\u003e\n \u003cp\u003eNEB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 114px;\"\u003e\n \u003cp\u003eMspJI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eUn-processed F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 101px;\"\u003e\n \u003cp\u003eMethyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003eMspJI F\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eTarget\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 67px;\"\u003e\n \u003cp\u003eReads % Average\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eHuman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e88.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e77.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e89.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e90.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e86.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e73.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cem\u003eC. neoformans\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.0084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.0075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.2149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.0109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.9374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.4186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cem\u003eK. pneumoniae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e19.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e7.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e9.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e19.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cem\u003eS. aureus\u0026lrm;\u0026lrm;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e6.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e6.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 67px;\"\u003e\n \u003cp\u003eCoverage % Average\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cem\u003eC. neoformans\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e----\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cem\u003eK. pneumoniae\u0026lrm;\u0026lrm;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e10.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e10.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e9.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e16.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e13.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cem\u003eS. aureus\u0026lrm;\u0026lrm;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e52.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e57.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e81.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e57.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e32.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e55.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eIV\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;The Reads Per Million (RPM), and Coverage Percent of some Microbes Included in the Throat Swab Sample Treated with NEBNext Microbiome DNA Enrichment Kit\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"824\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 303px;\"\u003e\n \u003cp\u003eTS Proceeded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 295px;\"\u003e\n \u003cp\u003eTS Un-Proceeded\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eNo. of Reads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eRPM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eCoverage%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eNo. of Reads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eRPM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eCoverage%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e# Reads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e2,334,282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e2,946,142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eNo. of Reads Mapped to Homo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1,826,322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e782,391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e2,472,890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e839,366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eNo. of Reads Mapped to Candida albicans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e28,839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e12,355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e30,403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e10,320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eNo. of Reads Mapped to \u003cem\u003eHaemophilus influenzae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e24,919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e10,675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e43.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e16,884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e5,731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e38.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eNo. of Reads Mapped to \u003cem\u003eHaemophilus parainfluenzae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e47,539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e20,366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e81.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e36,034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e12,231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e77.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eNo. of Reads Mapped to \u0026nbsp;\u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e28,913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e12,386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e55.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e21,329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e7,240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e51.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"King Abdulaziz University","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":"Clinical metagenomics, host DNA depletion, microbiome DNA enrichment, infectious diseases","lastPublishedDoi":"10.21203/rs.3.rs-5991450/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5991450/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOne of the biggest obstacles in applying metagenomics in the clinical field is the increase of human genome reads in the resulting sequences. In this study, three depletion methods were compared that utilize the methylation of cytosine in human DNA as a mechanism of action. Two commercial kits NEBNext Microbiome DNA Enrichment Kit and MethylMiner\u0026trade; Methylated DNA Enrichment Kit bind methylated cytosines to specific antibodies, along with MspJI restriction enzyme that digests methylated cytosines. The depletion methods were applied to a HeLa cell suspension (as a source for the human genome) spiked with well-characterized bacterial isolates of \u003cem\u003eStaphylococcus aureus, \u0026lrm;\u0026lrm;Klebsiella pneumoniae\u003c/em\u003e and \u003cem\u003eCryptococcus neoformans\u003c/em\u003e. The products of the depletion methods were run in parallel with undepleted samples as controls on Miseq.\u0026nbsp;The outcome of the NEBNext Microbiome DNA Enrichment Kit showed the most significant decrease in human genome (p\u0026thinsp;=\u0026thinsp;0.005) compared to undepleted samples and a significant increase in both \u003cem\u003eK. pneumoniae\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.005) and \u003cem\u003eS. aureus\u003c/em\u003e reads (p\u0026thinsp;=\u0026thinsp;0.014). While MethylMiner\u0026trade; Methylated DNA Enrichment kit showed a significant increase for \u003cem\u003eC. neoformans\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.007) besides a significant increase in the percent coverage of the references for \u003cem\u003eC. neoformans\u003c/em\u003e and \u003cem\u003eK. pneumoniae\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.046 and p\u0026thinsp;=\u0026thinsp;0.004, respectively). Results showed that both kits could help in depleting human DNA and enriching spiked pathogens in the study sample. The findings of this study are recommended to be validated on a large number of clinical samples.\u003c/p\u003e","manuscriptTitle":"Comparative Study of Host DNA Depletion Methods for Microbial Metagenomics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-11 15:48:31","doi":"10.21203/rs.3.rs-5991450/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":"93b28073-3dca-4733-98a1-55bd949c7736","owner":[],"postedDate":"February 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44056682,"name":"Molecular Biology"}],"tags":[],"updatedAt":"2025-02-11T15:48:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-11 15:48:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5991450","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5991450","identity":"rs-5991450","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}