Benchmarking microbial DNA enrichment protocols from human intestinal biopsies

Dmitrij Marchukov; Jiaqi Li; Pascal Juillerat; Benjamin Misselwitz; Bahtiyar Yilmaz

doi:10.3389/fgene.2023.1184473

Marchukov, Dmitrij; Li, Jiaqi; Juillerat, Pascal; Misselwitz, Benjamin; Yilmaz, Bahtiyar

2023-04-26 00:00:00

TYPE Original Research PUBLISHED 26 April 2023 DOI 10.3389/fgene.2023.1184473 Benchmarking microbial DNA enrichment protocols from OPEN ACCESS EDITED BY Zheng Kuang, human intestinal biopsies Carnegie Mellon University, United States REVIEWED BY 1† 2,3† 2,3,4 Dmitrij Marchukov , Jiaqi Li , Pascal Juillerat , Honghua Hu, Jinhua Institute of Zhejiang University, 2,3 2,3 Benjamin Misselwitz and Bahtiyar Yilmaz * China Bruno Fosso, 1 2 University Hospital Zürich, University of Zürich, Zürich, Switzerland, Department of Visceral Surgery and University of Bari Aldo Moro, Italy Medicine, Bern University Hospital, University of Bern, Bern, Switzerland, Maurice Müller Laboratories, Justin P. Shaffer, Department for Biomedical Research, University of Bern, Bern, Switzerland, Crohn’s and Colitis Center, University of California, San Diego, Gastroenterologie Beaulieu, Lausanne, Switzerland United States *CORRESPONDENCE Bahtiyar Yilmaz, [email protected] Shotgun metagenomic sequencing is a powerful tool for studying bacterial These authors have contributed equally communities in their natural habitats or sites of infection, without the need for to this work and share ﬁrst authorship cultivation. However, low microbial signals in metagenomic sequencing can be RECEIVED 11 March 2023 overwhelmed by host DNA contamination, resulting in decreased sensitivity for ACCEPTED 10 April 2023 PUBLISHED 26 April 2023 microbial read detection. Several commercial kits and other methods have been developed to enrich bacterial sequences; however, these assays have not been CITATION Marchukov D, Li J, Juillerat P, Misselwitz B tested extensively for human intestinal tissues yet. Therefore, the objective of this and Yilmaz B (2023), Benchmarking study was to assess the effectiveness of various wet-lab and software-based microbial DNA enrichment protocols approaches for depleting host DNA from microbiome samples. Four different from human intestinal biopsies. Front. Genet. 14:1184473. microbiome DNA enrichment methods, namely the NEBNext Microbiome DNA doi: 10.3389/fgene.2023.1184473 Enrichment kit, Molzym Ultra-Deep Microbiome Prep, QIAamp DNA Microbiome COPYRIGHT kit, and Zymo HostZERO microbial DNA kit, were evaluated, along with a software- © 2023 Marchukov, Li, Juillerat, controlled adaptive sampling (AS) approach by Oxford Nanopore Technologies Misselwitz and Yilmaz. This is an open- (ONT) providing microbial signal enrichment by aborting unwanted host DNA access article distributed under the terms of the Creative Commons Attribution sequencing. The NEBNext and QIAamp kits proved to be effective in shotgun License (CC BY). The use, distribution or metagenomic sequencing studies, as they efﬁciently reduced host DNA reproduction in other forums is contamination, resulting in 24% and 28% bacterial DNA sequences, permitted, provided the original author(s) and the copyright owner(s) are credited respectively, compared to <1% in the AllPrep controls. Additional optimization and that the original publication in this steps using further detergents and bead-beating steps improved the efﬁcacy of journal is cited, in accordance with less efﬁcient protocols but not of the QIAamp kit. In contrast, ONT AS increased accepted academic practice. No use, distribution or reproduction is permitted the overall number of bacterial reads resulting in a better bacterial metagenomic which does not comply with these terms. assembly with more bacterial contigs with greater completeness compared to non-AS approaches. Additionally, AS also allowed for the recovery of antimicrobial resistance markers and the identiﬁcation of plasmids, demonstrating the potential utility of AS for targeted sequencing of microbial signals in complex samples with high amounts of host DNA. However, ONT AS resulted in relevant shifts in the observed bacterial abundance, including 2 to 5 times more Escherichia coli reads. Furthermore, a modest enrichment of Bacteroides fragilis and Bacteroides thetaiotaomicron was also observed with AS. Overall, this study provides insight into the efﬁcacy and limitations of various methods for reducing host DNA contamination in human intestinal samples to improve the utility of metagenomic sequencing. KEYWORDS gut micobiome, host DNA depletion, metagemonic, phyloseq, human small intestine, microbial enrichment Frontiers in Genetics 01 frontiersin.org Marchukov et al. 10.3389/fgene.2023.1184473 1 Introduction Shotgun metagenomic sequencing allows the simultaneous analysis of all genetic material present in a sample, regardless of The gut microbiota is a complex community of microorganisms the organisms. This approach enables the identiﬁcation of numerous living in the mammalian digestive tract (Martin et al., 2007; Hilt et al., genes and their variants, along with the reconstruction of enzymatic 2014; Sender et al., 2016). The highly co-evolved mutualism between pathways, thereby providing valuable insights into the functional inhabitants on our body surfaces and the host immune system has capabilities of the microbial community (Ranjan et al., 2016; promoted beneﬁcial co-existence and interdependency over millions Robinson et al., 2021). In addition, recent advancements in of years (Young, 2017). The role of the bacterial microbiota in microbiome research have expanded our ability to investigate the maintaining homeostasis starts at birth and continues throughout microbiota’s functional and genetic proﬁle in speciﬁc regions of the life (Dominguez-Bello et al., 2010; Mueller et al., 2015). It is notably intestine, due to the development of bacterial proﬁling techniques evident that viable gut microbiota is crucial for maintaining the host that can be applied to biopsies rather than stool samples possible health status (Lloyd-Price et al., 2019), and this is in nearly everyone’s (Korem et al., 2015; Suez et al., 2018; Saffarian et al., 2019; Yilmaz interests to keep the habitat and its distinctive niches healthy (Stecher et al., 2019; Zeevi et al., 2019; Yilmaz et al., 2022). This approach has et al., 2005; Smith et al., 2007; Hooper and Macpherson, 2010; Yilmaz been successfully utilized to unravel the molecular and cellular et al., 2014; Macpherson et al., 2018; Uchimura et al., 2018; Lynch and mechanisms underlying gut-associated diseases. For instance, a Hsiao, 2019). The composition of the gut microbiota remains study conducted by Franzosa et al. utilized shotgun metagenomic relatively stable over the years within individuals in the absence of sequencing of colonic biopsies to identify gene-level differences in major events such as medications or surgery. Over time, gut microbial the microbial community between patients with Crohn’s disease and strains undergo genetic changes via various mechanisms (e.g., healthy individuals (Franzosa et al., 2019). This approach revealed mutations, horizontal and vertical gene transfer), and selection that Crohn’s disease was associated with signiﬁcant alterations in resulting in rapid adaption and/or long-term evolution of sub- bacterial metabolic pathways, including amino acid metabolism, strains. These processes can lead to positive and negative dynamic energy production, and xenobiotic biodegradation. Furthermore, structural and functional changes in the gut, which in turn might also the use of biopsy-based bacterial proﬁling has allowed for a better impact human health (Yilmaz et al., 2021). understanding of the microbial communities’ spatial organization in Changes in the gut microbiota have been associated with a wide the intestine, with studies showing differences in microbial range of diseases, including inﬂammatory bowel diseases (IBD) composition and diversity across various intestinal regions, (Lloyd-Price et al., 2019; Yilmaz et al., 2019), celiac diseases including the duodenum, jejunum, ileum, and colon. For (Olivares et al., 2018), colorectal cancer (CRC) (Feng et al., 2015; instance, a study by Leite et al. (2020) using 16 S rRNA gene Liang et al., 2017; Yu et al., 2017)(Cheng et al., 2020), chronic sequencing of duodenal biopsies from healthy individuals inﬂammation and metabolic diseases (Cox et al., 2014) such as revealed a distinct microbial community structure compared to obesity (Ley et al., 2005)(Backhed et al., 2004; Turnbaugh et al., that observed in fecal samples, highlighting the importance of 2006) and diabetes (Stewart et al., 2018; Zhou et al., 2022). However, analyzing speciﬁc regions of the intestine to gain a more studying the intestinal microbiome in the context of these diseases comprehensive understanding of the microbiota’s functional and poses unique technical challenges. Identifying the diversity of gut genetic proﬁle. Therefore, the use of biopsy-based bacterial proﬁling microbiota using culture-based methods can be a laborious and has provided a promising avenue for investigating the molecular and time-consuming process that is often unable to capture the full range cellular mechanisms underlying gut-associated diseases and holds of microbial species present. However, some studies have attempted great potential for future microbiome research. to address this limitation by using over 60 different culture Bacterial metagenomic sequencing requires a sufﬁcient conditions to isolate the most abundant taxa. In these studies, abundance of microbial DNA without large-scale host DNA the researchers were able to successfully culture an average of contaminations. However, biopsies or whole-tissue isolates 95% of the operational taxonomic units (OTUs) present at contain large bulks of host DNA, vastly outnumbering microbial greater than 0.1% abundance in fecal samples. (Browne et al., DNA (de Albuquerque et al., 2022). This phenomenon is not limited 2016; Lau et al., 2016). In recent years, molecular-based to intestinal biopsies. We have recently conducted a study with approaches that do not rely on cultivation, such as 16 S rRNA ileostomy patients, where we identiﬁed highly dynamic components gene sequencing and metagenomics, have brought a paradigm shift of the microbiota present in the small intestine. These components to our comprehension of the human microbiome’s involvement in were found to be highly responsive to dietary changes introduced health and disease. These methods enable a thorough examination after an overnight fast. (Yilmaz et al., 2022). The ratio of microbial/ of the microbial community, including the detection of previously host DNA oscillates in accordance with fasting and feeding, and the un-cultivable bacteria and the evaluation of their functional appearance and disappearance of microbial sub-strains were also potential (Loman et al., 2012). Although 16 S rRNA amplicon strongly associated with the provision of nutrition. This results in a sequencing is an expeditious and cost-effective approach for higher ratio of host/microbe DNA in the fasting state, while the identifying the taxonomic composition of a sample (Schriefer introduction of food leads to blooming in bacterial populations and et al., 2018), it is insufﬁcient for characterizing the functional a lower ratio of host/microbe DNA (Yilmaz et al., 2022). In this type landscape of the gut microbiome to answer inquiries regarding of situation, characterizing the functional and genetic proﬁle of low- microbial activities (Franzosa et al., 2018). Therefore, alternative abundance bacteria in microbiome samples can be a difﬁcult task, strategies, such as shotgun metagenomic approaches, are needed to particularly if the sample is contaminated with host DNA. To investigate the functional potential of the microbiome (Qin et al., overcome this challenge, it is essential to perform host DNA 2010). depletion as the ﬁrst step before conducting deep shotgun Frontiers in Genetics 02 frontiersin.org Marchukov et al. 10.3389/fgene.2023.1184473 metagenomic sequencing. By removing host DNA upstream of biopsies (~2 mm) from 10 subjects, with ﬁve biopsies per subject. sequencing, it is possible to increase the detection of low- Additionally, we included a second test group comprising abundance bacteria, which would otherwise remain undetected. 10 subjects to assess the effectiveness of a non-commercial Nevertheless, the optimal approach to achieve host DNA method (Bruggeling et al., 2021) in combination with two depletion remains undetermined. commercial kits. In this group, three biopsies per subject were To reduce host DNA content and increase the yield of microbiota collected. The procedures used in this study included total DNA DNA prior to sequencing (Heravi et al., 2020), several commercial kits extraction, microbial DNA enrichment, and two variations of the and general laboratory methods have been developed over the past few published laboratory-optimized depletion method (Bruggeling et al., years. Some of them have already proven useful in enriching microbial 2021). DNA from liquid samples such as saliva (Marotz et al., 2018), blood The Bern Human Intestinal Community project was approved (Feehery et al., 2013), sonicated ﬂuid from prosthetic joint components by the Bern Cantonal Ethics Commission (Ref: KEK-BE: 251/14) (Thoendel et al., 2016), human milk (Yap et al., 2020), and with signed informed consent obtained from all participants. cerebrospinal ﬂuid (Hasan et al., 2016; Ji et al., 2020), but also from Additionally, the bowel cleansing study was approved by ethical solid materials such as human breast tissue (Costantini et al., 2018), and approval number 336/2014. Biopsy samples were collected from tissue from an infected diabetic foot (Heravi et al., 2020). Surprisingly, to subjects registered for a screening ileo-colonoscopy without any the best of our knowledge, commercially available kits for the depletion gastrointestinal symptoms and without functional intestinal of host DNA from intestinal tissues have not been systematically symptoms and negative results in all additional workups. The compared or tested, except for a single study that developed a new cohort comprised of 15 males and 5 females within the age range technique to address this challenge. In this study, researchers optimized of 40–60 years old. It is noteworthy that none of the participants had the sample lysis step by incorporating additional detergents and bead- taken antibiotics or any regular medications for the 6 months beating protocols to achieve an efﬁcient host DNA depletion preceding the sampling. The licensed gastroenterologists collected (Bruggeling et al., 2021). The method was demonstrated to be biopsy samples and clinical data of all healthy subjects. Colonic relatively effective in reducing host DNA contamination in human biopsies were initially collected into 2 mL microfuge tubes fecal and mucosal samples. Notably, the approach resulted in higher containing 500 µL RNAlater (Sigma-Aldrich) and stored at −20 C bacterial read yield and a more accurate representation of the microbial until DNA extraction. community compared to the commercially available kits. Structured clinical metadata were prospectively gathered based The present study aimed to evaluate the effectiveness of host on pre-determined standards, documented electronically using DNA depletion methods, including commercially available kits, a Research Electronic Data Capture (REDCap) (Harris et al., 2009) laboratory-optimized protocol, and the software-controlled and handled in R (http://www.r-project.org) using the xlsx and data. enrichment approach of Oxford Nanopore, in enhancing frame packages. Assessment of the microbiota composition from bacterial DNA yields from human intestinal biopsy samples. Our intestinal biopsies was then analyzed according to numerous main objective was to enhance bacterial DNA yields from human parameters such age, sex, and sampling location. Statistical intestinal biopsy samples, and the ﬁndings highlight the limitations analyses were performed using Student’s t-test, Wilcoxon’s rank of existing microbial DNA enrichment tools and the potential sum test, and Pearson’s chi-squared test to assess differences impact of different enrichment methods on the identiﬁcation of between groups. bacterial groups. Despite the challenges of host DNA depletion from intestinal biopsies, we were able to increase the proportion of bacterial DNA to 30%–45% of total DNA in some cases. We also 2.2 Microbial DNA extraction and host DNA observed that different microbial enrichment methods could lead to depletion shifts in the proportion of identiﬁed bacteria groups in each sample. Interestingly, we observed that the software-controlled enrichment Microbial DNA extraction is a critical step in metagenomic approach of Oxford Nanopore increased the base pair numbers studies that can signiﬁcantly impact downstream analyses. To associated with bacterial DNA allowing to assemble the genome ensure reproducibility and minimize bias, we followed the metagenomically but did not signiﬁcantly increase the percentage of manufacturer’s instructions for DNA extraction using bacterial reads compared to commercially available kits. Our study commercially available kits. Speciﬁcally, we employed the kits provides valuable insights into the effectiveness of different shown in Figure 1, which have been extensively validated and microbial DNA enrichment methods and highlights the potential optimized for high yield and purity of DNA from a diverse range for the software-controlled enrichment approach of Oxford of microbial samples. However, these kits and computer-based Nanopore to improve bacterial DNA yield and enable the approaches have not been extensively tested for human intestinal identiﬁcation of rare intestinal bacteria. biopsies. 2.2.1 Total DNA/RNA extraction 2 Materials and methods Total DNA was isolated using AllPrep DNA/RNA Mini Kit (Qiagen) as described before (Yilmaz et al., 2019). 600 µL of RLT 2.1 Sample collection and ethics statement Plus Buffer containing 6 µL beta-mercaptoethanol and a 3 mm bead were added into the tube. Biopsies were homogenized by the Retsch To evaluate the performance of four host DNA depletion kits Tissue Lyser (Qiagen) at 30/frequency for 5 min. Supernatants were and one total DNA extraction control kit, we collected endoscopic transferred into AllPrep DNA mini spin column and centrifuged at Frontiers in Genetics 03 frontiersin.org Marchukov et al. 10.3389/fgene.2023.1184473 FIGURE 1 Schematic representation of DNA isolation protocol strategies used in this study. The bacterial DNA was extracted from biopsies using ﬁve different methods, which included protocols provided by the manufacturers and the laboratory-optimized protocol developed by Bruggeling and colleagues. (Bruggeling et al., 2021). These methods comprised unselective cell lysis kits (Qiagen AllPrep and NEBNext) and selective cell lysis kits (HostZero/QIAamp Microbiome and Molyzm UltraDeep), with or without microbiome enrichment. The resulting DNA samples were then sequenced using Illumina NovaSeq 6,000 in 150 bp paired-end mode. Additionally, DNA samples obtained through the Qiagen AllPrep DNA/RNA extraction kit were also analyzed using adaptive sequencing technologies from Oxford Nanopore. 9000 g for 30 s. DNA attached to spin columns was subjected to (10000g, 5min), the supernatant was discarded, and 100 μLof clean-up using 500 μL of Buffer AW1 and AW2 afterwards. As a last Microbial Selection Buffer and 1 μL of Microbial Selection step, DNA samples were eluted with 30 μL nuclease-free water into Enzyme were added to each tube for incubation at 37 C for 1.5 mL microfuge tubes and stored (−20 C) until proceeding with 30 min. To enhance the depletion of host DNA, 20 μLof downstream steps. The concentration and purity of the isolated Proteinase K were added to the sample and incubated at 55 C for DNA samples were evaluated by NanoDrop (Thermo Scientiﬁc). 30 min 100 μL of DNA/RNA Shield (2X Concentrate) was added. Of note, RNA was extracted following the protocol instructions even For microbial DNA isolation, each sample was treated with ZR though not used in our study. BashingBead Lysis Tube and 750 μL of ZymoBIOMICS Lysis Solution. The Retsch Tissue Lyser (Qiagen) was used for 5 min at a 2.2.1.1 HostZERO microbial DNA kit frequency of 30/min. Next, 400 μL of supernatant was transferred to This kit initially applies the physical homogenization of tissue another collection tube. After adding 1200 μL of ZymoBIOMICS samples with bead-beating, followed by selective chemical lysis of DNA Binding Buffer to the tubes, mixing was done by thoroughly eukaryotic host cells using the Host Depletion Solution, with the pipetting the entire volume up and down ﬁve times. Each sample intention to keep microbial cells intact. In the host depletion part of was then transferred to the Zymo-SpinTM IC-Z Column, and the protocol, 200 μL Host Depletion was added to each sample and ZymoBIOMICS DNA Wash Buffer 1 and Wash Buffer 2 were incubated at room temperature for 15min. Following centrifugation applied in the washing step. After the ﬁnal centrifugation (10000g, Frontiers in Genetics 04 frontiersin.org Marchukov et al. 10.3389/fgene.2023.1184473 2 min) of the washing steps, 30 μL DNase-free water was applied to concentration 1X and tubes were agitated by rotating at room the center of the Zymo-SpinTM IC-Z Column. DNA was eluted by temperature for 15 min. Each tube was then placed on the centrifugation at 10000g for 1 min and stored at −20 C. magnetic rack for 5 min until the beads were collected on the wall of the tube and the solution was clear. The supernatant was 2.2.2 QIAamp DNA microbiome kit removed without disturbing the beads and transferred to a clean This kit also works based on the principle of lysing host cells ﬁrst microcentrifuge tube. This supernatant contains the target microbial and depleting host DNA enzymatically while keeping microbial cells DNA. Afterwards, the ethanol precipitation protocol was followed to intact for the downstream microbial DNA extraction process. elute captured host DNA. 2.5X pure ethanol was added to each Brieﬂy, 500 μL of Buffer AHL was added to each tube, followed sample and then incubated on ice for 10 min. Afterwards, ethanol by incubation at room temperature for 30 min. After a was removed by a centrifugation step at 16000 g for 30 min and centrifugation step at 10000 g for 10 min, the supernatant was pellets were air-dried. removed. 190μL of Buffer RDD and 2.5 μL of Benzonase were added into each tube and incubated at 37 C for 30 min on a 2.2.5 Host DNA depletion by the method proposed thermomixer with shaking at 600rpm) After the addition of by Bruggeling et al. 20 μL Proteinase K, samples were again incubated at 56 C for Bruggeling et al. (2021) proposed a bacterial DNA isolation 30 min on a thermomixer (600rpm). Then, 200 μL Buffer ATL method optimized for the human gut biopsy tissue. We followed the was added to each sample and transferred into Pathogen Lysis protocol described in this manuscript. Brieﬂy, bacteria loosely Tube L. After lysis, samples were heated to 95 C for 5min. bound to the surface of the biopsy were separated by vortexing Following centrifugation, 40 μL Proteinase K was added to each and transferred to another microfuge tube. 20 μL proteinase K and sample, vortexed, and incubated at 56 C for 30 min. Next, 200 μL 0.0125% saponin were then used for digestion and lysis of human Buffer APL was added to each tube followed by incubation at 70 C cells while keeping bacterial cells intact. The resulting cell for 10 min. Afterwards, 200 μL ethanol was added to the lysate and suspension thus contained lyzed human cells and intact bacterial mixed by pulse-vortexing for 15–30 s. 700 μL of the mixture was cells. Next, DNase treatment was applied for human DNA depletion. then transferred into the QIAamp UCP Mini spin column. Washing In the end, each sample had reduced human DNA content and steps using 500 μL of Buffer AW1 and AW2 were done following the intact bacteria. Bacterial cells were then lyzed by utilizing a instructions of the protocol. After the ﬁnal centrifugation of the specialized bead-beating protocol, using 0.5 KU/mL mutanolysin washing steps, 30 μL DNase-free water was applied to the center of (Sigma) and a brief heat shock to ensure susceptibility for the membrane. DNA was eluted by centrifuging at 10000 g for 1 min mechanical lysis. and stored at −20 C. We altered the protocol slightly to increase the yield of microbial DNA: In step 3 of the original protocol, instead of vortexing the 2.2.3 Molzym ultra-deep microbiome prep kit tubes for 5 min samples were put in a bead-beater at 10 Hz for 5 min This kit utilizes a combination of mechanical and enzymatic without beads. Further, instead of adding 2 μL TurboDNAse in 10x lysis to effectively release DNA from cells and includes a bead- Turbo DNAse buffer, we used 2.5 μL DNase I in Buffer RDD of the beating step for efﬁcient cell lysis. This allows the degradation of RNase Free DNase Set by Qiagen. Moreover, in the original protocol, free-ﬂoating and human DNA and isolates the genomic DNA of 20 μL mutanolysin per sample was used, but we reduced it to half. microbes. The DNA was then puriﬁed using silica-based spin HostZERO Microbial DNA Kit and the QIAamp DNA Microbiome column technology. To reduce the interference of host DNA in Kit were tested separately for bead-beating and microbial DNA microbial DNA sequencing, this method selectively lyzed human extraction. cells using CM buffer, followed by degradation of host-released DNA using human DNase (MolDNase B), leaving bacterial cells 2.2.6 Library preparation and shotgun intact. Bacterial cells were then concentrated by centrifugation, and metagenomic sequencing DNA was extracted using enzymes that speciﬁcally target bacterial Due to a low DNA concentration in some groups of samples, we cell walls. To ensure consistency and reproducibility, all steps of the prepared the libraries using the Nextera XT kit, which requires a protocol were conducted precisely following the manufacturer’s minimum of 2 ng DNA as the starting material. DNA libraries were instructions. prepared according to the Nextera DNA Library Prep (Illumina) as instructed in the manufacturer’s protocol and sequenced on 2.2.4 NEBNext microbiome DNA enrichment kit NovaSeq 6,000 (Illumina, 150bp, PE mode). The metaWRAP- In contrast to the three kits described above, the starting material Read_qc module was applied to ﬁlter out the human genome‒ of this kit is total DNA. The NEBNext Microbiome DNA contaminated reads, remove adaptor sequences, and low-quality enrichment kit (New England Biolabs) contains magnetic beads reads, and produce quality reports for each of the sequenced samples that selectively bind to the CpG-methylated host DNA (Feehery prior to the microbial abundance estimation (Uritskiy et al., 2018). et al., 2013). This step facilitates the enrichment of bacterial DNA This pipeline contains the FASTQC (Andrews, 2015) and the and the depletion of host DNA. Brieﬂy, total DNA was extracted BMTagger modules (Rotmistrovsky, 2011). using the AllPrep DNA/RNA Mini Kit (Qiagen) using a slow bead Every sample was subjected to Illumina sequencing, while an beating step at a frequency of 10/min for 5 min. Host methylated additional 5 samples from the initial group that were extracted using DNA was captured using 160 μL of MBD2-Fc-bound magnetic AllPrep later underwent ONT sequencing as well. beads and prepared according to the manufacturer’s instructions. Before conducting any subsequent diversity and taxonomy The undiluted bind/wash Buffer (5X) was added to make the ﬁnal analysis, the read counts of each sample were divided by the total Frontiers in Genetics 05 frontiersin.org Marchukov et al. 10.3389/fgene.2023.1184473 number of reads in that sample, using the library size normalization Taxonomy classiﬁcation and quality control analysis of long- method. The taxonomy proﬁle was assessed using the read sequences were performed using the BugSeq workﬂow which Kraken2 pipeline with a custom RefSeq database following the uses a combination of tools and databases to classify reads into developer’s guideline (Wood et al., 2019) and the Kraken report taxonomic groups and also identify potential contaminant was generated in https://github.com/DerrickWood/kraken2/blob/ sequences (Fan et al., 2021; Chandrakumar et al., 2022). Brieﬂy, master/docs/MANUAL.markdown#custom-databases. To generate the reads were quality-controlled using fastp with a minimum read the CustomDB taxonomy directory with the necessary information, length of 100bp and minimum average read quality of Phred 7 we executed the kraken2-build command with the "--download- (Chen et al., 2018). Then, the reads were mapped to BugSeq’s taxonomy” option. This allowed us to obtain the accession number curated database containing microbial sequences, the human to taxon maps, taxonomic names, and tree information from NCBI. genome, and contaminants using minimap2 in “map-ont” mode However, the information was limited to complete genomes of with the “-a” ﬂag (Li, 2018). The alignments were then reassigned archaea, bacteria, fungi, plants, protozoa, and virus. For using Pathoscope based on a Bayesian statistical framework and the taxonomy classiﬁcations, we utilized Kraken2, with the following lowest common ancestor of alignments was taken (Francis et al., command line serving as a representative example: kraken2 --use- 2013). Finally, the lowest common ancestor of the reassigned reads names--db/home/ubelix/dbmr/terziev/CustomDB--fastq-input-- was calculated using Recentrifuge (Marti, 2019). The output report-zero-counts--conﬁdence 0.1 --threads 12 --minimum-base- obtained with this pipeline was saved in csv ﬁles which were quality 0 --paired--gzip-compressed {input_1. fastq.gz} {input_2. then used to prepare the corresponding tables and ﬁgures in this fastq.gz} -- output {output.reads} --report {output.report} > input. study using GraphPad Prism Version 9.5.1. kraken. 2.2.9 Statistical analysis 2.2.7 Library preparation and sequencing with All statistical analyses were performed using R version 3.6.1 or Oxford Nanopore Technologies (ONT) Prism 9 (GraphPad Software, San Diego, CA). Differences between 1 µg of high molecular weight DNA samples obtained using the groups after library size normalization were evaluated using one- AllPrep DNA/RNA kit were used for Oxford Nanopore adaptive way ANOVA (parametric), followed by Tukey’s honest signiﬁcant sequencing. Library preparation was performed using the SQK- difference test or the two-stage step-up method of Benjamini, LSK110 kit (Oxford Nanopore Technologies, Oxford) following the Krieger, and Yekutieli, as a post hoc test. The effect size was genomic DNA ligation protocol (https://community.nanoporetech. calculated using Cohen’s d in Excel using the following com/protocols/genomic-dna-by-ligation-sqk-lsk110/). Finally, the formula = ABS (AVERAGE (group1) - AVERAGE (group2))/ libraries were loaded separately onto different Nanopore R9.4. (SQRT (((COUNT (group1)—1) * STDEV. S (group1, group2)^2 1 ﬂow cells (FLO-MIN106), one for sequencing with Adaptive + (COUNT (group2) - 1) * STDEV. S (group2,group1)^2)/(COUNT sampling (AS) and one for control sequencing. Both ﬂow cells (group1) + COUNT (group2)—2))). were run simultaneously on a GridION X5 device (MinKNOW The computation of alpha and beta diversity was carried out version 21.11.7; Guppy 5.1.13; Oxford Nanopore Technologies) using different metrics, such as the Shannon index for alpha [ONT]) for up to 72 h. diversity and Aitchison distance for beta diversity. These computations were performed using the phyloseq package in R. 2.2.8 Bioinformatic analysis of Oxford Nanopore (McMurdie and Holmes, 2013; Callahan et al., 2016). Statistical Technologies (ONT) adaptive sampling (AS) analyses were performed using Mann-Whitney U tests for alpha sequencing data diversity and Adonis (PERMANOVA) for beta diversity with The output of AS sequencing runs consists of nanopore reads pairwise comparison (Benjamini-Hochberg false discovery rate in a FASTQ format, accompanied by a csv ﬁle that lists the correction) using pairwiseAdonis R package to conﬁrm the classiﬁcation of each read made by the ReadUntil API (https:// strength (McMurdie and Holmes, 2013; Callahan et al., 2016). github.com/nanoporetech/read_until_api) available in the Microbial changes were tested using multivariate analysis by MinKNOW interface on the GridION machine. This linear models (MaAsLin2) R package (Morgan et al., 2012; classiﬁcation is based on read matching to the user-provided Mallick et al., 2021). Differences of p < 0.05 or adj-p < 0.05 were reference sequence(s), as follows: Under a “depletion” AS runs considered signiﬁcant in all statistical analyses. as follows: the initial 400–600 bases of a strand that translocate through a given pore are used to classify the reads by the ONT’s ReadUntil API. Each read that passed through the nanopore was 3 Results aligned to the human reference genome while it was being sequenced. The alignment occurred at intervals of several bases, 3.1 Commercial kits can enrich the microbial and three types of decisions were made: 1) “no_decision”—the DNA but cannot entirely deplete host DNA read was continued and realigned to the reference(s) after several from intestinal biopsy samples bases (“no decision”), 2) “stop_receiving”—the read was fully sequenced and accepted (“accepted”), and 3) “unblock”—the Depletion of host DNA from biopsies is crucial for identifying sequencing was immediately terminated, and the read was the bacteria present in a speciﬁc region of the human intestine, as rejected. In the “rejected” case, the voltage is reversed at the well as for characterizing the genetic features of these bacteria via pore level and the DNA will be expelled from the pore, metagenomic analysis. We evaluated several commercially available preventing further sequencing Payne et al. (2021). DNA depletion kits, including the HostZERO Microbial DNA kit, Frontiers in Genetics 06 frontiersin.org Marchukov et al. 10.3389/fgene.2023.1184473 TABLE 1 Quality control of DNA extracted by the multiple kits from endoscopic biopsy samples. DNA concentration, purity, and DNA integrity number (DIN) are recorded for each sample. The average values with standard deviations (±) are shown for each category. Extraction Method Sample Size DNA 260/280 ratio 260/230 ratio DN concentration (ng/μL) AllPrep 10 173.28 ± 204.3 1.90 ± 0.01 2.35 ± 0.05 8.25 ± 0.05 NEBNext 6 19.4 ± 6.8 2.01 ± 0.30 2.11 ± 0.30 8.35 ± 0.15 HostZero 8 1.3 ± 1.1 1.75 ± 0.21 0.39 ± 0.21 7.55 ± 0.13 Moyzm 10 53.23 ± 46.1 2.10 ± 0.25 0.85 ± 0.05 8.55 ± 0.35 QIAamp 9 2.92 ± 1.9 2.07 ± 0.22 0.47 ± 0.11 7.25 ± 0.42 AllPrep2 9 285.4 ± 185.1 1.85 ± 0.05 2.15 ± 0.10 8.15 ± 0.11 QIAamp + Lab-optimized 8 15.48 ± 35.6 2.25 ± 0.27 1.25 ± 0.55 8.25 ± 0.15 HostZero + Lab-optimized 6 1.8 ± 1.7 1.85 ± 0.40 0.49 ± 0.01 7.69 ± 0.13 Molzym Ultra-Deep Microbiome Prep Kit, NEBNext Microbiome microbial DNA population to ~28% on average. Similarly, the DNA Enrichment Kit, and QIAamp DNA Microbiome Kit, to assess NEBNext kit enriched the microbial DNA population, yielding their suitability for extracting DNA from intestinal biopsy samples. an average of ~24% microbial DNA (Figure 2A). This These kits employ different strategies for lysing host cells and demonstrates that some of the commercially available enzymatically degrading host DNA, except for the NEBNext kit, microbial DNA enrichment kits are effective in reducing host which utilizes a different approach by selectively removing CpG- DNA from intestinal tissue samples, speciﬁcally the NEBNext methylated host DNA from total DNA extracted from AllPrep and QIAamp methods. However, not all extraction kits were DNA/RNA Mini Kit (Figure 1). able to enrich microbial DNA. Based on the host DNA/ The concentration and purity of the isolated DNA samples were microbial DNA ratios, we concluded that the HostZero and analyzed by NanoDrop (Thermo Scientiﬁc). The purity with Molzym kits did not affect the host DNA content: Extraction different extraction kits was in an acceptable range with a with the control AllPrep kit yielded on average ~1.0% microbial 260 nm/280 nm absorption ratio varying between 1.75 and 2.10 DNA, whereas the Molyzm and HostZero kits resulted in an (Table 1). In addition, the DNA was not fragmented with any of the average of only ~0.2% and ~7.0% bacterial DNA, respectively extraction kits, as indicated by a Bioanalyzer 2,100 measured DNA (Figure 2A). integrity number (DIN) between 7.25 and 8.55. Additional tests We previously demonstrated that investigating SNPs and indicated some impurities with Molzym, QIAamp Microbiome, and structural variants in the most abundant taxa requires over ~90% HostZERO tests revealing relatively low 260/230 ratios. This low of microbial DNA in a given sample with more than 50 million reads ratio is likely due to traces of residual guanidine from the lysis buffer (Yilmaz et al., 2022). However, as shown above, none of the used in column-based kits. However, such impurities typically do commercial kits was able to reduce the host DNA content but not affect downstream sequencing analysis, as stated by the did not achieve the desired purity of ≥90% (Figure 2A). Therefore, manufacturers’ application note. Notably, seven samples were we attempted to optimize the commercial kit protocols (laboratory- excluded from the analysis due to low DNA yield, leaving optimized protocol) by adding additional vortexing steps as well as 43 samples for further analysis. saponin incubation steps as described by Bruggeling et al. (2021) Metagenomic sequencing using the 150 bp PE mode on an before performing all extraction steps of the respective kit. We tested Illumina NovaSeq platform yielded a total of 1′002′860′693 reads this modiﬁcation for the QIAamp and HostZero kits and compared from 43 extracted DNA samples (Supplementary Table S1). Of these the results to those obtained using the AllPrep kit. Our results reads, only 97′015′448 were assigned to the microbial portion using showed that the AllPrep kit with optimization yielded on average the Kraken2 pipeline with a custom dataset containing microbial ~3.0% microbial DNA, slightly higher than without the optimization and human databases (Supplementary Table S1). Notably, steps (Figure 2). While for the QIAamp kit, the optimization did not 66′469′799 reads failed to match any of the databases and hence signiﬁcantly enrich microbial DNA (~13%), the HostZero kit with they were not further analyzed. These reads are likely derived from laboratory-based optimization increased microbial DNA yield to up plant DNA in line with our recent study, which showed that to 20% (Figure 2B). Overall, these results suggest that while Kraken2 could effectively identify plant DNA even in samples commercial kits can reduce host DNA contents in samples, they with abundant host material (Yilmaz et al., 2022). Since plant may not be sufﬁcient for advanced microbial genetic analysis. DNA was not the focus of this study, these reads were not However, since simple additional steps such as extra vortexing further analyzed. and saponin supplementation improved the efﬁcacy, suggesting The most effective kit for microbial DNA enrichment was that there may be room for further improvement in microbial the QIAamp DNA microbiome kit, which enriched the DNA enrichment protocols. Frontiers in Genetics 07 frontiersin.org Marchukov et al. 10.3389/fgene.2023.1184473 FIGURE 2 Microbial enrichment kits reduce host DNA in extracted DNA samples. (A) Percentage of host DNA in samples prepared using different microbiome DNA enrichment methods was compared to the percentage of host DNA extracted using a total DNA extraction kit (AllPrep). (B) As in (A), only the methods of the indicated microbiome DNA enrichment kits were modiﬁed as described by Bruggeling et al. Asterisks for p-values: *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.001. The Cohen’s d value for each comparison is as follows: HostZero versus AllPrep (d = 1.22), Molysis versus AllPrep (d = 0.44), NEBNext versus AllPrep (d = 1.36), QIAamp versus AllPrep (d = 1.72), QIAamp + Lab-optimized versus AllPrep (d = 1.52) and HostZero + Lab- optimized versus AllPrep (d = 1.79). 3.2 Impact of host depletion with different subjecting to extraction using HostZero, NEBNext, and QIAamp extraction kits on microbial community kits compared to the standard AllPrep kit. Moreover, HostZero and abundance and composition QIAamp kits yielded in an increase in the relative abundance of the Micrococcaceae family (Micrococcus luteus species) from the The level of microbial DNA enrichment differs among these kits. Actinobacteria phylum, as well as the Staphylococcaceae family Although none of these enrichments were adequate for conducting a (Staphylococcus aureus species) and Streptococcaceae family comprehensive genetic proﬁle of the most prevalent taxa in the gut, (Streptococcus genus) of the Firmicutes phylum, even though to a we next investigated the similarity of the microbial community lesser extent (Figure 3C). composition in samples extracted with different kits (Figure 3). The Taxa shifts could also be demonstrated when the lab-based microbiota of the HostZero and QIAamp kits show a greater optimization protocol was applied before the usage of the diversity of species, even with the laboratory-optimized HostZero and QIAamp kits (Figure 3D). Changes in taxa optimization protocol (Figure 3A). Further, relative composition observed with the HostZero lab-optimized approach were similar differences of the intestinal microbiota were found with NEBNext, to those without the lab-optimized protocol shown in Figure 3C. HostZero and QIAamp kits compared to standard AllPrep kit, Furthermore, under these conditions, the HostZero kit led to the assessed by PCA with Aitchison distance (p < 0.01) (Figure 3B). enrichment of the Bacteroidaceae family (Bacteroides genus) of These ﬁndings remained robust, also when the laboratory-optimized Bacteroidetes and the Lachnospiraceae family (Lachnoclostridium protocol was utilized (p < 0.05). Furthermore, our analysis of all and Blautia genera) of Firmicutes, but the QIAamp kit did not show samples collectively revealed a positive correlation between this effect (Figure 3D). These ﬁndings suggest that shifts in the microbial enrichment and alpha diversity, with statistical observed bacterial compositional due to the usage of different host signiﬁcance observed (p < 0.05) (Supplementary Figure S1A). DNA depletion kits affect only a relatively small number of taxa, Additionally, Supplementary Figure S1B reveals a clustering primarily from the Proteobacteria and Firmicutes phyla and less pattern among the samples based on their microbial DNA from Actinobacteria and Bacteroidetes. abundance. Variations in host depletion methods affected certain phyla and families of bacteria much stronger than others. Speciﬁcally, we 3.3 Bacterial sequence enrichment in human observed signiﬁcant changes in the Actinobacteria, Bacteroidetes, intestinal samples using ONT adaptive Firmicutes, and Proteobacteria phyla (Figures 3C, D; Supplementary sampling Table S2). Within the Proteobacteria phylum, the Pseudomonadales and Enterobacterales orders, as well as the Xanthomonadaceae Our ﬁndings showed that the ability to detect bacterial strains in (Stenotrophomonas genus), Burkholderiaceae, Enterobacteriaceae, intestinal biopsies with high levels of host DNA (>98%) is rather and Sphingomonadaceae families, were more enriched after insufﬁcient or ineffective with the existing wet-lab procedures of Frontiers in Genetics 08 frontiersin.org Marchukov et al. 10.3389/fgene.2023.1184473 FIGURE 3 Shifts in the observed bacterial composition induced by various host depletion kits. The bacterial DNA of human intestinal biopsies was analyzed by shotgun metagenomic analysis. (A) Alpha diversity between different host depletion kits was measured using the Shannon index and presented on box-and-whisker plots displaying quartiles, range, and standard deviations. (B) Differences in microbial composition between the groups were analyzed with Aitchison distance. Ellipsoids represent the 95% conﬁdence interval of the position of each group. The non-parametric Mann-Whitney U-test and the Adonis test were used to determine statistically signiﬁcant differences between groups regarding alpha diversity (A) and beta diversity (B), respectively. (C) A heatmap was generated to show the relative abundance of each taxon that differed between host depletion kits compared to the standard AllPrep kit. (D) A similar heatmap was generated for host depletion kits combined with the lab-optimized approach compared to the standard AllPrep kit. A p-value less than 0.05 was considered signiﬁcant, and signiﬁcant taxa are shown on the right panel of each heatmap with the color representing associations calculated using -log (q-value)*sign (coefﬁcient), where "+" represents (Continued) Frontiers in Genetics 09 frontiersin.org Marchukov et al. 10.3389/fgene.2023.1184473 FIGURE 3 (Continued) an adj-p-value <0.05. Asterisks for p-values: *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.001. The Cohen’s d value for each comparison is as follows for (A): HostZero versus AllPrep (d = 1.33), Molysis versus AllPrep (d = 1.82), NEBNext versus AllPrep (d = 0.03), QIAamp versus AllPrep (d = 1.47), QIAamp + Lab-optimized versus AllPrep (d = 1.15) and HostZero + Lab-optimized versus AllPrep (d = 1.65). FIGURE 4 ONT AS enriches sequencing yield and the number of sequenced bacterial reads from biopsies. (A) The comparison between the percentage of host DNA in samples sequenced with (AS, n = 5) and without (Control, n = 5) the adaptive sampling approach was based on multiple parameters, including total bases, the total number of reads, and the mean read length (base pair). The bar plots show the most abundant enriched taxa classiﬁed from (B) reads and (C) metagenomic assemblies for samples sequenced on ONT with and without adaptive sampling. Samples ending with “AS” annotation are sequenced with the ONT AS approach, and the rest are without this approach. Asterisks for p-values: *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.001. selective lysis of host and microbial cells or selective removal of efforts to reduce it, the prevalence of host DNA reads remained CpG-methylated host DNA. Therefore, we carried out an alternative relatively high in all samples. However, the length distribution of to lab-based depletion or enrichment approaches which is based on host DNA reads was restricted to approximately 500 base pairs, a software-controlled enrichment method by depletion of unwanted suggesting a discernible impact on both the mean read length and DNA during sequencing with providing a target DNA sequence the read length N50. (Martin et al., 2022) and it is called adaptive sampling (AS). ONT AS Our assessment of AS efﬁciency by comparing the host and method allows the currently being sequenced DNA fragment in a microbial read numbers revealed a statistically signiﬁcant, albeit given pore to be compared instantly with provided references to relatively modest depletion rate (between 0.5% and 0.7%) determine whether to sequence the DNA further (accepted or compared to traditional wet-lab-based approaches undecided) or reject it from the pore (rejected), which increases (Figure 4A). Due to the removal of human reads from the the sequencing capacity for molecules of interest (Loose et al., 2016). sequencing pool, which comprised the majority of reads in To test the capacity of host DNA depletion with AS, we used ﬁve human biopsy samples, sequencing with the AS approach DNA samples primarily extracted with the AllPrep kit and yielded shorter human DNA read lengths. Additionally, the sequenced on Illumina platform using 150 bp PE mode (Figures ejection of DNA strands from the pore requires a recovery 2A, 3). We re-sequenced these samples using ONT, with one group time, leading to a lower number of reads generated with the serving as a “control group” without adaptive sampling and the AS approach. Consequently, the total output of the AS approach other one as a “AS group” with potential reduction or depletion of was reduced due to both the lower number and shorter length of host DNA reads (Figure 4). reads, while allowing for a substantially greater number of non- Overall, ONT adaptive sampling yielded 50% less total bases of human bases to be sequenced in our compartment of interest raw data (average of ~20′915′948′394 bp in the control group and (Supplementary Table S3; Figure 4A). ~11′745′064′707 bp in AS group) and 3 times more reads (average Taxonomic proﬁling revealed AS led to a 2–5-fold increase in of 4′700′719 reads in the control group and 15′331′338 reads in the the number of reads corresponding to Escherichia coli in analyzed depletion group) (Figure 4A; Supplementary Table S3). Despite samples compared to traditional ONT sequencing without AS Frontiers in Genetics 10 frontiersin.org Marchukov et al. 10.3389/fgene.2023.1184473 (Figure 4B; Supplementary Table S4). Additionally, we observed a genome and sequencing only those areas in order to identify the modest enrichment of Bacteroides fragilis and Bacteroides areas of interest accurately. Therefore, the key difference between thetaiotaomicron in sample 4AS. In a supplementary analysis, we these two approaches is that kit-based microbial DNA enrichment investigated the efﬁcacy of AS approach in generating better approaches are more robust and can be used across most hosts, while metagenomic assemblies from human intestinal biopsy samples. adaptive sampling methods used in ONT require prior knowledge of Our ﬁndings revealed that the AS enabled us to assemble more the host for accurate results. However, the overall efﬁcacy of bacterial contigs with greater completeness compared to the non-AS different assays varied, and some methods yielded acceptable approach (Supplementary Table S5; Figure 4C). Speciﬁcally, in results with up to 28% of host DNA depletion. However, no sample 4AS, we were able to assemble approximately 57% method depleted 90% of the host DNA, which is required for (3.5 Mbp) of the B. fragilis genome with 4X coverage, while only highly sophisticated analyses such as bacterial genome analyses. 3% (166 Kbp) of its genome could be assembled from sequence Further, potential biases, such as the preferential enrichment of generated without AS (Supplementary Table S5; Figure 4C). speciﬁc microbial taxa remain a concern. Moreover, our results demonstrated that the greater genome In this study, we ﬁrst assessed the capacity and performance of completeness achieved with AS allowed us to recover various commercially available host DNA depletion kits, such as the antimicrobial resistance markers in sample 4AS, which were HostZERO Microbial DNA kit, the Molzym Ultra-Deep not detectable in the non-AS sample 4. Speciﬁcally, we were Microbiome Prep Kit, the NEBNext Microbiome DNA able to identify a CepA beta-lactamase and a tetracycline- Enrichment Kit, and the QIAamp DNA Microbiome Kit for resistant ribosomal protection protein in sample 4AS. In extracting DNA from intestinal biopsy samples (Figure 1). These contrast, these markers were not identiﬁed in the non-AS kits employ different techniques for lysing host cells and sample 4. AS also enabled the identiﬁcation of two plasmids enzymatically degrading host DNA. The NEBNext Microbiome in sample 4AS, which were not identiﬁed in sample 4 (Table 2). DNA Enrichment Kit and QIAamp DNA Microbiome Kit Overall, these results demonstrate the potential utility of the AS depleted host DNA by up to ~28% (Figure 2). The NEBNext kit approach for the targeted sequencing of speciﬁcmicrobial taxa in uses a distinct approach by selectively eliminating CpG-methylated complex samples containing high amounts of host DNA and host DNA from total DNA extracted using the AllPrep DNA/RNA highlight the potential advantages of the AS approach for Mini Kit, and our results conﬁrm the potential utility of this generating high-quality metagenomic assemblies and approach. In a previous study with complex respiratory samples, identifying important biological features, such as antimicrobial NEBNext also showed an effective host DNA depletion (Thoendel resistance markers and plasmids. et al., 2016). However, in previous analyses, this method showed poorer results, such as no effective host DNA reduction from saliva samples (Marotz et al., 2018), and relatively low host DNA depletion 4 Discussion from resected arthroplasty components and sonicated ﬂuids from prosthetic joint infections (Nelson et al., 2019). Metagenomic shotgun sequencing of bacterial populations and The tested wet-lab-based enrichment methods are not without advanced downstream analysis techniques are powerful techniques limitations and biases. Host DNA depletion can introduce a bias to assess the impact of environmental insults or host-derived factors. toward the identiﬁcation of speciﬁc microorganisms. The kits are However, obtaining sufﬁcient bacterial DNA from intestinal tissues designed to remove host-associated DNA and proteins but may also can be challenging due to the presence of high amounts of host remove microorganisms that are either closely associated with host DNA, which vastly outnumbers microbial DNA. Therefore, cells or show DNA characteristics similar to mammalian DNA. This substantially decreasing the amount of human DNA is crucial for can lead to an underrepresentation of speciﬁc microorganisms in the the successful application of metagenomic sequencing. Host DNA ﬁnal enriched sample. When analyzing shotgun metagenomic depletion kits are a recent development in the ﬁeld aiming to enrich datasets for shifts in richness and taxonomy proﬁle, we observed microbial DNA in host-associated samples such as blood, feces, that all kits, except for the Molzym kit, affected certain bacterial urine, saliva, or biopsies. These kits and previous work have phyla and families stronger than others. Speciﬁcally, signiﬁcant advanced a number of strategies; however, a systematic changes were observed in the Actinobacteria, Bacteroidetes, comparison of these approaches alone and in combination has Firmicutes, and Proteobacteria phyla. Within the Proteobacteria not been done. To address this, we tested host DNA depletion phylum, the Pseudomonadales and Enterobacterales orders, as well from human intestinal biopsies using i) wet-lab approaches using as the Xanthomonadaceae, Burkholderiaceae, Enterobacteriaceae, commercial kits and a protocol inspired by Bruggeling et al. (2021) and Sphingomonadaceae families, were more enriched after the and ii) a software-based enrichment protocol using a nanopore extraction using HostZero, NEBNext, and QIAamp kits compared sequencing platform. to the standard AllPrep kit (Figure 3). The reasons for the selective Kit-based microbial DNA enrichment approaches are designed enrichment or decrease for the mentioned taxa are unclear. On the to be effective across most hosts, regardless of the type of microbe other hand, the microbiota of the HostZero and QIAamp kits present in the sample. They rely on a series of predetermined steps showed a greater diversity of species compared to the standard that are optimized to extract the DNA of interest. In contrast, a AllPrep kit. Therefore, these kits have a higher potential to detect software-based adaptive sampling method in ONT requires prior bacteria of lower abundance in intestinal biopsies, which might be knowledge of the host. It is based on the electrical properties of DNA beneﬁcial in some situations. molecules as they pass through tiny pores. The adaptive sampling A potential alternative to host DNA depletion kits is the use of approach involves selecting speciﬁc areas of interest within the host Oxford Nanopore Technologies’ (ONT) adaptive sampling (AS) feature, Frontiers in Genetics 11 frontiersin.org Marchukov et al. 10.3389/fgene.2023.1184473 TABLE 2 ONT adaptive sampling improves the detection of plasmids. Two plasmids were identiﬁed in sample 4 with adaptive sampling but not without AS. Cluster IDs reﬂect unique taxonomic identiﬁers for plasmids and are stable over time. MRF: Metapair information and oriT: origin of transfer. Plasmid MPF oriT Median size Coverage Predicted host Nearest NCBI Replicon Relaxase cluster ID type type(s) across range accession type(s) type(s) samples (bp) AA336 MPF MOB 103819 bp 5X Enterobacterales LT985217 IncFIA MOB F F F AG294 —— 17450 bp 3X Escherichia CP019906 —— which has been shown to increase sequencing depth in bacterial from the small intestine of six cured colorectal cancer patients who sequences without altering microbial composition when a human fasted overnight and consumed a standardized breakfast over 6–10 h reference genome is provided. In fact, recent studies have (Yilmaz et al., 2022). Our results showed that microbial reads demonstrated that ONT’s adaptive sampling method reliably increases obtained from Illumina 150 pb paired-end sequencing increased overall diversity and sequencing depth in clinical metagenomic samples signiﬁcantly as time progressed after feeding. However, the sub- such as ~113-fold increase in clinical samples of respiratory tract strains of the blooming bacteria could only be identiﬁed once infections (Gan et al., 2021), ~30-fold enrichment of 148 human microbial reads reached over 78% of the total DNA within a genes associated with hereditary cancers (Kovaka et al., 2021), a ~14- given sample. These ﬁndings highlight the importance of effective fold increase of the least abundant species in a mock community (Martin host DNA depletion to enhance microbial read recovery and et al., 2022), and a 40-fold enrichment of a ZymoBIOMICS mock downstream metagenomic analysis. Based on the previous and metagenomic community (Payne et al., 2021). In our hands, adaptive current ﬁndings, we conclude that current commercial kits have sampling yielded a modest enrichment of bacterial sequences, non- the ability to partially deplete host DNA; however, the depletion etheless, the overall higher number of bacterial reads enabled a more rates achieved are not sufﬁcient for sub-strain analysis or structural complete assembly of bacterial genomes and a better identiﬁcation of analysis of the bacterial genomes, even of abundant bacterial species. bacterial plasmids. However, even with adaptive sampling, complete In conclusion, the present study provides valuable insights into genomic assembly of even the most abundant species has not been the efﬁcacy and limitations of host DNA depletion methods for possible, indicating the need for further improvement. microbiome studies in human intestinal samples. It also highlights It is worth noting, however, that ONT AS approach also changed the necessity for further development of more effective methods to the overall identiﬁed bacterial composition by depleting some microbial optimize bacterial DNA yields. With current technologies, researchers sequences. Particularly, sequencing of the genetic material of the designing an analysis pipeline involving microbial DNA enrichment Enterobacteriaceae family, such as Escherichia coli, was favoured by steps must scrutinize several factors when selecting the most suitable this method, similar to the results observed with the DNA depletion kits approach for their research. Speciﬁcally, parameters such as the (Figure 4B). On the other hand, one sample by ONT AS had enriched sample type, the bacterial species or pathogen(s) of interest, cost, sequences of two Bacteroides taxa, which had not been affected by DNA and the level of enrichment should be considered. It is crucial to note depletion kits (Figures 4B,C). These observations highlight potential that the starting proportion of bacterial DNA in a sample highly limitations of adaptive sampling, and further investigations are inﬂuences the enrichment factor. Therefore, a sample with lower warranted to determine the optimal sequencing approach for microbial content will experience a higher fold enrichment than a different types of microbiome studies. One limitation of our study sample with a higher initial microbial DNA content, provided that an was that we did not evaluate the wet-lab technique using a host DNA equal amount of host DNA is removed. For instance, it is unfeasible to depletion kit and subsequently employing adaptive sampling in ONT. achieve a 500-fold enrichment if microbial DNA initially constituted This might help to increase the number of bacterial reads in a tested only ≤1% of the total DNA (Shi et al., 2022). In these situations, it may sample; however, this will also increase the overall sequencing cost per be worth exploring the use of alternative technologies for bacterial sample. Furthermore, we did not examine the potential usage of genome analysis, such as single-cell or long-read sequencing, which formalin-ﬁxed parafﬁn-embedded (FFPE) tissue, which could serve may be less impacted by host DNA contamination. as a valuable resource for microbial characterization of tissue sections studied previously. However, microbial DNA extracted FFPEs might be affected by various factors, such as damage caused during the ﬁxation Data availability statement and embedding process, and a potentially low quantity of microbial DNA in the sample. Generally, the yield of DNA from FFPE is lower The datasets generated for this study are available through the than those obtained from fresh or frozen tissue, which suggests that the Sequence Read Archive NCBI. The Nanopore datasets for this study tested protocols in this study may not necessarily lead to improved can be found in the BioProject ID: PRJNA943380 and BioSample: microbial enrichment for FFPEs. SAMN33717862 (http://www.ncbi.nlm.nih.gov/bioproject/943380). Host DNA depletion up to 30%–50% can be considered an acceptable range for taxonomy classiﬁcation using shotgun metagenomic approaches. However, it is important to note that Ethics statement having over half of the reads assigned to host DNA still poses a challenge in bacterial genome assembly and identiﬁcation of SNPs The studies involving human participants were reviewed and and structural variants. In a recent study, we collected stoma content approved by Bern Cantonal Ethics Commission (Ref: KEK-BE: 251/ Frontiers in Genetics 12 frontiersin.org Marchukov et al. 10.3389/fgene.2023.1184473 14). The patients/participants provided their written informed Nanopore dataset was supported by the BugSeq Platform (https:// consent to participate in this study. bugseq.com) and we thank to Dr. Sam Chorlton for guiding us with the interpretation of the data. Author contributions Conﬂict of interest DM and BY designed the experiments. PJ collected the biopsy samples. DM and JL sequenced the biopsy samples. BY and JL The authors declare that the research was conducted in the analyzed the data. DM, BM, and BY wrote the manuscript. All absence of any commercial or ﬁnancial relationships that could be authors contributed to revisions of the manuscript, and read, and construed as a potential conﬂict of interest. approved the submitted version. Publisher’s note Funding All claims expressed in this article are solely those of the authors and This work was supported by SNF Ambizione Grant (PZ00P3_ do not necessarily represent those of their afﬁliated organizations, or 185880) and Novartis Foundation for Medical-Biological Research those of the publisher, the editors and the reviewers. Any product that (#19A013) to BY. BY has also received funding from SNF Starting may be evaluated in this article, or claim that may be made by its Grant TMSGI3_211300. The funder was not involved in the study manufacturer, is not guaranteed or endorsed by the publisher. design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication. Supplementary material Acknowledgments The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.1184473/ We also thank the University of Bern Next-Generation full#supplementary-material Sequencing (NGS) Platform at VetSuisse Faculty for sequencing SUPPLEMENTARY FIGURE S1 and the Interfaculty Bioinformatics Unit for computational Diversity differences based on host DNA abundance. The alpha and beta infrastructure. We thank all healthy subjects who donate the diversity differences were quantiﬁed using the Shannon index and the intestinal biopsies for their commitment. We also thank the staff Aitchison distance, respectively, in relation to host DNA abundance. (A) The correlation between host DNA abundance and Shannon index was of the University Hospital of Visceral Medicine and Surgery, for shown for all ﬁve kits used in the study with the default setting (left helping PJ with endoscopic sample collections. We are also grateful panel), as well as for three kits with lab-optimized protocols (right to Dr. Alban Ramette (Institute for Infectious Diseases, University of panel). Each sample is represented by a dot, and statistical signiﬁcance wasobservedinbothpanels(p-value < 0.05). (B) The differences in host Bern) and Dr. Loïc Borcard (Institute for Infectious Diseases, DNA abundance with Aitchison distance, with the left panel displaying University of Bern) for their help with sequencing our samples in results for all ﬁve kits used with the manufacturer’s instruction and the Nanopore with the adaptive sampling approach. Analysis of the right panel showing results for three kits with lab-optimized protocols. References Andrews, S. (2015). A quality control tool for high throughput sequence data. [Online]. through the analysis of multi hypervariable 16S-rRNA gene regions. Sci. Rep. 8, 16893. doi:10.1038/s41598-018-35329-z Backhed, F., Ding, H., Wang, T., Hooper, L. V., Koh, G. 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