The impact of storage buffer and storage conditions on fecal samples for bacteriophage infectivity and metavirome analysis

doi:10.21203/rs.3.rs-2491764/v1

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The impact of storage buffer and storage conditions on fecal samples for bacteriophage infectivity and metavirome analysis

https://doi.org/10.21203/rs.3.rs-2491764/v1

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published 28 Aug, 2023

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Background: There is an increasing interest in investigating the human gut virome, for its influence on the gut bacterial community and its putative influence on the trajectory towards health or disease. Most gut virome studies are based on sequencing of stored fecal samples. However, relatively little is known about how conventional storage buffers and storage conditions affect infectivity of bacteriophages and downstream metavirome sequencing.

Results: We demonstrate that the infectivity and genome recovery rate of the different spiked bacteriophages (T4, c2 and Phi X174) is variable and highly dependent on the storage buffers. Regardless of storage temperature and timespan, all the tested phages immediately lost their infectivity either completely (DNA/RNA Shield) or more than 90% (StayRNA and RNAlater). Generally, SM buffer at 4°C exhibited good maintenance of phage infectivity up to 30 days and phage genomes up to 100 days, while CANVAX was the most effective buffer that kept all spiked phage genomes for at least 100 days. Prolonged storage time (500 days) at –80°C influenced the viral diversity differently in the different buffers. The samples stored in CANVAX or DNA/RNA Shield buffer have the least shifts in metavirome composition after prolonged storage, but produced more contigs classified as “uncharacterised”. In addition, compared to SM buffer, these storage buffers lead to a higher fraction of bacterial contamination in metavirome-sequencing libraries. We found this problem was due to the inactivation of DNAses used for removing extra-cellular DNA during virome extraction, which could be partly solved by additional washing steps before virome extraction.

Conclusion: Fecal sample storage conditions (buffer, time, temperature) strongly influences bacteriophage infectivity and viral composition as determined by plaque assay and metavirome sequencing. The choice of buffer had stronger influence compared to storage temperature and time and could introduce bias for viral sequencing and analysis. Based on these results, we recommend SM buffer for the storage of the fecal virome samples at 4°C for isolation of viruses and –80°C for metagenomic applications if practically feasible (access to cold storage etc.). If other buffers were already used for storage, samples should be purified from these buffers before extraction and sequencing.

Bacteriophage

Storage buffer

Storage condition

Infectivity

Genome recovery

Metavirome

Sequencing

The mammalian gut is inhabited by a complex community of microbes (collectively referred to as the gut microbiome, GM), which is mainly composed of bacteria, but other biological entities such as bacteriophages (or phages; bacterial viruses) are also important GM members. The GM plays important roles in host metabolism and immune system regulation and GM dysbiosis is linked to development and severity of many diseases [1-3]. In the gut, phages are approximately as abundant as bacteria and can influence bacterial diversity, abundance and function [4, 5]. Interestingly, imbalances in the gut virome have been associated with various diseases such as obesity, diabetes, alcoholic liver disease, necrotizing enterocolitis and malnutrition [1, 6-8]. Furthermore, the transfer of fecal virome communities from a healthy host to a diseased host, has been shown to reverse the disease phenotype [6, 9-11]. Therefore, there is a strong interest in studying fecal phages (and the gut virome as such) to elucidate the role of the gut virome in the etiology of gut-related diseases. Importantly, the validity and reproducibility of such studies are relying on the quality of the collected biological samples.

Inappropriate storage of fecal samples can alter the distribution of specific taxa and introduce biases in sequencing results and subsequently down-stream analysis [12-16]. Several studies have reported that sample collection and storage methods have profound effects on the bacterial community profile determined by sequencing-based microbiome characterization tools, which demonstrate the importance of proper storage of fecal samples to ensure the accuracy of bacterial metagenomic analyses [13, 17-21], but little attention has been given to the preservation of gut virome. Currently, stabilization buffers are mostly tested for their efficiencies on the gut bacteria [14, 22, 23], it is unclear to what extend these buffers can maintain the infectivity of bacteriophages during storage, and the effect of storage conditions on downstream fecal virome analysis has not yet been established.

The aims of this study were to investigate the effects of storage conditions (buffer, time and temperature) on the infectivity of three representative phages from the major phage families in the gut as well as determining the influence of storage conditions on metavirome sequencing outcomes.

The effect of temperature (25°C, 4°C, −20°C and −80°C) was investigated in 5 different storage buffers (StayRNA, CANVAX, DNA/RNA Shield, RNAlater and SM buffer) with the aim of determining the effect of storage conditions on the infectivity of the phages T4, c2, and Phi X174 (up to 100 days of storage) as well as the overall virome composition and viral genome preservation (for up to 500 days) (Fig. 1).

Buffers influence phage infectivity with different manners

Initially, we determined the proportion of infectious phages that can be recovered immediately after spiking (day0). As shown in Fig. 2 and Table S3, buffer DNA/RNA Shield inactivates all the tested phages rapidly (T4, c2, and Phi X174), regardless of storage temperature. The same was observed for c2 in CANVAX. While in StayRNA and RNAlater varioius fractions of the tested phages were staying infectious (0.07 ~ 17.0%), SM buffer maintained the highest infectivities for all tested phages (34.6 ~ 39.7%).

Next, we determined the effect of different buffers on the phage infectivity at different time points during 100 days of storage at various temperatures. At 25°C, the infectivity of Phi X174 and T4 decreased rapidly the first 30 days of storage in RNAlater and StayRNA, and no plaques could be observed thereafter (Fig. 2A and E), while c2 remained infective after 100 days of storage (Fig. 2C). Colder temperature (4°C) was generally in favor of phage stability. CANVAX and SM buffer showed similar efficiency for both phage c2 and T4, but CANVAX maintained the highest infectivity for Phi X174 (Fig. 2A, C and E). The infectivity of the Phi X174 and c2 showed substantial decrease (between 6 to10 folds) in StayRNA and RNAlater after 30 days storage, while T4 retained with a relativly small fractions (0.09% ~ 0.13%) for up to 100 days of storage.

In general, all the tested phages could be plaqued during the storage period at −20°C and −80°C, if the infectivity was maintained at the time of spiking (Table S3, Fig. 2B, D and F), though the plaque efficiency was quite different (from 0.13% to 60.8%). SM buffer and CANVAX showed better ability than StayRNA and RNAlater to maintain infectivity. Storage at −80°C was better at maintaining infectivity than −20°C for c2 in SM buffer and T4 in CANVAX. Short time storage at 25°C followed by transfer to fridge (4°C) or freezer (−80°C) resulted in a decline of infectivity of the tested phages comparable to the above single-stage temperature exposures (Fig. S1A-C).

Buffers maintain phage genomic content

The recovery of the genomic content of the phages T4, c2, and Phi X174 spiked into fecal samples stored in the different buffers was determined by qPCR. All the spiked-in phage genomes were detectable in all the tested buffers to larger or smaller degree after spiking (Fig. 3 and Table S3). CANVAX showed the best phage genome recovery rate for all phages, but also DNA/RNA Shield and SM buffer allowed good recovery (Table S3). All the tested buffers preserved the genomes short time (one day) at all tested temperatures, but after long-time (100 days) storage at 25°C a marked reduction in the fraction of genomes recovered was seen (Fig. 3A and C). When stored at lower temperatures (4°C, −20°C and −80°C) all the genomes were stable for up to 100 days. CANVAX showed the best ability to preserve the phage genomes (Fig. 3B, D and F), followed by SM buffer.

We also observed that initial temperature abuse during storage (25°C for 2 days and then transferred to 4°C or −80°C) did not cause significant degradation of phage genomes, suggesting that samples obtained under e.g. field conditions without immediate access to cold storage are still validatable for further metagenomic analysis (Fig. S1D-F).

Buffer and storage time affect gut virome diversity

Based on the experiments determining the infectivity and genomic recovery rate of the tested phages, we concluded that 4°C and −80°C are favorable for phage storage. We then investigated how the viral community changed over time in response to different buffers and temperatures by comparing fecal viromes obtained after being stored for 0, 14 and 500 days at refrigeration (4°C) and freezing (−80°C) temperatures, respectively (Fig.1).

The buffers DNA/RNA Shield and CANVAX yielded the highest amount of nucleic acids, but with relatively low purity (Table S4), and the fraction of sequencing reads found to be of bacterial or fungal origin were much higher in CANVAX (>70.5%) and DNA/RNA Shield (>64.6%) stored samples compared to SM buffer (~30%). In line with this, a higher proportion of sequencing reads could be matched to viral databases when stored in SM buffer (9.0% ~ 14.3%) as compared with CANVAX (0.90% ~ 1.18%) and DNA/RNA Shield (1.74% ~ 4.45%) (Fig. 4A).

Although it appeared that the buffers had a strong influence on the distribution of viral and non-viral DNA, there were no significant differences among all the tested buffers in viral alpha diversity at the two tested storage temperatures (Fig. 4B). With long-time storage (500 days) a significant decrease in the viral Shannon diversity index was seen (p<0.01), while the number of observed vOTUs did not change relative to the samples stored 14 days (Fig. S2A and S2B).

The majority of classified viruses were prokaryotic viruses (phages) belonging to the order of Caudovirales (e.g. Siphoviridae, Myoviridae and Podoviridae) and the order of Petitvirales (Microviridae), but most of the identified viruses cannot be classified at the family level (Fig. 4C). The “unknown” category was larger when samples were stored in CANVAX (37% ~ 47%) and DNA/RNA Shield (24% ~ 47%) compared to SM buffer (<10%) (Fig. 4C and Table S5). Further, Caudovirales constituted a higher fraction of the contigs when stored in SM buffer (~ 60%) than all the other buffers (Table S5). Siphoviridae were the most abundant identified family in the baseline (in SM buffer, day0) and SM buffer samples, but was less abundant in CANVAX (p=0.002, Wilcox test) and DNA/RNA Shield (p=0.004, Wilcox test) regardless of storage temperature and time.

Bray-Curtis distance-based metrics showed clear differences among the viromes stored in different buffers (Fig. 4D, PERMANOVA, p=0.019), with samples stored in CANVAX clustering close to samples stored in DNA/RNA Shield, while the StayRNA samples clustered with samples stored in RNAlater. Short-time storage (14 days) at refrigeration (4°C) and freezing (−80°C) temperatures did not strongly affect the viral distribution (PERMANOVA, p=0.225), but long-time storage (500 days) led to a distinct shift in viral distribution (PERMANOVA, p=0.014), especially for the viral community in StayRNA, RNAlater and SM buffer. Notably, short-time storage in SM buffer at −80°C clustered closest to the baseline (day0) sample.

Buffers induce non-viral sequencing bias

The abundance of spiked phages (T4, c2, and Phi X174) recovered from the SM buffer by sequencing was higher than that in the rest of the tested buffers, especially for the ssDNA phage Phi X174 (Fig. 5A). However, we found all the buffers except SM buffer have higher level of bacterial genome contamination, as contigs showed very low abundance (near to zero) in SM buffer but high abundance in StayRNA, RNAlater and CANVAX encoded a lot of hypothetical proteins (HTP) from bacterial genomes or draft genomes. (Fig. 5B and C). We determined these sequences to be “sneaker contigs” that were originally derived from bacterial genomes or draft genomes but not completely removed during virome purification (Fig. 5C, Fig. S3).

The virome purification protocol contains a nuclease treatment step to remove environmental DNA before the viral capsids are lysed and viral DNA/RNA purified. We hypothesized that the addition of RNAlater and CANVAX buffers may inhibit the nuclease activity. To test this, we spiked exogenous viral DNA into the two buffers and treated with the nuclease while SM buffer was used as a control. We found that both RNAlater and CANVAX completely inactivated the nuclease activity and therefore leave extracelluar DNA undigested (Fig. S4A). Repeated washing steps with SM-buffer allowed us to counteract this inhibition in RNAlater samples, but not CANVAX (Fig. S4B). In line with removing inhibition from the RNAlater samples, our metavirome sequencing result also showed the RNAlater samples treated with extra washing step had a similar virome diversity to that in SM buffer (Fig. 6C), while the virome sequencing quality of CANVAX-stored samples was not improved accordingly(Fig. 6A and 6B).

Storage conditions for fecal samples aiming at subsequent virus/phage isolation or metavirome sequencing have not been thoroughly investigated previously. Here, we studied the impact of storage conditions (buffer, temperature, time) on the infectivity of spiked bacteriophages and the overall fecal viral community composition.

One of the most important prerequisites for successful viral application is its infectivity. Here we found the infectivity of the different bacteriophages was quite variable and appeared to be highly dependent on storage buffer and temperature. Our results showed that phages immediately lost most of their infectivities (>60%) after spiking (Fig. 2 and Table S3), and all the tested phages with non-enveloped structures completely abolished their infectivity in DNA/RNA Shield and the same for the long-tailed phage c2 phage in CANVAX buffer. This may result from the low pH values of these buffers (Table S4), as the isoelectric point (IS) affects the net surface charge of the coat protein of non-enveloped viruses and impairs their ability to attach to their host [24-26]. Another explanation may be due to the high osmotic stress in both DNA/RNA Shield and CANVAX which lead to osmotic shock and possibly cause the inactivation of bacteriophages [27]. Consistent with previous studies, our result showed that the commonly used SM buffer overall maintained phage infectivity well [28-33], possibly due to its neutral pH and that the ions in the buffer can interact with capsids and stabilize the protein structure [34].

Several 16S rRNA gene-based studies have shown that the use of preservation buffers (such as RNAlater) for protecting the bacteria at room temperature (RT) was not always effective [22, 23]. This is also applicable to phage storage at unfavorable temperatures which lead to a faster degradation of the protein structures that make up the capsid [35], which is in agreement with our results showing that phages stored at 25°C lose their infectivity faster than when stored at lower temperatures. At −20°C the tailed phages (c2 and T4) showed a faster decrease in infectivity than the non-tailed phage Phi X174, probably due to slow freezing that can induce more crystals damaging the structure of the fragile phage tail [36]. Hence, it is wise to ensure fast freezing of the samples if they are intended for phage isolation in virome studies [37, 38]. In many situations storage at 4°C (or in wet ice at 0°C) is convenient, and phages' infectivities from SM buffer and CANVAX at 4°C are better than at −20 and −80°C.

Importantly, as long as the phages were stabilized in buffers, the storage time and temperature (except for 25°C for a long time) do not affect the genome recovery efficiency as determined by qPCR (Fig. 3), suggesting that the overall phage structure was preserved and the phage DNA was intact [32]. After meta-virome sequencing Bray–Curtis dissimilarity metrics showed that samples stored in DNA/RNA Shield grouped with CANVAX and RNAlater with StayRNA respectively, suggesting they may have similar chemical components (Fig. 4). Except for a few low coverage regions of Phi X174 and T4 in CANVAX and DNA/RNA Shield (Table S6), all the sequences of the spiked-in phages could be detected, indicating all the buffers have preserved the viral DNA in the viral particles. The recovered abundance of spiked phage Phi X174 is higher than c2 and T4 with the same amount of spiked-in phage as determined by qPCR. This may be due to the Phi X174 genome is easier to extract or its circular ssDNA genome is preferentially amplified by MDA or both [39]. The low bacterial/eukaryotic DNA contamination from the samples stored in SM buffer is consistent with our previous observations [32, 33]. Both CANVAX and DNA/RNA Shield can preserve more nucleic acids from the samples and yielded higher DNA concentrations (Table S3) but with a lower purity and a higher degree of bacterial and eukaryotic DNA contamination (Fig. 4).

The elimination of non-viral nucleic acids during virome extraction is vital. However, all the tested storage buffers except SM buffer can lead to higher bacterial contamination of the viromes due to their inhibition of nuclease activity used to remove planktonic bacterial during virome extraction (Fig. 6 and Fig. S4). Importantly, the inhibition of the nuclease activity in RNAlater can be relieved by SM buffer washing, probably by removing the chemicals that causes deactivation, but this did not work for CANVAX stored samples (Fig. 6 and Fig. S4). The SM buffer washing step can be used for both StayRNA and RNAlater if samples are already stored in these two buffers, but deeper sequencing and more careful viral contig verification may be needed for samples saved in CANVAX and DNA/RNA Shield.

Our data shows the importance of storage conditions for virome study. We found that the phages infectivity was kept better in SM buffer and stored at 4°C. However, when the isolation of active phages from samples is not necessary, the use of SM buffer and stored at −80°C warrants more stable and less potential contaminations for metavirome study. If the StayRNA or RNAlater buffer were used, intensive sample washing with SM buffer before the nuclease treatment should be performed.

Phage propagation and plaque assays

3 phages from different families, namely phage T4 (Myoviridae), phage c2 (Siphoviridae), and phage Phi X174 (Microviridae) (Table S1) were produced in this study. The host of phage T4, Escherichia coli DSM 613, was grown in LB broth (Merck, Kenilworth, NJ, USA) at 37°C with shaking at 225 rpm. Phage c2’s host, Lactococcus lactis MG1363, was grown in M17 broth (Merck, Kenilworth, NJ, USA) supplemented with 5 mM CaCl₂ at 30°C without shaking. For the host of Phi X174, Escherichia coli ATCC 13706 was grown in BHI broth (Merck, Kenilworth, NJ, USA) containing 10 mM CaCl₂ and MgCl₂ at 37°C and shaking at 225 rpm. For phage propagation, phages were incubated with their respective host bacteria overnight and then subject to centrifugation at 5000× g for 30 min at 4°C to remove cell debris. The phage stocks were prepared by the 0.45 µm filter and stored at 4°C. Phages’ infectivities in the filtrates were enumerated by plaque assay as described below.

Preparation of phage spiked fecal samples

A fresh fecal sample was donated by an anonymous healthy adult donor and mixed thoroughly with 5 different preservation buffers, namely StayRNA (A&A Biotechnology, Gdynia, Poland), CANVAX (Canvax Biotech, Córdoba, Spain), DNA/RNA Shield (Zymo Research, Irvine, CA, USA), RNAlater (Sigma-Aldrich) and SM (Sodium chloride/Magnesium sulfate) buffer (lab preparation, 200 mM NaCl, 10 mM MgSO₄, 50 mM Tris-HCl (1 M, pH 7.5)). Then the fecal suspensions were spiked with different volumes of phages to a final concentration of 3 × 10⁵plaque-forming units per milliliter (3 × 10⁵ PFU/mL) of each phage and mixed gently to form the spiked fecal samples.

Storage at different temperatures and times

The above prepared spiked fecal samples were stored at different temperatures (room temperature at 25°C, refrigeration at 4°C, freezing at −20°C and −80°C) and the infectivity of phages was detected at different time points (0, 1, 7, 14, 30, and 100 days) by plaque assays. Briefly, 100 μL of the spiked fecal sample was centrifuged and at 12,000 rpm for 10 mins and 10 μL of the supernatant with different dilutions containing the phages (c2, Phi X174, and T4) was mixed with 200 μL of their respective overnight cultured hosts LactococcuslLactis MG1363, Escherichia coli ATTC 13706B1 and Escherichia coli DSM 613 and left to settle for 10 min at room temperature. 5 mL of media containing 0.5% agarose pre-warmed at 40°C was mixed with the phage sample and bacterial culture and poured to the top of a pre-warmed agar plate (1.5%). The double-layer plates were first solidified at room temperature and then incubated overnight at the corresponding growth temperature of the bacterial host. On the next day, the phage plaques were counted, and PFU/mL was calculated. The infectivity of the phage was calculated as the following formula:

Phage infectivity (%) = Plaque amounts/Dilution factors/Added volume of diluted phage/The initial concentration of phage×100. The genome recovery rate of the spiked phages was determined by qPCR at 0, 1 and 100 days. Besides, we also simulated a field sampling condition that fecal samples cannot be stored under refrigeration or freezing condition immediately. Herein, we kept the spiked fecal sample at 25°C for 2 days and then transferred them to 4°C and −80°C for 14 days for the infectivity and genome recovery study, respectively (Fig. 1). We also stored the same batch of spiked fecal samples for the virome diversity study, where a baseline sample was prepared at day-0, and samples stored at 4°C and −80°C for 14-day, −80°C for 500-day were prepared for metavirome sequencing.

Purification and pretreatment of virome DNA

Phage isolation and purification were carried out according to our previous method with minor modifications [33]. Briefly, the Centriprep 50K was replaced by Centrisart^® I centrifugal ultrafiltration unit (MWCO 100 kDa, Sartorius Stedim Biotech GmbH) and the up-concentration step was done at 2500× g for 30 min at 4°C or 25°C. Extra centrifugation times were applied for some difficult samples (Table S4). QIAmp viral RNA mini kit (Qiagen, Hilden, Germany) was used for the extraction of viral DNA/RNA from the concentrated virome solution. And then the extracted nucleic acids were amplified by Multiple Displacement Amplification (MDA) with the Genomephi V3 kit (GE Healthcare Life Science, Marlborough, MA, USA), and the amplification time was done at 30°C for 30 min. Finally, the amplified DNA was cleaned with a Genomic DNA Clean & Concentrator^TM kit (Zymo Research, Irvine, CA, USA).

Phage quantification by quantitative real-time PCR (qPCR)

The DNA from phage T4, c2, and Phi X174 was quantified by real-time qPCR using SYBR Green Master Mix (Roche, Basel, Switzerland) on CFX96 Touch Real-Time PCR Detection System (Bio-Rad, Hercules, California, USA). 10 μM of forward and reverse primers targeting the specific T4, c2 and Phi X174 genome were added to 20 μL reactions, which were run using the following setup: initial stage at 50°C for 2 min, hot start at 95°C for 10 min, followed by 40 cycles of (i) 95°C for 15 s, (ii) 55°C for 20 s, and (iii) 60°C for 40 s [33]. Serial 10-time dilutions of phages (T4, c2, and Phi X174) genomic DNA were used to generate standard curves. After the qPCR amplification, a melting curve analysis (95°C for 15 s, 60°C for 60 s, 95°C for 30 s and 60°C for 15 s) was performed. Each reaction was performed in duplicates and the designed primers for specific phages and targeted positions were listed in Table S2.

To investigate the activity of universal nuclease in the buffer of RNAlater, CANVAX and SM buffer, the exogenous DNA was spiked into the above buffers and carried out the same extraction procedure. And also the fecal samples kept in the above buffers was up-concentrated and washing with SM buffer up to 5 times to verify if washing can improve fecal virome quality.

Metavirome sequencing and data pre-processing

The concentration of the MDA amplified and cleaned DNA was measured by Qubit dsDNA HS Assay Kit (ThermoFisher Scientific, Waltham, MA, USA). The library was constructed using the Nextera XT kit (Illumina, San Diego, CA, USA) and purified by AMPure XP beads according to the manufacturer’s protocol. Constructed libraries were sequenced using 2×150 bp paired-end settings on an Illumina NextSeq550 platform.

The average sequencing depth for the metavirome was 3,646,735 reads/sample (Table S6, min. 661,636 reads and max. 6,529,708 reads). The raw reads were trimmed from adaptors and barcodes and the high quality sequences (>95% quality) using Trimmomatic v0.35 [40], with a minimum size of 50nt were retained for further analysis. High quality reads were de-replicated and checked for the presence of Phi X174 using BBMap (bbduk.sh) [41]. Virus-like particle-derived DNA sequences were subjected to within-sample de-novo assembly-only using Spades v3.13.1 [42]. and the contigs with a minimum length of 2,200 nt, were retained. Contigs generated from all samples were pooled and de-replicated at 90% identity using BBMap (dedupe.sh) [41] . Prediction of viral contigs/genomes was carried out using VirSorter2 [43] (“full” categories | dsDNAphage, ssDNA, RNA, Lavidaviridae, nucleocytoplasmic large DNA viruses (NCLDV) | viralquality ≥ 0.66), vibrant [44] (High-quality | Complete), and checkv [45] (High-quality | Complete). Taxonomy was inferred by blasting viral ORF against viral orthologous groups [46] and the Lowest Common Ancestor (LCA) for every contig was estimated based on a minimum e-value of 10e-5. Following assembly, quality control, and annotations, reads from all samples were mapped against the viral (high-quality) contigs (vOTUs) using the bowtie2 [47] and a contingency-table of reads per Kbp of contig sequence per million reads sample (RPKM) was generated, here defined as vOTU-table. Code describing this pipeline can be access in github: github.com/jcame/virome_analysis-FOOD.

Data analysis

The infectivity and genome recovery were visualized by GraphPad Prism (v8.0.1) or R software (v4.1.2). Analysis of viral community α- and β-diversity were performed using packages Phyloseq (v1. 36.0) [48] and Vegan (v2.5.6) in R. For α-diversity analysis, all the indexes were calculated with t.test using packages ggsignif (v0.6.3). Bray-Curtis distance metrics were calculated for β-diversity analysis and unconstrained ordination was performed using principal coordinate analysis (PCoA). R package gggenomes was used to visualize the functional genes of annotated viral contigs [49].

GM: gut microbiome; NA: not assigned; RT: room temperature; MDA: multiple Displacement Amplification; ssDNA: single-stranded DNA; dsDNA: double-stranded DNA; PCoA: principal coordinates analysis; vOTU: viral-operational taxonomic unit; ORFs: open reading frames; NCLDV: nucleocytoplasmic large DNA viruses; (LCA): Lowest Common Ancestor; SGBs: species-level genome bins, PERMANOVA: Permutational multivariate analysis of variance

Ethics approval and consent to participate

The participate was informed by oral and written consent.

Consent for publication

Not applicable.

Availability of data and materials

The viral metagenome sequences raw data produced in this study is available through the NCBI Sequence Read Archive under BioProject accession number PRJNA926386. Code for virome pre-analysis is available from github: github.com/jcame/virome_analysis-FOOD.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

This work was supported by research grants (23145 and 36242) from VILLUM FONDEN.

Authors’ contributions

Research topic and content was designed by DSN and LD. Experiments were performed by XZ and AA. Data collection was performed by XZ, AA, WK and AG. Data analysis was done by XZ, JC, DSN and LD. The manuscript was written and revised by XZ, JC, DSN and LD. All authors read and approved the final manuscript for submission.

Acknowledgments

We thank the donors and our colleagues at section of microbiology and fermentation, department of food science, University of Copenhagen for their participation and cooperation. We thank China Scholarship Council (CSC) Grant’s support to XZ (201906870027).

Khan Mirzaei M, Khan MAA, Ghosh P, Taranu ZE, Taguer M, Ru J, Chowdhury R, Kabir MM, Deng L, Mondal D et al: Bacteriophages Isolated from Stunted Children Can Regulate Gut Bacterial Communities in an Age-Specific Manner. Cell Host Microbe 2020, 27(2):199-212 e195.
Scanlan PD: Bacteria–Bacteriophage Coevolution in the Human Gut: Implications for Microbial Diversity and Functionality. Trends in Microbiology 2017, 25(8):614-623.
Thompson LR, Zeng Q, Kelly L, Huang KH, Singer AU, Stubbe J, Chisholm SW: Phage auxiliary metabolic genes and the redirection of cyanobacterial host carbon metabolism. Proc Natl Acad Sci (USA) 2011, 108(39):E757-764.
Van Belleghem JD, Dąbrowska K, Vaneechoutte M, Barr JJ, Bollyky PL: Interactions between bacteriophage, bacteria, and the mammalian immune system. Viruses 2019, 11(1):10.
Shkoporov AN, Hill C: Bacteriophages of the Human Gut: The “Known Unknown” of the Microbiome. Cell Host Microbe 2019, 25(2):195-209.
Rasmussen TS, Mentzel CMJ, Kot W, Castro-Mejia JL, Zuffa S, Swann JR, Hansen LH, Vogensen FK, Hansen AK, Nielsen DS: Faecal virome transplantation decreases symptoms of type 2 diabetes and obesity in a murine model. Gut 2020, 69(12):2122-2130.
Duan Y, Llorente C, Lang S, Brandl K, Chu H, Jiang L, White RC, Clarke TH, Nguyen K, Torralba M et al: Bacteriophage targeting of gut bacterium attenuates alcoholic liver disease. Nature 2019, 575(7783):505-511.
Kaelin EA, Rodriguez C, Hall-Moore C, Hoffmann JA, Linneman LA, Ndao IM, Warner BB, Tarr PI, Holtz LR, Lim ES: Longitudinal gut virome analysis identifies specific viral signatures that precede necrotizing enterocolitis onset in preterm infants. Nat Microbiol 2022, 7(5):653-662.
Zuo T, Wong SH, Lam K, Lui R, Cheung K, Tang W, Ching JYL, Chan PKS, Chan MCW, Wu JCY et al: Bacteriophage transfer during faecal microbiota transplantation in Clostridium difficile infection is associated with treatment outcome. Gut 2018, 67(4):634-643.
Broecker F, Russo G, Klumpp J, Moelling K: Stable core virome despite variable microbiome after fecal transfer. Gut Microbes 2017, 8(3):214-220.
Lin D, Lin, H. C.: Fecal Virome Transplantation. In: Bacteriophages in Therapeutics. London: IntechOpen; 2021.
Choo JM, Leong LE, Rogers GB: Sample storage conditions significantly influence faecal microbiome profiles. Sci Rep 2015, 5:16350.
Cardona S, Eck A, Cassellas M, Gallart M, Alastrue C, Dore J, Azpiroz F, Roca J, Guarner F, Manichanh C: Storage conditions of intestinal microbiota matter in metagenomic analysis. Bmc Microbiol 2012, 12:158.
Costea PI, Zeller G, Sunagawa S, Pelletier E, Alberti A, Levenez F, Tramontano M, Driessen M, Hercog R, Jung FE et al: Towards standards for human fecal sample processing in metagenomic studies. Nat Biotechnol 2017, 35(11):1069-1076.
Shaw AG, Sim K, Powell E, Cornwell E, Cramer T, McClure ZE, Li MS, Kroll JS: Latitude in sample handling and storage for infant faecal microbiota studies: the elephant in the room? Microbiome 2016, 4(1):40.
Thomas V, Clark J, Dore J: Fecal microbiota analysis: an overview of sample collection methods and sequencing strategies. Future Microbiol 2015, 10(9):1485-1504.
Gorzelak MA, Gill SK, Tasnim N, Ahmadi-Vand Z, Jay M, Gibson DL: Methods for Improving Human Gut Microbiome Data by Reducing Variability through Sample Processing and Storage of Stool. PLoS One 2015, 10(8):e0134802.
Roesch LFW, Casella G, Simell O, Krischer J, Wasserfall CH, Schatz D, Atkinson MA, Neu J, Triplett EW: Influence of fecal sample storage on bacterial community diversity. Open Microbiol J 2009, 3:40-46.
Dominianni C, Wu J, Hayes RB, Ahn J: Comparison of methods for fecal microbiome biospecimen collection. Bmc Microbiol 2014, 14:103.
Han M, Hao L, Lin Y, Li F, Wang J, Yang H, Xiao L, Kristiansen K, Jia H, Li J: A novel affordable reagent for room temperature storage and transport of fecal samples for metagenomic analyses. Microbiome 2018, 6(1):43.
Lobocka MB, Glowacka A, Golec P: Methods for Bacteriophage Preservation. Methods Mol Biol 2018, 1693:219-230.
Vandeputte D, Tito RY, Vanleeuwen R, Falony G, Raes J: Practical considerations for large-scale gut microbiome studies. Fems Microbiol Rev 2017, 41(Supp_1):S154-S167.
Song SJ, Amir A, Metcalf JL, Amato KR, Xu ZZ, Humphrey G, Knight R: Preservation Methods Differ in Fecal Microbiome Stability, Affecting Suitability for Field Studies. Msystems 2016, 1(3).
Taj MK, Ling JX, Bing LL, Qi Z, Taj I, Hassani TM, Samreen Z, Yunlin W: Effect of dilution, temperature and ph on the lysis activity of T4 phage against E.Coli BL21. J Anim Plant Sci 2014, 24(4):1252-1255.
Michen B, Graule T: Isoelectric points of viruses. J Appl Microbiol 2010, 109(2):388-397.
Suarez V, Moineau S, Reinheimer J, Quiberoni A: Argentinean Lactococcus lactis bacteriophages: genetic characterization and adsorption studies. J Appl Microbiol 2008, 104(2):371-379.
Jończyk E, Kłak M, Międzybrodzki R, Górski A: The influence of external factors on bacteriophages—review. Folia Microbiologica 2011, 56(3):191-200.
Reyes A, Haynes M, Hanson N, Angly FE, Heath AC, Rohwer F, Gordon JI: Viruses in the faecal microbiota of monozygotic twins and their mothers. Nature 2010, 466(7304):334-338.
Minot S, Sinha R, Chen J, Li HZ, Keilbaugh SA, Wu GD, Lewis JD, Bushman FD: The human gut virome: Inter-individual variation and dynamic response to diet. Genome Res 2011, 21(10):1616-1625.
Minot S, Bryson A, Chehoud C, Wu GD, Lewis JD, Bushman FD: Rapid evolution of the human gut virome. P Natl Acad Sci USA 2013, 110(30):12450-12455.
Minot S, Grunberg S, Wu GD, Lewis JD, Bushman FD: Hypervariable loci in the human gut virome. Proc Natl Acad Sci (USA) 2012, 109(10):3962-3966.
Castro-Mejia JL, Muhammed MK, Kot W, Neve H, Franz CM, Hansen LH, Vogensen FK, Nielsen DS: Optimizing protocols for extraction of bacteriophages prior to metagenomic analyses of phage communities in the human gut. Microbiome 2015, 3:64.
Deng L, Silins R, Castro-Mejía JL, Kot W, Jessen L, Thorsen J, Shah S, Stokholm J, Bisgaard H, Moineau S et al: A Protocol for Extraction of Infective Viromes Suitable for Metagenomics Sequencing from Low Volume Fecal Samples. 2019, 11(7):667.
Silva YJ, Costa L, Pereira C, Cunha A, Calado R, Gomes NCM, Almeida A: Influence of environmental variables in the efficiency of phage therapy in aquaculture. Microb Biotechnol 2014, 7(5):401-413.
Ackermann H-W, Tremblay D, Moineau S: Long-term bacteriophage preservation. World Frferation for Culture Collection Newslett 2004, 38:35-40.
Warren JC, Hatch MT: Survival of T3 Coliphage in Varied Extracellular Environments. I. Viability of the Coliphage During Storage and in Aerosols. 1969, 17(2):256-261.
Olson MR, Axler RP, Hicks RE: Effects of freezing and storage temperature on MS2 viability. J Virol Methods 2004, 122(2):147-152.
Malik DJ, Sokolov IJ, Vinner GK, Mancuso F, Cinquerrui S, Vladisavljevic GT, Clokie MRJ, Garton NJ, Stapley AGF, Kirpichnikova A: Formulation, stabilisation and encapsulation of bacteriophage for phage therapy. Adv Colloid Interface Sci 2017, 249:100-133.
Kim KH, Bae JW: Amplification Methods Bias Metagenomic Libraries of Uncultured Single-Stranded and Double-Stranded DNA Viruses. Appl Environ Microb 2011, 77(21):7663-7668.
Bolger AM, Lohse M, Usadel B: Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30(15):2114-2120.
Bushnell B: BBMap: A Fast, Accurate, Splice-Aware Aligner. 2014.
Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD et al: SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing. J Comput Biol 2012, 19(5):455-477.
Guo J, Bolduc B, Zayed AA, Varsani A, Dominguez-Huerta G, Delmont TO, Pratama AA, Gazitua MC, Vik D, Sullivan MB et al: VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome 2021, 9(1):37.
Kieft K, Zhou Z, Anantharaman K: VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. Microbiome 2020, 8(1):90.
Nayfach S, Camargo AP, Schulz F, Eloe-Fadrosh E, Roux S, Kyrpides NC: CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat Biotechnol 2021, 39(5):578-585.
Groups VO: VOGDB WEBSITE LAUNCHED. 2022.
Langmead B, Salzberg SL: Fast gapped-read alignment with Bowtie 2. Nat Methods 2012, 9(4):357-U354.
McMurdie PJ, Holmes S: phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 2013, 8(4):e61217.
Thomas HM, J. A.；Kristina, H.: gggenomes: A Grammar of Graphics for Comparative Genomics. R package version 0959000 2022.

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The impact of storage buffer and storage conditions on fecal samples for bacteriophage infectivity and metavirome analysis

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