Bias of the Mock Community Composed of Common Strains Isolated from Clinical Specimens

doi:10.21203/rs.3.rs-1304839/v1

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Bias of the Mock Community Composed of Common Strains Isolated from Clinical Specimens

https://doi.org/10.21203/rs.3.rs-1304839/v1

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Background: The mock communities (MCs) bias of common species from clinical specimens should be considered when we use a microbiome in the clinical microbiology laboratory. The aims of this study were to MCs using clinically important common species and to investigate the bias of MC use in the clinical laboratory.

Results: Five MCs incorporating 32 bacterial strains isolated from clinical specimens were included. We analyzed the diversity of operational taxonomic units (OTU), the relative abundance, and the taxonomic assignment using paired-end sequencing of the 16S rRNA V3−V4 region. The Shannon index revealed the best correlation with the actual number of MC species for diversity. We determined that the OTU with relative abundance (log) of 0.001 is most appropriate to explain the community. The relative abundance was higher in Bacteroidetes and Fusobacteria; however, low relative abundance was shown in Aeromonas caviae, Burkholderia cepacia, Enterobacter cloacae, Pseudomonas aeruginosa, and Clostridioides difficile. It is suggested that the relative abundance was low in gram-positive bacteria and those with 60%−70% GC content but was not related to genome size or 16S rRNA copy number.

Conclusions: We investigated the characteristics of MC composed as common clinical isolates and confirmed the bias of MC. These data could be used for microbiome research on clinical specimens.

Metagenomics

Bioinformatics

Detection

Diagnosis

Diversity

Attempts to use microbiome analysis in a clinical laboratory are increasing with the enhancement of next-generation sequencing (NGS) technology [1-3]. However, the method has a few serious limitations for such use, such as a lack of standardization of the research process, distortion of bioinformatics, and inconsistent results [4-6].

Recently, it was recommended to use a mock community (MC) to overcome these limitations and increase the reliability of the results [7, 8]. An MC is composed of several bacterial strains, and we can establish the standardization of experimental process including DNA extraction, PCR, NGS, and bioinformatics pipelines using MC [7-11]. We could compare the data among researchers and reduce unnecessary duplication by others [12-14].

The bias limits can be reduced by using MC [12, 14], although it cannot replace the whole microbial community characteristics of specimens [15]. A few commercial MCs are provided by the American Type Culture Collection, BEI resource, and Zymo Research, although these are limited to use for basic research in specific sites such as oral tissue, skin, gut, and vagina. It is important to consider the GC contents, genome size, results of gram strain, and 16S rRNA copy number when we design a new MC.

The aims of this study were to design MCs using clinically important common species and to investigate the bias of MCs for use in the clinical laboratory

Mock community

Five MCs using 32 bacteria were constructed to include clinically common and important bacteria (Table 1). All bacterial species were confirmed by biochemical testing, MALDI-TOF MS, and 16S rRNA sequencing.

The criteria for each MC were as follows: the genome size (range 1.8Mb to 5.8Mb; mean: 3.9Mb) in MC1, s GC content (range 27.0%−66.7%; mean: 44.18%) in MC2, 16S rRNA copy number (range 4−12; mean: 6) in MC 3, and 4, a total of 32 bacteria and an even distribution of gram staining results in MC 5.

All five MCs contained a total of 8.0 × 10⁸ cells and consisted of 9−32 bacterial species. MC5 contained all 32 bacteria (each 1.6 × 10⁷ CFU) with 12 gram-positive and 20 gram-negative bacteria. The phylum is composed of 1 Actinobacteria, 11 Firmicute, 3 Bacteroidetes, 1 Fusobacteria, and 16 Proteobacteria. MC1 contained 9 bacteria (each 8.9 × 10⁷ CFU) with 4 Firmicutes, 1 Bacteroidetes, and 4 Proteobacteria. MC2 contained 12 bacteria (each 6.7 × 10⁷ CFU) with 4 Firmicutes, 1 Fusobacteria, and 8 Proteobacteria. MC3 contained 16 bacteria (each 5.0 × 10⁷ CFU) with 6 Firmicutes, 2 Bacteroidetes, 1 Fusobacteria, and 7 Proteobacteria. MC4 contained 18 bacteria (each 4.4 × 10⁷ CFU) of species not included in MC3 except Streptococcus oralis and Aeromonas caviae. For GC contents and genome size, NCBI registration information for the type strain of each species was used, and the 16s rRNA copy number was based on the average of rrnDB (Ribosomal RNA Database; University of Michigan). The GC contents, genome size, and 16S rRNA copy number of all MCs consisted of an average of 44.7% (STDEV 2.4), 3.9 Mb (STDEV 0.2), and 6.5 (STDEV 0.3), respectively.

We used phosphate-buffered saline as a negative control for DNA library construction.

NGS and bioinformatic pipelines

DNA extraction from MCs was performed using the Maxwell 16 LEV Blood DNA Kit (Promega, USA). The DNA library construction and NGS analysis were performed by paired-end sequencing of V3−V4 regions according to the manual of the Illumina MiSeq System. The EzBioCloud pipeline (https://www.ezbiocloud.net/contents/16smtp) was used for NGS data analysis. In pre-processing, merging of paired-end reads was performed using the VSEARCH program, and low-quality reads (<Q25) were filtered [16]. As many as 100,000 reads were used for data analysis, and QC processing was performed to exclude low-quality, non-target, and chimeric amplicons. The UCHIME with chimera-free reference DB was used to detect any chimera [17]. Operational taxonomic unit (OTU) picking was performed by UCLUST using an open-reference method. Taxonomic assignment was performed through VSEARCH using the EzBioCloud 16S database and determined on the basis of 97% of 16S similarity [16, 18].

Diversity of OTU

The species richness of α-diversity was estimated according to the ACE [19], Chao1 [20], and Jackknife [21] methods. The species evenness was calculated by the Shannon and Simpson index [22].

Relative abundance and interpretation of taxonomic assignment

We used “fold error” to calculate the bias of relative abundance. Fold error is defined as the relative abundance of the ideal value, and the ideal value is calculated the same way for each MC (100 divided by the number of species). The Ez-BioCloud 16S database uses a concept of “group,” It classifies a group of several bacterial species that cannot be distinguished by 16S rRNA sequences. Of the 32 species included in this study, 23 were identified as a “group”: Klebsiella aerogenes, Klebsiella pneumoniae, and Salmonella enterica were included in the Enterobacteriaceae group (Table 1). The Staphylococcus aureus group included S. aureus and S. epidermidis, and the Streptococcus pneumoniae group included S. pneumoniae and S. pyogenes. For these species, we divided the total relative abundance of the group by the number of species for calculation of the fold error. Others were defined as the results of misidentified species other than those of the expected MC species.

Statistics

Correlation analysis was calculated by the Pearson correlation coefficient, and polynomial logistics regression analysis was performed for bacterial variance. The p value was calculated by a two-side test, and it was considered significant when it equaled < 0.05.

Change of NGS raw data during bioinformatics pipeline

Total reads for all MCs were at least 100,000 each, ranging from 123,255 (MC2) to 184,711 (MC5) (Table 2). After pre-filterimg, total reads were between 84,090 (MC4) and 100,000 (MC1, 3, and 5). All 5 MCs showed sufficient valid reads of more than 70,000. The percent of identified reads for MC2 and MC4 were 98.9% and 98.0%, respectively. The identification rates of MC1 (67.4%), MC3 (87.1%), and MC5 (89.6%) were relatively low. However, this was caused by the inclusion of a strain of Sphingobacterium, a suspected new species. When we treat the result of this strain (Sphingobacterium_uc) as correct, the final identification rates of all MCs were more than 98%. There are differences in the identified reads among 5 MCs although each MC contains the same CFUs of bacteria. The rates of misidentified reads ranged from 0.9% in MC3 to 3.6% in MC4.

Taxonomic assignment

The number of OTUs among the 5 MCs was between 65 (MC4) and 126 (MC5), and the number of species was 48 (MC1)−89 (MC5) (Table 2, Figure 1A). On average, 1.27 (MC2) to 2.10 (MC1 OTUs were matched to one species.

The ratio of the number of detected species to that of expected species was between 2.8 (MC4 and MC5) and 5.3 (MC1). The ratio of those at the genus level was between 1.8 (MC5) and 4.0 (MC1). These ratios were slightly decreased when an MC contained a large number of species.

Calculation of α-diversity by OTU

The OTU, ACE, Chao1, and Jackknife indices reflecting species richness showed a moderate correlation (r = 0.56−0.62) with the number of expected MC species (Table 2). For species evenness, the correlation between the Shannon index (range: 1.85−2.65) and the number of expected MC species was high (r = 0.82).

There were few changes in the number of OTUs if the valid reads exceeded 60,000 in all MCs (Figure 1A). The OTU ranks with relative abundance (log) > 0.001 were 16 (MC1), 18 (MC2 and MC3), 26 (MC4), and 38 (MC5) (Table 2, Figure 1B).

Relative abundance at the species level

There were significant differences in the relative abundance, although all expected MC species were identified to the species level (Figures 2 and 3). At the phylum, the relative abundance was highest in Bacteroidetes and also high in Fusobacteria within each MC. The relative abundance of Fusobacterium was highest (26.42%) in MC2 because MC2 does not contain Bacterioidetes. In this study, Bacteroidetes compromise Bacteroides fragilis (6.8−7.8), Chryseobacterium gleum group (4.4−4.8), and Sphingobacterium uc (2.0−2.8) The. Morganella morganii group (1.6−1.7) and Acinetobacter baumannii (1.0−2.49) showed high fold error except in Bacterioidetes and Fusobacterium.

The proportion of species with fold error between 0.5 and 1.5 was 33% (3 of 9), 50% (6 of 12), 13% (2 of 16), 28% (5 of 18), and 13% (4 of 32) among MC1, MC2, MC3, MC4, and MC5, respectively. Staphylococcus aureus (MC1), B. cepacia (MC2), C. difficile (MC3), E. cloacae (MC4), and P. aeruginosa (MC4 and MC5) had the lowest fold error in each MC (Figure 2). The cut-off value of the relative abundance that can identify all species among MCs was the highest in MC1 (1.94%) and lowest in MC5 (0.01%) (Table 2).

A total of 422 species with 30 included in MCs were identified in the negative control (Figure 2). The relative abundance of the 30 species of MCs ranges from a minimum of 0.001% (Enterococcus faecalis) to a maximum of 0.58% (Enterobacteriaceae group).

Misidentified results at the species level of 5 MCs

We defined the misidentified results in the MCs when the final identification results showed a different species in the same genus or a different genus from a specific species. A different species in the same genus was confirmed in most species, and most of them were of the Enterobacteriaceae group, Enterococcus, and Streptococcus among MCs (Figure 4). However, the relative abundance of these was low: between 0.46% (MC3) and 1.11% (MC5). Corynebacterium striatum, Clostridioides difficile, Clostridium perfringens, Aeromonas caviae, Hemophilus influenzae, and Stenotrophomonas maltophilia were identified at the species level. Pantoea, Erwinia, Cronobacter, Cosenzaea, and Raoultella were identified although they were not included in the MCs (Table 3).

Difference of relative abundance by bacterial characteristics

The fold error was significantly lower in gram-positive bacteria (Figure 3). There was no difference of fold error by 16S rRNA copy number or genome size. Bacteria with GC contents of 60% to 70% showed significantly lower fold error.

For the diagnostic use of the microbiome in the clinical microbiology laboratory, it is necessary to verity the experimental procedures and data analysis using bacteria isolated from clinical specimens [23, 24]. In this study, we tried to confirm the bias of microbiomes using 5 MCs incorporating 32 clinical isolates.

It is well known that at least 60,000 raw reads are necessary to analyze a microbiome using biological samples. It has been reported that more than 100,000 raw reads are essential for reliable community analysis [25]. In this study, we could obtain more than 100,000 raw reads with 70,000 valid reads. We believe that 60,000 valid reads are enough to analyze the microbial community because there are no changes of OTU, and the rarefaction threshold was reached at 60,000 reads in all MCs.

The OTU to species ratio was 1.3−2.1. This is attributable to the misperception of sequencing error as OTU [26]. We could identify all species included in the 5 MCs when analyzing OTUs with a relative abundance (log) of 0.001 or higher. We believe that the use of the relative abundance (log) 0.001 can eliminate sequencing errors, and we could perform the community analysis accurately.

The species diversity can be expressed as the species richness and evenness. The richness index was different from the actual number of species in MC when we use ACE, CHAO, and Jackknife. When we calculated the Shannon and Simpson indices for species evenness, the former was highly correlated with the actual number of species in the MC. So, we concluded that the Shannon index is the best indicator of the species diversity for an MC as in a previous report [27].

For many bacteria, a partial sequence (V3−V4 region) of the 16S rRNA gene is identical or very similar in many bacteria, so it is common that one strain is expressed as two species, although it may have the opposite effect. However, the Ez-Biocloud 16S database has the advantage of reducing this erroneous result with the use of a “group.” This composes as a group for several species that cannot be distinguished by 16S rRNA amplicons. In this study, 23 of 32 species were reported as a group in this database. It is possible to analyze the microbiome data to the species level if we use the Ez-Biocloud 16S database including the concept of “group,” although many previous reports allow the identification only to the genus level.

The relative abundance was different even though the same contents of bacteria were included in each MC. We confirmed that the relative abundance was extremely low in P. aeruginosa, E. cloacae, A. caviae, C. difficile, B. cepacia, and S. aureus (Figure 2). So, it should be noted that it is likely to be calculated markedly less than the original for these species when we use clinical specimens for the microbiome. In addition, these should be considered to determine the cut-off value of the community.

It has been reported that relative abundance differs according to phylum, bacterial cell wall, GC content, genome size, and 16S rRNA copy number [28, 29]. We also confirmed the phylum of Bacteroidetes (B. fragilis, Sphingobacterium sp., C. indologenes), and Fusobacterium (F. nucleatum) showed high relative abundance. In addition, the relative abundance of gram-positive bacteria and GC contents of 60% to 70% was low. This bias can occur during the process of DNA extraction and PCR amplification for microbiome analysis using NGS [8, 29].

We investigated the characteristics of MC composed as common clinical isolates and confirmed the bias of MC. These data could be used for microbiome research on clinical specimens.

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and material

All the assemblies were deposited into the NCBI SRA database with the following accession number: PRJNA801011

Competing interests

The authors declare that they have no competing interests

Funding

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019R1I1A3A01061803) and by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HR21C1003).

Authors' contributions

EYK created a mock community and analyzed metagenome data. SHK and JY checked the analyzed data and reviewed the draft. JHS presented the conceptualization and methodology of the study. EYK and JHS were the major contributors to the drafting of the manuscript. All authors read and approved the final manuscript.

Acknowledgements

Not applicable

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Table 1

Composition and characteristics of mock communities

Phylum	Species	Gram stain	16S rRNA GCN^†	Genome size (Mb)	GC content (%)	MC
Actinobacteria	Corynebacterium striatum	+	4.0	2.86	59.3				MC4	MC5
Firmicutes	Bacillus cereus	+	13.6	5.76	35.0	MC1			MC4	MC5
	Clostridioides difficile	+	11.9	4.17	28.6			MC3		MC5
	Clostridium perfringens	+	9.9	3.47	28.1		MC2		MC4	MC5
	Enterococcus faecalis	+	4.0	2.97	37.4	MC1	MC2	MC3		MC5
	Enterococcus faecium	+	6.0	2.91	37.9			MC3		MC5
	Staphylococcus aureus^*	+	5.6	2.84	32.7		MC2		MC4	MC5
	Staphylococcus epidermidis^*	+	5.8	2.52	32.0	MC1			MC4	MC5
	Streptococcus agalactiae	+	6.8	2.08	35.4				MC4	MC5
	Streptococcus oralis^**	+	4.0	1.97	41.1	MC1		MC3	MC4	MC5
	Streptococcus pneumoniae^**	+	4.0	2.09	39.6		MC2	MC3		MC5
	Streptococcus pyogenes	+	5.8	1.79	38.4			MC3		MC5
Bacteroidetes	Bacteroides fragilis	-	6.0	5.27	43.4				MC4	MC5
	Sphingobacterium sp.	-	5.3	5.27	39.8	MC1		MC3		MC5
	Chryseobacterium indologenes	-	6.0	4.89	37.2			MC3		MC5
Fusobacteria	Fusobacterium nucleatum	-	4.5	2.41	27.0		MC2	MC3		MC5
Proteobacteria	Acinetobacter baumannii	-	5.9	3.97	39.0		MC2	MC3		MC5
	Aeromonas caviae	-	10.0	4.54	61.4	MC1	MC2	MC3	MC4	MC5
	Burkholderia cepacia	-	5.5	8.49	66.7		MC2		MC4	MC5
	Enterobacter cloacae	-	8.0	4.96	55.1				MC4	MC5
	Escherichia coli	-	7.0	5.13	50.6		MC2		MC4	MC5
	Haemophilus influenzae	-	6.0	1.85	38.0	MC1	MC2		MC4	MC5
	Klebsiella aerogenes^***	-	8.0	5.26	55.0	MC1		MC3		MC5
	Klebsiella pneumoniae^***	-	8.0	5.59	57.2		MC2		MC4	MC5
	Moraxella catarrhalis	-	4.0	1.92	41.6			MC3		MC5
	Morganella morganii	-	4.0	3.96	51.0			MC3		MC5
	Neisseria gonorrhoeae	-	4.0	2.14	52.4		MC2		MC4	MC5
	Proteus mirabilis	-	6.9	3.96	38.8			MC3		MC5
	Salmonella enterica^***	-	6.9	4.8	52.1	MC1			MC4	MC5
	Serratia marcescens	-	7.0	5.2	59.8			MC3		MC5
	Stenotrophomonas maltophilia	-	3.8	4.57	66.4				MC4	MC5

^*, ^**, ^***, 16s rRNA V3-V4 region sequence not differentiated.

^†, gene copy number.

Table 2

Metagenomic results of 5 MCs

	PBS (0)	MC1 (9)	MC2 (12)	MC3 (16)	MC4 (18)	MC5 (32)
Reads by NGS data processing
Raw data (total reads)	23,760	170,464	123,255	165,036	124,951	184,711
Pre-filter^* (Q25)	17,846	100,000	90,529	100,000	84,090	100,000
(QC) Low quality amplicons	3,464	161	192	131	113	137
(QC) Non-target amplicons	2	0	1	0	0	0
(QC) Chimeric amplicons	587	11,143	20,051	13,476	9,807	13,697
Valid reads	13,793	88,696	70,285	86,393	74,170	86,166
Identified reads^†(%)^‡	12,884 (93.4)	59,796 (67.4)	69,531 (98.9)	75,259 (87.1)	72,655 (98.0)	77,186 (89.6)
Misidentified reads^§ (%)^‡	13,793 (100)	2349 (2.6)	1583 (2.3)	735 (0.9)	2645 (3.6)	2369 (2.7)
Taxonomic assignment (detected/expected, fold)
OTU (r=0.56)^¶	441	101	76	101	65	126
Phylum	12	5/3 (1.7)	4/3 (1.3)	5/4 (1.3)	4/4 (1.0)	5/5 (1.0)
Genus	200	36/9 (4.0)	34/12 (2.8)	28/13 (3.1)	34/16 (2.1)	46/25 (1.8)
Species	422	48/9 (5.3)	60/12 (5.0)	50/16 (3.1)	50/18 (2.8)	89/32 (2.8)
Species richness
ACE (r=0.62)^¶	449.32	107.89	85.45	104.69	71.64	139.74
CHAO (r=0.61)^¶	499.57	102.69	80.80	101.85	66.75	133.31
Jackknife (r=0.60)^¶	466.00	113.00	92.00	109.00	73.00	146.00
Species evenness
Expected Shannon (r=0.98)^¶	−	2.2	2.49	2.77	2.89	3.47
Shannon (r=0.82)^¶	4.67	1.85	2.33	2.21	2.07	2.65
Expected Simpson (r=-0.92)^¶	−	0.11	0.08	0.06	0.06	0.03
Simpson (r=-0.51)^¶	0.03	0.21	0.13	0.15	0.23	0.11
OTU rank
Relative abundance (log) > 0.001	286	16	18	18	26	38
Cut off value for MC species
The lowest relative abundance (%)	−	1.94	1.85	0.03	0.06	0.01
Relative abundance > 0.01
Identification of species	422	24	32	35	35	59
Identification of MC species	−	8^**	12	15^**	16^**	28^**
Misidentification of species (Similar species)^††	422	16 (12)	20 (13)	20 (15)	19 (8)	31 (20)

^*, After pre-filtering, no more than 100,000 reads were used for analysis.

^†, Number of reads identified at the species level.

^‡, Ratio of each read divided by valid reads.

^§, Number of reads misidentified at the species level.

^¶, Correlation coefficient with number of MC species.

^**, Contains species indistinguishable from the 16s rRNA V3-V4 region.

^††, Identified bacteria as species similar to MC species.

Table 3

Relative abundance of misidentified species

Genus	Misidentified species	MC1	MC2	MC3	MC4	MC5
Lactococcus	Lactococcus lactis group	0.01
Citrobacter	Citrobacter koseri				0.19	0.14
Cosenzaea	Cosenzaea myxofaciens			0.08		0.04
Cronobacter	Cronobacter dublinensis group					0.02
Cronobacter	Cronobacter sakazakii	0.13			0.02	0.01
Erwinia	Erwinia aphidicola	0.01
	Erwinia iniecta			0.06		0.02
	Erwinia tasmaniensis				0.01
Kosakonia	Kosakonia cowanii group				0.09	0.02
Pantoea	AKIU_s		0.01
	OCMY_s		0.02		0.04	0.02
	Pantoea agglomerans group		0.08		0.11	0.08
	Pantoea coffeiphila		0.25	0.03	0.11	0.04
	Pantoea theicola		0.02		0.01
	Pantoea_uc		0.06	0.06	0.25	0.11
Pectobacterium	Pectobacterium carotovorum group		0.05		0.01
Pseudescherichia	Pseudescherichia vulneris				0.01
Raoultella	Raoultella planticola group	0.12		0.03		0.02

No competing interests reported.

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Bias of the Mock Community Composed of Common Strains Isolated from Clinical Specimens

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Abstract

Figures

Background

Methods

Mock community

NGS and bioinformatic pipelines

Diversity of OTU

Relative abundance and interpretation of taxonomic assignment

Statistics

Results

Change of NGS raw data during bioinformatics pipeline

Taxonomic assignment

Calculation of α-diversity by OTU

Relative abundance at the species level

Misidentified results at the species level of 5 MCs

Difference of relative abundance by bacterial characteristics

Discussion

Conclusion

Declarations

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Consent for publication

Availability of data and material

Competing interests

Funding

Authors' contributions

Acknowledgements

References

Tables

Additional Declarations

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