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ISSN 2304-3415, Russian Open Medical Journal 1 of 7
2022. Volume 11. Issue 4 (December). Article CID e0413 ... .. , DOI: 10.15275/rusomj.2022.0413 '_MiCrobiology
Original article
Difficulties of Enterobacteriaceae genome annotation in deciphering gastrointestinal microbiome datasets obtained by 16S rRNA gene amplicon sequencing
Elizaveta S. Klimenko, Natalya L. Belkova, Anna V. Pogodina, Lubov V. Rychkova, Marina A. Darenskaya
Research Center for Family Health and Problems of Human Reproduction, Irkutsk, Russia
Received 21 February 2022, Revised 14 April 2022, Accepted 26 August 2022
© 2022, Russian Open Medical Journal
Abstract: Sequencing of the 16S rRNA gene amplicon is the cornerstone of the method for studying diverse bacteria in complex microbial communities. However, its use is complicated by an error rate of 10-17% when annotating 16S rRNA gene sequences. In our study, we examined the degree of accuracy of the taxonomic database of Enterobacteriaceae, compiled using the SILVA 132 reference database and a previously obtained dataset, viz. the microbiome of the gastrointestinal tract in adolescents with normal body weight and obesity. Material and Methods — In this study, previously obtained 16S rRNA gene amplicon sequencing data were used, and the deciphering was carried out using the QIIME2 2019.4 platform. Phylogenetic analysis was performed using MEGA X software.
Results — Phylogenetic analysis of this family based on the studied V3-V4 fragment was hampered by polyphyly among some genera, and for half of the variants of the amplicon sequences it was not possible to clarify their genus. Statistical analysis did not reveal significant differences between the samples.
Conclusion — Although the average values of bacterial genera in the studied groups intuitively differed from each other, statistical analysis did not reveal significant differences between the samples. However, it can be assumed that a more detailed study of taxonomic diversity, taking into account factors, such as enterotype, duration of breastfeeding and family history, may reveal differences in the frequency distribution.
Keywords: gut microbiome; taxonomic annotation; adolescents; opportunistic microorganisms; 16S amplicon sequencing.
Cite as Klimenko ES, Belkova NL, Pogodina AV, Rychkova LV, Darenskaya MA. Difficulties of Enterobacteriaceae genome annotation in deciphering gastrointestinal microbiome datasets obtained by 16S rRNA gene amplicon sequencing. Russian Open Medical Journal 2022; 11: e0413.
Correspondence to Elizaveta S. Klimenko. Address: 16 Timiryazev St., Irkutsk 664003, Russia. Phone: +79501033652. E-mail: klimenko.elizabet@gmail.com.
Introduction
The human microbiome is a complex ecosystem consisting of the genetic material of more than 1 trillion microorganisms living inside the host [1]. Advances in massively parallel DNA sequencing technologies led to a significant increase in knowledge about microorganisms found in the natural environment, food systems, and the human body. In particular, sequencing of the 16S rRNA gene amplicon is a key method for studying the diversity and phylogeny of bacteria. This approach allows the simultaneous identification of most bacteria in complex microbial communities. Although the analysis of 16S rRNA gene diversity presents significant prospects for the study of bacteria from various habitats, the problems of standardizing approaches to sample preparation, DNA sequencing, and data analysis for obtaining reliable information on the composition, structure, and diversity of bacterial populations remain [2].
In studies of the microbiota of most ecosystems or habitats, identification at the species or strain level increases the ecological and/or clinical significance of the results, compared with identification at the genus level. For example, identification at the species level is often critical for host-associated microbial communities, as these communities often include commensal and pathogenic species of the same genus. In addition, some bacterial
taxa include species that are specific to several localities and inhabit strictly defined niches of a given environment [3]. High-throughput sequencing of near-full-length 16S rRNA gene fragments (e.g., PacBio single-molecule circular consensus sequences, real-time sequencing, and whole genome sequencing) was expected to improve detection accuracy to species and strain levels. However, due to the greater availability and lower cost of ribosomal amplicon metasequencing, molecular epidemiological studies of the bacterial microbiota of humans, other animals, plants, and the environment are currently conducted on a population scale (i.e., thousands of samples) [4].
Bacterial reference databases with wide phylogenetic diversity, such as SILVA, RDP, and Greengenes, play a key role in data analysis [5-7]. Nevertheless, the taxonomic annotation of 16S rRNA gene sequences is incorrect in 10-17% of cases [8]. SILVA and RDP are regularly updated and represent extensive and complete libraries of 16S rRNA gene sequences from all studied habitats. In contrast, the Greengenes database was last updated in 2013 [7]. Taxonomic assignment using a reference database for large arrays of sequences and pipelines of metagenomic data processing platforms is associated with a certain percentage of misidentifications. Thus, correction of the taxonomic position is required using phylogenetic analysis and sequences of type strains.
ISSN 2304-3415, Russian Open Medical Journal
2022. Volume 11. Issue 4 (December). Article CID e0413 DOI: 10.15275/rusomj.2022.0413_
Table 1. General characteristics of study participants
Sample code Group Gender Age BMI BMI Z-score Obesity grade Obesity numeric
D01 Control Female 15 21.72 0.44 Control 0
D13 Control Male 14 19.84 0.19 Control 0
D14 Control Male 12 18.78 0.33 Control 0
D28 Control Female 13 17.27 -0.81 Control 0
D29 Control Male 16 17.0 0.31 Control 0
D03 Control Female 14 19.2 -0.64 Control 0
D30 Control Female 14 19.5 -0.51 Control 0
D31 Control Male 15 19.52 -0.34 Control 0
D32 Control Male 15 19.8 -0.23 Control 0
D33 Control Female 17 21.16 -0.02 Control 0
D34 Control Female 15 20.52 0.1 Control 0
D35 Control Male 15 20.23 0.1 Control 0
D36 Control Male 17 21.83 0.09 Control 0
D04 Control Male 16 18.07 -1.12 Control 0
D41 Control Male 13 17.02 -0.72 Control 0
D42 Control Male 13 20.51 0.59 Control 0
D43 Control Female 16 21.36 0.18 Control 0
D44 Control Male 17 22.01 0.28 Control 0
D45 Control Female 13 20.52 0.59 Control 0
D46 Control Male 14 20.89 0.49 Control 0
D47 Control Male 17 23.28 0.54 Control 0
D05 Control Female 13 19.29 0.34 Control 0
D11 Obesity Male 15 36.75 3.34 Severe 3
D12 Obesity Female 15 37.36 3.16 Severe 3
D15 Obesity Male 16 33.82 2.84 Moderate 2
D17 Obesity Female 14 36.1 3.11 Severe 3
D18 Obesity Female 17 37.97 3.26 Severe 3
D02 Obesity Female 16 29.6 2.04 Moderate 1
D20 Obesity Male 16 40.91 3.88 Severe 3
D21 Obesity Female 15 30.1 2.37 Moderate 1
D22 Obesity Male 15 26.3 2.36 Moderate 1
D23 Obesity Female 17 27.9 1.73 Moderate 1
D25 Obesity Female 15 34.7 2.9 Moderate 2
D26 Obesity Female 12 31.12 2.89 Moderate 2
D27 Obesity Male 14 32.7 2.96 Moderate 2
D39 Obesity Male 13 29.14 2.56 Moderate 2
D40 Obesity Male 12 31.31 3.03 Moderate 2
D06 Obesity Male 16 28.82 2.03 Moderate 1
D07 Obesity Male 15 35.54 3.13 Severe 3
D09 Obesity Male 13 26.19 2.19 Moderate 1
Our previous studies described the frequency and structure of obesity-associated cardiometabolic risk factors in a cohort of children and adolescents [9], changes in the biochemical status of obese youths [10-12], and analyzed the intestinal microbiota [13, 14]. Species of Bifidobacterium are present in the gastrointestinal tract of a healthy person, and a change in the number and composition of their species is a sign of intestinal dysbiosis. Features of the gut microbiome associated with obesity include a decrease in Bifidobacterium counts and reduced phylotype diversity. Bifidobacterium species were also examined, for which accurate species identification could not be performed using V3-V4 variable regions or standards for amplicon sequencing [15]. In addition, gut microbiome dysbiosis in obese adolescents was associated with altered species spectrum of enteric bacteria. Members of this family include both normal intestinal microbes (Escherichia coli) and opportunistic pathogens, such as Klebsiella spp. In this regard, accurate species identification is essential for obtaining complete information about the composition of representatives of this family in the intestinal microbiocenosis [16]. The goal of our study was to evaluate the correctness of the taxonomic identification of enteric bacteria by means of using the SILVA 132 reference database. The present study was conducted
on samples of patients with normal body weight and obesity, for whom the results of both bacteriological analysis [16] and amplicon sequencing data were available.
Material and Methods
When studying the educational preferences of students studying at the universities of the Stavropol Krai and practicing Islam, 1,500 questionnaires were evaluated, which was a sufficient reference sample.
Brief description of the research material
The study was approved by the Ethics Committee of the Research Center for Family Health and Problems of Human Reproduction (RC FHPHR), Protocol No. 6 of 21 December 2015. The RC FHPHR previously studied the intestinal microbiome of adolescents with normal body weight and obesity [13, 15]. The general characteristics of the patients are presented in Table 1. Laboratory studies were carried out using standard operating procedures (SOP), IHMS_SOP 03 V2 and IHMS_SOP 06 V2, developed in the course of implementing the project of the international consortium, International Human Microbiome Standards. Amplicon analysis of the V3-V4 variable regions in the 16S rRNA gene was performed at Novogene (China). Primary data were deposited in the NCBI Sequence Read Archive (SRA) under accession numbers SRR11006336-SRR11006339, SRR11006343, and SRR11006351-SRR11006388 (PRJNA604466) [14]. The amplicon libraries were processed using the algorithms of the QIIME2 2019.4 platform [17].
Bioinformatics data processing, phylogenetic and statistical analysis
We used SILVA 132 reference database for taxonomic assignment. To elucidate the phylogeny of amplicon sequence variants (ASVs), identified as Enterobacteriaceae sequences, sequences of the complete 16S rRNA gene of type strains of all Enterobacteriaceae family species were used.
A total of 63 nucleotide sequences were included in the tree, identified by comparison with the SILVA 132 reference database as belonging to the family Enterobacteriaceae, along with typical bacterial strains of this family. Multiple alignment and phylogenetic tree construction were performed using MEGA X software [15]. DNA sequence alignment was originally performed using the MUSCLE algorithm with default settings. The alignment was then visually checked to correct obvious alignment errors and remove areas of questionable alignment. The maximum likelihood method was employed to construct the phylogenetic tree. Statistical support for phylogeny was implemented using bootstrap (1,000 iterations). Bootstrap values >85% were considered highly supported, values of 75-84% were classified as moderately supported, and values of 50-74% were categorized as poorly supported. Values <50% were not specified [19].
Results
Basic statistics for library analysis
Molecular genetic analysis performed 2,590,453 reads. The number of reads per sample ranged from 52,945 to 77,290. A total of 2,890 phylotypes (ASVs) were identified, and the range per sample was 342-564. Depth of sequencing evaluation via the
ISSN 2304-3415, Russian Open Medical Journal
2022. Volume 11. Issue 4 (December). Article CID e0413 DOI: 10.15275/rusomj.2022.0413_
Michaelis-Menten approximation showed that the composition of the microbiome at the ASV level was underestimated by an average of 2.04%.
General characteristics of the representation of Enterobacteriaceae
The Enterobacteriaceae content in the total microbiome ranged from 0.76 to 23.45%, and there were no significant differences between the control and obesity groups.
A total of 63 ASVs were assigned to the Enterobacteriaceae family. Sensu the taxonomy of the SILVA reference database, this family is represented by the genera Citrobacter, Enterobacter, Klebsiella, Proteus, Raoultella, Serratia and two undifferentiated groups (Escherichia-Shigella and Hafnia-Obesumbacterium) in the adolescent gut microbiome. In addition to these genera, ASVs have also been examined that could not be assigned to any genus (unidentified Enterobacteriaceae). Among all ASVs, only 65be was present in all samples, and this ASV was identified by SILVA as an Escherichia-Shigella phylotype (Figure 1). None of the ASVs exhibited significant differences in size between the obesity and control groups (Supplementary materials).
Taxonomy of Enterobacteriaceae
At the time of writing, this family was represented by 32 genera and 124 species. The following species had subspecies: Enterobacter cloacae, Enterobacter hormaechei, Klebsiella pneumoniae, Klebsiella quasipneumoniae, Klebsiella variicola, and Salmonella enterica. The December 2017 update of the SILVA reference database contains the genera Proteus, Serratia, Hafnia, and Obesumbacterium, which are not currently included in Enterobacteriaceae according to LPSN (https://lpsn.dsmz.de/). Proteus was moved to Morganellaceae [17], Serratia to Yersiniaceae, Hafnia and Obesumbacterium to Hafniaceae, and Pantoea to Erwiniaceae family.
Phylogenetic analysis revealed that the genera belonging to Enterobacteriaceae, according to the studied fragments, were polyphyletic, and they formed mixed clades (Figure 2). Only the genera Cedecea, Gibbsiella, Phytobacter, Pseudocitrobacter, Franconibacter, Mangrovibacter, Izhakiella, Rosenbergiella, Trabulsiella, and some groups of Citrobacter, Klebsiella, and Kosakonia were monophyletic and formed clades with good statistical support. Twenty-five ASVs were identified to the generic level, whereas 11 ASVs were assigned to the Escherichia-Shigella cluster. The identification matched for 22 ASVs, whereas the reclassification affected 14 ASVs. Twenty-seven ASVs remained identified only at the family level (Enterobacteriaceae).
Sequences identified as taxa that do not currently belong to Enterobacteriaceae formed separate clades (Nos. 1-4, Figure 2). The identification of ASV 5535 as a member of Proteus was not confirmed. Clade #1 contains sequences identified as Hafnia-Obesumbacterium and unidentified (UI) Enterobacteriaceae. The remaining clades included both sequences characterized by SILVA as representatives of reclassified genera and re-identified by phylogeny. Clade #3 contained Escherichia-Shigella and Pantoea sequences, while clade #4 included Escherichia-Shigella and Serratia sequences. All of them featured medium to strong bootstrap support. Clade #2 contained sequences identified as Enterobacter, Pantoea, and UI Enterobacteriaceae, and branch nodes had weak bootstrap support. For these sequences, a search for the nearest homologs was performed using BLAST software (Table 2). SILVA identification was identical for 11 ASVs. Among the mismatched were representatives of the genera Erwinia, Pantoea, Serratia and Yersinia.
The reclassified taxa accounted for 0.009-3.6% of the total microbiome. After the taxonomy correction, the content of several genera in the gut microbiome was changed, including Enterobacter, Klebsiella, and the Escherichia-Shigella group. The frequency distribution of other taxa was sporadic (Figure 3). Analysis of the overall frequency of ASVs with the same generic identification did not reveal significant differences between obese and control groups in counts for any genus.
Table 2. Search for the closest homology using BLAST
ASV Homology (%) Sequence accession No. Identification
Clade 1
00a3 98.76 NR_116898 Hafnia paralvei ATCC 29927
3491 99.75 NR_116603/NR_112985 Obesumbacterium proteus NCIMB 8771/Hafnia alvei JCM 1666
4381 100 NR_025334/NR_112985 Obesumbacterium proteus 42/Hafnia alvei JCM 1666
5bab 99.01 NR_119214/NR_104925 Raoultella planticola DSM 3069/Ewingella americana CIP 81.94
5d95 99.50 NR_116603/NR_112985 Obesumbacterium proteus NCIMB 8771/Hafnia alvei JCM 1666
692e 99.01 NR 044152 Yersinia massiliensis 50640
Clade 2
34c5 98.26 NR_041970 Erwinia amylovora DSM 30165
768e 99.01 NR_025635 Klebsiella variicola F2R9
93d8 99.01 NR_041970 Erwinia amylovora DSM 30165
ac23 99.01 NR_148649 Enterobacter bugandensis 247BMC
c27b 98.51 NR 104724 Erwinia aphidicola X 001
Clade 3
1e31 100 NR_116755 Pantoea dispersa LMG 2603
78e4 99.75 NR_118122 Pantoea wallisii LMG 26277
be6c 99.75 NR_116246 Pantoea eucrina LMG 2781
dd27 99.75 NR_116755 Pantoea dispersa LMG 2603
f963 100 NR 116114 Pantoea deleyi LMG 24200
Clade 4
2614 100 NR_114043 Serratia marcescens NBRC 102204
a21f 99.75 NR_044385 Serratia nematodiphila DZ0503SBS1
ba84 99.75 NR_036886/NR_114043 Serratia marcescens subsp. sakuensis KRED/Serratia marcescens NBRC 102204
ec2b 100 NR 044385 Serratia nematodiphila DZ0503SBS1
ISSN 2304-3415, Russian Open Medical Journal
2022. Volume 11. Issue 4 (December). Article CID e0413 DOI: 10.15275/rusomj.2022.0413_
Figure 1. Frequency heatmap of isolated ASVs in gastrointestinal microbiomes in obesity and control groups of youths.
Hence, the phylogenetic analysis of this family for the studied V3-V4 fragment was complicated by the polyphyly of some genera. The genus of half of the ASVs could not be specified.
Discussion
Dysbiosis is mainly associated with an increased number of pathobionts, such as Escherichia and Klebsiella spp. caused by a reduction in the number of taxa with useful metabolic activity, including lactobacilli and bifidobacteria. Dysbiosis is also associated with a decrease in biodiversity, i.e., a reduction in the number of microbial species present in the microbiome and lower complexity of the microbial community [21]. As for gastrointestinal microbiota in obese and normal-weight children, the former category has fewer counts of Bifidobacterium and higher counts of E. coli. Studies have shown that a high number of bifidobacteria in infancy and adulthood protects against obesity [22]. It was also revealed that with a decrease in body weight in children achieved by modifying their diet, the numbers of Bifidobacterium and Lactobacillus increases, while the number of enterobacteria decreases [23]. In inflammatory bowel disease, there is an increase in proteobacteria, viz. intestinal bacteria, including the opportunistic pathogens E. coli and K. pneumoniae, which increases mucosal inflammation and the risk of infections. Many
studies have described a decrease in the number of bifidobacteria and lactobacilli and an increase in the numbers of Enterobacter in patients with irritable bowel syndrome (IBS) and diarrhea. Other researchers linked IBS with Campylobacter, Yersinia, Salmonella, Shigella and E. coli. The heterogeneity of the results is explained by the variety of methods used to determine the microbiota and the different criteria for enrolling patients [21]. While there is controversy as to which types of bacteria are associated with being overweight, some specific genera and species of bacteria appear important.
It is known that even the complete sequence of the 16S rRNA gene has a low low discriminatory power. Branching of genera and species within this family during phylogenesis, based on the 16S rRNA gene, has a significant stochasticity depending on the used algorithms and analyzed bacteria [20]. According to the results of some studies, it can be said that the entire Enterobacteriales order is generally characterized by polyphyletic branching and the absence of connected monophyletic groups [20, 24]. The used fragment is not optimal for phylogenetic analysis of this family. Different parts of the genome may have distinct phylogenetic similarities to other taxa. In other words, a group can be monophyletic for some parts of the genome and simultaneously paraphyletic for other parts. In analytical results, this may reflect either analytical ambiguity or actual phylogenetic inconsistencies.
ISSN 2304-3415, Russian Open Medical Journal
2022. Volume 11. Issue 4 (December). Article CID e0413 DOI: 10.15275/rusomj.2022.0413_
B
j-Butti
rf-*
• ASV 19c6 Enterobacter sp.
Enterobacter hormaechei subsp. hoffmannii DSM 14563 Enterobacter quasihormaechei WCHEsl20003 Enterobacter hormaechei subsp. xiangfangensis 10-17 [■Enterobacter hormaechei subsp. oharae EN-314 'Enterobacter hormaechei subsp. steigerwaltii EN 562 -Enterobacter bugandensis 247BMC
—Pluralibacter gergoviaeJCM 1234
i-Pluralibacter pyrinus KCTC 2520
i O ASV dd27 Ur O ASV 1963 ' U O ASV le31
[i- O ASV 78e4 m ' O ASV be6c
^^^^^"■■Franconibacter -O ASV dc72
C
—Escherichia marmotae HT073016 -Shigella dysenteriae ATCC 13313
• ASV 982c Escherichia-Shigella , Escherichia albertii NBRC 107761 -Shigella boydii P288 Shigella sonnei CECT 4887 Escherichia fergusonii NBRC 102419 Shigella flexnen ATCC 29903 -Esherichia coli ATCC 11775 Pseudescherichia vulnerls ATCC 33821
• ASV 76fd Escherichia-Shigella
• ASV b20c Escherichia-Shigella
• ASV el3d Escherichia-Shigella
• ASV 3aa2 Escherichia-Shigella
• ASV 4fec Escherichia-Shigella
• ASV 65be Escherichia-Shigella
• ASV 6e73 Escherichia-Shigella
• ASV 7ece Escherichia-Shigella
• ASV 9ef9 Escherichia-Shigella ASV bf40 Escherichia-Shigella O ASV 5535
Enterobacillus tribolii IG-V01 Shimweilia blattae DSM 4481 Enterobacter sakazakü ATCC 29544 Shimweilia pseudoproteus 521 O ASV 2614 O ASV ba84 O ASV ec2b O ASV a21f Slccibacter colletis 1383
Buttiauxella Izardll DSM 9397 Buttiauxella warmboldiae DSM 9404 'Buttiauxella noackiae NSW 11 Buttiauxella gaviniae DSM 9393 r— Buttiauxella agrestls CDC 1176-81
ji-Buttiauxella brennerae DSM 9396
«L-Buttiauxella ferragutiae DSM 9390 O ASV e7aa — Lelhottia jeotgali PFL01
Lelliottia nimipressuralis LMG 10245 Enterobacter soli LF7
# ASV (bSe Kluyvera sp. Kluyvera cryocrescens ATCC 33435
Raoultella terrigena ATCC 33257
• ASV a72b Citrobacter sp. 'Citrobacter europaeus 97/79
Lelliottia amnigena JCM 1237
• ASV 8866 Scandinavium sp. Scandinavium goeteborgense CCUG 66741 O ASV c099
|-Kluyvera ascortoata ATCC 33433
• ASV ebb5 Kluyvera sp. j • ASV caOl Kluyvera sp. 'Kluyvera intermedia JCM 1238 O ASV 9932
(• ASV f6b3 Citrobacter sp. Citrobacter werkmanii NBRC 105721 • ASV 8e7b Citrobacter sp. Citrobacter portucalensis A60
Citrobacter braakii CDC 80-58 Citrobacter murliniae CDC 2970-59 'Citrobacter (reundil ATCC 8090 |Citrobacter pasteurii CIP 55.13
• ASV 92e8 Citrobacter sp.
v='
M rShir 1—Sl
Ki
Clade 4
-rt»
¡Klebsiella grimontii SB73
_. 'Klebsiella michtganensis W14
r—I '-Klebsiella pasteurii SPARK836C1
'-Yhl
Yokenella regensburgei JCM 2403
ASV la53 Raoultella sp. Raoultella planticola ATCC 33531 omithinolytica ATCC 31898 ASV 78b4 Raoultella sp. • ASV c6e3 Raoultella sp. Raoultella electrica 1GB
i- • ASV
(—Raoultella Raoultella or • A
Gibbsielia Kluyvera georgiana ATCC 51603
mi— O ASV00a3 ' O ASV 5bab O ASV 692e
i— O ASV 4381
- O ASV 3491
L- O ASV 5d95
— Citrobacter youngae CIP 105016
■Cedecea
"Mangrovibactet ■Rosenbergiella
— Biostraticola tofi BF 36
— Limnobaculum parvum HYN0051
♦ Comamonas terrigena IMI 359870
—Enterobacter wuhouensls WCHEsl 20002
j-Enterobacter huaxiensis 90008
Leclercia adecarboxylata NBRC 102595 Enterobacter chuandaensis 90028 -Enterobacter mort R-18-2
chengduensis WCHECI-C4 'Enterobacter sichuanensis WCHECL1597 -Enterobacter ludwigii DSM 16688
-Enterobacter tabaci YIM Hb # ASV c357 Enterobacter sp. 'Enterobacter roggenkampii DSM 16690
r # A
i—Klebsi | 0 AS P- Klebsiel
ASV 8ede Klebsiella sp. Klebsiella pneumoniae subsp. ozaenae ATCC 11296 ASV b499 Klebsiella sp. Klebsiella africana 200023 [(Klebsiella quaslpneumoniae subsp. quasipneumoniae 01A030 Ii • ASV e28a Klebsiella sp.
_rKlebsiella granulomatis (genomospecies)
i* '-Klebsiella pneumoniae subsp. rtiinoscleromatis NCTC S046 [Klebsiella quasipneumoniae subsp. similipneumoniae 07A044 • ASV 5bbO Klebsiella sp.
• ASV 902S Klebsiella sp.
• ASV b2fa Klebsiella sp. -Klebsiella alba CW-D 3
-Klebsiella pneumoniae subsp. pneumoniae DSM 30104
t9 ASV 83fS Klebsiella sp.
• ASV ala8 Klebsiella sp. ■Klebsiella variicola subsp. tropica SB5531 O ASV 34c 5 O ASV c27b O ASV 93d8 O ASV 768e O ASV ac23
Siccibacter turicensis LMG 23731 • ASV dac7 Klebsiella sp. ppKIebsiella oxytoca ATCC 13182
|-j '-Klebsiella indica TOUT106
j- ' O ASV 07f7
-j L O ASV 7189 L Salmonella enterica subsp. arizonae DSM 9386
C-Citrobacter amalonaticus CIP 82.89 Citrobacter farmeri CDC 2991-81 Citrobacter rodentium NBRC 105723 - Citrobacter sedlakii NBRC 105722
-Salmonella bongori DSM 13772
-Escherichia hermannii NBRC 105704 — Kosakonia cowanii CIP 107300 -Metakosakonia masslliensis JC163
Phytobacter Kosakonia quasisacchari WCHEsl20001 — Enterobacter timonensis mt20
Kosakonia arachidis Ah 143 Kosakonia oryziphila REICA 142 Kosakonia oryzae Ola 51
Kosakonia radicincitans D5/23 Kosakonia pseudosacchari JM-387 Kosakonia sacchari SP1 L Kosakonia oryzendophytica REICA 082 Enterobacter cloacae subsp. cloacae ATCC 13047
-Enterobacter siamensis C2361
• ASV c72a Salmonella sp.
-Salmonella subterránea FRCI
• ASV 6925 Enterobacter sp.
'-Enterobacter oligotrophies CCA6
I Trabulsiella
rTs* f
Citrobacter kosen CIP 82.87 Salmonella enterica subsp. houtenae DSM 9221 Salmonella enterica subsp. Indica DSM 14848 • ASV S7e8 Salmonella sp. Salmonella enterica subsp. dianzonae DSM 14847 Salmonella enterica subsp. salamae DSMZ 9220
Figure 2. Phylogenetic tree of the studied ASVs and sequences of type strains of Enterobacteriaceae. A - The outer group is marked with a diamond-shaped marker. Grey clusters denote monophyletic genera that do not include ASVs. Black markers denote ASVs that were assigned to a specific genus or taxonomic group. White markers denote unidentified ASVs. The scale is five substitutions per 100 base pairs (bp). B - Extended group 1. Gray clusters denote monophyletic genera not including ASV. Black markers identify ASVs that were assigned to a specific genus or taxonomic group. White markers denote unidentified ASVs. The scale is one substitution per 100 bp. C - Extended group 2. Grey clusters denote monophyletic genera that did not include ASVs. Black markers denote ASVs that were assigned to a specific genus or taxonomic group. White markers denote unidentified ASVs. The scale is two substitutions per 100 bp.
Figure 3. Frequency distribution (% of the total count in the family) of Enterobacteriaceae family representatives in control and obesity groups.
ISSN 2304-3415, Russian Open Medical Journal
2022. Volume 11. Issue 4 (December). Article CID e0413 DOI: 10.15275/rusomj.2022.0413_
The composition of the microbial community depends on such factors as the time of breastfeeding, family history (health status of the mother and other family members), and the dominant component of the microbial community that determines the human enterotype. The health status of the mother during pregnancy (past inflammatory and infectious diseases) can affect early (intrauterine) colonization of the child's body with bacteria, such as Enterobacter, Enterococcus, Lactobacillus, Photorhabdus and Tannerella [25]. The main stage of colonization of the child's body by symbiotic bacteria occurs at the time of birth. The mode of delivery largely determines the future composition of the microbiome. The microbiome of children born by caesarean section is very different from the microbiome of children born by vaginal delivery. Breastfeeding is the second step in the colonization of the baby's intestines after birth. The method (artificial or natural) and the time of feeding strongly influence the composition of the intestinal microbiome, determining the dominant and minor components of the community. Feeding has the greatest impact on the diversity of representatives of the genus Bifidobacterium [25]. The dominant component of the community influences the minor components. There are several approaches to typing the intestinal microbial community according to the dominant component. The first approach involves the use of partitioning around medoids (PAM) and dividing them into three groups (Bacteroides, Prevotella and Ruminococcus). The second approach is based on the Dirichlet multinomial mixtures (DMM) and gives a division into 4 groups (Ruminococcaceae [R], Prevotella [P], Bacteroides 1 [B1] and Bacteroides 2 [B2]). In some cases, an additional enterotype is identified with a predominance of representatives of the Enterobacteriaceae (H) family - but, as a rule, it is associated with the presence of inflammatory diseases, alcohol dependence, or other ailments. Enterotypes do not depend on gender, age, ethnicity or geography. Rather, they depend on the characteristics of long-term nutrition [26].
Conclusion
Most taxa were characterized by the presence of a single sample; no dependence on division into groups was observed. However, it can be assumed that a more detailed study of taxonomic diversity, taking into account factors, such as enterotype, duration of breastfeeding and family history, may reveal differences in the frequency distribution. Future studies should also include the analysis of these samples using the whole genome sequencing technology due to such type of information retrieval analysis on a larger scale.
Conflict of interest
The authors declare that they have no conflicts of interest.
Funding
This study was conducted with the financial support by the Council for Presidential Grants of the Russian Federation (NSh- 3382.2022.1.4).
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.
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ISSN 2304-3415, Russian Open Medical Journal
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Elizaveta S. Klimenko - Junior Researcher, Department of Epidemiology and Microbiology, Research Center for Family Health and Human Reproductive Problems, Irkutsk, Russia. https://orcid.org/0000-0003-0979-8816.
Natalya L. Belkova - PhD, Associate Professor, Leading Researcher, Department of Epidemiology and Microbiology, Research Center for Family Health and Human Reproductive Problems, Irkutsk, Russia. https://orcid.org/0000-0001-9720-068X.
Anna V. Pogodina - MD, Lead Researcher, Department of Pediatrics, Research Center for Family Health and Human Reproductive Problems, Irkutsk, Russia. https://orcid.org/0000-0001-8533-3119. Lubov V. Rychkova - MD, Corresponding Member of Russian Academy of Sciences, Chair of the Department of Pediatrics, Director of Research Center for Family Health and Human Reproductive Problems, Irkutsk, Russia. https://orcid.org/0000-0002-0117-2563.
Marina A. Darenskaya - PhD, Lead Researcher, Department of Personalized and Preventive Medicine, Research Center for Family Health and Human ReproductiveProblems, Irkutsk, Russia. https://orcid.org/0000-0003-3255-2013.
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