UDC 61

 

CHARACTERISTICS OF INTESTINAL MICROBIOME IN TODDLERS

BORN TO MOTHERS WITH GESTATIONAL DIABETES MELLITUS

AS A PREDICTOR OF METABOLIC DISORDERS

 

L.A. Kharitonova, MD, Professor, Head of the Department of Pediatrics with Infectious Diseases in Children

FSAEI HE Russian National Research Medical University named after N.I. Pirogov

of the Ministry of Health of the Russian Federation,

(117997, Russian Federation, Moscow, Ostrovityanova str., d1)

E-mail: doc.29gkb@gmail.com

 

T.A. Mayatskaya, Assistant of the Department of Pediatrics with Infectious Diseases in Children

FSAEI HE Russian National Research Medical University named after N.I. Pirogov

of the Ministry of Health of the Russian Federation,

(117997, Russian Federation, Moscow, Ostrovityanova str., d1)

E-mail: doc.29gkb@gmail.com

 

A.M. Zatevalov, Doctor of Biological Sciences, Head of the Laboratory

of Diagnostics and Prevention of Infectious Diseases

FBIS MRIEM named after G.N. Gabrichevsky of Rospotrebnadzor,

(125212, Russian Federation, Moscow, Admiral Makarov str., 10)

E-mail: doc.29gkb@gmail.com

 

A.A. Mayatskii

GBUZ MO "Central Clinical Psychiatric Hospital named after F.A. Usoltsev",

"Lyubertsy psychoneurological dispensary"

(127083, Russian Federation, Moscow, 8 Marta str.)

E-mail: doc.29gkb@gmail.com

 

Abstract. The standard bacteriological examination can cultivate only approximately 80% of microorganisms from feces, limiting the power of the intestinal microbiome study. For a more accurate assessment of the intestinal microbiome in children, we used the 16S ribosomal RNA gene amplicon sequencing to identify uncultivated pathogens. We evaluated the metabolic activity of the intestinal microbiome and its volatile fatty acid profile. The study aimed to determine a correlation between the imbalance of intestinal microbiota and its functional activity in children born to mothers with gestational diabetes mellitus by studying their intestinal microecology to predict the health status of the cohort. The studied groups significantly differed in the distribution of types and species of intestinal microorganisms: the balance shifted towards conditionally pathogenic and pathogenic bacteria in children of mothers with gestational diabetes mellitus. A dysbiotic intestinal microbial community can adversely affect the children born to mothers with gestational diabetes mellitus.

Keywords: intestinal microbiome, gestational diabetes mellitus, young children, metabolism, butyric acid, acetic acid, functional activity.

 

Introduction

The relevance of studying the human microbiome and its impact on the body’s health is undeniable. A human microbiome is a set of genomes of microbial populations that live on human somatic cells. With their gene composition exceeding the number of human genes by over 100 times, microorganisms form an additional "open organ" [12]. A microbiome within a certain habitat forms a "functional core" that is characterized by its species' composition as well as metabolic and other molecular functions [15]. A microbiome should normally be able to resist certain external or internal changes and recover from them. The definition of a healthy microbiome may also include the presence of specific microbial gene families and metabolic and regulatory pathways that contribute to a sustainable host-associated ecosystem [17, 11]. Thus, a microbiome integrates with the body via a close relationship of various types including symbiosis or parasitism.

The microbiological community of the gastrointestinal tract – the intestinal microbiome (IM) – is the most stable and autonomous microbiome in the human body, and it has been extensively studied over the past decades. IM is now known to be involved not only in the digestion and the macro- and micronutrient exchange, but also in other physiological processes: the regulation of the background immunological reactivity, the regulation of endocrine and nervous systems, detoxication, anticarcinogenic activity, and the body homeostasis. However, diseases, genetic predisposition, and dietary or lifestyle habits can change the relationship between the body and its microbiota. According to recent studies, diabetes mellitus (DM) can affect IM. An increasing number of studies examine IM in pregnant women suffering from gestational diabetes mellitus (GDM). These studies deserve the attention of the scientific and medical community, as changes in a woman's body during pregnancy with GDM may lead to negative outcomes that affect a child’s development, such as alterations in gut microbiota (dysbiosis) [2, 13].

IM development during childhood includes several stages. First, the settlement of microorganisms in the infant’s gut occurs over the first five days after birth. Second, a biofilm forms from 6 to 29 days post-natal. Third, from 1 month to 11 months 29 days, microorganisms struggle for survival, which results in the most pronounced dynamic changes of IM. By the age of one year, an individual human microbiome is formed under the influence of exogenous factors; IM develops a stable core structure and reaches adult characteristics by the age of three years [5]. To the best of our knowledge, no prospective studies have been conducted on IM in young children born to mothers with GDM, which would employ novel medical technologies and 16S ribosomal RNA (rRNA) sequencing of bacteria in particular. Therefore, our comprehensive study of IM in this cohort of children is both clinically relevant and urgently needed.

Materials and methods

A prospective tudy of the intestinal microbiome (IM) was conducted in 105 children aged 1–3 years, including 75 children born to mothers with gestational diabetes mellitus (GDM) and 30 children of mothers without GDM (control group, CG). The study was carried out in the Maternity Hospital and the Pediatric Department of the Bauman Municipal Clinical Hospital No. 29 of the Moscow Department of Health from 2017 to 2021. The study included clinically healthy children born from a single-fetus pregnancy, at full term, and by natural childbirth. The subjects underwent a comprehensive screening and neither had signs of acute illness at the time of examination nor got sick over the previous month. Children who received antibiotics or probiotics within the last six months were excluded from the study. Informed consent was obtained from each parent and/or guardian, and the study was approved by the local ethics committee (LEK) (extract from the minutes of the meeting of the LEK of the Pirogov Russian National Research Medical University No. 180 dated December 17, 2018).

The quantitative distribution of the main intestinal microorganism types and species in children was assessed with the method of ngs-sequencing of feces - DNA was isolated using the DNeasyPowerLyzerMicrobialKit (QIAgen) kits according to the manufacturer's instructions. Amplicon libraries of 16S rRNA were prepared using PCR with universal primers on region V4. Biochemical analysis of feces with quantification of the short-chain fatty acid (SCFA) concentrations by gas-liquid chromatography of acidified fecal supernatant was performed to assess the function of IM.

We measured intestinal dysbiosis by quantifying the biodiversity of the microbial community using the Shannon index (Н). H represents the number of microorganism species in the intestinal microbial community and is calculated by the formula:

H=-i=1npilog2pi

 

where

pi=xii=1nxi

 

The normalized Shannon index ranges from 0 to 1, which is considered suitable for interpreting the diversity of a microbial community. The data obtained during the study were statistically processed using Statistica 8.0 and MS Office Excel 2010 software. For all statistical tests, the alpha error threshold p < 0.05 was set. Descriptive analysis of quantitative and ordinal data was performed by calculating mean values, mean error, maximum and minimum values, standard deviation, 95% confidence interval for mean, median and interquartile range; analysis of variance (Student's parametric t-criteria or Mann-Whitney and Wilcoxon non-parametric criteria); testing statistical hypotheses using the Chi-square test; Pearson correlation analysis was used to establish the strength and directivity of the correlation relationship of quantitative variables, and Spearman rank correlation analysis was used between qualitative ones.

Results

The sequencing of 16S bacterial rRNA indicated the following quantitative distribution of the main intestinal microorganisms in children of the studied groups (Fig. 1).

 

Fig. 1. Distribution of the main colon microorganisms in children aged 1–3 years. GDM – children born to mothers with gestational diabetes mellitus; CG – control group (without GDM)

 

Fig. 1 illustrates that Actinobacteria and Firmicutes dominate IM in both groups, which is natural for children of this age provided that the Firmicutes family includes indigenous obligates and facultative anaerobes. However, the second dominant type of Actinobacteria contains more guanine and cytosine in its DNA than Firmicutes and thus may comprise both aerobic and anaerobic gram-positive bacteria [6]. Within the Actinobacteria type, Bifidobacteria spp. are the most common bacteria found in the normal colon microbiota in young children [16]. Proteobacteria and Bacteroidetes are quantitatively inferior to Actinobacteria and Firmicutes, which corresponds to the structure of a differentiated microbiome in young children [9].

Both the general distribution of the dominant bacteria and their ratio are important to maintain a healthy IM and promote homeostasis of the internal systems of the body. We assessed the ratio of the dominant bacteria, as shown in Fig. 2.

 

 

Fig. 2. The ratio of Actinobacteria and Firmicutes in the colon of 1–3-year-old children. GDM – children born to mothers with gestational diabetes mellitus; CG – control group (without GDM)

 

The median representation of Actinobacteria relative to Firmicutes decreases in children of mothers with GDM compared to CG (Fig. 2). The dysbiotic changes in the IM community can increase the risks of developing pathology in this cohort of children starting from an early age.

We studied dysbiosis by calculating the biodiversity (Shannon) index of the microbial community in young children (Fig. 3).

 

Fig. 3. Shannon index of the microbial community in 1–3-year-old children studied by 16S rRNA sequencing

 

We found higher IM biodiversity in GDM compared to CG (Fig. 3). This provides additional evidence of dysbiotic changes in the microbiome structure of children born to mothers with GDM. The difference in microbiota species composition between the studied groups of children confirms this notion (Table 1).

 

Table 1

Species composition of the intestinal microbiota in 1–3-year-old children, n=105 (Ме [min; max])

Types of microorganisms	GDM, n=75	CG, n=30
Actinobacteria
Actinomyces spp.	3.52 [0 – 15.73] *	0 [0 - 0.1]
Rothia mucilaginosa	0.92 [0.37 - 2.6]	0.5 [0 - 2.47]
Bifidobacterium adolescentis	0.12 [0 - 1.63]	0.37 [0.1 - 2.53]
Bifidobacterium spp.	0.08 [0 - 3.8]	0.17 [0 - 3.13]
Eggerthella spp.	0.05 [0 - 0.33]	0 [0 - 0.07]
Adlercreutzia spp.	0 [0 - 0.13]	0 [0 - 0]
Varibaculum spp.	0 [0 - 0.03]	0 [0 - 0]
Bifidobacterium bifidum	0 [0 - 0]	0 [0 - 0]
Collinsella stercoris	0 [0 - 0]	0 [0 - 0.03]
Eggerthella lenta	0 [0 - 0]	0 [0 - 0]
Bacteroidetes
Bacteroides spp.	0.02 [0 - 0.13]	0 [0 - 0.07]
Bacteroides uniformis	0 [0 - 0.07]	0.03 [0 - 0.9]
Bacteroides caccae	0 [0 - 0.07]	0 [0 - 0]
Prevotella copri	0 [0 - 0.03]	0 [0 - 0]
Parabacteroides spp.	0 [0 - 0.03]	0 [0 - 0]
Sediminibacterium spp.	0 [0 - 0]	0 [0 - 0.17]
Bacteroides ovatus	0 [0 - 0]	0 [0 - 0.4]
Firmicutes
Bulleidia moorei	20.98 [3.43 - 33.83]	45.8 [24.5 - 56.8]
Streptococcus spp.	16.67 [7.33 - 24.73]	10.2 [0.13 - 22.4]
Clostridium hiranonis	5.12 [1.97 - 10.7] *	0.43 [0 - 2.33]
Peptoniphilus spp.	1.68 [0.23 - 6.1]	0.67 [0.2 - 1.17]
Clostridiales	1.32 [0 - 3.4]	0 [0 - 8.7]
Streptococcus agalactiae	0.27 [0.07 - 1.1]	0.07 [0 - 0.37]
Dialister spp.	0.23 [0 - 5,7]	0 [0 - 4.3]
Turicibacter spp.	0.15 [0 - 0.5]	0.13 [0 - 0.73]
Coprococcus catus	0.05 [0.03 - 0.07] *	0 [0 - 0.03]
Dorea spp.	0.05 [0 - 0.3]	0.5 [0.07 - 1.83]
Blautia producta	0.03 [0 - 0.73] *	0 [0 - 0.37]
Peptostreptococcus anaerobius	0 [0 - 1.37]	0.03 [0 - 0.3]
Anaerostipes spp.	0 [0 - 0.47]	0 [0 - 0.07]
Roseburia spp.	0 [0 - 0.33]	0 [0 - 0]
Veillonellaceae	0 [0 - 0.33] *	0 [0 - 0]
Peptostreptococcus spp.	0 [0 - 0.2]	0 [0 - 0.03]
Lachnospiraceae	0 [0 - 0.2]	0 [0 - 0]
Clostridium neonatale	0 [0 - 0.1]	0.07 [0 - 0.47]
Clostridium butyricum	0 [0 - 0.07]	0 [0 - 0]
Ruminococcus bromii	0 [0 - 0.03]	0 [0 - 0]
Ruminococcus torques	0 [0 - 0.03]	0 [0 - 0.03]
Lachnobacterium spp.	0 [0 - 0.03]	0 [0 - 0.03]
Lactobacillaceae	0 [0 - 0.03]	0 [0 - 0.03]
Lactococcus spp.	0 [0 - 0]	0 [0 - 0]
Lachnospira spp.	0 [0 - 0]	0 [0 - 0]
Veillonella spp.	0 [0 - 0]	0 [0 - 0]
Ruminococcus spp.	0 [0 - 0]	0 [0 - 0]
Coprococcus spp.	0 [0 - 0]	0 [0 - 0]
Lactobacillales	0 [0 - 0]	0 [0 - 0]
Dorea formicigenerans	0 [0 - 0]	0 [0 - 0]
Anaerococcus spp.	0 [0 - 0]	0 [0 - 0]
Enterococcaceae	0 [0 - 0]	0 [0 - 0]
Clostridium perfringens	0 [0 - 0]	0 [0 - 0]
Staphylococcus spp.	0 [0 - 0]	0 [0 - 0]
Lactobacillus spp.	0 [0 - 0]	0 [0 - 0]
SMB53 spp.	0 [0 - 0]	0 [0 - 0.03]
Bacillus spp.	0 [0 - 0]	0 [0 - 0]
Erysipelotrichaceae	0 [0 - 0]	0 [0 - 0.3]
Proteobacteria
Acinetobacter spp.	2.25 [1.07 - 6.63]	3.5 [0.67 - 12.3]
Xanthobacteraceae	1.48 [0.13 - 3.93]	0.53 [0 - 1.1]
Nitrosomonadaceae	1.07 [0.2 - 3.83]	0.43 [0 - 2.9]
Hydrocarboniphaga spp.	0.13 [0 - 0.4]	0 [0 - 0.2]
Bilophila spp.	0.08 [0 - 0.47]	0.07 [0 - 0.5]
Burkholderia bryophila	0 [0 - 0.03]	0 [0 - 0]
Thiotrichaceae	0 [0 - 0.03]	0 [0 - 0]
Proteobacteria	0 [0 - 0]	0 [0 - 0]
Sutterella spp.	0 [0 - 0]	0 [0 - 0]
Enterobacteriaceae	0 [0 - 0]	0 [0 - 0]
Vibrionaceae	0 [0 - 0]	0 [0 - 0.17]
Tenericutes
RF39	0 [0 - 0]	0 [0 - 0]
Verrucomicrobia
Akkermansia muciniphila	0 [0 - 0]	0 [0 - 0]
Cyanobacteria
Streptophyta	0 [0 - 0]	0 [0 - 0]
Euryarchaeota
ANME-1	0 [0 - 0]	0 [0 - 0]
Bacteria
Bacteria	0.5 [0 - 2.77]	0.17 [0 - 1.07]

 

GDM – gestational diabetes mellitus; CG – control group

To assess the statistical significance of the differences in occurrence frequencies, the Mann-Whitney U-test was used. (*) indicates a statistically significant difference between GDM and CG, p<0.05; n – the number of children.

 

As shown in Table 1, the results of next-generation sequencing (NGS) suggest the predominance of Firmicutes. The diversity of the bacteria decreases quantitatively in the following order:

• In the GDM group: Bulleidia moorei, 21%, Streptococcus spp., 17%, Clostridium hiranonis, 5%, Actinomyces spp., 4%, Acinetobacter spp., 2%, Peptoniphilus spp., 2%, Rothia mucilaginosa, 1%, Nitrosomonadaceae, 1%, Clostridiales, 1%, Xanthobacteraceae, 1%.
• In the control group: Bulleidia moorei, 46%, Streptococcus spp., 10%, Acinetobacter spp., 3%.

Ten dominant types of microorganisms were identified in children born to mothers with GDM, and three types – in CG. In both groups, the dominant species is Bulleidia moorei from the Firmicutes type. The relative representation of Bulleidia moorei is lower in children of mothers with GDM than in CG (20.98 log₁₀/CFU and 45.8 log₁₀/CFU; p<0.01). Clostridium hiranonis prevails in children of mothers with GDM compared to CG (5.12 log₁₀/CFU and 0.43 log₁₀/CFU; p=0.01). Clostridium hiranonis belongs to cluster XI of the genus Clostridium (a cluster including opportunistic pathogens such as Cl. difficile) and can cause infections under favorable conditions [18]. Actinomyces spp. is also increased in the GDM group but not in CG (3.52 [0-15.73] and 0 [0-0.1]; p=0.04). The genus Actinomyces (type Actinobacteria) includes human saprophytes secreting biologically active substances that are capable of selectively suppressing the viability of other bacteria and regulating the IM composition [14]. However, they can also cause actinomycosis and contribute to adverse changes in the immune system [8]. An increase in saprophytic flora in children of mothers with GDM compared to CG may imply a more competitive environment within the IM community since intestinal saprophytes contain many enzymes, multiply more actively, and produce a variety of bactericidal and bacteriostatic substances to fight pathogenic bacteria [7].

Coprococcus catus is more frequent in children of mothers with GDM than in CG (0.05 [0.03-0.07]; 0 [0-0.03]; p=0.03). Since it produces butyric acid and has anti-inflammatory properties, its relative growth in children born from mothers with GDM might be a compensatory mechanism for regulating the IM community in the context of dysbiosis. Unlike CG, these children can also have more Veillonellaceae (0 [0-0.33]; 0 [0-0]; p=0.04) and Blautia producta (0.03 [0-0.73]; 0 [0-0.37]; p=0.049). These species represent the resident microflora but they can cause inflammation in the intestine under certain conditions [3] and are found in people with irritable bowel syndrome [10]. All this suggests that children of mothers with GDM have higher risks of developing inflammatory processes in the intestine against the background of dysbiosis, despite beneficial bacteria.

Thus, 1–3-year-old children born to mothers with GDM show signs of increased dysbiosis and a higher diversity of opportunistic and pathogenic bacteria in the intestine. Additionally, we found a higher percentage of opportunistic bacteria with potentially beneficial properties and saprophytes that can restrain the growth of pathogens. Nevertheless, the growth of saprophytes cannot compensate for the dysbiosis caused by the pathogenic microbiota. Over time, the prolonged imbalance of the microbial flora in children born to mothers with GDM may affect their general condition.

Human health depends not only on the quantitative pattern of the intestinal microflora but also to a greater extent on the functional activity of microorganisms that produce volatile fatty acids, which are involved in all energy processes of the body. Therefore, we evaluated the functional activity of IM by assessing the volatile fatty acids profile (Table 2.).

 

Table 2

Functional activity of the intestinal microbiome in 1–3-year-old children, n=105 (Ме [min; max])

Indices	GDM n=75	CG n=30
Concentration of SCLC in feces, mmol/g
C2	49.47 [26.43 - 80.57]	40.70 [16.32 - 79.51]
C3	12.83 [8.87 - 20.35]	14.85 [2.98 - 21.13]
iC4	1.57 [0.99 - 2.82]	1.17 [0.81 - 1.88]
C4	8.03 [3.29 - 15.40]	8.15 [2.27 - 14.31]
iC5	1.15 [0.68 - 2.52]	0.62 [0.40 - 1.59]
C5	0.29 [0.15 - 0.94]	0.2 [0.09 - 0.34]
iC6	0.05 [0.04 - 0.08]	0.06 [0.03 - 0.08]
C6	0.06 [0.04 - 0.08]	0.04 [0.03 - 0.06]
TM	77.27 [47.59 - 130.30]	74.85 [31.25 - 137.91]
Indices, units
SI	0.59 [0.41 - 0.89]	0.50 [0.35 - 0.90]
II	0.40 [0.23 - 0.69]	0.35 [0.15 - 0.86]
Acetic:propionic:butyric acid ratio, %
С2	67.14 [59.14 - 73.66]	69.82 [57.69 - 79.14]
С3	21.18 [15.88 - 26.16]	20.62 [10.39 - 26.47]
С4	10.80 [7.43 - 15.42]	10.98 [7.03 - 15.18]
Relative representation, %
С2	63.02 [52.99 - 70.7]	66.70 [53.88 - 74.20]
С3	19.68 [15.15 - 23.53]	19.47 [9.75 - 25.85]
iC4	2.40 [1.22 - 4.18]	2.01 [1.12 - 3.99]
C4	10.33 [7.16 - 13.91]	9.79 [6.88 - 14.66]
iC5	1.88 [0.85 - 3.45]	1.56 [0.48 - 3.8]
C5	0.43 [0.23 - 1.36]	0.33 [0.17 - 1.38]
iC6	0.08 [0.04 - 0.13]	0.08 [0.04 - 0.16]
C6	0.07 [0.04 - 0.11]	0.06 [0.03 - 0.14]

 

GDM – gestational diabetes mellitus; CG  – control group; C2 – acetic acid concentration; C3 – propionic acid concentration; iC4 – isobutyric acid concentration; C4 – butyric acid concentration; iC5 – isovaleric acid concentration; C5 – valerian acid concentration; iC6 – isocaproic acid concentration; C6 – caproic acid concentration; TM – total metabolites (total concentration of volatile fatty acids); SI – structural index; II – isoacid index; C2% – the proportion of acetic acid in the acetic:propionic:butyric acid ratio; C3% – the proportion of propionic acid in the acetic:propionic:butyric acid  ratio; C4% – the proportion of butyric acid in the acetic:propionic:butyric acid ratio; C2 – relative representation of acetic acid concentration; C3 – relative representation of propionic acid concentration; iC4 – relative representation of isobutyric acid concentration; C4 – relative representation of butyric acid concentration; iC5 – relative representation of isovaleric acid concentration; C5 – relative representation of the valerian acid concentration; iC6 – relative representation of the isocaproic acid concentration; C6 – relative representation of the caproic acid concentration.

The data were evaluated by the Student's t-test; (*) indicates a statistically significant difference between GDM and CG, p<0.05.

 

Table 2 demonstrates no significant difference in the SCFA concentrations between CG and the children of mothers with GDM. The results support the hypothesis of the quantitative compensation of SCFA  in children of mothers with GDM due to the diversity of opportunistic and pathogenic bacteria species.

To assess the metabolic pathways of the interactions within IM, we calculated the correlations of the SCFA concentrations in children of the studied groups (Fig. 4 a, b).

 

A                                                                                                                           B

Fig. 4. Correlation analysis of the SCFA concentrations in the feces

of 1–3-year-old children born to mothers with GDM (a) or without GDM (b)

 

Fig. 4 demonstrates unidirectional direct correlations of concentrations of acetic, propionic, and butyric acids with the total SCFA both in CG and in children from mothers with GDM. The ratio of acetic, propionic, and butyric acids is an important marker of the integrity of the IM community [19]. The ratio of these metabolites remains constant within a small range of concentrations if the indigenous microflora maintains symbiotic relationships within the microbial tissue complex of the colon. Otherwise, as in dysbiotic disorders, the constancy of the SCFA production is supported by the high biodiversity of IM with the participation of both indigenous and conditionally pathogenic IM [1].

In children of mothers with GDM, the content of acetic acid is directly proportional to that of butyric and propionic acids, whereas in CG, acetic acid has a direct correlation only with propionic acid but not with butyric acid. Indigenous bacteria that produce propionic acid in moderate amounts play an important anti-inflammatory role in human subcutaneous adipose tissue. Propionic acid is produced by both indigenous and conditionally pathogenic bacteria. Its excessive accumulation in the colon's lumen increases the permeability of the intestinal wall to pathogens and toxic substances. The correlations between the structural index and the isoacid index in CG and children of mothers with GDM are unidirectional but in the group GDM not favorable for compensatory metabolic pathways since their biocenosis is structured incorrectly.

To associate the functional activity of the microbial community with the biodiversity index, the SCFA concentrations were correlated with the Shannon index determined by NGS sequencing (Table 3).

 

Table 3

Correlation analysis of the SCFA concentrations and the Shannon index in 1–3-year-old children, n=105

Parameter	Shannon Index (H`)
	GDM, n=75	CG, n=30
С2	-0.01	0.69
С3	0.03	-0.35
С4	-0.27	-0.64
iС4	0.14	-0.58
iC5	-0.22	-0.82
С5	-0.03	-0.78
iC6	-0.43	-0.15
С6	-0.35	-0.33
SI	-0.28	-0.7
II	-0.41	-0.38

GDM – gestational diabetes mellitus; CG – control group; C2 – acetic acid concentration; C3 – propionic acid concentration; iC4 – isobutyric acid concentration; C4 – butyric acid concentration; iC5 – isovaleric acid concentration; C5 – valerian acid concentration; iC6 – isocaproic acid concentration; C6 – caproic acid concentration; TM – total concentration of volatile fatty acids; SI – structural index; II – isoacid index.

Statistically significant correlation coefficients are highlighted in bold (p<0.05); n – the number of children.

 

Table 3 shows that isocaproic acid correlates inversely with the Shannon index in children born to mothers with GDM. In contrast to CG, the biodiversity index in GDM correlates directly with acetic acid. A short-term growth of the luminal microflora (predominantly transient) may increase the acetic acid concentration without violating the consortium of IM biofilm in healthy children. The rest of the monocarboxylic acids are produced by anaerobic microorganisms that are mostly present in the intestine parietally as a biofilm, which includes indigenous representatives of the microbiome. In CG, the structural index and the monocarboxylic acids index inversely correlate with the Shannon biodiversity index. The imbalance in the resident microflora in children of mothers with GDM is unlikely to be corrected within the microbial community, which further supports the disturbance of the IM structure.

To clarify the revealed patterns of metabolic activity of IM, we calculated the correlation of the main SCFA indices and classes of microorganisms in children of the studied groups (Fig. 5).

 

A                                                                         B

 

Fig. 5. Correlation of the metabolic activity of intestinal microbiocenosis and the intensity of bacterial colonization

of feces in 1–3-year-old children born to mothers with GDM (a) and without GDM (b)

 

We found significant correlations of the SCFA concentrations in feces with Bacteroidetes, Proteobacteria, and Firmicutes microorganisms in children from mothers with GDM, and only with Firmicutes in CG (Fig. 5). For children of the GDM group, a significant direct correlation was found between the SCFA concentrations and Firmicutes, while an inverse correlation was observed with Bacteroidetes and Proteobacteria. In CG, only Firmicutes correlated directly, with no significant relationship found with Bacteroidetes and Proteobacteria.

The IM imbalance in children of mothers with GDM can be a risk factor for the development of generalized pathology. Bacteroidetes are involved in the carbohydrate fermentation, protein utilization, and biotransformation of bile acids, and modern literature emphasizes the importance of balanced IM for preventing the risks of generalized pathology. Moreover, Proteobacteria normally constitute a minority in the structure of a healthy differentiated microbiome. Significant impact on the representation of Proteobacteria can lead to an alteration of the structural index that, in turn, indicates the displacement of useful bacteria from IM.

The direct correlation of Firmicutes with propionic acid suggests a compensatory mechanism of IM regulation. In CG, we found a significant direct correlation of Firmicutes with an increase in the acetic acid concentration and a decrease in the structural index, valerian acid, and isovaleric acid. Since Firmicutes are the most widely represented group of microorganisms, including both facultative and obligate anaerobes, their number and large representation in IM can affect the structure of the microbiome under various conditions.

Discussion

Plenty of studies on the relationship of microorganisms with the host provide disparate and even contradictory information and little is known about the effect of a dysbiotic microbiome of the mother on the child’s microbiome and his/her health [4]. According to our study, the composition of IM and its functional activity in 1–3-year-old children born to mothers with GDM differ both qualitatively and quantitatively from the control group. Despite a higher metabolic activity, the mechanisms of IM regulation against the background of emerging dysbiosis in children of mothers with GDM are similar to normal ones. However, increased biodiversity of conditionally pathogenic and pathogenic microflora prevents IM from maintaining qualitative homeostasis of its structure and is likely to adversely affect the child's health. Our data support the studies demonstrating a high functional activity of the microbiome in the context of host pathology [20], which confirms the unfavorable prognosis of IM activation in children of mothers with GDM because of decompensated regulation of the microbiome's composition.

Children of mothers with GDM can have increased future risks of disruption in the regulation of the vital body processes due to the accumulation of toxic substances in the intestine, violation of the integrity of the bacterial film, and the transport of pathogens and their waste products into the bloodstream. Dysbiotic changes of IM in children born to mothers with GDM can further lead to chronic inflammation in the colon mucosa and the progression of metabolic disorders with age.

Conclusion

The IM composition in 1–3-year-old children born to mothers with GDM differs significantly from that in children from the control group and is dysbiotic in nature. It is characterized by a high variety of opportunistic and pathogenic bacteria. The metabolic activity of IM is also high in this cohort of children due to the activity of conditionally pathogenic flora, which may disrupt compensatory mechanisms and trigger generalized metabolic disorders in the future. Therefore, it is crucial not only to determine the combination of different bacteria (whether they are pathogenic, conditionally pathogenic, or beneficial) but also to assess their functional (metabolic) activity since each species can shift the intra-bacterial balance to either the negative or positive side, exerting a corresponding effect on the body.

 

References

1. Ardatskaya MD, Minushkin ON. Probiotics in the treatment of functional bowel disease. Experimental and clinical gastroenterology 2012; 3, 106-13 (In Russian).
2. Brown J, de Vos WM, DiStefano PS, Dore J, Huttenhower C, Knight R et al. Translating the human microbiome. Nat Biotechnol 2013; 31, 304-8. doi: 10.1038/nbt.2543.
3. Campbell C, Adeolu M, Gupta RS. Genome-based taxonomic framework for the class Negativicutes: division of the class Negativicutes into the orders Selenomonadales emend. Acidaminococcales ord. nov. and Veillonellales ord. nov. Int J Syst Evol Microbiol 2015; 65(9),3203-15. doi: 10.1099/ijs.0.000347.
4. Clarke, S, Murphy EF, Nilaweera K, Ross R, Shanahan F, O’Toole PW, Cotter PD. The gut microbiota and its relationship to diet and obesity. Gut Microbes 2012; 3, 186-202. doi: 10.4161/gmic.20168.
5. Dzhaparidzе LA, Soldatova OA. Formation of the children microbiota: its role in overall metabolism. J Infectology 2022; 14(1), 20-30. doi: 10.22625/2072-6732-2022-14-1-20-30.
6. Ehrenberg HM, Durnwald CP, Catalano P, Mercer BM. The influence of obesity and diabetes on the risk of cesarean delivery. Am J Obstet Gynecol 2004; 191(3), 969-74. doi: 10.1016/j.ajog.2004.06.057.
7. Khmelnitsky OK, Khmelnitskaya NM. Pathomorphology of human mycoses. St. Petersburg: Publishing house of St. Petersburg MAPO, 2005, p. 432 (In Russian).
8. Kurbatova IV, Plahotnaya GA. "Atypical" actinomycosis – microbiological aspects and clinical manifestations. Attending physician 2008; 5, 8. URL: https://amp.lvrach.ru/2008/05/5157391 (In Russian).
9. Ley RE. Obesity and the human microbiome. Curr Opin Gastroenterol 2010; 26(1), 5-11. doi: 10.1097/MOG.0b013e328333d751.
10. Malinen E, Rinttilä T, Kajander K, Mättö J, Kassinen A, Krogius L et al. Analysis of the fecal microbiota of irritable bowel syndrome patients and healthy controls with real-time PCR. Am J Gastroenterol 2005; 100(2), 373-82. doi: 10.1111/j.1572-0241.2005.40312.x.
11. Martiny JB, Jones SE, Lennon JT, Martiny AC. Microbiomes in light of traits: a phylogenetic perspective. Science 2015; 350 (6261). doi: 10.1126/science.aac9323.
12. Natividad JM, Verdu EF. Modulation of intestinal barrier by intestinal microbiota: pathological and therapeutic implications. Pharmacol Res 2013; 69(1), 42-51. doi: 10.1016/j.phrs.2012.10.007.
13. Rudge MVC, Calderon IM, Ramos MD, Peraçoli JC, Pim A. Hypertensive disorders in pregnant women with diabetes mellitus. Gynecol Obstet Invest 1997; 44(1), 11-5. doi: 10.1159/000291401
14. Sergeeva AG, Kuimova NG. Aktinomycetes as producers of bioactive substances. Bulletin of Physiology and Pathology of Respiration 2006, S22, 88-9. (In Russian).
15. Shafquat A, Joice R, Simmons SL, Huttenhower C. Functional and phylogenetic assembly of microbial communities in the human microbiome. Trends Microbiol 2014; 22(5), 261-6. doi: 10.1016/j.tim.2014.01.011.
16. Ursova NI. Microbiocenosis of open biological systems of the body in the process of adaptation to the environment. Rus med magazine 2004; 12(16), 957-9. (In Russian).
17. Xu Z, Malmer D, Langille MGI, Way SF, Knight R. Which is more important for classifying microbial communities: who’s there or what they can do? ISME J 2014; 8, 2357-9. doi: 10.1038/ismej.2014.157.
18. Yutin N, Galperin MY. A genomic update on clostridial phylogeny: Gram-negative spore formers and other misplaced clostridia. Environ Microbiol 2013; 15(10), 2631-41. doi: 10.1111/1462-2920.12173.
19. Zatevalov AV, Selkova EP, Afanasiev SS, Aleshkin AV, Mironov AYu, Gusarova MP, Gudova NV. The evaluation of microbiological disorders of microflora of oropharinx and intestine using mathematical modeling technique. Klinicheskaya Laboratornaya Diagnostika (Russian Clinical Laboratory Diagnostics) 2016; 61(2), 117-21 (In Russian).
20. Zatevalov AМ, Selkova EP, Aleshkin AV, Grenkova TA. Assessment of critical butyric acid concentrations in feces of patients on enteral tube feeding in intensive care units. Fundamental and Clinical Medicine 2017; 2(1), 14-22 (In Russian).

 

References

1.   Ardatskaya MD, Minushkin ON. Probiotics in the treatment of functional bowel disease. Experimental and clinical gastroenterology 2012; 3, 106-13 (In Russian).

2.   Brown J, de Vos WM, DiStefano PS, Dore J, Huttenhower C, Knight R et al. Translating the human microbiome. Nat Biotechnol 2013; 31, 304-8. doi: 10.1038/nbt.2543.

3.   Campbell C, Adeolu M, Gupta RS. Genome-based taxonomic framework for the class Negativicutes: division of the class Negativicutes into the orders Selenomonadales emend. Acidaminococcales ord. nov. and Veillonellales ord. nov. Int J Syst Evol Microbiol 2015; 65(9),3203-15. doi: 10.1099/ijs.0.000347.

4.   Clarke, S, Murphy EF, Nilaweera K, Ross R, Shanahan F, O’Toole PW, Cotter PD. The gut microbiota and its relationship to diet and obesity. Gut Microbes 2012; 3, 186-202. doi: 10.4161/gmic.20168.

5.   Dzhaparidze LA, Soldatova OA. Formation of the children microbiota: its role in overall metabolism. J Infectology 2022; 14(1), 20-30. doi: 10.22625/2072-6732-2022-14-1-20-30.

6.   Ehrenberg HM, Durnwald CP, Catalano P, Mercer BM. The influence of obesity and diabetes on the risk of cesarean delivery. Am J Obstet Gynecol 2004; 191(3), 969-74. doi: 10.1016/j.ajog.2004.06.057.

7.   Khmelnitsky OK, Khmelnitskaya NM. Pathomorphology of human mycoses. St. Petersburg: Publishing house of St. Petersburg MAPO, 2005, p. 432 (In Russian).

8.   Kurbatova IV, Plahotnaya GA. "Atypical" actinomycosis – microbiological aspects and clinical manifestations. Attending physician 2008; 5, 8. URL: https://amp.lvrach.ru/2008/05/5157391 (In Russian).

9.   Ley RE. Obesity and the human microbiome. Curr Opin Gastroenterol 2010; 26(1), 5-11. doi: 10.1097/MOG.0b013e328333d751.

10. Malinen E, Rinttilä T, Kajander K, Mättö J, Kassinen A, Krogius L et al. Analysis of the fecal microbiota of irritable bowel syndrome patients and healthy controls with real-time PCR. Am J Gastroenterol 2005; 100(2), 373-82. doi: 10.1111/j.1572-0241.2005.40312.x.

11. Martiny JB, Jones SE, Lennon JT, Martiny AC. Microbiomes in light of traits: a phylogenetic perspective. Science 2015; 350 (6261). doi: 10.1126/science.aac9323.

12. Natividad JM, Verdu EF. Modulation of intestinal barrier by intestinal microbiota: pathological and therapeutic implications. Pharmacol Res 2013; 69(1), 42-51. doi: 10.1016/j.phrs.2012.10.007.

13. Rudge MVC, Calderon IM, Ramos MD, Peraçoli JC, Pim A. Hypertensive disorders in pregnant women with diabetes mellitus. Gynecol Obstet Invest 1997; 44(1), 11-5. doi: 10.1159/000291401

14. Sergeeva AG, Kuimova NG. Aktinomycetes as producers of bioactive substances. Bulletin of Physiology and Pathology of Respiration 2006, S22, 88-9. (In Russian).

15. Shafquat A, Joice R, Simmons SL, Huttenhower C. Functional and phylogenetic assembly of microbial communities in the human microbiome. Trends Microbiol 2014; 22(5), 261-6. doi: 10.1016/j.tim.2014.01.011.

16. Ursova NI. Microbiocenosis of open biological systems of the body in the process of adaptation to the environment. Rus med magazine 2004; 12(16), 957-9. (In Russian).

17. Xu Z, Malmer D, Langille MGI, Way SF, Knight R. Which is more important for classifying microbial communities: who’s there or what they can do? ISME J 2014; 8, 2357-9. doi: 10.1038/ismej.2014.157.

18. Yutin N, Galperin MY. A genomic update on clostridial phylogeny: Gram-negative spore formers and other misplaced clostridia. Environ Microbiol 2013; 15(10), 2631-41. doi: 10.1111/1462-2920.12173.

19. Zatevalov AV, Selkova EP, Afanasiev SS, Aleshkin AV, Mironov AYu, Gusarova MP, Gudova NV. The evaluation of microbiological disorders of microflora of oropharinx and intestine using mathematical modeling technique. Klinicheskaya Laboratornaya Diagnostika (Russian Clinical Laboratory Diagnostics) 2016; 61(2), 117-21 (In Russian).

20. Zatevalov AM, Selkova EP, Aleshkin AV, Grenkova TA. Assessment of critical butyric acid concentrations in feces of patients on enteral tube feeding in intensive care units. Fundamental and Clinical Medicine 2017; 2(1), 14-22 (In Russian).

 

Материал поступил в редакцию 01.02.24

 

 

ОСОБЕННОСТИ МИКРОБИОМА КИШЕЧНИКА У ДЕТЕЙ РАННЕГО ВОЗРАСТА, РОЖДЁННЫХ ОТ МАТЕРЕЙ С ГЕСТАЦИОННЫМ САХАРНЫМ ДИАБЕТОМ,

КАК ПРЕДИКТОР МЕТАБОЛИЧЕСКИХ НАРУШЕНИЙ У ПОТОМСТВА

 

Л.А. Харитонова, д.м.н., проф., зав. кафедрой педиатрии с инфекционными болезнями у детей

ФГАОУ ВО РНИМУ им. Н.И. Пирогова МЗ РФ

(117997, Российская Федерация, г. Москва, ул. Островитянова, д1)

E-mail: doc.29gkb@gmail.com

 

Т.А. Маяцкая, ассистент кафедры педиатрии с инфекционными болезнями у детей

ФГАОУ ВО РНИМУ им. Н.И. Пирогова МЗ РФ

(117997, Российская Федерация, г. Москва, ул. Островитянова, д1)

E-mail: doc.29gkb@gmail.com

 

А.М. Затевалов, д.б.н., Гл.н.с лаборатории Диагностики и профилактики инфекционных заболеваний

ФБУН МНИИЭМ им. Г.Н. Габричевского Роспотребнадзора, ул. Адмирала Макарова

(125212, Российская Федерация, г. Москва, ул. Адмирала Макарова, 10)

E-mail: doc.29gkb@gmail.com

 

А.А. Маяцкий

ГБУЗ МО «Центральная клиническая психиатрическая больница им.Ф.А.Усольцева»

(127083, Российская Федерация, г. Москва, улица 8 Марта, д. 1)

E-mail: doc.29gkb@gmail.com

 

Аннотация. Актуальность. Важной проблемой исследования микробиома кишечника является невозможность культивировать около 80% микроорганизмов при помощи стандартного бактериологического исследования кала. В связи с чем, в нашем исследовании для более точной оценки микробиома кишечника у изучаемой когорты детей был использован метод секвенирования ампликона гена рибосомной РНК 16S, что позволило выявить некультивируемые патогены. В том числе была проведена оценка метаболической активности микробиома кишечника с определением летучих жирных кислот. Цель данного исследования совершенствование ранней диагностики нарушений микроэкологии кишечника у детей, рожденных от матерей с гестационным сахарным диабетом, путем изучения видового состава и состояния функциональной активности микробиома кишечника методом NGS секвенирования. Результаты. Были выявлены значимые различия распределения типов и видов микроорганизмов в кишечнике в исследуемых группах детей: у детей от матерей с ГСД происходит смещение равновесия в сторону условно-патогенных и патогенных бактерий. Показаны особенности метаболической активности дисбиотического микробного сообщества кишечника и его потенциально неблагоприятное воздействие на организм детей, рожденных от матерей с гестационным сахарным диабетом.

Ключевые слова: микробиом кишечника, гестационный сахарный диабет, дети раннего возраста, метаболизм, масляная кислота, уксусная кислота, функциональная активность.