WO2021237312A1

WO2021237312A1 - Method for quantitative evaluation (mqe both for rapid prognosis of disbiosis in infants -up to 1 year of age(mqe/rpdi-1y), and for multi-factural diseases (mqe/mfd)

Info

Publication number: WO2021237312A1
Application number: PCT/BG2020/050001
Authority: WO
Inventors: Valko KALINKIN IVANOV; Deyan PROKOPOV KRUMOV; Ilia ILIEV NIKOLOV; Tonka VASILEVA ATANASOVA; Veselin BIVOLARSKI PETROV; Mariana NIKOLOVA MANOLOVA; Daniela MOLLOVA GEORGIEVA
Original assignee: Gamma Consult - Kalinkin, Prokopov & Sie Gps
Priority date: 2020-05-27
Filing date: 2020-05-28
Publication date: 2021-12-02
Also published as: BG113148A; BG67567B1; EP4162081A1

Abstract

The invention regarding the present disclosure relates to a method for in vitro assessment and prognosis for balancing the intestinal microbiota in newborns in the age of one month to one year, based on an integrated approach, by calculating correlation coefficients between key indicators, divided into three main groups: 1. Correlation between composition and amount of microorganisms in breast milk and in the feces of the child; 2. Correlation between enzyme profile and activity of enzymes responsible for the assimilation and metabolism of milk components (lactose, lipids, oligosaccharides, proteins) in samples of breast milk and feces of the child; 3. Correlation between the amount of metabolites obtained from the metabolism of the main components of breast milk from the microbiota in breast milk and in the feces of the child. The method in conjunction with the invention provides an opportunity for rapid prognosis of dysbiosis in infants in the age group of up to 1 year. In this way, it will find application in the algorithm for accurate diagnosis of the intestinal microbiota of newborns aged one month to one year. The method also helps to quickly establish the type of dysbiosis and the degree of violation.

Description

METHOD FOR QUANTITATIVE EVALUATION (MQE) BOTH FOR RAPID PROGNOSIS OF DYSBIOSIS IN INFANTS - UP TO 1 YEAR OF AGE (MQE/RPDI- 1Y), AND FOR MULTI-FACTORIAL DISEASES (MQE/MFD)

I. TECHNICAL FIELD

The present invention relates to the field of medical diagnostics, pharmacy, biotechnology.

II. BACKGROUND OF THE DISCLOSURE

There is various evidence that breastfeeding provides significant protection against infectious and inflammatory diseases, various disabilities and improves cognitive abilities. (Bartick and Reinhold, 2010; Dieterich et al., 2013; Kramer et al., 2009).. This is largely due to the properties of breast milk and its oligosaccharides which are available to affect microbial colonization in newborns. The performed studies showed that babies are colonized with approximately 107 different types of microorganisms in the first week of life Palmer et al. (2007). These close relationships of the body with the gastrointestinal microflora can be the basis and seem to be critical to the health of the newborn, as well as the risk of developing various other non-communicable diseases. Optimal breastfeeding actually remains a highly effective public health strategy for infant survival, especially to reduce mortality from gastroenteritis and pneumonia in developing countries(Bhutta et al., 2013). Breastfeeding studies showed the protective role of breast milk against many chronic and immune conditions, in particular, type 1 diabetes, necrotizing enterocolitis, asthma and leukemia.(Bartick and Reinhold, 2010).

The development of the microbiota in the newbom's digestive system is a gradual and dynamic process, which is determined by several factors, such as mode of birth, prematurity, diet, related diseases and antibiotic therapy, as well as the impact of the environment, which requires personalized approach (Wall et al. 2009). In addition, the colonization process is strongly influenced by diet (breast milk and / or formula. During the first month after birth, bifidobacteria and coliforms (particularly E. coli) are predominant, followed by Lactobacillus spp. and Bacteroides and varies widely between individuals. (Wall et al., 2009). Changes in the ratio of the dominant representatives of the intestinal microflora of newborns appeared after about a year of life, mainly as a result of the introduction of a new food in the baby's diet. The number of representatives of Lactobacillus spp., Bacteroides spp. and Clostridia increases while bifibroids and E. coli decrease. Finally, at the age of about 2 years, the intestinal microbial communities reach a composition similar to that found in the intestines of adults. (Koenig et al. 2011). Bifidobacterium longum is the most common primary colonizer of the gastrointestinal tract of newborns (Palmer et al., 2007).

Human milk has many mechanisms to protect newborns: immune response cells, immunoglobulins, innate protective proteins, peptides, free fatty acids, cytokines and chemokines, glycans and oligosaccharides. (Ballard and Morrow, 2013. Among the various bioactive components of human milk, oligosaccharides are the most common and include significantly more than 100 different bioactive carbohydrates, composed of 3 to 32 monosaccharides (Newburg et al., 2005). As a class, oligosaccharides are 6-12 g / L from breast milk and are largely synthesized from lactose. (Bode, 2012). Human colostrum contains approximately 22-24 g / L of milk oligosaccharides. (Newburg et al., 1995; Urashima et al., 2012. In human milk, oligosaccharides are the third most important component after lactose and lipids and their amount is much higher than the amount of total protein. (Newburg et al., 1995). The structure of more than 100 human oligosaccharides has been characterized up to date (Urashima et al., 2011a,b; Kobata, 2010). Characterization of the physiology of bifidobacteria is largely focused on their ability to metabolize various dietary oligosaccharides.

This genus of bacteria has a set of transmembrane permeases that ensure the metabolism of various carbohydrate polymers, such as dietary fiber, including human milk oligosaccharides, which pass undigested to the distal parts of the digestive tract. (Moro et al., 2006; Macfarlane and Cummings, 1999). Only a few species of bifidobacteria are capable of metabolizing and using human milk oligosaccharides as a carbon source. Such species are typically isolated mainly from the microflora of the gastrointestinal tract of newborns.lt has been found that bifidobacteria associated with the newborn are able to assimilate mainly lacto-N-tetraoses (LNT; Gal β 1-3GlcNAc β 1-3 Gal β 1-4Glc) or lacto-N-neotetraoses (LNnT; Gal β 1- 4GlcNAc β 1-3 Gal β 1-4Glc (LoCascio et al., 2007).

The spices B. bifidum secretes extracellular 1,2-α-1-fucosidase (EC 3.2.1.63), which cleaves terminal 1,2-α-fucosyl bonds that protect galactose residues in lacto-N-biosis, thus allowing continuation of catabolic processes until their full assimilation (Katayama et al., 2004; Nagae et al., 2007). In addition, lacto-N-biosidase (EC3.2.1.140) releases lacto-N-biosis from lacto- N-tetraose and other milk oligosaccharides that lack fucosylation or sialylation.(Wada et al., 2008).

Once obtained, the lacto-N-biose is transported across the cell membrane by a special ABC transporter that integrates the lacto-N-biose binding subunit.(Suzuki et al., 2008; Wada et al., 2007). It is also known that B. longurn uses endo-ot-N -acetylgalactosaminidase (EC 3.2.1.97) to extract galacto-N-biosis from O-linked mucin glycans(Fujita et al., 2005). In fact, the presence of both endo-N-acetylgalactosaminidase and fucosidase activity allows B. bifidum to degrade mucin (Ruas-Madiedo et al., 2008). Data on the absorption of oligosaccharides from breast milk by lactobacilli and other concomitant microflora are scarce, with no specific correlations in the direction of type of enzyme-enzyme activity-type of microorganism- amount of microorganisms.

Metabolites are a consequence of the physiological state of the human body from one side, and from the other of the physiological activity of the accompanying microflora in the intestinal tract. This makes them an ideal way to track changes caused by illness or treatment.Assessing the amount and diversity of metabolites is a particularly important point in understanding the many interactions between genetic factors, the environment, and the microbiota. At key points in metabolic processes they inter sect, where the low molecular weight metabolites mediate these relationships.

Researchers consider the microbiome as a potential source of biomarkers for various diseases. Although this may be a favorable area for biomarkers, there are many examples of different metabolites being associated with the same disease, which reduces their reliability as biomarkers.

Another indicator whose potential is being investigated for a biomarker is the pH values of faecal samples. There is evidence that they directly correlate with the bacterial species that colonize the baby's intestines. For example, a direct relationship has been found between lower pH values of faecal samples and significantly reduced amounts of potentially pathogenic bacterial populations (ie Clostridiaceae, Enterobacteriaceae, Peptosteptoccocaceae and Veillonellaceae). Another example is the found correlation between the abundance of specific species of Enterobacteriaceae, which cause inflammation of the intestines and the presence of colic in infants. It was found also correlation between the abundance of specific species of Enterobacteriaceae, which cause inflammation of the intestines and the presence of colic in infants. These adverse effects may be due to the fact that lipopolysaccharides derived from enterobacteria induce stronger inflammatory activity than other lipopolysaccharide- producing bacteria.These adverse effects may be due to the fact that lipopolysaccharides derived from enterobacteria induce stronger inflammatory activity than other lipopoly saccharide-producing bacteria.

Asthma is the most common chronic disease of childhood. Canadian scientists have found a correlation between changes in the intestinal microbiome in infants up to one year (dysbiosis), which affect the development of asthma.The effects of microbial intestinal dysbiosis on atopic wheezing in a population living in developing countries with a low economic standard have been studied. Microbial dysbiosis at 3 months of age is associated with the later development of atopic wheezing. For example, dysbiosis has been observed in Ecuadorian infants involving various bacterial taxa, including several microscopic fungal taxa. Fecal short-chain fatty acid levels in three-month-old infants with atopic wheezing show clear trends of increased acetate concentration and decreased caproic acid concentration.

Some of the defense mechanisms of human milk are due to oligosaccharides. Given the growing incidence of diseases associated with unregulated microbiota and host immunity, there is a global interest in testing new nutritional approaches and functional foods corresponding to those found in breast milk to optimize host health and restore the bacterial flora.

The data described so far from the literature prove that the test models used both for diagnostic purposes and for control of the microbiota in children up to one year of age cannot provide an algorithm for rapid and reliable assessment of the intestinal microbiota and its association with various disorders, because they are linear, related to only one indicator.

The development of a quantitative assessment method for the rapid prediction of dysbiosis in infants up to 1 year of age and as well for the study multifactorial diseases including abnormalities in the human microbiota requires an interdisciplinary approach and the final assessment shoud include data from different research methods with specific computational techniques.

III. TECHNICAL SUMMARY OF THE INVENTION The objective of the invention is to provide a method based on in vitro quantitative assessment, which provides the ability to quickly predict dysbiosis in infants up to 1 year age, which can be used to create a model (algorithm) for accurate diagnosis of intestinal microbiota in newborns from one month to one year age and the method helps to quickly establish the type of dysbiosis and the degree of disorder. The method is based on an interdisciplinary approach in analyzing the quantitative and qualitative composition of the microbiota, the type and quantity of key enzymes and metabolites secreted by the microbiome and data analysis through an information system.

In the last few years, studies have found that both colostrum and breast milk from healthy women contain bacteria, including staphylococci, streptococci, corynebacteria, lactic acid bacteria, propionic acid bacteria, and bifidobacteria (Fernandez et al., 2013). Recently, the application of various techniques, including metagenomic approaches, has confirmed the presence of DNA from these and other bacterial genera in breast milk and colostrum (Hunt et al., 2011; Cabrera-Rubio et al., 2012; Fernandez et al., 2013; Jost et al., 2013, 2014; Ward et al., 2013; Jimenez et al., 2015). Therefore, these biological fluids are continuous sources of live bacteria to the gastrointestinal tract (McGuire and McGuire, 2015). Other studies have shown that there is a transfer of bacterial strains from mother to baby during breastfeeding (Albesharat et al., 2011; Martin et al., 2012; Jost et al., 2014). Despite some established correlations of the amount and composition of the intestinal microbiota with the detection of certain diseases, the molecular biological methods used are long and expensive for everyday practice, which makes them inaccessible for mass use. There are no correlations with the data on key enzymes and metabolites responsible for the absorption of specific components of colostrum and breast milk and the dynamics of their concentration when changing the diet and overcoming intestinal dysbiosis.

Some bacteria in the baby's oral cavity can contaminate milk during breastfeeding due to the flow of milk back into the milk ducts (Ramsey et al., 2004). Within 24 hours after birth, colostrum has been found to contain typical oral bacteria such as Veillonella, Leptotrichia and Prevotella (Cabrera-Rubio et al., 2012). Although the origin of the human salivary microbiome is still quite poorly studied (Zaura et al., 2014), Streptococcus spp. appears to be one of the dominant species in both adults (Nasidze et al., 2009; Yang et al., 2012) and infants (Bearfield et al., 2002; Cephas et al., 2011). Streptococci are also among the dominant species in human colostrum and breast milk (Jimenez et al., 2008a, b; Hunt et al., 2011; Martin et al., 2015). Maternal oral health has been shown to correlate closely with the likelihood of developing dental caries in a child (Zaura et al., 2014). Some compounds in human milk (eg., human milk oligosaccharides, proteins) may affect the colonization of certain species indirectly. For example, milk proteins, such as caseins and lactoferrin, have been found to have the ability to inhibit the attachment of cariogenic streptococci to hydroxyapatite and to promote the attachment of commensal bacteria in vitro (Johansson and Lif Holgerson, 2011). There are no large-scale and long-term studies characterizing the development of the oral microflora during the neonatal period and childhood. In initial studies, it was thought that oral colonization by bacteria that cause dental caries can occur after emergence of the first milk teeth in the newborn, as later, however, it has been shown that the cariogenic Streptococcus mutans may be present in the oral cavity of the baby before the appearance of hard tissues there (Law et al., 2007). Epidemiological studies reveal a number of environmental exposures associated with asthma. This could explain the escalation of asthma over the last 30 years. Babies at risk of asthma experience transient states of intestinal dysbiosis and changes in the metabolism (eg., urobilinogen) during the first 3 months of life. These metabolites or differences in microbiomes are used in the design of prior use of probiotics or ca serve serve as a prognostic biomarker. A model has been developed by combining estimates of ecosystem performance and the generation of ecosystem services for the definition of dysbiosis in infants. According to it, the intestinal microbiome is characterized by: (1) no disturbances in the composition and amounts of taxa when changing diet or taking antibiotics; (2) high susceptibility to invasion by external taxa; and (3) poor use of the available resources (including breast milk oligosaccharides). The healthy/balanced state of the microbiota is characterized as a highly functioning ecosystem when the intestinal microbiome is: (1) stable over time in the composition and amount of taxa; (2) resistant to invasion by allochthonous bacteria; and (3) demonstrate high conversion of breast milk oligosaccharides into end products and biomass beneficial to infants. This model is agnostic by method and index of choice and provides a quantitative indicator and objective approach to assessing the microbiome, but it is limited mainly to data on the composition and quantity of microorganisms, and is not based on correlations between composition and quantity of microorganisms, enzyme activity responsible for the absorption of oligosaccharides from breast milk and the amount of metabolites secreted by the microbiota.

The present patent application refers to the development of a specific method for in vitro quantification of fast prediction of dysbiosis in infants up to 1 year age at which it achieves the ability to control the human microbiome, as in the method, according to the description takes into account on the one hand common indicators such as age and gender, and on the other hand the specific indicators of human health. The proposed method is based on the processing of data from analyzes of the diversity and ratio of microorganisms in the intestinal tract and other body fluids (saliva, breast milk, feces), amount of metabolites used as biomarkers and reporting the level of enzyme activity of enzymes used as biomarkers, allowing the individual characteristics of people to be taken into account.

The method covered by this patent application is based on an integrated approach for in vitro analysis of the intestinal microbiota balance in children from one to twelve months age and a prognosis for its recovery in established dysbiosis by calculating correlation coefficients between key indicators divided into three main groups:

1. Correlation between composition and amount of microorganisms in breast milk and in the feces of the child;

2. Correlation between enzyme profile and activity of enzymes responsible for the assimilation and metabolism of milk components (lactose, lipids, oligosaccharides, proteins) in samples of breast milk and feces of the child;

3. Correlation between the amount of metabolites obtained from the metabolism of the main components of breast milk from the microbiota in breast milk and in the feces of the child.

The in vitro quantitative assessment method for the rapid prediction of dysbiosis in infants up to one year of age and for multifactorial diseases requires the following course of analysis: 1. Protocol for quantitative analysis of the human microbiota by real-time qPCR. The presented protocol can be used to identify abnormalities for dysbiosis analysis in newborns from one to twelve months and to be included in an algorithm for identification of condition, abnormalities and rules for restoring the balance of the microbiota. It will be possible to quantify different groups of microorganisms from the beneficial and pathogenic microflora which to be used for calculation of the ratios between individual species (Table 1). Essential for determining the degree of dysbiosis represent ratios bifidobacteria/bacteroids; lactobacilli/bacteroids; (bifidobacteria + lactobacilli)/bacteroids; (bifidobacteria + lactobacilli/yeast; (bifidobacteria + lactobacilli)/E. coli. Another important indicator is the ratio between bifidobacteria and the main enzymes responsible for the utization of the main components of breast milk (lactose, lipids, proteins, oligosaccharides).

Table 1. Model of comparison of the data from the studied 12 indicators.

The values of the individual ratios vary depending on age, health and diet. The range of ratios in healthy individuals is presented in Table 6.

2. Analysis of enzymes in breast milk and faecal samples from infants from one to twelve months age, which are responsible for the metabolism of the main components of breast milk (lactose, oligosaccharides, lipids, proteins) - β-galactosidase, α-galactosidase, α-glucosidase, β-glucosidase, α-fucosidase, lipase, protease. For the purposes of the method, the ratios between the individual enzymes are calculated, which gives information about the type of metabolic processes that take place, and on the other hand provides information about the capacity of the beneficial microflora to maintain the balance of intestinal microflora. The enzyme α-fucosidase provides direct information on the adhesion potential of the beneficial microflora (bifidobacteria and lactobacilli) on the epithelial cells of the intestinal mucosa, while the other enzymes provide information on the rate of reproduction of beneficial microorganisms and their place in this specific ecological niche. The data on enzyme activity correlate with the data on the amount of metabolites found in the respective samples. The range of ratios of the values of the enzyme activities is described in Table 6.

3. Analysis of the amount of short-chain fatty acids in breast milk and faecal samples of infants from one to twelve mounths age. The quantitative analysis of lactate, acetate, propionate and butyrate in samples of breast milk and feces of infants up to one year of age provide objective information about the metabolic processes carried out with the participation of the accompanying microbiota. The presence of short-chain fatty acids is a direct confirmation of the functional activity of bifidobacteria and lactobacilli. The ratio between the different groups of microorganisms described in the first point of the proposed method and the amount of short-chain fatty acids provides essential information about the balance of the microbiota and its functional state. In addition, for the purposes of the proposed method, the ratios between the enzymatic activities of the enzymes of the second point (the second stage of the analysis) and the amount of short-chain fatty acids can be calculated. The range of ratios of the values of the enzyme activities is described in Table 6.

4. Calculation of the correlations between the ratios of the individual components to define the status of the microbiome in children from one to twelve months of age. Our in vitro model for determining intestinal dysbiosis in children from one to twelve months of age as a condition of the specific ecological niche in the intestinal tract correlates with a reduction in the risk of acute and chronic diseases. In addition, the model can provide a framework for evaluating the effectiveness of strategies for the prevention and treatment of intestinal dysbiosis.

IV. EXAMPLES

Example 1. In vitro study of 12 indicators by assessing the balance of the microbiota in infants aged from one to twelve months:

1. A protocol for the quantitative analysis of the human microbiota by real-time qPCR

The presented protocol can be used to identify deviations for analysis of dysbiosis in newborns aged from one to twelve months and to be included in an algorithm for identifying the condition, deviations and rules for restoring the balance of the microbiota.

A. Primers and fluorescently labeled probes for real-time qPCR detection of bacterial groups and species in samples from target groups

The primer pairs and fluorescently labeled probes for real-time qPCR detection of bacterial species and communities in samples from different target groups are presented in Table 2.

Table 2. Primers and fluorescently labeled probes for real-time qPCR detection of bacterial groups and species used in the present study

2. Control bacterial strains

Bacterial strains used to control the specificity of the primers and labeled probes, as well as for quantitative assessment of individual bacterial species and communities are presented in Table 3. The strains were purchased from the Bulgarian National Bank for Industrial Microorganisms and Cell Cultures (NBIMCC) and the German Collection of Microorganisms and Cell Cultures (DSMZ). The strains were grown aerobically or anaerobically in selective growth media as recommended by DSMZ and NBIMCC. For each culture are made a series of dilutions, which were plated onto appropriate agar medium to determine the total number of bacteria - colony-forming units (CFU). Additionally, 1.0 ml samples were taken from the dilutions and centrifuged under the following conditions: 12000rpm / 3min. The bacterial cell pellets were stored at -80 ° C until use for analysis.

Table 3. Bacterial strains used as controls in this study

3. Extraction of DNA from bacterial cultures and samples from the target groups

Bacterial genomic DNA from control strains and isolates from target groups was extracted using the Blood and Tissue kit DNeasy (Qiagen, Hilden, Germany).

Microbial DNA from faecal samples and breast milk was isolated using the QIAamp DNA Stool Mini Kit (Qiagen, Hilden, Germany). Initially, 200 mg of faecal sample / breast milk was diluted in 1.8 ml of sterile PBS buffer. To diluted faecal sample (200 μl), 20 μl of lysozyme (150 mg/ml) and 130 μl of lysis buffer (2.0 mM Na₂EDTA; 20 mM Tris-HCl; 1.2% Triton X-100) were added. The suspension was incubated at 37°C for 30 minutes, and DNA was extracted according to the manufacturer's instructions.

4. Quantification of DNA

The amount and purity of bacterial genomic DNA isolated from the standard strains and from the tested samples are determined spectrophotometrically. The absorption of suitably diluted DNA samples at wavelengths of 260 nm and 280 nm is determined. The measurements are performed on a Shimadzu UV-2600 spectrophotometer at a light path of 8.5 mm, after resetting the apparatus against the cuvette with elution buffer. The homogeneity of DNA was determined by by electrophoresis in 1% (w/v) agarose gel and ethidium bromide staining. The samples with DNA were stored at -20 °C.

5. Real-time qPCR

The real-time qPCR reactions for detection and quantification of bacteria were performed using SYBR Green PCR Master Mix (Applied Biosystems). The reactions were carried out in 96-well plates with a final volume of 25 μL. The Real-time qPCR was performed using a Real-Time PCR CFX96 Touch (BIO-RAD) with software version 3.0.1224 under conditions for each of the target bacterial groups listed in Table 4.

Table 4. Primers and reaction conditions for real-time qPCR quantification of bacteria in faecal samples

TaqMan Universal PCR Master Mix (Applied Biosystems) was used in real-time qPCR reactions to detect and quantify bacteria with fluorescently labeled probes. The reactions were carried out in 96-well plates with a final volume of 25 μL, under conditions for each of the target bacterial groups listed in Table 5.

Table 5. Primers, fluorescently labeled probes and reaction conditions for real-time qPCR quantification of bacteria in fecal samples.

Real-time qPCR assays for quantification of bacteria in faecal samples and breast milk were performed with a minimum of two replicates of different assays.

6. Electrophoretic analysis of DNA fragments obtained by qPCR

To check the size and integrity of the amplimers obtained after conducting real-time qPCR, agarose gel electrophoresis of the reaction products was performed. Separation of the DNA fragments was performed in a 2% agarose gel at 35V and 25 mA current. The separated fragments were visualized fluorescently - by treating the gel with ethidium bromide and illuminating with UV light. The approximate size of the DNA fragments is determined by comparison with a standard containing small DNA fragments of known size - SmartLadder SF (Eurogentec, Belgium).

7. Statistical data processing

Statistical data processing and its graphical presentation is performed with SYSTAT software, version 13.2 and SigmaPlot version 12.0 (Systat Software Inc., USA).

II. Analysis of enzymes in samples of breast milk and feces from the newborn babies 1. Determination of enzymatic activity of β-galactosidase (EC 3.2.1.23)

The enzymatic reaction was performed under the following conditions: 100 mM sodium acetate buffer, pH 6.0, containing 2 mM o-nitrophenyl-ß-D-galactopyranoside (ONP-Gal) solution and suitably diluted cells or supernatant after cell disintegration at 37 °C. The reaction was stopped with 1 M sodium carbonate. One unit of enzyme activity catalyzes the production of 1.0 μmol o-nitrophenol per minute at pH 6.0 and 37 °C. The o-nitrophenol content was determined spectrophotometrically at 410 nm wavelength on a Beckman Coulter DU 800 spectrophotometer.

All experiments to determine enzyme activity were performed in triplicate.

2. Determination of α-galactosidase activity (EC 3.2.1.22)

The enzyme reaction was performed under the following conditions: 400 mM citrate buffer, pH 4.0, containing 9.9 mM solution of p-nitrophenyl-α-D-galactopyranoside (PNP-Gal) (prepared in distilled water by dissolving p-nitrophenyl-α-D-galactopyranoside, Sigma cat. № N-0877) and suitably diluted cells or supernatant after cell disintegration at 37 °C. The reaction was stopped with 200 mM borate buffer, pH 9.8. One unit of activity converts 1.0 μmole of p-nitrophenyl-α-D-galactopyranoside to o-nitrophenol and D-galactose in one minute at pH 4.0 and 37 °C.

The p-nitrophenol content was determined spectrophotometrically at 405 nm wavelength on a Beckman Coulter DU 800 spectrophotometer.

All experiments to determine enzyme activity were performed in triplicate.

3. Determination of α-L-fucosidase activity (EC 3.2.1.51)

The enzyme reaction was performed under the following conditions: 100 mM citrate buffer, pH 6.5, containing 10 mM solution of p-nitrophenyl-α-L-fucopyranoside (PNP-FUC) (prepared by diluting p-nitrophenyl α-L-fucopyranoside, Sigma cat. № N3628) with distilled water and suitably diluted cells at 37 °C. The reaction was stopped with 200 mM borate buffer, pH 9.8. One unit of activity converts 1.0 μmole of p-nitrophenyl-α-L-fucopyranoside to p-nitrophenol and L-fucopyranoside in one minute at pH 6.5 and 37 °C. The p-nitrophenol content was determined spectrophotometrically at 405 nm wavelength on a Beckman Coulter DU 800 spectrophotometer.

All experiments to determine enzyme activity were performed in triplicate.

4. Determination of α-glucosidase activity (EC 3.2.1.20)

The enzymatic reaction was performed under the following conditions: 100 mM acetate buffer, pH 6.0, containing 1.25 mM p-nitrophenyl-α-D-glucopyranoside solution (prepared by diluting p-nitrophenyl-α-D-glucopyranoside, Sigma cat. № N3628 with distilled water and suitably diluted cells at 37 °C. The reaction was stopped with 0.5 M sodium carbonate. One unit of activity converted 1.0 μmole of p-nitrophenyl α-D-glucopyranoside to p-nitrophenol and D-glucopyranoside per minute at pH 6.0 and 37 °C. The p-nitrophenol content was determined spectrophotometrically at 405 nm wavelength on a Beckman Coulter DU800 spectrophotometer.

All experiments to determine enzyme activity were performed in triplicate.

5. Determination of β-glucosidase activity (EC 3.2.1.28)

The enzyme reaction was performed under conditions: 0.1 M acetate buffer, pH 5.0, containing 20 mM solution of p-nitrophenyl-ß-D-glucopyranoside (ONP-Glu) and suitably diluted cells at 37 °C. The reaction was stopped with 0.2 M sodium carbonate. One unit of activity converts 1.0 μmole of p-nitrophenyl-α-D-glucopyranoside to p-nitrophenol and D- glucopyranoside in one minute at pH 5.0 and 37 °C. The p-nitrophenol content was determined spectrophotometrically at 405 nm wavelength on a Beckman Coulter DU 800 spectrophotometer.

All experiments to determine enzyme activity were performed in triplicate.

6. Determination of protease activity The protease activity of the cells and the supernatant was examined in the presence of casein (Fluka) as a substrate. One unit (U) of protease activity is defined as the amount of enzyme that hydrolyzes casein to 1 μmol tyrosine in 1 minute at pH 7.5 and 37 °C.

The enzyme reaction was performed under the following conditions: in 50 mM phosphate buffer, pH 7.5, dissolved 0.65% (w/v) casein and 1.0 ml of appropriately diluted sample (cells or supernatant). The reaction proceeds for 10 minutes at 37 °C in a water bath. The reaction was stopped by the addition of 110 mM trichloroacetic acid (TCA) (Merck) at 37 °C. The samples were incubated with TCA for 30 minutes at the same temperature. To remove undigested casein, the samples were centrifuged at 9000 rpm/15 min, 4 °C. The supernatant was analyzed for the amount of tyrosine released from casein degradation in the presence of 500 mM Na₂CO₃ and 0.5 M Folin reagent solution (Merck) at 37 °C for 30 minutes. The absorbance at λ = 660 nm (A_{660 nm}) was measured on a Beckman Coulter DU 800 spectrophotometer against a control containing distilled water instead of a sample. From the measured values at A_{660 nm}, the concentration of tyrosine (μmol) is determined by standard curve. L-tyrosine (Sigma- Aldrich) is used as a standard to construct the standard curve.

All experiments to determine protease activity were performed with a minimum of three replicates.

7. Determination of lipase activity (EC 3.1.1.3)

The enzymatic reaction was performed under the following conditions: 0.1 M acetate buffer, pH 7.6, containing 0.8 mM p-nitrophenyl palmitate solution and suitably diluted cells at 37 °C. The reaction was stopped with 0.2 M lead acetate (II). One unit of activity converts 1.0 μmole from p-nitrophenyl palmitate to p-nitrophenol and palmitate in one minute at pH 7.6 and 37 °C. The p-nitrophenol content was determined spectrophotometrically at 405 nm wavelength on a Beckman Coulter DU 800 spectrophotometer.

All experiments to determine enzyme activity were performed in triplicate.

III. Analysis of short-chain fatty acids in breast and faecal samples of infants from one to twelve months.

The following protocol has been developed for the detection of short-chain fatty acids in faecal samples:

1. Isolation of short-chain fatty acids from excrement.

To 100 mg of fresh faecal sample or breast milk, 2% H3PO4 or 0.05% H2SO4 is added in a ratio of 1:10. The samples were homogenized very well on a vortex 2 times for 2 min. The resulting homogenate was centrifuged for 10 min, 13 000 rpm, then the supernatant was removed. 200μL were taken from the supernatant and extracted with 200 μL of organic solvent (CH2C12, AcCN, EtOH, EtOAc). The samples were homogenized, then centrifuged for 5 min, 13 000 rpm. The resulting organic layer was separated and subjected to HPLC analysis.

2. HPLC method for analysis of short chain fatty acids.

Konik-Tech HPLC system (Spain) was used, equipped with an Aminex HPX-87H Ion Exclusion Column (7.8 mm id x 30 cm, Biorad, Richmond, CA) and a Micro-Guard Cation-H precolumn (4.6 mm id x 30 mm, Biorad). The samples were determined with a UV detector at a wavelength of 210 nm, with a mobile phase of 0.005M H₂SO₄, a flow rate of 0.6 ml/min and a column temperature T=40 °C. The identification of the peaks was performed during the retention times according to the standards of short-chain fatty acids: lactic acid, acetic acid, propionic acid and butenoic acid. For the quantification of short-chain fatty acids, standard curves have been prepared in concentrations of 25 - 1.56 mmol / L. Correlation equations describing the peak area (y) and the concentration of short-chain fatty acids (x, mmol/L) are derived. The standard curves have linear ranges between 25 - 1.65 mmol/L and a correlation coefficient r2 > 0.9999.

Table 6. Range of values of the measured indicators in healthy children aged from one to twelve months.

As a result of the analyzes of the tested breast milk and feces of a newborn babies aged between one and twelve months, the following coefficients are calculated:

1. Ratio of Bifidobacteria/Bacteroids.

2. The ratio of the amount of Bifidobacteria/Lactobacilli

3. The ratio of the amount of Bifidobacteria + Lactobacilli/Bacteroids

4. The ratio of the amount of Bifidobacteria + Lactobacilli/Yeast

5. The ratio of the amount of Bacteroids/Yeast

6. The ratio of the amount of Bifidobacteria + Lactobacilli/E. coli

7. The ratio of the amount of Bifidobacteria + Lactobacilli/β-galactosidase activity

8. The ratio of the amount of Bifidobacteria + Lactobacilli/α-fucosidase activity

9. The ratio of enzyme activities β-galactosidase/α-galactosidase

10. The ratio of enzyme activities β-galactosidase/α-fucosidase

11. The ratio of enzyme activities β-galactosidase/lipase

12. The ratio of enzyme activities β-galactosidase/protease

13. The ratio of enzyme activities β-galactosidase + α-galactosidase/α-fucosidase

14. Ratio of lactic acid/acetic acid/butyric acid metabolites

It is calculated as a percentage between the values for the number of the two groups of microorganisms in breast milk, using the weighted average value for the respective bacteria in the norm of Table 2 and the arithmetic mean value obtained for the number of bacteria in the test sample. For example, it is assumed for bifidobacteria that (10³ -10⁵ ) = 100% and for lactobacilli (10⁴-10⁶) = 100%. When in both groups of microorganisms the quantity in the samples is 100%, the coefficient between the two groups of microorganisms is one.

The trends in reading the ratios are expressed in Table 7, which are obtained at the corresponding ratio of the indicator from the vertical of the table to the indicator from the horizontal of the table.

Table 7. Trends in the ratios between the individual indicators, with a red arrow indicates a positive effect on the microbiota, and with a blue negative effect on the microbiota.

Example 2. In vitro study of 6 indicators by assessing the balance of the microbiota in infants aged one to twelve months:

1. A protocol for the quantitative analysis of the human microbiota by real-time qPCR The presented protocol can be used to identify deviations for analysis of dysbiosis in newborns aged from one to twelve months and to be included in an algorithm for identifying the condition, abnormalities deviations and rules for restoring the balance of the microbiota.

The primer pairs and fluorescently labeled probes for real-time qPCR detection of bacterial species and communities in samples from different target groups are presented in Table 8.

Table 8. Primers and fluorescently labeled probes for real-time qPCR detection of bacterial groups and species used in the present study

2. Control bacterial strains

Bacterial strains used to control the specificity of the primers and labeled probes, as well as for quantitative assessment of individual bacterial species and communities are presented in Table 9. The strains were purchased from the Bulgarian National Bank for Industrial Microorganisms and Cell Cultures (NBIMCC) and the German Collection of Microorganisms and Cell Cultures (DSMZ). The strains were grown aerobically or anaerobically in selective growth media as recommended by DSMZ and NBIMCC. For each culture are made a series of dilutions, which were plated onto appropriate agar medium to determine the total number of bacteria - colony-forming units (CFU). Additionally, 1.0 ml samples were taken from the dilutions and centrifuged under the following conditions: 12000rpm / 3min. The bacterial cell pellets were stored at -80 ° C until use for analysis. Table 9. Bacterial strains used as controls in this study

3. Extraction of DNA from bacterial cultures and samples from the target groups

4. Quantification of DNA

5. Real-time qPCR The real-time qPCR reactions for detection and quantification of bacteria were performed using SYBR Green PCR Master Mix (Applied Biosystems). The reactions were carried out in 96-well plates with a final volume of 25 μL. The Real-time qPCR was performed using a Real-Time PCR CFX96 Touch (BIO-RAD) with software version 3.0.1224 under conditions for each of the target bacterial groups listed in Table 10.

Table 10. Primers and reaction conditions for real-time qPCR quantification of bacteria in faecal samples

TaqMan Universal PCR Master Mix (Applied Biosystems) was used in real-time qPCR reactions to detect and quantify bacteria with fluorescently labeled probes. The reactions were carried out in 96-well plates with a final volume of 25 μL, under conditions for each of the target bacterial groups listed in Table 11. Table 11. Primers, fluorescently labeled probes and reaction conditions for real-time qPCR quantification of bacteria in fecal samples.

6. Electrophoretic analysis of DNA fragments obtained by qPCR

7. Statistical data processing

II. Analysis of enzymes in samples of breast milk and feces from the newborn babies

1. Determination of enzymatic activity of β-galactosidase (EC 3.2.1.23)

All experiments to determine enzyme activity were performed in triplicate.

2. Determination of α-L-fucosidase activity (EC 3.2.1.51) The enzyme reaction was performed under the following conditions: 100 mM citrate buffer, pH 6.5, containing 10 mM solution of p-nitrophenyl-α-L-fucopyranoside (PNP-FUC) (prepared by diluting p-nitrophenyl-α-L-fucopyranoside, Sigma cat. № N3628) with distilled water and suitably diluted cells at 37 °C. The reaction was stopped with 200 mM borate buffer, pH 9.8. One unit of activity converts 1.0 μmol of p-nitrophenyl-α-L-fucopyranoside to p-nitrophenol and L-fucopyranoside in one minute at pH 6.5 and 37 °C. The p-nitrophenol content was determined spectrophotometrically at 405 nm wavelength on a Beckman Coulter DU 800 spectrophotometer.

All experiments to determine enzyme activity were performed in triplicate.

1. Isolation of short-chain fatty acids from excrement.

2. HPLC method for analysis of short chain fatty acids.

Konik-Tech HPLC system (Spain) was used, equipped with an Aminex HPX-87H Ion Exclusion Column (7.8 mm id x 30 cm, Biorad, Richmond, CA) and a Micro-Guard Cation-H precolumn (4.6 mm id x 30 mm, Biorad). The samples were determined with a UV detector at a wavelength of 210 nm, with a mobile phase of 0.005M H₂SO₄, a flow rate of 0.6 ml/min and a column temperature T=40 °C.

The identification of the peaks was performed during the retention times according to the standards of short-chain fatty acids: lactic acid, acetic acid, propionic acid and butenoic acid. For the quantification of short-chain fatty acids, standard curves have been prepared in concentrations of 25 - 1.56 mmol / L. Correlation equations describing the peak area (y) and the concentration of short-chain fatty acids (x, mmol/L) are derived. The standard curves have linear ranges between 25 - 1.65 mmol/L and a correlation coefficient r2 > 0.9999.

Table 12. Range of values of the measured indicators in healthy children aged from one to twelve months.

1. Ratio of Bifidobacteria/Bacteroids

2. The ratio of the amount of Bifidobacteria/Lactobacilli

3. The ratio of the amount of Bifidobacteria + Lactobacilli/Bacteroids

4. The ratio of the amount of Bifidobacteria + Lactobacilli/β-galactosidase activity

5. The ratio of the amount of Bifidobacteria + Lactobacilli/α-fucosidase activity

6. The ratio of enzyme activities β-galactosidase/α-fucosidase

7. Ratio of lactic acid/acetic acid/butyric acid metabolites

The calculations are made as in Example 1.

Table 13. Trends in the ratios between the individual indicators, with a red arrow indicates a positive effect on the microbiota, and with a blue negative effect on the microbiota.

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Claims

32. Zaura, E., Nicu, E.A., Krom, B.P., Keijser, B.J., 2014. Acquiring and maintaining a normal oral microbiome: current perspective. Frontiers in Cell Infection and Microbiology 24, 85.

33. Newburg, D.S., Naubauer, S.H., 2005. Carbohydrates in milks: analysis, quantities and significance. In: Jensen, R.G. (Ed.), Handbook of Milk Composition. Academic Press, San Diego, pp. 273-349.

34. Wada, J., et al., 2008. Bifidobacterium bifidum lacto-N-biosidase, a critical enzyme for the degradation of human milk oligosaccharides with a type 1 structure. Applied and Environmental Microbiology 74 (13), 3996-4004.

35. Suzuki, R., et al., 2008. Structural and thermodynamic analyses of solute-binding protein from Bifidobacterium longum specific for core 1 disaccharide and lacto-N-biose I. Journal of Biological Chemistry 283 (19), 13165-13173.

36. Fujita, K., et al., 2005. Identification and molecular cloning of a novel glycoside hydrolase family of core 1 type O-glycan-specific endo-alpha-N-acetylgalactosaminidase from Bifidobacterium longum . Journal of Biological Chemistry 280 (45), 37415-37422.

37. Ruas-Madiedo, P., et al., 2008. Mucin degradation by Bifidobacterium strains isolated from the human intestinal microbiota. Applied and Environmental Microbiology 74 (6), 1936-1940.

38. Cabrera-Rubio, R., Collado, M.C., Laitinen, K., Salminen, S., Isolauri, E., Mira, A., 2012. The human milk microbiome changes over lactation and is shaped by maternal weight and mode of delivery. American Journal of Clinical Nutrition 96 (3), 544-551.

39. Fernandez, L., Langa, S., Martin, V., Maldonado, A., Jimenez, E., Martin, R., Rodriguez, J.M., 2013. The human milk microbiota: origin and potential roles in health and disease. Pharmacological Research 69, 1-10.

40. Jost, T., Lacroix, C., Braegger, C., Rochat, F., Chassard, C., 2014. Vertical mother- neonate transfer of maternal gut bacteria via breastfeeding. Environmental Microbiology 16 (9), 2891-2904.

41. Ward, T.L., Hosid, S., loshikhes, I., Altosaar, I., 2013. Human milk metagenome: a functional capacity analysis. BMC Microbiology 13, 116.

PATENT CLAIMS

1. Method for Quantitative Evaluation (MQE) both for Rapid Prognosis of Dysbiosis in Infants - up to 1 year of age (MQE/RPDI-1Y), and for Multi-Factorial Diseases (MQE/MFD) - by assessing the quantitative and qualitative composition of the microbiota, type and amount of enzymes and metabolites, characterized in that it includes the stages of following Claims:

• Conducting of In-Vitro Analyses of diversity and ratios of microorganisms in the intestinal tract and body fluids from mother and child; as well as the amounts of metabolites, used as biomarkers; and reporting the level of enzyme activity of enzymes used as biomarkers.

• Using a mathematical model for Processing of Data, collected on previous stages, by producing a result, which demonstrates:

• Correlation between Composition and Amount of Microorganisms in the body fluids of the mother and the child;

• Correlation between Enzyme Profile and Activity of Enzymes responsible for the absorption and metabolism of body fluid components in the mother and child body fluid samples;

• Correlation between the Amount of Metabolites retrieved from the metabolism of the main components of the body fluid of the microbiota in the body fluids of mother and child.

2. Method according to claim 1, characterized in that the other body fluids are selected from the group of: saliva, breast milk, faeces.

3. Method according to any of Claims 1 or 2, characterized in that the microorganisms are selected from the group of: Bifidobacteria, Lactobacilli, Bacteroidetes, Clostridii, Enterococaceae, E. Coli.

4. Method according to any ofClaims 1 to 3, characterized in that the enzymes are selected from the group consisting of: Alpha-Galactosidase, Beta-Galactosidase, Alpha- Glucosidase, Beta-Glucosidase, Beta-Glucosidase, Alpha-Fucosidase, Lipase, Protease.

5. Method according to any of claims 1 to 4, characterized in that the metabolites are selected from the group consisting of: Lactic Acid, Acetic Acid, Propionic Acid, Butyric Acid.

6. Method according to any one of the Claims 1 to 5, characterized in that the correlation is one or more of: a) the correlation is expressed as a ratio of the quantity of Bifidobacteria over the quantity of Bacteroides: b) the correlation is expressed as a ratio of the quantities of Bifidobacteria and Lactobacili taken together over the quantity of Bacteroides: c) the correlation is expressed as a ratio of the quantities of Bifidobacteria and Lactobacili taken together over the quantity of Yeasts: d) the correlation is expressed as a ratio of the quantity of Bacteroides over the quantity of Yeasts: e) the correlation is expressed as a ratio of the quantities of Bifidobacteria and Lactobacili taken together over the quantity of E.Coli; f) the correlation is expressed as a ratio of the quantities of Bifidobacteria and Lactobacili taken together over the activity of Beta-Galactosidase; g) the correlation is expressed as a ratio of the quantities of Bifidobacteria and Lactobacili taken together over the activity of Alpha-Fucosidase; h) the correlation is expressed as a ratio of the enzyme activity of Beta-Galactosidase over the enzyme activity of Alpha-Galactosidase; i) the correlation is expressed as a ratio of the enzyme activity of Beta-Galactosidase over the enzyme activity of Alpha-Fucosidase; j) the correlation is expressed as a ratio of the enzyme activity of Beta-Galactosidase over the enzyme activity of Fipase; k) the correlation is expressed as a ratio of the enzyme activity of Beta-Galactosidase over the enzyme activity of Protease; l) the correlation is expressed as a ratio of the enzyme activities of Beta- Galactosidase and Alpha-Galactosidase taken together over the enzyme activity of Alpha-Fucosidase; m) the correlation is expressed as a ratio of the quantities of the metabolites (Factic Acid, Acetic Acid, and Butyric Acid) over the quantities of the enzymes (Beta- Galactosidase, Alpha-Galactosidase, and Alpha-Fucosidase):