Genomic and bioinformatic analysis of the effect of antibiotic use on microbiota levels in breast milk

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Methods: In the study, milk samples taken from 12 mothers who were hospitalized in our neonatal unit and received antibiotic treatment with a diagnosis of sepsis, and 12 mothers who were hospitalized in the same period and had babies with similar characteristics were analyzed in terms of microbiota. Genomic DNA Extraction, 16S Amplicon Sequencing and bioinformatic analyses were performed. Results: Chao Index was 0.241 with relatively low richness in both samples as within sample analysis. Beta diversity values showed significant diversity between sample groups (p<0.05) and species turnover was significant for both groups (p<0.05) with high diversity level for healthy group compared to patient group. Jaccard similarity and diversity index fitting results showed that nonmetric fitted function had 97.7% explanatory value, whereas linear fitting function had 94.5% explanatory value. Bray-Curtis dissimilarity index had 12.1% and Jaccard similarity and diversity index had 9.2% explanatory values showing significant similarity and diversity. Lefse analysis results for groups showed that all Linear discriminant analysis (LDA) values for all species in the study and control group were statistically significant (p<0.05). 14 species were identified in healthy groups, whereas 8 species were identified in the study group. Phylum mean was higher in the control group, whereas class, order, family, genus and species percentage means were higher in the study group. However, all differences for relative abundance were insignificant between control and study groups (p>0.05). Conclusion: Antibiotic use in breastfeeding mothers decreased the microbiota level, leading to a significant decrease in microbiome diversity in milk. Although there was no significant decrease in total microbiome levels, there was a significant decrease in microbiota diversity and there was a more significant decrease in beneficial strains than in harmful strains, and the microbiota balance was disrupted. Pediatrics Breast milk microbiota genomic analysis bioinformatic analysis Figures Figure 1 Figure 2 1. Introduction Breastfeeding is one of the most important stages in an individual's life, and during this process, the baby not only fulfills its daily vital functions, but also needs nutrients for organ development and physiological development [ 1 ]. In addition, clinical studies conducted in recent years have shown that individuals who are breastfed have fewer diseases in the future [ 2 ]. Today, breast milk is reported as a prognostic factor and risk factor for many health problems, from some types of cancer [ 3 , 4 ] to allergy [ 5 ] and chronic diseases [ 6 ]. Based on this, public studies are being organized all over the world on the importance of breastfeeding, and it is aimed to encourage expectant mothers and mothers to breastfeed [ 7 , 8 ]. On the other hand, despite the importance of breast milk, how the mother is fed, what she eats, and what kind of standard of living she has are also important in terms of breast milk. One of the factors that can be considered to affect the quality of breast milk is the concept of microbiota, which is a subject closely related to nutrition and the gastrointestinal system. Although there are many different definitions or conceptual frameworks for microbiota today, the term microbiota is basically a community formed by symbiotic and communal microorganisms, including archaea, bacteria, fungi and viruses [ 9 , 10 ]. In general, it refers to the beneficial and harmful organisms found in the human digestive system and especially in the intestines [ 11 ]. The microbiome, on the other hand, refers to the totality of all microorganisms such as fungi, bacteria and viruses in the body and their genes. While the microbiota is conceptually the ecosystem formed by these microorganisms, the concept of microbiome refers to the relationships of these microorganisms with their environment and their genetic material [ 12 , 13 ]. In other words, microbiota is a superconcept that includes microbiome. Many benefits of microbiota in the human body have been revealed, and it plays a vital role in the digestive system, from the synthesis of essential vitamins such as B, B12, and K to protection against skin infections such as acne and eczema, from resistance to fungal infections to mineral absorption [ 14 , 16 ]. Studies have shown that microbiota provides resistance to many important diseases such as asthma and allergies, cardiovascular diseases, cancer, and ulcerative colitis. Although studies have been conducted on the positive effects of microbiota on human health [ 9 – 16 ], there are no sufficient studies on the transmission from mothers' milk to babies and on baby health. Although there are limited studies on this subject that examine the microbiome content of breast milk, there are no sufficient studies that examine breast milk in terms of antibiotic resistance. Therefore, this study aimed to reveal the differences in microbiota by examining the milk of mothers who were monitored in neonatal intensive care and who did and did not receive antibiotic treatment. 2. Methods 2.1. Research Model In this experimental prospective cross-sectional study, milk samples taken from 12 mothers who were hospitalized in our neonatal unit and received antibiotic treatment with a diagnosis of sepsis, and 12 mothers who were hospitalized in the same period and had babies with similar characteristics were analyzed in terms of microbiota. Genomic DNA Extraction, 16S Amplicon Sequencing and bioinformatic analyses were performed. 2.2. Sample Collection Milk samples are taken into 50 ml sterile single-use falcon tubes. Before collecting breast milk, the area around the breast and the tip should be cleaned. After the cleaning process, the first one or two drops of milk are discarded and then milked to 5mL. After milking, the milk is stored at the reference method − 80°C conditions for no more than 30 minutes. 2.3. Genomic DNA Extraction from Milk Samples (DiaRex) Milk samples were obtained using the DiaRex® Stool Genomic DNA Extraction Kit (Cat No: SD-0323, Ankara). Briefly, 2 mL of milk sample was centrifuged at 16,000 g x for 10 minutes. At the end of centrifugation, the fat and liquid layers were removed from the fractionated milk sample. 250 µl of Lysis (LBD) solution was added to the pellet. Then, 15 mg of glass and 10 zircon beads were added and the homogenizer was applied at 4000 rpm for 2 x 20 seconds. After homogenization, 25 µl of Proteinase K (PKD) solution was added and incubation was carried out at 56°C for 60 minutes. At the end of incubation, the entire content was centrifuged at 5000 g for 5 minutes and the supernatant was transferred to a new tube. 200 µl Stool Lysis (SLD) solution was added to the supernatant and incubation was performed at 70°C for 10 minutes. After incubation, 250 µl absolute ethanol was added to the lysate and the entire content was transferred to the column. The column was centrifuged at 8000 g x 1 minute and then transferred to a new tube. Then, after washing according to the kit protocol, 100 µl Elution (EBD) solution was added and after 2 minutes of incubation, 8000 g x 1 minute centrifuged to obtain genomic DNA. 2.4. 16S Amplicon Sequencing Extracted genomic DNA was amplified with 16S V3-V4 314F-860R primer sets. Library preparation was performed with Nextera XT DNA library preparation kit and indexes (Illumina). Pooled libraries cleaned with specific size selection were applied following the manufacturer's protocol (AMPure XP, Beckman Coulter). After library preparation, MiSeq (Illumina) instrument was used to run sequencing. 2.5. Bioinformatic Analysis Bidirectional (2x250) Illimuna reads were loaded into the qiime2 system. First, it was determined that all samples were close to each other on a depth of 100X and no sample was excluded at this stage. Quality cleaning and chimera detection were performed using the DADA2 algorithm (via q2-dada2) on qiime2. Parts with quality scores mostly below 30 were excluded and ASVs (Amplicon Sequence Variants) were created. The obtained ASVs were mapped to the Silva 138 ( https://www.arb-silva.de/documentation/release-138/ ) database and taxonomic tables were created. For data visualization and biostatistical analyses, files created with qiime2 were processed using the R 4.1 programming language R studio. Basic differences within and between groups were detected with Alpha diversity and Beta diversity analyses. Taxonomic densities between groups were detected using the DeSeq2 package. With Lefse analysis, the most basic differences between the groups were determined as LDA scores greater than 4. 2.6. Statistical Analysis Scale parameters were described by using means, standard deviations, median and ranges. Shannon’s Diversity Index, Simpson’s Dominance Index and Chao Index were used for Alpha diversity. Nonmetrik fit and linear fit models were used for ordination distance and observed dissimilarity relation in order to evaluate linearization deviations [ 17 , 18 ]. Permutational analysis with Bray-Curtis dissimilarity index and Jaccard similarity and diversity index were used for diversity and similarity analysis. Kolmogorov Smirnov test was used for normality of parameters. Mann Whitney U test was used for differences between groups. R-studio and SPSS 25.0 for windows were used for statistical evaluation of parameters at 95% Confidence Interval. 2.7. Ethical considerations Ethical approval was taken from Balıkesir University Medicine Faculty Clinical Research Ethical Committee with 01.03.2024 date and E-94025189-050.04-358285 grand number. 3. Results Age mean of patient group was 29.58 ± 6.78 and control group was 29.83 ± 7.02 with statistically insignificant difference (p > 0.05). Number of pregnancies were similar in both groups (p > 0.05), and most of patients and control groups had one or two pregnancies. Gestational week, birth weight and blood sample date differences were also insignificant (p > 0.05). In both groups, nutrition types were similar, and normal diet was more common in both group (p > 0.05) (Table 1 ). Table 1 Baseline characteristics of patient and control groups with difference analysis results Patient (n = 12) Control (n = 12) p value Age, years, mean ± SD 29.58 ± 6.78 29.83 ± 7.02 0.932 a Number of pregnancies, n (%) 1 5 (41.7) 5 (41.7) p > 0.05 2 5 (41.7) 5 (41.7) 3 2 (16.7) 2 (16.7) Gestational week, mean ± SD 39.25 ± 3.65 38.92 ± 1.16 0.478 a Birth weight, kg, mean ± SD 3130.83 ± 262.69 3212.50 ± 311.54 0.755 a Blood sample date, day, mean ± SD 3.75 ± 2.45 3.75 ± 1.60 0.551 a Nutrition type, n (%) Normal 7 (58.3) 8 (66.7) 0.939 b Vegan 1 (8.3) 1 (8.3) Functional 2 (16.7) 2 (16.7) Gluten free 2 (16.7) 1 (8.3) a. Mann Whitney U test, b. Chi-Square Likelihood ratio, SD: Standard Deviation. The Shannon Index is an estimator shows richness of species, and 0.089 levels means low diversity within samples. Simpson’s dominance index was also in accordance with Shannon’s Diversity Index, and had 0.061 index value with insignificant dominance within samples. Chao Index was 0.241 with relatively low richness in both samples as within sample analysis. Beta diversity values showed significant diversity between sample groups (p < 0.05) and species turnover was significant for both groups (p < 0.05) with high diversity level for healthy group compared to patient group (Table 2 ). Table 2 Alpha and Beta diversity results for Operational Taxonomic Units (OUT) Alpha Diversity (Within sample) Indicator Value Shannon’s Diversity Index Diversity 0.089 Simpson’s Dominance Index Dominance 0.061 Chao Index Richness 0.241 Observed Observation 0.241 Beta Diversity-Species turnover Observed Delta 26686 Expected Delta 27785 Significance 0.009 Jaccard similarity and diversity index fitting results showed that nonmetric fitted function had 97.7% explanatory value, whereas linear fitting function had 94.5% explanatory value (Fig. 1 ). Permutational analysis of variance for beta diversity results showed that weighted and unweighted UniFrac levels were similar for groups with insignificant explanatory level (p > 0.05) within groups. However, Bray-Curtis dissimilarity index had 12.1% and Jaccard similarity and diversity index had 9.2% explanatory values showing significant similarity and diversity (Table 3 ). Table 3 Permutational analysis of variance for beta diversity Df Sum of Squares R 2 F Pr(> F) Bray-Curtis dissimilarity index 1.131 0.122 3.064 0.002 Jaccard similarity and diversity index 0.935 0.092 2.238 0.002 Weighted UniFrac 0.008 0.072 1.696 0.160 Unweighted UniFrac 0.366 0.056 1.305 0.106 UniFrac: Unique fraction metric. Mean class vector value for control group was higher than study group, and range was also higher. However, explanatory value of similarity was 10.2%, and dissimilarity differences between groups were insignificant (p > 0.05) (Fig. 2 ). Lefse analysis results for groups showed that all LDA values for all species in the study and control group were statistically significant (p < 0.05). In this respect, Lefse outputs of both groups were similar, in accordance of Beta Diversity results. 14 species were identified in healthy groups, whereas 8 species were identified in the study group (Table 4 ). Table 4 Lefse analysis results for groups and p values Enrich group Spicy LDA p value p (adjusted) Patient group Study Staphylococcus_s 5.57965225 0.00028304 0.00028304 Study Staphylococcus 5.57772884 0.00035454 0.00035454 Study Staphylococcaceae 5.57771025 0.00035454 0.00035454 Study Bacillales 5.57393233 0.00044268 0.00044268 Study Bacilli 5.37082878 0.02607774 0.02607774 Study Finegoldia 3.33283711 0.0024503 0.0024503 Study Finegoldia_s 3.33283013 0.0024503 0.0024503 Study Dorea formicigenerans 2.76768967 0.0392788 0.0392788 Control group Control Lactobacillales 5.16049142 0.01636311 0.01636311 Control Streptococcus 5.12720096 0.0464989 0.0464989 Control Streptococcaceae 5.12718397 0.0464989 0.0464989 Control Streptococcus_s 5.09638935 0.04038046 0.04038046 Control Micrococcaceae 3.87684208 0.01330056 0.01330056 Control Carnobacteriaceae 3.65797408 0.00802004 0.00802004 Control Granulicatella 3.63099875 0.02875133 0.02875133 Control Granulicatella_s 3.63084431 0.02875133 0.02875133 Control Listeriaceae 3.51695084 0.0324268 0.0324268 Control Brochothrix_s 3.50518697 0.0324268 0.0324268 Control Brochothrix 3.50349441 0.0324268 0.0324268 Control Micrococcus 2.87795619 0.0324268 0.0324268 Control Micrococcus_s 2.87141144 0.0324268 0.0324268 Control Rhizobiaceae 2.51371344 0.0324268 0.0324268 Phylum mean was higher in the control group, whereas class, order, family, genus and species percentage means were higher in the study group. However, all differences for relative abundance were insignificant between control and study groups (p > 0.05) (Table 5 ). Table 5 Relative abundance test results between groups Percentages (%) Group p value a Control Study Mean ± SD Median (Min-Max) Mean ± SD Median (Min-Max) Phylum 5.88 ± 16.82 0.01 (0.00-67.82) 5.88 ± 13.78 0.02 (0.00-45.90) 0.877 Class 3.03 ± 11.64 0.01 (0.00-65.09) 3.57 ± 10.49 0.04 (0.00-42.87) 0.514 Order 1.96 ± 6.08 0.02 (0.00-37.83) 2.17 ± 6.52 0.03 (0.00-35.92) 0.942 Family 1.09 ± 4.49 0.01 (0.00-36.31) 1.25 ± 4.68 0.02 (0.00-35.97) 0.624 Genus 0.66 ± 4.18 0.01 (0.00-45.60) 0.82 ± 4.32 0.01 (0.00-42.16) 0.799 Species 1.15 ± 4.87 0.04 (0.00-38.98) 1.32 ± 4.98 0.07 (0.00-36.98) 0.259 a. Mann Whitney U Test, SD: Standard Deviation. 4. Discussion In this study, the effect of antibiotic use on the microbiota in breast milk was investigated and milk samples from 12 mothers who took antibiotics and 12 mothers who did not were analyzed. The results obtained in the study showed that although there was no significant difference in diversity within the group in mothers who took antibiotics and those who did not, there were significant differences between the groups and in the number of strains. The number of species in mothers who used antibiotics was 8, while it was 12 in those who did not use antibiotics, and Staphylococcus_s, Staphylococcus and Staphylococcaceae species were present in both groups. Studies that reveal the importance of breast milk in terms of nutrition and baby development generally show that breast milk is the most important basic food for individuals in the first six months of their lives, regardless of its content and amount [ 1 – 4 ]. When its content is weak or its amount is low, it is supported by formula, but breast milk is the most basic source of nutrition for babies. Although there are not enough studies on microbiota in babies, current theoretical or conceptual studies emphasize that microbiota is important in babies, and affected from many factors [ 19 – 23 ]. In their study, Ma et al [ 19 ] showed that different formula-fed causes significant microbiota differences between neonatal groups. In another study, Combellick et al [ 21 ] reported that hospitalization for delivery effect micobiota of baby. In their literature review, Layuk et al [ 23 ] reported that giving early breastfeeding and initiation of microbiota has significant effect on immunity and digestion. Results of studies show that microbiota in babies has a sensitive balance in human life. However, more clinical and experimental studies are needed on this subject. Studies have been conducted on the permeability of breast milk. In these studies, it is generally concluded that although breast milk is selective in terms of passing harmful materials, it does pass antibiotics and various genetic and hormonal materials [ 24 – 27 ]. Today, there are still not enough studies on whether some drugs pass into breast milk and to the baby through breastfeeding. Although there are many reasons for this, the ethical and other difficulties of working on babies come to the forefront in this regard. In literature, very few studies conducted on effect of antibiotics on breastfeeding and microbiome. In studies on breast milk, there are studies reporting that antibiotics pass into the milk [ 28 – 30 ]. Korpela et al [ 28 ] reported that antibiotics usage in breastfeeding may weaken intestinal microbiota. Azad et al [ 29 ] reported that usage of intrapartum antibiotics may reduce microbiota. Lemas et al [ 30 ] showed that maternal antibiotics usage may correlated with microbiome weakness and obesity. However, studies on their effects on the microbiome of mother milk are not studied comprehensively. In our study, although the microbiome distributions of mothers who took antibiotics and those who did not take them were similar, there were fewer bacterial species in the mothers who took antibiotics in terms of diversity. Moreover, Staphylococcus_s, Staphylococcus and Staphylococcaceae species, which are known to be harmful to the microbiome, were present in both groups. These results show that antibiotic use has a negative and unbalanced effect on the microbiome in breast milk. In this respect, in mothers who have to use antibiotics, mothers should be supported with probiotics and symbiotics in order to ensure microbiome health. In addition, it is useful to support and reveal with further studies the extent to which the microbiome in breast milk is taken by the child, to what extent the body accepts it and how much the body needs. When we look at the results in general, the microbiome culture quality of mothers who used and did not use antibiotics was quite low. There are many variables in this, from nutritional level to food quality, from food safety to eating habits. More comprehensive, large-budget, multi-center studies are needed to reveal both the causes and consequences of the bacterial colonization balance disruption caused by antibiotics. In this way, it may be possible to obtain the health benefits that microbiota will provide to individuals in later periods of life. Limitations of the Study The most important limitation of the study is that there has not been enough study on the transfer of microbiota from breast milk and therefore, the results of the study could not be compared with different centers, different regions or demographic characteristics. Although this situation makes the study important as a pioneer in the field, more extensive studies with multicenters and larger samples are needed. Another important limitation of the study is its cost. Genomic DNA Extraction 16S Amplicon Sequencing and bioinformatics analyses are currently very costly processes. Therefore, the number of samples in the study was limited due to cost limitations. In addition, due to cost limitations, the study reflects the results obtained from single-center, specific time period samples. There is a need to examine the role of seasonal effects with studies in different time periods and demographic effects with multi-center studies. Contributions of the study to literature and clinical practice The most important contribution of the study to the literature is that it is a pioneering study in the literature, as there are not enough studies in this field. Although studies have been conducted on the role of breast milk in the development of the baby, it is possible to make inferences about the mechanism underlying this thanks to this study. In addition, the study can be a source and basis for further research. The most important contribution of the study to clinical practice is that it quantitatively demonstrates the relationship between breast milk, antibiotic use and microbiota, and shows that supplementation with synbiotic and probiotic supplements is necessary to restore the disrupted microbiota in mothers using antibiotics. Although previous studies have been conducted to suggest that the microbiota will be disrupted in mothers using antibiotics and that this will have an impact on the health of the baby, these are mostly conceptual and not evidence-based studies. The research results have demonstrated this evidence-based. 5. Conclusion According to the results of the study, antibiotic use in breastfeeding mothers decreased the microbiota level and caused a significant decrease in microbiome diversity in milk. Although there was no significant decrease in total microbiome levels, there was a significant decrease in microbiota diversity and there was a more significant decrease in beneficial strains compared to harmful strains, and the microbiota balance was disrupted. These results reveal the nutritional damage of antibiotic use in breastfeeding mothers not only to the mother herself but also to the baby through milk. However, in cases of sepsis and many similar medical diseases and complications, antibiotic use may be mandatory despite all its harms. In this case, it would be beneficial to support both the expectant mother and the baby in terms of nutrition with at least the use of synbiotics or probiotics. Abbreviations DNA Deoxyribonucleic Acid EBD Elution solution LBD Lysis solution LDA Linear discriminant analysis Effect Size PKD Proteinase K solution SD Standard Deviation SLD Stool Lysis solution Declarations Ethics approval and consent to participate Ethical approval was taken from Balıkesir University Medicine Faculty Clinical Research Ethical Committee with 01.03.2024 date and E-94025189-050.04-358285 grand number. Patient consents for publication were taken from participants. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding Not applicable. Authors' contributions FDK and ÖKA carried out data collection, statistical analysis, and out writing with finalizing the article. Acknowledgements Authors thank Kadir Yılmaz, İstanbul Commerce University, for his valuable statistical support. 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JAMA Pediatr 170(8):750–757 Azad, M. B., Konya, T., Persaud, R. R., Guttman, D. S., Chari, R. S., Field, C. J.,… To, T. (2016). Impact of maternal intrapartum antibiotics, method of birth and breastfeeding on gut microbiota during the first year of life: a prospective cohort study. BJOG: An International Journal of Obstetrics & Gynaecology , 123 (6), 983–993 Lemas, D. J., Yee, S., Cacho, N., Miller, D., Cardel, M., Gurka, M., … Shenkman,E. (2016, December). Exploring the contribution of maternal antibiotics and breastfeeding to development of the infant microbiome and pediatric obesity. In Seminars in Fetal and Neonatal Medicine (Vol. 21, No. 6, pp. 406–409). WB Saunders Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6235909","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":429349635,"identity":"a99f5cc1-2e89-4b94-aa82-492c887503c3","order_by":0,"name":"ÖZLEM KEMER AYCAN","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYDCCA1CCj4GB8fGPCiCHmbmBOC1sQLXGDGdAWhiJ18ImzdgG4hHQwnf7AOPngjM29mzs3cnGhfNqo/nbgVp+VGzDqUXyXAKz9IwbacxsPGc3Pp657XjujMOMDYw9Z27j1GIAdL00z4fDbGwSuZsNeLcdy20AamFmbMOrhfk3UAsPm/zbbRK8c47lzidCC5s0z43DEmwSvNukeRtqcjcQ0iJ5hrHNmudMmgEbT+5mwxnHDuRuBGo5iM8vfGeYD9/mOWZjz89+duODDzV1ufPOHz744EcFbi3osXAYTB7Aox4D1JGieBSMglEwCkYIAAD8mVsELpoA9gAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-2462-0939","institution":"Balıkesir Üniversitesi","correspondingAuthor":true,"prefix":"","firstName":"ÖZLEM","middleName":"KEMER","lastName":"AYCAN","suffix":""},{"id":429349636,"identity":"62ca901f-8484-47ca-a134-f6ff28ff61e2","order_by":1,"name":"Damla Kurt","email":"","orcid":"","institution":"Balıkesir Üniversitesi","correspondingAuthor":false,"prefix":"","firstName":"Damla","middleName":"","lastName":"Kurt","suffix":""}],"badges":[],"createdAt":"2025-03-16 06:01:48","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6235909/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6235909/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78734603,"identity":"e530ed23-b017-4b72-bd9e-abd2713e2757","added_by":"auto","created_at":"2025-03-18 08:01:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":301194,"visible":true,"origin":"","legend":"\u003cp\u003eJaccard similarity and diversity index fitting results\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6235909/v1/e3566c676b9ed2e73da9a103.png"},{"id":78734602,"identity":"55001f2e-9cf3-46ad-be2f-7ada0ff141b6","added_by":"auto","created_at":"2025-03-18 08:01:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":161398,"visible":true,"origin":"","legend":"\u003cp\u003eDissimilarity rank for groups and ranges\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6235909/v1/1674b1626e50ff1668f05c39.png"},{"id":78736963,"identity":"e87e8769-c6ae-4b0a-abdb-284dfa019b17","added_by":"auto","created_at":"2025-03-18 08:25:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1224345,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6235909/v1/ee104b69-ffdc-44a9-bf9e-e8c91c68da39.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGenomic and bioinformatic analysis of the effect of antibiotic use on microbiota levels in breast milk\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBreastfeeding is one of the most important stages in an individual's life, and during this process, the baby not only fulfills its daily vital functions, but also needs nutrients for organ development and physiological development [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In addition, clinical studies conducted in recent years have shown that individuals who are breastfed have fewer diseases in the future [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Today, breast milk is reported as a prognostic factor and risk factor for many health problems, from some types of cancer [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] to allergy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and chronic diseases [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Based on this, public studies are being organized all over the world on the importance of breastfeeding, and it is aimed to encourage expectant mothers and mothers to breastfeed [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. On the other hand, despite the importance of breast milk, how the mother is fed, what she eats, and what kind of standard of living she has are also important in terms of breast milk. One of the factors that can be considered to affect the quality of breast milk is the concept of microbiota, which is a subject closely related to nutrition and the gastrointestinal system.\u003c/p\u003e \u003cp\u003eAlthough there are many different definitions or conceptual frameworks for microbiota today, the term microbiota is basically a community formed by symbiotic and communal microorganisms, including archaea, bacteria, fungi and viruses [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In general, it refers to the beneficial and harmful organisms found in the human digestive system and especially in the intestines [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The microbiome, on the other hand, refers to the totality of all microorganisms such as fungi, bacteria and viruses in the body and their genes. While the microbiota is conceptually the ecosystem formed by these microorganisms, the concept of microbiome refers to the relationships of these microorganisms with their environment and their genetic material [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In other words, microbiota is a superconcept that includes microbiome.\u003c/p\u003e \u003cp\u003eMany benefits of microbiota in the human body have been revealed, and it plays a vital role in the digestive system, from the synthesis of essential vitamins such as B, B12, and K to protection against skin infections such as acne and eczema, from resistance to fungal infections to mineral absorption [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Studies have shown that microbiota provides resistance to many important diseases such as asthma and allergies, cardiovascular diseases, cancer, and ulcerative colitis.\u003c/p\u003e \u003cp\u003eAlthough studies have been conducted on the positive effects of microbiota on human health [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], there are no sufficient studies on the transmission from mothers' milk to babies and on baby health. Although there are limited studies on this subject that examine the microbiome content of breast milk, there are no sufficient studies that examine breast milk in terms of antibiotic resistance. Therefore, this study aimed to reveal the differences in microbiota by examining the milk of mothers who were monitored in neonatal intensive care and who did and did not receive antibiotic treatment.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Research Model\u003c/h2\u003e\n \u003cp\u003eIn this experimental prospective cross-sectional study, milk samples taken from 12 mothers who were hospitalized in our neonatal unit and received antibiotic treatment with a diagnosis of sepsis, and 12 mothers who were hospitalized in the same period and had babies with similar characteristics were analyzed in terms of microbiota. Genomic DNA Extraction, 16S Amplicon Sequencing and bioinformatic analyses were performed.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Sample Collection\u003c/h2\u003e\n \u003cp\u003eMilk samples are taken into 50 ml sterile single-use falcon tubes. Before collecting breast milk, the area around the breast and the tip should be cleaned. After the cleaning process, the first one or two drops of milk are discarded and then milked to 5mL. After milking, the milk is stored at the reference method \u0026minus;\u0026thinsp;80\u0026deg;C conditions for no more than 30 minutes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Genomic DNA Extraction from Milk Samples (DiaRex)\u003c/h2\u003e\n \u003cp\u003eMilk samples were obtained using the DiaRex\u0026reg; Stool Genomic DNA Extraction Kit (Cat No: SD-0323, Ankara). Briefly, 2 mL of milk sample was centrifuged at 16,000 g x for 10 minutes. At the end of centrifugation, the fat and liquid layers were removed from the fractionated milk sample. 250 \u0026micro;l of Lysis (LBD) solution was added to the pellet. Then, 15 mg of glass and 10 zircon beads were added and the homogenizer was applied at 4000 rpm for 2 x 20 seconds. After homogenization, 25 \u0026micro;l of Proteinase K (PKD) solution was added and incubation was carried out at 56\u0026deg;C for 60 minutes. At the end of incubation, the entire content was centrifuged at 5000 g for 5 minutes and the supernatant was transferred to a new tube. 200 \u0026micro;l Stool Lysis (SLD) solution was added to the supernatant and incubation was performed at 70\u0026deg;C for 10 minutes. After incubation, 250 \u0026micro;l absolute ethanol was added to the lysate and the entire content was transferred to the column. The column was centrifuged at 8000 g x 1 minute and then transferred to a new tube. Then, after washing according to the kit protocol, 100 \u0026micro;l Elution (EBD) solution was added and after 2 minutes of incubation, 8000 g x 1 minute centrifuged to obtain genomic DNA.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. 16S Amplicon Sequencing\u003c/h2\u003e\n \u003cp\u003eExtracted genomic DNA was amplified with 16S V3-V4 314F-860R primer sets. Library preparation was performed with Nextera XT DNA library preparation kit and indexes (Illumina). Pooled libraries cleaned with specific size selection were applied following the manufacturer\u0026apos;s protocol (AMPure XP, Beckman Coulter). After library preparation, MiSeq (Illumina) instrument was used to run sequencing.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. Bioinformatic Analysis\u003c/h2\u003e\n \u003cp\u003eBidirectional (2x250) Illimuna reads were loaded into the qiime2 system. First, it was determined that all samples were close to each other on a depth of 100X and no sample was excluded at this stage. Quality cleaning and chimera detection were performed using the DADA2 algorithm (via q2-dada2) on qiime2. Parts with quality scores mostly below 30 were excluded and ASVs (Amplicon Sequence Variants) were created. The obtained ASVs were mapped to the Silva 138 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.arb-silva.de/documentation/release-138/\u003c/span\u003e\u003c/span\u003e) database and taxonomic tables were created. For data visualization and biostatistical analyses, files created with qiime2 were processed using the R 4.1 programming language R studio. Basic differences within and between groups were detected with Alpha diversity and Beta diversity analyses. Taxonomic densities between groups were detected using the DeSeq2 package. With Lefse analysis, the most basic differences between the groups were determined as LDA scores greater than 4.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6. Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eScale parameters were described by using means, standard deviations, median and ranges. Shannon\u0026rsquo;s Diversity Index, Simpson\u0026rsquo;s Dominance Index and Chao Index were used for Alpha diversity. Nonmetrik fit and linear fit models were used for ordination distance and observed dissimilarity relation in order to evaluate linearization deviations [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. Permutational analysis with Bray-Curtis dissimilarity index and Jaccard similarity and diversity index were used for diversity and similarity analysis. Kolmogorov Smirnov test was used for normality of parameters. Mann Whitney U test was used for differences between groups. R-studio and SPSS 25.0 for windows were used for statistical evaluation of parameters at 95% Confidence Interval.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.7. Ethical considerations\u003c/h2\u003e\n \u003cp\u003eEthical approval\u0026nbsp;was taken from Balıkesir University Medicine Faculty Clinical Research Ethical Committee with 01.03.2024 date and E-94025189-050.04-358285 grand number.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eAge mean of patient group was 29.58\u0026thinsp;\u0026plusmn;\u0026thinsp;6.78 and control group was 29.83\u0026thinsp;\u0026plusmn;\u0026thinsp;7.02 with statistically insignificant difference (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Number of pregnancies were similar in both groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and most of patients and control groups had one or two pregnancies. Gestational week, birth weight and blood sample date differences were also insignificant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In both groups, nutrition types were similar, and normal diet was more common in both group (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of patient and control groups with difference analysis results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatient (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.58\u0026thinsp;\u0026plusmn;\u0026thinsp;6.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.83\u0026thinsp;\u0026plusmn;\u0026thinsp;7.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.932\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of pregnancies, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (41.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (16.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational week, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.478\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth weight, kg, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3130.83\u0026thinsp;\u0026plusmn;\u0026thinsp;262.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3212.50\u0026thinsp;\u0026plusmn;\u0026thinsp;311.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.755\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood sample date, day, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.75\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.551\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutrition type, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (58.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.939\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (8.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunctional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (16.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGluten free\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (8.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ea. Mann Whitney U test, b. Chi-Square Likelihood ratio, SD: Standard Deviation.\u003c/p\u003e \u003cp\u003eThe Shannon Index is an estimator shows richness of species, and 0.089 levels means low diversity within samples. Simpson\u0026rsquo;s dominance index was also in accordance with Shannon\u0026rsquo;s Diversity Index, and had 0.061 index value with insignificant dominance within samples. Chao Index was 0.241 with relatively low richness in both samples as within sample analysis. Beta diversity values showed significant diversity between sample groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and species turnover was significant for both groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with high diversity level for healthy group compared to patient group (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAlpha and Beta diversity results for Operational Taxonomic Units (OUT)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlpha Diversity (Within sample)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShannon\u0026rsquo;s Diversity Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimpson\u0026rsquo;s Dominance Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDominance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChao Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRichness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBeta Diversity-Species turnover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eObserved Delta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26686\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eExpected Delta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eJaccard similarity and diversity index fitting results showed that nonmetric fitted function had 97.7% explanatory value, whereas linear fitting function had 94.5% explanatory value (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePermutational analysis of variance for beta diversity results showed that weighted and unweighted UniFrac levels were similar for groups with insignificant explanatory level (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) within groups. However, Bray-Curtis dissimilarity index had 12.1% and Jaccard similarity and diversity index had 9.2% explanatory values showing significant similarity and diversity (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePermutational analysis of variance for beta diversity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePr(\u0026gt;\u0026thinsp;F)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBray-Curtis dissimilarity index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJaccard similarity and diversity index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeighted UniFrac\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnweighted UniFrac\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUniFrac: Unique fraction metric.\u003c/p\u003e \u003cp\u003eMean class vector value for control group was higher than study group, and range was also higher. However, explanatory value of similarity was 10.2%, and dissimilarity differences between groups were insignificant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLefse analysis results for groups showed that all LDA values for all species in the study and control group were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In this respect, Lefse outputs of both groups were similar, in accordance of Beta Diversity results. 14 species were identified in healthy groups, whereas 8 species were identified in the study group (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLefse analysis results for groups and p values\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnrich group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpicy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLDA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep (adjusted)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003ePatient group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eStaphylococcus_s\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.57965225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00028304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00028304\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eStaphylococcus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.57772884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00035454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00035454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eStaphylococcaceae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.57771025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00035454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00035454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBacillales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.57393233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00044268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00044268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBacilli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.37082878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02607774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02607774\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinegoldia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.33283711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0024503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0024503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinegoldia_s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.33283013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0024503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0024503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDorea formicigenerans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.76768967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0392788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0392788\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eControl group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLactobacillales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.16049142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01636311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01636311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eStreptococcus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.12720096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0464989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0464989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eStreptococcaceae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.12718397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0464989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0464989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eStreptococcus_s\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.09638935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04038046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04038046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMicrococcaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.87684208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01330056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01330056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarnobacteriaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.65797408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00802004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00802004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGranulicatella\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.63099875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02875133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02875133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGranulicatella_s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.63084431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02875133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02875133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eListeriaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.51695084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0324268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0324268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrochothrix_s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.50518697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0324268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0324268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrochothrix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.50349441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0324268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0324268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMicrococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.87795619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0324268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0324268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMicrococcus_s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.87141144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0324268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0324268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRhizobiaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.51371344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0324268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0324268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePhylum mean was higher in the control group, whereas class, order, family, genus and species percentage means were higher in the study group. However, all differences for relative abundance were insignificant between control and study groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelative abundance test results between groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePercentages (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ep value\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (Min-Max)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian (Min-Max)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhylum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5.88\u0026thinsp;\u0026plusmn;\u0026thinsp;16.82\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01 (0.00-67.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.88\u0026thinsp;\u0026plusmn;\u0026thinsp;13.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02 (0.00-45.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.03\u0026thinsp;\u0026plusmn;\u0026thinsp;11.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01 (0.00-65.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.57\u0026thinsp;\u0026plusmn;\u0026thinsp;10.49\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04 (0.00-42.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.96\u0026thinsp;\u0026plusmn;\u0026thinsp;6.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02 (0.00-37.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;6.52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03 (0.00-35.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01 (0.00-36.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;4.68\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02 (0.00-35.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01 (0.00-45.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;4.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01 (0.00-42.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04 (0.00-38.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;4.98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07 (0.00-36.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ea. Mann Whitney U Test, SD: Standard Deviation.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, the effect of antibiotic use on the microbiota in breast milk was investigated and milk samples from 12 mothers who took antibiotics and 12 mothers who did not were analyzed. The results obtained in the study showed that although there was no significant difference in diversity within the group in mothers who took antibiotics and those who did not, there were significant differences between the groups and in the number of strains. The number of species in mothers who used antibiotics was 8, while it was 12 in those who did not use antibiotics, and \u003cem\u003eStaphylococcus_s, Staphylococcus and Staphylococcaceae\u003c/em\u003e species were present in both groups.\u003c/p\u003e \u003cp\u003eStudies that reveal the importance of breast milk in terms of nutrition and baby development generally show that breast milk is the most important basic food for individuals in the first six months of their lives, regardless of its content and amount [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. When its content is weak or its amount is low, it is supported by formula, but breast milk is the most basic source of nutrition for babies. Although there are not enough studies on microbiota in babies, current theoretical or conceptual studies emphasize that microbiota is important in babies, and affected from many factors [\u003cspan additionalcitationids=\"CR20 CR21 CR22\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In their study, Ma et al [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] showed that different formula-fed causes significant microbiota differences between neonatal groups. In another study, Combellick et al [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] reported that hospitalization for delivery effect micobiota of baby. In their literature review, Layuk et al [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] reported that giving early breastfeeding and initiation of microbiota has significant effect on immunity and digestion. Results of studies show that microbiota in babies has a sensitive balance in human life. However, more clinical and experimental studies are needed on this subject.\u003c/p\u003e \u003cp\u003eStudies have been conducted on the permeability of breast milk. In these studies, it is generally concluded that although breast milk is selective in terms of passing harmful materials, it does pass antibiotics and various genetic and hormonal materials [\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Today, there are still not enough studies on whether some drugs pass into breast milk and to the baby through breastfeeding. Although there are many reasons for this, the ethical and other difficulties of working on babies come to the forefront in this regard.\u003c/p\u003e \u003cp\u003eIn literature, very few studies conducted on effect of antibiotics on breastfeeding and microbiome. In studies on breast milk, there are studies reporting that antibiotics pass into the milk [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Korpela et al [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] reported that antibiotics usage in breastfeeding may weaken intestinal microbiota. Azad et al [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] reported that usage of intrapartum antibiotics may reduce microbiota. Lemas et al [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] showed that maternal antibiotics usage may correlated with microbiome weakness and obesity. However, studies on their effects on the microbiome of mother milk are not studied comprehensively. In our study, although the microbiome distributions of mothers who took antibiotics and those who did not take them were similar, there were fewer bacterial species in the mothers who took antibiotics in terms of diversity. Moreover, \u003cem\u003eStaphylococcus_s, Staphylococcus\u003c/em\u003e and \u003cem\u003eStaphylococcaceae\u003c/em\u003e species, which are known to be harmful to the microbiome, were present in both groups. These results show that antibiotic use has a negative and unbalanced effect on the microbiome in breast milk. In this respect, in mothers who have to use antibiotics, mothers should be supported with probiotics and symbiotics in order to ensure microbiome health. In addition, it is useful to support and reveal with further studies the extent to which the microbiome in breast milk is taken by the child, to what extent the body accepts it and how much the body needs. When we look at the results in general, the microbiome culture quality of mothers who used and did not use antibiotics was quite low. There are many variables in this, from nutritional level to food quality, from food safety to eating habits. More comprehensive, large-budget, multi-center studies are needed to reveal both the causes and consequences of the bacterial colonization balance disruption caused by antibiotics. In this way, it may be possible to obtain the health benefits that microbiota will provide to individuals in later periods of life.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLimitations of the Study\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe most important limitation of the study is that there has not been enough study on the transfer of microbiota from breast milk and therefore, the results of the study could not be compared with different centers, different regions or demographic characteristics. Although this situation makes the study important as a pioneer in the field, more extensive studies with multicenters and larger samples are needed.\u003c/p\u003e \u003cp\u003eAnother important limitation of the study is its cost. Genomic DNA Extraction 16S Amplicon Sequencing and bioinformatics analyses are currently very costly processes. Therefore, the number of samples in the study was limited due to cost limitations. In addition, due to cost limitations, the study reflects the results obtained from single-center, specific time period samples. There is a need to examine the role of seasonal effects with studies in different time periods and demographic effects with multi-center studies.\u003c/p\u003e \u003cp\u003e \u003cem\u003eContributions of the study to literature and clinical practice\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe most important contribution of the study to the literature is that it is a pioneering study in the literature, as there are not enough studies in this field. Although studies have been conducted on the role of breast milk in the development of the baby, it is possible to make inferences about the mechanism underlying this thanks to this study. In addition, the study can be a source and basis for further research.\u003c/p\u003e \u003cp\u003eThe most important contribution of the study to clinical practice is that it quantitatively demonstrates the relationship between breast milk, antibiotic use and microbiota, and shows that supplementation with synbiotic and probiotic supplements is necessary to restore the disrupted microbiota in mothers using antibiotics. Although previous studies have been conducted to suggest that the microbiota will be disrupted in mothers using antibiotics and that this will have an impact on the health of the baby, these are mostly conceptual and not evidence-based studies. The research results have demonstrated this evidence-based.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eAccording to the results of the study, antibiotic use in breastfeeding mothers decreased the microbiota level and caused a significant decrease in microbiome diversity in milk. Although there was no significant decrease in total microbiome levels, there was a significant decrease in microbiota diversity and there was a more significant decrease in beneficial strains compared to harmful strains, and the microbiota balance was disrupted.\u003c/p\u003e \u003cp\u003eThese results reveal the nutritional damage of antibiotic use in breastfeeding mothers not only to the mother herself but also to the baby through milk. However, in cases of sepsis and many similar medical diseases and complications, antibiotic use may be mandatory despite all its harms. In this case, it would be beneficial to support both the expectant mother and the baby in terms of nutrition with at least the use of synbiotics or probiotics.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDeoxyribonucleic Acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEBD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eElution solution\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLBD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLysis solution\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLinear discriminant analysis Effect Size\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePKD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProteinase K solution\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStool Lysis solution\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eEthical approval was taken from Balıkesir University Medicine Faculty Clinical Research Ethical Committee with 01.03.2024 date and E-94025189-050.04-358285 grand number. Patient consents for publication were taken from participants.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthors' contributions\u003c/h2\u003e \u003cp\u003eFDK and \u0026Ouml;KA carried out data collection, statistical analysis, and out writing with finalizing the article.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eAuthors thank Kadir Yılmaz, İstanbul Commerce University, for his valuable statistical support.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVictora CG, Bahl R, Barros AJ, Fran\u0026ccedil;a GV, Horton S, Krasevec J, Rollins NC (2016) Breastfeeding in the 21st century: epidemiology, mechanisms, and lifelong effect. lancet 387(10017):475\u0026ndash;490\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMasi AC, Stewart CJ (2024) Role of breastfeeding in disease prevention. 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WB Saunders\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Balıkesir University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Breast milk, microbiota, genomic analysis, bioinformatic analysis","lastPublishedDoi":"10.21203/rs.3.rs-6235909/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6235909/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The aim of this study was to reveal the differences in microbiota by examining the milk of mothers who were monitored in neonatal intensive care and received and did not receive antibiotic treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e In the study, milk samples taken from 12 mothers who were hospitalized in our neonatal unit and received antibiotic treatment with a diagnosis of sepsis, and 12 mothers who were hospitalized in the same period and had babies with similar characteristics were analyzed in terms of microbiota. Genomic DNA Extraction, 16S Amplicon Sequencing and bioinformatic analyses were performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Chao Index was 0.241 with relatively low richness in both samples as within sample analysis. Beta diversity values showed significant diversity between sample groups (p\u0026lt;0.05) and species turnover was significant for both groups (p\u0026lt;0.05) with high diversity level for healthy group compared to patient group. Jaccard similarity and diversity index fitting results showed that nonmetric fitted function had 97.7% explanatory value, whereas linear fitting function had 94.5% explanatory value. Bray-Curtis dissimilarity index had 12.1% and Jaccard similarity and diversity index had 9.2% explanatory values showing significant similarity and diversity. Lefse analysis results for groups showed that all Linear discriminant analysis (LDA) values for all species in the study and control group were statistically significant (p\u0026lt;0.05). 14 species were identified in healthy groups, whereas 8 species were identified in the study group. Phylum mean was higher in the control group, whereas class, order, family, genus and species percentage means were higher in the study group. However, all differences for relative abundance were insignificant between control and study groups (p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Antibiotic use in breastfeeding mothers decreased the microbiota level, leading to a significant decrease in microbiome diversity in milk. Although there was no significant decrease in total microbiome levels, there was a significant decrease in microbiota diversity and there was a more significant decrease in beneficial strains than in harmful strains, and the microbiota balance was disrupted.\u003c/p\u003e","manuscriptTitle":"Genomic and bioinformatic analysis of the effect of antibiotic use on microbiota levels in breast milk","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-18 08:01:44","doi":"10.21203/rs.3.rs-6235909/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"91b25e16-f27f-4726-b605-6f95a26a2efb","owner":[],"postedDate":"March 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45733926,"name":"Pediatrics"}],"tags":[],"updatedAt":"2025-03-18T08:01:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-18 08:01:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6235909","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6235909","identity":"rs-6235909","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}