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what does chemo do to the intestines over 6 months period

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Effect of chemotherapy on the microbiota and metabolome of human being milk, a example report

  • Amy McMillan1,2,
  • Michelle Angelinithree,
  • Gregory B Gloor4,
  • Mark Sumarah5,half-dozen,
  • Jeremy P Burton1,2 &
  • Gregor Reid1,2

Microbiome volume 2, Article number:24 (2014) Cite this article

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  • 66 Citations

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Abstract

Background

Homo milk is an important source of bacteria for the developing infant and has been shown to influence the bacterial composition of the neonatal gut, which in turn tin affect disease take chances subsequently in life. Human milk is as well an important source of nutrients, influencing bacterial composition but also directly affecting the host. While recent studies accept emphasized the adverse effects of antibody therapy on the infant microbiota, the effects of maternal chemotherapy have non been previously studied. Hither nosotros report the furnishings of drug administration on the microbiota and metabolome of human milk.

Methods

Mature milk was collected every two weeks over a 4 calendar month catamenia from a lactating woman undergoing chemotherapy for Hodgkin's lymphoma. Mature milk was besides collected from healthy lactating women for comparison. Microbial profiles were analyzed by 16S sequencing and the metabolome by gas chromatography–mass spectrometry.

Findings

Chemotherapy acquired a pregnant divergence from a healthy microbial and metabolomic profile, with depletion of genera Bifidobacterium, Eubacterium, Staphylococcus and Cloacibacterium in favor of Acinetobacter, Xanthomonadaceae and Stenotrophomonas. The metabolites docosahexaenoic acid and inositol known for their beneficial effects were also decreased.

Decision

With milk contents beingness critical for shaping baby immunity and development, consideration needs to be given to the impact of drugs administered to the mother and the long-term potential consequences for the health of the infant.

Background

Colonization of the neonatal gut plays a pivotal function in gastrointestinal, immunological and neurological development, with 1 of the initial sources of bacteria being the mother'southward milk [1, two]. Breastfed infants have been shown to have lower incidences of asthma, diarrhea, and necrotizing enterocolitis compared with formula-fed infants [3]. This protective effect may exist due, in part, to the types of leaner present in milk, as infants fed formula supplemented with probiotics were better protected against these conditions compared to those just fed formula [four–6]. The leaner acquired during infancy tin can influence affliction risk after in life and play a major office in determining the future composition of the developed microbiome [5]. Thus, factors that bear on the milk microbiota accept important health consequences for the child not simply during development just also into adulthood. In addition to the microbiota, the metabolites of human being milk, such as fatty acids, carbohydrates, proteins and vitamins, also play an of import role in babe evolution and long-term health [7–10].

Mail service-delivery, many women are prescribed pharmaceutical agents for various reasons. While most over-the-counter drugs and antibiotics are non contraindicated during breast feeding [eleven, 12], when it comes to chemotherapeutics, the recommendation is that breastfeeding should be avoided until the drug has been cleared from the milk [13]. In a case report of a 70 mg infusion of cisplatin, no detectable levels were institute in milk after 66 hours [14], and in another case written report using doxorubicin (trade name Adriamycin), no detectable levels were seen after 72 hours [15]. In our particular study, the field of study was brash that breastfeeding could resume 12 days after each chemotherapy session.Here we present the outset report on the effects of chemotherapy on microbial and metabolomic profiles in human being milk over a 4-month menstruation in a breastfeeding woman undergoing handling for Hodgkin's lymphoma (Figure 1).

Figure 1
figure 1

Summary of clinical information and sample collection. Milk samples were collected from a lactating woman undergoing chemotherapy for Hodgkin's lymphoma. Milk samples were nerveless every 2 weeks over a four-month menses. At each session milk was nerveless fifteen to 30 minutes earlier (sample A) and after (sample B) chemotherapy. The duration of chemotherapy treatment was 2 hours. No milk was nerveless at calendar week viii due to scheduling conflicts.

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Methods

Clinical samples and written report pattern

Upstanding approving was obtained from Western Research Ethics Board and Lawson Health Research Institute, London, Ontario, Canada. Subjects provided written consent for sample drove and subsequent analyses.

Milk collection and processing

Mature milk was collected from a lactating woman undergoing the ABVD chemotherapy authorities (Adriamycin (twoscore mg), Bleomycin (xvi units), Vinblastine (nine.6 mg), Dacarbazine (600 mg) for Hodgkin's lymphoma at the London Wellness Sciences Center, London, Ontario. Mature milk was also nerveless from 8 healthy women recruited from London, Ontario and the surrounding area. Wearing sterile gloves the women cleaned their nipple and surrounding area with sterile saline swabs to reduce the presence of skin bacteria. Milk was collected using a sterile HygieniKit Milk Collection Organization (Ameda, Buffalo Grove, IL, Usa) fastened to an electric breast pump. Between 5 and fifteen ml of milk was pumped into a sterile tube and kept on ice until transfer to the laboratory, which occurred within i hour of collection. Samples were aliquoted and stored at -20°C until DNA extraction.

DNA isolation

After thawing on ice, 2 ml of milk were spun down at thirteen,0001000 for x minutes and the supernatant discarded. The pellet was and then homogenized in 1.4 ml of ASL buffer (QIAamp® DNA Stool Kit, QIAGEN: Valencia, CA, Us) and 400 mg of 0.ane mm diameter zirconium-drinking glass chaplet (BioSpec Products, Bartlesville, OK, USA). Mechanical and chemic lyses were performed by bead beading at 4,800 rpm for 60 due south, then 60 s on ice (repeated twice) using a mini-beadbeater-one (BioSpec Products) and then incubated at 95°C for five minutes. Subsequent procedures were performed using the QIAGEN QIAamp® Dna Stool Kit according to the manufacturer's protocol, with the exception of the last step in which the column was eluted with 120 μl of elution buffer. DNA was stored at -twenty°C until further use.

V6 16S rRNA cistron sequencing

PCR distension

The genomic DNA isolated from the clinical samples was amplified using the barcoded primers V6-LT: 5′CCATCTCATCCCTGCGTGTCTCCGACTCAGNNNNNCWACGCGARGAACCTTACC3′ and V6-RT: v′ CCTCTCTATGGGCAGTCGGTGATACRACACGAGCTGACGAC3′, which amplify the V6 hypervariable region of the 16S rRNA gene. The PCR was carried out in a forty μl reaction containing 5 μl of DNA template (or nuclease-free h2o as a negative command), 1.v mM MgClii, 0.eight μM of each primer, 4 μl of ten× PCR Buffer (Invitrogen, Burlington, ON, Canada), 0.2 mM dNTPs, 0.05U Taq Polymerase (Invitrogen) and 0.15 μg/μl bovine serum albumin. Thermal cycling was carried out in an Eppendorf Mastercyler nether the following weather: initial denaturation at 95°C for 2 minutes followed by 25 cycles of 95°C for 1 minute, 55°C for one minute and 72°C for ane minute. After amplification, the Deoxyribonucleic acid concentration was measured with the Qubit® two.0 Fluorometer (Invitrogen) using the broad range analysis. Equimolar amounts of each PCR production were pooled together and purified using the QIAquick PCR purification kit (QIAGEN). The PCR purified sample was then sent to the London Regional Genomics Eye, London, Ontario, Canada for V6 16S rRNA gene sequencing using the Ion Torrent platform as per the eye's standard operating process.

Sequence processing and taxonomic consignment

Custom Perl and Bash scripts were used to de-multiplex the reads and assign barcoded reads to private samples. Reads were kept if the sequence included a perfect lucifer to the barcode and the V6 16S rRNA factor primers. Reads were clustered by 97% identity into operational taxonomic units (OTUs) using UClust v.3.0.617 [16]. OTUs that represented ≥1% of the reads in at least one sample were kept, while those that did not run into the cutoff were discarded. Taxonomic assignments for each OTU were fabricated past the Ribosomal Database Project (RDP) SeqMatch tool [17]. From the elevation twenty matches to the RDP named isolates database, the full taxonomy was retained for matches with the highest S_ab score. For multiple top matches with equal scores, the highest common taxonomy was retained (for example, genus level if multiple species matched as well). Since the maximum number of matches displayed per sequence is 20, the RDP taxonomic assignments were verified by BLAST confronting the Greengenes named isolates database with an output of 100 hits [18]. Taxonomy was assigned based on hits with the highest percentage identities and coverage. If multiple hits fulfilled this criterion, classification was re-assigned to a higher mutual taxonomy. In instances where the highest percentage identity/coverage yielded a unmarried lucifer, if this were <90% and the S_ab score from RDP was <0.vii, taxonomy was assigned at the family level instead of at the genus level. A summary of each OTU classification and its sequence is shown in Additional file i. The raw sequencing reads generated in this report have been deposited to the NCBI Short Read Archive (SRA) database [SRA:SRP041626].

Data analysis

Weighted UniFrac distances were calculated in QIIME [19] by using a phylogenetic tree of OTU sequences built with FastTree [twenty] and based on an OTU sequence alignment with MUSCLE [21]. The QIIME pipeline was likewise used to summate Shannon's variety index (logarithms with base 2) and to generate principal coordinate analysis (PCoA) plots. Weighted UniFrac distances compare microbial profiles (presence/absenteeism and affluence) between samples (i.due east., beta-diversity) [22] while Shannon's diversity index evaluates the microbial diversity within a sample (i.e., alpha variety). The college the Shannon's variety index, the more diverse a sample is and a value of zero indicates the presence of only ane species [23]. PCoA plots let ane to visualize the UniFrac distance matrix and plot the values on a set of orthogonal axes that capture the greatest amount of variation between all samples tested. For beta-diversity analyses, the data set was rarified to the everyman read count/sample, which was 734 reads. A summary of clinical data, including total number of sequence reads per sample, is shown in Additional file 2. Barplots, boxplots and stripcharts were all generated in R [24].

Statistical analysis

The ALDEx R package version 2 [25] was used to compare genera between the non-treatment and chemotherapy handling groups (as portrayed in the boxplots). Microbiome data represent proportional distributions and are thus not independent of each other. This ways that a decrease in one organism volition inevitably pb to a concomitant increase in another organism. For example, if a sample has two organisms A (l%) and B (50%) and A is completely killed by an antibiotic, the proportion of B in that sample will now exist 100% even if its actual abundance has not changed. The ALDEx R parcel estimates the technical variation inherent in loftier-throughput sequencing by Monte-Carlo sampling from a Dirichlet distribution [26]. The Monte-Carlo replicates are transformed using the centered log-ratio transformation, which takes the logarithm of the Monte-Carlo estimates of organism abundances in each sample divided past the per-sample geometric mean organism abundance [27]. This transformation has several desirable properties that exercise not exist in proportional information, notably subcomposition coherence and linear sample independence. Data transformed in this fashion permit the use of standard statistical tests to decide significance. Values reported in this manuscript represent the expected values of 128 Dirichlet Monte-Carlo instances. A value of zero indicates that organism abundance is equal to the geometric mean affluence. Thus, organisms more abundant than the mean will have positive values and those less abundant than the hateful will take negative values. Base two was used for the logarithm and so differences between values represent fold changes. Statistical significance for these comparisons was determined by a Mann-Whitney U test with P < 0.05 and a fake discovery charge per unit (FDR) of < 0.1 using the q values output by the fdrtool R package [24].

The unpaired Student's t-exam was used to compare Shannon'south diversity index (P < 0.05).

Sample training gas chromatography-mass spectrometry

To extract metabolites, 100 μl of milk were mixed with 400 μl pure methanol. Samples were vortexed for fifteen south and centrifuged for ten minutes at 9,000grand. Supernatants (200 μl) were transferred to gas chromatography-mass spectrometry (GC-MS) vials and ii.5 μl of ribitol solution (2 mg/ml) was added to each vial as an internal standard. Samples were dried to completeness using a SpeedVac. Afterwards drying, 100 μl of two% methoxyamine•HCl in pyridine (MOX) was added to each sample for derivitization and samples were incubated at l°C for ninety minutes. North-methyl-N-(trimethylsilyl) trifluoroacetamide (100 μl) was then added to each vial and incubated at fifty°C for 30 minutes. Samples were transferred to microinserts earlier running on GC-MS (Agilent 7890A GC, 5975 inert MSD with triple centrality detector, 30 m DB5-MS column with 10 chiliad duraguard column). Samples were run using 1 μl injections on browse mode and a solvent delay of 10 minutes. Run time was threescore minutes per sample. Each sample was run twice non-consecutively to ensure consistency throughout the sequence.

GC-MS data assay

Chromatogram files were converted to ELU format using the AMDIS Mass Spectrometry software [28]. Chromatograms were aligned and abundance of metabolites calculated using the Spectconnect software [29] with the support threshold set to depression. In gild to determine if differences between week 0 and chemotherapy (weeks two to sixteen) existed, principle component assay (PCA) was conducted in SIMCA (Umetrics, San Jose, CA, USA) using the relative affluence matrix output from Spectconnect. Data were mean centered and pareto scaled prior to PCA. Contained unpaired t-tests with Bonferroni correction were calculated in Excel to determine metabolites that were significantly contradistinct past chemotherapy (P < 0.05). Compounds that besides contributed to the separation of week 0 from chemotherapy samples co-ordinate to the PCA loadings plot (compounds in bottom left quadrant) were chosen for further investigation.

Findings

Chemotherapy affected both bacterial diversity and bacterial profiles in human being milk. Bacterial diversity within samples was lower in milk collected throughout chemotherapy compared with milk samples collected at week 0 and from healthy lactating women (Figure 2). Bacterial profiles at calendar week 0 were similar to those from good for you women, although this changed within two weeks of treatment (Figure 3A). Samples collected at weeks 4 to 16 shared similar profiles and differed from week two and from week 0/good for you samples (Figure 3A). These differences were non due to natural changes over fourth dimension, equally the bacterial community in two milk samples analyzed from a healthy field of study did not change over a 4-month menstruation (Figure 3A, green samples). The bar plot in Figure 3B shows the bacterial communities present in these samples with a striking increase in abundances of Acinetobacter and Xanthomonadaceae in milk collected during chemotherapy. A comparison of relative abundances of Acinetobacter, Xanthomonadaceae and Stenotrophomonas (a genus belonging to the Xanthomonadaceae family) betwixt the chemotherapy (weeks 4 to sixteen) and non-treatment (calendar week 0 and healthy samples) groups is displayed in Figure iv and were significantly higher during chemotherapy. We as well examined the differences between three bacteria believed to confer benign wellness effects to the baby, Bifidobacterium, Eubacterium and Lactobacillus. The offset 2 were significantly decreased during chemotherapy whereas no differences were observed for Lactobacillus (Effigy 4). Overall, a full of 22 out of the 49 genera identified were differentially abundant between the 2 groups (Additional file three). While the core microbiome (that is, taxa that were nowadays in 100% of the samples) was somewhat similar between the 2 groups, it is interesting to note that Stenotrophomonas was nowadays in every chemotherapy sample and Lactobacillus and Eubacterium were present in every good for you and calendar week 0 sample (Additional file 4).

Figure 2
figure 2

Changes in bacterial variety as a result of chemotherapy. Bacterial diversity within a sample (i.due east. alpha variety) was measured by calculating Shannon'south diversity index. Each bespeak on the graph represents a subject area with the line representing the mean for all samples within a group. The college the index the greater the bacterial diversity found within a sample. The hateful of the 'Wk0/H' grouping (week 0 and healthy samples) was 4.three, and that of the chemotherapy group (weeks 2 to 16) was two.8. Groups were statistically dissimilar from each other every bit measured by unpaired Pupil'south t-test (P < 0.05).

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Figure 3
figure 3

16S rRNA sequencing analysis of bacteria in human milk. Milk samples were collected from a lactating woman undergoing chemotherapy as described in Figure 1 as well as from viii healthy lactating women. (A) Weighted UniFrac PCoA plot. Each milk sample, represented by a coloured circle, is plotted on this three-dimensional, three-centrality plane representing 84% of the variation observed betwixt all samples. Samples that cluster together are similar in biota composition and abundance. Orange circles represent samples collected from weeks 4 to 16 of chemotherapy, bluish circles represent samples nerveless at week ii of chemotherapy, purple circles stand for samples nerveless at week 0, blood-red circles correspond milk samples from healthy lactating women (only one time signal) and green circles correspond milk samples from a healthy lactating women collected 4-months apart. Every bit shown past the plot, there were three distinct groups: (i) week 0 samples and salubrious milk samples; (ii) calendar week two of chemotherapy; and (iii) weeks 4 to xvi of chemotherapy. Data were rarified to 735 reads/sample. (B) Barplot showing the relative abundances of different genera in each sample. Each bar represents a bailiwick and each coloured box a different genus. The height of the coloured boxes represents the relative abundance of that genus inside the sample. Genera that were less than ii% abundant in a given sample were placed in the 'Remaining fraction' at the acme of the graph (grey boxes).

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Figure 4
figure 4

Comparison of relative proportions of bacterial taxa betwixt treatments. Boxplots comparing vi bacterial taxa between samples collected during chemotherapy (weeks 4 to 16) and those without treatment (week 0 and healthy samples (Wk0/H)). The box signifies the 75% (upper) and 25% (lower) quartiles and thus shows where 50% of the samples lie. The black line within the box represents the median. The whiskers stand for the lowest datum still within one.5 interquartile range (IQR) of the lower quartile and the highest datum still within 1.5 IQR of the upper quartile. Outliers are shown with open circles. The value '0' represents the geometric hateful abundance; thus, values higher up 0 are more arable and values less than 0 are less abundant than the geometric mean. Significant differences were observed between the two groups for all taxa graphed (Mann-Whitney U test P < 0.05, FDR <0.one).

Full size paradigm

The metabolic profile also changed as a result of chemotherapy (Figure 5) and was similar between weeks 2 and xvi, but dissimilar to that observed at calendar week 0. A total of 226 metabolites were detected past our GC-MS method, 12 of which were significantly different between the week 0 and chemotherapy (weeks 2 to xvi) groups (Table 1). Additional file 5 shows the relative abundances of all metabolites detected in milk and stripcharts in Additional file six testify the distribution of key metabolites detected between the two groups.

Effigy v
figure 5

Principle component analysis of metabolites in breast milk at calendar week 0 and during chemotherapy. (A) Scoreplot displaying the distribution of samples based on metabolites alone, where the distance between samples represents how similar the metabolome of those samples are. Each point represents the boilerplate of ii technical replicates. (B) Loadings plot. Each point represents a metabolite. Metabolites present in a given quadrant of the loadings plot are nowadays in highest abundance in samples present in the same quadrant of the scoreplot (A).

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Tabular array 1 Metabolites significantly altered by chemotherapy

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Discussion

This study shows that a course of chemotherapy has significant effects on bacterial and metabolic profiles in homo milk, moving away from those of salubrious lactating women. Of note, the subject field did non report any additional drug use, antibiotics, illness or major changes in nutrition over the course of the study.

The consequences of decreased bacterial diversity in human being milk and the implications on the child are still unknown; however, the decreased milk variety could impact intestinal diversity and information technology has been shown that low intestinal diversity in the first weeks of life is associated with necrotizing enterocolitis [30] and an increased risk of allergy and atopy in school-historic period children [31]. Lower intestinal diversity has also been observed in children with type 1 diabetes compared to age-matched controls [32].

In addition to overall changes in microbial profiles, we observed a significant subtract in the relative abundance of Bifidobacterium in the chemotherapy group compared to the non-handling group. Bifidobacterium is the predominant organism in the gut of breastfed infants, attributed to its ability to metabolize the human being milk oligosaccharides present in large amounts in milk [33, 34]. Maternal levels can too affect gut Bifidobacterium affluence, with low levels in milk correlating with low levels in the neonatal gut [35]. The potential consequences of decreased numbers of Bifidobacterium being passed on from the female parent to the neonate could exist an increased risk of asthma or obesity later in life. Loftier levels of Bacteroides have been reported in the gut of infants with low levels of Bifidobacterium[36] and early colonization with high Bacteroides counts has been associated with an increased adventure of developing asthma and obesity [37–39]. In addition, depleted levels of Bifidobacterium have been shown to promote colonization of opportunistic pathogens such as Klebsiella and Citrobacter[36].

Staphylococcus, including coagulase negative species, are one of the predominant organisms in human milk [40–42], and were also significantly reduced as a upshot of chemotherapy. Information technology has been shown that numerous human milk isolates of Staphylococcus epidermidis can inhibit the growth of Staphylococcus aureus[43], the main causative amanuensis of mastitis, which is a painful inflammatory condition of the breast that often leads to premature abeyance of breastfeeding in many women. While we were non able to identify the Staphylococcus in our samples downward to the species level with 16S rRNA factor sequencing, culture analysis on mannitol salt agar plates did bear witness that the Staphylococcus isolates were not Southward. aureus and the select few that were tested were coagulase negative. This reduction of Staphylococcus (likely coagulase negative species) as a issue of chemotherapy could brand lactating women more than prone to infections, affecting both themselves and their infants. Like Bifidobacterium, Staphylococcus is passed from the milk to the neonate, with college numbers in the intestine of breastfed compared to formula-fed infants [one]. Interestingly, a metagenome analysis revealed the presence of immunosuppressive motifs in bacterial Dna from man milk, with the majority of these belonging to Staphylococcus[41]. The exposure of the neonate to this Dna, either ingested from the milk or through live bacteria that have released their DNA once in the gut, could help to regulate the infant'southward immune response against a variety of innocuous bacterial, environmental and food antigens.

The utilization of bacterial products by other bacteria is termed metabolic cross-feeding and plays an of import office in bacterial selection. For instance, the byproducts of bacterial metabolism, such every bit lactate and acetate production, are utilized as an energy source by many butyrate-producing leaner [44–47] such equally Eubacterium, which was decreased in our chemotherapy group. Butyrate is important for health, as information technology reduces inflammation and metabolic diseases, promotes colonic repair and protects confronting colon cancer [48, 49]. On the other hand, some pathogens persist and crusade disease only in the presence of certain commensal bacteria [50], likely due to the metabolites produced. Thus, changes in bacterial communities in human milk volition inevitably alter the metabolic milieu, selecting for bacteria able to utilize those metabolites. Equally a result, potential shifts from a healthy and balanced intestinal microbiota can occur, having important consequences on health.

Docosahexaenoic acid (DHA), inositol and an unknown polyunsaturated fatty acid were amongst the 12 metabolites that differed between calendar week 0 and weeks 2 to xvi, with reduced levels detected during chemotherapy (weeks two to 16). DHA is the near abundant long chain polyunsaturated fatty acid in the brain, retina and nerve cells and is supplied mainly through breast milk [51]. DHA deficiencies lead to reduced brain, heart and neuronal development [51] and it has been observed that breastfed infants take better visual acuity and neuronal development compared to those fed formula [52]. Reduced levels of DHA and alpha-linolenic acid (a precursor of DHA) accept been reported in milk of mothers with atopic children compared to milk from mothers with not-atopic children [53, 54]. Another chief metabolite in the neonatal brain is inositol, which is important for osmoregulation, cellular diet and detoxification [55]. Palmitic acid levels were as well reduced during chemotherapy, though the results were not pregnant. Palmitic acid is the most abundant lipid in human milk and has been shown to increment bone strength in infants [56] and limit abdominal damage and pro-inflammatory immune responses in mice [57]. While changes in metabolite concentrations do occur over the course of lactation, especially between colostrum and mature milk, no changes in the above metabolites over the course of the first year of life have been observed in mature milk [58–61]. Due to the high variability in milk metabolites between individuals, we did not have plenty samples from healthy women to make substantive claims as to how the Hodgkin's patient compared, although at that place were no obvious differences in the metabolic profiles of control samples taken at early compared to subsequently stages of lactation.

We recognize that the main limitation of the report is its single instance written report content of a patient undergoing chemotherapy. However, the findings were revealing. Of notation, even with the illness, her milk microbiota earlier treatment was similar to that of good for you lactating women and merely after intervention did the microbiota patterns alter. It has been reported by Hunt et al.[40] that the milk microbiota of a specific private is stable over time, consistent with our study from the good for you sample nerveless four months apart. We believe that the changes in the microbiota are a result of therapeutic agents and non specific to just one patient.

Conclusions

Bacterial and metabolic compositions in human milk, and so disquisitional for amnesty and baby development, tin can change significantly after maternal exposure to chemotherapeutic agents. Further larger cohort studies are warranted to examine microbiota and metabolomic changes associated with chemotherapy and other medications prescribed to lactating mothers and the consequences for the microbiome, the metabolome and long-term health of infants.

Supporting data

The data set supporting the results of this article are included in Boosted files i, 2, three, four, v and 6.

Abbreviations

DHA:

docosahexaenoic acid

FDR:

false discovery rate

GC-MS:

gas chromatography-mass spectrometry

OTU:

operational taxonomic unit

PCA:

principal component assay

PCR:

polymerase chain reaction

PCoA:

chief coordinate analysis

RDP:

Ribosomal Database Project.

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Acknowledgements

We would like to thank Jean Macklaim for her advice and guidance in analyzing the 16S rRNA sequencing data and Tim McDowell at Agriculture and Agri-Food Canada for technical support with GC-MS. Funding sources to individuals: CU - CIHR Strategic Training Program in Cancer Enquiry and Technology Transfer; and the Translational Breast Cancer Studentship; AM - Ontario Graduate Scholarship. Written informed consent was obtained from the patients for publication of this manuscript and accompanying images.

Writer information

Affiliations

  1. Lawson Health Enquiry Found, 268, Grosvenor Street, London, ON, N6A 4V2, Canada

    Camilla Urbaniak, Amy McMillan, Jeremy P Burton & Gregor Reid

  2. Department of Microbiology & Immunology, Western University, London, ON, N6A 5C1, Canada

    Camilla Urbaniak, Amy McMillan, Jeremy P Burton & Gregor Reid

  3. Perinatal and Women'due south Health, London Health Sciences Middle, London, ON, N6A 4L6, Canada

    Michelle Angelini

  4. Department of Biochemistry, Western University, London, ON, N6A 5C1, Canada

    Gregory B Gloor

  5. Department of Chemistry, Western University, London, ON, N6A 5C1, Canada

    Marker Sumarah

  6. Agriculture and Agri-Food Canada, London, ON, N5V 4T3, Canada

    Marker Sumarah

Respective author

Correspondence to Gregor Reid.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

CU designed the study, recruited subjects, nerveless and prepared the samples for microbial analysis, analyzed 16S rRNA sequencing data and wrote the manuscript. AM performed GC-MS analysis on the milk samples and reviewed the manuscript. MA helped with study blueprint and recruitment and reviewed the manuscript. JB provided input into study design and reviewed the manuscript. MS contributed essentially to the pattern and conquering of the metabolomic data, analysis and manuscript review. GG provided co-supervision, teaching and input into microbiome acquisition and analysis and manuscript review. GR conceptualized the study, helped with study design and manuscript writing, supervised data collection and analysis and provided financial back up. All authors read and canonical the last manuscript.

Electronic supplementary material

40168_2014_53_MOESM1_ESM.xlsx

Boosted file 1: Tabular array S1: Summary of taxonomic results and full-length V6 16S rRNA sequence of each OTU. (XLSX 33 KB)

Boosted file 2: Table S2: Summary of clinical information. (XLSX 10 KB)

40168_2014_53_MOESM3_ESM.dr.

Boosted file iii: Table S3: Comparing of relative abundances of different genera detected in milk between the chemotherapy and non-treatment groups. Values in the 2nd and third columns represent the base ii logarithm of the median affluence in all samples within a group (that is, Wk0/H samples (non-treatment group) or Wk4-16 samples (chemotherapy group) relative to the geometric hateful abundance, which has a value of 0. Thus, positive values are college than the geometric mean and are thus more abundant than negative values, which are lower than the geometric hateful. Significant differences were based on FDR values of <0.1. Out of the 49 genera identified, 22 were significantly different between the 2 groups. (Physician 102 KB)

Additional file 4: Table S4: Taxa present in every sample within a group. (XLSX 45 KB)

40168_2014_53_MOESM5_ESM.xlsx

Boosted file five: Table S5: Relative abundances of metabolites identified in breast milk by GC-MS. Relative abundance values were constructed by the Spectconnect program, which assigns the sample with the greatest concentration (elevation area) of each metabolite an arbitrary value. All other samples are so fabricated proportional to this value according to peak area. Values represent an average of ii technical replicates. *Metabolite identity confirmed by accurate standards. The metabolites referred to with numbers in the first column stand for un-annotated metabolites. The P values between the week 0 and chemotherapy groups were calculated using an unpaired t-test with Bonferroni correction. (XLSX 38 KB)

40168_2014_53_MOESM6_ESM.pdf

Additional file six: Figure S1: Comparison of relative abundance of metabolites in milk between week 0 and chemotherapy. The relative abundances of DHA, inositol and palmitic acid are shown above, with each point on the graph representing a different milk sample. DHA and inositol were significantly decreased past chemotherapy (unpaired t-test with Bonferroni correction, P < 0.05). While palmitic acid was also decreased by chemotherapy, these results were not statistically pregnant. Values represent an average of ii technical replicates. (PDF 53 KB)

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Urbaniak, C., McMillan, A., Angelini, M. et al. Effect of chemotherapy on the microbiota and metabolome of human milk, a instance written report. Microbiome 2, 24 (2014). https://doi.org/10.1186/2049-2618-2-24

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Keywords

  • 16S rRNA factor sequencing
  • Homo milk microbiome
  • Metabolome

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