Depletion of Faecalibacterium prausnitzii in the gut microbiota of children diagnosed with cancer

doi:10.21203/rs.3.rs-1214620/v1

PDF

Research Article

Depletion of Faecalibacterium prausnitzii in the gut microbiota of children diagnosed with cancer

https://doi.org/10.21203/rs.3.rs-1214620/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted 17 Mar, 2022

You are reading this latest preprint version

Background: A beneficial composition of the microbiome is essential for maintaining human health and to protect the body against pathogens. The identification of microbes with oncogenic potential suggests a possible link between changes in the gastrointestinal microbiome composition and the onset of cancer. Childhood cancer is still the predominant cause of death among children over a year in Western countries. However, a profound understanding of the changes in the composition of the microbiome in the onset or progression of childhood cancer is still lacking.

Methods: We characterized the gut microbiota in a cohort of Norwegian children diagnosed with cancer using fecal samples before the onset of treatment. Samples were collected in the Norwegian Childhood Cancer Biobank (NCCB) and compared to a control group of children attending two diverse kindergartens in Oslo, Norway. The data of 16S rRNA gene amplicon sequencing were analyzed by non-metric multidimensional scaling (NMMS) of amplicon sequence variants (ASVs) relative abundance data.

Results: Our results revealed 11 prevalent 16S rRNA gene sequence variants with significantly differential occurrence between the patient and the control groups. Eight of these taxa were depleted in children diagnosed with cancer, with three of these eight classified as Faecalibacterium prausnitzii.

Conclusions: Our observations indicate that the gut microbiota may play an important role in understanding the physiological changes associated with childhood cancer. However, further studies are required to validate if variations in the microbial community could be used as an early indicator of the presence of cancer in the future.

Cancer

children

fecal

gastrointestinal

gut

microbiome

and microbiota.

The adult human gastrointestinal microbiome is a stable and complex community comprised of trillions of microorganisms [1] (Cho and Blaser 2012). The establishment of the microbial community in infants is widely recognized as a fundamental developmental process [2–5] (Tamburini et al. 2016, Bokolichet al 2016, Chu et al. 2017). Humans are born with an essentially sterile gastrointestinal tract, microbial colonization starts during birth, and stabilizes in an adult-like structure around three years of age [2] (Milani et al. 2017).

The human gut microbiome plays several essential roles for health and physiology for the host, including influencing the development of the immune system, protection from pathogens, and the production of vitamins and short-chain fatty acids. It has been suggested by [6] (Levy et al. 2017), that the gut microbiome may be influenced by host factors that could be used to indicate gut dysbiosis. In the case of Ulcerative colitis (UC) and Crohn’s disease (CD), shifts in the bacterial community with depletion of Feacalibacterium prausnitzii have been found [7] (Zhang et al., 2019). Similar shifts have been observed in colon cancer [8] (Lopez-Siles et al., 2016).

The link between cancer and the microbiome can be causal, as some microbes are considered oncogenic [9] (Chang and Parsonnet, 2020). It is still unclear, whether the general composition of the gut microbiota can influence the development of cancer [10] (Zitvogel et al., 2017), e.g. if certain configurations can render higher individual susceptibility to the disease. It is possible that the composition of the gut microbiota is altered by changes in the host physiology resulting from cancer, with important implications for the gut environment, and thus for host health and treatment prospects.

Childhood cancer is still the predominant cause of death among children over a year in Western countries despite significantly improved treatment and increased knowledge of the molecular mechanisms of various cancers. Pediatric cancers are entirely different in comparison to adult cancers, in terms of frequencies, clinical behavior, and the potential factors involved in the onset of the disease. Current knowledge about the role of the gut microbiome in childhood cancer is scarce [11] (Bai et al. 2018). It has, however, been shown that the gastrointestinal microbial community differs between pediatric/adolescent Acute Lymphomatic Leukemia (ALL) patients versus a healthy control group at the time of diagnosis [12] (Rajagopala et al. 2016), indicating that the gastrointestinal microbiome may play a role in the establishment and progression of the disease.

To our knowledge, this is one of the first studies investigating the gut microbiota of children diagnosed with cancer. We collected fecal samples from patients diagnosed with cancer prior to the beginning of the treatment regime. In addition, as normal controls, we obtained fecal samples from children attending two different kindergartens in Oslo. We performed 16S rRNA gene amplicon sequencing of the sample set in order to look for differences in the gut microbiota composition between the children with cancer versus the healthy controls.

Participants

Fecal samples from children diagnosed with cancer (n = 48) were collected and withdrawn from the Norwegian Childhood Cancer Biobank (NCCB), where biological samples are collected prior to diagnosis and stored according to standard protocols [13] (Hermansen et al 2021).

Fecal samples from healthy children were collected as part of another study during the first half of 2020, in order to investigate the effects of environmental exposure on the microbiome in children. One subpopulation included children (age 3–5 years) attending a traditional Norwegian kindergarten (n = 10), and the other subpopulation was based on samples from children spending their time in a forest kindergarten (age 2–5 years). A major factor that differentiates a traditional kindergarten from a forest kindergarten is the amount of time spent outdoors and in close contact with nature. Both populations of children (n = 15) resided in Oslo and exhibited no obvious signs of physical ailments or cognitive deficits. Fecal samples were collected at home by the parents or at kindergarten by the teachers and preserved in 95% ethanol.

DNA extraction and sequencing

Fecal DNA was extracted using the MagAttract PowerSoil DNA KF kit (Qiagen) according to the manufacturer’s instructions. Library preparation for DNA sequencing was carried out as previously described [14] (de Muinck et al. 2017), targeting the V4 region of the 16S rRNA gene with the 515f-806r primer pair. 2x300 paired-end sequencing was performed using the MiSeq platform at the Norwegian Sequencing Centre.

Data analysis and statistics

Sequence read demultiplexing was carried out using a custom routine developed at NSC (https://github.com/nsc-norway/triple_index-demultiplexing). Further sequence data processing was performed using the Divisive Amplicon Denoising Algorithm as implemented in the dada2 v1.16 R-package [15] (Callahan et al. 2016). Nine samples were excluded from further analysis in the childhood cancer cohort due to labeling issues, and six were removed due to low read numbers, leaving 33 samples for analyses. The total number of sequences for these samples, after filtering, pair merging, and chimera removal, was 3,428,605 (mean 59,114 ± 29,457 s.d.). Taxonomic classification of amplicon sequence variants (ASVs) was done using the Ribosomal Database Project v16 training set [16] (Cole et al. 2014). Singleton ASVs were removed from the data, as were ASVs that did not receive a reliable taxonomic assignment at the phylum level. This resulted in a total of 1224 ASVs (mean 124 ± 48 s.d.).

The following R packages were used: PERMANOVA was carried out using the adonis2 function in the Vegan v2.5.6, using Bray-Curtis distances. The random forest model was computed using random Forest v4.6.14 [17] (Ho, 1995). Significance of the random forest model was calculated using rfUtilities v2.1.5. Differential occurrence analysis of ASVs based on the negative binomial distribution was carried out using DESeq2 v1.28.1 [18] (Love 2014), with reported p-values subjected to Benjamini-Hochberg correction for multiple hypothesis testing. For DESeq2 testing, we wanted to focus on prevalent ASVs that occurred in several individuals. Therefore, the count data was filtered to only include ASVs that occurred at a minimum of 100 sequence reads in a minimum of five individuals, leaving 133 ASVs.

Of the 33 patients included in the final analyses, 12 had been diagnosed with tumors of the central nervous system (CNS), 13 with solid tumors (ST), and eight with leukemia. The cohort of patients included children within an age range from newborn to 17 years. In particular in the ST group was the median age two years (inter quartile range 0–11) relative to 4.5 (3.5–6.5) for leukemia and 9.5 (3–15) for CNS tumors. An initial non-metric multidimensional scaling (NMDS) analysis demonstrated the large effect age had on the microbiota in this cohort (Figure S1). [19] Yatsunenko et al. (2012) and others [20] (Stewart et al. 2018) have reported that the gut microbiota develops an adult composition by three years of age. Thus, we divided the patients into those less than three years old and those three years or older. A PERMANOVA model including both, diagnosis and age group, did not demonstrate any significant effects on diagnosis (p = 0.27), while age group was highly significant (p < 0.001). Diversity was significantly reduced (Figures S2a and S2b) in the patients younger than three years, both relative to those three years and older and healthy controls, in terms of species richness and Shannon entropy (p < 0.002 for all comparisons, Wilcoxon rank sum test). This result was in line with the expectations [19] (e.g. Yatsunenko et al. 2012). Thus, in order to carry out a more realistic comparison with our kindergarten controls, we excluded children younger than three years (11 in total) from our patient cohort in the following analyses. Within the patients of age three years and over, there was no significant effect of age on the microbiota (p = 0.57, PERMANOVA). Furthermore, all patients will henceforth be treated as a single group in the comparisons with the control group (in the control group, none of the children was below three years).

Children diagnosed with cancer had a significantly different microbiota composition (R² = 0.09, p < 0.001, PERMANOVA) compared to the microbiotas of the cohort of children sampled from the kindergartens (Fig. 1). This result was confirmed by a machine learning approach using a random forest classification model. This resulted in a classification accuracy of 97.87%, with only one misclassified sample out of 47 (p < 0.001, permutation test).

We observed a marginally higher Shannon entropy in the patient group relative to controls (p < 0.046, Wilcoxon rank sum test, Figure S2b), while ASV richness did not differ significantly between the groups (Figure S2a). At the phylum level, we observed an increased mean relative abundance of Actinobacteria in the patient group (p = 0.028, Wilcoxon rank sum test) (Figure S3), but no other significant differences.

ASV level analysis using DESeq2 identified 11 ASVs with significantly (Benjamini-Hochberger corrected p < 0.05) differential occurrence between the patient and the control group (Table 1). Eight of these ASVs were depleted in children diagnosed with cancer. Strikingly, three of these eight were classified as Faecalibacterium prausnitzii (ASV2, ASV3, and ASV6), and all three were among the four ASVs with the highest significance for differential occurrence. The combined relative abundance of these three ASVs were significantly higher in the control cohort (p < < 0.001) (Fig. 2). All three groups were highly prevalent in the data (Figures S4a, b, and c) and additional testing confirmed the DESeq2 results (Wilcoxon rank sum test p < 0.001 for all comparisons). No significant relationship was observed between the relative abundance of these three ASVs and the age parameter in the patient group (Figures S5a, b, and c). Furthermore, all three ASVs were found in both, the patients and the controls. The other five ASVs depleted in patients were Haemophilus parainfluenzae, Alistipes finegoldii, and Sutterella wadsworthiensis, as well as two members of the Clostridium IV and XIVa clusters. The three ASVs that were significantly overrepresented in the patient group, were Prevotella copri, Dorea longicatena, and a member of the Clostridium XIVa clustered.

ASV2, ASV3, and ASV6 were also variables of high importance for successful discrimination between the patients and the controls in the random forest model (Figures S6a and b). The random forest model also identified the following short chain fatty acid producing taxa as being among the top 20 variables of highest importance for successful classification: Roseburia intestinalis, Ruminococcus, Anaerostipes hadrus, Roseburia, Coprococcus comes, Intestinimonas, Lachnospiracea, Roseburia inulinivorans, and Blautia faeces.

Outline of the study

We characterized the fecal microbiota in a cohort of children diagnosed with cancer, before starting treatment, as well as in 25 healthy children attending kindergarten. The cohort of patients included children ranging from newborn to 17 years of age. While the overall age effect within that cohort was large, excluding children younger than three years removed this effect. Previous work has shown that the gut microbiota tends to stabilize at an adult-like stage by three years of age [19, 20] (Yatsunenko et al., 2012, Stewart et al., 2018). Thus, while not optimal, removing the youngest children from the patient cohort should largely account for age differences relative to controls.

Differences in the sampling procedure

Sampling procedure was different in the patient and the control group. Specifically, the samples from the kindergarten cohorts were collected at home by the parents or at kindergarten by the teachers into tubes containing 95% ethanol. Samples from the cohort diagnosed with cancer were collected at the hospital and frozen directly upon sampling, without any kind of preservation medium. In a study analyzing 1200 samples, the authors found that these preservation methods did not significantly interfere with the biological signature of the samples [21] (Song et al., 2016). A second study by [22] (Moossavi et al. 2019), found consistent microbiota profiles in samples preserved in ethanol and by freezing. This study also did not observe discernible differences in Faecalibacterium abundances. Similar conclusions have been drawn in other studies [23] (Marotz et al. 2021), [24] (Byrd et al. 2019) [25] (Blekhman et al. 2016).

Decreased relative abundance of Feacalibacterium prausnitzii in the diagnosed cohort

The most significant result was the depletion of three abundant ASVs classified as Faecalibacterium prausnitzii in children diagnosed with cancer. The depletion was highly significant for all three strains, and overall F. prausnitzii was decreased by a factor of four in patients relative to the controls. F. prausnitzii is a dominant member of the human microbiota and its occurrence in the gut correlates negatively with health outcomes, like inflammatory bowel disease and colorectal cancer [8, 26] (Lopez-Siles 2016, 2017). Due to its high butyrate production and anti-inflammatory properties, it is considered to present a promising candidate for probiotic formulations [27] (Ferreira-Halder et al. 2017). Short chain fatty acids (acetate, propionate and butyrate) (Reviewed in [28] Blaak et al., 2020) are produced by bacteria in the colon and are important in vital gut processes, like gastrointestinal motility and energy utilization. For example, butyrate is the main nutrient source for colonocytes, and it has been shown to have protective properties against colorectal cancer [29] (Archer et al., 1998). Furthermore, butyrate can reduce inflammation of the intestinal mucosa through regulation of immune signaling (Reviewed in [30] Deleu et al., 2021). F. prausnitzii is one of the most important butyrate producers in the gastrointestinal tract and thus of high interest in biomedical research. Modulation of this species, as well as other SCFA producers, may have consequences for treatment efficacy in cancer. Here in this study, several other short chain acid producing taxa were identified as important for classification of microbiota profiles into the patient and control group. We would like to point out that the present study is a small pilot, with imperfect age matching between patients and controls, and the results presented here should be substantiated in larger scale studies.

Our results indicate that changes in the microbial community involved in short chain fatty acid production in the gut may function as an early indicator of the presence of cancer. In particular, the butyrate producing F. prausnitzii was depleted by a factor of four in the patient group. F. prausnitzii depletion has also been reported in gastrointestinal conditions, like inflammatory bowel disease [26] (Lopez-Siles 2017 ISMEJ), indicating that this species could be a more general indicator of disease-associated dysbiosis. From the data presented here, it is unclear, whether F. prausnitzii depletion results from cancer, or if a dysbiotic microbiome can actually play a role in the development of disease. Large prospective studies are needed to distinguish between these two scenarios.

ALL, Acute Lymphomatic Leukemia; ASV, amplicon sequence variant; CD, Crohn’s disease; F. prausnitzii, Faecalibacterium prausnitzii; NCCB, Norwegian Childhood Cancer Biobank; CNS, central nervous system; NMDS, non-metric multidimensional scaling; ST, solid tumor; and UC, Ulcerative colitis.

Ethics approval and consent to participate

The presented project was a retrospective case control study, approved by the Regional Committee for Medical and Health Research Ethics (REK nr: 2018/2491 and 2019/66945). For all samples, informed consent was provided from all subjects and/or their legal guardian(s) and all methods were performed in accordance with the relevant guidelines and regulations.

Consent for publication

'Not applicable'.

Availability of data and materials

DNA sequence data are available in the NCBI Sequence Read Archive under the accession ID PRJNA814879.

Competing interests

The authors declare that they have no competing interests.

Funding

For collection and handling of the samples provided by the NCCB, we received funding from The Norwegian Childhood Cancer Society, the foundations of the Oslo University Hospital (Fondsstiftelsen and Barnestiftelsen).

For the kindergarten study, we received funding from the U.S.-Norway Fulbright Foundation. The funding bodies had no direct influence in the design of the study and collection, analysis, or interpretation of data or in writing the manuscript.

Author's contributions

LOB and MCMK initiated the overall study. LOB and MCMK conceived and designed the childhood cancer, EJdM, PT, NN, and PJF the control study. EJdM, PT, NN, PJF, VMS, and JUH tested and established various methods for the study. NR and JUH collected, prepared and managed the samples from children diagnosed with cancer, EJdM, NN, and PJF from children of the control study from Norwegian kindergartens. EJdM, PT, and VMS performed DNA extraction and sequencing. EJdM and PT established the analysis pipeline and carried out the data analysis. EJdM, PT, MCMK, and LOB interpreted the data. EJdM and PT wrote the draft of the manuscript, JUH, MCMK, NN, and PFJ critical reviewed the manuscript, and EJdM and LOB finalized it. All authors read and approved the final manuscript.

Acknowledgements

We wish to thank all participants, their parents, and the kindergarten teachers for their help and valuable contributions.

Cho I, Blaser MJ: The human microbiome: at the interface of health and disease. Nat Rev Genet 2012, 13(4):260–270.
Milani C, Duranti S, Bottacini F, Casey E, Turroni F, Mahony J, Belzer C, Delgado Palacio S, Arboleya Montes S, Mancabelli L et al: The First Microbial Colonizers of the Human Gut: Composition, Activities, and Health Implications of the Infant Gut Microbiota. Microbiol Mol Biol Rev 2017, 81(4).
Tamburini S, Shen N, Wu HC, Clemente JC: The microbiome in early life: implications for health outcomes. Nat Med 2016, 22(7):713–722.
Bokulich NA, Chung J, Battaglia T, Henderson N, Jay M, Li H, A DL, Wu F, Perez-Perez GI, Chen Y et al: Antibiotics, birth mode, and diet shape microbiome maturation during early life. Sci Transl Med 2016, 8(343):343ra382.
Chu DM, Ma J, Prince AL, Antony KM, Seferovic MD, Aagaard KM: Maturation of the infant microbiome community structure and function across multiple body sites and in relation to mode of delivery. Nat Med 2017, 23(3):314–326.
Levy M, Kolodziejczyk AA, Thaiss CA, Elinav E: Dysbiosis and the immune system. Nat Rev Immunol 2017, 17(4):219–232.
Zhang Q, Wu Y, Wang J, Wu G, Long W, Xue Z, Wang L, Zhang X, Pang X, Zhao Y et al: Accelerated dysbiosis of gut microbiota during aggravation of DSS-induced colitis by a butyrate-producing bacterium. Sci Rep 2016, 6:27572.
Lopez-Siles M, Martinez-Medina M, Suris-Valls R, Aldeguer X, Sabat-Mir M, Duncan SH, Flint HJ, Garcia-Gil LJ: Changes in the Abundance of Faecalibacterium prausnitzii Phylogroups I and II in the Intestinal Mucosa of Inflammatory Bowel Disease and Patients with Colorectal Cancer. Inflamm Bowel Dis 2016, 22(1):28–41.
Chang AH, Parsonnet J: Role of bacteria in oncogenesis. Clin Microbiol Rev 2010, 23(4):837–857.
Zitvogel L, Daillere R, Roberti MP, Routy B, Kroemer G: Anticancer effects of the microbiome and its products. Nat Rev Microbiol 2017, 15(8):465–478.
Bai J, Behera M, Bruner DW: The gut microbiome, symptoms, and targeted interventions in children with cancer: a systematic review. Support Care Cancer 2018, 26(2):427–439.
Rajagopala SV, Yooseph S, Harkins DM, Moncera KJ, Zabokrtsky KB, Torralba MG, Tovchigrechko A, Highlander SK, Pieper R, Sender L et al: Gastrointestinal microbial populations can distinguish pediatric and adolescent Acute Lymphoblastic Leukemia (ALL) at the time of disease diagnosis. BMC Genomics 2016, 17(1):635.
Hermansen JU, Wojcik DM, Robinson N, Pahnke J, Haugland HK, Jamtoy AH, Flaegstad T, Halvorsen H, Lund B, Baumbusch LO et al: The Norwegian childhood cancer biobank. Cancer Rep (Hoboken) 2021:e1555.
de Muinck EJ, Trosvik P, Gilfillan GD, Hov JR, Sundaram AYM: A novel ultra high-throughput 16S rRNA gene amplicon sequencing library preparation method for the Illumina HiSeq platform. Microbiome 2017, 5(1):68.
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP: DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 2016, 13(7):581–583.
Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, Brown CT, Porras-Alfaro A, Kuske CR, Tiedje JM: Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res 2014, 42(Database issue):D633-642.
Ho TK: Random Decision Forests. In Proceedings of 3rd international conference on document analysis and recognition 1995, Vol. 1:278–282.
Love MI, Huber W, Anders S: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014, 15(12):550.
Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG, Contreras M, Magris M, Hidalgo G, Baldassano RN, Anokhin AP et al: Human gut microbiome viewed across age and geography. Nature 2012, 486(7402):222–227.
Stewart CJ, Ajami NJ, O'Brien JL, Hutchinson DS, Smith DP, Wong MC, Ross MC, Lloyd RE, Doddapaneni H, Metcalf GA et al: Temporal development of the gut microbiome in early childhood from the TEDDY study. Nature 2018, 562(7728):583–588.
Song SJ, Amir A, Metcalf JL, Amato KR, Xu ZZ, Humphrey G, Knight R: Preservation Methods Differ in Fecal Microbiome Stability, Affecting Suitability for Field Studies. mSystems 2016, 1(3).
Moossavi S, Engen PA, Ghanbari R, Green SJ, Naqib A, Bishehsari F, Merat S, Poustchi H, Keshavarzian A, Malekzadeh R: Assessment of the impact of different fecal storage protocols on the microbiota diversity and composition: a pilot study. BMC Microbiol 2019, 19(1):145.
Marotz C, Cavagnero KJ, Song SJ, McDonald D, Wandro S, Humphrey G, Bryant M, Ackermann G, Diaz E, Knight R: Evaluation of the Effect of Storage Methods on Fecal, Saliva, and Skin Microbiome Composition. mSystems 2021, 6(2).
Byrd DA, Chen J, Vogtmann E, Hullings A, Song SJ, Amir A, Kibriya MG, Ahsan H, Chen Y, Nelson H et al: Reproducibility, stability, and accuracy of microbial profiles by fecal sample collection method in three distinct populations. PLoS One 2019, 14(11):e0224757.
Blekhman R, Tang K, Archie EA, Barreiro LB, Johnson ZP, Wilson ME, Kohn J, Yuan ML, Gesquiere L, Grieneisen LE et al: Common methods for fecal sample storage in field studies yield consistent signatures of individual identity in microbiome sequencing data. Sci Rep 2016, 6:31519.
Lopez-Siles M, Duncan SH, Garcia-Gil LJ, Martinez-Medina M: Faecalibacterium prausnitzii: from microbiology to diagnostics and prognostics. ISME J 2017, 11(4):841–852.
Ferreira-Halder CV, Faria AVS, Andrade SS: Action and function of Faecalibacterium prausnitzii in health and disease. Best Pract Res Clin Gastroenterol 2017, 31(6):643–648.
Blaak EE, Canfora EE, Theis S, Frost G, Groen AK, Mithieux G, Nauta A, Scott K, Stahl B, van Harsselaar J et al: Short chain fatty acids in human gut and metabolic health. Benef Microbes 2020, 11(5):411–455.
Archer S, Meng S, Wu J, Johnson J, Tang R, Hodin R: Butyrate inhibits colon carcinoma cell growth through two distinct pathways. Surgery 1998, 124(2):248–253.
Deleu S, Machiels K, Raes J, Verbeke K, Vermeire S: Short chain fatty acids and its producing organisms: An overlooked therapy for IBD? EBioMedicine 2021, 66:103293.

Table 1 is available in the Supplemental Files section.

No competing interests reported.

DEseqtab.xlsx
Table 1. Amplicon sequence variants (ASVs) level analysis using DESeq2 identified 11 ASVs with significant differential occurrence between the patient and the control group (Benjamini-Hochberger corrected p<0.05).
Supplementv03.docx
Supplementary figure 1. Non-metric multidimensional scaling (NMMS) of amplicon sequence variants (ASVs) relative abundance data, based on Bray-Curtis between-sample distances, for the entire patient group. In the plot, each sample is represented by a number indicating the age (in years) of each patient at the time of sampling. The specific diagnosis is indicated by color. Supplementary figure 2. ASV richness (a) and Shannon entropy (b) in the healthy control group (Ctrl), patients three years or older (>=3), and patients younger than three years (<3). (Black lines inside boxes are medians, boxes represent the interquartile range, and the whiskers represent 1.5 times the interquartile range). Supplementary figure 3. Relative abundance of the main bacterial phyla in the patient (three years or older) and the control group. Supplementary figure 4. Relative abundances of ASV2 (a), ASV3 (b), and ASV6 (c). These ASVs were classified as Feacalibacterium prausnitzii and were found to be significantly depleted in the patient group relative to the healthy controls. (Black lines indicate means, yellow dots represent the individual relative abundances of the controls, and blue dots represent the individual relative abundances in the children three years of age and older in the diagnosed group). Supplementary figure 5. Relative abundances of ASV2 (a), ASV3 (b), and ASV6 (c) as a function of age in the patient group. These ASVs were classified as Feacalibacterium prausnitzii and were found being significantly depleted in the patient group relative to the healthy controls. There was no significant relationship between abundance and age for any of these ASVs (linear models). Supplementary figure 6. Top twenty most important variables (ASVs) for discriminating between patients and controls in the Random forest classification model, as measured by (a) mean decrease in accuracy and (b) mean decrease in Gini coefficient. In (a) the ASVs classified as F. prausnitzii are ASV2 and 6, respectively (from most to less important). In (b) the ASVs classified as F. prausnitzii are ASV2 6 and 3, respectively.

PDF

Version 1

posted 17 Mar, 2022

You are reading this latest preprint version

Depletion of Faecalibacterium prausnitzii in the gut microbiota of children diagnosed with cancer

Status:

Version 1

Abstract

Figures

Background

Methods

Participants

DNA extraction and sequencing

Data analysis and statistics

Results

Discussion

Outline of the study

Differences in the sampling procedure

Conclusions

Abbreviations

Declarations

References

Table

Competing Interests

Supplementary Files

Status:

Version 1