Literature DB >> 29202044

Faecalibacterium Gut Colonization Is Accelerated by Presence of Older Siblings.

Martin Frederik Laursen1, Rikke Pilmann Laursen2, Anni Larnkjær2, Christian Mølgaard2, Kim F Michaelsen2, Hanne Frøkiær3, Martin Iain Bahl1, Tine Rask Licht1.   

Abstract

Faecalibacterium prausnitzii is a highly abundant human gut microbe in healthy individuals, but it is present at reduced levels in individuals with gastrointestinal inflammatory diseases. It has therefore been suggested to constitute a marker of a healthy gut and is associated with anti-inflammatory properties. However, factors affecting the colonization of F. prausnitzii in the human gut during early life are very poorly understood. By analysis of 16S rRNA amplicon sequencing data from three separate infant study populations, we determined the colonization dynamics of Faecalibacterium and factors affecting its establishment in the gut. We found that in particular, the presence of older siblings was consistently associated with Faecalibacterium gut colonization during late infancy and conclude that acquisition of Faecalibacterium is very likely to be accelerated through transfer between siblings. IMPORTANCEFaecalibacterium prausnitzii has been suggested to constitute a key marker of a healthy gut, yet the factors shaping the colonization of this highly oxygen-sensitive, non-spore-forming species in the intestinal environment remain poorly understood. Here, we provide evidence from three separate infant study populations that F. prausnitzii colonization in the gut happens during late infancy and is affected by the number of older siblings in the family. We conclude that Faecalibacterium acquisition is highly likely to be accelerated by contact between siblings. Bearing in mind the immunoregulatory properties of F. prausnitzii and the well-established protective effects against allergic disorders related to the presence of older siblings, early colonization of this species may have profound consequences for child health.

Entities:  

Keywords:  Faecalibacterium; infancy; siblings

Year:  2017        PMID: 29202044      PMCID: PMC5705805          DOI: 10.1128/mSphere.00448-17

Source DB:  PubMed          Journal:  mSphere        ISSN: 2379-5042            Impact factor:   4.389


OBSERVATION

Starting around birth, the infant gut is colonized by a bacterial community, which gradually increases in richness and diversity (1, 2). A number of factors, including mode of delivery, breastfeeding, and introduction of complementary feeding affect this colonization. After approximately 3 years, the gut microbiota of the child is comparable to that of an adult (3). Faecalibacterium prausnitzii, which is the sole species within the genus Faecalibacterium, is one of the most prevalent and abundant human gut microbes (4), and it has been suggested that it constitutes a marker of gut health due to its lower abundance in patients suffering from inflammatory bowel diseases compared to healthy controls and to its immunoregulatory properties (5, 6). Currently, very little is known about factors that impact the colonization of F. prausnitzii during early life and the putative contribution of this species to immune system development (7). Animal studies have shown that prior colonization with other microbes is necessary to obtain robust establishment of F. prausnitzii in germfree rodents (8, 9), and it has been suggested that depletion of oxygen in the gut or putatively another type of conditioning of the gut environment by other microbes is required for F.  prausnitzii to colonize the gut (7). The sensitivity of F. prausnitzii to low pH and bile salts explains the observation that it predominantly colonizes the distal gastrointestinal tract of humans (6), but it remains to be established how this highly oxygen-sensitive, non-spore-forming bacterium is transmitted between individuals and which factors affect its colonization process. We therefore analyzed 16S rRNA amplicon sequencing data from fecal samples originating from three previous studies of Danish infants entitled SKOT I (10), SKOT II (11), and ProbiComp (12), respectively. Comparison of Faecalibacterium abundance to a number of selected variables allowed identification of factors influencing colonization of Faecalibacterium. F. prausnitzii is not detected or found at very low levels during the first 4 to 6 months of life, but it increases rapidly during the first years (13–15). By investigation of three independent study populations, SKOT I (n = 114), SKOT II (n = 113), and ProbiComp (n = 255), we found that the relative abundance and prevalence of Faecalibacterium increased significantly during the period from late infancy (8 to 12 months old) to toddlerhood (14 to 19 months old) (Fig. 1A to F). In contrast to the SKOT infants, which were sampled as close as possible to 9  and 18 months of age, the infants in the ProbiComp study were sampled over a larger age span (Fig. 1G). This enabled correlation analysis between age and Faecalibacterium abundance and revealed a significant positive correlation at late infancy, but no such correlation at toddlerhood (Fig. 1H), suggesting that Faecalibacterium colonization reaches a plateau (Fig. 1I). To investigate how external factors and host variables may affect the relative abundance of Faecalibacterium during late infancy, we analyzed the association of its abundance with (i) gender, (ii) gestational age at birth, (iii) Caesarean section, (iv) breastfeeding, (v) pets, (vi) older siblings, and (vii) anthropometrics in all three infant study populations. Among these factors, only the presence of older siblings in the home was consistently correlated with the relative abundance of Faecalibacterium across the three study populations (Fig. 2A). Both the relative abundance and the prevalence of Faecalibacterium were higher during late infancy in infants with older siblings than in infants with no older siblings in the three populations (see Fig. S1 in the supplemental material). These differences were no longer present in toddlerhood, and Faecalibacterium was detected in almost all individuals (Fig. S1). Combination of the three data sets (n = 482) and stratification of individuals with no (n = 270), one (n = 156), or two or more (n = 56) older siblings revealed a stepwise increase in relative abundance (P < 0.0001 by the Kruskal-Wallis test) and prevalence (P for the trend [Pfortrend] of 0.0002 by the chi-square test) of Faecalibacterium with increasing number of older siblings during late infancy (Fig. 2B). Moreover, age-adjusted partial correlation analysis between relative abundance of Faecalibacterium during infancy and the number of older siblings revealed that this association was highly significant (rho = 0.23; P = 6.4 × 10−7; n = 482). The association was no longer present when the children became toddlers, suggesting that at this age, children will have acquired Faecalibacterium from other encounters. As infants with older siblings are putatively introduced earlier to solid foods, we investigated diet as a potential confounder, since we have previously found that diet influences the microbiota (11). By analysis of detailed dietary records obtained for the SKOT cohorts (11, 16), we found that transition to family foods (rho = 0.16; P = 0.018; n = 217) and dietary intake of rye bread (rho = 0.20; P = 0.003; P value for the corrected false-discovery rate [PFDRcorrected] of 0.07; n = 217), but not other dietary parameters, were associated with Faecalibacterium (Fig. S2). However, the association between relative abundance of Faecalibacterium and the number of older siblings persisted after adjustment for both transition to family foods (rho = 0.26; P = 0.0001; n = 217) and intake of rye bread (rho = 0.25; P = 0.0001; n = 217). Together, this shows that rather than gender, mode of delivery, breastfeeding, complementary diet, exposure to pets or anthropometric measures, it is the exposure to older siblings that predominantly affects Faecalibacterium acquisition during late infancy.
FIG 1 

Colonization dynamics of Faecalibacterium prausnitzii. (A to C) Mean relative abundance of Faecalibacterium in SKOT I, SKOT II, and ProbiComp during late infancy (8 to 12 months old) (green) and toddlerhood (14 to 19 months old) (blue). Values are means plus standard deviations (SD) (error bars). Mean values that are significantly different (P < 0.0001) by Mann-Whitney test are indicated by a bar and four asterisks. (D to F) Prevalence of Faecalibacterium in SKOT I, SKOT II, and ProbiComp during late infancy (green) and toddlerhood (blue). Mean values that are significantly different (P < 0.0001) by Fisher exact test are indicated by a bar and four asterisks. (G) Variation in age at the two sampling points (sampling point 1 [S1], late infancy; S2, toddlerhood) for SKOT I, SKOT II, and ProbiComp. Boxes indicate 25th to 75th percentiles, with median values marked as a line and whiskers indicating minimum and maximum values. The dotted line indicates the age that distinguishes late infancy from toddlerhood. (H) Spearman’s rank correlations of age versus relative abundance of Faecalibacterium during late infancy (green) and toddlerhood (blue) in ProbiComp. Red lines indicate robust nonlinear regression fit to the data points. (I) Proposed colonization dynamics of Faecalibacterium as a function of age based on data from this study and previous studies (13–15).

FIG 2 

Faecalibacterium colonization during late infancy is impacted by older siblings. (A) Heatmap of Spearman’s rank correlation coefficients between the relative abundance of Faecalibacterium and selected variables during late infancy in SKOT I, SKOT II, and ProbiComp. Statistical significance is indicated by asterisks as follows: **, P = 0.01; ***, P < 0.001. (B and C) Relative abundances (B) and prevalence of Faecalibacterium (C) in the combined data set (n = 482) during late infancy and toddlerhood stratified according to the number of older siblings. Mean values plus SD (error bars) are shown in panel B. Statistical significance is indicated by asterisks as follows: ****, P < 0.0001 by the Kruskal-Wallis test; ***, P < 0.001 by the chi-square test for trend.

(A to C) Mean relative abundance (plus SD) of Faecalibacterium in SKOT I, SKOT II, and ProbiComp during late infancy and toddlerhood stratified according to the presence of older siblings. Statistical significance is indicated by asterisks as follows: **, P = 0.01; ***, P < 0.001 by the Mann-Whitney test. (D to F) Prevalence of Faecalibacterium in SKOT I, SKOT II, and ProbiComp during late infancy and toddlerhood stratified according to the presence of older siblings. Statistical significance is indicated by asterisks as follows: *, P < 0.05; ***, P < 0.001 by the Fisher exact test. Download FIG S1, EPS file, 0.02 MB. Heatmap of Spearman’s rank correlation coefficients between relative abundance of Faecalibacterium during late infancy and dietary factors in SKOT I, SKOT II, and combined (SKOT I plus SKOT II). Statistical significance is indicated as follows: *, P < 0.05; #, FDR-corrected P value of 0.07. P values within food groups were FDR corrected for multiple testing. Download FIG S2, EPS file, 0.1 MB. Colonization dynamics of Faecalibacterium prausnitzii. (A to C) Mean relative abundance of Faecalibacterium in SKOT I, SKOT II, and ProbiComp during late infancy (8 to 12 months old) (green) and toddlerhood (14 to 19 months old) (blue). Values are means plus standard deviations (SD) (error bars). Mean values that are significantly different (P < 0.0001) by Mann-Whitney test are indicated by a bar and four asterisks. (D to F) Prevalence of Faecalibacterium in SKOT I, SKOT II, and ProbiComp during late infancy (green) and toddlerhood (blue). Mean values that are significantly different (P < 0.0001) by Fisher exact test are indicated by a bar and four asterisks. (G) Variation in age at the two sampling points (sampling point 1 [S1], late infancy; S2, toddlerhood) for SKOT I, SKOT II, and ProbiComp. Boxes indicate 25th to 75th percentiles, with median values marked as a line and whiskers indicating minimum and maximum values. The dotted line indicates the age that distinguishes late infancy from toddlerhood. (H) Spearman’s rank correlations of age versus relative abundance of Faecalibacterium during late infancy (green) and toddlerhood (blue) in ProbiComp. Red lines indicate robust nonlinear regression fit to the data points. (I) Proposed colonization dynamics of Faecalibacterium as a function of age based on data from this study and previous studies (13–15). Faecalibacterium colonization during late infancy is impacted by older siblings. (A) Heatmap of Spearman’s rank correlation coefficients between the relative abundance of Faecalibacterium and selected variables during late infancy in SKOT I, SKOT II, and ProbiComp. Statistical significance is indicated by asterisks as follows: **, P = 0.01; ***, P < 0.001. (B and C) Relative abundances (B) and prevalence of Faecalibacterium (C) in the combined data set (n = 482) during late infancy and toddlerhood stratified according to the number of older siblings. Mean values plus SD (error bars) are shown in panel B. Statistical significance is indicated by asterisks as follows: ****, P < 0.0001 by the Kruskal-Wallis test; ***, P < 0.001 by the chi-square test for trend. Study populations and samplings. Data from three infant study populations were included in the present study. The study protocol for ProbiComp was approved by the Committees on Biomedical Research Ethics for the Capital Region of Denmark (H-4-2014-032), and the study was registered at clinicaltrials.org (NCT02180581). The study protocols for the SKOT cohorts were approved by the Committees on Biomedical Research Ethics for the Capital Region of Denmark (H-KF-2007-0003 and H-3-2010-122). All parents signed a consent form. The ProbiComp study (n = 290) was a double-blind randomized placebo-controlled study where infants, aged 8 to 13 months and starting daycare within 12 weeks after the start of the study were randomly assigned to receive a combination of the two probiotics, BB-12 (Bifidobacterium animalis subsp. lactis BB-12 strain) and LGG (Lactobacillus rhamnosus GG), or placebo during the 6-month study period (17). Inclusion criteria were single birth and starting daycare at 8 to 14 months of age. Exclusion criteria were low birth weight (<2,500 g), gestational age at birth of <36 weeks, chronic illness, regular medication, antibiotics within 4 weeks prior to the start of the study, and non-Danish speaking parents. Fecal samples were obtained before (n = 265; age 8 to 13 months) and after (n = 210; age 14 to 19 months) the study and were stored at −80°C until DNA extraction and microbiota profiling as described previously (12). The fecal microbiotas were profiled in 255 and 201 samples before and after the study, respectively, and the treatment had no impact on the fecal microbiota (12). The SKOT cohort studies monitored children during the first 3 years of life with the overall aim to investigate relationships between early diet, growth development, and later disease risks. In SKOT I (18), infants from a random sample of mothers were recruited (n = 311), whereas in SKOT II (19), infants of obese mothers (body mass index [BMI] of >30 kg/m2) were recruited (n = 184). Inclusion criteria for both cohorts were single birth, full-term delivery, age of 9 months ± 2 weeks at the first visit, and absence of chronic illness. Participants in both cohorts were examined at 9 (±2 weeks), 18 (±4 weeks), and 36 (±12 weeks) months of age, and fecal samples were collected and stored at −80°C until DNA extraction. The fecal microbiota data from the first two sampling points in subsets of 114 (SKOT I) and 113 (SKOT II) infants have been published previously (11). In all study populations, fecal samples were freshly delivered on the morning of the visit or had been stored in the participant’s home, either in the freezer (−18°C) or in the fridge (4°C) for maximally 24 h before storage at −80°C. Information on gender, gestational age, Caesarean section, breastfeeding (months of breastfeeding only and the frequency of breastfeeding at sampling times), presence of older siblings and furred pets was collected from parental interviews at the sampling times in all three study populations. Anthropometrics were obtained at examinations and Z-scores were calculated as described previously (11, 17). Food records were obtained and analyzed in the SKOT studies as described previously (11, 19). DNA extraction and microbiota profiling. The procedures for DNA extraction, PCR amplification, and sequencing of the V3 region of the 16S rRNA gene have been described previously (11, 12). Briefly, DNA was extracted (PowerLyzer PowerSoil DNA isolation kit [catalog no. 12855-100; MoBio]), the V3 region of the 16S rRNA gene was amplified (Phusion High-Fidelity PCR kit [catalog no. F-553L; Thermo Fisher Scientific]) with sample-specific barcoded forward primers (PBU [5′-A-adapter-TCAG-barcode-CCTACGGGAGGCAGCAG-3′]) and the universal reverse primer (PBR [5′-trP1-adapter-ATTACCGCGGCTGCTGG-3′]) according to the following PCR program: (i) 30 s at 98°C; (ii) 24 cycles, with 1 cycle consisting of 15 s at 98°C and 30 s at 72°C; (iii) 5 min at 72°C. PCR products were purified (HighPrep PCR magnetic beads [catalog no. AC-60005; MAGBIO]), DNA quantities were measured (Qubit dsDNA [double-stranded DNA] HS [high-sensitivity] assay [catalog no. Q32851; Invitrogen]), and samples were pooled to obtain equimolar libraries, which were sequenced using the Ion OneTouch and Ion PGM (personal genome machine) platform with an Ion 318 Chip kit. Sequence data were analyzed as described previously for SKOT data (11) and ProbiComp data (12), and the levels of all sequences classified as Faecalibacterium by the RDP classifier, with a confidence threshold of 0.5 (20) against the Greengenes database v. 13.8 (21) were pooled within each sample and normalized to the total reads in that sample. Statistics. Mann-Whitney, Kruskal-Wallis, Spearman’s rank correlations, Fisher exact test, and chi-square tests were performed with the GraphPad Prims software (v. 7.0; GraphPad Software, Inc., La Jolla, CA). When indicated, P values were corrected for multiple testing using the false-discovery rate (FDR) (22). Partial Spearman’s rank correlation analysis adjusted for age or dietary parameters was performed in R (version 3.1.0; R Core Team. 2014. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria) using the ppcor package. Accession number(s). Sequencing data are deposited at NCBI Sequence Read Archive with the accession number SRP100762 under BioProject PRJNA360073 for ProbiComp study and accession number SRP052851 under BioProject PRJNA273694 for SKOT cohorts.
  21 in total

1.  Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB.

Authors:  T Z DeSantis; P Hugenholtz; N Larsen; M Rojas; E L Brodie; K Keller; T Huber; D Dalevi; P Hu; G L Andersen
Journal:  Appl Environ Microbiol       Date:  2006-07       Impact factor: 4.792

Review 2.  Faecalibacterium prausnitzii: from microbiology to diagnostics and prognostics.

Authors:  Mireia Lopez-Siles; Sylvia H Duncan; L Jesús Garcia-Gil; Margarita Martinez-Medina
Journal:  ISME J       Date:  2017-01-03       Impact factor: 10.302

3.  Determinants of blood glucose and insulin in healthy 9-month-old term Danish infants; the SKOT cohort.

Authors:  A L Madsen; L Schack-Nielsen; A Larnkjaer; C Mølgaard; K F Michaelsen
Journal:  Diabet Med       Date:  2010-12       Impact factor: 4.359

Review 4.  Faecalibacterium prausnitzii and human intestinal health.

Authors:  S Miquel; R Martín; O Rossi; L G Bermúdez-Humarán; J M Chatel; H Sokol; M Thomas; J M Wells; P Langella
Journal:  Curr Opin Microbiol       Date:  2013-07-03       Impact factor: 7.934

5.  Maternal obesity and offspring dietary patterns at 9 months of age.

Authors:  L B B Andersen; C B Pipper; E Trolle; R Bro; A Larnkjær; E M Carlsen; C Mølgaard; K F Michaelsen
Journal:  Eur J Clin Nutr       Date:  2014-12-03       Impact factor: 4.016

6.  Probiotics and Child Care Absence Due to Infections: A Randomized Controlled Trial.

Authors:  Rikke Pilmann Laursen; Anni Larnkjær; Christian Ritz; Hanne Hauger; Kim Fleischer Michaelsen; Christian Mølgaard
Journal:  Pediatrics       Date:  2017-07-03       Impact factor: 7.124

7.  Identification of metabolic signatures linked to anti-inflammatory effects of Faecalibacterium prausnitzii.

Authors:  Sylvie Miquel; Marion Leclerc; Rebeca Martin; Florian Chain; Marion Lenoir; Sébastien Raguideau; Sylvie Hudault; Chantal Bridonneau; Trent Northen; Benjamin Bowen; Luis G Bermúdez-Humarán; Harry Sokol; Muriel Thomas; Philippe Langella
Journal:  MBio       Date:  2015-04-21       Impact factor: 7.867

8.  Age-associated effect of kestose on Faecalibacterium prausnitzii and symptoms in the atopic dermatitis infants.

Authors:  Yasuhiro Koga; Shouji Tokunaga; Jun Nagano; Fuyuhiko Sato; Kenta Konishi; Takumi Tochio; Youko Murakami; Natsuko Masumoto; Jun-Ichirou Tezuka; Nobuyuki Sudo; Chiharu Kubo; Rumiko Shibata
Journal:  Pediatr Res       Date:  2016-08-16       Impact factor: 3.756

9.  Bacteroides thetaiotaomicron and Faecalibacterium prausnitzii influence the production of mucus glycans and the development of goblet cells in the colonic epithelium of a gnotobiotic model rodent.

Authors:  Laura Wrzosek; Sylvie Miquel; Marie-Louise Noordine; Stephan Bouet; Marie Joncquel Chevalier-Curt; Véronique Robert; Catherine Philippe; Chantal Bridonneau; Claire Cherbuy; Catherine Robbe-Masselot; Philippe Langella; Muriel Thomas
Journal:  BMC Biol       Date:  2013-05-21       Impact factor: 7.431

10.  Having older siblings is associated with gut microbiota development during early childhood.

Authors:  Martin Frederik Laursen; Gitte Zachariassen; Martin Iain Bahl; Anders Bergström; Arne Høst; Kim F Michaelsen; Tine Rask Licht
Journal:  BMC Microbiol       Date:  2015-08-01       Impact factor: 3.605

View more
  15 in total

1.  Effects of digested Cheonggukjang on human microbiota assessed by in vitro fecal fermentation.

Authors:  Vineet Singh; Nakwon Hwang; Gwangpyo Ko; Unno Tatsuya
Journal:  J Microbiol       Date:  2021-02-01       Impact factor: 3.422

2.  The establishment of the gut microbiota in 1-year-aged infants: from birth to family food.

Authors:  Mirco Vacca; Benedetta Raspini; Francesco Maria Calabrese; Debora Porri; Rachele De Giuseppe; Marcello Chieppa; Marina Liso; Rosa Maria Cerbo; Elisa Civardi; Francesca Garofoli; Hellas Cena; Maria De Angelis
Journal:  Eur J Nutr       Date:  2022-02-25       Impact factor: 4.865

3.  Contact with adult hen affects development of caecal microbiota in newly hatched chicks.

Authors:  Tereza Kubasova; Miloslava Kollarcikova; Magdalena Crhanova; Daniela Karasova; Darina Cejkova; Alena Sebkova; Jitka Matiasovicova; Marcela Faldynova; Alexandra Pokorna; Alois Cizek; Ivan Rychlik
Journal:  PLoS One       Date:  2019-03-06       Impact factor: 3.240

4.  Metagenomic growth rate inferences of strains in situ.

Authors:  Akintunde Emiola; Wei Zhou; Julia Oh
Journal:  Sci Adv       Date:  2020-04-22       Impact factor: 14.136

Review 5.  Composition and Function of Chicken Gut Microbiota.

Authors:  Ivan Rychlik
Journal:  Animals (Basel)       Date:  2020-01-08       Impact factor: 2.752

6.  Gut Microbiome Changes in Patients with Active Left-Sided Ulcerative Colitis after Fecal Microbiome Transplantation and Topical 5-aminosalicylic Acid Therapy.

Authors:  Dagmar Schierová; Jan Březina; Jakub Mrázek; Kateřina Olša Fliegerová; Simona Kvasnová; Lukáš Bajer; Pavel Drastich
Journal:  Cells       Date:  2020-10-13       Impact factor: 6.600

7.  Early Life Microbiota Colonization at Six Months of Age: A Transitional Time Point.

Authors:  Benedetta Raspini; Mirco Vacca; Debora Porri; Rachele De Giuseppe; Francesco Maria Calabrese; Marcello Chieppa; Marina Liso; Rosa Maria Cerbo; Elisa Civardi; Francesca Garofoli; Maria De Angelis; Hellas Cena
Journal:  Front Cell Infect Microbiol       Date:  2021-03-26       Impact factor: 5.293

8.  The Alteration of Intestinal Microbiota Profile and Immune Response in Epinephelus coioides during Pathogen Infection.

Authors:  Joan Tang Xiao Joe; Yung-Che Tseng; Jen-Leih Wu; Ming-Wei Lu
Journal:  Life (Basel)       Date:  2021-01-28

9.  Infant Skin Bacterial Communities Vary by Skin Site and Infant Age across Populations in Mexico and the United States.

Authors:  Alejandra Núñez-de la Mora; Katherine R Amato; Melissa B Manus; Sahana Kuthyar; Ana Gabriela Perroni-Marañón
Journal:  mSystems       Date:  2020-11-03       Impact factor: 6.496

10.  Effects of Different Human Milk Oligosaccharides on Growth of Bifidobacteria in Monoculture and Co-culture With Faecalibacterium prausnitzii.

Authors:  Lianghui Cheng; Mensiena B G Kiewiet; Madelon J Logtenberg; Andre Groeneveld; Arjen Nauta; Henk A Schols; Marthe T C Walvoort; Hermie J M Harmsen; Paul de Vos
Journal:  Front Microbiol       Date:  2020-10-30       Impact factor: 5.640

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.