Fluent Essays

The guidelines is given below, and there are three article about nutrition, you are required to use AMA style, follow the guidelines strictly, that's all, the requirements are in the guideline plz read guideline and those three paper carefully, otherwise I will not give a credit to the paper NitN: Sample Final Paper NUT11 Word count: 792 Anemic? An Iron Fish Might Be What All You Need Approximately 30% of the world’s population suffers from anemia.1 Anemia is a condition where the body does not have enough of the protein called hemoglobin,2 which is the part of red blood cells that carries oxygen to the tissues in our body.1 As a result, individuals with anemia often experience tiredness, headaches, decreased cognitive abilities, impaired immunity, and poor pregnancy outcomes.2 Iron deficiency is the most common cause of anemia1—referred to as iron- deficiency anemia (IDA)—and most commonly affects women and children.2 Typically anemia is treated with iron tablets; however, getting tablets to poor and remote villages is difficult and costs money. Furthermore, many women find that they feel nauseated when taking iron pills, which may discourage them from taking the supplement. Almost 50% of the women in rural Cambodian villages experience anemia, but treatment with iron supplements was unsuccessful.1 In the 1980s, researchers showed that cooking in iron pots would increase the amount of iron in the food that was consumed,2 which could help prevent IDA. As reported in the news recently, a Canadian scientist had the idea to use a lump of iron in place of iron cookware. After many iterations of the iron-ingot, scientists finally settled on the shape of a fish because it is a symbol of luck in Cambodian culture, and the Lucky Iron Fish project was born.1 They claim that the Lucky Iron Fish can be used to treat anemia in these Cambodian villages. When the ingot is boiled in water or soup for at least 10 minutes some iron leaches into the liquid. Then the iron fish is removed and lemon juice is to the food to help the body absorb the iron when the food is consumed. They claim that using the Lucky Iron Fish every day would provide enough iron to meet 75% of the iron that a woman should consume. The news reports that several hundred Cambodian NitN: Sample Final Paper NUT11 women tested the ingot for a year, and at the end half of the women who used the ingot weren’t anemic anymore. They further claim that the ingot is better than supplements. In the original research that tested the effectiveness of the Lucky Iron Fish, the researchers actually had three different groups for their year-long study: iron-ingot, iron-ingot with basic nutrition education, or a control that received neither.2 Ultimately, the researchers grouped both of the iron-ingot groups together because they found that the nutrition lessons didn’t have an effect, likely because the women that received the nutrition education couldn’t afford to purchase the foods that the lessons encouraged them to eat. One critical piece of information that the news failed to mention is that the women needed to add an acidifier, such as a teaspoon of citrus juice, while boiling the ingot in plain water in order for water to make the ingot leach iron. Many Cambodian soups contain acidic fruits, so additional juice isn’t needed in this case. Even though the Lucky Iron Fish was tested in a smaller group that the news article reported, it actually seems to work better than the news suggested. In the control group, the prevalence of anemia and IDA, which was measured using blood tests, remained similar from the start to the end of the study. In the groups that received the iron-ingot, the prevalence of anemia dropped from 57% to 11% and the prevalence of IDA dropped from 13% to 2%. The original study didn’t directly compare to supplements, so we don’t know if the iron-ingot works better than supplements for alleviating deficiency. Nevertheless, the researchers didn’t notice any negative side effects, which is a benefit over supplements. The prevalence of anemia in women in developing nations ranges from 11% in Nicaragua to 50% in Côte d’Ivoire.3 However, anemia can also be caused by other micronutrient deficiencies, such as vitamins A and B12 or folate, repeated infections, or malaria.1 Additionally, the inflammation that results from infection inhibits the absorption of iron,3 which may increase iron requirements of individuals living in regions with a high burden of infection. Thus, before deciding NitN: Sample Final Paper NUT11 whether to use the Lucky Iron Fish, health officials need to consider both the amount of anemia that can be attributed to iron deficiency and the level of exposure to inflammation in a given region. In countries with a high prevalence of IDA and low to moderate infection rates, the Lucky Iron Fish could be a cost effective and successful method of treating IDA. However, the Iron Fish may not be as effective in countries with high infection rates or multiple micronutrient deficiencies. In my opinion, the news reporter could have added a bit more about the limitations of the Lucky Iron Fish; however, the rest of the information provided in the news article was accurate. References 1. Roxby P. Why an iron fish can make you stronger. BBC News. http://www.bbc.com/news/health-32749629. Published May 17, 2015. Accessed July 29, 2017. 2. Charles CV, Dewey CE, Hall A, Hak C, Channary S, Summerlee AJS. A Randomized Control Trial Using a Fish-Shaped Iron Ingot for the Amelioration of Iron Deficiency Anemia in Rural Cambodian Women. Tropical Medicine & Surgery. 2015; 3(3): 195. DOI: 10.4172/2329-9088.1000195. 3. Petry N, Olofin I, Hurrell RF, et al. The Proportion of Anemia Associated with Iron Deficiency in Low, Medium, and High Human Development Index Countries: A Systematic Analysis of National Surveys. Nutrients. 2016; 8: 693. DOI: 10.3390/nu8110693. ARTICLE Received 1 Dec 2015 | Accepted 13 May 2016 | Published 24 Jun 2016 A key genetic factor for fucosyllactose utilization affects infant gut microbiota development Takahiro Matsuki1,*, Kana Yahagi1,*, Hiroshi Mori2,*, Hoshitaka Matsumoto1,*, Taeko Hara1, Saya Tajima1, Eishin Ogawa3, Hiroko Kodama3, Kazuya Yamamoto2, Takuji Yamada2, Satoshi Matsumoto1,** & Ken Kurokawa2,4,5,** Recent studies have demonstrated that gut microbiota development influences infants’ health and subsequent host physiology. However, the factors shaping the development of the microbiota remain poorly understood, and the mechanisms through which these factors affect gut metabolite profiles have not been extensively investigated. Here we analyse gut microbiota development of 27 infants during the first month of life. We find three distinct clusters that transition towards Bifidobacteriaceae-dominant microbiota. We observe considerable differences in human milk oligosaccharide utilization among infant bifido- bacteria. Colonization of fucosyllactose (FL)-utilizing bifidobacteria is associated with altered metabolite profiles and microbiota compositions, which have been previously shown to affect infant health. Genome analysis of infants’ bifidobacteria reveals an ABC transporter as a key genetic factor for FL utilization. Thus, the ability of bifidobacteria to utilize FL and the presence of FL in breast milk may affect the development of the gut microbiota in infants, and might ultimately have therapeutic implications. DOI: 10.1038/ncomms11939 OPEN 1 Yakult Central Institute, 5-11 Izumi, Kunitachi-shi, Tokyo 186-8650, Japan. 2 Department of Biological Information, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan. 3 Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi-Ku, Tokyo 117-8605, Japan. 4 Earth-Life Science Institute, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan. 5 National Institute of Genetics, Center for Information Biology, Yata 1111, Mishima, Shizuoka 411-8540, Japan. * These authors contributed equally to this work. ** These authors jointly supervised this work. Correspondence and requests for materials should be addressed to T.M. (email: [email protected]). NATURE COMMUNICATIONS | 7:11939 | DOI: 10.1038/ncomms11939 | www.nature.com/naturecommunications 1 mailto:[email protected] http://www.nature.com/naturecommunications I t is becoming increasingly apparent that the bacterial ecosystem in our gut has a profound influence on human health and disease. The gut microbiota contributes to immune system maturation, energy harvesting and sympathetic nervous system development. In particular, the composition and meta- bolite profiles of gut microbiota have been associated with pathogen resistance1–3, inflammatory responses4 and adiposity5,6. Initial gut microbe colonization begins immediately after birth, and bacterial ecosystems develop within the first few days. Previous studies have reported that the composition of the infant gut microbiota differs from that of adults7–9, that substantial variation occurs between individuals6,10,11 and that bifidobacteria predominate in most infants11–13. Recent studies also demon- strated that environmental factors including the mode of delivery and feeding affect the gut microbiota assemblage and that the process is not random6,13,14. Furthermore, it has been indicated that the gut microbiota development during infancy can have long-lasting effects on the individual’s future health15–18. However, little is known about their pattern of progression, factors that drive the assembly of infant gut microbiota and how these factors affect metabolite profiles. Here we investigated gut microbiota compositions and metabolic profiles for 217 stool samples obtained from 27 infants during their first month of life (202 samples from 12 infants were analysed longitudinally and 15 samples from 15 infants were studied in follow-up). The dynamics and equilibria of the developing microbiota were investigated, and their associations with metabolites were evaluated. We subsequently analysed phenotypes and genotypes of isolated bifidobacteria, and found a key genetic factor affecting infant gut microbiota composition and metabolite profile. Results Early development of gut microbiota. To investigate the dynamics of gut microbiota immediately after birth, we analysed the sequences of the V1–V2 region of the 16S rRNA genes obtained from 12 infants born by normal delivery (Supplementary Table 1) using the 454 GS Junior platform. We obtained stool samples every day during the first week after birth and every other day thereafter until 1 month of age (B17 stool samples per infant; 202 samples in total). A total of 588,293 pyrosequencing reads (average 2,912±1,397 reads per sample; Supplementary Table 2 and Supplementary Fig. 1) were analysed using an open-source Quantitative Insights Into Microbial Ecology (QIIME) software pipeline19 (Supplementary Table 3). Figure 1a shows an age-dependent, gut microbiota composition heatmap for each subject at the bacterial family level. The analysis demonstrated that there are major variations in both the composition and dynamic progression among individuals. Over- all, the composition of the infant’s microbiota was relatively simple, being composed of only a few dominant bacterial families. The displacement of predominant bacteria occurred within only a few days. We observed an increased average abundance of Bifidobacteriaceae, a-diversities and total bacterial cell counts, as well as decreased average abundances of Enterobacteriaceae and Staphylococcaceae (Supplementary Fig. 2). Characteristics of the taxonomic composition observed among the samples were clearly distinguished by principal coordinate analysis (PCoA) and partitioning around medoids (PAM)20 on the basis of bacterial family composition data (Fig. 1b and Supplementary Data 1). Values of the Calinski-Harabasz (CH) index with PAM clustering suggest that the infant microbiota could be divided into three clusters (Supplementary Fig. 3), which were characterized by the predominance of Bifido- bacteriaceae, Enterobacteriaceae or Staphylococcaceae (Fig. 1c). We subsequently observed sequential transitions occurring from Staphylococcaceae- to Enterobacteriaceae- and/or Entero- bacteriaceae- to Bifidobacteriaceae-dominated microbiota, with considerable individual variation in the day of the transition (Fig. 1d). Transitions in the opposite direction were rarely observed. The results suggest that the best-adapted bacterial family in the infant gut may be Bifidobacteriaceae, followed by Enterobacteriaceae and Staphylococcaceae. Microbiota in 1-month-old infants. Subsequently, we performed 16S rRNA gene-library analysis using additional faecal samples; in addition to the faecal samples from 12 infants (subjects A–L, shown in Fig. 1 on day 29), we obtained faecal samples from their parents (n¼22) and another 15 breast-fed infants at approxi- mately 1 month after birth (Supplementary Tables 4 and 5). A total of 147,010 high-quality reads (average 3,058±1,232 reads per sample; Supplementary Data 2) were analysed, and the resulting family composition heatmap is shown in Fig. 2a. The results of PCoA and PAM clustering based on bacterial family compositions suggest that the microbiota of 1-month-old infants stratified into two clusters that were distinct from the adult cluster (Fig. 2b and Supplementary Fig. 4). The majority of infants (n¼18, designated as cluster B) were characterized as having a significantly high abundance of Bifidobacteriaceae, and the minor cluster (n¼9, designated as cluster E) had a significantly high abundance of facultative anaerobes such as Enterobacteriaceae, Enterococcaceae and Staphylococcaceae (Fig. 2c and Supplementary Figs 5 and 6). The 22 adult samples formed a single cluster (designated cluster AD), showing significantly higher abundances of Lachnospiraceae, ‘Clostridiales incertae sedis XIV’, Bacteroidaceae, Ruminococcaceae and Peptostreptococcaceae, as well as higher a-diversity compared with the two infant microbiota clusters (Fig. 2c and Supplementary Figs 5–7). The correlation coefficients observed between bacterial family abundances in infant and adult microbiota are illustrated in network diagrams (Fig. 2d and Supplementary Figs 8 and 9). Analysis of the infant stool samples indicated that the abundance of predominant Bifidobacteriaceae negatively correlated with those of Enterobacteriaceae, Enterococcaceae, Clostridiaceae and Staphylococcaceae. Furthermore, the network diagram for adults indicated that bacterial family compositions and their associa- tions were different from those of infants. Bacterial lineages and gut environments. To understand how gut microbiota affect the host’s physiology, it is important to understand the entire gut ecosystem, not only in terms of microbiota compositions but also in terms of the metabolites produced by the bacteria. Therefore, we investigated the pH and organic acid concentrations of each infant’s stool (Supplementary Data 3) and assessed the correlation between these parameters and bacterial family abundances (Fig. 3a and Supplementary Fig. 10). We found that increased Bifidobacteriaceae abundance positively correlated with organic acid concentrations and total bacterial counts but negatively correlated with pH. In contrast, the abundance of facultative anaerobes such as Enterobacter- iaceae and Staphylococcaceae correlated with decreased organic acid concentrations, decreased total bacterial counts and increased pH (Fig. 3a), as well as decreased Bifidobacteriaceae abundance (Fig. 2d). Previous studies have reported that bifidobacteria produce acetate and lactate as their metabolites1,21, and that some of these strains (for example, Bifidobacterium longum ss. infantis ATCC 15697) can efficiently utilize some human milk oligosaccharide (HMO) components22–25. Therefore, we hypothesized that ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms11939 2 NATURE COMMUNICATIONS | 7:11939 | DOI: 10.1038/ncomms11939 | www.nature.com/naturecommunications http://www.nature.com/naturecommunications bifidobacteria consume the remaining oligosaccharides in the infant gut, causing elevated acetate and lactate concentrations and decreased pH. Furthermore, we examined the concentrations of major residual HMO components in infant stools by high- performance liquid chromatography (HPLC; Supplementary Data 3 and Supplementary Figs 11 and 12)26. As expected, HMO consumption in the gut was associated with increased abundances of Bifidobacteriaceae (Fig. 3a), increased organic acid concentrations and decreased pH values (Fig. 3b). However, some infants showed high faecal oligosaccharide concentrations despite the presence of Bifidobacteriaceae (Fig. 3c). Therefore, we examined the species composition of bifidobacteria in these infants (Fig. 3c). We also analysed the oligosaccharide profiles of breast milk provided by their mothers, and found that three infants (subjects I29, TB16 and TB19) received breast milk from non-secretor mothers (lacking 2’-fucosyllactose (2’-FL) and 2’, 3-difucosyllactose (DFL), Supplementary Table 6). However, we found it difficult to explain these discordant results. These observations prompted us to isolate bifidobacteria from the infant faeces to assess their ability to utilize HMOs in vitro. The 29 isolated strains were cultured in medium containing HMOs as the carbon source and their growth was monitored (Fig. 3d and Supplementary Figs 13 and 14). Interestingly, 14 of the 29 strains exhibited remarkable growth in HMO medium Subject ID Days after birth Enterobacteriaceae Bifidobacteriaceae Staphylococcaceae Bacteroidaceae Enterococcaceae Porphyromonadaceae Streptococcaceae Clostridiaceae Veillonellaceae Pasteurellaceae Lactobacillaceae Micrococcaceae Lachnospiraceae Propionibacteriaceae Peptostreptococcaceae A B C D E F G H I J K L 2 → 29 1 → 29 1 → 29 1 → 29 1 → 29 2 → 29 5 → 29 2 → 29 3 → 29 3 → 292 → 29 2 → 29 0 10 50 100 Abundance (%) a EnterobacteriaceaeBifidobacteriaceaeBifidobacteriaceae- predominant Enterobacteriaceae- predominant Staphylococcaceae- predominant Staphylococcaceae A b u n d a n ce ( % ) 100 50 0 100 50 0 100 50 0 a a c a a B E S Cluster B E S Cluster B E S Cluster b b b b B E S PC1(50%) P C 2 (1 8 % ) Days after birthInfant ID J E F G I D L A H B C K – – – – – S S S S S S S S S S S S S S S S S S E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E EE E E E E E E E E EEE E E E E E E E E E E EE E E E E EE E E E EE E E E E E E E E E E E E E E E NT NT NT NT NT NT NT NT NT 1 2 3 4 5 6 7 9 11 13 15 17 19 21 23 25 27 29 B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B b c d D19 Figure 1 | Infant gut microbiota community profiles during the first month of life. (a) Microbiota profiles in stool samples from 12 subjects (n¼202; B17 sampling days per subject), temporally ordered from left to right. Each row represents taxonomic groups at the family level. The top 15 families are displayed and sorted according to relative abundance. Abundances are represented using the colour scale. (b) Characteristics of infant gut microbiota, illustrated by PCoA and PAM clustering analyses. Data from individuals (points) were clustered, and the centres of gravity (rectangles) were computed for each class. The coloured ellipses encompass 67% of the samples in each cluster. (c) Box plots showing the relative abundances of the main contributors to each cluster. Different letters (a–c) above the boxes indicate significant differences between clusters (Po0.05, Mann–Whitney U-test with Bonferroni’s correction). (d) Temporal shift from Staphylococcaceae- or Enterobacteriaceae-dominant microbiota to Bifidobacteriaceae-dominant microbiota. S, Staphylococcaceae-dominated (yellow); E, Enterobacteriaceae-dominated (blue); B, Bifidobacteriaceae-dominated (red); NT, not tested; —, sample not provided. NATURE COMMUNICATIONS | DOI: 10.1038/ncomms11939 ARTICLE NATURE COMMUNICATIONS | 7:11939 | DOI: 10.1038/ncomms11939 | www.nature.com/naturecommunications 3 http://www.nature.com/naturecommunications Taxon (family) Enterobacteriaceae Enterobacteriaceae Enterobacteriaceae- predominant Bifidobacteriaceae Bifidobacteriaceae Bifidobacteriaceae- predominant Lachnospiraceae Lachnospiraceae Lachnospiraceae-, Bacteroidaceae-, and Ruminococcaceae-predominant Bacteroidaceae Bacteroidaceae Clostridiales (XIV) Clostridiales (XIV) Enterococcaceae Enterococcaceae Streptococcaceae Veillonellaceae Veillonellaceae Staphylococcaceae Staphylococcaceae Porphyromonadaceae Clostridiaceae Propionibacteriaceae Peptostreptococcaceae Peptostreptococcaceae Ruminococcaceae Ruminococcaceae Prevotellaceae Sum of the others Enterobacteriaceae Enterobacteriaceae Bifidobacteriaceae Bifidobacteriaceae Lachnospiraceae Eubacteriaceae ErysipelotrichaceaeBacteroidaceae Bacteroidaceae Coriobacteriaceae Clostridiales XIV Enterococcaceae Streptococcaceae Veillonellaceae Staphylococcaceae Porphyromonadaceae Porphyromonadaceae Clostridiaceae Propionibacteriaceae Peptostreptococcaceae Ruminococcaceae Prevotellaceae Other(clostlidiales) T B 1 4 T B 0 6 T B 2 1 T B 1 5 T B 1 0 T B 2 0 T B 2 9 Jd 2 9 G d 2 9 m o A m o B m o D m o E m o L m o F m o H fa G fa D fa F fa J fa B fa A fa H fa K fa C m o l m o K fa E fa l fa L m o J E d 2 9 T B 0 3 T B 0 9 T B 1 6 T B 2 5 T B 2 6 T B 1 9 T B 0 5 L d 2 9 B d 2 9 D d 2 9 Id 2 9 F d 2 9 C d 2 9 K d 2 9 A d 2 9 H d 2 9 T B 0 7 0 10 50 100 Abundance (%) A b u n d a n ce ( % ) A b u n d a n ce ( % ) A b u n d a n ce ( % ) 100 50 0 B E AD B E AD B E AD B E ADB E ADB E AD B E AD B E AD B E AD 80 40 0 70 35 0 40 20 0 60 40 20 0 30 20 10 0 40 20 0 20 10 0 10 5 0 a b c a b c a b c a a b a a b a a b a a b a a b Cluster Cluster Cluster E AD B P C 2 (2 7 % ) PC1(43%) Abundance (average) >30% >10% >1% Correlation >0.6 < –0.6 < –0.5 < –0.4 < –0.3 >0.5 >0.4 >0.3 Infant Adult a b d c Figure 2 | Gut microbiota community profiles of 27 1-month-old infants and 22 adults. (a) Bacterial families representing more than 1% (on average) of the microbiota in infants or adults are shown on the colour scale. Samples were hierarchically clustered by measuring Euclidean distances with complete- linkage clustering, as shown in the upper tree. (b) Characteristics of infant and adult gut microbiota, as illustrated by PCoA and PAM clustering analyses. Cluster B, Bifidobacteriaceae-predominant; cluster E, Enterobacteriaceae-predominant; cluster AD, adult-type microbiota. (c) Abundances of the main contributors to each cluster. Different letters (a–c) indicate significant differences between clusters (Po0.05, Mann–Whitney U-test with Bonferroni’s correction). Differences in other bacterial families are shown in Supplementary Fig. 7). (d) Network diagram showing co-occurrence relationships among the main contributors in infants and adults. Node sizes indicate the abundances of each bacterial family, and the widths of the edges reflect the calculated Spearman’s rank correlation coefficient. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms11939 4 NATURE COMMUNICATIONS | 7:11939 | DOI: 10.1038/ncomms11939 | www.nature.com/naturecommunications http://www.nature.com/naturecommunications Enterobacteriaceae Enterobacteriaceae Bifidobacteriaceae Bifidobacteriaceae Bacteroidaceae Bacteroidaceae Enterococcaceae Streptococcaceae Veillonellaceae Staphylococcaceae Porphyromonadaceae Clostridiaceae Correlation coefficient –1.0 0 1.0 Propionibacteriaceae Organic acids (mM) HMO (mM) A ve ra g e ± s .d . 54±39 21±24 5.1±15 4.4±9 4.2±7.6 2.6±8.5 2.4±4.3 2.4±5.3 1.1±2.4 1.1±2.6 A ce ta te ( 3 0 .5 ± 3 5 .3 ) L a ct a te ( 1 2 .9 ± 3 1 ) F L ( 2 8 .8 ± 2 8 .2 ) L N T + L N n T ( 3 .5 ± 5 .2 ) L N F P + L N D F H ( 2 4 .7 ± 1 7 .5 ) S u cc in a te ( 8 .9 ± 1 2 ) T o ta l a ci d s (5 4 .7 ± 6 5 .1 ) T o ta l H M O ( 5 7 ± 4 1 .7 ) N o . o f b a ct e ri a ( L o g 1 0 .2 ± 0 .4 ) p H ( 5 .9 ± 0 .6 ) –0.53 0.47 0.67 0.61 0.54 0.57–0.67–0.49 –0.43 –0.49 –0.43 0.45 0.46 0.47 0.44 –0.42 0.48 0.46 0.51–0.49 –0.60 –0.47 –0.46 –0.58 –0.58 –0.390.59 –0.59 –0.48 –0.40 –0.56 0.50 0.39 0.33 0.36 0.35–0.32 –0.15 –0.34 0.35 0.33 0.20 0.34 –0.31 –0.040.160.00–0.08–0.230.320.210.120.32 –0.15–0.29–0.24–0.140.200.34–0.06 –0.13 0.02 0.20 0.11 0.39 0.32 0.29 0.38 0.31 –0.25 –0.15 –0.22 –0.13 –0.18 –0.21 0.13 0.22 0.29 0.380.31–0.03–0.03 –0.23 –0.35 –0.07 –0.01 –0.23 –0.21 0.13 0.31 0.22 0.19 0.400.250.10–0.25–0.330.25 0.09 0.26 180 120 60 0 0 50 100 150 0 50 100 150 HMO (mM) HMO (mM) 7.5 6.5 5.5 4.5 p H Cluster B Cluster E � = –0.77 P < 0.01 � = 0.67 P < 0.01 1.2 0.9 0.6 0.3 0.0 0 10 20 30 40 Incubation (h) B. longum ss. infantis B. longum ss. longum B. longum B. pseudocatenulatum B. kashiwanohense B. ps'catenulatum B. breve B. breve B. dentium B. bifidum B. dentium B. bifidum O D 6 0 0 O D 6 0 0 a t 4 0 h 100 75 50 25 0 A b u n d a n ce ( % ) A ce ta te ( m M ) 150 100 50 0 C o n ce n tr a tio n ( m M ) Fucosyllactose LNT&LNnT LNFP&LNDFH 100 75 50 25 0 A b u n d a n ce ( % ) G 2 9 E 2 9 J2 9 T B 2 6 T B 2 5 T B 2 5 T B 1 6 T B 0 9 T B 0 3 T B 2 9 I2 9 D 2 9 B 2 9 L 2 9 T B 0 5 T B 1 9 H 2 9 A 2 9 T B 2 0 T B 1 0 T B 1 5 C 2 9 T B 0 7 T B 2 1 T B 0 6 T B 1 4 F 2 9 K 2 9 G 2 9 E 2 9 J2 9 T B 2 6 T B 1 6 T B 0 9 T B 0 3 T B 2 9 I2 9 D 2 9 B 2 9 L 2 9 T B 0 5 T B 1 9 H 2 9 A 2 9 T B 2 0 T B 1 0 T B 1 5 C 2 9 T B 0 7 T B 2 1 T B 0 6 T B 1 4 F 2 9 K 2 9 T B 2 5 G 2 9 E 2 9 J2 9 T B 2 6 T B 1 6 T B 0 9 T B 0 3 T B 2 9 I2 9 D 2 9 B 2 9 L 2 9 T B 0 5 T B 1 9 H 2 9 A 2 9 T B 2 0 T B 1 0 T B 1 5 C 2 9 T B 0 7 T B 2 1 T B 0 6 T B 1 4 F 2 9 K 2 9 2′-FL 3-FL DFL LNT LNnT Glc+GalGlcNAcFucLacLNDFH LNFP OD IN -F 2 9 M e d iu m IN -0 7 C A -K 2 9 a C A -K 2 9 b B R -A 2 9 B R -0 6 B R -I 2 9 C A -C 2 9 B R -1 0 B R -1 5 B R -C 2 9 C A -B 2 9 C A -0 5 C A -D 2 9 B R -H 2 9 B R -L 2 9 B R -0 7 L O -K 2 9 a L O -0 6 L O -C 2 9 L O -K 2 9 b D E -2 9 L O -1 0 L O -2 1 B R -1 9 B R -2 1 B I- 1 4 B R -1 4 B R -2 0 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 25 20 15 10 5 0 C o n ce n tr a tio n ( m M ) a b d c e Figure 3 | Relationships between bacterial family abundances and gut environments. (a) Spearman’s rank correlation coefficients between bacterial family abundances and gut environmental factors such as pH, organic acid concentrations and faecal oligosaccharide concentrations are shown in numerical and colour-scale formats. (b) Spearman’s rank correlations of oligosaccharide concentrations with pH values and acetate concentrations. (c) Relationships between bacterial abundances, faecal oligosaccharide concentrations and the relative abundances of bifidobacterial species. The upper tree shows hierarchical clustering on the basis of the bacterial family compositions. (d) Growth curves of 29 bifidobacterial strains in medium containing HMOs (see Supplementary Fig. 14 for more details). (e) Glycoprofiles of bacterial supernatants after 40 h of cultivation. Samples are ordered based on their OD600 values after 40 h of cultivation. NATURE COMMUNICATIONS | DOI: 10.1038/ncomms11939 ARTICLE NATURE COMMUNICATIONS | 7:11939 | DOI: 10.1038/ncomms11939 | www.nature.com/naturecommunications 5 http://www.nature.com/naturecommunications (saturating OD60040.7), but 15 strains did not (saturating OD600o0.3). Culture supernatants were collected to investigate the remaining oligosaccharides. Although most bifidobacterial strains utilized lacto-N-tetraose (LNT), there were considerable differences in the utilization of fucosyllactose (sum of 20-FL, 3-fucosyllactose and DFL), which is the main component of HMOs27,28 (Fig. 3e and Supplementary Fig. 15). These results indicated that efficient FL utilization is not a universal property of infant bifidobacteria and is instead strain-dependent. Bifidobacterial genomes and FL utilization. To gain insight into how the strains showed differences in FL utilization, we deter- mined the draft genomes of all 29 strains (Table 1 and Supplementary Table 7) and subsequently performed OrthoMCL clustering analysis using bi-directional BLAST alignments (Supplementary Fig. 16a). Initially, we investigated the presence of fucosidase genes (glycoside hydrolase family classifications: GH95 and GH29) and confirmed that all strains exhibiting robust growth in an HMO-containing medium possessed at least one fucosidase gene (Table 1 and Supplementary Fig. 16b). However, we found that 6 out of 15 strains showing limited growth in the HMO-containing medium possessed a fucosidase gene. We analysed the phylogeny and subcellular localization of the fuco- sidase genes. We found that most fucosidases, except for that of B. bifidum BI-14, are intracellular enzymes, with only minor sequence differences occurring between FL-utilizing and non-FL- utilizing strains (Supplementary Fig. 17). Subsequently, we searched for other genes responsible for FL utilization and discovered that the presence of a homologous group (HG_2571) corresponded with the FL-utilization pheno- type in all strains, except for B. bifidum BI-14 (Table 1 and Supplementary Fig. 16c). BLAST searches against the KEGG database indicated that the homologous sequences were highly similar to substrate-binding protein (SBP), which participates in the multiple-sugar ABC transporter system (KEGG entry K02027; Supplementary Fig. 16c). Furthermore, we found that two permease genes of the multiple-sugar ABC transporter system (K02025 and K02026) and the fucosidase gene (GH95) were adjacent to the SBP gene in most strains (Fig. 4a). On the basis of these findings, we hypothesized that the ABC transporter mediates FL transportation into bacterial cells. This hypo- thesis could explain the absence of the transporter in the B. bifidum BI-14 strain, which utilizes FL because that strain expresses extracellular membrane-bound fucosidases (Supple- mentary Fig. 17). To determine whether the putative ABC transporter SBP for FL (denoted FL-SBP) mediates FL utilization, the gene was knocked out in B. breve BR-A29 using homologous recombination (Supplementary Fig. 18). After confirming that the FL-SBP gene was knocked out, growth of the knockout strain was investigated in the HMO medium. In contrast with the original BR-A29 strain, the FL-SBP gene-knockout strain showed limited growth in the HMO medium (Fig. 4b). Furthermore, we confirmed that FL was not utilized by the FL-SBP gene-knockout strain (Fig. 4c), demonstrating that the FL-SBP is responsible for FL utilization. HMO-utilizing bifidobacteria affect gut ecosystems. Having identified the differences in FL utilization among bifidobacteria, we subdivided the Bifidobacteriaceae-dominated microbiota (cluster B) into those colonized by FL-utilizing bifidobacteria (designated cluster B1; n¼11) and those dominated with non- FL-utilizing bifidobacteria (cluster B2; n¼7; Supplementary Fig. 19). Furthermore, we compared the faecal organic acids, pH, HMO and microbiota compositions of these two subgroups with those of Enterobacteriaceae-dominated microbiota (cluster E; n¼9). Compared with clusters B2 and E, cluster B1 showed significantly higher acetate concentrations and lower pH and residual oligosaccharide concentrations (Po0.05, Mann– Whitney U-test with Bonferroni’s correction; Fig. 5 and Supplementary Fig. 20). In addition, cluster B1 had significantly higher Bifidobacteriaceae and lower Enterobacteriaceae abun- dances. In contrast, there were no significant differences in faecal acetate concentrations, pH or oligosaccharide concentrations between clusters B2 and E. Discussion Gut microbiota development in healthy, full-term infants has been investigated using culture-based enumeration29, molecular analysis with 16S rRNA gene-targeting primers or probes10–12,30, or, more recently, using metagenomic approaches8,9,13,31. These intensive observational studies have demonstrated that the modes of delivery10,13, feeding10,13,29 and use of antibiotics12 can influence the development of the infants’ microbiota. In addition, recent investigations have suggested that maternal HMO secretion type31, environmental exposures10,14 and bacterial transmission and propagation30 can also alter the Table 1 | Summary of the draft genomes of the 29 bifidobacterial strains. Strains Genome size (Mbp) No. of CDSs Growth with HMO (OD60040.7) a-L-fucosidase HG_2571 GH29 GH95 SBP of ABC transporter B. breve BR-06, BR-10, BR-14, BR-15, BR-20, BR-21, BR-A29, BR-I29 (eight strains) 2.2–2.7 1,928–2,368 þ � þ þ B. breve BR-07, BR-19, BR-C29, BR-H29, BR-L29 (five strains) 2.2–2.5 1,955–2,224 � � þ � B. longum ss. infantis IN-07, IN-F29 (two strains) 2.6–2.7 2,356–2,441 þ þ þ þ B. longum ss. longum LO-06, LO-10, LO-21, LO-C29, LO-K29a, LO-K29b (six strains) 2.4–2.7 1,987–2,209 � � � � B. pseudocatenulatum CA-C29, CA-K29a, CA-K29b (three strain) 2.2–2.5 1,825–2,168 þ � þ þ B. pseudocatenulatum CA-05, CA-B29, CA-D29 (three strains) 2.2–2.3 1,896–1,904 � � � � B. bifidum BI-14 2.2 1,779 þ þ þ � B. dentium DE-29 2.6 2,127 � þ � � HMO, human milk oligosaccharide; SBP, substrate-binding protein. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms11939 6 NATURE COMMUNICATIONS | 7:11939 | DOI: 10.1038/ncomms11939 | www.nature.com/naturecommunications http://www.nature.com/naturecommunications assemblage of infants’ microbiota. However, the overall understanding of gut microbiota development is still limited because of the small number of subjects studied9, the low frequency of sample analysis8,13,31 and/or the limitations in the enumeration methods used10–12. In addition, metabolite profiles, bacterial strain isolation and related phenotype and genotype Strain FL utilization IN-F29 IN-07 CA-C29 CA-K29b CA-K29a … NitN: Final Paper Guidelines NUT11 Nutrition research is frequently presented in the news. Sometimes the information presented is vague or contradictory to other articles, so how do you know what to believe? The overarching purpose of this group of assignments is for you to investigate and think critically about how the news interprets and reports nutrition science. You may find that the news correctly reports the outcomes of a scientific article; however, you may also find that the news either misinterprets or drastically overemphasizes the size or importance of the reported results. Note: In order to ensure that all university grade submission deadlines are met, we can only accept final papers up to 24 hours late unless there is a valid and documented reason for late submission. For this assignment, you will be writing your final paper based on your outline. Don’t forget to look at any comments your TA has given you for improvement! The paper must be in your own words. General Notes: • 750 ± 100 words (about 3 pages). Don’t forget to put the word count in the header. • Double spaced, 12pt font, 1 inch margins. • In-text citations and a reference list using the AMA style guidelines. • Sources (at minimum): o News article o Original research article o Review article • You are not required to have a certain number of technical terms; however, if you use a technical term remember to define it. • Upload PDFs of all of your articles. • Upload your paper as a Word document to Canvas. Title: Give your paper a creative and catchy title. Introduction: State the overall claim being made by the news article. Introduce the health or performance issue and the influencing factor that are being addressed in the news article. Provide information on why the health or performance issue is important, as well as some information on how the influencing factor may be linked to the health or performance issue. Body Paragraphs: The body of your paper should first summarize and describe the stance of the news article. Then introduce and describe the original study presented in the research article. As with your other written assignments, you do not need to describe every detail of the study. Consider the important aspects of study design (intervention, dosage, duration, and important subject characteristics), variables and how they were measured, and the main results. Critically evaluate how the media reported the science. Are the results correct? Is the significance of the results stated in the NitN: Final Paper Guidelines NUT11 media reflective of the actual results? Are there other results that the news article did not mention that they should have? Are there methodological flaws in the study that the news failed to consider? Finally, discuss the broader context for the results you have presented. There are several different avenues that you could pursue in discussing the broader context; pick one that flows logically from the information presented in your previous paragraphs. Do the results align with the prevailing opinion in the scientific community? The news may have accurately reported a single paper, but several other papers may have opposing results. Other researchers may have alternative interpretations of the results or be able to point out methodological flaws in the original research. The original research may also discuss important interpretations or limitations that the news failed to report, and a review could help you to develop a better understanding of that discussion. Is the story over? Are there other avenues of related research worth exploring? Conclusion: Give the overall conclusion about the claim presented in the news taking the scientific evidence into account. Also provide your personal opinion on how well the media did in reporting the science. You should refer to the findings from your original research article and the review article and draw a logical conclusion from the evidence. Does this assignment change how you will evaluate nutrition science reported in the media? References: You should include both in-text citations and an associated reference list at the end of the paper using AMA style guidelines. Please look at the FAQ on AMA guidelines and refer back to the Nutrition in the News lecture for full details. Your source for the latest research news Science News from research organizations Date: Source: Summary: Share: Early diet of infants, not maternal obesity, influences development of gut microbiome February 11, 2016 American Society for Microbiology After the age of nine months, the development of the infant gut microbiota is driven by the transition to family foods, not maternal obesity, according to results from a new study. The gut microbiota is a complex community of microorganisms that live in the digestive tract. Children are essentially born without microbes in their gut, and they are immediately colonized upon birth. The next several years are critical in establishing a person's endogenous gut microbiota. a b e g d FULL STORY After the age of nine months, the development of the infant gut microbiota is driven by the transition to family foods, not maternal obesity, according to results from a new study. The study was published online this week in mSphere, an open-access journal of the American Society for Microbiology. "Our results reveal that the transition from early infant feeding to family foods is a major determinant for gut microbiota development," said senior author Tine Rask Licht, PhD, professor and head of the Research Group for Microbiology and Immunology, National Food Institute, Technical University of Denmark, Soborg, Denmark. "Maternal obesity did not influence microbial diversity or specific taxon abundances during the complementary feeding period." The gut microbiota is a complex community of microorganisms that live in the digestive tract. Children are essentially born without microbes in their gut, and they are immediately colonized upon birth. The next several years are critical in establishing a person's endogenous gut microbiota. Later in life, the gut Cite This Page: American Society for Microbiology. "Early diet of infants, not maternal obesity, influences development of gut microbiome." ScienceDaily. ScienceDaily, 11 February 2016. <www.sciencedaily.com/releases/2016/02/160211142225.htm>. microbiota can change in response to factors such as diet, but only slightly. Each adult has a very distinct gut microbiota. "When you look at an adult's gut microbiota, it is more or less like a fingerprint," explained Professor Licht. The gut microbiota is strongly affected by diet and has been linked with obesity. Children of obese parents have a higher risk of developing obesity, and this is only partially explained by genetic predisposition. While many previous studies have focused on the impact of early infant diet, particularly breastfeeding, few studies have addressed the influence of maternal obesity on the infant gut microbiota, which can occur either through microbes transmitted during birth or through the dietary habits of the family. To shed light on the issue, Martin Laursen, a PhD student at Technical University of Denmark, and colleagues compared the gut microbiotas of two cohorts of infants, one born from a random sample of healthy mothers (n=114) and the other born from obese mothers (n =113). The researchers analyzed stool samples from the children at nine months and 18 months. By nine months, most children have transitioned, at least partially, to a complementary diet. Microbiota data were compared to breastfeeding patterns and detailed individual dietary recordings. The major determinants of gut microbiota development were breastfeeding duration and composition of the complementary diet. In both cohorts, gut microbial composition were strongly affected by introduction of family foods with high protein and fiber contents. "We found that introduction of family foods is the main driver of development of the complex microbial ecosystem in the gut at age 9 months. The food determines the diversity and the composition of the microbiota, and this is very important," said Professor Licht. "It is well known that breast feeding has a great impact on gut microbiota, but nobody has addressed the effect of diet at this age before." Story Source: Materials provided by American Society for Microbiology. Note: Content may be edited for style and length. Journal Reference: 1. Laursen, M.F. et al. Infant Gut Microbiota Development Is Driven by Transition to Family Foods Independent of Maternal Obesity. mSphere, February 2016 DOI: 10.1128/mSphere.00069-15 MLA APA Chicago RELATED STORIES Alternations in Gut Microbiota in Pregnancy and Lactation Jan. 17, 2019 — Recent studies have shown that maternal gut microbiota in humans primes the offspring's immune and metabolic development during pregnancy and lactation. Due to environmental factors that are ... read more Gut Microbiota of Infants Predicts Obesity in Children Oct. 23, 2018 — Evaluating the gut microbiota of infants may help identify children who are at risk for becoming overweight or obese. The research revealed that gut microbiota composition at two years of life is ... read more Rat Study Shows Gut Microbes Play a Role in Colon Cancer Susceptibility July 13, 2016 — The microscopic organisms that live in our gut do more than help us digest food. 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Views expressed here do not necessarily reflect those of ScienceDaily, its staff, its contributors, or its partners. Financial support for ScienceDaily comes from advertisements and referral programs, where indicated. Infant Gut Microbiota Development Is Driven by Transition to Family Foods Independent of Maternal Obesity Martin Frederik Laursen,a Louise B. B. Andersen,b Kim F. Michaelsen,b Christian Mølgaard,b Ellen Trolle,a Martin Iain Bahl,a Tine Rask Lichta National Food Institute, Technical University of Denmark, Søborg, Denmarka; Department of Nutrition, Exercise and Sports, University of Copenhagen, Frederiksberg, Denmarkb ABSTRACT The first years of life are paramount in establishing our endogenous gut microbiota, which is strongly affected by diet and has repeatedly been linked with obesity. However, very few studies have addressed the influence of maternal obesity on infant gut microbiota, which may occur either through vertically transmit- ted microbes or through the dietary habits of the family. Additionally, very little is known about the effect of diet during the complementary feeding period, which is potentially important for gut microbiota development. Here, the gut microbiotas of two different cohorts of infants, born either of a random sample of healthy mothers (n � 114), or of obese mothers (n � 113), were profiled by 16S rRNA amplicon se- quencing. Gut microbiota data were compared to breastfeeding patterns and de- tailed individual dietary recordings to assess effects of the complementary diet. We found that maternal obesity did not influence microbial diversity or specific taxon abundances during the complementary feeding period. Across cohorts, breastfeed- ing duration and composition of the complementary diet were found to be the ma- jor determinants of gut microbiota development. In both cohorts, gut microbial composition and alpha diversity were thus strongly affected by introduction of fam- ily foods with high protein and fiber contents. Specifically, intake of meats, cheeses, and Danish rye bread, rich in protein and fiber, were associated with increased alpha diversity. Our results reveal that the transition from early infant feeding to family foods is a major determinant for gut microbiota development. IMPORTANCE The potential influence of maternal obesity on infant gut microbiota may occur either through vertically transmitted microbes or through the dietary habits of the family. Recent studies have suggested that the heritability of obesity may partly be caused by the transmission of “obesogenic” gut microbes. However, the findings presented here suggest that maternal obesity per se does not affect the overall composition of the gut microbiota and its development after introduction of complementary foods. Rather, progression in complementary feeding is found to be the major determinant for gut microbiota establishment. Expanding our understand- ing of the influence of complementary diet on the development and establishment of the gut microbiota will provide us with the knowledge to tailor a beneficial pro- gression of our intestinal microbial community. KEYWORDS: 16S rRNA sequencing, breastfeeding, complementary diet, family foods, infant gut microbiota, maternal obesity Despite the temporal resilience and stability of the gut microbiota, long-term diet(1) and major diet shifts (2) are known to affect the human gut microbiota. Infancy and early childhood constitute a period in life in which the microbiota is characterized by relatively low stability and high responsiveness toward influencing factors. During this period, dietary factors have major implications for the establishment of the gut Received 25 November 2015 Accepted 6 January 2016 Published 10 February 2016 Citation Laursen MF, Andersen LBB, Michaelsen KF, Mølgaard C, Trolle E, Bahl MI, Licht TR. 2016. Infant gut microbiota development is driven by transition to family foods independent of maternal obesity. mSphere 1(1):e00069-15. doi:10.1128/ mSphere.00069-15. Editor Garret Suen, University of Wisconsin, Madison Copyright © 2016 Laursen et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license. Address correspondence to Tine Rask Licht, [email protected] Diet affects infant gut microbiota RESEARCH ARTICLE Host-Microbe Biology crossmark Volume 1 Issue 1 e00069-15 msphere.asm.org 1 o n A p ril 2 4 , 2 0 2 0 b y g u e st h ttp ://m sp h e re .a sm .o rg / D o w n lo a d e d fro m http://orcid.org/0000-0002-6399-9574 http://dx.doi.org/10.1128/mSphere.00069-15 http://dx.doi.org/10.1128/mSphere.00069-15 http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ http://crossmark.crossref.org/dialog/?doi=10.1128/mSphere.00069-15&domain=pdf&date_stamp=2016-2-10 msphere.asm.org http://msphere.asm.org/ microbiota (3, 4). While many previous studies have focused on early infant diet (5, 6), particularly breastfeeding and formula feeding, only a few have addressed the effects of the complementary (solid-food) diet of infants in the period after 6 months of age (7). As the gut microbial population is not fully established until the age of 3 to 5 years (8, 9), it is important to understand how it is influenced by the transition from early infant feeding to family foods during the complementary feeding period, which is defined by the WHO as the period from 6 until 18 to 24 months of age (10). The established adult gut microbiota has been linked with a range of metabolic, autoim- mune, and allergic diseases (9). Specifically, the intestinal microbiome has repeatedly been linked to obesity in animal models (11–15) as well as in human studies (16, 17). By transplantation of fecal microbial communities from human twins discordant for obesity into germfree mice, it has been shown that a greater increase in body mass and adiposity occurs in mice transplanted with obese donor microbiota than with the corresponding microbiota from the lean donor twin, suggesting the importance of gut microbes over human genetics in the etiology of obesity (18). Indeed, children of obese parents have a higher risk of developing obesity, and this is not explained solely by human genetic predisposition. Since gut microbes can be transferred from mother to infant during birth (19), an obesity-associated microbiota may be transferred from an obese pregnant woman to her offspring. It is well documented that obese mothers on average breastfeed for a shorter time than normal-weight mothers (20) and that breastfeeding has a protective effect on obesity in offspring (21). Further, parental obesity is associated with lower socio-economic status and specific dietary patterns (22) that may affect the type of complementary diet introduced to the infant and thereby the development of the gut microbiota (9), as well as contribute to future obesity risk (23). Indeed, diet-microbiota interactions have been shown to be key players in the development of obesity (18). Therefore, we compared the gut microbiota profiles of two different cohorts of Danish infants at the ages of 9 and 18 months, designated SKOT I (24) and SKOT II (25), respectively (SKOT is a Danish abbreviation for dietary habits and well-being of young children). SKOT I includes infants from a random sample of mothers (mean body mass index [BMI], 22.9 kg/m2), and SKOT II includes infants of obese mothers (mean BMI, 35.1 kg/m2). To elucidate the impact of (i) maternal obesity and (ii) dietary factors on infant gut microbiota development, associations between specific features of the gut microbiota and dietary factors were investigated with a focus on breastfeeding and complementary diet composition. RESULTS Gut microbiota development during the complementary feeding period is inde- pendent of maternal obesity. To assess the impact of maternal obesity on gut microbiota establishment in offspring, we sequenced the V3 region of 16S rRNA genes from fecal samples of 227 individuals at both 9 and 18 months of age in the two SKOT cohorts. These cohorts are different with respect to maternal obesity and generally differ in terms of socio-economic status, C-section prevalence, and early infant feeding but differ only slightly with respect to infant body composition measures (Table 1). Between-sample diversity (beta diversity) of the gut microbiota in the two cohorts was investigated by principal-coordinate analysis (PCoA) of the Bray-Curtis dissimilarity indices and showed clustering according to age rather than cohort (Fig. 1A). Distances to the group centroid for each point, as an estimate of beta diversity, illustrated no differences between cohorts (Fig. 1A). However, greater beta diversity was observed at 9 months than at 18 months in both cohorts, in line with previous reports (26–28). Levels of within-sample diversity (alpha diversity), as estimated by the Shannon index, the number of observed genera, and Pielou’s evenness index of the communities, were not significantly different between the two cohorts at either 9 months or 18 months of age (Fig. 1B). However, there was a significant increase in these alpha diversity measures from 9 to 18 months in both cohorts. On a compositional level, the gut microbiotas across time and cohorts were dominated by four phyla, Firmicutes (64.2%), Actinobacteria (23.4%), Bacteroidetes (7.7%), and Proteobacteria (4.3%), while less than Laursen et al. Volume 1 Issue 1 e00069-15 msphere.asm.org 2 o n A p ril 2 4 , 2 0 2 0 b y g u e st h ttp ://m sp h e re .a sm .o rg / D o w n lo a d e d fro m msphere.asm.org http://msphere.asm.org/ 0.5% belonged to other phyla or were unclassified. On average, 98.3% of the commu- nities belonged to 24 bacterial families (Fig. 2). Despite large interindividual variation, average bacterial communities assessed at the phylum level as well as at the family level at 9 months and 18 months were highly similar between SKOT I and II (Fig. 2). Indeed, according to PCA of family-level composition, samples clustered according to age rather than cohort and showed the relative contributions of bacterial families to the variation in the data set (Fig. 3A). After correction for multiple testing, we found no significant differences between cohorts with respect to relative abundances of bacterial phyla, families, or genera at either 9 or 18 months, and no differences in the changes occurring from 9 to 18 months between the two cohorts were identified (Fig. 3B). In contrast, over time, Lachnospiraceae, Ruminococcaceae, Eubacteriaceae, Rikenellaceae, TABLE 1 Characteristics of the SKOT cohort subsets used in this studya Parameter (unit of measure) Value for SKOT I (n � 114) Value for SKOT II (n � 113) P valueb Mother BMI at the infant age of 9 mo (mean kg/m2 � SD) 22.9 � 3.2 35.1 � 4.2 �0.0001 (MWT) Work situation Job (%) 80.7 76.1 Student (%) 14.9 8.0 No job (%) 4.4 15.9 0.007 (�2) Education level Basic (%) 12.3 32.7 Short (%) 11.4 12.4 Medium (%) 32.5 34.5 Long (%) 43.9 20.4 �0.0001 (�2) Household income per yearc �650,000 DKK (%) 44.7 49.0 �650,000 DKK (%) 55.3 51.0 0.587 (FET) Infant Birth Wt for age at birth (mean Z score � SD) 0.35 � 0.84 0.81 � 1.04 0.0003 (tW) Length for age at birth (mean Z score � SD) 1.20 � 0.96 1.65 � 1.11 0.013 (t) BMI for age at birth (mean Z score � SD) �0.41 � 0.91 �0.08 � 1.13 0.015 (tW) C-section prevalence (%) 13.3 33.7 0.0006 (FET) Gestational age at birth (mean no. of wks � SD) 40.2 � 1.2 40.3 � 1.3 0.448 (t) Sex Male (%) 47.4 53.1 Female (%) 52.6 46.9 0.427 (FET) Early infant feeding Age at introduction to complementary feeding (mean no. of mos � SD) 4.4 � 0.7 4.2 � 0.6 0.0018 (MWT) Duration of exclusive breastfeeding (mean no. of mos � SD) 3.6 � 1.8 2.6 � 2.0 0.0006 (MWT) Total duration of breastfeeding (mean no. of mos � SD) 8.1 � 3.8 6.6 � 4.5 0.0068 (MWT) Anthropometry Wt for age at 9 mo (mean Z score � SD) 0.46 � 0.92 0.83 � 0.93 0.003 (t) Length for age at 9 mo (mean Z score � SD) 0.23 � 0.90 0.88 � 0.97 �0.0001 (t) BMI for age at 9 mo (mean Z score � SD) 0.45 � 1.03 0.46 � 0.95 0.939 (t) Subscapularis skinfold thickness for age at 9 mo (mean Z score � SD) 0.19 � 1.24 0.39 � 0.99 0.184 (tW) Waist circumference at 9 mo (mean cm � SD) 45.64 � 3.07 44.95 � 2.99 0.090 (t) Wt for age at 18 mo (mean Z score � SD) 0.50 � 0.84 0.70 � 0.85 0.094 (t) Length for age at 18 mo (mean Z score � SD) 0.07 � 0.92 0.42 � 0.97 0.006 (t) BMI for age at 18 mo (mean Z score � SD) 0.64 � 0.97 0.63 � 0.87 0.945 (t) Subscapularis skinfold thickness for age at 18 mo (mean Z score � SD) 0.59 � 1.11 0.82 � 1.20 0.141 (t) Waist circumference at 18 mo (mean cm � SD) 46.83 � 2.88 46.61 � 2.52 0.550 (t) aData are from reference 25. bStatistical significance was evaluated by the Mann-Whitney test (MWT), the chi-square test (�2), Fischer’s exact test (FET), Student’s t test (t), and Student’s t test with Welch’s correction (tW). cDKK, Danish krone. Transition to Family Foods Affects Gut Microbiota Volume 1 Issue 1 e00069-15 msphere.asm.org 3 o n A p ril 2 4 , 2 0 2 0 b y g u e st h ttp ://m sp h e re .a sm .o rg / D o w n lo a d e d fro m msphere.asm.org http://msphere.asm.org/ and Sutterellaceae were significantly increased in both cohorts, and Bifidobacteriaceae, Actinomycetaceae, Veillonellaceae, Enterobacteriaceae, Lactobacillaceae, Enterococ- caceae, Clostridiales incertae sedis XI, Carnobacteriaceae, and Fusobacteriaceae were significantly decreased in both cohorts (Fig. 3B; see Table S1 in the supplemental material). This is in agreement with a previous study of the SKOT I cohort using quantitative-PCR (qPCR)-based microbiota assessment (8) and with studies involving other cohorts (27–30). These results suggest that maternal obesity per se does not influence gut microbiota development during the complementary feeding period. The high gut microbiota similarity between the two cohorts, independently sampled during different time periods, allowed a high-powered characterization of infant gut microbi- -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 SKOT I 9m SKOT II 9m SKOT I18m SKOT II 18m PC1 (11.0%) P C 2 (2 .3 % ) S K O T I S K O T II S K O T I S K O T II 0.0 0.2 0.4 0.6 0.8 1.0 9 months 18 months *** *** ns ns D is ta nc e to c en tr oi d S K O T I S K O T II S K O T I S K O T II 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 9 months 18 months ns *** *** ns S ha nn on in de x S K O T I S K O T II S K O T I S K O T II 0 20 40 60 80 100 ns *** *** ns 9 months 18 months O bs er ve d ge ne ra S K O T I S K O T II S K O T I S K O T II 0.0 0.2 0.4 0.6 0.8 1.0 9 months 18 months ns *** *** ns P ie lo u' s ev en ne ss in de x Beta diversity Alpha diversity A B FIG 1 Gut microbial beta and alpha diversity is independent of maternal obesity but changes over time. (A) PCoA plot based on Bray-Curtis dissimilarity, with the centroid for each group shown with a black boarder. The distance to the group centroid for each point provides a measure of homogeneity of variance, used to estimate beta diversity. PC1 and PC2, principal coordinates 1 and 2, respectively. (B) Alpha diversity measures as estimated by the Shannon index, observed genera, and Pielou’s evenness index. Boxes indicate 25th to 75th percentiles, with mean values marked as a line and whiskers indicating minimum and maximum values. ns, not significant; *, P < 0.05; **, P < 0.01; ***, P < 0.001 (according to Tukey’s honestly significant difference test for beta diversity and paired [within cohorts, across time points] or unpaired [across cohorts at the same time point] t tests for alpha diversity measures). Laursen et al. Volume 1 Issue 1 e00069-15 msphere.asm.org 4 o n A p ril 2 4 , 2 0 2 0 b y g u e st h ttp ://m sp h e re .a sm .o rg / D o w n lo a d e d fro m msphere.asm.org http://msphere.asm.org/ ota development and identification of the main factors explaining variation in gut microbiota. Limited influence of C section, gestational age at birth, and prior use of antibiotics. The mode of delivery (31), gestational age at birth (within a normal range of full-term delivery) (32), and use of antibiotics (33) have all previously been shown to impact the infant gut microbiota. In the SKOT cohorts, neither microbial community compositions nor alpha diversity measures at 9 months were significantly different between individuals born by C section and those born vaginally (see Table S2 in the supplemental material). We did, however, note a decreased relative abundance of Bacteroidaceae (P � 0.003, false-discovery-rate-corrected P values [q] � 0.072) in infants born by C section in SKOT II (Table S2), in line with results of previous studies (27, 31, 34). Gestational age at birth was not associated with gut microbiota composition or alpha diversity at 9 months (Table S3), and the use of antibiotics 2 weeks before the sample was taken (current antibiotic use was an exclusion criterion) could not explain the variation in gut microbial diversity at 9 or 18 months (Table S4). All infants in the present study were delivered at full term (range, 37 to 42 weeks), C-section prevalence was low in SKOT I (Table 1), and for only a few infants was the use of oral antibiotics during the 2 weeks prior to sampling registered (10 individuals in total). Further, the relative late sampling point (9 months of age) may explain discrepancies with prior studies. FIG 2 Composition of gut microbiota in SKOT I and SKOT II. Relative abundances of bacterial phyla (small panels) and families (large panels) in the SKOT I cohort (n � 114) at the ages of 9 months (A) and 18 months (B) and in the SKOT II cohort (n � 113) at the ages of 9 months (C) and 18 months (D). Boxes indicate 25th to 75th percentiles, with mean relative abundances marked as lines and whiskers indicating the range (minimum/maximum) multiplied by the interquartile range (25th to 75th percentiles) from the boxes. Bacterial families are ranked by average relative abundances at the age of 9 months. Detailed information can be found in Table S1 in the supplemental material. Transition to Family Foods Affects Gut Microbiota Volume 1 Issue 1 e00069-15 msphere.asm.org 5 o n A p ril 2 4 , 2 0 2 0 b y g u e st h ttp ://m sp h e re .a sm .o rg / D o w n lo a d e d fro m msphere.asm.org http://msphere.asm.org/ Duration of exclusive breastfeeding, rather than age at introduction of complementary feeding, is reflected in late-infancy gut microbiota. Danish mothers are advised to exclusively breastfeed their infants until the age of approxi- mately 6 months and to continue partial breastfeeding until the infant is about 1 year old. It is additionally recommended to introduce complementary foods (apart from infant formula) at about the age of 6 months but not before the age of 4 months (35). As we have previously reported (25), infants in the SKOT I cohort were both exclusively and partially breastfed significantly longer than infants in the SKOT II cohort. Addition- ally, age at the introduction of complementary foods was significantly lower in SKOT II than in SKOT I (Table 1). Despite the fact that no infants in either of the cohorts were exclusively breastfed beyond the age of 6 months, the recorded duration of exclusive breastfeeding was associated with the relative abundance of specific bacterial taxa at the age of 9 months. This was most pronounced in SKOT I, possibly due to the longer average duration of exclusive breastfeeding in this cohort (Fig. 4A and B). However, differences in the effects of duration of exclusive breastfeeding on microbiota between cohorts were modest and not large enough to evoke detectable significant differences between the two cohorts at the age of 9 months (Fig. 1 to 3). In both cohorts, the duration of exclusive breastfeeding was negatively correlated with Lachnospiraceae (e.g., the genera Dorea, Coprococcus, Blautia, Pseudobutyrivibrio, and Roseburia) and genera within Ruminococcaceae (e.g., Ruminococcus, Anaerotruncus, Oscillibacter, Clos- tridium IV, and Butyricicoccus), encompassing species known to utilize plant-derived complex carbohydrates and resistant starch introduced with solid foods (36). Also, Erysipelotrichaceae, Peptostreptococcaceae, and Eubacteriaceae were negatively affected BA SKOT I La ch no sp ira ce ae Bi fid ob ac ter iac ea e Ba cte ro ida ce ae Ru mi no co cc ac ea e Ve illo ne lla ce ae En ter ob ac ter iac ea e Co rio ba cte ria ce ae Er ys ipe lot ric ha ce ae St re pto co cc ac ea e Pe pto str ep toc oc ca ce ae Cl os trid iac ea e Pr ev ote lla ce ae En ter oc oc ca ce ae La cto ba cil lac ea e Po rp hy ro mo na da ce ae Ri ke ne lla ce ae Pa ste ur ell ac ea e Su tte re lla ce ae Ac ida mi no co cc ac ea e Ac tin om yc eta ce ae Cl os trid ial es In ce rta eS ed is XI Eu ba cte ria ce ae Fu so ba cte ria ce ae Ca rn ob ac ter iac ea e -6 -4 -2 0 2 4 6 *** *** ns *** ** *** * ** ns ns ns ns *** ** ns *** ns ** ns *** * *** *** *** Lo g 2( Fo ld c ha ng e) SKOT II La ch no sp ira ce ae Bi fid ob ac ter iac ea e Ba cte ro ida ce ae Ru mi no co cc ac ea e Ve illo ne lla ce ae En ter ob ac ter iac ea e Co rio ba cte ria ce ae Er ys ipe lot ric ha ce ae St re pto co cc ac ea e Pe pto str ep toc oc ca ce ae Cl os trid iac ea e Pr ev ote lla ce ae En ter oc oc ca ce ae La cto ba cil lac ea e Po rp hy ro mo na da ce ae Ri ke ne lla ce ae Pa ste ur ell ac ea e Su tte re lla ce ae Ac ida mi no co cc ac ea e Ac tin om yc eta ce ae Cl os trid ial es In ce rta eS ed is XI Eu ba cte ria ce ae Fu so ba cte ria ce ae Ca rn ob ac ter iac ea e -6 -4 -2 0 2 4 6 *** *** ns *** *** *** ns ns ns *** ** *** *** ns *** ** *** * *** ns ns * ** * Lo g 2( Fo ld c ha ng e) Firmicutes Actinobacteria Bacteroidetes Proteobacteria Fusobacteria ns Phylum FIG 3 Gut microbiota composition is independent of maternal obesity but changes over time. (A) PCA biplot of the relative abundances of bacterial families at 9 and 18 months of age in SKOT I and SKOT II. Ellipses indicate 95% confidence intervals for each group, while arrows show loadings. var., variance. (B) Log2-transformed fold changes of relative abundances of bacterial families between the ages of 9 and 18 months within SKOT I and SKOT II. Error bars indicate the standard error of the mean. ns, not significant; *, q < 0.05; **, q < 0.01; ***, q < 0.001 (according to false-discovery-rate-corrected [5%] paired Wilcoxon signed-rank tests of relative abundances at 9 months versus 18 months). No significant differences were found between the fold changes of bacterial families occurring in the two cohorts after we performed false-discovery-rate-corrected (5%) Mann-Whitney tests. Laursen et al. Volume 1 Issue 1 e00069-15 msphere.asm.org 6 o n A p ril 2 4 , 2 0 2 0 b y g u e st h ttp ://m sp h e re .a sm .o rg / D o w n lo a d e d fro m msphere.asm.org http://msphere.asm.org/ by the duration of exclusive breastfeeding (Fig. 4A). Positive correlations with exclusive breastfeeding were observed in both cohorts for Bifidobacteriaceae (Bifidobacterium), which are known to utilize the lactose and human milk oligosaccharides found in breast milk (37), and Veillonellaceae (e.g., Veillonella and Megasphaera), known lactate utilizers (38, 39). In addition, Pasteurellaceae (Haemophilus) abundances were positively corre- lated with the duration of exclusive breastfeeding (Fig. 4A and B). Although not significant in both cohorts, lactic acid bacteria (Lactobacillaceae, Enterococcaceae, Strep- tococcaceae) and other bacteria known to be present in human milk, like Prevotella (40), and on breast tissue, like Enterobacteriaceae (Escherichia and Klebsiella) (41), were positively correlated with duration of exclusive breastfeeding (Fig. 4A and B). At the age of 9 months, 97 infants (nSKOT I � 59, nSKOT II � 38) were still partially breastfed. Additionally, the estimated average daily breast milk intake at the age of 9 months was strongly correlated with gut microbiota composition and confirmed the associations obtained for the duration of exclusive breastfeeding (see Table S5 in the supplemental material). Consistently with our previous report (8), the effects of breastfeeding on microbial composition were limited at 18 months (Table S6). Some infants are fed with infant formula as a replacement or a supplement to breastfeeding for a period prior to the introduction of complementary foods. However, age at the introduction of com- plementary foods (range, 3 to 6 months) did not correlate with abundances of specific bacterial families at 9 months (Table S7). Furthermore, alpha diversity measures at 9 months were negatively correlated with the duration of exclusive breastfeeding, whereas age of introduction to complementary feeding was generally not correlated with alpha diversity measures, although a weak negative association with observed Family Genus 0 1 2 3 4 5 6 0.0 0.5 1.0 1.5 2.0 2.5 3.0 rho = -0.182 p = 0.008 Duration of e xclusiv e bre astfe e ding (months) S h an n o n in d e x ≤ 3 4 5 6 0.0 0.5 1.0 1.5 2.0 2.5 3.0 rho = -0.047 p = 0.490 Age at introduction to comple me ntary fe e ding (months) S h an n o n in d e x 0 1 2 3 4 5 6 0 10 20 30 40 50 60 70 rho = -0.236 p = 0.0005 Duration of e xclusiv e bre astfe e ding (months) O b se rv e d g e n e ra ≤ 3 4 5 6 0 10 20 30 40 50 60 70 rho = -0.139 p = 0.039 Age at introduction to comple me ntary fe e ding (months) O b se rv e d g e n e ra 0 1 2 3 4 5 6 0.0 0.2 0.4 0.6 0.8 1.0 rho = -0.144 p = 0.035 Duration of e xclusiv e bre astfe e ding (months) P ie lo u' s ev en ne ss ≤ 3 4 5 6 0.0 0.2 0.4 0.6 0.8 1.0 rho = -0.027 p = 0.692 Age at introduction to comple me ntary fe e ding (months) P ie lo u' s ev en ne ss BA C FIG 4 Duration of exclusive breastfeeding is reflected in late-infancy gut microbiota. Hierarchical clustering of Spearman’s rank correlations of duration of exclusive breastfeeding with gut microbial composition at 9 months of age at the family (A) and genus (B) levels in SKOT I and II. *, P < 0.05; **, P < 0.01; ***, P < 0.001. (C) Spearman’s rank correlations of alpha diversity measures (Shannon index, observed genera, and Pielou’s evenness index) to duration of exclusive breastfeeding (0 to 6 months) and age at introduction of complementary feeding (3 to 6 months) for compiled data from SKOT I and II. Boxes indicate 25th to 75th percentiles, with mean values marked as a line and whiskers indicating minimum and maximum values. Transition to Family Foods Affects Gut Microbiota Volume 1 Issue 1 e00069-15 msphere.asm.org 7 o n A p ril 2 4 , 2 0 2 0 b y g u e st h ttp ://m sp h e re .a sm .o rg / D o w n lo a d e d fro m msphere.asm.org http://msphere.asm.org/ genera at 9 months was observed (Fig. 4C). These results suggest that breastfeeding duration, rather than the timing of introduction of complementary foods, is reflected in gut microbiota composition during late infancy, as recently proposed in a study of gut microbiome data from Swedish infants (27). Composition of complementary diet during late infancy affects gut micro- biota composition. A validated 7-day food registration was performed by the parents when the infants were 9 months old (42). On the macronutrient level, no significant differences in fat or carbohydrate intake were observed between the two cohorts; however, protein intake was significantly higher (P � 0.0001, Student’s t test) in SKOT II, while SKOT I infants had a significantly higher (P � 0.016, Student’s t test) fiber intake (Fig. 5A). To capture the complete picture of the complementary diet of the infants in both cohorts at 9 months of age, we previously (25) divided the complete dietary recordings into 23 food groups (defined in Table 2). By PCA of the compiled SKOT I and II subsets of data included in this study (n � 217), the previously defined (25) principal components named family foods (PC1) and health-conscious food (PC2) were gener- ated (Fig. 5B). The family foods component describes the transition from early infant foods (with low loadings of breast milk, formula, and porridge) to foods introduced during late infancy (with high loadings of meat, milk, cheese, animal fat, and rye bread). Macronutrients 0 10 20 30 40 50 60 70 80 90 100 Carbohydrate Prote in Fat SKOT I SKOT II *** ns ns P er ce nt o f t ot al e ne rg y in ta ke 0 1 2 3 4 * Fibre Fi br e in ta ke (m g/ kJ ) Bifidobacteriaceae -4 -2 0 2 4 6 8 0 20 40 60 80 100 rho = -0.241 q = 0.008 Fam ily food PC1 (13.2%) R el at iv e ab un da nc e of B if id ob ac te ri ac ea e (% ) Scores -4 -2 0 2 4 6 8 -6 -4 -2 0 2 4 6 H e al th -C o n s ci o u s f o o d Fam ily food SKOT I SKOT II PC1 (13.2%) P C