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We found that in breast-fed group, α diversity remained unchanged before 3 months of age, but increased significantly in 6 months of age. Previously studies have reported that faecal bacterial diversity increases with age, indicating a more complex microbial community over time8,9. Studies have shown that infants who are exclusively breast-fed have lower microbial diversity, compared with formula-fed babies whose gut microbiota is more diverse and similar to older children10,11,12. The difference of gut microbial diversity between breast-fed and formula-fed babies is also reported in animal research in tiger cubs13. We also found that among different groups, α diversity was lower in breast-fed group than formula-fed groups in 40 days of age. In adults, low gut microbial diversity has been linked to diseases in recent studies. In infants, breast milk may be the major determinant of a lower gut microbial diversity, because specific bacteria are selected for degrading particular oligosaccharides in breast milk. The predomination of infant-type Bifidobacteria during breastfeeding results in a low bacterial diversity, but it is beneficial for babies’ health. For example, the infant-type Bifidobacteria has a large impact on the maturation of the immune system, which may help reduce the incidence of infections in children. However, some diseases have been associated with a reduced microbial diversity in early life, such as eczema and asthma, which have been linked to low microbial diversity in 1 week–4 months of age. But the low microbial diversity is not coupled to Bifidobacterium abundance in these studies, and no reports have shown negative impacts of breastfeeding on development of asthma or allergies. The causality of lower diversity to diseases remains to be identified. What’s more, research has suggested that an immature gut microbial community can be “repaired” by introduction of adult-like microbes increasing greatly during introduction of solid foods in 6 months of age, which is within the development window of opportunity. Findings in adults cannot be inferred to infants regarding the association of gut microbial diversity with diseases, since the microbial ecosystem and the immune system of infants are quite different from adults4.
Bifidobacterium represented the most predominant genus and Enterobacteriaceae the second in all groups at all time-points in our study. Previous study also indicates that all infants have significant levels of Enterobacteriaceae and Bifidobacteriaceae at family level in 2 months of age. The abundance of a single genus usually constitutes the most in family level evaluation. Roger et al. have indicated that Bifidobacterium accounts for 40–60% on average of the total faecal microbiota of a 2-week old new born10. In our study, in 40 days of age, Bifidobacterium accounted for 46.2% in breast-fed group, and 32.2–33.0% in formula-fed groups, which was precisely classified according to feeding types. Bifidobacterium is present in the first few months and decreases as age goes on to almost zero by 18 months old14. Enterobacteriaceae also decreases with time7,8. This is consistent with the European study of 531 infants, which indicates the decrease trend in Bifidobacteriaceae and Enterobacteriaceae species from 6 weeks of age until 4 weeks after solid foods introduction, regardless of differences in feeding patterns15. We found that in breast-fed group, Bifidobacterium decreased from 46.2% in 40 days to 41.4% in 3 months and 29.9% in 6 months of age. In formula-fed groups, after solid foods introduction, Bifidobacterium decreased from 32.2% in 3 months to 31.7% in 6 months of age in formula A group, but increased from 33.0 to 39.0% in formula B group, indicating that different formulas may have different effects on microbiota. In our study, solid foods were introduced from 4 to 6 months of age, so they affected only the last time point in 6 m. We found that in 40 days of age, Bifidobacterium and Bacteroides were significantly higher, while Streptococcus and Enterococcus copy numbers were significantly lower in breast-fed group than they were in formula A-fed group. Lachnospiraceae was lower in breast-fed group than that in formula B-fed group. Veillonella and Clostridioides were lower in breast-fed group than that in formula A and B-fed groups. In 3 months of age there were less Lachnospiraceae and Clostridioides in breast-fed group than formula-fed groups. Other differences of microbiota were shown in Figs. 5 and 6.
After birth, the most important determinant of infant gut microbial colonization is breastfeeding. Studies have shown that breastfeeding is associated with higher levels of Bifidobacterium1,2,16, which is consistent with our study. The genus Bifidobacterium possesses multiple benefits, such as modulation of the immune system, production of vitamins, remission of atopic dermatitis symptoms in infants and decrease in rotavirus infections and lactose intolerance in children and adults10,17. Bifidobacteria is reported to be associated with diminished risk of allergic diseases18 and excessive weight gain19. Higher level of Bifidobacteria also indicates better immune responses to vaccines20.
Bacteroides is among several beneficial bacteria in the earlier neonatal phase, which has important and specific functions in the development of mucosal immune system6. The early activation of mucosal immune system may provide human body lifelong protection from health disorders6. Bacteroides is also linked with increased diversity and faster maturation of gut2. Koenig has studied 1 baby for 2.5 years after its birth and found that Bacteroides genus is absent before the introduction of solid foods21. However, Yassour M. et al. have reported that many infants present a significant Bacteroides species in the first 6 months, before the introduction of solid foods, in a longitudinal study of 39 children in their first 3 years of life14. We also found that there was Bacteroides in the first 6 months of life in all groups. Bacteroides was significantly higher in breast-fed infants, ranking third in 40 days (0.095) in breast-fed group, but decreased as time went on to 0.059 in 3 m and 0.039 in 6 m.
Besides Bacteroides, other health promoting bacteria like Clostridia has been reported to be vital to provide mucosal barrier homeostasis during the neonatal period, which is necessary in the immature intestine6. Formula-fed infants tend to have a more diverse microbial community with increased Clostridia species9,12, which is in accordance with our finding. We also found Veillonella was lower in breast-fed infants than formula-fed ones. Although there is an analysis indicating that Veillonella has been associated with a lower incidence of asthma, it has not taken feeding patterns into consideration22. So more data are needed to clarify the specific roles of certain bacteria with regard to feeding types.
Studies have shown that breast milk keeps the gut in a condition with a lower abundance of Veillonellaceae, Enterococcaceae, Streptococcaceae9,11,23 and Lachnospiraceae7, which is consistent with our results. Some researchers have indicated that higher level of Streptococcus sp. is seen in patients suffered from type 1 diabetes2. There may be other negative effects of these bacteria, but we still know little about them.
The subsequent big change in diet is the introduction of solid foods in 4–6 months of age, which is largely associated with changes in infant gut microbiota. A case study has found an increase in Bacteroidetes at phylum level after solid foods are introduced21. They have indicated that Bacteroidetes is specialized in the decomposition of complex plant polysaccharides21, and it is also associated with faster maturation of the intestinal microbial community2. In our study, after solid foods introduction, percentage of Bacteroides at genus level increased in formula A-fed group, from 0.023 to 0.028, but kept almost the same from 0.009 to 0.008 in formula B-fed group. While in breast-fed group, a decreased percentage of Bacteroides was found from 0.059 in 3 m to 0.039 in 6 m. The trends are different according to different feeding patterns. Pannaraj et al. believe that daily breastfeeding as a part of milk intake continues to affect the infant gut microbial composition, even after solid foods introduction8. But in our study, differences in gut microbiota between breast-fed group and formula-fed groups were not seen any more after solid foods were introduced. As for studies of gut microbiota, the taxonomic level of bacteria adopted in research may affect the results. We focused on microbiota mainly at genus level, resulting in certain discrepancies with some other articles at phylum or species level.
There were significant differences of microbiota between formula A-fed and formula B-fed groups in our study. We found that Pediococcus was less in formula A-fed group than that in formula B-fed group in 40 days. Many research articles have not taken the differences of formulas into consideration, especially retrospective studies. Even breast-fed group is mixed with formulas in some reports. So there must be some inaccuracies of their findings.
Except for feeding patterns, several factors are associated with the microbiota over the first year of life, which is a key period for the gut colonization, such as the mode of delivery, antibiotic exposure, geographical location, household siblings, and furry pets2,9. During the first days of life, the gut microbiota in infants born by vaginal delivery (VD) is similar to that in maternal vagina and intestinal tract, whereas in infants born by caesarean section delivery (CS) the gut microbiota shares characteristics with that of maternal skin. We noticed that the genera of Bacteroides and Parabacteroides were negatively correlated with CS. This was consistent with findings in many other studies, in which the difference of Bacteroides remains in 4 and 12 months of age7,9, and we also found the negative correlation of Bacteroides with CS existed not only in 40 days but also in 6 months of age. The increased morbidity reported extensively in infants born by CS is likely led by altered early gut colonization partially24. Accumulating data have indicated that antibiotic-mediated gut microbiota turbulence during the vital developmental window in early life period may lead to increased risk for chronic non-infectious diseases in later life24. There is a high detection rate of gut Enterococcus in antibiotic-treated infants in their early postnatal period among 26 infants born in a mean gestational age of 39 weeks25. We also found that the relative abundance of Enterococcus was positively correlated with antibiotics usage. The overgrowth of Enterococcus may be caused by antibiotic selection25.
In conclusion, by a larger cohort study than before, differences in gut microbiota among infants who were fed exclusively by breast milk or a single kind of formulas were obtained from this study, contributing further to our understanding of early gut microbial colonization, with more solid data than previous studies with mixed feeding patterns. Faecal diversity was lower in breast-fed infants than formula-fed ones in early life period, but increased significantly after solid foods introduction. A low diversity of the gut microbiota in early life appeared to characterize a healthy gut, if caused by breastfeeding, which was different from theories in adults. There were differences in bacterial composition in infants according to different feeding types, and even different formulas had different effects on microbiota, which we could not ignore in future research. This study presented initial data facilitating further research that will help us understand the importance of breastfeeding to gut microbiota in early life period. More

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Data collection
We hypothesized that the evolution of underground species affected protein networks in a unique manner in which various types of protein domains served as building blocks of protein evolution. To study the evolution of protein networks, we collected genomic, proteomic, and protein domain classification data, namely, fully sequenced genomes with coding sequences and annotated proteomes, together with protein ortholog assignments, from 32 species living in three broad ecological niches, namely subterranean, fossorial, and aboveground (Table 1, and listed in Materials and Methods). We first sought overall statistics regarding the number of proteins and the number of corresponding orthologous protein families. Overall PPI statistics were calculated, including those predicting PPIs in organisms for which experimentally verified PPI data are missing. We used the KEGG orthologs (KO) group of orthologous proteins in KEGG (Kyoto Encyclopaedia of Genes and Genomes)17 to reproduce gain and loss of protein domains in orthologous proteins. We collected 1,350,898 proteins from the studied organisms that belong to 624,787 KO groups (10,314 are unique ortholog groups). The matching number of interactors and networks for every organism were exhaustively calculated for all these proteins (Fig. 1). We found that 361,615 of the 1,350,898 proteins are distributed among 5,879,879 (predicted and real) PPIs. The mean number of interactors per protein within each habitat, namely, aboveground (A), fossorial (F), and subterranean (S) were 32.07, 32.48, and 32.67, respectively (see details in the supplementary results and in Tables S1–S3). This shows that the number of interactors per protein is similar for organisms from different ecologies.
Table 1 All organisms included in the PASTORAL database, with a complete number of proteins in the corresponding proteome.
Full size table

Figure 1

The study overview. Fully sequenced genomes with coding sequences and annotated proteomes were collected from 32 species living in three broad ecological niches: subterranean, fossorial, and aboveground. For collected proteins (1,350,898), protein domains, protein disordered regions, and KEGG orthologous annotation (624,787) were predicted using the Pfam search tool53 along with HMMER60 , IUPred2A44, and the KEGG database17, respectively. Next, 5,879,879 PPIs were evaluated using our previously developed ChiPPI tool15. Briefly, ChiPPI uses a domain-domain co-occurrence table. When a certain domain is missing, ChiPPI evaluates the corresponding missing interactors in the PPI network15, based on real PPI data (363,816) as obtained from BioGrid (release 3.4.163)16. Finally, PPI data are organized in PASTORAL, a dedicated database.

Full size image

Additional analysis of PPI features for orthologous proteins (516 KOs) common to all organisms were similar across ecologies. These features included the number of interactors, the number of PPIs, and global/individual clustering coefficients (supplementary results, Figures S1, S2, Table S4). Thus, we studied PPI properties of genes encoding products related to stresses that differ across the ecologies considered, such as hypoxia. Our findings confirm our hypothesis that the design principles of the evolution of underground species involve various types of protein domains serving as building blocks of protein evolution.
Analysis of the PPIs of stress-response proteins cluster organisms according to habitat
To examine how organisms might have adapted to the various stresses in each habitat, we analyzed mutations and changes in the PPIs encoded by stress response genes. Heat-shock, hypoxia, and circadian stresses differ considerably between aboveground and underground environments, and are likely to drive evolutionary selection of proteins that provide optimal function in each niche1,9. We assumed that organisms subject to a shared ecological experience would face similar environmental stresses. PPI networks of stress-related proteins would thus be expected to differ substantially according to ecology.
To test our hypothesis, we performed clustering analysis of all the organisms included in our study, based on mutations and PPI network features, and compared the results for each classification. Such analysis included all orthologous stress-response, hypoxia, heat-shock, and circadian stress proteins (Table 1). In total, 85,173 PPIs related to stress-response proteins were found to be distributed among 1,103 proteins. These comprised of 730 heat shock proteins in 71,940 PPIs, 254 hypoxia-related proteins in 10,256 PPIs, and 119 circadian proteins in 2,977 PPIs (Table 1, Tables S1–S7). All orthologous stress-response genes (KO groups) were obtained by querying the KEGG database with the terms “heat-shock”, “hypoxia”, and “circadian” terms. The results are listed in Table 2, while the corresponding lists of proteins are found in Tables S5, S6 and S7, respectively.
Table 2 KEGG Orthologs: Heat-shock (upper), hypoxia-related (middle) and circadian (bottom) proteins.
Full size table

Next, we performed clustering analysis based on sequence mutations and PPI features for the full set of heat-shock, hypoxia, and circadian stress proteins (Table 2). Remarkably, proteins related to hypoxia, heat-shock, and circadian stresses in the 32 organisms studied did not all cluster according to shared ecology based on sequence mutations (Fig. 2A) but significantly did so on the basis of “PPI network clustering coefficient” (Fig. 2B–D; p value (AU)  More

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