Background: Adoption of thermal processing of the diet drives human evolution and gut microbiota diversity changes in a dietary habit-dependent manner. However, whether thermal processing of food triggers gut microbial variation remains unknown. Herein, we compared the microbiota of non-thermally processed and thermally processedfood (NF andTF) andinvestigatedgut microbiota associated with NF andTFincatfish Silurus meridionalis and C57BL/6 mice to assess effects of thermal processing of food on gut microbiota and to further identify the differences in host responses. Results: We found no differences in overall microbial composition and structure in the pairwise NF and TF, but identified differential microbial communities between food and gut. Both fish and mice fed TF had significantly lower gut microbial diversity than those fed NF. Moreover, thermal processing of food triggered the changes in their microbial communities. Comparative host studies further indicated host species determined gut microbial assemblies, even if fed with the same food. Fusobacteria was the most abundant phylum in the fish, and Bacteroidetes and Firmicutes dominated in the mice. Besides the consistent reduction of Bacteroidetes and the balanced Protebacteria, the response of other dominated gut microbiota in the fish and mice to TF was taxonomically opposite at the phylum level, and those further found at the genus level. Conclusions: Our results reveal that thermal processing of food strongly contributes to the reduction of gut microbial diversity and differentially drives microbial alterations in a host-dependent manner, suggesting specific adaptations of host-gut microbiota in vertebrates responding to thermal processing of food. These findings open a window of opportunity to understand the decline in gut microbial diversity and the community variation in human evolution and provide new insights into the host-specific microbial assemblages associated with the use of processing techniques in food preparation in humans and domesticated animals. Keywords: Thermal treatment, Food, Vertebrate, Host selection, Gut microbiota Background discussed topic . Diet is central to the evolution of The emergence of metazoans has undoubtedly involved modern humans  and other vertebrates, such as mutualistic relationships with diverse microorganisms horses , and equally important in the evolution of that have presumably been a vital part of the evolution their microbial communities . Comparative studies of of vertebrates . Indeed, co-evolution between humans microbial communities are revealing factors that affect and the microbiota in the gastrointestinal tract is a much microbial diversity such as host genotype  and a range of environmental factors, particularly diet [7, 8]. * Correspondence: email@example.com The type and quantity of food consumed by modern Department of Fishery Resources and Environment, College of Fisheries, humans are changing rapidly. The consumption of Huazhong Agricultural University, Wuhan, People’s Republic of China thermally processed (e.g., cooked) or sanitized foods is Hubei Provincial Engineering Laboratory for Pond Aquaculture, Wuhan, People’s Republic of China © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Zhang and Li Microbiome (2018) 6:99 Page 2 of 14 increasing due to their more effective digestion and pre- responses that were elicited by thermal processing of food vention of infectious diseases. This largely contrasts with were evolutionarily conserved in the last common an- our closest primate relatives who continue to consume cestor of all the animals or independently shaped in each raw foods, unavoidably, where a large number of micro- host. Recently, host phylogeny has been reported to in- organisms colonize. Dietary shifts in humans over time fluence microbial community structure [6, 14], reflecting have presumably occurred in three stages: from an in- the conserved convergence of gut microbiota within a crease in the sharing of plant roots, bulbs, and tubers in host. Thus, we further predicted diverse adaptive trajec- early Homo species  to an increased meat intake in tories of gut microbiota in distantly different phylogenetic Homo sapiens during the Pleistocene and to the adop- hosts such as aquatic and terrestrial animals to thermally tion of agriculture practices and domestication of ani- processed food. mals almost 10,000 years ago. Mammals and fish species with evolutionally unique Dietary shifts result in specific changes in gut micro- phylogenies are the most representative animals in our biota that can distinguish human populations based on biosphere, living in ecologically different habitats. Some their subsistence strategies (i.e., histories and lifestyles) studies on interactions of host-gut microbiota and . Studies focusing on both urban-industrialized soci- microbial differences in humans and animal models are eties and traditional peoples with distinctly different documented [6, 14], though the microbial variation asso- dietary compositions indicate significant divergences in ciated with thermal processing of food remains largely gut microbiota . Recent work by Obregon-Tito et al. unclear. It is necessary to expand studies to this aspect  explored the association between lifestyle and gut in order to fully disentangle the gut microbial variation microbiota in hunter-gatherers and traditional agricul- of the host and how the microbiome has co-evolved with tural communities in Peru and an urban-industrialized the host. We selected both mice and fish to discern the community in the US and showed that some microbes effect of thermal processing of food on host-gut micro- have been lost in urban-industrialized societies. More- biota. On the one hand, mice contain a plenty of hom- over, a progressive loss of gut microbial diversity has ologous genes in humans, favoring the more closely been demonstrated from the adoption of a low evolutionary conservation, which can help in under- microbial-accessible carbohydrate diet over generations standing gut microbial changes in humans over the in mice . evolution; On the other hand, we further explored Modern microbiota deviates substantially from our whether host responses are differently derived in ter- ancestors, and the diversity has decreased over time restrial mammals and aquatic animals by comparing [11, 12]. In general, differences in gut microbiota between gut microbiota of mice and fish. humans and other omnivorous primates are attributed to In this study, we targeted male C57BL/6 mice and dramatic lifestyle changes such as the transition from raw southern catfish (Silurus meridionalis) that are capable to cooked food, farming, and industrialization. Thus, it is of feeding on both non-thermally and thermally proc- possible that microbial diversity and composition were essed food to investigate the impact of thermal process- altered at various key stages of human evolution . ing of food on gut microbial assemblages and compare There is a long evolutionary history of thermal processing the host responses of gut microbiota to thermally proc- in the diet throughout human evolution, from the first use essed food. We provide evidence that thermal processing of fire by our early ancestors to utilization of multiple of food markedly dictates microbial diversity and com- cooking technologies in modern societies. Thermal pro- munity structure and that both animal hosts respond in cessing is a socially unique human practice and represents a remarkably divergent way to thermally processed food. a great advancement in food utilization. To date, many domesticated animals are also gradually transitioning to Methods thermally processed food prepared by humans in Animal intervention industry-oriented developing societies. While the diet is Four-week-old juvenile southern catfish from a pair of an important mediator of gut microbial diversity, the po- parents in a local fish farm were transported to the tential role of thermal processing associated with food College of Fisheries, Huazhong Agricultural University, preparation in shaping gut microbiota has not been Wuhan, China. The fish were reared in the tank with the explored, other than the effect of shifting the diet alone. same culture condition for environmental adaptation for Given the evidence of an important role of thermal pro- 1 week. Male C57BL/6 mice at 4 weeks of age were cessing in the diet over human evolution, we hypothesized obtained from the Hubei Research Center of Laboratory that the adoption of thermal processing in the diet has Animals, Wuhan, China. All mice were kept in a cage been an integral factor tailoring specific microbial struc- for 1 week of adaptation with free access to water in ture signatures in modern humans and domesticated an air-conditioned laboratory under the controlled animals. However, it is indistinguishable that whether host experimental temperature (26 ± 2 °C). Grass carp Zhang and Li Microbiome (2018) 6:99 Page 3 of 14 (Ctenopharyngodon idella) (big fish) fillets and stone (NG vs TG; NS vs TS) samples were collected about moroko (Pseudorasbora parva) (small fish) were used every 2 weeks during the experimental periods for ana- as the experiment foods in this study. Each food was lyses of food microbiota and nutritional characteristics used in duplicate: one as NF and the other as TF. including proximate composition, fatty acids, and amino The heating-up procedure to prepare the TF in a acids. The NS group of four samples was contaminated, method of steaming was set for 15 min (the highest so the samples were not used in subsequent sequencing. temperature, 100 °C; lasting 2~3 min). Lastly, four The catfish were fed two food sources with different food groups, non-thermally and thermally processed treatments (NG vs TG; NS vs TS) for assessing the effect grass carp (NG and TG) and non-thermally and ther- of food type, food treatment, and their interactions on mally processed stone moroko (NS and TS), were ob- microbial assemblies. At the end of the experiments, tained. The catfish and mice were supplied with their four catfish from two tanks in each group (n = 2 fish/ original food (water earthworm Limnodrilus hoffmeis- tank) were killed with an overdose of anesthetic MS- teri for fish; chow for mice) and then transferred to 222. The posterior intestine (approximately half of the combinations of original food and experimental food intestinal tract) was removed after dissection using a prepared for better dietary adaptation during the sterile scalpel and forceps. Similarly, four mice from two period of acclimatization. Experimental food supplies cages in each group (n = 2 mice/cage) were separately were increased gradually with the decline in original caged into four sterile cages for fecal sampling. In total, food supplies until the original food was totally re- four samples from each grouped fish and mice were ob- placed by the experimental food at the start of the tained for microbial analysis. All samples were separately experiments. Comparable catfish were randomly di- placed in sterile 1.5 ml tubes and stored at − 80 °C until vided into four groups (n = 2 tanks/group) with separ- analysis. ate non-recirculating water supplies in each tank (water temperature, 24.2~28.6 °C) to avoid cross- DNA extraction of microbial samples contamination of microbiota; meanwhile, mice were DNA was extracted from all samples using a QIAamp divided into two groups (n = 2 cages/group). NG and DNA Stool Mini Kit (Qiagen, Hilden, NRW, Germany) TG were supplied to two groups of catfish (named as following the manufacturer’s instructions with modifica- F_NG and F_TG) and two groups of mice (M_NG tions. In brief, 1 ml of lysis buffer was added to ~ 100 mg and M_TG). And two other groups of catfish were fecal samples or ~ 200 mg gut and food samples and then supplied with NS and TS (F_NS and F_TS), respect- vortexed horizontally until homogeneity was achieved. ively. The uneaten food residues were removed from The samples were incubated at 95 °C for 5 min and centri- the tanks within 1 h after fish feeding and from the fuged for 2 min at full speed. The resulting DNA pellets cages within 2~3 h for mice. Both animal species were dissolved in 120 μl TAE buffer. The final DNA con- were fed three times daily (in the morning, dusk, and centration was determined using NanoDrop ND-2000 midnight). The fish and mice experiments lasted 8 (ThermoFisher Scientific, Hudson, NH, USA), and DNA and 9 weeks, respectively. The body weight of the fish quality was evaluated by agarose gel electrophoresis. and mice were measured at the end of the experi- ment (Additional file 1:Table S1). Amplification and sequencing of bacterial 16S rRNA genes Sample collection The V4–V5 hypervariable regions of the 16S rRNA In this study, the genetic backgrounds of southern genes were amplified with primers 515-Forward (5′- catfish from the same broodstocks and the identical GTGCCAGCMGCCGCGGTAA-3′) and 907-Reverse culture conditions to the largest degree reduce indi- (5′-CCGTCAATTCCTTTGAGTTT-3′). A specific pri- vidual variations in gut microbial community. Thus, mer with unique barcodes was used for identifications of we did not collect gut samples prior to experiments. different samples. PCR was performed in triplicate with By contrast, it is not sure whether mice had the same 15 μl of Phusion High-Fidelity PCR Master Mix (NEB, parents despite the same age. In order to assess if Ipswich, MA, USA), 0.2 μM of forward and reverse early microbial variations among individuals confuse primers, and 10 ng of template DNA per 30 μl reaction. the late resultant differences, four fecal samples were Thermal cycling consisted of an initial denaturation step randomly collected from mice before experimental at 98 °C for 1 min, followed by 30 cycles of denaturation food intervention (named as M_BA). at 98 °C for 10 s, annealing at 50 °C for 30 s, and elong- Grass carp were purchased from a local market and ation at 72 °C for 30 s, with a final elongation at 72 °C stone moroko were obtained from Lake Liangzi as the for 5 min. Identical PCR products were combined in experimental foods. Grass carp were dissected with the equal amounts, and the mixtures were purified with viscera, and the head was removed. Paired NF and TF GeneJET Gel Extraction Kit (ThermoFisher Scientific, Zhang and Li Microbiome (2018) 6:99 Page 4 of 14 Hudson, NH, USA). Sequencing libraries were con- Statistical analysis structed using an NEB Next UltraTM DNA Library Prep Before data analysis, Shapiro-Wilks test was used to ver- Kit (NEB, Ipswich, MA, USA) for Illumina (San Diego, ify homogeneity of variance. Student’s t test was used to CA, USA) following the manufacturer’s recommenda- detect differences in proximate composition and alpha tions, and index codes were added. Library quality was diversity of paired food groups when data met the assessed using a Qubit 2.0 Fluorometer (ThermoFisher homogeneity of variance; otherwise, Welch’s t test or un- Scientific, Hudson, NH, USA) and Agilent Bioanalyzer equal variance t test was used. Differences in profiles of 2100 system (Agilent, Santa Clara, CA, USA). The overall fatty acids and amino acids of the two foods with resulting amplicons were sequenced on the Illumina different treatments were statistically analyzed using HiSeq 2500 platform. two-way permutational multivariate analysis of variance (PERMANOVA). To evaluate sample dispersion within groups, we calculated inter-sample dissimilarity based Sequence processing, taxonomy assignments and on weighted UniFrac distance and tested the significance community structure analyses within groups using one-way ANOVA with Tukey’s HSD Raw sequence data were processed using QIIME post hoc test. Differences in beta-diversity of gut micro- Pipeline-Version 1.7.0 . All sequences were trimmed biota of mice before and after food intervention were and assigned to each sample based on their barcodes statistically assessed using one-way PERMANOVA. Fur- (barcode mismatches = 0). Overlapping paired-end reads ther, host species and food treatment effects on gut were merged using FLASH-1.2.8 software . Merged microbiota were evaluated using two-way PERMA- sequences (read length > 300 bp, without ambiguous NOVA. Herein, we contrasted “mice vs fish” and “non- base “N,” and average base quality score > 30) were used thermally processed food vs thermally processed food” for further analysis. All sequence reads were sorted and tested for their interactive effects. Similarly, to de- based on their unique barcodes. Chimeric sequences tect whether there were food type and thermal treatment were removed using the UCHIME algorithm . Se- effects on gut microbiota in fish, we also used the quences were subsampled to the same sequence depth method where “grass carp fillets and stone moroko” and using daisychopper.pl  for downstream analysis. Se- “non-thermally processed food vs thermally processed quences were clustered into operational taxonomic units food” were contrasted. All univariate testing and all (OTUs) with CD-HIT algorithm using a 97% identity multivariate testing with 9999 permutations were per- cutoff, and singletons were removed. Phylogenetic affili- formed in SPSS Statistics 20 and Past 3.0, respectively. A ation sequences were analyzed by the Ribosomal p value < 0.05 was considered statistically significant. Database Project classifier . Assessments of within- community diversity (alpha diversity) and between- Results community diversity (beta diversity) were implemented Biochemical compositions of experimental food in QIIME with in-house Perl scripts. Alpha diversity was The proximate composition of the food is shown estimated using four different metrics including Shan- (Additional file 1: Table S2). Thermal processing had no non and Simpson indices for biodiversity and observed effects on fat and ash contents in the two foods (p> 0.05 species and Chao1 for microbial species richness. In for both). The thermal processing decreased water content addition, we compared microbial compositions of top 50 of grass carp fillets and stone moroko (p< 0.001 for both). OTUs between the experimental food and gut using It caused a slight decrease of protein content for grass UPGMA method based on Bray-Curtis distances to- carp fillets (p< 0.01), but not for stone moroko (p> 0.05, gether with a heat map of abundance data and further Additional file 1: Table S2). Regardless of thermal process- visualized gut samples for beta-diversity analysis using ing, the protein content in grass carp fillets was higher Principal Coordinate Analysis (PCoA) based on UniFrac compared to stone moroko (on average, 15.44 vs 13.01%, matrices. To further explore key phylotypes that may p< 0.001), but the ash content was lower (1.26 vs 3.14%, contribute to the observed differences in microbial com- p< 0.001). The two experimental foods contained low munities, linear discriminant analysis (LDA) effect size and similar levels of fat contents (on average, 2.22% (LEfSe) algorithm was performed (http://huttenhower. for grass carp fillets and 2.27% for stone moroko, sph.harvard.edu/galaxy) combining Kruskal-Wallis test p > 0.05) and had no differences in water contents or Wilcoxon rank-sum test with LDA scores to estimate (p >0.05). the effect size of differentially abundant features with We further analyzed the profiles of fatty acids and biological consistency and statistical significance (herein, amino acids of the experimental foods. Principle Com- α value for the statistical test was set at 0.05 and thresh- ponent Analysis (PCA) based on fatty acid profiles old on the LDA score for discriminative features was showed that thermal treatment did not result in overall more than 3.0). fatty acid changes (two-way PERMANOVA, p> 0.05, Zhang and Li Microbiome (2018) 6:99 Page 5 of 14 Additional file 1: Figure S1a) and that food samples clus- normalized the sequence number against the sample tered according to food type (p< 0.001). Based on amino with the lowest sequence number obtained by random acid profiles, PCA also showed sample separations of subsampling. High coverage values (average = 99%) were grass carp fillets and stone moroko (two-way PERMA- obtained for sequences in all samples, indicating that the NOVA, p< 0.05, Additional file 1: Fig. S1b) were not sequencing depth was sufficient. dependent on food treatment (p> 0.05). These results indicate that food type predominantly affects the profiles Thermal processing does not significantly affect food of fatty acids and amino acids rather than thermal treat- microbiota ment in this study. Microbial taxonomic compositions of three groups of experimental foods are showed in Additional file 1: Overview of high-throughput sequencing data Figure S2. The results identified that dominant phyla We characterized 28 gut microbiota samples from four were Proteobacteria, Firmicutes, Bacteroidetes,and Fuso- groups of catfish (F_NG, F_TG, F_NS, and F_TS) and bacteria, together accounting for an average of 99.3, 99. three groups of mice (M_BA, M_NG, and M_TG), and 4, and 98.8% of all classifiable sequences in NG, TG, and 12 food microbiota samples from three food groups TS, respectively. TS group had higher abundance of (NG, TG and TS). In total, 2,549,512 raw sequences Proteobacteria and lower abundance of Firmicutes were obtained. After performing quality trimming and (Additional file 1: Figure S2a) compared to NG and TG chimera checking, we obtained 2,028,760 high-quality groups. There were no significant differences in the processed sequences with a mean length of 371 bp, ac- abundance of each phylum between NG and TG groups counting for 80% of all valid sequences, with an average (Fig. 1a, Additional file 1: Figure S2a). At the genus level, of 50,719 sequences (ranging from 29,083 to 63,992) per Acinetobacter and Veillonella, followed by Cetobacter- sample. To minimize bias due to sequencing depth, we ium and unclassified Neisseriaceae, dominated in Genus level Phylum level Acinetobacter Streptococcus Proteobacteria Veillonella Comamonadaceae* Firmicutes Neisseriaceae* Clostridium Bacteroidetes Cetobacterium Ruminococcaceae* Fusobacteria Sutterella Oscillospira Other Bacteroidaceae* Porphyromonadaceae* Enhydrobacter Peptostreptococcus Rikenellaceae* Porphyromonas S24-7* Elizabethkingia Plesiomonas* Comamonadaceae Other Aeromonadaceae* Desulfovibrionaceae* NG Bacteroides Helicobacteraceae* Alcaligenaceae* Proteiniclasticum TG Fusobacterium Lactococcus Comamonas Other Wautersiella 6.4 NG 0.96 1100 1700 p =0.674 TG p =0.267 p = 0.981 p =0.676 6.0 0.94 1000 0.92 900 5.6 0.90 800 5.2 0.88 700 4.8 0.86 600 1000 Alpha diversity Fig. 1 Microbial relative abundance and alpha diversity in the food. Microbiota in food, grass carp fillets, fed both the catfish, and mice was assessed. Donut charts of the relative abundance at the a phylum and b genus levels. Outer and inner donuts represent the relative abundance in non-thermally processed and thermally processed grass carp (NG and TG) food, respectively. c Multiple indices for alpha diversity estimation. The symbol “*” denotes unclassified OTUs at a taxonomic higher or lower level Shannon Simpson Observed species Chao1 Zhang and Li Microbiome (2018) 6:99 Page 6 of 14 microbial communities of NG and TG groups, which food type (p< 0.01) significantly contributed to changes in consisted of approximately half of all the sequences microbial structure, yet no significant effects were detected (Fig. 1b, Additional file 1: Figure S2b). Despite these taxa for their interactions (p> 0.05) (Table 2). In addition to being detected in TS, the compositional abundances dif- food treatment and food type, their interactions disclosed fered from those in NG and TG groups. Halomonas was significant effects on fish gut microbial members the most abundant genus in TS group (13.5%), but it was (Additional file 1: Table S4). PCoA was used to visualize an significantly lower in NG (0.5%) and TG (0.5%) overview of gut microbial communities in catfish or mice groups (Additional file 1:FigureS2b). As shownin fed with the paired food at the OTU level. Separation was Additional file 1: Figure S2c, TS group separated from clear in both microbial members (unweighted UniFrac, NG and TG groups (weighted UniFrac, one-way PER- Fig. 2d), and microbial structure (weighted UniFrac, MANOVA, p< 0.001), but the two groups were not Fig. 2e) for catfish according to their food treatments. (p> 0.05). Similarly, for alpha diversity, there were no This pattern also occurred in mice (unweighted UniFrac, significant differences between NG and TG groups Fig. 2f; weighted UniFrac, Fig. 2g). Clustering analysis (p> 0.05 for all alpha diversity metrics, Fig. 1c). based on Bray-Curtis metrics of the top 50 genera further confirmed the distinctness of gut microbial communities: Thermal processing of food decreases gut microbial both catfish and mice samples clustered together 100% of diversity the time according to their treatment group (Fig. 3). In TG led to significantly lower Shannon index diversity in the catfish, a distinct sub-cluster nested within different food catfish gut (p< 0.05) and had near-significant effects on re- sources with the same food treatment was observed duced species evenness (p = 0.051 for Simpson index) and (Fig. 3a). In addition, food had an approximate clustering species richness (p = 0.075 for observed species and that was clearly separated from that of catfish (Bray-Cur- p = 0.051 for Chao1) compared with NG (Fig. 2a). In tis, one-way PERMANOVA, p< 0.001, Fig. 3a) and mice line with these results, overall lower alpha diversity (p< 0.001, Fig. 3b). These results suggest that, in addition were observed in F_TS than F_NS (p <0.05 for to host species and food itself, thermal processing of food Shannon index, p = 0.075 for Simpson index, p <0.01 also shapes gut microbial communities in vertebrates. for observed species and Chao1; Fig. 2b). Similarly, alpha diversity measurements revealed lower values Gut microbiota differentially responds to thermal of Shannon and Simpson indices in M_TG compared processing of food to M_NG (p< 0.05 for both; Fig. 2c). No shifts in Gut microbial taxa at the phylum level were dominated microbial species richness were indicated by ob- by Fusobacteria, Proteobacteria, Bacteroidetes, and Fir- served species and Chao1 (p> 0.05 for both; Fig. 2c). micutes in catfish and mice (Fig. 4a, Additional file 1: Overall, these results reveal that thermal processing Figure S3c). Microbial samples from mice before food of food results in general decreases in gut microbial intervention were sequenced and were compared to diversity. those after the intervention (Additional file 1: Figure S3c). The most abundant taxon in all mice was Bacteroi- Thermal processing of food alters gut microbial detes (56.2%), followed by Firmicutes (27.9%). The abun- community dance of Bacteroidetes was higher in M_BA (67.3%) Gut microbial communities differed in mice before and compared to M_NG (55%) and M_TG (46.3%), whereas after food intervention (weighted UniFrac, one-way PER- therewas alower levelof Proteobacteria in M_BA MANOVA, p< 0.001; Additional file 1:FigureS3a); how- (6.8%) than M_NG (14.1%) and M_TG (15.5%). However, ever, intra-group microbial dissimilarities did not change thermal processing of food increased the abundance of (one-way ANOVA, p> 0.05; Additional file 1:FigureS3b). Firmicutes in mice (M_NG = 25.6% and M_TG = 35.6%). Using catfish and mice fed NG and TG to assess sources of No differences in Proteobacteria were observed between gut microbial differences, the results showed the overall mi- M_NG and M_TG, similar to the results obtained in two crobial community structure was strongly associated with different paired groups of catfish (Fig. 4a). Fusobacteria effects of host species (weighted UniFrac, two-way PER- dominated in the gut of catfish, with a decreased MANOVA, p< 0.001), food treatment (p< 0.01), and their abundance in F_NG compared to F_TG (48.5 vs 64.4%, interactions (p< 0.05) (Table 1). The main effects and the Fig. 4a). The reduction was also observed in F_NS com- interactive effects were also found on microbial members pared to F_TS (56.4 vs 66.9%). By contrast, F_NG and F_ (Additional file 1: Table S3). Further, using catfish fed grass NS had higher abundant Bacteroidetes (20.0 vs 11.0% and carp fillets (NG and TG) and stone moroko (NS and TS) to 21.0 vs 14.4%) and Firmicutes (7.9 vs 4.7% and 8.8 vs 4. assess effects of food treatment, food type and their interac- 5%) than the corresponding groups. The Firmicutes-Bac- tions on fish gut microbiota, we found that food treatment teroidetes ratio increased significantly in M_TG than M_ (weighted UniFrac, two-way PERMANOVA, p< 0.001) and NG (p< 0.05, Additional file 1: Figure S4). The ratio was Zhang and Li Microbiome (2018) 6:99 Page 7 of 14 Fig. 2 (See legend on next page.) Zhang and Li Microbiome (2018) 6:99 Page 8 of 14 (See figure on previous page.) Fig. 2 Thermal processing of food affects gut microbial community of the catfish and mice. Thermal processing of food decreases alpha diversity of the microbial community of catfish fed a grass carp fillets and b small stone moroko, and of mice fed c grass carp fillets. Principal coordinate analysis based on unweighted UniFrac distance for d catfish and e mice, and weighted UniFrac distance for f catfish and g mice shows that thermal processing of food induces significant changes in the gut microbial community of the catfish and mice stable between F_NG and F_TG (p > 0.05) and decreased vice versa (Fig. 5a, c, and e), indicating opposite patterns in F_TS compared to F_NS (p< 0.05). Figure 4b shows of gut microbial enrichment between fish and mice the predominant taxa in catfish and mice at the genus responding to thermal processing of food. The differen- level. Thermal processing of food resulted in increased tial shifts were also observed at the OTU level (Fig. 3). trends of the most abundant genera, such as Cetobacter- These results strongly suggest host-specific alterations of ium (F_NG = 48.5% and F_TG = 63.4%; F_NS = 56.3%, gut microbiota by thermal processing of food. F_TS = 65.9%) in fish, and unclassified S24-7 (M_NG = 26.9% and M_TG = 31.1%) and Oscillospira (M_NG = Discussion 12.6% and M_TG = 18.2%) in mice (Fig. 4b, Additional The factors mediating community assembly and struc- file 1: Figure S3d). These taxa disclosed the opposite ture are of a hotspot in microbial ecology in terrestrial changes in fish and mice. Similarly, the Bacteroides sig- and aquatic animals. The key roles of microbial commu- nificantly increased in fish by thermal processing of nities are associated with the development and acclima- food (F_NG = 0.9% and F_TG = 1.4%; F_NS = 0.9% and tion of host to environmental changes [1, 2]. Diet is F_TS = 1.4%), whereas it decreased dramatically in mice known to drive host evolution and affect gut microbiota (M_NG = 9.6% and M_TG =1.6%); meanwhile, this ob- assemblages [3–5]. In this study, the results further ex- servation was suitable to unclassified Rikenellaceae in hibited that thermal processing of food effectively re- both the animals (Fig. 4b). sulted in a reduced gut microbiota diversity of both fish The strict version (all against all) of LEfSe was and mice and affected their microbial communities, with assigned to robustly identify abundant microbial taxa extensively distinct responses of symbiotic gut micro- with a log LDA score above 3.0 that were statistically biota between the hosts. The findings corroborate our different between biological classes in this study. LEfSe hypothesis that thermal processing of food drives the as- analysis revealed 25 and 21 phylotypes in F_NG and F_ sembly of complex gut microbiota in vertebrates associ- TG (Fig. 5a), 12 and 16 phylotypes in F_NS and F_TS ated with host-specific adaptive selection. (Fig. 5c), and 11 and 40 phylotypes in M_NG and M_ We constrained the most possible diet effects by pro- TG (Fig. 5e) for distinguishing taxonomic differences viding mice and catfish hosts with the same food so that between the paired groups of catfish and mice. Of the microbial changes observed could be correlated with the phylotypes in fish, ten were simultaneously enriched in effect of the host. The gut microbiota of mice and catfish F_NG and F_NS; meanwhile, ten were overrepresented almost shared the same microbial divisions, although the in F_TG and F_TS. As shown in the biologically clades dominating taxa within the divisions obviously differed. (Fig. 5b, d, and f), taxonomic distributions further con- In mice, Bacteroidetes and Firmicutes were the two most firmed specific gut microbial taxa from phylum to genus important divisions, with the majority of microbial associated with thermal processing of food, such as the phylotypes; in contrast, gut microbial community of the overrepresented Firmicutes and Fusobacteria in TF-fed catfish was composed mainly of Fusobacterium and Pro- fish and the overrepresented Bacteroidetes in NF-fed fish teobacteria. These results are broadly similar to those of (Fig. 5b, d). Moreover, many specific abundant taxa in gut microbiota in murine and teleost fish . The ex- NF-fed fish were overrepresented in TF-fed mice, and tensive divergences in gut microbiota between mice and Table 1 Two-way PERMANOVA based on weighted UniFrac Table 2 Two-way PERMANOVA based on weighted UniFrac distance testing whether gut microbial communities have distance testing whether gut microbial communities have differences between mice and catfish fed non-thermal and differences in catfish fed grass carp fillets and stone moroko thermal processing grass carp fillets with non-thermal and thermal processing Source d.f. SS MS Pseudo-Fp Source d.f. SS MS Pseudo-Fp Host 1 0.6571 0.6571 211.71 0.0001 Treatment 1 0.1285 0.1285 7.3559 0.0002 Treatment 1 0.0272 0.0272 8.7553 0.0087 Diet 1 0.0816 0.0816 4.6703 0.006 Interaction 1 0.0289 0.0289 9.3063 0.0105 Interaction 1 0.0267 0.0267 1.5256 0.1943 Residual 12 0.0372 0.0031 Residual 12 0.2097 0.0175 Total 15 0.7504 Total 15 0.4464 Zhang and Li Microbiome (2018) 6:99 Page 9 of 14 Fig. 3 Hierarchical clustering dendrogram of microbiota in the gut and food. Dendrograms of gut microbiota in a catfish and b mice coupled with microbiota of food simultaneously supplied to the catfish and mice for the top 50 genera, based on Bray-Curtis distance metrics. The heat map depicts the relative abundance of each genus in each sample. The color scale for the heat map is displayed in the upper right corner of the figure fish strongly indicate that host species determines an es- results seem to provide no conclusive evidence that the sential role in microbial assemblages , consistent with gut microbial alterations of the mice and fish are unre- observations from previous studies of different mammals lated to the food microbiota. The reason for this is due  and other wild and domesticated animals [22, 23]. to thermal processing might diminish or kill microbial In contrast to host genetics as the endogenous determin- activities that may alter the microbial interactions be- ant of gut microbiota, food is an important exogenous tween the gut and food. However, the downfall is intrin- driving factor for microbial configurations. The meticu- sic to thermal processing of food, and it occurs in lous comparison in the catfish fed two different food sequencing based on microbial ecology studies. In gen- sources with thermal and non-thermal processing re- eral, small proportions of gut microbiota were derived vealed differential gut microbiota within intra-species from the microbiota present in food and other sur- host, confirming the effect of food on gut microbial rounding environments, which has been documented in composition and structure. The alterations might be as- some studies focusing on different animals [26, 27]. This sociated with the differences in macro- and micro- is also supported by dramatic differences in microbial nutrients of food components. Notably, the significantly abundances and compositions between the two animals’ higher ash content in stone moroko food containing gut and their foods in the present study. Thus, it could much fishbone is assuredly related to abundant of min- be inferred that the differences in gut microbiota of the eral elements, especially calcium, which can manipulate host responding to the pairwise food are mainly driven by gut microbiota [24, 25]. thermal processing of food rather than food microbiota. Our study centered on whether the impact of the gut Convergent shifts in gut microbial diversity were ob- microbiota is facilitated by thermal processing of food. served in two groups of TF-fed catfish. An increase of As expected, thermal processing significantly altered the highly abundant microbiota in host, such as Cetobacter- communities concurrently in fish and mice. Unlike ium in F_TG and F_TS, is likely responsible for a decrease commercial foods consumed by humans and many do- of other taxa and eventually leads to a low microbial diver- mesticated animals that are usually less enriched in sity. Supporting this, a similar phenomenon was found in microbiota and even sterile due to heavily heated pro- M_TG. Likely, thermal processing of diet dysregulates the cessing, it is easy to carry some microbiota from the sur- competitive mechanisms of the less abundant species rounding environment when food is unprocessed or downregulated by the diet, which then releases the domin- slightly processed. Thus, we intentionally aimed to de- ant microbiota from competitive exclusion, enabling it to tect the microbiota of food used in this study and then expand in abundance. The emerging picture appears to excluded the potential influences of microbial structure follow the community ecological theory, which predicts of food on the communities in the gut because of an un- that highly abundant species monopolize most of the re- changed alpha diversity and stable microbial community sources in the habitats, and over time accelerate the re- observed in the paired NF and TF. Unfortunately, the ductions and extinctions of rare species in communities Zhang and Li Microbiome (2018) 6:99 Page 10 of 14 1.0 Phylum level Fusobacteria Proteobacteria Bacteroidetes 0.8 Firmicutes Tenericutes Deferribacteres Cyanobacteria 0.6 Verrucomicrobia Actinobacteria Other 0.4 0.2 0.0 M_NG F_NG F_TG F_NS F_TS M_TG Fish Mice Cetobacterium Bacteroides Unclassified S24-7 F_NG 0.9 b 0.12 0.4 F_TG F_NS 0.09 0.3 F_TS 0.6 M_NG 0.06 0.2 M_TG 0.3 0.03 0.1 0.0 0.00 0.0 Unclassified Unclassified Oscillospira 0.06 0.06 0.3 Rikenellaceae Clostridiales 0.04 0.04 0.2 0.02 0.02 0.1 0.00 0.00 0.0 Fish Mice Fish Mice Fish Mice Fig. 4 Taxonomic compositions and changes of gut microbiota in the catfish and mice. Relative abundance and the change trend caused by thermally processed food of dominant gut microbiota at the phylum level (a) and genus level (b). The arrow indicates the change trend of the gut microbiota in thermally processed food-fed catfish or mice compared to that in the corresponding non-thermally processed-fed counterparts. ↑ increase, → stable, ↓ decrease with initial uneven abundance patterns compared with As hypothesized, our results indicate that TF triggers more even abundance patterns . Dietary intervention diverse taxon-specific inter-species changes. Compared can improve low gene richness of gut microbiota, which to NF-fed individuals, TF-fed counterparts maintained has the potential to benefit the host . Conversely, it or reduced the ratios of Firmicutes and Bacteroidetes in also plays negative roles in gut microbial assembly. The catfish, while an elevated ratio occurred in TF-fed mice human microbial diversity is greatly depleted compared with more body weight (Additional file 1: Table S1). The with our closest living ape relatives . As proposed by resulting changes in mice match the previous results ob- Gillings et al. , a history of a series of ecological and served in obese individuals compared to their lean coun- evolutionary drivers strongly contributes to the declining terparts in human . Such a trend is likely to generate microbial diversity over human evolution, and the use of correlations between diet-related gut microbiota and fire in diet preparation is presumed as an initial factor host fitness , but diverse patterns are driven by dif- lowering the diversity. Herein, we present the first evi- ferent populations responding to a Western diet . As dence that thermal processing of food markedly decreases detected in more details at the genus level, in this study, the microbial diversity in the gut of two vertebrates, pro- we found opposite patterns of changes in many domin- viding new insights into the sharp decreases of the micro- ant taxa between fish and mice, which has not been bial diversity in humans. previously reported in different hosts fed the same diet. Relative abundance Relative abundance Zhang and Li Microbiome (2018) 6:99 Page 11 of 14 Fig. 5 (See legend on next page.) Zhang and Li Microbiome (2018) 6:99 Page 12 of 14 (See figure on previous page.) Fig. 5 LEfSe analysis identifying taxonomic differences in the gut microbiota of the catfish and mice responding to thermally processed food. Key phylotypes of differently abundant taxa were identified using linear discriminant analysis (LDA) combined with effect size (LEfSe) algorithm. Histograms of LDA scores of 16S gene sequences in F_NG and F_TG (a), F_NS and F_TS (c), and M_NG and M_TG (e) are shown, with a cutoff value of LDA score (log ) above 3.0. a, c, and e F_NG, F_NS, and M_NG-enriched taxa are indicated with a negative LDA score (red), and taxa enriched in the F_TG, F_TS, and M_TG are characterized by a positive score (green). The symbols “# and ¶” denote enriched taxa in the F_TG and/or F_TS; however, those in the M_NG, where the symbol “#” simultaneously denotes the same enriched taxa in F_TG, F_TS, and M_NG; the symbols “& and $” denote enriched taxa in the F_NG and/or F_NS, but those in the M_TG, where the symbol “$,” denotes the same enriched taxa in the F_NG and F_NS. b, e, and f Cladograms are derived from LEfSe analysis of differential gut microbial taxa. The central point denotes the root of the tree of bacteria and expanded to each ring representing the next lower taxonomic level from phylum to genus. Each circle’s diameter represents the relative abundance of the taxon in gut microbial community. The symbol “*” denotes unclassified OTUs at a taxonomic higher or lower level A simple explanation is that the effects of thermal pro- supporting the effect of cooked food on gut microbiota. cessing of food on gut microbiota rely upon host phyl- Particularly significant is the finding that human gut ogeny and/or that differences in host-specific response bacterial enzymes could degrade xenobiotics unique to to thermal processing of food shaping gut microbiota TF . Therefore, thermal processing that is incorpo- are likely to be genetically driven. This further highlights rated into dietary transitions from a very early hominid that host and thermal processing of food would interact diet, to a Neolithic diet, and to today’s typical of West for modulating gut microbiota. More intriguingly, this diet rich in high protein and fat foods, such as red meat raises several questions: whether different host responses and baked potatoes and coffee, further promotes the as- to TF apply to other populations and if different micro- sembly of the present-day human gut microbiota. bial changes correlate to microbial function redundancy An alternative perspective to microbial alterations by for the host during the adaptation. More research is TF is that an increased energy intake is triggered by an needed, not only on representative mammals such as incorporation of animal products such as cooked meat humans and closely related mice family but also on com- into diets that appears to have accelerated the human monly domesticated animals, such as poultry and sal- evolution in terms of the morphological development, mons, to better understand the patterns present in this resulting in larger brain and body size, and smaller gut study. [36, 37]. Similarly, Carmody et al. (2011) found that Generally, gut microbial communities vary geographic- thermal processing significantly increases energy intake ally with the host in large part because they are suited to and leads to larger individuals in mice . These imply local diet and lifestyle and the local adaptation could be that TF modulates gut microbial communities to alter enhanced by incorporation and acquisition of exogenous host energy intake and fat deposition . The correl- genes by gut microbiota . Changes in early hominid ation between altered gut microbiota and diet to diet came with the adoption of cooking, yet it is almost large degree could be considered as a consequence of impractical to obtain direct evidence associated with the interactions between gut microbiota and nutrient cooking or processed foods shaping gut microbiota over loading/calorie intake [7, 41]. Since the advent of cook- human evolution because few fecal samples in early hu- ing, ancestral humans gradually adapted to diversified man history are available. Nevertheless, our results TF and therefore experienced an increased access to might support that divergences of gut microbiota correl- energy-rich food. Unexpectedly, recent lifestyle change ate to the diversified dietary habits. For example, some has negative impacts on the so-called “forgotten organ,” people in a dietary culture show a preference for plant- the human gut microbiota . Thus, gut microbiota based food, and some in other cultures consume meat- may in turn act on the adaptations of host physiology heavy food, even non-thermal processed such as sashimi and metabolic pathways , which eventually contrib- and raw beef. When a diet consists largely of TF that utes to the evolutionary trajectories of host and symbi- can produce end products toxic to humans, such as otic microbiota . acrylamide and ammonia, due to Maillard reaction dur- In addition, our study also has implications for under- ing the heating, thermal processing may have shaped as- standing microbial community differences and variations semblages of human gut microbes for adapting to the among populations. Since gut enterotypes firstly pro- gut ecosystem by introducing products of the Maillard posed based on differential members of gut microbiota reaction which can be further degraded by the resident associated with dietary characteristics in humans , microbiota. In in vitro gut model, Tuohy et al. (2006) some work, subsequently, indicate strong diet effects on found heated protein reduced the numbers of beneficial enterotype status [44–47], but lack consensus of entero- microbiota such as Bifidobacteria and Lactobacilli and types . In two studies focusing on mice fed a similar increased the numbers of detrimental microbiota , diet by Wu et al.  and Wang et al. , despite the Zhang and Li Microbiome (2018) 6:99 Page 13 of 14 clear evidence for contributions of a long-term dietary mechanisms underlying by which how TF modulates history on enterotypes, the opposite effects of Bacter- gut microbial communities, more in particular quan- oides-dominant enterotypes were observed in a short tify the effect size of different thermal processing term. A very recent study has revealed that enterotypes techniques, from daily life to lifespan scales, combin- have no capabilities of reflecting resident microbial com- ing with habitual food on gut microbiota. munities across diverse human populations, nor were they able to effectively distinguish the communities Additional files when those reanalyzed by removing their Bacteroides and Prevotella members . The Bacteroides-dominant Additional file 1: This file contains all the supporting information that is associated with the manuscript, including four additional figure captions enterotype dominates when diet rich in animal protein and legends and four additional tables. The figures are included in and fat are consumed, whereas the Prevotella-dominant separate files and labeled Figures S1–S4. (ZIP 1360 kb) enterotype is believed to be prevalent in individuals with high carbohydrate diets . However, in this study, Abbreviations even though supplied with the same food source, mice DNA: Deoxyribonucleic acid; F_NG: Fish fed non-thermally processed grass carp; F_NS: Fish fed non-thermally processed stone moroko; F_TG: Fish fed with TF had dramatically a lower abundance level of thermally processed grass carp; F_TS: Fish fed thermally processed stone Bacteroides compared to those with NF. This means that moroko; LDA: Linear discriminant analysis; LEfSe: Linear discriminant analysis if enterotype-like clusters continue to be driven by a key effect size; M_BA: Mice before experimental food intervention; M_NG: Mice fed non-thermally processed grass carp; M_TG: Mice fed thermally processed microbial taxon, our finding would further highlight the grass carp; NF: Non-thermally processed food; NG: Non-thermally gaps in enterotype categorization. Despite the controver- processed grass carp; NS: Non-thermally processed stone moroko; sies, these results illustrate that gut microbiota are PCA: Principal component analysis; PCoA: Principal coordinate analysis; PERMANOVA: Permutational multivariate analysis of variance; rRNA: Ribosomal driven not only by host diet but also by the evolution of ribonucleic acid; TF: Thermally processed food; TG: Non-thermally processed feeding ecology and life history . Taken together, grass carp; TS: Non-thermally processed stone moroko substantial gut microbial shifts may have occurred dur- ing the human evolution via adaptation to dietary transi- Acknowledgements We sincerely thank Sena S De Silva and Elizabeth Rettedal for their suggestions tions in terms of both foodstuffs and their processing and revisions on this manuscript and Tianyi Zhang and Weitong Xu for their methods such as thermal processing, concurrently af- assistance in animal management and sample collections. We are also grateful fecting the mutualistic interactions of the host-gut to Wei Chi, Rong Tang, Li Li, and Xi Zhang for their helpful suggestions during the experiments. microbiota. Likewise, whether gaps in the Prevotella- dominant enterotype occur in host feeding on plant- Funding based TF is worth exploring further. The study was supported by the Twelfth 5-year National Key Science and Technology Research Program of China (2012BAD25B06) and the Fundamental Research Funds for the Central Universities (project nos. 2662015PY119 and Conclusions 2014PY041). The authors declare that they have no competing interests. Ecological and evolutionary factors, such as dietary com- ponents and lifestyles, have been used to disentangle Availability of data and materials The raw sequence files of this article are available at the Sequence Read host-gut microbiota adaptations and interplay, though Archive of NCBI under accession number PRJNA352094. the effect of thermal processing associated with food preparations on gut microbiota has been unexplored so Authors’ contributions ZZ and DL designed the study. ZZ contributed to sample collection, analysis far. Our data indicate, in addition to host genotype and of samples, and data analysis. ZZ wrote the paper. DL revised and edited the diet, thermal processing that significantly reduced gut paper. All authors read and approved the final manuscript. microbial diversity and altered the communities in fish and mice. We found opposite patterns of changes in Ethics approval and consent to participate The animals used in this study were treated in accordance with the approval many predominant microbial compositions between fish of the Scientific Ethics Committee of Huazhong Agricultural University under and mice, suggesting that specific adaptive trajectory of approved permit number HZAUMO-2016-026. host-gut microbiota driven by TF in aquatic and terres- Competing interests trial animals might be thoroughly specialized within host The authors declare that they have no competing interests. populations from the perspectives of a long-term evolu- tionary history. These results would be beneficial for elu- Publisher’sNote cidating decreases of microbial diversity and changes of Springer Nature remains neutral with regard to jurisdictional claims in published community structure. Given the prevalence of thermal maps and institutional affiliations. processing of food worldwide, the difference in food Received: 13 November 2017 Accepted: 19 April 2018 processing, such as boiled, baked and steamed, and the potentially profound effect of non-food factors on gut mi- crobial community and function, best practice currently References would appear to undertake further studies on the 1. Woese CR. On the evolution of cells. Proc Nati Acad Sci USA. 2002;99:8742–7. Zhang and Li Microbiome (2018) 6:99 Page 14 of 14 2. Turnbaugh PJ, Ley RE, Hamady M, Fraser-Liggett C, Knight R, Gordon JI. The 29. Cotillard A, Kennedy SP, Kong LC, Prifti E, Pons N, Le Chatelier E, et al. human microbiome project: exploring the microbial part of ourselves in a Dietary intervention impact on gut microbial gene richness. Nature. 2013; changing world. Nature. 2007;449:804–10. 500:585–8. 3. Teaford MF, Ungar PS. Diet and the evolution of the earliest human 30. Ley RE, Turnbaugh PJ, Klein S, Gordon JI. Microbial ecology: human gut ancestors. Proc Natl Acad Sci U S A. 2000;97:13506–11. microbes associated with obesity. Nature. 2006;444:1022–3. 4. Mihlbachler MC, Rivals F, Solounias N, Semprebon GM. Dietary change and 31. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An evolution of horses in North America. Science. 2011;331:1178–81. obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444:1027–31. 5. Hiltunen T, Becks L. Consumer co-evolution as an important component of 32. Amato KR, Yeoman CJ, Cerda G, Schmitt CA, Cramer JD, Miller ME, et al. the eco-evolutionary feedback. Nat Commun. 2014;5:5226. Variable responses of human and non-human primate gut microbiomes to 6. Li X, Yu Y. Feng W, Yan Q, Gong Y. Host species as a strong determinant of a Western diet. Microbiome. 2015;3:1–9. the intestinal microbiota of fish larvae. J Microbiol 2012; 50:29–37. 33. Hehemann J, Correc G, Barbeyron T, Helbert W, Czjzek M, Michel G. Transfer 7. De Filippo C, Cavalieri D, Di Paola M, Ramazzotti M, Poullet JB, Massart S, of carbohydrate-active enzymes from marine bacteria to Japanese gut et al. Impact of diet in shaping gut microbiota revealed by a comparative microbiota. Nature. 2010;464:908–12. study in children from Europe and rural Africa. Proc Natl Acad Sci U S A. 34. Tuohy KM, Hinton DJ, Davies SJ, Crabbe MJ, Gibson GR, Ames JM. Metabolism 2010;107:14691–6. of Maillard reaction products by the human gut microbiota––implications for 8. Sonnenburg ED, Smits SA, Tikhonov M, Higginbottom SK, Wingreen NS, health. Mol Nutr Food Res. 2006;50:847–57. Sonnenburg JL. Diet-induced extinctions in the gut microbiota compound 35. Gloux K, Berteau O, El OH, Béguet F, Leclerc M, Doré J. A metagenomic over generations. Nature. 2016;529:212–5. β-glucuronidase uncovers a core adaptive function of the human intestinal 9. Yeakel JD, Bennett NC, Koch PL, Dominy NJ. The isotopic ecology of African microbiome. Proc Natl Acad Sci U S A. 2011;108:4539–46. mole rats informs hypotheses on the evolution of human diet. P Roy Soc 36. Aiello LC, Wheeler P. The expensive-tissue hypothesis: the brain and the Lond B Bio. 2007;274:1723–30. digestive system in human and primate evolution. Curr Anthropol. 1995;36: 10. Obregon-Tito AJ, Tito RY, Metcalf J, Sankaranarayanan K, Clemente JC, Ursell 199–221. LK, et al. Subsistence strategies in traditional societies distinguish gut 37. Milton K. A hypothesis to explain the role of meat-eating in human microbiomes. Nat Commun. 2015;6:6505. evolution. Evol Anthropol. 1999;8:11–21. 11. Moeller AH, Li Y, Ngole EM, Ahuka-Mundeke S, Lonsdorf EV, Pusey AE, et al. 38. Carmody RN, Weintraub GS, Wrangham RW. Energetic consequences of Rapid changes in the gut microbiome during human evolution. Proc Natl thermal and nonthermal food processing. Proc Nati Acad Sci USA. 2011;108: Acad Sci U S A. 2014;111:16431–5. 19199–203. 12. Sonnenburg ED, Sonnenburg JL. Starving our microbial self: the deleterious 39. Backhed F, Ding H, Wang T, Hooper LV, Koh GY, Nagy A, et al. The gut consequences of a diet deficient in microbiota-accessible carbohydrates. microbiota as an environmental factor that regulates fat storage. Proc Natl Cell Metab. 2014;20:779–86. Acad Sci U S A. 2004;101:15718–23. 13. Gillings MR, Paulsen IT, Tetu SG. Ecology and evolution of the human 40. Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, Mende DR, et al. microbiota: fire, farming and antibiotics. Genes. 2015;6:841–57. Enterotypes of the human gut microbiome. Nature. 2011;473:174–80. 14. Sullam KE, Essinger SD, Lozupone CA, O’Connor MP, Rosen GL, Knight R, 41. Jumpertz R, Le DS, Turnbaugh PJ, Trinidad C, Bogardus C, Gordon JI, et al. et al. Environmental and ecological factors that shape the gut bacterial Energy-balance studies reveal associations between gut microbes caloric communities of fish: a meta-analysis. Mol Ecol. 2012;21:3363–78. load and nutrient absorption in humans. Am J Clin Nutr. 2011;94:58–65. 15. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello 42. O'Hara AM, Shanahan F. The gut flora as a forgotten organ. EMBO Rep. EK, et al. QIIME allows analysis of high-throughput community sequencing 2006;7:688–93. data. Nat Methods. 2010;7:335–6. 43. Serino M, Luche E, Gres S, Baylac A, Bergé M, Cenac C, et al. Metabolic 16. Magoc T, Salzberg SL. FLASH: fast length adjustment of short reads to adaptation to a high-fat diet is associated with a change in the gut improve genome assemblies. Bioinformatics. 2011;27:2957–63. microbiota. Gut. 2012;61:543–53. 17. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves 44. Quercia S, Candela M, Giuliani C, Turroni S, Luiselli D, Rampelli S, et al. From sensitivity and speed of chimera detection. Bioinformatics. 2011;27:2194–200. lifetime to evolution: timescales of human gut microbiota adaptation. Front 18. Gilbert JA, Field D, Swift P, Newbold L, Oliver A, Smyth T, et al. The seasonal Microbiol. 2014;5:587. structure of microbial communities in the Western English Channel. Environ 45. Wu GD, Chen J, Hoffmann C, Bittinger K, Chen YY, Keilbaugh SA, et al. Microbiol. 2009;11:3132–9. Linking long-term dietary patterns with gut microbial enterotypes. Science. 19. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid 2011;334:105–8. assignment of rRNA sequences into the new bacterial taxonomy. Appl 46. Lim MY, Rho M, Song YM, Lee K, Sung J, Ko G. Stability of gut enterotypes Environ Microbiol. 2007;73:5261–7. in Korean monozygotic twins and their association with biomarkers and 20. Kostic AD, Howitt MR, Garrett WS. Exploring host-microbiota interactions in diet. Sci Rep. 2014;4:7348. animal models and humans. Genes Dev. 2013;27:701–18. 47. Wang J, Linnenbrink M, Künzel S, Fernandes R, Nadeau MJ, Rosenstiel P, 21. Ley RE, Hamady M, Lozupone C, Turnbaugh PJ, Ramey RR, Bircher JS, et al. et al. Dietary history contributes to enterotype-like clustering and functional Evolution of mammals and their gut microbes. Science. 2008;320:1647–51. metagenomic content in the intestinal microbiome of wild mice. Proc Nati 22. Roeselers G, Mittge EK, Stephens WZ, Parichy DM, Cavanaugh CM, Guillemin Acad Sci USA. 2014;111:E2703–10. K, et al. Evidence for a core gut microbiota in the zebrafish. ISME J. 2011;5: 48. Jeffery IB, Claesson MJ, O'Toole PW, Shanahan F. Categorization of the gut 1595–608. microbiota: enterotypes or gradients? Nat Rev Micro. 2012;10:591–2. 23. Scupham AJ, Patton TG, Bent E, Bayles DO. Comparison of the cecal 49. Gorvitovskaia A, Holmes SP, Huse SM. Interpreting Prevotella and microbiota of domestic and wild turkeys. Microb Ecol. 2008;56:322–31. Bacteroides as biomarkers of diet and lifestyle. Microbiome. 2016;4:15. 24. Chaplin A, Parra P, Laraichi S, Serra F, Palou A. Calcium supplementation 50. Amato KR. Co-evolution in context: the importance of studying gut microbiota modulates gut microbiota in a prebiotic manner in dietary obese mice. Mol in wild animals. Microbiome Science and Medicine. 2013;1:10–29. Nutr Food Res. 2016;60:468. 25. Borda-Molina D, Vital M, Sommerfeld V, Rodehutscord M, Camarinha-Silva A. Insights into broilers’ gut microbiota fed with phosphorus, calcium, and phytase supplemented diets. Front Microbiol. 2016;7:2033. 26. Kohl KD, Dearing MD. Wild-caught rodents retain a majority of their natural gut microbiota upon entrance into captivity. Env Microbiol Rep. 2014;6:191–5. 27. Zhang Z, Li D, Refaey MM, Xu W, Tang R, Li L. Host age affects the development of southern catfish gut bacterial community divergent from that in the food and rearing water. Front Microbiol. 2018;9:495. 28. Tokeshi M. Species coexistence: ecological and evolutionary perspectives: John Wiley & Sons. 2009.
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Published: May 31, 2018