Microbiota in the ptarmigan intestine—An Inuit delicacy and its potential in popular cuisine

Mads Bjørn Bjørnsen; Nabila Rodríguez Valerón; Diego Prado Vásquez; Esther Merino Velasco; Anders Johannes Hansen; Aviaja Lyberth Hauptmann

doi:10.1371/journal.pone.0305317

Abstract

The consumption of prey intestines and their content, known as gastrophagy, is well-documented among Arctic Indigenous peoples, particularly Inuit. In Greenland, Inuit consume intestines from various animals, including the ptarmigan, a small herbivorous grouse bird. While gastrophagy provides the potential to transfer a large number of intestinal microorganisms from prey to predator, including to the human gut, its microbial implications remain to be investigated. This study addresses this gap by investigating the microbial composition of the Greenlandic rock ptarmigan’s gastrointestinal tract by analyzing the crop, stomach, and intestines while also comparing it with the microbiota found in garum, a fermented sauce made from ptarmigan meat and intestines. Through 16S rRNA gene sequencing, we assessed whether garum made from ptarmigan intestines provides access to microbial diversity otherwise only accessible through gastrophagy. Our findings reveal that garum made from ptarmigan intestines displayed distinct flavors and microbial composition similar to that found in the ptarmigan gut and intestines, highlighting the potential role of fermented products in mediating food microbial diversity associated with Indigenous food practices. Furthermore, our study underscores the broader importance of understanding microbial diversity in different food systems, particularly in the context of shifting dietary patterns and concerns about diminishing food microbial diversity. By elucidating the microbial richness gained through gastrophagy this research contributes to a deeper understanding of traditional and Indigenous foodways and their implications for human gut health.

Citation: Bjørnsen MB, Valerón NR, Vásquez DP, Velasco EM, Hansen AJ, Hauptmann AL (2024) Microbiota in the ptarmigan intestine—An Inuit delicacy and its potential in popular cuisine. PLoS ONE 19(12): e0305317. https://doi.org/10.1371/journal.pone.0305317

Editor: Franck Carbonero, Washington State University - Spokane, UNITED STATES OF AMERICA

Received: May 28, 2024; Accepted: September 12, 2024; Published: December 23, 2024

Copyright: © 2024 Bjørnsen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data is available at the NCBI SRA repository with accession number PRJNA1162031.

Funding: The project was funded by the following institutions: the Aage V. Jensen Foundation and the Danish National Research Foundation. ALH received: Aage V. Jensen Foundation. Granted 2022 https://avjcf.org/ AJH received Danish National Research Foundation. DNRF CEH grant DNRF143 https://dg.dk/en/ The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The study of the microbiology of foods has conventionally dealt primarily with pathogenic microorganisms posing threats to food safety and quality. However, with the rapid expansion of our understanding of the human gut microbiome, there is an increasing focus on the importance of food microbial diversity for human health [1]. Exposure to microbial diversity, including non-pathogenic bacteria in foods, has important implications for human gut microbiome diversity and proper functioning [2]. Fermented foods can play a crucial role in promoting gut health by introducing beneficial microbes into the digestive system [3, 4]. Despite this, except in the case of fermented foods, food microbiology studies are often uncoupled to considerations of food as a source of microbial diversity for the human gut (as exemplified by recent thematic issues on the topic of food microbial diversity [5]) with a few notable exceptions [6, 7]. Thus, there is a need to explore the microbial diversity and composition of various foods beyond the lens of food safety alone, as it has the potential to impact human health positively. As a result, we currently have a limited understanding of the gut microbial implications of other microbially rich foods. An example of such food is gastrophagy, which is the eating of intestines and intestinal content, a food practice that can potentially transfer high numbers of intestinal microbes between trophic levels in the food web. Gastrophagy is common in non-industrialized food cultures [8]. It is well-documented among the Arctic Indigenous peoples [9–12], particularly among Inuit [13]. So far, research on gastrophagy has focused on taste, culture, and nutrition [13] but not the apparent potential for food microbial diversity. Previous studies from our lab have shown that the Indigenous foods of Inuit in Greenland, made according to tradition, harbor a more diverse food microbiota than their industrial counterparts [14]. We have argued that eating intestines as part of the Inuit diet has nutritional and microbial implications for the human gut microbiota [15]. In this study, we take an additional step towards understanding the microbiological potential of Inuit microbe-rich foods by assessing the microbiota of orunit—the intestines and intestinal content of the ptarmigan, which has traditionally been eaten raw [16, 17]. The rock ptarmigan (Lagopus muta) is a small grouse species in the Arctic and subarctic areas. Traditionally, the ptarmigan was mainly treasured for its intestines, eaten raw rather than its meat [16]. Today, cooked meat is an integrated part of local diets in Greenland, while eating its intestines is rare. We included an assessment of the microbiological potential of using ptarmigan intestines in a popular gastronomic context, namely garum. Garum is a traditional fermented fish sauce from ancient Roman and Greek cuisine. It is originally made by fermenting typically small oily fishlike anchovies and is known for its rich flavor [18]. In our case, the garum was made of the ptarmigans’ meat and intestines.

Previous research has focused on the microbial composition of cecal samples from wild rock ptarmigans in Arctic Norway and Svalbard [19] and compared wild Japanese rock ptarmigans and captive Svalbard rock ptarmigans [20]. Firmicutes, Actinobacteria, Bacteroidetes, and Synergistetes were shown to be the dominant phyla in the wild rock ptarmigans. In contrast, a more homogenized composition dominated by only the phyla Firmicutes and some Bacteroidota was found in the captive rock ptarmigans, which supports our earlier research showing that a richer source of microbial diversity is found in non-industrialized foods [15]. No research that we know of has delved into the composition of the Greenlandic rock ptarmigan microbiota, and no former studies have approached it from the perspective of gastrophagy and the potential microbial importance of Indigenous peoples’ food cultures.

By utilizing 16S rRNA gene amplicon sequencing, we analyzed the microbial composition along the gastrointestinal tract of Greenlandic rock ptarmigan as well as the microbial composition of garum made from the ptarmigan meat and intestines. We investigated whether the microbial composition changed through the intestinal tract and whether the microbiota found in the ptarmigan’s intestines was integrated into the garum microbiota. This study aimed to describe the microbial composition of Greenlandic rock ptarmigans and the potential microbial diversity available through gastrophagy. Additionally, we aimed to determine if the microbial diversity present in the intestines could be transferred to popular cuisines. By including an assessment of the microbial potential of ptarmigan intestines in the context of popular gastronomy, namely Koji garum, we wished to highlight the relevance of ptarmigan gastrophagy not only as part of the past but also in the present and the future.

Materials and methods

Ethics statement

The animals included in this study–Greenlandic rock ptarmigans (Lagopus muta)–are not endangered or protected. The samples for this project were collected with approval from the Government of Greenland (NO. G23-018) during the regulated hunting season in the fall/winter of 2021. Greenland does not have an Institutional Animal Care and Use Committee, and no other approval beyond the sample permission listed above is required in Greenland.

Sample collection

Samples were obtained from 11 rock ptarmigans from the Nuuk fjord system in West Greenland. Bacterial swabs were collected from the crop, stomach (gizzard), and intestine (cecum) in triplicates using sterile swabs (HAGMED®, Rawa Mazowiecka, Poland) in the autumn of 2021 and stored at -80°C. Ptarmigan meat and intestines were used to make garum. For the garum, pearled barley (Aurion, Denmark) was soaked for 12 hours at 4°C and then washed with water. The barley was steamed at 100°C for 90 minutes and then cooled down until 30°C and inoculated with Aspergillus oryzae (Barley white koji, Higuchi Matsunosuke Shoten Co., Ltd) for 36 hours at 31°C and 80% humidity in an incubator (IF110plus, Memmert, GmbH + Co. KG, Schwabach, Germany).

To prepare the garum, the 11 ptarmigan birds were brought frozen to the kitchen, whereafter they were defrosted and cleaned from feathers, separating meat from intestines. Subsequently, barley koji was then mixed with either ptarmigan intestines (two samples) and meat (two samples), separately (four samples), in a ratio of 1:2 w/w (Table 1). Finally, 6% salt was added, mixed, and stored in a sealed glass jar at 28°C for one month. Samples from the four different garums were also taken in triplicates. In total, 111 samples were taken: crop (n = 33), stomach (n = 33), intestines (n = 33), and garum (n = 12).

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Table 1. The garum content is produced in duplicates from the ptarmigan intestines and meat, respectively.

https://doi.org/10.1371/journal.pone.0305317.t001

Taste evaluation

The taste of garum was evaluated by 12 trained chefs and researchers from Basque Culinary Center (San Sebastian, Spain) familiar with fermented meat and fish sauce by using check-all-that-apply (CATA) with an adapted description of 33 taste attributes for fermented sauce and garum [21] S1 Table. Three ml of each garum was served in a small transparent plastic cup at room temperature 20°C.

DNA extraction, amplification, and sequencing

We extracted DNA from a total of 118 samples, including 111 ptarmigan samples, one negative control from an unused Type AD sterile swab (HAGMED®, Rawa Mazowiecka, Poland), and six DNA extraction kit controls using the DNeasy PowerSoil Pro Kit (Qiagen®, Hilden, Germany) and following the manufacturer’s instructions with a few modifications as described below. The swab tip was cut off with a sterile scissor and placed into the PowerBead Pro tube, where 800 μl of solution C1 was added and vortexed briefly. Hereafter, the samples were incubated at 65°C for 10 minutes. The DNA concentration was assessed by a Qubit fluorometer (Invitrogen) and stored at -20°C until further processing. A clean-up process with DNeasy PowerClean Pro Cleanup kit (Qiagen®, Hilden, Germany) and OneStep PCR Inhibitor Removal Kit (Zymo Research®, CA, U.S.A.) was performed to remove the remaining PCR inhibitors using the manufacturer’s instructions.

qPCR visualization of selected samples from each sample type showed that combining both clean-up processes gave the best amplification results. The V3 and V4 regions of the 16S rRNA gene were amplified through PCR from the extracted DNA using the primers 341F: ACTCCTACGGGAGGCAGCAG [22] and 806R: GGACTACHVGGGTWTCTAAT [23]. Furthermore, two PCR setups were used as some samples did not amplify, and others were overamplified. The following PCR setup was used for the garum samples: 95°C for 10 min, followed by 32 cycles of 95°C for 15 sec, 56°C for 20 sec, and 72°C for 40 sec. This was followed by a single cycle of 72°C for 10 min, whereafter it was stored at -20°C until further processed. Samples from the crop, stomach, and intestines had the following PCR setup: 95°C for 10 min, followed by 32 cycles of 95°C for 15 sec, 56°C for 20 sec, and 72°C for 40 sec. Followed by a single cycle of 72°C for 10 min and then stored at -20°C until further processed. During this process, we added six PCR controls, leaving us with a total of 124 samples for the downstream library build. PCR products were resolved with gel electrophoresis and stored at -20°C. The PCR product was pooled to make amplicon libraries with Illumina TrueSeq DNA PCR-free library, with each PCR product equally represented by assessing the gel electrophoresis results and dividing the samples into groups depending on the results. The library was built on Ilumina TrueSeq DNA PCR-free Library following the manufacturer’s instructions, and the final libraries were sent for Illumina MiSeq v3 2 x 300 bp paired-end sequencing. Negative control, DNA extraction kit controls, and PCR product controls were included throughout all stages of the laboratory process. The samples were sequenced a second time to ensure enough data for further processing.

Bioinformatics and statistical analysis

Pre-processing and data analysis.

For 16S rRNA gene amplicon sequencing data, raw reads were extracted from the Illumina Miseq® System in fastq format. The paired-end reads were first demultiplexed and trimmed for primers using cutadapt v. 4.2 [24]. Reads with an expected error rate -e 0.13, containing one or more Ns or a length below 170 bp, were discarded. Furthermore, each sample was demultiplexed into sense and anti-sense reads for subsequent processing, as described by Frøslev and colleagues (2017). Through the DADA2 framework [25], the sense and anti-sense reads were processed independently until the identification of chimeric sequences [26]. The algorithm was used to filter and denoise; chimeras were removed before generating the sense, and the anti-sense ASV table contained ASV read counts for each merged sample. Data from the two individual sequence runs were merged, combining the reads from both runs into one ASV table [27]. Taxonomy assignments were performed against the SILVA 16S taxonomy database v.138.1 [28]. R package Decontam v. 1.20.0 [29] was used to detect ASVs present in controls and determine the probability for those to be contaminants, where 4 ASVs were identified as such, whereafter controls and blanks were filtered out of the data. Furthermore, 190 ASVs not annotated to at least bacterial Phylum were removed from downstream analyses, and singletons and doubletons were removed from the dataset. Seven samples with <5.000 reads, comprising 3 crops and 4 stomach samples, were removed from the dataset, leaving 104 samples for further analysis [30, 31].

Statistics and visualization.

Statistical analysis and visualization of graphs were conducted in the “R” software RStudio v. 2023.06.0 [32]. Rarefaction curves were plotted using vegan package v. 2.6.4 [33, 34]. Student t-test was used to test for bacterial differences at the phylum and genus level. ggplot2 package v. 3.4.4 [35] was used to visualize barplots of the microbial communities found in the sample types. Alpha diversity for four diversity indices was calculated: Richness, Shannon, Simpson, and Faith phylogenetic diversity, for which a phylogenetic tree was built using package ape v. 5.7.1.4 [36]. The diversity was tested for normal distribution using the Shapiro-Wilks test. Whereafter, the non-parametric Kruskal-Wallis test was used to compare the diversity indices across the sample types for significant differences (p <0.05), with the p-value adjusted for False Discovery Rate method [37]. The R package Phyloseq v. 1.44.0 [38, 39] was used to calculate the Richness, Shannon, and Simpson indices, and the R package picante v. 1.8.2 [40] was used to calculate the phylogenetic diversity indices. The sample’s core microbiome was identified using the R package Microbiome v. 1.22.0 [41]

The unweighted Unifrac dissimilarity distance matrix was used for beta diversity analysis and visualized using a Non-Metric Multi-dimensional scaling (NMDS) plot was performed on the distance matrix to visualize the dissimilarity between the microbial communities and colored by metadata variable using the phyloseq v. 1.44.0 [38, 39] and vegan packages. The R package vegan was used to compare bacterial community composition between the groups using a Permutational Multivariate Analysis of Variance (PERMANOVA) with 999 permutations using Adonis2 [33] on the matrix to determine if dissimilarity was significant between sample types pairwiseAdonis package v. 0.4.1 [42]. Statistically significant results were considered at p-value <0.05, with Bonferroni used for p-value adjustment for multiple comparisons.

Fisher’s exact test was used when the Cochran Q test did not apply to the CATA data to determine whether there were significant differences between the tasting attributes associated with garum intestines and garum meat, statistically significant results were considered at p-value <0.05.

Results

We sequenced the V3-V4 region of the 16S rRNA with Illumina Miseq v3, which yielded 32,682,297 forward and 32,682,297 reverse reads from two runs. After filtering and quality process, we ended up with 104 samples (Crop = 30), (Intestines = 33), (Stomach = 29), and (Garum = 12) retaining a total of 8.466,809 combined sequences with an average number of sequences per sample of 81,411 clustered into 3090 ASVs, which represented 2 Kingdoms, 23 phyla, 33 classes, 81 orders, 136 families, and 251 genera. ASVs accumulation curves reached asymptote, confirming sufficient sequencing depth to cover the microbial diversity in all samples (S1 Fig).

Composition of ptarmigan intestinal microbiota

The ptarmigan gut, including crop, stomach, and intestines, harbored a microbial community of 2979 ASVs. The crop contained 809 ASVs and was dominated mainly by Firmicutes (68.24% ±31.2) and Proteobacteria (22.32% ±25.03) at the phylum level (Fig 1A). The stomach contained 1782 ASVs and was dominated by Firmicutes (35.75% ±10.73), Actinobacteriota (34.13% ±14.99), and Proteobacteria (10.96% ±16.55). Finally, the intestines contained 1534 ASVs and were dominated by Campylobacterota (24.66% ±33.39), Firmicutes (24.45% ±19.38), Actinobacteriota (22.25% ±19.53), and Proteobacteria (12.68% ±22.58). At the genus level, the crop contained mainly Streptococcus (61.2% ±36.57), Acinetobacter (8.16% ±16.79), and unknown bacteria (4.01% ±7.46) (Fig 1B). The stomach had unknown bacteria (42.46% ± 12.69), Olsenella (10.5% ± 9.5), and Actinomyces (8.99% ± 8.40). Finally, the intestines were dominated by unknown bacteria (44.48% ±24.11), Helicobacter (24.87% ±33.65), and Olsenella (6.68% ±7.33). We also analyzed the garum samples consisting of 285 ASVs, mainly comprised of two phyla, Firmicutes (86.77% ±16.91) and Actinobacteriota (11.78% ±15.08). When we divided the garum samples into the two sub-types, from intestines and meat, we saw that the garum made from intestines had 250 ASVs and, at the phylum level, mainly Firmicutes (73.55% ±14.48) and Actinobacteriota (23.55% ±12.94). In contrast, the garum made from meat consisted of only 66 ASVs. It was mainly dominated by Firmicutes (99.99% ±0.01%) (Fig 1A). At the genus level, all garum samples were primarily comprised of Staphylococcus (71.24% ±23.02), unknown bacteria (10.36% ±13.15) and Latilactobacillus (9.56% ±8.45). The garum from intestines consisted of Staphylococcus (71.24% ±23.02), unknown bacteria (10.36% ±13.15) and Latilactobacillus (9.56% ±8.45), while the garum made from meat consisted of Staphylococcus (88.41% ±13.51) and Latilactobacillus (8.85% ±11.14) (Fig 1B).

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Fig 1. Taxonomic composition across sample types at phylum and genus level.

Microbial composition comparison of the 10 most abundant taxa between sample types. Each horizontal column indicates the average microbial relative abundance within each sample type (Crop, n = 30; Stomach, n = 29; Intestines, n = 33; Garum Intestines, n = 6; Garum Meat, n = 6) at (A) phylum level (B) genus level.

https://doi.org/10.1371/journal.pone.0305317.g001

From the 10 most abundant taxa at the phylum level, the crop microbiota is distinguished from both the stomach and intestinal microbiota by having a significantly higher abundance of Firmicutes (p < 0.001 in both cases) and a higher amount of Proteobacteria (p < 0.001 in both cases). Furthermore, the crop has a significantly lower abundance of Desulfobacterota (p < 0.001) compared to the stomach and of Actinobacteriota (p < 0.001) compared to the intestines. The stomach microbiota is differentiated from the crop and intestinal microbiota by a significantly higher abundance of Actinobacteria (p < 0.001 and p = 0.003, respectively) and Desulfobacterota (p < 0.001 and p = 0.039, respectively) and Synergistota (p < 0.001 and p = 0.01, respectively). Lastly, the intestinal microbiota was distinguished from both the crop and stomach microbiota by a significantly higher abundance of Campylobacterota (p < 0.001 in both cases).

At the genus level, the crop microbiota is distinguished from both the stomach and intestinal microbiota, with a significantly higher abundance of Streptococcus (p < 0.001 in both cases). Furthermore, the crop has a significantly lower abundance of Olsenella (p < 0.001 in both cases), Colidextribacter (p < 0.001 in both cases), and Actinomyces (p < 0.001) compared to intestines. The stomach microbiota is distinguished from the crop and intestinal microbiota, with a significantly higher abundance of Actinomyces (p < 0.001 and p = 0.012, respectively). Finally, the intestinal microbiota is distinguished from both the microbiota of the crop and the stomach by a significantly higher abundance of Helicobacter (p < 0.001 in both cases).

The crop core microbiota was defined as a subset of ASVs that had a prevalence in over 50% of the samples possessing a relative abundance exceeding 0.0001% of the sample type [43, 44]. The core microbiota of the crop samples was comprised of 5 ASVs, representing 0.62% of the total ASV diversity in the crop microbiota. Notably, 92.3% of the sequenced reads attributed to these core ASVs belonged to the genus Streptococcus, a member of the Firmicutes, and were detected in 96.6% of the samples. Among the remaining ASVs, three were affiliated with the phylum Proteobacteria (4.3%), with one occurring in 63.3% of the samples, and the other two in 53.3% of the samples. Finally, one ASV belonged to the phylum Cyanobacteria (3.2%), also prevalent in 53.3% of the samples.

Alpha-diversity of the ptarmigan gastrointestinal tract and garum

Four indices of alpha diversity were calculated: the Richness, Faith phylogenetic diversity, Shannon index, and Simpson index (Fig 2).

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Fig 2. Comparison of alpha diversity metrics of the microbial communities in the ptarmigan gastrointestinal tract and garum.

Boxplots of the alpha diversity of the ptarmigans’ gastrointestinal tract and the two types of garum. The indices used are richness (A), phylogenetic diversity, faith (B), ASV diversity, Shannon index (C), and Simpson index (D). The vertical line inside the boxes is the median obtained from the samples analyzed; each dot represents the alpha diversity value of one sample (Crop, n = 30; Stomach, n = 29; Intestines, n = 33; Garum Intestines, n = 6; Garum meat, n = 6).

https://doi.org/10.1371/journal.pone.0305317.g002

For all alpha diversity metrics, a Shapiro-Wilks test was performed to test if the samples were normally distributed for all four metrics (p < 0.001). As none of the metrics were normally distributed, a pairwise Wilcoxon test with “fdr” was used for multiple comparisons to test which of the sample types were significantly different from each other. For the richness and phylogenetic diversity metrics, all sample types were significantly different except for crop compared to garum meat. The Shannon and Simpson diversity significantly differed between the stomach and the crop, intestines, garum intestines, and garum meat (p < 0.001, in all cases) S2 Table.

Beta diversity of bacterial communities

Beta diversity was assessed using the Unifrac unweighted distance metric in an NMDS plot to illustrate the differences in the bacterial communities in the ptarmigans’ gastrointestinal tract and the two types of garum (Fig 3). Using PERMANOVA a significant difference was found between crop and garum intestines (p = 0.01, R² 0.11), crop and garum meat (p = 0.01, R² 0.14), crop and intestines (p = 0.01, R² 0.16), crop and stomach (p = 0.01, R² 0.20), garum intestines and garum meat (p = 0.01, R² 0.47), garum intestines and intestines (p = 0.01, R² 0.11), garum intestines and stomach (p = 0.05, R² 0.15), garum meat and intestines (p = 0.01, R² 0.24) and garum meat and stomach (p = 0.01, R² 0.31) S3 Table. Furthermore, we see that the crop had the highest average distance within the samples (0.5515), followed by intestines (0.45), stomach (0.4235), garum intestines (0.3828), and garum meat (0.2619) (Table 2).

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Fig 3. Non-metric multidimensional scaling of samples from ptarmigan’s gastrointestinal tract and garum.

Bacterial community clustering was assessed based on Unifrac unweighted distance and visualized with a Non-Metric Multidimensional (NMDS) plot. The NMDS plot displays the distance in the bacterial community composition between Crop (pink), Stomach (purple). Intestines (green), Garum Intestines (light blue), and Garum Meat (orange). Each circle represents the ASV composition from one sample (Crop, n = 30; Stomach, n = 29; Intestines, n = 33; Garum Intestines, n = 6; Garum meat, n = 6).

https://doi.org/10.1371/journal.pone.0305317.g003

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Table 2. Betadiversity distance of each sample type.

https://doi.org/10.1371/journal.pone.0305317.t002

Integration of intestinal microbiota into garum fermented sauce

Samples from the crop shared 47 and 31 ASVs with the stomach and intestine, respectively (Fig 4A). The stomach and intestines shared 834 ASVs, and 117 ASVs were shared between all three sample types. When comparing the microbiota of the two different garum types with the intestines, we see that the intestinal microbiota and the garum intestine microbiota shared 137 ASVs, the garum intestines and the garum meat shared 25 ASVs, and the garum meat and intestines only shared 2 ASVs, while 6 ASVs were shared among all three sample types (Fig 4B).

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Fig 4. Shared microbiota between sample types.

Venn diagram shows the number of unique and shared ASVs between sample types. Ptarmigan’s gastrointestinal tracts microbiota (Crop, n = 31 (pink); Stomach, n = 29 (purple); Intestines, n = 33 (green)) (A) and between the ptarmigan’s intestinal microbiota and the two types of garum (Intestines, n = 33 (green); Garum Intestines, n = 6 (light blue); Garum Meat, n = 6 (orange)) (B).

https://doi.org/10.1371/journal.pone.0305317.g004

Taste evaluation

There was no taste difference between Garum Intestines 1 and Garum Intestines 2 nor between Garum Meat 1 and Garum Meat 2 after the initial tasting. The data was combined into one Garum Intestines (1+2 combined) and one Garum Meat (1+2 combined). Results from the CATA were assessed using the Cochran Q test or the Fisher exact test if the Cochran Q test was not applicable. Of the 33 tasting attributes, “Black Olives” and “Game Flavor” were found to have significant differences between the CATA results (p > 0.001 and p = 0.004, respectively), with garum intestines having significantly higher counts of “yes” in both cases. A complete list of CATA results is listed in S4 Table.

Discussion

Using a 16S rRNA gene amplicon sequencing strategy, we studied the composition and diversity of the ptarmigans’ gastrointestinal microbiota from a culinary perspective. We compared the microbial composition in the ptarmigans’ gastrointestinal tract with two types of garum made from the ptarmigans’ meat and intestines, respectively. This is the first reported study of the Greenlandic rock ptarmigans’ gut microbiota and the first study where the gut microbiota has been put in a food context.

Microbial composition of the gastrointestinal tract of the rock ptarmigan and its relevance for the human gut microbiota

The microbial composition varies throughout the ptarmigan’s gastrointestinal tract. The crop has a richness of 809 ASVs, with a high fraction of the phylum Firmicutes, where the genera Streptococcus sp. represents the most ASVs, with one highly dominating ASV being the biggest part of the crop’s core microbiota. In many avian species, the crop has a pH level of around 5.5 [45], in which most lactic acid bacteria (LAB) classified under the phylum Firmicutes have the best growth conditions. Research suggests that the crop microbiota in poultry serves as a natural barrier that limits and kills pathogens [46]. As an example, some strains of S. cristatus found in the crop have been associated with reducing the carriage of Salmonella in poultry [47]. In addition, LAB is known for its ability to produce lactic acid as a metabolic byproduct, which adds to an acidic environment that inhibits the growth of certain pathogens. Other studies have found a positive relationship between a high abundance of Firmicutes in the gastrointestinal tract and the immune system in domesticated chickens [48–50]. In our data, LAB, including L. delbrueckii and S. cristatus, were identified in the crop microbiota, supporting the idea that the crop of the Greenland rock ptarmigan could have some protective effect against pathogens.

The crop samples exhibited a significantly reduced number of ASVs compared to the stomach and intestinal samples (Fig 2) while having a higher microbial beta diversity (Table 2). Higher beta diversity among the crop samples indicates a less stable microbial community in the crop compared to downstream sites in the intestines. This, alongside that the crop’s core microbiota comprised only 5 ASVs, further suggests that the crop’s microbiota has a higher fraction of allochthonous microbes. In Fig 4A, we see that the crop samples have a total of 809 ASVs, the stomach samples have 1782 ASVs, and the intestinal samples have 1534 ASVs. Furthermore, we also see in Fig 4A that the microbial composition of the ptarmigan crop shares far less ASVs with either the stomach or the intestines. Taken together, these results might suggest that the microbial community found in the crop could act as a microbial barrier between the outside environment and the lower digestive tract.

Our results from this study differ from previous studies looking into the microbial composition of the ptarmigans’ gastrointestinal tract. The dominant phyla found in wild rock ptarmigans from Norway [19] and Japan [20] were Firmicutes, Actinobacteriota, Bacteroidota, and Synergistetes. In our data, the dominant phyla were Campylobacterota in the intestinal microbiota, followed by Firmicutes and Actinobacteriota. The genus Helicobacter was the most abundant genera after unknown bacteria found in the ptarmigan intestinal microbiota, with 25 different ASVs (Fig 1B). The presence of Helicobacter spp. in the intestines could be a source of microbial diversity through gastrophagy from ptarmigan with potential relevance to human health. H. pylori has been associated as a cause of diseases and with protective abilities on human health [51, 52]. The bacteria is found in most human gut microbiota, especially in non-industrialized countries, and when present, it is often the dominant microorganism in the stomach [51, 53, 54]. Some Helicobacter species are specifically adapted to certain hosts, which may lower the risk of disease transfer to humans [55, 56]. In addition, H. pylori is particularly adapted to colonize the human stomach and is thus rarely found in animals, being naturally present only in a limited range of animal hosts [57].

Many factors, including geography, dietary habits, physical activity, and medication usage, influence the microbial composition in the human gut [58]. Dietary transitions in Greenland have meant a divergence from Indigenous animal-sourced Inuit food culture towards more agricultural and processed imported diets [59]. Dietary transitions continuously impact Indigenous peoples’ health, and we need a greater understanding of the potential consequences on the human gut microbiota [60]. In the case of the ptarmigan, its integration into non-Indigenous foodways, eating the cooked meat rather than the raw intestinal content, is likely to have resulted in a reduced potential for food microbial diversity. Reduced microbial diversity in the human gut microbiota and its associated negative consequences are more common in Western populations than in populations living more traditional lifestyles [61]. Previous studies show that microbial composition is higher in traditional food in Greenland than in industrial counterparts [15]. Marco and colleagues (2020) suggest that including safe live microbes in our diet could confer health benefits and should be further investigated. It has been found that diets with live microbes were associated with several health benefits [3, 4]. Consumption of safe microbes has been associated with improved immune systems, reduced risk of chronic diseases, and improved gut function [62, 63]. While the notion of adding probiotics is advocated, it is notable that many probiotic sources contain a relatively limited number of microbial species, and recent studies have argued that not only bacteria labeled as probiotics should be considered beneficial for human health [64, 65]. A recent study done by Hill and colleagues (2023) argues that live microbes from food, including fermented foods, give access to a higher microbial diversity, which could stop the decline of the microbial diversity we see in Western societies caused by processed foods [66, 67] as more and more data point towards that these western diets often have a meager amount of live microbes [68]. Out of the top five phyla identified in this study, four of them (Firmicutes, Actinobacteriota, Proteobacteria, and Bacteroidota) are also among the top phyla found in the human gut and may be potential sources of gut microbial diversity.

Garum, integrating gastrophagy into gastronomy

When comparing the alpha diversity metrics between the two types of garum, garum intestines (GI) and garum meat (GM) (Fig 2), we found that the GI displayed a significantly higher microbial diversity than the GM in both richness and phylogenetic diversity metrics (p < 0.01). This suggests that by including intestines and their content in the garum instead of only using meat, we see a higher microbial diversity added to the garum. In other words, by using gastrophagy in our gastronomy, we gain access to a more diverse food microbiota; as we see in Fig 4B, the GI shares 143 ASVs with the ptarmigan intestines while GM shares only 8 ASVs with the ptarmigan intestines. Furthermore, the GI shares more ASVs with the intestines compared to how many ASVs that are not shared. In addition, flavor assessments show that the two garums have distinct flavors. Out of the 33 tasting attributes, “Black Olives” and “Game flavor” exhibited significant differences between GI and GM, with the GI having a higher score for these flavors. The GI has a higher fraction of Other and Unknown genera of Bacteria compared to GM as well as Actinomyces, not prevalent in GM. In further studies it would be relevant to test whether the higher diversity of microbes in GI perhaps in combination with the potential of microbes such as Actinomyces to metabolize carbohydrates into a variety of acids can help to increase the suppression of pathogens in combination with contributing to allowing access to the richer microbial diversity found in the intestines potentially enhancing the gut health.

Among the shared taxa was the genus Bifidobacteria. This family of bacteria, which is naturally found in the human microbiota [69], has been associated with protection from enteropathogenic infection [70] and has been positively associated with treating human diseases [71]. Other taxa identified were the order Lactobacillales, where the genera Lactiplantibacillus and Latilactobacillus were found. In the GI microbiota, 12 and 5 different ASVs belonging to these genera were identified, respectively. For the GM microbiota, only 4 ASVs belonging to this order and 1 ASV for each genus were identified. Both genera are classified as LAB and are associated with a positive impact on human health. For instance, Lb. plantarum, which is known to enhance the flavor and preserve the nutritional composition found in food, like amino acids and vitamins. [72]. Lat. sakei has been reported to enhance the production of short-chain fatty acids, which are known to improve gut health [73, 74]. Both types of bacteria have been reported to be present in the human gastrointestinal tract and fermented food. Our results show that using gastrophagy—using intestines rather than meat alone—in garum provides access to a significantly broader array of microorganisms with potential relevance to human health. Introducing gastrophagy into popular culinary practices, such as utilizing garum made from ptarmigan intestines in the kitchen, offers a novel approach to augmenting microbial diversity loss in dishes with added potential flavor implications. This aligns with ongoing efforts to reintegrate diverse microbial exposure into contemporary diets, acknowledging the potential health and sensory benefits associated with such microbes.

Incorporating garum into our project introduces a novel gastronomic dimension to the ptarmigan microbiota. Historically renowned in various cultures, garum exemplifies food preservation through fermentation [75]. This approach acknowledges the adaptability of traditional practices to culinary contexts, emphasizing the resilience and relevance of ptarmigan gastrophagy in present and future foodscapes. Moreover, the exploration of garum as a medium for preserving microbial diversity from ptarmigan gastrophagy expands the scope of our study beyond the biological aspects. It opens avenues for cultural and gastronomic conversations, positioning Indigenous culinary practices as dynamic and adaptable components of a living heritage.

Conclusions

In conclusion, this study contributes to research at the intersection of traditional diets, human gut microbial diversity, and contemporary gastronomy. It underscores the imperative to acknowledge and preserve the microbial richness inherent in Indigenous dietary practices, advocating for a reassessment of dietary shifts that may compromise this diversity. Ptarmigan gastrophagy is a source of food microbial diversity in a world where the food is becoming less and less microbially diverse. A way to activate the microbial richness found in the ptarmigans’ gastrointestinal tract could be through garum or other fermented food practices.

Future research

Relevant next steps from this research are to assess the impact of eating orunit—ptarmigan intestines—and foods made from it on the human gut microbiome to test whether ptarmigan gastrophagy adds to the diversity of the human gut microbiota and how this impacts human health and wellbeing.

Supporting information

S1 Fig. Rarefaction curve of the sample types.

Rarefaction curve of the sample types of crop (A), stomach (B), intestines (C) and the two types of garum meat and intestines (D). The curves confirm a sufficient sequencing depth to cover the microbial diversity found in the samples as they reach asymptote.

https://doi.org/10.1371/journal.pone.0305317.s001

(DOCX)

S1 Table. Tasting attributes for the two garum types used for CATA.

https://doi.org/10.1371/journal.pone.0305317.s002

(DOCX)

S2 Table. The p-value for the pairwise Wilcoxon test of the alpha diversity indices.

P-value of the alpha diversity metrics richness, faith phylogenetic diversity, Shannon diversity index and Simpsons diversity index. Pairwise Wilcoxon test was performed. P value of 0.05 is set with the p-value adjusted for False Discovery Rate method.

https://doi.org/10.1371/journal.pone.0305317.s003

(DOCX)

S3 Table. PERMANOVA results for the dissimilarity between the bacterial community clustering.

PERMANOVA results for the bacterial community clustering based on Unifrac unweighted distance. P value of 0.05 is set with the p-value adjusted with Bonferroni for multiple comparisons.

https://doi.org/10.1371/journal.pone.0305317.s004

(DOCX)

S4 Table. CATA results for the two garum types.

P-value of the tasting attributes. The appropriate statistical comparison of 2 groups either Cochran Q test or Fishers exact test was performed. P-value of 0.05 is set.

https://doi.org/10.1371/journal.pone.0305317.s005

(DOCX)

Acknowledgments

The authors would like to extend a special thanks to the hunters who have harvested the ptarmigan. Thanks to Anne Nívika Grødem and Ove Grødem. Furthermore, we would like to thank Ida Broman Nielsen for her work in the laboratory.

References

1. Marco ML, Hill C, Hutkins R, Slavin J, Tancredi DJ, Merenstein D, et al. Should There Be a Recommended Daily Intake of Microbes? Journal of Nutrition. 2020;150: 3061–3067. pmid:33269394
- View Article
- PubMed/NCBI
- Google Scholar
2. Prescott SL. A butterfly flaps its wings: Extinction of biological experience and the origins of allergy. Annals of Allergy, Asthma and Immunology. American College of Allergy, Asthma and Immunology; 2020. pp. 528–534. pmid:32474160
- View Article
- PubMed/NCBI
- Google Scholar
3. Hill C, Tancredi DJ, Cifelli CJ, Slavin JL, Gahche J, Marco ML, et al. Positive Health Outcomes Associated with Live Microbe Intake from Foods, Including Fermented Foods, Assessed using the NHANES Database. Journal of Nutrition. 2023;153: 1143–1149. pmid:36822397
- View Article
- PubMed/NCBI
- Google Scholar
4. Marco ML, Heeney D, Binda S, Cifelli CJ, Cotter PD, Foligné B, et al. Health benefits of fermented foods: microbiota and beyond. Current Opinion in Biotechnology. Elsevier Ltd; 2017. pp. 94–102. https://doi.org/10.1016/j.copbio.2016.11.010 pmid:27998788
5. Doulgeraki AI, Tassou CC. Food microbial diversity. Microorganisms. MDPI; 2021. pmid:34946157
- View Article
- PubMed/NCBI
- Google Scholar
6. David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature. 2014;505: 559–563. pmid:24336217
- View Article
- PubMed/NCBI
- Google Scholar
7. Lang JM, Eisen JA, Zivkovic AM. The microbes we eat: Abundance and taxonomy of microbes consumed in a day’s worth of meals for three diet types. PeerJ. 2014;2014: 1–39. pmid:25538865
- View Article
- PubMed/NCBI
- Google Scholar
8. Buck LT, Berbesque JC, Wood BM, Stringer CB. Tropical forager gastrophagy and its implications for extinct hominin diets. J Archaeol Sci Rep. 2016;5: 672–679.
- View Article
- Google Scholar
9. Eskimoliv Nansen F. Kristiania: H. Aschehoug & Co.s.; 1891.
10. Eidlitz K. Food and Emergency Food in the Circumpolar Area. University of Uppsala; 1969.
11. Fediuk K. Vitamin C in the Inuit Diet: past and present. Dissertation. McGill University. 2000. Available: https://escholarship.mcgill.ca/concern/theses/xp68kj14m.
12. Andersen SM. Vitamins and minerals in the traditional Greenland diet. National Environmental Research Institute. Copenhagen NV; 2005. Available: http://www2.dmu.dk/1_viden/2_Publikationer/3_fagrapporter/rapporter/FR528.PDF.
13. Buck LT, Stringer CB. Having the stomach for it: A contribution to Neanderthal diets? Quat Sci Rev. 2014;96: 161–167.
- View Article
- Google Scholar
14. Hauptmann AL, Paulová P, Castro-Mejía JL, Hansen LH, Sicheritz-Pontén T, Mulvad G, et al. The microbial composition of dried fish prepared according to Greenlandic Inuit traditions and industrial counterparts. Food Microbiol. 2020;85. pmid:31500717
- View Article
- PubMed/NCBI
- Google Scholar
15. Hauptmann AL, Paulová P, Hansen LH, Sicheritz-Pontén T, Mulvad G, Nielsen DS. Microbiota in foods from Inuit traditional hunting. PLoS One. 2020;15. pmid:31935269
- View Article
- PubMed/NCBI
- Google Scholar
16. Thalbitzer W. The Ammassalik Eskimo: Contributions to the Ethnology of the East Greenland Natives. Meddelelser om Grønland. Meddelelser Om Grønland 39; 1912.
- View Article
- Google Scholar
17. Gilberg A. Eskimo Doctor. W. W. Norton; 1948.
18. Papalas AJ, Curtis RI. Garum and Salsamenta: Production and Commerce in Materia Medica. The Classical World. 1992;86: 50.
- View Article
- Google Scholar
19. Salgado-Flores A, Tveit AT, Wright AD, Pope PB, Sundset MA. Characterization of the cecum microbiome from wild and captive rock ptarmigans indigenous to Arctic Norway. PLoS One. 2019;14: 1–21. pmid:30856229
- View Article
- PubMed/NCBI
- Google Scholar
20. Ushida K, Segawa T, Tsuchida S, Murata K. Cecal bacterial communities in wild Japanese rock ptarmigans and captive Svalbard rock ptarmigans. Journal of Veterinary Medical Science. 2016;78: 251–257. pmid:26468217
- View Article
- PubMed/NCBI
- Google Scholar
21. Jeon H. Effect of testing conditions on consumer acceptance of soy sauce. Master’s Thesis, Ewha Womans University, Seoul, Republic of Koera. 2016.
22. Herlemann DPR, Labrenz M, Jürgens K, Bertilsson S, Waniek JJ, Andersson AF. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME Journal. 2011;5: 1571–1579. pmid:21472016
- View Article
- PubMed/NCBI
- Google Scholar
23. Takai K, Horikoshi K. Rapid detection and quantification of members of the archaeal community by quantitative PCR using fluorogenic probes. Appl Environ Microbiol. 2000;66: 5066–5072. pmid:11055964
- View Article
- PubMed/NCBI
- Google Scholar
24. Martin M. Cutadapt Removes Adapter Sequences From High-Throughput Sequencing Reads. EMBnet Journal. 2011;17: 2803–2809.
- View Article
- Google Scholar
25. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13: 581–583. pmid:27214047
- View Article
- PubMed/NCBI
- Google Scholar
26. Frøslev TG, Kjøller R, Bruun HH, Ejrnæs R, Brunbjerg AK, Pietroni C, et al. Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates. Nat Commun. 2017;8. pmid:29084957
- View Article
- PubMed/NCBI
- Google Scholar
27. Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME Journal. 2017;11: 2639–2643. pmid:28731476
- View Article
- PubMed/NCBI
- Google Scholar
28. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013;41: 590–596. pmid:23193283
- View Article
- PubMed/NCBI
- Google Scholar
29. Davis NM, Proctor DiM, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome. 2018;6. pmid:30558668
- View Article
- PubMed/NCBI
- Google Scholar
30. Alberdi A, Aizpurua O, Gilbert MTP, Bohmann K. Scrutinizing key steps for reliable metabarcoding of environmental samples. Methods Ecol Evol. 2018;9: 134–147.
- View Article
- Google Scholar
31. Cao Q, Sun X, Rajesh K, Chalasani N, Gelow K, Katz B, et al. Effects of Rare Microbiome Taxa Filtering on Statistical Analysis. Front Microbiol. 2021;11: 1–15. pmid:33510727
- View Article
- PubMed/NCBI
- Google Scholar
32. RStudio: Integrated Development for R. Boston, MA: Posit Software, PBC; 2020. Available: http://www.rstudio.com/.
33. Jari Oksanen, Simpson Gavin L., Guilamume Blachet F., Roeland Kindt, Pierre Legendre, Minchin Peter R., et al. Vegan: community ecology package. R package version 2.6.4. 2019. Available: https://cran.r-project.org/package=vegan.
- View Article
- Google Scholar
34. Dixon P. VEGAN, a package of R functions for community ecology. Journal of Vegetation Science. 2003;14: 927–930.
- View Article
- Google Scholar
35. Wickham H. Elegant Graphics for Data Analysis: ggplot2. Applied Spatial Data Analysis with R. Springer New York LLC; 2008. https://doi.org/10.1007/978-0-387-98141-3
36. Paradis E, Schliep K. Ape 5.0: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 2019;35: 526–528. pmid:30016406
- View Article
- PubMed/NCBI
- Google Scholar
37. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. 1995;57: 289–300. Available: https://www-jstor-org.ep.fjernadgang.kb.dk/stable/2346101.
- View Article
- Google Scholar
38. McMurdie PJ, Holmes S. Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS One. 2013;8. pmid:23630581
- View Article
- PubMed/NCBI
- Google Scholar
39. McMurdie PJ, Holmes S. Shiny-phyloseq: Web application for interactive microbiome analysis with provenance tracking. Bioinformatics. 2015;31: 282–283. pmid:25262154
- View Article
- PubMed/NCBI
- Google Scholar
40. Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics. 2010;26: 1463–1464. pmid:20395285
- View Article
- PubMed/NCBI
- Google Scholar
41. Lahti L, Shetty S. Microbiome: tools for microbiome analysis in R. Version 2.1.24. 2017. Available: https://github.com/microbiome/microbiome?tab=readme-ov-file.
- View Article
- Google Scholar
42. Martinez Arbizu P. pairwiseAdonis: Pairwise multilevel comparison using adonis. R package version 0.4. In: R Cran [Internet]. 2020 p. 1. Available: https://github.com/pmartinezarbizu/pairwiseAdonis.
- View Article
- Google Scholar
43. Grond K, Santo Domingo JW, Lanctot RB, Jumpponen A, Bentzen RL, Boldenow ML, et al. Composition and Drivers of Gut Microbial Communities in Arctic-Breeding Shorebirds. Front Microbiol. 2019;10: 1–12. pmid:31649627
- View Article
- PubMed/NCBI
- Google Scholar
44. Bodawatta KH, Koane B, Maiah G, Sam K, Poulsen M, Jønsson KA. Species-specific but not phylosymbiotic gut microbiomes of New Guinean passerine birds are shaped by diet and flight-associated gut modifications. Proceedings of the Royal Society B: Biological Sciences. 2021;288: 14–17. pmid:33878920
- View Article
- PubMed/NCBI
- Google Scholar
45. Marmion M, Ferone MT, Whyte P, Scannell AGM. The changing microbiome of poultry meat; from farm to fridge. Food Microbiol. 2021;99. pmid:34119108
- View Article
- PubMed/NCBI
- Google Scholar
46. Moen B, Rudi K, Svihus B, Skånseng B. Reduced spread of Campylobacter jejuni in broiler chickens by stimulating the bird’s natural barriers. J Appl Microbiol. 2012;113: 1176–1183. pmid:22817452
- View Article
- PubMed/NCBI
- Google Scholar
47. Zhang G, Ma L, Doyle MP. Salmonellae reduction in poultry by competitive exclusion bacteria Lactobacillus salivarius and Streptococcus cristatus. J Food Prot. 2007;70: 874–878. pmid:17477255
- View Article
- PubMed/NCBI
- Google Scholar
48. Liao XD, Ma G, Cai J, Fu Y, Yan XY, Wei XB, et al. Effects of Clostridium butyricum on growth performance, antioxidation, and immune function of broilers. Poult Sci. 2014;94: 662–667. pmid:25717087
- View Article
- PubMed/NCBI
- Google Scholar
49. Zhang L, Li J, Yun TT, Qi WT, Liang XX, Wang YW, et al. Effects of pre-encapsulated and pro-encapsulated Enterococcus faecalis on growth performance, blood characteristics, and cecal microflora in broiler chickens. Poult Sci. 2015;94: 2821–2830. pmid:26371326
- View Article
- PubMed/NCBI
- Google Scholar
50. Grond K, Sandercock BK, Jumpponen A, Zeglin LH. The avian gut microbiota: community, physiology and function in wild birds. J Avian Biol. 2018;49: 1–19.
- View Article
- Google Scholar
51. Miller AK, Williams SM. Helicobacter pylori infection causes both protective and deleterious effects in human health and disease. Genes Immun. 2021;22: 218–226. pmid:34244666
- View Article
- PubMed/NCBI
- Google Scholar
52. Zuo ZT, Ma Y, Sun Y, Bai CQ, Ling CH, Yuan FL. The Protective Effects of Helicobacter pylori Infection on Allergic Asthma. Int Arch Allergy Immunol. 2021;182: 53–64. pmid:33080611
- View Article
- PubMed/NCBI
- Google Scholar
53. Bik EM, Eckburg PB, Gill SR, Nelson KE, Purdom EA, Francois F, et al. Molecular analysis of the bacterial microbiota in the human stomach. Proc Natl Acad Sci U S A. 2006;103: 732–737. pmid:16407106
- View Article
- PubMed/NCBI
- Google Scholar
54. Cover TL, Blaser MJ. Helicobacter pylori in Health and Disease. Gastroenterology. 2009;136: 1863–1873. pmid:19457415
- View Article
- PubMed/NCBI
- Google Scholar
55. Dubois A, Berg DE, Incecik ET, Fiala N, Heman-Ackah LM, Del Valle J, et al. Host specificity of Helicobacter pylori strains and host responses in experimentally challenged nonhuman primates. Gastroenterology. 1999;116: 90–96. pmid:9869606
- View Article
- PubMed/NCBI
- Google Scholar
56. Ferrero RL, Jenks PJ. In Vivo Adaptation to the Host. In: Mobley HL, Mendz GL, Hazell SL, editors. Helicobacter pylori: Physiology and Genetics. Washington DC: ASM Press; 2001. pp. 583–592. https://doi.org/10.1128/9781555818005.ch46
57. Lee A, Fox J, Hazell S. Pathogenicity of Helicobacter pylori: a Perspective. Infect Immun. 1993; 1601–1610. pmid:8478048
- View Article
- PubMed/NCBI
- Google Scholar
58. Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG, Contreras M, et al. Human gut microbiome viewed across age and geography. Nature. 2012. pp. 222–227. pmid:22699611
- View Article
- PubMed/NCBI
- Google Scholar
59. Pars T, Osler M, Bjerregaard P. Contemporary use of traditional and imported food among Greenlandic Inuit. Arctic. 2001;54: 22–31.
- View Article
- Google Scholar
60. Skelly E, Kapellas K, Cooper A, Weyrich LS. Consequences of colonialism: A microbial perspective to contemporary Indigenous health. Am J Phys Anthropol. 2018;167: 423–437. pmid:30159882
- View Article
- PubMed/NCBI
- Google Scholar
61. Sonnenburg ED, Smits SA, Tikhonov M, Higginbottom SK, Wingreen NS, Sonnenburg JL. Diet-induced extinctions in the gut microbiota compound over generations. Nature. 2016;529: 212–215. pmid:26762459
- View Article
- PubMed/NCBI
- Google Scholar
62. Berding K, Bastiaanssen TFS, Moloney GM, Boscaini S, Strain CR, Anesi A, et al. Feed your microbes to deal with stress: a psychobiotic diet impacts microbial stability and perceived stress in a healthy adult population. Mol Psychiatry. 2023;28: 601–610. pmid:36289300
- View Article
- PubMed/NCBI
- Google Scholar
63. Marco ML, Sanders ME, Gänzle M, Arrieta MC, Cotter PD, De Vuyst L, et al. The International Scientific Association for Probiotics and Prebiotics (ISAPP) consensus statement on fermented foods. Nat Rev Gastroenterol Hepatol. 2021;18: 196–208. pmid:33398112
- View Article
- PubMed/NCBI
- Google Scholar
64. Dimidi E, Cox SR, Rossi M, Whelan K. Fermented foods: Definitions and characteristics, impact on the gut microbiota and effects on gastrointestinal health and disease. Nutrients. MDPI AG; 2019. pmid:31387262
- View Article
- PubMed/NCBI
- Google Scholar
65. Rezac S, Kok CR, Heermann M, Hutkins R. Fermented foods as a dietary source of live organisms. Front Microbiol. 2018;9. pmid:30197628
- View Article
- PubMed/NCBI
- Google Scholar
66. Sonnenburg JL, Sonnenburg ED. Vulnerability of the industrialized microbiota. Science (1979). 2019;366. pmid:31649168
- View Article
- PubMed/NCBI
- Google Scholar
67. Margină D, Ungurianu A, Purdel C, Tsoukalas D, Sarandi E, Thanasoula M, et al. Chronic inflammation in the context of everyday life: Dietary changes as mitigating factors. International Journal of Environmental Research and Public Health. MDPI AG; 2020. pp. 1–30. pmid:32531935
- View Article
- PubMed/NCBI
- Google Scholar
68. Sonnenburg ED, Sonnenburg JL. The ancestral and industrialized gut microbiota and implications for human health. Nat Rev Microbiol. 2019;17: 383–390. pmid:31089293
- View Article
- PubMed/NCBI
- Google Scholar
69. Lloyd-Price J, Abu-Ali G, Huttenhower C. The healthy human microbiome. Genome Med. 2016;8. pmid:27122046
- View Article
- PubMed/NCBI
- Google Scholar
70. Fukuda S, Toh H, Taylor TD, Ohno H, Hattori M. Acetate-producing bifidobacteria protect the host from enteropathogenic infection via carbohydrate transporters. Gut Microbes. 2012;3: 449–454. pmid:22825494
- View Article
- PubMed/NCBI
- Google Scholar
71. Chen J, Chen X, Ho CL. Recent Development of Probiotic Bifidobacteria for Treating Human Diseases. Front Bioeng Biotechnol. 2021;9. pmid:35004640
- View Article
- PubMed/NCBI
- Google Scholar
72. Yilmaz B, Bangar SP, Echegaray N, Suri S, Tomasevic I, Lorenzo JM, et al. The Impacts of Lactiplantibacillus plantarum on the Functional Properties of Fermented Foods: A Review of Current Knowledge. Microorganisms. 2022;10: 1–18. pmid:35456875
- View Article
- PubMed/NCBI
- Google Scholar
73. Chen Y, Yu L, Qiao N, Xiao Y, Tian F, Zhao J, et al. Latilactobacillus curvatus: A candidate probiotic with excellent fermentation properties and health benefits. Foods. 2020;9: 1–20. pmid:32993033
- View Article
- PubMed/NCBI
- Google Scholar
74. Silva YP, Bernardi A, Frozza RL. The Role of Short-Chain Fatty Acids From Gut Microbiota in Gut-Brain Communication. Front Endocrinol (Lausanne). 2020;11: 1–14. pmid:32082260
- View Article
- PubMed/NCBI
- Google Scholar
75. Zhu Y, Tramper J. Koji—where East meets West in fermentation. Biotechnol Adv. 2013;31: 1448–1457. pmid:23850857
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Marco ML, Hill C, Hutkins R, Slavin J, Tancredi DJ, Merenstein D, et al. Should There Be a Recommended Daily Intake of Microbes? Journal of Nutrition. 2020;150: 3061–3067. pmid:33269394
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Prescott SL. A butterfly flaps its wings: Extinction of biological experience and the origins of allergy. Annals of Allergy, Asthma and Immunology. American College of Allergy, Asthma and Immunology; 2020. pp. 528–534. pmid:32474160
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Hill C, Tancredi DJ, Cifelli CJ, Slavin JL, Gahche J, Marco ML, et al. Positive Health Outcomes Associated with Live Microbe Intake from Foods, Including Fermented Foods, Assessed using the NHANES Database. Journal of Nutrition. 2023;153: 1143–1149. pmid:36822397
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Marco ML, Heeney D, Binda S, Cifelli CJ, Cotter PD, Foligné B, et al. Health benefits of fermented foods: microbiota and beyond. Current Opinion in Biotechnology. Elsevier Ltd; 2017. pp. 94–102. https://doi.org/10.1016/j.copbio.2016.11.010 pmid:27998788

[ref5] 5. Doulgeraki AI, Tassou CC. Food microbial diversity. Microorganisms. MDPI; 2021. pmid:34946157
View Article
PubMed/NCBI
Google Scholar

[15] View Article

[16] PubMed/NCBI

[17] Google Scholar

[ref6] 6. David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature. 2014;505: 559–563. pmid:24336217
View Article
PubMed/NCBI
Google Scholar

[19] View Article

[20] PubMed/NCBI

[21] Google Scholar

[ref7] 7. Lang JM, Eisen JA, Zivkovic AM. The microbes we eat: Abundance and taxonomy of microbes consumed in a day’s worth of meals for three diet types. PeerJ. 2014;2014: 1–39. pmid:25538865
View Article
PubMed/NCBI
Google Scholar

[23] View Article

[24] PubMed/NCBI

[25] Google Scholar

[ref8] 8. Buck LT, Berbesque JC, Wood BM, Stringer CB. Tropical forager gastrophagy and its implications for extinct hominin diets. J Archaeol Sci Rep. 2016;5: 672–679.
View Article
Google Scholar

[27] View Article

[28] Google Scholar

[ref9] 9. Eskimoliv Nansen F. Kristiania: H. Aschehoug & Co.s.; 1891.

[ref10] 10. Eidlitz K. Food and Emergency Food in the Circumpolar Area. University of Uppsala; 1969.

[ref11] 11. Fediuk K. Vitamin C in the Inuit Diet: past and present. Dissertation. McGill University. 2000. Available: https://escholarship.mcgill.ca/concern/theses/xp68kj14m.

[ref12] 12. Andersen SM. Vitamins and minerals in the traditional Greenland diet. National Environmental Research Institute. Copenhagen NV; 2005. Available: http://www2.dmu.dk/1_viden/2_Publikationer/3_fagrapporter/rapporter/FR528.PDF.

[ref13] 13. Buck LT, Stringer CB. Having the stomach for it: A contribution to Neanderthal diets? Quat Sci Rev. 2014;96: 161–167.
View Article
Google Scholar

[34] View Article

[35] Google Scholar

[ref14] 14. Hauptmann AL, Paulová P, Castro-Mejía JL, Hansen LH, Sicheritz-Pontén T, Mulvad G, et al. The microbial composition of dried fish prepared according to Greenlandic Inuit traditions and industrial counterparts. Food Microbiol. 2020;85. pmid:31500717
View Article
PubMed/NCBI
Google Scholar

[37] View Article

[38] PubMed/NCBI

[39] Google Scholar

[ref15] 15. Hauptmann AL, Paulová P, Hansen LH, Sicheritz-Pontén T, Mulvad G, Nielsen DS. Microbiota in foods from Inuit traditional hunting. PLoS One. 2020;15. pmid:31935269
View Article
PubMed/NCBI
Google Scholar

[41] View Article

[42] PubMed/NCBI

[43] Google Scholar

[ref16] 16. Thalbitzer W. The Ammassalik Eskimo: Contributions to the Ethnology of the East Greenland Natives. Meddelelser om Grønland. Meddelelser Om Grønland 39; 1912.
View Article
Google Scholar

[45] View Article

[46] Google Scholar

[ref17] 17. Gilberg A. Eskimo Doctor. W. W. Norton; 1948.

[ref18] 18. Papalas AJ, Curtis RI. Garum and Salsamenta: Production and Commerce in Materia Medica. The Classical World. 1992;86: 50.
View Article
Google Scholar

[49] View Article

[50] Google Scholar

[ref19] 19. Salgado-Flores A, Tveit AT, Wright AD, Pope PB, Sundset MA. Characterization of the cecum microbiome from wild and captive rock ptarmigans indigenous to Arctic Norway. PLoS One. 2019;14: 1–21. pmid:30856229
View Article
PubMed/NCBI
Google Scholar

[52] View Article

[53] PubMed/NCBI

[54] Google Scholar

[ref20] 20. Ushida K, Segawa T, Tsuchida S, Murata K. Cecal bacterial communities in wild Japanese rock ptarmigans and captive Svalbard rock ptarmigans. Journal of Veterinary Medical Science. 2016;78: 251–257. pmid:26468217
View Article
PubMed/NCBI
Google Scholar

[56] View Article

[57] PubMed/NCBI

[58] Google Scholar

[ref21] 21. Jeon H. Effect of testing conditions on consumer acceptance of soy sauce. Master’s Thesis, Ewha Womans University, Seoul, Republic of Koera. 2016.

[ref22] 22. Herlemann DPR, Labrenz M, Jürgens K, Bertilsson S, Waniek JJ, Andersson AF. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME Journal. 2011;5: 1571–1579. pmid:21472016
View Article
PubMed/NCBI
Google Scholar

[61] View Article

[62] PubMed/NCBI

[63] Google Scholar

[ref23] 23. Takai K, Horikoshi K. Rapid detection and quantification of members of the archaeal community by quantitative PCR using fluorogenic probes. Appl Environ Microbiol. 2000;66: 5066–5072. pmid:11055964
View Article
PubMed/NCBI
Google Scholar

[65] View Article

[66] PubMed/NCBI

[67] Google Scholar

[ref24] 24. Martin M. Cutadapt Removes Adapter Sequences From High-Throughput Sequencing Reads. EMBnet Journal. 2011;17: 2803–2809.
View Article
Google Scholar

[69] View Article

[70] Google Scholar

[ref25] 25. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13: 581–583. pmid:27214047
View Article
PubMed/NCBI
Google Scholar

[72] View Article

[73] PubMed/NCBI

[74] Google Scholar

[ref26] 26. Frøslev TG, Kjøller R, Bruun HH, Ejrnæs R, Brunbjerg AK, Pietroni C, et al. Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates. Nat Commun. 2017;8. pmid:29084957
View Article
PubMed/NCBI
Google Scholar

[76] View Article

[77] PubMed/NCBI

[78] Google Scholar

[ref27] 27. Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME Journal. 2017;11: 2639–2643. pmid:28731476
View Article
PubMed/NCBI
Google Scholar

[80] View Article

[81] PubMed/NCBI

[82] Google Scholar

[ref28] 28. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013;41: 590–596. pmid:23193283
View Article
PubMed/NCBI
Google Scholar

[84] View Article

[85] PubMed/NCBI

[86] Google Scholar

[ref29] 29. Davis NM, Proctor DiM, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome. 2018;6. pmid:30558668
View Article
PubMed/NCBI
Google Scholar

[88] View Article

[89] PubMed/NCBI

[90] Google Scholar

[ref30] 30. Alberdi A, Aizpurua O, Gilbert MTP, Bohmann K. Scrutinizing key steps for reliable metabarcoding of environmental samples. Methods Ecol Evol. 2018;9: 134–147.
View Article
Google Scholar

[92] View Article

[93] Google Scholar

[ref31] 31. Cao Q, Sun X, Rajesh K, Chalasani N, Gelow K, Katz B, et al. Effects of Rare Microbiome Taxa Filtering on Statistical Analysis. Front Microbiol. 2021;11: 1–15. pmid:33510727
View Article
PubMed/NCBI
Google Scholar

[95] View Article

[96] PubMed/NCBI

[97] Google Scholar

[ref32] 32. RStudio: Integrated Development for R. Boston, MA: Posit Software, PBC; 2020. Available: http://www.rstudio.com/.

[ref33] 33. Jari Oksanen, Simpson Gavin L., Guilamume Blachet F., Roeland Kindt, Pierre Legendre, Minchin Peter R., et al. Vegan: community ecology package. R package version 2.6.4. 2019. Available: https://cran.r-project.org/package=vegan.
View Article
Google Scholar

[100] View Article

[101] Google Scholar

[ref34] 34. Dixon P. VEGAN, a package of R functions for community ecology. Journal of Vegetation Science. 2003;14: 927–930.
View Article
Google Scholar

[103] View Article

[104] Google Scholar

[ref35] 35. Wickham H. Elegant Graphics for Data Analysis: ggplot2. Applied Spatial Data Analysis with R. Springer New York LLC; 2008. https://doi.org/10.1007/978-0-387-98141-3

[ref36] 36. Paradis E, Schliep K. Ape 5.0: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 2019;35: 526–528. pmid:30016406
View Article
PubMed/NCBI
Google Scholar

[107] View Article

[108] PubMed/NCBI

[109] Google Scholar

[ref37] 37. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. 1995;57: 289–300. Available: https://www-jstor-org.ep.fjernadgang.kb.dk/stable/2346101.
View Article
Google Scholar

[111] View Article

[112] Google Scholar

[ref38] 38. McMurdie PJ, Holmes S. Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS One. 2013;8. pmid:23630581
View Article
PubMed/NCBI
Google Scholar

[114] View Article

[115] PubMed/NCBI

[116] Google Scholar

[ref39] 39. McMurdie PJ, Holmes S. Shiny-phyloseq: Web application for interactive microbiome analysis with provenance tracking. Bioinformatics. 2015;31: 282–283. pmid:25262154
View Article
PubMed/NCBI
Google Scholar

[118] View Article

[119] PubMed/NCBI

[120] Google Scholar

[ref40] 40. Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics. 2010;26: 1463–1464. pmid:20395285
View Article
PubMed/NCBI
Google Scholar

[122] View Article

[123] PubMed/NCBI

[124] Google Scholar

[ref41] 41. Lahti L, Shetty S. Microbiome: tools for microbiome analysis in R. Version 2.1.24. 2017. Available: https://github.com/microbiome/microbiome?tab=readme-ov-file.
View Article
Google Scholar

[126] View Article

[127] Google Scholar

[ref42] 42. Martinez Arbizu P. pairwiseAdonis: Pairwise multilevel comparison using adonis. R package version 0.4. In: R Cran [Internet]. 2020 p. 1. Available: https://github.com/pmartinezarbizu/pairwiseAdonis.
View Article
Google Scholar

[129] View Article

[130] Google Scholar

[ref43] 43. Grond K, Santo Domingo JW, Lanctot RB, Jumpponen A, Bentzen RL, Boldenow ML, et al. Composition and Drivers of Gut Microbial Communities in Arctic-Breeding Shorebirds. Front Microbiol. 2019;10: 1–12. pmid:31649627
View Article
PubMed/NCBI
Google Scholar

[132] View Article

[133] PubMed/NCBI

[134] Google Scholar

[ref44] 44. Bodawatta KH, Koane B, Maiah G, Sam K, Poulsen M, Jønsson KA. Species-specific but not phylosymbiotic gut microbiomes of New Guinean passerine birds are shaped by diet and flight-associated gut modifications. Proceedings of the Royal Society B: Biological Sciences. 2021;288: 14–17. pmid:33878920
View Article
PubMed/NCBI
Google Scholar

[136] View Article

[137] PubMed/NCBI

[138] Google Scholar

[ref45] 45. Marmion M, Ferone MT, Whyte P, Scannell AGM. The changing microbiome of poultry meat; from farm to fridge. Food Microbiol. 2021;99. pmid:34119108
View Article
PubMed/NCBI
Google Scholar

[140] View Article

[141] PubMed/NCBI

[142] Google Scholar

[ref46] 46. Moen B, Rudi K, Svihus B, Skånseng B. Reduced spread of Campylobacter jejuni in broiler chickens by stimulating the bird’s natural barriers. J Appl Microbiol. 2012;113: 1176–1183. pmid:22817452
View Article
PubMed/NCBI
Google Scholar

[144] View Article

[145] PubMed/NCBI

[146] Google Scholar

[ref47] 47. Zhang G, Ma L, Doyle MP. Salmonellae reduction in poultry by competitive exclusion bacteria Lactobacillus salivarius and Streptococcus cristatus. J Food Prot. 2007;70: 874–878. pmid:17477255
View Article
PubMed/NCBI
Google Scholar

[148] View Article

[149] PubMed/NCBI

[150] Google Scholar

[ref48] 48. Liao XD, Ma G, Cai J, Fu Y, Yan XY, Wei XB, et al. Effects of Clostridium butyricum on growth performance, antioxidation, and immune function of broilers. Poult Sci. 2014;94: 662–667. pmid:25717087
View Article
PubMed/NCBI
Google Scholar

[152] View Article

[153] PubMed/NCBI

[154] Google Scholar

[ref49] 49. Zhang L, Li J, Yun TT, Qi WT, Liang XX, Wang YW, et al. Effects of pre-encapsulated and pro-encapsulated Enterococcus faecalis on growth performance, blood characteristics, and cecal microflora in broiler chickens. Poult Sci. 2015;94: 2821–2830. pmid:26371326
View Article
PubMed/NCBI
Google Scholar

[156] View Article

[157] PubMed/NCBI

[158] Google Scholar

[ref50] 50. Grond K, Sandercock BK, Jumpponen A, Zeglin LH. The avian gut microbiota: community, physiology and function in wild birds. J Avian Biol. 2018;49: 1–19.
View Article
Google Scholar

[160] View Article

[161] Google Scholar

[ref51] 51. Miller AK, Williams SM. Helicobacter pylori infection causes both protective and deleterious effects in human health and disease. Genes Immun. 2021;22: 218–226. pmid:34244666
View Article
PubMed/NCBI
Google Scholar

[163] View Article

[164] PubMed/NCBI

[165] Google Scholar

[ref52] 52. Zuo ZT, Ma Y, Sun Y, Bai CQ, Ling CH, Yuan FL. The Protective Effects of Helicobacter pylori Infection on Allergic Asthma. Int Arch Allergy Immunol. 2021;182: 53–64. pmid:33080611
View Article
PubMed/NCBI
Google Scholar

[167] View Article

[168] PubMed/NCBI

[169] Google Scholar

[ref53] 53. Bik EM, Eckburg PB, Gill SR, Nelson KE, Purdom EA, Francois F, et al. Molecular analysis of the bacterial microbiota in the human stomach. Proc Natl Acad Sci U S A. 2006;103: 732–737. pmid:16407106
View Article
PubMed/NCBI
Google Scholar

[171] View Article

[172] PubMed/NCBI

[173] Google Scholar

[ref54] 54. Cover TL, Blaser MJ. Helicobacter pylori in Health and Disease. Gastroenterology. 2009;136: 1863–1873. pmid:19457415
View Article
PubMed/NCBI
Google Scholar

[175] View Article

[176] PubMed/NCBI

[177] Google Scholar

[ref55] 55. Dubois A, Berg DE, Incecik ET, Fiala N, Heman-Ackah LM, Del Valle J, et al. Host specificity of Helicobacter pylori strains and host responses in experimentally challenged nonhuman primates. Gastroenterology. 1999;116: 90–96. pmid:9869606
View Article
PubMed/NCBI
Google Scholar

[179] View Article

[180] PubMed/NCBI

[181] Google Scholar

[ref56] 56. Ferrero RL, Jenks PJ. In Vivo Adaptation to the Host. In: Mobley HL, Mendz GL, Hazell SL, editors. Helicobacter pylori: Physiology and Genetics. Washington DC: ASM Press; 2001. pp. 583–592. https://doi.org/10.1128/9781555818005.ch46

[ref57] 57. Lee A, Fox J, Hazell S. Pathogenicity of Helicobacter pylori: a Perspective. Infect Immun. 1993; 1601–1610. pmid:8478048
View Article
PubMed/NCBI
Google Scholar

[184] View Article

[185] PubMed/NCBI

[186] Google Scholar

[ref58] 58. Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG, Contreras M, et al. Human gut microbiome viewed across age and geography. Nature. 2012. pp. 222–227. pmid:22699611
View Article
PubMed/NCBI
Google Scholar

[188] View Article

[189] PubMed/NCBI

[190] Google Scholar

[ref59] 59. Pars T, Osler M, Bjerregaard P. Contemporary use of traditional and imported food among Greenlandic Inuit. Arctic. 2001;54: 22–31.
View Article
Google Scholar

[192] View Article

[193] Google Scholar

[ref60] 60. Skelly E, Kapellas K, Cooper A, Weyrich LS. Consequences of colonialism: A microbial perspective to contemporary Indigenous health. Am J Phys Anthropol. 2018;167: 423–437. pmid:30159882
View Article
PubMed/NCBI
Google Scholar

[195] View Article

[196] PubMed/NCBI

[197] Google Scholar

[ref61] 61. Sonnenburg ED, Smits SA, Tikhonov M, Higginbottom SK, Wingreen NS, Sonnenburg JL. Diet-induced extinctions in the gut microbiota compound over generations. Nature. 2016;529: 212–215. pmid:26762459
View Article
PubMed/NCBI
Google Scholar

[199] View Article

[200] PubMed/NCBI

[201] Google Scholar

[ref62] 62. Berding K, Bastiaanssen TFS, Moloney GM, Boscaini S, Strain CR, Anesi A, et al. Feed your microbes to deal with stress: a psychobiotic diet impacts microbial stability and perceived stress in a healthy adult population. Mol Psychiatry. 2023;28: 601–610. pmid:36289300
View Article
PubMed/NCBI
Google Scholar

[203] View Article

[204] PubMed/NCBI

[205] Google Scholar

[ref63] 63. Marco ML, Sanders ME, Gänzle M, Arrieta MC, Cotter PD, De Vuyst L, et al. The International Scientific Association for Probiotics and Prebiotics (ISAPP) consensus statement on fermented foods. Nat Rev Gastroenterol Hepatol. 2021;18: 196–208. pmid:33398112
View Article
PubMed/NCBI
Google Scholar

[207] View Article

[208] PubMed/NCBI

[209] Google Scholar

[ref64] 64. Dimidi E, Cox SR, Rossi M, Whelan K. Fermented foods: Definitions and characteristics, impact on the gut microbiota and effects on gastrointestinal health and disease. Nutrients. MDPI AG; 2019. pmid:31387262
View Article
PubMed/NCBI
Google Scholar

[211] View Article

[212] PubMed/NCBI

[213] Google Scholar

[ref65] 65. Rezac S, Kok CR, Heermann M, Hutkins R. Fermented foods as a dietary source of live organisms. Front Microbiol. 2018;9. pmid:30197628
View Article
PubMed/NCBI
Google Scholar

[215] View Article

[216] PubMed/NCBI

[217] Google Scholar

[ref66] 66. Sonnenburg JL, Sonnenburg ED. Vulnerability of the industrialized microbiota. Science (1979). 2019;366. pmid:31649168
View Article
PubMed/NCBI
Google Scholar

[219] View Article

[220] PubMed/NCBI

[221] Google Scholar

[ref67] 67. Margină D, Ungurianu A, Purdel C, Tsoukalas D, Sarandi E, Thanasoula M, et al. Chronic inflammation in the context of everyday life: Dietary changes as mitigating factors. International Journal of Environmental Research and Public Health. MDPI AG; 2020. pp. 1–30. pmid:32531935
View Article
PubMed/NCBI
Google Scholar

[223] View Article

[224] PubMed/NCBI

[225] Google Scholar

[ref68] 68. Sonnenburg ED, Sonnenburg JL. The ancestral and industrialized gut microbiota and implications for human health. Nat Rev Microbiol. 2019;17: 383–390. pmid:31089293
View Article
PubMed/NCBI
Google Scholar

[227] View Article

[228] PubMed/NCBI

[229] Google Scholar

[ref69] 69. Lloyd-Price J, Abu-Ali G, Huttenhower C. The healthy human microbiome. Genome Med. 2016;8. pmid:27122046
View Article
PubMed/NCBI
Google Scholar

[231] View Article

[232] PubMed/NCBI

[233] Google Scholar

[ref70] 70. Fukuda S, Toh H, Taylor TD, Ohno H, Hattori M. Acetate-producing bifidobacteria protect the host from enteropathogenic infection via carbohydrate transporters. Gut Microbes. 2012;3: 449–454. pmid:22825494
View Article
PubMed/NCBI
Google Scholar

[235] View Article

[236] PubMed/NCBI

[237] Google Scholar

[ref71] 71. Chen J, Chen X, Ho CL. Recent Development of Probiotic Bifidobacteria for Treating Human Diseases. Front Bioeng Biotechnol. 2021;9. pmid:35004640
View Article
PubMed/NCBI
Google Scholar

[239] View Article

[240] PubMed/NCBI

[241] Google Scholar

[ref72] 72. Yilmaz B, Bangar SP, Echegaray N, Suri S, Tomasevic I, Lorenzo JM, et al. The Impacts of Lactiplantibacillus plantarum on the Functional Properties of Fermented Foods: A Review of Current Knowledge. Microorganisms. 2022;10: 1–18. pmid:35456875
View Article
PubMed/NCBI
Google Scholar

[243] View Article

[244] PubMed/NCBI

[245] Google Scholar

[ref73] 73. Chen Y, Yu L, Qiao N, Xiao Y, Tian F, Zhao J, et al. Latilactobacillus curvatus: A candidate probiotic with excellent fermentation properties and health benefits. Foods. 2020;9: 1–20. pmid:32993033
View Article
PubMed/NCBI
Google Scholar

[247] View Article

[248] PubMed/NCBI

[249] Google Scholar

[ref74] 74. Silva YP, Bernardi A, Frozza RL. The Role of Short-Chain Fatty Acids From Gut Microbiota in Gut-Brain Communication. Front Endocrinol (Lausanne). 2020;11: 1–14. pmid:32082260
View Article
PubMed/NCBI
Google Scholar

[251] View Article

[252] PubMed/NCBI

[253] Google Scholar

[ref75] 75. Zhu Y, Tramper J. Koji—where East meets West in fermentation. Biotechnol Adv. 2013;31: 1448–1457. pmid:23850857
View Article
PubMed/NCBI
Google Scholar

[255] View Article

[256] PubMed/NCBI

[257] Google Scholar

Figures

Abstract

Introduction

Materials and methods

Ethics statement

Sample collection

Taste evaluation

DNA extraction, amplification, and sequencing

Bioinformatics and statistical analysis

Pre-processing and data analysis.

Statistics and visualization.

Results

Composition of ptarmigan intestinal microbiota

Alpha-diversity of the ptarmigan gastrointestinal tract and garum

Beta diversity of bacterial communities

Integration of intestinal microbiota into garum fermented sauce

Taste evaluation

Discussion

Microbial composition of the gastrointestinal tract of the rock ptarmigan and its relevance for the human gut microbiota

Garum, integrating gastrophagy into gastronomy

Conclusions

Future research

Supporting information

S1 Fig. Rarefaction curve of the sample types.

S1 Table. Tasting attributes for the two garum types used for CATA.

S2 Table. The p-value for the pairwise Wilcoxon test of the alpha diversity indices.

S3 Table. PERMANOVA results for the dissimilarity between the bacterial community clustering.

S4 Table. CATA results for the two garum types.

Acknowledgments

References