Exploring the Influence of Urbanization on Gut Mycobiota through Dietary Changes in Burkina Faso

Exploring the Influence of Urbanization on Gut Mycobiota through Dietary Changes in Burkina Faso | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring the Influence of Urbanization on Gut Mycobiota through Dietary Changes in Burkina Faso Sonia Renzi, Niccolò Meriggi, Monica Di Paola, Giovanni Bacci, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4073876/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Aug, 2025 Read the published version in BMC Microbiology → Version 1 posted 11 You are reading this latest preprint version Abstract Background How the evolution of dietary habits has impacted the yeasts associated with our gut is largely unknown. The impact of urbanization and globalization on human nutrition and the composition of gut microbial communities are considered driving forces behind the rise in non-communicable diseases. While previous studies in developing countries have investigated changes in the bacterial component of the gut microbiota during the transition from rural to urban areas, the modifications in the intestinal fungal communities are completely unexplored. In this study, we examined the impact of urbanization and dietary shifts on the composition of the gut mycobiota in families residing in rural, semi-urbanized, and urban areas in Burkina Faso. We compared these findings with families living in the urban area of Florence (Italy) as a reference for a globalized lifestyle. Results Our research revealed a significant reduction in the alpha diversity of the intestinal mycobiota as individuals transitioned from rural to urban areas. Members of rural households exhibited greater fungal richness and biodiversity compared to those in urban households, including affluent families in the capital city, Ouagadougou. We observed that the fungal diversity varied in households as a function of the rural-to-urban transition gradient, and we identified 33 fungal amplicon sequence variants (ASVs), including 12 fungal species, as associated with distinct areas with specific lifestyle and dietary patterns as indicators of the rural-to-urban transition. Conclusion The household-level survey of rural and urban communities in Burkina Faso highlighted the effect of urbanization on the lifestyle and subsequent composition of the participants' intestinal mycobiota. A greater diversity of fungal taxa emerged in the rural cohort, along with the presence of distinct species with potential pathogenic traits. This finding suggests that the continuous exposure to pathogenic fungi and the ensuing interaction with the immune system may contribute to the maintenance of lower incidence and severity of non-communicable diseases (NCDs) in non-globalized communities. In agreement with the “hygiene hypothesis”, the lack of yeast diversity could provide a potential explanation for the higher prevalence of inflammatory and immune-related disorders in urbanized regions across the world. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The human gut microbiota, comprising bacteria, fungi, archaea, and viruses, plays a crucial role in influencing host metabolism, immune function, and overall human well-being [ 1 , 2 ]. At the same time, the microbiota is influenced by different factors, such as diet, lifestyle, and geography, that significantly shape the structure of microbial communities. Within environmental factors, urbanization, which is associated with changes in lifestyle, socio-economic status, and dietary habits, results in a shift from traditional, fiber-rich diets to hyper-caloric diets high in fats, proteins, and processed foods commonly found in Western countries. This transition plays a crucial role in altering the structure of the gut microbiota [ 3 – 5 ]. In particular, dietary shift leads to a reduction in gut microbiota diversity, particularly affecting beneficial bacteria involved in fiber fermentation, with a consequent decrease in short-chain fatty acids productions (SCFAs) [ 3 – 9 ]. Improved socio-economic conditions in urban settings contributes to enhanced hygiene practices, increased antibiotic use, and food sterilization, all of which adversely affect the gut microbiota by reducing or eliminating potentially beneficial microorganisms [ 3 , 4 , 6 ]. The "hygiene hypothesis" and the concept of "missing microbes" explain the depletion of ancestral microorganisms due to Western-related behaviors and practices [ 10 , 11 ]. Impairment of the microbiota also relates to the increase of non-communicable diseases (NCDs), including diet-related disorders, inflammatory and immune-related conditions, cardiovascular diseases, chronic kidney disease, respiratory diseases, and cancers [ 6 – 9 ]. Additionally, the ability of the mycobiota, i.e., fungal communities, to modulate host immunity has gained attention for their role in health and disease [ 12 ]. Fungal adaptation in the human gastrointestinal tract occurs through maternal transmission, environmental factors, and food exposure [ 13 – 16 ]. The development of gut fungal communities in the first years of life is crucial to the maturation of host immune competence [ 17 – 22 ]. Studies on adult gut mycobiota reveal two key findings: the existence of a core mycobiota and a dynamic fungal community influenced by dietary patterns and diverse environments [ 23 – 26 ]. Fermented products can provide to the human diet beneficial yeast strains belonging to the genera Saccharomyces, Candida, Pichia, Debaryomyces, Kluyveromyces, Hanseniaspora , capable of producing benefits to human health [ 27 , 28 ]. In Africa, these yeasts are widely represented in traditional fermentations crafted from both plant and animal-derived raw materials. In a survey conducted by Johansen and colleagues [ 29 ] of 43 traditional fermented foods from sub-Saharan Africa, it was found that S. cerevisiae emerged as the predominant yeast species in 77% of the analyzed fermented products, followed by Pichia kudriavzevii (anamorph Candida krusei ) at 60%, Candida tropicalis at 47%, and Kluyveromyces marxianus (anamorph Candida kefyr ) at 44%. This fungal exposure can occur through environmental contamination, direct food assimilation, or the modification of intestinal communities following a dietary shift [ 30 ]. Despite current knowledge, the impact of the environment in shaping the fungal mycobiota remains largely unexplored [ 31 , 32 ]. This study aims to describe the impact of transitioning from rural to urban environments on the intestinal mycobiota of African populations living in areas at different levels of urbanization (rural, semi-urbanized, and urban areas) in Burkina Faso [ 33 ]. We also compared the gut fungal community of these African cohorts with an Italian cohort representing a population characterized by Western conditions [ 34 ]. Our findings reveal a significant influence of lifestyle on the decreasing gradient of fungal diversity in the three African cohorts according to the level of urbanization. Biodiversity variations were observed within family members in rural and semi-urbanized areas, highlighting a shift in fungal profiles during the rural-to-urban transition. Material and Methods Study populations The research took place in Burkina Faso, specifically in the Boulkiemde province, spanning from February 2019 to January 2020. In this investigation, we enrolled a total of 30 households in good health from three different cohorts in Burkina Faso, comprising 147 individuals of varying ages, from 1 to 73 years old. These households were carefully chosen from three distinct locales with different urbanization levels: rural villages, the semi-urbanized area of Nanoro town, and the urban area of the capital city, Ouagadougou. These selection criteria align with the details outlined in our earlier publication by Casari et al. in 2022 [34]. Participants’ enrollment was performed during the dry season with the support of the Nanoro Health and Demographic Surveillance System (HDSS), a surveillance organ established by the Clinical Research Unit of Nanoro (CRUN). Data on lifestyle, dietary habits, socio-economic conditions, hygiene, sanitation, health status at the time of enrollment and previous illnesses, were collected in a database by means of a questionnaire already analyzed by Casari et al. 2022 [34]. The main characteristics of the African cohorts are the following : (i) 10 polygamous households, composed by one father, at least 2 mothers and up to 4 children (n=51 individuals), randomly selected by three rural villages (Boulpon, Poessi, and Godo, details in Casari et al. 2022 [34]) located in Boulkimede province. The populations of these rural villages live on subsistence farming and raise small animals (e.g., chickens and goats). Their housing are clusters of huts built using soil, wood, and straw, without electricity and scarce access to water. These households are in poor contact with urban areas. Their diet is traditional and predominantly plant-based (i.e., local cereals, legumes, and herbs). Western influences on diet are almost totally absent. This population is at high risk of infectious diseases and malnutrition. (ii) 10 families, either polygamous or monogamous, with one father, one or two mothers, and up to 4 children (n=51 individuals), living in the semi-urbanized area of the small town of Nanoro. People live in groups of small brick houses with scarce access to electricity. Water is collected from public wells. Their diet is still traditional and plant-based, but different foods can be found at local markets, including meat and dried fish that they eat once-a-week, and occasionally processed and Western-like foods. In this area, people are at risk of infectious diseases and malnutrition. (iii) 10 monogamous families, with one mother and father and up to 4 children (n= 45 individuals) living in the capital city of Ouagadougou, selected among wealthy urban families in medium-high economic conditions. This population lives in concrete or brick buildings with access to electricity and private water sources. They have a predominantly Burkinabè diet, but have access to a variety of foods, including Western and processed foods available in supermarkets. These families have good hygiene and sanitation, and the risk of infection and malnutrition is extremely reduced. In addition, a total of 143 subjects belonging to 45 different families, composed of 2 parents and up to 4 children, living in the urban areas of Florence (Tuscany, Italy) have been enrolled as representatives of an urban population with a Western-like diet and lifestyle. Inclusion and exclusion criteria of study participants Inclusion criteria for the African cohorts were as follows: (i) households and families composed of a biological father and from 1 to more mothers and up to 4 children; (ii) belonging to the Mossi ethnic group; (iii) apparently in good health status. Exclusion criteria: individuals who had fever (>38.5 °C) in the previous 72 hours, in order to exclude acute illnesses at the time of enrollment. For Italian families, inclusion criteria are: (i) families composed of 2 parents and up to 4 children living in urban areas in Florence province (Italy); (ii) good health status. Subjects with acute illnesses, including infections, at the time of enrollment were not included in the project. Furthermore, in order to avoid conceptual and descriptive biases, the subjects with gastrointestinal diseases such as Crohn disease and ulcerative colitis were removed from the current study due to the impact of these pathologies on the intestinal microbiota (A total of 8 children with Crohn’s disease and 12 children with ulcerative colitis from Italian cohort were removed from the whole subjects cohort included in the bioproject mentioned in the section ‘Availability of data and materials’). To avoid technical biases during the analysis, we also removed two samples, 1 from the Italian cohort and 1 from the Nanoro cohort, which presented no fungal counts (samples named ‘IT-IBD-21-F1’ and ‘F304N’ were removed from the whole subjects cohort included in the bioproject mentioned in the section ‘Availability of data and materials’). For all the enrolled African and Italian participants included in the analyses, information obtained by the questionnaires about health status, diseases, vaccinations, dietary habits, and socio-economic conditions were extensively reported in Supplementary Table 1. The pruning procedures based on the exclusion criteria mentioned above produced a final dataset composed of 122, 45, 50 and 51 subjects from the Italian, Ouagadougou, Nanoro and Rural villages cohorts, respectively, on which the statistical analyses were carried out. Data deposition Sequence data are deposited in the European Nucleotide Archive (ENA) under accession code PRJEB59322. The dataset available in the EBI-ENA database includes ITS1 raw sequences from both healthy subjects and patients with inflammatory bowel disease. For the computational analysis of this study, only healthy subjects belonging to families were considered. Ethics In Burkina Faso, the study was carried out in accordance with the recommendations of the National Ethics Committee of Burkina Faso that granted ethical clearance (reference number 2018-8-104). For the Italian families, the study was approved by the Ethics Committee of Meyer Children Hospital, Florence, Italy (reference number 187/2018). Adult participants gave their informed consent. Parents or primary caregivers signed off on children under the age of 18. Confidentiality was maintained for data and sample collection by assigning each participant an identifying code. Sample collection and DNA extractio n For each participant, a stool sample was collected by a commercial sterile collection tube supplied with RNAlater (Thermo Fisher Scientific), then stored at -80°C in order to preserve nucleic acids. The DNeasy PowerSoil Pro Kit (Qiagen) was used to extract total DNA from 250 mg (wet weight) of each fecal sample according to the manufacturer's instructions. The Qubit 4 Fluorometer (Thermo Fisher Scientific) and the 1x dsDNA High Sensitivity Kit were used to assess DNA concentration before the downstream analyses. ITS1 amplification, library preparation and sequencing For each DNA sample, the fungal internal transcribed spacer 1 (ITS1) was amplified using a specific primer set for the ITS1 rDNA region (ITS1f: 5′‐CTTGGTCATTTAGAGGAAGTAA‐3′ and ITS2r: 5′‐GCTGCGTTCTTCATCGATGC‐3′) [35], with overhang Illumina adapters. Library preparation was performed according to the Fungal Metagenomic Sequencing Demonstrated Protocol (Document # 1000000064940 v01). Sequencing was performed on the Illumina MiSeq platform (Illumina) with the V3 chemistry 600 cycle PE300 protocol at the Department of Biology, University of Florence, Italy. Amplicon sequence variance assemblage Both primer sequences were removed by using cutadapt version 1.15 [36] in paired-end mode. If a primer sequence was not found, the entire sequence was discarded along with its pair to reduce possible contamination. The raw sequences were processed using DADA2 pipeline version 1.14.1 [37] to infer amplicon sequence variants (ASVs). The low quality reads were filtered using the “filterAndTrim” function with a maximum number of expected error thresholds of 2 for forward and reverse read pairs and a minimum cut-off length of 70bp. Error rate estimation (“learnErrors” function) and denoising (“dada” function) with default parameters were performed. Denoised reads were merged using the “mergePairs” function, discarding those with any mismatches and/or an overlap length shorter than 20 bp. Chimeric sequences were also removed (“removeBimeraDenovo” function) and taxonomic classification was produced by using DECIPHER package version 2.14.0 against the Warcup database for fungal ITS1 [38]. All sequence variants not classified as Fungi and/or with no fungal counts were removed from the dataset to properly perform the downstream analyses. Statistical analysis Statistical analyses were performed in R environment version 4.2.2 [39]. Sequencing depth was depicted by rarefaction curves generated by using the “ggrare” function of “ranacapa” package version 0.1.0 [40]. Mean relative abundance was calculated using “microbiomeutilities” package version 1.0.17 [41]. Variations in fungal diversity (beta diversity analysis) were inspected using “vegan” package version 2.6.4 [42]. In detail, samples distribution was displayed by principal coordinate analysis (PCoA) using the “cmdscale” function of the “stat” package performed on distance matrices based on Bray-Curtis diversity index and Jaccard similarity coefficient to infer quantitative and qualitative differences, respectively. The data were normalized before multidimensional analysis removing counts present less than once within the sample dataset, and then transformed in relative abundances to reduce coverage bias among samples. Permutational multivariate analysis of variance using distance matrices (adonis PERMANOVA) was performed to inspect differences between sample groups by using the “adonis2” function of “vegan” package. Adonis PERMANOVA was tested on a multiple factor formula (formula = ∼Area * Family + Age + Sex) considering the interaction between area and family degree due to the crossed design experiment. Pairwise comparisons on variance among geographical areas were assessed using pairwise adonis PERMANOVA by using the “pairwise.adonis” function from “pairwiseAdonis” package version 0.4 [43] adjusting the resulting p-values with Holm correction method. The same model formula was used to inspect differences in alpha diversity metrics, first fitted the model formula using linear model (“lm” function of “stats” R package) then inspected by analysis of variance ANOVA type II from “car” package version 3.1.0 [44]. Covariation between nutritional data (amounts of macronutrients represented by the percentages of carbohydrates, simple sugar, total protein, animal protein, vegetable protein, fats, and gr/1000 Kcal of fibers) and beta diversity was assessed by fitting the nutritional parameters onto the principal components analysis (PCA) by using the ‘envfit’ function in ‘vegan’ package setting a number of permutations of 1000. PCA was built on VST (variance stabilizing transformation) scaled abundance matrix by using DESeq2 package version 1.38.3 [45] after single counts removal and estimated using the “rda” function of the “vegan” package. Differences in nutritional parameters among sample groups were assessed by Wilcoxon test by using the “wilcox_test” function from the “rstatix” package version 0.7.2 adjusting the resulting p-values with Benjamini-Hochberg correction method [46]. To assess differences on variance related to the family degree only, thus excluding the geographic area effect, the adonis PERMANOVA was also performed on multiple factors formula (formula = ∼Family member + Age range + Sex) after separating the dataset according to four geographical areas. All multivariate analyses were conducted with a number of permutations of 1000 and the r-squared values were used to inspect the amount of variance explained. The dispersion among sample group centroids was computed using the ”betadisper” function of “vegan” package and the differences in dispersion were tested using the analysis of variance (ANOVA) and Tukey post-hoc test (TukeyHSD function from “stats” R package) to inspect significant pairwise contrasts. After singleton removal, the differential abundance analysis was performed using the likelihood-ratio test (LRT) method by the “DESeq” function from the “DESeq2” package. LRT was used to test significance of change in deviance between a full model (Area + Family) and reduced model (Family) provided in the model formula. Statistically significant counts (alpha = 0.05) were scaled using variance stabilizing transformation (VST) then clustered using “pheatmap” package version 1.0.12 [47]. Differences in alpha diversity metrics and taxa relative abundances were assessed using the “ggpubr” package [46]. Figures were produced by using “ggplot2” package version 3.4.2 [48] and edited using open-source graphics editor Inkscape (http://inkscape.org/). Results Changes in fungal diversity depending on geographical area and family member Beta diversity analysis was assessed to describe distribution and similarity between sample groups according to different geographical areas and family structure. The sample distribution of African and Italian family cohorts was observed in Principal Coordinates Analysis (PCoA) performed on distance matrices achieved by quantitative (Bray-Curtis) and qualitative (Jaccard) indexes (Fig. 1a and Fig. S6a). Both multidimensional analyses showed a clear separation of the Italian families cohort from the three African cohorts, regardless of the different level of urbanization of Burkinabè households (Fig. 1a Fig. S6a). Differences in beta diversity were then validated by multivariate permutational analysis of variance (adonis PERMANOVA) (see material and methods for additional details), demonstrating that the “area” factor (i.e., Italy, Ouagadougou, Nanoro and Rural villages) and the interaction between “area” and “family member” (i.e., father, mother, or child) were the only statistically significant factors affecting the fungal diversity (Table S4). This means that the mycobiota switches first in the four different areas and secondly in the households, but only as a function of the area of origin. In fact, the households showed significant variations in mycobiota only in Nanoro and the rural villages areas. The sample dispersion tested considering the aforementioned factors showed statistical significance only for the “area” and the interaction between “area” and “family member” (ANOVA, p< 0.001 for both Bray-Curtis and Jaccard indexes). However, pairwise tests (Tukey post-hoc) on all 66 pair combinations showed that this significance was driven solely by 17 and 14 significant comparisons for the Bray-Curtis and Jaccard distances, respectively (“Area:Family” for Bray-Curtis and Jaccard indexes in Table S5), indicating that the lack of homogeneity was due to few comparisons compared to the total samples present in the dataset. Pairwise adonis PERMANOVA was performed to highlight differences in fungal diversity between each area showing that the mycobiota varied between all areas except for Ouagadougou vs Nanoro comparison (Fig. 1c and Fig. S6c). The highest value of explained variance was observed between rural villages and Italian cohorts comparison (Fig. 1c and Fig. S6c), highlighting the greater degree of dissimilarity in their mycobiota. Subsequently, to evaluate the impact of the “family member” factor on sample distribution in each family cohort, the dataset was analyzed dividing it according to the four different areas. We did not find significant differences in fungal diversity within the family members of the Italian cohort and that of Ouagadougou capital city, as depicted by the adonis PERMANOVA (Table S7) and highlighted in multidimensional analysis (PCoA based on Bray-Curtis distance in Fig. 1b). Differently from Italy and Ouagadougou, the family members of the households from rural villages and the semi-urbanized area of Nanoro showed significant variations in fungal diversity (Table S7). The adonis pairwise PERMANOVA analysis identified the family member pairs that significantly influenced the multivariate analysis, i.e. children vs mothers (R2: 0.06, Holm p-adj: 0.02) in the Nanoro cohort, and fathers vs mothers (R2: 0.07, Holm p-adj: 0.03) in the rural villages cohort. Differences in alpha diversity measures (Observed, Shannon index and Inverse Simpson index) among different areas were also inspected. Analysis of variance (ANOVA type II) showed that all diversity metrics were significantly influenced by the area variable (Table S8). The pairwise comparisons (Wilcoxon test with Benjamini-Hochberg correction method) showed that the alpha diversity metrics were overall higher in the cohort with the lowest level of urbanization (i.e. households from rural villages), as shown by Wilcoxon test (Fig. 1d and Table S9). Pairwise comparison showed significant differences in the total number of observed ASVs among all cohorts, except for the comparison between cohorts from Ouagadougou and Nanoro (Fig. 1d and Table S9). Shannon index metrics showed significant differences in fungal diversity between Italian cohorts and the three African cohorts, and between rural villages and families from Ouagadougou capital city (Fig. 1d and Table S9). Moreover, by using the Inverse Simpson index, we found statistically significant differences in fungal diversity between the Italian cohort and rural villages, and between the Italian cohort and the Nanoro cohort, respectively (Fig. 1d and Table S9). These results highlight the impact of urbanization on fungal diversity metrics indicating the presence of a gradient with increasing values in accordance with the reduction of the level of urbanization. Each geographical cohort presented different dietary habit, which implied a different intake of the main nutrients considered, i.e., total kcal per day, fiber intake (grams per 1000 kcal), percentages of animal proteins, vegetable proteins, total proteins, total carbohydrates, total sugars, and total fats (specific comparisons between cohorts are reported in Fig. S1 and Fig. S2). Therefore, we performed environmental fitting analysis ( envfit ) to assess whether the nutritional parameters described above drive variation in the fungal communities. The analysis showed that changes in gut mycobiota were significantly associated with seven of eight nutritional parameters tested (Fig. 2a and Fig.2b), this means that changes in the fungal community associated with the geographical areas were also associated with variations in macronutrients intake. Based on the square correlation coefficient, the intake of different proportions of protein sources (animal and vegetable proteins) represents the factors that mainly affect fungal distribution. The distribution of daily macronutrient intake values significantly depicted by the environmental fitting analysis is represented in Fig. 3c for each geographical area. The distribution of values was consistent with the environmental fitting results. Environmental fitting on principal component analysis (PCA) showed a significant correlation with fungal distribution of the Italian cohort and the increase in percentage consumption of total protein, animal protein, sugars, and fats, whereas the increased consumption of grams of fiber per 1000 kcal and the percentage of vegetable proteins and carbohydrates were depicted (Fig. 2a). Differences among cohorts in fungal abundances follow a gradient dependent on rural-to urban transition We performed prevalence-abundance estimation to depict the most representative ASVs within the four different cohorts. To deepen the taxonomic variability associated with each cohort, we highlighted the most representative ASVs following prevalence and occupancy thresholds (see supplementary materials and methods for additional details). In accordance with the threshold parameters, the analysis showed that ASV_1 (Candida sp VVT 2012) was a part of the most representative core membership of Italian and Ouagadougou cohorts, whereas ASV_2 ( Pichia ) was representative of core membership the African cohorts only and ASV_6 ( Epicoccum sorghi ) as core memberships of Nanoro and Rural villages cohorts (Fig. S4 and Table S2). This highlighted how the specific and wide representative ASVs within the dataset were also shared among cohorts in accordance with the level of urbanization. To inspect differences in fungal distribution within sample groups, we performed a log-likelihood ratio test (LRT) using DESeq2. The analysis showed a total of 33 ASVs differentially abundant among the family cohorts (Table S10). These ASVs represented 1.99% of the total ASVs profiled in the whole fungal communities (33 out of the 1655 identified ASVs), and 59.9% of the total fungal abundance (mean abundance within the sample dataset: 50.5% and standard error: 1.83%). Sequence variant clustering (by Ward D2 Linkage method) was performed to observe the distribution of the 33 identified ASVs among the four cohorts. A pattern of 8 ASVs (Fig. 3) was referred to the Italian cohort that clustered separately from the African cohorts. The Italian pattern includes 3 ASVs assigned to 3 different fungal species (ASV_29: Rhodotorula mucilaginosa , ASV_71: Candida sake , ASV_239: Verticillium leptobactrum ) and 5 ASVs assigned to 4 fungal genera (ASV_88 and ASV_154: Eupenicillum , ASV_77: Debaryomyces , ASV_55: Penicillium , ASV_58: Eurotium ). African areas depicted 25 different ASVs, which included 9 fungal species (ASV_6: Epicoccum sorghi , ASV_10: Candida mesorugosa , ASV_12: Candida tropicalis , ASV_33: Aspergillus flavus , ASV_34: Kluyveromyces marxianus, ASV_70: Cryptococcus flavus, ASV_83: Aspergillus penicillioides, ASV_99: Malassezia restricta, ASV_134: Cyberlindnera fabianii ) and 10 fungal genera (ASV_2, ASV_7 and ASV_18: Pichia, ASV_3: Candida, ASV_9: Eurotium, ASV_20: Trichosporon, ASV_30: Davidiella, ASV_50 and ASV_82: Aspergillus, ASV_74: Gibberella, ASV_480: Rhizopus, ASV_646: Plectosphaerella ). Other ASVs with different taxonomic assignments were also depicted (Fig. S7 and Table S10). All significant ASVs and the related taxonomic annotations were reported in Table S10. Pairwise comparison by using the Wilcoxon test (Benjamini-Hochberg correction method) (Fig. 4 and Fig. S7; Table S11 and Table S12) showed that the relative abundance of the ASVs selected by LRT analysis, follows a gradient according to the rural-to-urban transition pattern. In particular, the species level variants Candida mesorugosa , Cryptococcus flavus , Cyberlindnera fabianii , Epicoccum sorghi , Kluyveromyces marxianus , and Malassezia restricta were enriched in the rural villages cohort, and their abundance progressively decrease according to the increase in the urbanization level (Fig. 4). We also identified fungal species that are more, or almost exclusively, enriched in the Italian cohorts compared to the Africans, such as Candida sake, Rhodotorula mucilaginosa and Verticilium leptobactrum (Fig. 4). Variations in fungal relative abundances as a function of the rural-to-urban transition were also visible in the non-species assigned ASVs identified by LRT and reported in Fig. S7. Some of these, such as the fungal genera Aspergillus , Candida and Pichia , showed significantly higher relative abundances in the African cohorts than in the Italian one (Fig. S7). Conversely, the genera Penicillium and Eupenicillium were markedly associated with the Italian cohort and almost absent in the African cohorts (Fig. S7). Discussion Urbanization triggers cultural, socio-economic, and lifestyle changes, including the globalization of diets. These shifts, alongside environmental agents and hygiene practices, alter the gut microbiome and contribute to an increase in non-communicable diseases (NCDs), like inflammatory bowel disease, arthritis, asthma, diabetes, obesity, and respiratory and cardiovascular conditions [49]. Previous studies with similar conditions associate urbanization in sub-Saharan African countries with a heightened prevalence of NCDs, surpassing infectious diseases in terms of occurrence [50,51]. Human socio-cultural adaptation is an ongoing process influenced by interactions with the environment. For example, the diverse vegetation and climate variations across Africa dictate specific agricultural methods, food preservation techniques, and consequently, dietary patterns. These aspects are closely linked to urbanization levels, which bring about substantial shifts in nutrition, hygiene practices, and medication usage. Our past study on lifestyle and dietary changes among rural, semi-urbanized and urban households in Burkina Faso underlined the connection between the rural-to-urban transition, obesity and the “double burden malnutrition” phenomenon [34]. The present study investigates shifts in mycobiota composition within three cohorts from Burkina Faso, all belonging to the same ethnic group. These established cohorts represent the transition from rural to urban living, where both adults and children share similar lifestyles and dietary habits within qualitatively defined environmental and household settings [34]. Comparison of these African cohorts to urban Italian families allows us to assess how differences in geography, environment, diet, and lifestyle contribute to variations in the yeast gut mycobiota. Studies on gut mycobiota [52] of healthy individuals following Western, animal-based, or plant-based diets, reveal potential associations between macronutrients (such as carbohydrates or saturated fatty acids) and specific fungal taxa. In a recent extensive study [53] mycobiota composition and variation of gut fungal communities in a European population were found to be significantly determined by 10 factors, mostly related to diet. Our analysis reveals a distinguishable shift in gut fungal diversity along an urbanization gradient. Consistent distinction between Italian and rural cohorts, affirm that a decrease in richness and biodiversity result from an increase in urbanization levels. Notably, diet emerged as a significant factor in determining fungal diversity, particularly in relation to different protein sources. Rural and semi-urbanized families displayed higher fungal diversity, potentially influenced by environmental exposure, poor sanitation, beside dietary habits. Conversely, urban families, with limited environmental exposure and processed food consumption, exhibited lower fungal diversity. Taking into account family effect, we observed higher fungal diversity among family members of rural and semi-urbanized households compared to urban families from the capital city and Italy. This result may indicate rural and semi-urbanized settings as a considerable driver of differences in fungal diversity. Differences in gut fungal composition were especially apparent in semi-rural and rural areas between mother to child and mother to father. An absence of this phenomena in urban areas, may be attributed to a lack of polygamy, differences in occupation, as well as shifts in family roles and activities within households [34]. Furthermore, differential abundance analysis identified several fungal amplicon sequence variants (ASVs) significantly associated with different geographical areas. A. flavus, A. penicillioides , and ASVs related to the Aspergillus genus were significantly enriched within the African groups. The decrease in abundance of Aspergillus in Clostridium difficile infections (CDI) has suggested it’s beneficial role in decreasing the risk of CDI [54]. Therefore, the association of A. penicillioides with healthy individuals rather than CDI was also corroborated because its abundance increased following fecal transplantation in CDI subjects [55]. C. tropicalis , here prevalent in the African cohorts, is often associated with traditional sub-Saharan fermented foods, especially in the cereal-based ones, which are also characterized by the presence of anti-nutritional factors such as phytate. C. tropicalis isolated from different fermented beverages were found to have high extracellular phytase activity [56], suggesting a possible role of the yeast in promoting nutrients bioavailability. However, despite the positive contribution that this yeast can make within fermentation, the pathogenic potential found in clinical isolates of C. tropicalis , as well as the opportunistic nature of yeasts belonging to the Candida genus, must be considered [57]. In particular, the species level variants Candida mesorugosa, Cryptococcus flavus, Cyberlindnera fabianii, Epicoccum sorghi, Kluyveromyces marxianus , and Malassezia restricta were enriched in the rural villages cohort, and their abundance progressively decrease according to the increase of the level of urbanization. C. mesorugosa has already been described as a pathogenic fungus causing several infections and was isolated from clinical samples in Ghana [58] and Brazil [59]. C. flavus is a basidiomycetous yeast often associated with the phyllosphere, and it has been isolated as an endophyte from rice leaves [60,61]. This yeast has shown interesting enzymatic activities such as amylase, carboxymethyl cellulase (CMCase), and xylanase activities [62,63]. C. fabianii , a rare opportunistic pathogen primarily isolated from the fermentation of alcoholic beverages and sugarcane [64-67], has also been implicated in pathogenesis in several case studies involving pediatric subjects [68-71]. The prevalence-abundance analysis in accordance with the LRT analysis corroborated the role of E. sorghi as a representative species of areas with a lower degree of urbanization (Nanoro and Rural villages). The wide occurrence of E. sorghi within low urbanized areas can be explained by contamination mechanisms; in fact, a natural fungal contamination in food consumed in Durban, South Africa was described by Olagunju et al. 2018 [72]. Additionally, the significant prevalence of K. marxianus along the rural gradient can be attributed to its widespread presence in various African fermented products [73]. Notably, K. marxianus is recognized for its immunoregulatory activity; in particular, it exerts the activation of Foxp3+ Treg cells, thus reducing the levels of inflammation and potentially preventing the development of chronic inflammatory disease in the long term [74]. M. restricta , classically associated with the skin microbiota, is also widely described as a component of the intestinal microbiota [75,76]. We hypothesize that the higher abundance of this fungal species in subjects from rural villages may be attributable to their lifestyle, for example by the absence of sanitization methods during food preservation and handling. Furthermore, M. restricta has been demonstrated to play a regulatory role in the immune system; Kesavan and collaborators reported a significant reduction in pro-inflammatory cytokines associated with Malassezia [77]. Thus, the presence of K. marxianus, C. tropicalis , and C. fabianii in association with African cohorts and, in some cases, in enrichment in the context of rural villages is easily traceable to the composition of the traditional Burkinabè diet. We also identified prevalent environmental fungal species like Rhodotorula mucilaginosa , which is almost exclusively enriched in the Italian cohorts compared to the African cohorts. In humans, R. mucilaginosa is described as a transient commensal microorganism found on the skin, nails, and in the gastrointestinal, urinary, and respiratory tracts [78]. Yeasts of this genus are deemed opportunistic pathogens, primarily impacting immunocompromised individuals [79]. The interaction between the human immune system and fungi involves a finely tuned mechanism that responds to opportunistic pathogenic fungi while maintaining tolerance to commensal ones. In turn, fungi can modulate host immunity, enhancing defenses against pathogenic infections, with a mechanism that is conserved across kingdoms, from invertebrates to mammals [80,81]. This immune equilibrium develops in the first 18 months of life, and the lack of early exposure to fungal antigens potentially correlates with an increase in risk for allergies and asthma [82]. Traditional lifestyles often involve greater exposure to environmental microbes and allergens due to factors such as living in rural areas, close contact with animals, and the consumption of unprocessed foods. These environmental exposures may play a role in shaping the immune system and reducing the risk of allergies. It’s plausible that the co-occurrence of fungal species with potentially immunogenic profiles in non-immunocompromised subjects (healthy donors) could trigger a synergistic effect that, over time, mitigates the onset of chronic inflammatory diseases. A reduction in early life exposure to yeasts and fungi in urban settings may thus have significant implications for the development of host immune recognition and tolerance, and potentially contribute to the onset of immune-related disorders in urbanized regions worldwide. Our hypothesis suggests that urbanization and the resulting decline in certain fungal species play a significant role in the onset of NCDs, especially chronic inflammatory disorders linked to Western lifestyles. Therefore, our results underscore the need for thorough exploration of both environmental and gut fungal communities in Africa and non-globalized regions to uncover the mechanisms underlying the connection between fungi and NCDs. Declarations Availability of data and materials The ITS1 raw sequences data has been deposited to the European Nucleotide Archive (ENA) under the accession code PRJEB59322. All results from statistical analyses were mentioned by writing in the main text, represented as figures in the main text or reported as supplementary tables and figures. Further information can be provided upon request to the corresponding author. Acknowledgements We would like to thank the study participants in Burkina Faso: all households of the rural villages of Boulpon, Godo and Poessi, the households of the town of Nanoro and the capital city of Ouagadougou. A special thanks to the field staff of the IRSS-URCN-Nanoro (BF) to Dieudonnè Sorgo from St. Camille Hospital-Nanoro (BF) and Maria José Caldes, Global Health Center Regione Toscana-Italy. We would also thank the study participants in Italy, the medical and nursing staff of the AOU Meyer children hospital of Florence that supported the activity of sample collection. Funding This research was funded by the Joint Programming Initiative, Eranet Cofound, a Healthy Diet for a Healthy Life (JPI-HDHL), TRANSMIC project (grant number 529051018); Bando Salute 2018 RISKCROHNBIOM project (grant number G84I18000160002) and Consorzio Interuniversitario per le Biotecnologie 2023. Authors’ information Sonia Renzi and Niccolò Meriggi contributed equally to this work as they are co-first authors. Authors and Affiliations Department of Biology, University of Florence, Sesto Fiorentino, 50019 Florence, Italy Sonia Renzi, Niccolò Meriggi, Giovanni Bacci, Monica Di Paola, Benedetta Cerasuolo, Agnese Gori & Duccio Cavalieri Meyer Children's Hospital IRCCS, Gastroenterology and Nutrition Unit, 50139 Florence, Italy Silene Casari, Alessia de Blasi & Paolo Lionetti Meyer Children's Hospital IRCCS, UP Dietetica, 50139 Florence, Italy Elena Banci Institut de Recherche en Sciences de la Santé-Clinical Research Unit of Nanoro (IRSS-URCN), Nanoro 18, Burkina Faso Salou Diallo, Berenger Kaborè, Karim Derra & Halidou Tinto National Research Council, Institute of Agricultural Biology and Biotechnology, Pisa, Italy Carlotta De Filippo Department of Internal Medicine, Radboud University Medical Center, Nijmegen, the Netherlands Quirjin de Mast Department of Neurology, Pharmacology, Psychology and Child Health (NEUROFARBA), University of Florence, 50139 Florence, Italy Paolo Lionetti Contributions D.C. and P.L. conceived the work; S.R., N.M., M.D.P. and C.D.F. wrote the manuscript; S.R. and B.C. produced the metagenomic libraries and sequencing; N.M. performed amplicon sequence variants inference and statistical analysis; G.B. contributed to amplicon sequence variants inference; A.G., S.C., E.B., and A.D.B. managed and analyzed nutritional diaries data; S.D., B.K., K.D., and H.T. coordinated the enrollment and sample collection in Burkina Faso. Q.D.M., D.C., P.L., S.R., N.M., M.D.P., C.D.F., B.C., G.B., A.G., S.C., E.B., A.D.B., S.D., B.K., K.D., and H.T. revised and approved the manuscript. Corresponding author Correspondence to Duccio Cavalieri, [email protected] Ethics declarations Ethics approval and consent to participate In Burkina Faso, the study was carried out in accordance with the recommendations of the National Ethics Committee of Burkina Faso that granted ethical clearance (reference number 2018-8-104). For the Italian families, the study was approved by the Ethics Committee of Meyer Children Hospital, Florence, Italy (reference number 187/2018). Adult participants gave their informed consent. Parents or primary caregivers signed off on children under the age of 18. Confidentiality was maintained for data and sample collection by assigning each participant an identifying code. Consent for publication Not applicable. Competing interests The authors declare no conflict of interest. References Gilbert JA, Blaser MJ, Caporaso JG, Jansson JK, Lynch SV, Knight R. Current understanding of the human microbiome. Nat Med. 2018;24:392–400. Kho ZY, Lal SK. The Human Gut Microbiome - A Potential Controller of Wellness and Disease. Front Microbiol. 2018;9:1835. De Filippo C, Cavalieri D, Di Paola M, Ramazzotti M, Poullet JB, Massart S, et al. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc Natl Acad Sci U S A. 2010;107:14691–6. De Filippo C, Di Paola M, Ramazzotti M, Albanese D, Pieraccini G, Banci E, et al. Diet, Environments, and Gut Microbiota. A Preliminary Investigation in Children Living in Rural and Urban Burkina Faso and Italy. Front Microbiol. 2017;8:1979. Kabwe MH, Vikram S, Mulaudzi K, Jansson JK, Makhalanyane TP. The gut mycobiota of rural and urban individuals is shaped by geography. BMC Microbiol. 2020;20:257. Zuo T, Ng SC. The Gut Microbiota in the Pathogenesis and Therapeutics of Inflammatory Bowel Disease. Front Microbiol. 2018;9:2247. Martínez I, Stegen JC, Maldonado-Gómez MX, Eren AM, Siba PM, Greenhill AR, et al. The gut microbiota of rural papua new guineans: composition, diversity patterns, and ecological processes. Cell Rep. 2015;11:527–38. Blaser MJ. The theory of disappearing microbiota and the epidemics of chronic diseases. Nat Rev Immunol. 2017;17:461–3. Vangay P, Johnson AJ, Ward TL, Al-Ghalith GA, Shields-Cutler RR, Hillmann BM, et al. US Immigration Westernizes the Human Gut Microbiome. Cell. 2018;175:962–e97210. Strachan DP. Hay fever, hygiene, and household size. BMJ. 1989;299:1259–60. Blaser MJ, Falkow S. What are the consequences of the disappearing human microbiota? Nat Rev Microbiol. 2009;7:887–94. Gow NAR, Netea MG. Medical mycology and fungal immunology: new research perspectives addressing a major world health challenge. Philos Trans R Soc Lond B Biol Sci. 2016;371:20150462. Fiers WD, Leonardi I, Iliev ID. From Birth and Throughout Life: Fungal Microbiota in Nutrition and Metabolic Health. Annu Rev Nutr. 2020;40:323–43. Strati F, Di Paola M, Stefanini I, Albanese D, Rizzetto L, Lionetti P et al. Age and Gender Affect the Composition of Fungal Population of the Human Gastrointestinal Tract. Front Microbiol. 2016;7. https://www.frontiersin.org/articles/ 10.3389/fmicb.2016.01227 . Strati F, Cavalieri D, Albanese D, De Felice C, Donati C, Hayek J, et al. Altered gut microbiota in Rett syndrome. Microbiome. 2016;4:41. Strati F, Cavalieri D, Albanese D, De Felice C, Donati C, Hayek J, et al. New evidences on the altered gut microbiota in autism spectrum disorders. Microbiome. 2017;5:24. Bliss JM, Basavegowda KP, Watson WJ, Sheikh AU, Ryan RM. Vertical and horizontal transmission of Candida albicans in very low birth weight infants using DNA fingerprinting techniques. Pediatr Infect Dis J. 2008;27:231–5. Boix-Amorós A, Martinez-Costa C, Querol A, Collado MC, Mira A. Multiple Approaches Detect the Presence of Fungi in Human Breastmilk Samples from Healthy Mothers. Sci Rep. 2017;7:13016. Boix-Amorós A, Puente-Sánchez F, du Toit E, Linderborg KM, Zhang Y, Yang B, et al. Mycobiome Profiles in Breast Milk from Healthy Women Depend on Mode of Delivery, Geographic Location, and Interaction with Bacteria. Appl Environ Microbiol. 2019;85:e02994–18. Collado MC, Isolauri E, Laitinen K, Salminen S. Effect of mother’s weight on infant’s microbiota acquisition, composition, and activity during early infancy: a prospective follow-up study initiated in early pregnancy. Am J Clin Nutr. 2010;92:1023–30. Wampach L, Heintz-Buschart A, Hogan A, Muller EEL, Narayanasamy S, Laczny CC, et al. Colonization and Succession within the Human Gut Microbiome by Archaea, Bacteria, and Microeukaryotes during the First Year of Life. Front Microbiol. 2017;8:738. Rook GA. Regulation of the immune system by biodiversity from the natural environment: an ecosystem service essential to health. Proc Natl Acad Sci U S A. 2013;110:18360–7. Szóstak N, Handschuh L, Samelak-Czajka A, Tomela K, Schmidt M, Pruss Ł, Milanowska-Zabel K, Kozlowski P, Philips A. Host Factors Associated with Gut Mycobiome Structure. mSystems. 2023;8(2):e0098622. 10.1128/msystems.00986-22 . Epub 2023 Feb 14. PMID: 36786595; PMCID: PMC10134842. Shuai M, Fu Y, Zhong HL, Gou W, Jiang Z, Liang Y, Miao Z, Xu JJ, Huynh T, Wahlqvist ML, Chen YM, Zheng JS. Mapping the human gut mycobiome in middle-aged and elderly adults: multiomics insights and implications for host metabolic health. Gut. 2022;71(9):1812–20. 10.1136/gutjnl-2021-326298 . Epub 2022 Jan 11. PMID: 35017200; PMCID: PMC9380515. Sun Y, Zuo T, Cheung CP, Gu W, Wan Y, Zhang F, Chen N, Zhan H, Yeoh YK, Niu J, Du Y, Zhang F, Wen Y, Yu J, Sung JJY, Chan PKS, Chan FKL, Wang K, Ng SC, Miao Y. Population-level configurations of gut mycobiome across 6 ethnicities in urban and rural China. Gastroenterology. 2021;160:272–e28611. 10.1053/j.gastro.2020.09.014 . Vonaesch P, Billy V, Mann AE, Morien E, Habib A, Collard JM, Dédé M, Kapel N, Sansonetti PJ, Parfrey LW,Afribiota Investigators. The eukaryome of African children is influenced by geographic location, gut biogeography, and nutritional status. microLife. 2023;4:uqad033. https://doi.org/10.1093/femsml/uqad033 . Jespersen L. Occurrence and taxonomic characteristics of strains of Saccharomyces cerevisiae predominant in African indigenous fermented foods and beverages. FEMS Yeast Res. 2003;3:191–200. Baschali A, Tsakalidou E, Kyriacou A, Karavasiloglou N, Matalas A-L. Traditional low-alcoholic and non-alcoholic fermented beverages consumed in European countries: a neglected food group. Nutr Res Rev. 2017;30:1–24. Johansen PG, Owusu-Kwarteng J, Parkouda C, Padonou SW, Jespersen L. Occurrence and Importance of Yeasts in Indigenous Fermented Food and Beverages Produced in Sub-Saharan Africa. Front Microbiol. 2019;10:1789. https://doi.org/10.3389/fmicb.2019.01789 . Kuncharoen N, Techo S, Savarajara A, et al. Dentification and lipolytic activity of yeasts isolated from foods and wastes. Mycol Int J Fugal Biol. 2020;11:279–86. 10.1080/21501203.2020.1745922 . Fiers WD, Gao IH, Iliev ID. Gut mycobiota under scrutiny: fungal symbionts or environmental transients? Curr Opin Microbiol. 2019;50:79–86. https://doi.org/10.1016/j.mib.2019.09.010 . Nenciarini S, Renzi S, di Paola M, Meriggi N, Cavalieri D. (2023). The yeast-human coevolution: Fungal transition from passengers, colonizers, and invaders. WIREs mechanisms of disease, e1639. Advance online publication. https://doi.org/10.1002/wsbm.1639 . Bosu WK. An overview of the nutrition transition in West Africa: implications for non-communicable diseases. Proc Nutr Soc. 2015;74:466–77. Casari S, Di Paola M, Banci E, Diallo S, Scarallo L, Renzo S, et al. Changing Dietary Habits: The Impact of Urbanization and Rising Socio-Economic Status in Families from Burkina Faso in Sub-Saharan Africa. Nutrients. 2022;14:1782. White TJ, Bruns T, Lee S, Taylor J. Amplification and direct sequencing of fungal ribosomal RNA Genes for phylogenetics. PCR Protoc Guide Methods Appl. San Diego: Academic; 1990. pp. 315–22. Martin M. CUTADAPT removes adapter sequences from high-throughput sequencing reads. EMBnet J 2011;17. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3. Deshpande V, Wang Q, Greenfield P, Charleston M, Porras-Alfaro A, Kuske CR, et al. Fungal identification using a Bayesian classifier and the Warcup training set of internal transcribed spacer sequences. Mycologia. 2016;108:1–5. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org . 2021. Kandlikar GS. ranacapa: Utility Functions and shiny App for Simple Environmental DNA Visualizations and Analyses. R package version 0.1. 2021. 2021. Shetty SA, Lahti L, microbiomeutilities. Utilities for Microbiome Analytics. http://microbiome.github.com/microbiome . 2020. https://microsud.github.io/microbiomeutilities/. Oksanen J, Simpson GL, Blanchet FG, Kindt R, Legendre P, Minchin PR et al. vegan: Community Ecology Package. 2022. https://CRAN.R-project.org/package=vegan . Martinez Arbizu P, pairwiseAdonis. Pairwise multilevel comparison using adonis. R package version 0.4. 2020. https://rdrr.io/github/nuriamw/micro4all/man/PairwiseAdonisFun.html . Fox J, Weisberg S. An {R} Companion to Applied Regression, Third Edition [Internet]. 3rd ed. SAGE Publications; 2018. https://socialsciences.mcmaster.ca/jfox/Books/Companion/ . Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550. Kassambara A. rstatix: Pipe-Friendly Framework for Basic Statistical Tests [Internet]. 2022. https://CRAN.R-project.org/package=rstatix . Kolde R. pheatmap: Pretty Heatmaps [Internet]. 2019. https://CRAN.R-project.org/package=pheatmap . Wickham H, Chang W. ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics — ggplot2-package. 2016 [cited 2023 Oct 8]; https://ggplot2.tidyverse.org/reference/ggplot2-package.html . Sonnenburg ED, Sonnenburg JL. The ancestral and industrialized gut microbiota and implications for human health. Nat Rev Microbiol. 2019;17:383–90. Gouda HN, Charlson F, Sorsdahl K, Ahmadzada S, Ferrari AJ, Erskine H, et al. Burden of non-communicable diseases in sub-Saharan Africa, 1990–2017: results from the Global Burden of Disease Study 2017. Lancet Glob Health. 2019;7:e1375–87. Melaku YA, Gill TK, Taylor AW, Appleton SL, Gonzalez-Chica D, Adams R, et al. Trends of mortality attributable to child and maternal undernutrition, overweight/obesity and dietary risk factors of non-communicable diseases in sub-Saharan Africa, 1990–2015: findings from the Global Burden of Disease Study 2015. Public Health Nutr. 2019;22:827–40. 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;505:559–63. Szóstak N, Handschuh L, Samelak-Czajka A, Tomela K, Schmidt M, Pruss Ł, et al. Host Factors Associated with Gut Mycobiome Structure. mSystems. 2023;8:e0098622. Cao Y, Wang L, Ke S, Villafuerte Gálvez JA, Pollock NR, Barrett C, et al. Fecal Mycobiota Combined With Host Immune Factors Distinguish Clostridioides difficile Infection From Asymptomatic Carriage. Gastroenterology. 2021;160:2328–e23396. Zuo T, Wong SH, Cheung CP, Lam K, Lui R, Cheung K, et al. Gut fungal dysbiosis correlates with reduced efficacy of fecal microbiota transplantation in Clostridium difficile infection. Nat Commun. 2018;9:3663. Ogunremi OR, Sanni AI, Agrawal R. Probiotic potentials of yeasts isolated from some cereal-based Nigerian traditional fermented food products. J Appl Microbiol. 2015;119:797–808. 10.1111/jam.12875 . Pfaller MA, Castanheira M. Nosocomial candidiasis: antifungal stewardship and the importance of rapid diagnosis. Med Mycol. 2016;54:myv076. 10.1093/mmy/myv076 . Adjapong G, Bartlett M, Hale M, Garrill A. The isolation of Candida rugosa and Candida mesorugosa from clinical samples in Ghana. Med Mycol. 2016;54:322–6. Chaves GM, Terçarioli GR, Padovan ACB, Rosas RC, Ferreira RC, Melo ASA, et al. Candida mesorugosa sp. nov., a novel yeast species similar to Candida rugosa, isolated from a tertiary hospital in Brazil. Med Mycol. 2013;51:231–42. Nasanit R, Tangwong-O-Thai A, Tantirungkij M, Limtong S. The assessment of epiphytic yeast diversity in sugarcane phyllosphere in Thailand by culture-independent method. Fungal biology. 2015;119(12):1145–57. https://doi.org/10.1016/j.funbio.2015.08.021 . Khunnamwong P, Jindamorakot S, Limtong S. Endophytic yeast diversity in leaf tissue of rice, corn and sugarcane cultivated in Thailand assessed by a culture-dependent approach. Fungal biology. 2018;122(8):785–99. https://doi.org/10.1016/j.funbio.2018.04.006 . Parachin NS, Siqueira S, de Faria FP, Torres FAG, de Moraes LMP. Xylanases from Cryptococcus flavus 563 isolate I-11: Enzymatic profile, isolation and heterologous expression of CfXYN1 in Saccharomyces 564 cerevisiae. J Mol Catal B: Enzymatic. 2009;59:52–7. Wanderley KJ, Torres FAG, Moraes LMP, Ulhoa CJ. Biochemical characterization of α-amylase from the 607 yeast Cryptococcus flavus. FEMS Microbiol Lett. 2004;231:165–9. Jindal N, Arora S, Dhuria N, Arora D. Cyberlindnera (Pichia) fabianii infection in a neutropenic child: importance of molecular identification. JMM Case Rep. 2015;1:2015–7. Nyanga LK, Nout MJ, Smid EJ, Boekhout T, Zwietering MH. Fermentation characteristics of yeasts isolated from traditionally fermented masau (Ziziphus mauritiana) fruits. Int J Food Microbiol. 2013;166:426–32. 10.1016/j.ijfoodmicro.2013.08.003 . Mukisa IM, Porcellato D, Byaruhanga YB, Muyanja CM, Rudi K, Langsrud T, Narvhus JA. The dominant microbial community associated with fermentation of Obushera (sorghum and millet beverages) determined by culture-dependent and culture-independent methods. Int J Food Microbiol. 2012;160:1–10. 10.1016/j.ijfoodmicro.2012.09.023 . Limtong S, Kaewwichian R, Yongmanitchai W, Kawasaki H. Diversity of culturable yeasts in phylloplane of sugarcane in Thailand and their capability to produce indole-3-acetic acid. World J Microbiol Biotechnol. 2014;30:1785–96. 10.1007/s11274-014-1602-7 . Al-Sweih N, Ahmad S, Khan S, Joseph L, Asadzadeh M, Khan Z. Cyberlindnera fabianii fungaemia outbreak in preterm neonates in Kuwait and literature review. Mycoses. 2019;62(1):51–61. Jindal N, Arora S, Dhuria N, Arora D. (2015). Cyberlindnera (Pichia) fabianii infection in a neutropenic child: importance of molecular identification. JMM case Rep, 2(4), e000033. Hof H, Amann V, Tauber C, Paulun A. Peritonitis in a neonate due to Cyberlindnera fabianii, an ascomycetic yeast. Infection. 2017;45(6):921–4. Pandey N, Paul P, Kumar D, Tilak R. (2020). Case report of a rare yeast Cyberlindnera fabianii fungemia in preterm twin neonates from North India: Diagnostic and therapeutic challenge. Indian J Case Rep, 563–5. Olagunju O, Mchunu N, Venter S, Guibert B, Durand N, Metayer I, Ijabadeniyi O. (2018). Fungal contamination of food commodities in Durban, South Africa. J Food Saf, 38(6), e12515. Motey GA, Johansen PG, Owusu-Kwarteng J, Ofori LA, Obiri-Danso K, Siegumfeldt H, et al. Probiotic potential of Saccharomyces cerevisiae and Kluyveromyces marxianus isolated from West African spontaneously fermented cereal and milk products. Yeast Chichester Engl. 2020;37:403–12. Smith IM, Baker A, Christensen JE, Boekhout T, Frøkiær H, Arneborg N, et al. Kluyveromyces marxianus and Saccharomyces boulardii Induce Distinct Levels of Dendritic Cell Cytokine Secretion and Significantly Different T Cell Responses In Vitro. PLoS ONE. 2016;11:e0167410. Spatz M, Richard ML. Overview of the Potential Role of Malassezia in Gut Health and Disease. Front Cell Infect Microbiol. 2020;10:201. 10.3389/fcimb.2020.00201 . PMID: 32528901; PMCID: PMC7265801. Victora CG, Bahl R, Barros AJD, França GVA, Horton S, Krasevec J, et al. Breastfeeding in the 21st century: epidemiology, mechanisms, and lifelong effect. Lancet. 2016;387:475–90. 10.1016/S0140-6736(15)01024-7 . Kesavan S, Walters CE, Holland KT, Ingham E. The effects of Malassezia on pro-inflammatory cytokine production by human peripheral blood mononuclear cells in vitro. Med Mycol. 1998;36:97–106. Raimondi S, Amaretti A, Gozzoli C, Simone M, Righini L, Candeliere F, Brun P, Ardizzoni A, Colombari B, Paulone S, Castagliuolo I, Cavalieri D, Blasi E, Rossi M, Peppoloni S. Longitudinal Survey of Fungi in the Human Gut: ITS Profiling, Phenotyping, and Colonization. Front Microbiol. 2019;10:1575. 10.3389/fmicb.2019.01575 . PMID: 31354669; PMCID: PMC6636193. Hof H. Rhodotorula spp. in the gut - foe or friend? GMS Infect Dis. 2019;7:Doc02. 10.3205/id000042 . PMID: 31538040; PMCID: PMC6734584. Rizzetto L, Ifrim DC, Moretti S, Tocci N, Cheng S-C, Quintin J, et al. Fungal Chitin Induces Trained Immunity in Human Monocytes during Cross-talk of the Host with Saccharomyces cerevisiae. J Biol Chem. 2016;291:7961–72. Meriggi N, Di Paola M, Vitali F, Rivero D, Cappa F, Turillazzi F, et al. Saccharomyces cerevisiae Induces Immune Enhancing and Shapes Gut Microbiota in Social Wasps. Front Microbiol. 2019;10:2320. Roussel S, Sudre B, Reboux G, Waser M, Buchele G, Vacheyrou M, et al. Exposure to moulds and actinomycetes in Alpine farms: a nested environmental study of the PASTURE cohort. Environ Res. 2011;111:744–50. Additional Declarations No competing interests reported. Supplementary Files FigureS1.png FigureS2.png FigureS3.png FigureS4.png FigureS5.png FigureS6.png Renzietal.2023SupplementaryBMC.docx Supplementarytable1.csv Supplementarytable2.csv Supplementarytable3.csv Supplementarytable4.csv Supplementarytable5.csv Supplementarytable9.csv Supplementarytable10.csv Supplementarytable11.csv Supplementarytable12.csv Cite Share Download PDF Status: Published Journal Publication published 16 Aug, 2025 Read the published version in BMC Microbiology → Version 1 posted Editorial decision: Revision requested 08 May, 2024 Reviews received at journal 02 May, 2024 Reviewers agreed at journal 29 Apr, 2024 Reviewers agreed at journal 19 Apr, 2024 Reviews received at journal 17 Apr, 2024 Reviewers agreed at journal 28 Mar, 2024 Reviewers agreed at journal 26 Mar, 2024 Reviewers invited by journal 26 Mar, 2024 Editor assigned by journal 25 Mar, 2024 Submission checks completed at journal 25 Mar, 2024 First submitted to journal 11 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4073876","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":284402615,"identity":"da5fc7e9-8678-48f7-8b95-5da700dea389","order_by":0,"name":"Sonia Renzi","email":"","orcid":"","institution":"Department of Biology, University of Florence","correspondingAuthor":false,"prefix":"","firstName":"Sonia","middleName":"","lastName":"Renzi","suffix":""},{"id":284402616,"identity":"f9b18fd8-3481-4af0-9752-22af04cd12b0","order_by":1,"name":"Niccolò Meriggi","email":"","orcid":"","institution":"Department of Biology, University of Florence","correspondingAuthor":false,"prefix":"","firstName":"Niccolò","middleName":"","lastName":"Meriggi","suffix":""},{"id":284402617,"identity":"e2359beb-f0ae-43e6-ad9a-bb5374e9f94c","order_by":2,"name":"Monica Di Paola","email":"","orcid":"","institution":"Department of Biology, University of Florence","correspondingAuthor":false,"prefix":"","firstName":"Monica","middleName":"Di","lastName":"Paola","suffix":""},{"id":284402620,"identity":"98103262-035d-44cc-8f79-6d5df0abdb3a","order_by":3,"name":"Giovanni Bacci","email":"","orcid":"","institution":"Department of Biology, University of Florence","correspondingAuthor":false,"prefix":"","firstName":"Giovanni","middleName":"","lastName":"Bacci","suffix":""},{"id":284402621,"identity":"7806c9a7-244d-41bf-88db-6ca058be43d1","order_by":4,"name":"Benedetta Cerasuolo","email":"","orcid":"","institution":"Department of Biology, University of Florence","correspondingAuthor":false,"prefix":"","firstName":"Benedetta","middleName":"","lastName":"Cerasuolo","suffix":""},{"id":284402624,"identity":"12566684-9a3e-4960-8933-959d258f4f1e","order_by":5,"name":"Agnese Gori","email":"","orcid":"","institution":"Department of Biology, University of Florence","correspondingAuthor":false,"prefix":"","firstName":"Agnese","middleName":"","lastName":"Gori","suffix":""},{"id":284402625,"identity":"34c8b8b1-8744-4ace-b232-bcc65234d9ea","order_by":6,"name":"Silene Casari","email":"","orcid":"","institution":"Meyer Children's Hospital IRCCS, Gastroenterology and Nutrition Unit","correspondingAuthor":false,"prefix":"","firstName":"Silene","middleName":"","lastName":"Casari","suffix":""},{"id":284402627,"identity":"e0de3fba-03d3-47c0-946d-33ffd080d935","order_by":7,"name":"Elena Banci","email":"","orcid":"","institution":"Meyer Children's Hospital IRCCS, UP Dietetica","correspondingAuthor":false,"prefix":"","firstName":"Elena","middleName":"","lastName":"Banci","suffix":""},{"id":284402628,"identity":"2ca204c8-543c-498a-9c0e-731d3027bad7","order_by":8,"name":"Alessia de Blasi","email":"","orcid":"","institution":"Meyer Children's Hospital IRCCS, Gastroenterology and Nutrition Unit","correspondingAuthor":false,"prefix":"","firstName":"Alessia","middleName":"","lastName":"de Blasi","suffix":""},{"id":284402629,"identity":"78ca4054-f767-4920-8f34-01140a6ea778","order_by":9,"name":"Salou Diallo","email":"","orcid":"","institution":"Institut de Recherche en Sciences de la Santé-Clinical Research Unit of Nanoro","correspondingAuthor":false,"prefix":"","firstName":"Salou","middleName":"","lastName":"Diallo","suffix":""},{"id":284402630,"identity":"0091d5d5-818b-4b77-97ed-5515edd1c47d","order_by":10,"name":"Berenger Kaborè","email":"","orcid":"","institution":"Institut de Recherche en Sciences de la Santé-Clinical Research Unit of Nanoro","correspondingAuthor":false,"prefix":"","firstName":"Berenger","middleName":"","lastName":"Kaborè","suffix":""},{"id":284402631,"identity":"a2538282-83f4-4eaf-b9cf-0e22a68112f9","order_by":11,"name":"Karim Derra","email":"","orcid":"","institution":"Institut de Recherche en Sciences de la Santé-Clinical Research Unit of Nanoro","correspondingAuthor":false,"prefix":"","firstName":"Karim","middleName":"","lastName":"Derra","suffix":""},{"id":284402632,"identity":"24ea5e98-6fdd-4ba8-a4f0-94936b8979b3","order_by":12,"name":"Halidou Tinto","email":"","orcid":"","institution":"Institut de Recherche en Sciences de la Santé-Clinical Research Unit of Nanoro","correspondingAuthor":false,"prefix":"","firstName":"Halidou","middleName":"","lastName":"Tinto","suffix":""},{"id":284402633,"identity":"ede6928c-4461-40da-85dc-88a9f44fd3ac","order_by":13,"name":"Carlotta Filippo","email":"","orcid":"","institution":"National Research Council, Institute of Agricultural Biology and Biotechnology","correspondingAuthor":false,"prefix":"","firstName":"Carlotta","middleName":"","lastName":"Filippo","suffix":""},{"id":284402634,"identity":"50a51a0f-703e-47e3-8601-ce7e51148618","order_by":14,"name":"Quirijn De Mast","email":"","orcid":"","institution":"Department of Internal Medicine, Radboud University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Quirijn","middleName":"","lastName":"De Mast","suffix":""},{"id":284402635,"identity":"e0116a73-b9a9-4110-9372-bc4976cbff11","order_by":15,"name":"Paolo Lionetti","email":"","orcid":"","institution":"Meyer Children's Hospital IRCCS, Gastroenterology and Nutrition Unit","correspondingAuthor":false,"prefix":"","firstName":"Paolo","middleName":"","lastName":"Lionetti","suffix":""},{"id":284402636,"identity":"2d09ad2b-91cc-4ef3-aa62-b60581168585","order_by":16,"name":"Duccio Cavalieri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIie2QsQrCMBCG46JLsGsKYl/hpOAk9FWuFJyqCC4dHDrp4gP0NXyDE8EueYBM0i5ODnVzEDHVTYhmdMgHF34OPu4ujDkc/wjpwnfskA7ca5sZMG6jsJfi5zpIrRgd+szQvlKXSemrpG6qjAVekVRUZadBqGZ7ogUbRAbFV9NQoGSjQk2BUC75WM2R6MtioNIui9f6HElA8Rq1ksKhsVECWTYUP5CHRQp2U6Dc6ik5chA/FF+e21vEaFduF4RH5EJeWkVwToYfK5P6essmwfDQ29W3FUbeJg0buk+iXm4Y80ZYdBwOh8NhzxNmqF7cJJdxkQAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Biology, University of Florence","correspondingAuthor":true,"prefix":"","firstName":"Duccio","middleName":"","lastName":"Cavalieri","suffix":""}],"badges":[],"createdAt":"2024-03-11 12:48:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4073876/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4073876/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12866-025-04278-9","type":"published","date":"2025-08-16T15:57:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53753793,"identity":"97ee6318-1255-46cf-bc69-ad835523350a","added_by":"auto","created_at":"2024-03-29 18:55:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":248074,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSample distribution among cohorts and impact of geographical areas and family members. \u003c/strong\u003ePrincipal Coordinates Analysis based on Bray-Curtis distance matrix depicted the distribution of the four different cohorts according to the areas. Samples groups were depicted using color scheme in the legend (Area). Samples are represented by using solid-coloured points whereas white-filled points represent centroids (a). Principal Coordinates Analysis based on Bray-Curtis distance matrix, for each single cohort, shows the distribution of the samples according to the family members. Samples groups were depicted using color scheme in the legend (Family). Samples are represented by using solid-coloured points whereas white-filled points represent centroids. R-squared values and significance from adonis PERMAOVA were reported above each panel. Significant effects were highlighted using asterisks (**, p\u0026lt;0.01; ***, p\u0026lt;0.001) (b). Heatmap displayed statistical significance for each comparison from pairwise adonis PERMANOVA (Holm correction method) based on Bray-Curtis distance matrix for each group combination. Explained variance rate was reported from lower value (blue scale) to higher value (red scale) and significant comparisons were reported using asterisks (ns, p\u0026gt;0.05; **, p\u0026lt;0.01) (c). Comparison of alpha diversity measures among cohorts according to the different levels of urbanization by using Wilcoxon test (Benjamini-Hochberg correction method). Statistically significant effects are reported using asterisks (*, p-value \u0026lt;0.05; **, p-value \u0026lt;0.01; ***, p-value \u0026lt;0.001; ****, p \u0026lt;0.0001) and the four different cohorts according to the different areas are mentioned in figures by using the following label code, It: Italy, Ou: Ouagadougou, Na: Nanoro, Ru: Rural villages.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4073876/v1/a36c9a94d28e821ac9da74d8.png"},{"id":53756533,"identity":"c685836e-2e6c-469e-af2e-f58fa44b3584","added_by":"auto","created_at":"2024-03-29 19:03:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67488,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBeta diversity and environmental variables fitting.\u003c/strong\u003e Principal component analysis (PCA) based on VST scaled abundance. Environmental variables (nutritional data) were used as factors to display correlation with the ordination configuration by envfit analysis. Arrows highlight the correlation of the environmental variables with the principal components, and color pattern shows the four different geographical areas (a). Histograms report the goodness of fit statistic, i.e., the squared correlation coefficient of \u003cem\u003eenvfit\u003c/em\u003e(Explained variance) by each covariate in the model, and asterisks represent the significant covariates (***, p-value\u0026lt; 0.001) (b). Hetmap reports the distribution of the significantly fitted (see \u003cem\u003eenvfit\u003c/em\u003e) environmental variables (Macronutrient intake) across the four different geographical areas. Macronutrient intake is expressed as log+1 of the percentage, except for the fibers expressed as log+1 of the grams per 1000 kcal. Macronutrients intake values are reported using color gradients in the legend (blue scale for low values and red scale for higher values) (c). The environmental variables tested are reported in the figure by using the following label code: percentages of vegetable proteins (VePr), percentage of animal proteins (AnPr), percentage of total carbohydrates (Ca), percentage of total proteins (Pr), percentage of total sugars (Su), fiber intake expressed in grams per 1000 kcal (Fi), percentage of total fats (Fa) and kcal per day (kcal).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4073876/v1/7580c3929365d219ac0d2d00.png"},{"id":53753789,"identity":"db8ae6b1-204a-4305-a45b-e0f5449eee0b","added_by":"auto","created_at":"2024-03-29 18:55:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":163278,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSequence variants clustering distributed among the studied cohorts.\u003c/strong\u003e Amplicon sequence variants whose abundances significantly differed within the dataset (loglikelihood ratio test of DESeq2) were clustered according to their mean variance-stabilized abundance. Euclidean distances were used as measure in the cluster dendrogram, as displayed in the rows and columns. Ward D2 linkage was used as a method to produce the hierarchical clusters. Significant VST scaled variants were reported on the heatmap rows, while sample distribution within the clustering was reported on the columns.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4073876/v1/a49ade555c7976cba1d20181.png"},{"id":53753791,"identity":"ea27046d-bb9c-4a3d-9423-1313f7a8d775","added_by":"auto","created_at":"2024-03-29 18:55:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":53618,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePairwise comparisons of the significant abundances at fungal species level.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRelative abundances of significant ASVs identified at Species level (by loglikelihood ratio test of DESeq2) displayed according to the four different areas. Differences in the relative abundance were tested by using the Wilcoxon test (Benjamini-Hochberg correction method). Each cohort was depicted following the color pattern in the legend. Significant comparison were displayed using asterisks (*, p-value \u0026lt;0.05; **, p-value \u0026lt;0.01; ***, p-value \u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eSamples were displayed according to the color pattern legend, based on belonging to the area and family members.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4073876/v1/6ba5faeffefb1c729cc03bf3.png"},{"id":89310577,"identity":"fa9f24c0-557b-45ae-b54f-381f1a06cdd9","added_by":"auto","created_at":"2025-08-18 16:08:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1193996,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4073876/v1/d9901e7e-1ccd-44d8-8017-4467103025ba.pdf"},{"id":53753788,"identity":"06fb88df-3173-428d-9afc-2e421310daa4","added_by":"auto","created_at":"2024-03-29 18:55:04","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":53774,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.png","url":"https://assets-eu.researchsquare.com/files/rs-4073876/v1/eea04d669c9dbe4abfb2bd95.png"},{"id":53756532,"identity":"c5cf14ab-ec98-46da-9ebf-389d77daedaa","added_by":"auto","created_at":"2024-03-29 19:03:04","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":53574,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.png","url":"https://assets-eu.researchsquare.com/files/rs-4073876/v1/cab0b12caa3369dd5facf7d6.png"},{"id":53756527,"identity":"4a58bb3e-c99a-43d4-be65-77b7d196ad11","added_by":"auto","created_at":"2024-03-29 19:03:04","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":140724,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.png","url":"https://assets-eu.researchsquare.com/files/rs-4073876/v1/0cc2e07a8a180e13eaf13780.png"},{"id":53756529,"identity":"12851945-847c-458f-9b1d-94ad4718b1cb","added_by":"auto","created_at":"2024-03-29 19:03:04","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":44123,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS4.png","url":"https://assets-eu.researchsquare.com/files/rs-4073876/v1/faed2a2ea3a996d124b7cad1.png"},{"id":53753806,"identity":"ef4dd4f6-c5e0-4583-a4b5-cea8d7d0db4a","added_by":"auto","created_at":"2024-03-29 18:55:04","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":138303,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS5.png","url":"https://assets-eu.researchsquare.com/files/rs-4073876/v1/b721ac2938fb5bc7d46ffe6a.png"},{"id":53756531,"identity":"b16d66ae-b6ce-4237-8bd8-91499b479b88","added_by":"auto","created_at":"2024-03-29 19:03:04","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":88419,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS6.png","url":"https://assets-eu.researchsquare.com/files/rs-4073876/v1/c7768fef1ed7e234acde46c6.png"},{"id":53753808,"identity":"b20b4813-7e6c-469f-8281-a8d853d5d3fc","added_by":"auto","created_at":"2024-03-29 18:55:04","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":559861,"visible":true,"origin":"","legend":"","description":"","filename":"Renzietal.2023SupplementaryBMC.docx","url":"https://assets-eu.researchsquare.com/files/rs-4073876/v1/a1295a418b56990294160fd6.docx"},{"id":53756534,"identity":"32213704-bc60-4080-8d54-3f283bf4a9a7","added_by":"auto","created_at":"2024-03-29 19:03:04","extension":"csv","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":116660,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.csv","url":"https://assets-eu.researchsquare.com/files/rs-4073876/v1/864a7aa6f512d3e3bb5bce10.csv"},{"id":53753803,"identity":"55388090-443b-4f22-9f8e-ce4e3e981dc4","added_by":"auto","created_at":"2024-03-29 18:55:04","extension":"csv","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":3562,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable2.csv","url":"https://assets-eu.researchsquare.com/files/rs-4073876/v1/0094eb4db1a3ccd8a5246496.csv"},{"id":53753797,"identity":"331ca8cd-d5be-4e33-9620-2b14d373dacb","added_by":"auto","created_at":"2024-03-29 18:55:04","extension":"csv","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":3562,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable3.csv","url":"https://assets-eu.researchsquare.com/files/rs-4073876/v1/7ae640d81239870525a5a789.csv"},{"id":53753805,"identity":"4de016da-0c3c-4d0c-8fb8-a12eaabe5e08","added_by":"auto","created_at":"2024-03-29 18:55:04","extension":"csv","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":17751,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable4.csv","url":"https://assets-eu.researchsquare.com/files/rs-4073876/v1/6f6b76cc44d750f46a5047e2.csv"},{"id":53753804,"identity":"d8c7520b-af6b-4f76-a0b7-8585a4f5e6f3","added_by":"auto","created_at":"2024-03-29 18:55:04","extension":"csv","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":17751,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable5.csv","url":"https://assets-eu.researchsquare.com/files/rs-4073876/v1/eda52b893683b6f3a83ed681.csv"},{"id":53753807,"identity":"41c13812-e4f0-4a82-862b-24599610ec5b","added_by":"auto","created_at":"2024-03-29 18:55:04","extension":"csv","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":6776,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable9.csv","url":"https://assets-eu.researchsquare.com/files/rs-4073876/v1/68282c7b1b96062dbbf8d9a0.csv"},{"id":53753802,"identity":"fe1e96e2-5bd9-4333-b9c7-7b32765820ff","added_by":"auto","created_at":"2024-03-29 18:55:04","extension":"csv","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":4860,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable10.csv","url":"https://assets-eu.researchsquare.com/files/rs-4073876/v1/944062b13771590ba3bfe385.csv"},{"id":53757992,"identity":"c1e1fc63-43bd-4a1c-aa90-03e3614f8576","added_by":"auto","created_at":"2024-03-29 19:11:04","extension":"csv","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":6970,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable11.csv","url":"https://assets-eu.researchsquare.com/files/rs-4073876/v1/887b4f25b8ec7f72c1e27131.csv"},{"id":53753796,"identity":"8fba18a3-dccb-4254-83d1-76f1f760cfaa","added_by":"auto","created_at":"2024-03-29 18:55:04","extension":"csv","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":6970,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable12.csv","url":"https://assets-eu.researchsquare.com/files/rs-4073876/v1/41be1b3cfb6ac50857bf1db4.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Influence of Urbanization on Gut Mycobiota through Dietary Changes in Burkina Faso","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe human gut microbiota, comprising bacteria, fungi, archaea, and viruses, plays a crucial role in influencing host metabolism, immune function, and overall human well-being [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. At the same time, the microbiota is influenced by different factors, such as diet, lifestyle, and geography, that significantly shape the structure of microbial communities. Within environmental factors, urbanization, which is associated with changes in lifestyle, socio-economic status, and dietary habits, results in a shift from traditional, fiber-rich diets to hyper-caloric diets high in fats, proteins, and processed foods commonly found in Western countries. This transition plays a crucial role in altering the structure of the gut microbiota [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn particular, dietary shift leads to a reduction in gut microbiota diversity, particularly affecting beneficial bacteria involved in fiber fermentation, with a consequent decrease in short-chain fatty acids productions (SCFAs) [\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7 CR8\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Improved socio-economic conditions in urban settings contributes to enhanced hygiene practices, increased antibiotic use, and food sterilization, all of which adversely affect the gut microbiota by reducing or eliminating potentially beneficial microorganisms [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The \"hygiene hypothesis\" and the concept of \"missing microbes\" explain the depletion of ancestral microorganisms due to Western-related behaviors and practices [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eImpairment of the microbiota also relates to the increase of non-communicable diseases (NCDs), including diet-related disorders, inflammatory and immune-related conditions, cardiovascular diseases, chronic kidney disease, respiratory diseases, and cancers [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, the ability of the mycobiota, i.e., fungal communities, to modulate host immunity has gained attention for their role in health and disease [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFungal adaptation in the human gastrointestinal tract occurs through maternal transmission, environmental factors, and food exposure [\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The development of gut fungal communities in the first years of life is crucial to the maturation of host immune competence [\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Studies on adult gut mycobiota reveal two key findings: the existence of a core mycobiota and a dynamic fungal community influenced by dietary patterns and diverse environments [\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFermented products can provide to the human diet beneficial yeast strains belonging to the genera \u003cem\u003eSaccharomyces, Candida, Pichia, Debaryomyces, Kluyveromyces, Hanseniaspora\u003c/em\u003e, capable of producing benefits to human health [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In Africa, these yeasts are widely represented in traditional fermentations crafted from both plant and animal-derived raw materials. In a survey conducted by Johansen and colleagues [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] of 43 traditional fermented foods from sub-Saharan Africa, it was found that S. cerevisiae emerged as the predominant yeast species in 77% of the analyzed fermented products, followed by \u003cem\u003ePichia kudriavzevii\u003c/em\u003e (anamorph \u003cem\u003eCandida krusei\u003c/em\u003e) at 60%, \u003cem\u003eCandida tropicalis\u003c/em\u003e at 47%, and \u003cem\u003eKluyveromyces marxianus\u003c/em\u003e (anamorph \u003cem\u003eCandida kefyr\u003c/em\u003e) at 44%. This fungal exposure can occur through environmental contamination, direct food assimilation, or the modification of intestinal communities following a dietary shift [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite current knowledge, the impact of the environment in shaping the fungal mycobiota remains largely unexplored [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This study aims to describe the impact of transitioning from rural to urban environments on the intestinal mycobiota of African populations living in areas at different levels of urbanization (rural, semi-urbanized, and urban areas) in Burkina Faso [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. We also compared the gut fungal community of these African cohorts with an Italian cohort representing a population characterized by Western conditions [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur findings reveal a significant influence of lifestyle on the decreasing gradient of fungal diversity in the three African cohorts according to the level of urbanization. Biodiversity variations were observed within family members in rural and semi-urbanized areas, highlighting a shift in fungal profiles during the rural-to-urban transition.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003e\u003cem\u003eStudy populations\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe research took place in Burkina Faso, specifically in the Boulkiemde province, spanning from February 2019 to January 2020. In this investigation, we enrolled a total of 30 households in good health from three different cohorts in Burkina Faso, comprising 147 individuals of varying ages, from 1 to 73 years old. These households were carefully chosen from three distinct locales with different urbanization levels: rural villages, the semi-urbanized area of Nanoro town, and the urban area of the capital city, Ouagadougou. These selection criteria align with the details outlined in our earlier publication by Casari et al. in 2022 [34]. Participants\u0026rsquo; enrollment was performed during the dry season with the support of the Nanoro Health and Demographic Surveillance System (HDSS), a surveillance organ established by the Clinical Research Unit of Nanoro (CRUN). Data on lifestyle, dietary habits, socio-economic conditions, hygiene, sanitation, health status at the time of enrollment and previous illnesses, were collected in a database by means of a questionnaire already analyzed by Casari et al. 2022 [34].\u003c/p\u003e\n\u003cp\u003eThe main characteristics of the African cohorts are the following :\u003c/p\u003e\n\u003cp\u003e(i) 10 polygamous households, composed by one father, at least 2 mothers and up to 4 children (n=51 individuals), randomly selected by three rural villages (Boulpon, Poessi, and Godo, details in Casari et al. 2022 [34]) located in Boulkimede province. The populations of these rural villages live on subsistence farming and raise small animals (e.g., chickens and goats). Their housing are clusters of huts built using soil, wood, and straw, without electricity and scarce access to water. These households are in poor contact with urban areas. Their diet is traditional and predominantly plant-based (i.e., local cereals, legumes, and herbs). Western influences on diet are almost totally absent. This population is at high risk of infectious diseases and malnutrition.\u003c/p\u003e\n\u003cp\u003e(ii) 10 families, either polygamous or monogamous, with one father, one or two mothers, and up to 4 children (n=51 individuals), living in the semi-urbanized area of the small town of Nanoro. People live in groups of small brick houses with scarce access to electricity. Water is collected from public wells. Their diet is still traditional and plant-based, but different foods can be found at local markets, including meat and dried fish that they eat once-a-week, and occasionally processed and Western-like foods. In this area, people are at risk of infectious diseases and malnutrition.\u003c/p\u003e\n\u003cp\u003e(iii) 10 monogamous families, with one mother and father and up to 4 children (n= 45 individuals) living in the capital city of Ouagadougou, selected among wealthy urban families in medium-high economic conditions. This population lives in concrete or brick buildings with access to electricity and private water sources. They have a predominantly Burkinab\u0026egrave; diet, but have access to a variety of foods, including Western and processed foods available in supermarkets. These families have good hygiene and sanitation, and the risk of infection and malnutrition is extremely reduced.\u003c/p\u003e\n\u003cp\u003eIn addition, a total of 143 subjects belonging to 45 different families, composed of 2 parents and up to 4 children, living in the urban areas of Florence (Tuscany, Italy) have been enrolled as representatives of an urban population with a Western-like diet and lifestyle.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eInclusion and exclusion criteria of study participants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eInclusion criteria for the African cohorts were as follows: (i) households and families composed of a biological father and from 1 to more mothers and up to 4 children; (ii) belonging to the Mossi ethnic group; (iii) apparently in good health status.\u003c/p\u003e\n\u003cp\u003eExclusion criteria: individuals who had fever (\u0026gt;38.5 \u0026deg;C) in the previous 72 hours, in order to exclude acute illnesses at the time of enrollment.\u003c/p\u003e\n\u003cp\u003eFor Italian families, inclusion criteria are: (i) families composed of 2 parents and up to 4 children living in urban areas in Florence province (Italy); (ii) good health status.\u003c/p\u003e\n\u003cp\u003eSubjects with acute illnesses, including infections, at the time of enrollment were not included in the project. Furthermore, in order to avoid conceptual and descriptive biases, the subjects with gastrointestinal diseases such as Crohn disease and ulcerative colitis were removed from the current study due to the impact of these pathologies on the intestinal microbiota (A total of 8 children with Crohn\u0026rsquo;s disease and 12 children with ulcerative colitis from Italian cohort were removed from the whole subjects cohort included in the bioproject mentioned in the section \u0026lsquo;Availability of data and materials\u0026rsquo;). To avoid technical biases during the analysis, we also removed two samples, 1 from the Italian cohort and 1 from the Nanoro cohort, which presented no fungal counts (samples named \u0026lsquo;IT-IBD-21-F1\u0026rsquo; and \u0026lsquo;F304N\u0026rsquo; were removed from the whole subjects cohort included in the bioproject mentioned in the section \u0026lsquo;Availability of data and materials\u0026rsquo;).\u003c/p\u003e\n\u003cp\u003eFor all the enrolled African and Italian participants included in the analyses, information obtained by the questionnaires about health status, diseases, vaccinations, dietary habits, and socio-economic conditions were extensively reported in Supplementary Table 1.\u003c/p\u003e\n\u003cp\u003eThe pruning procedures based on the exclusion criteria mentioned above produced a final dataset composed of 122, 45, 50 and 51 subjects from the Italian, Ouagadougou, Nanoro and Rural villages cohorts, respectively, on which the statistical analyses were carried out.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData deposition\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSequence data are deposited in the European Nucleotide Archive (ENA) under accession code PRJEB59322. The dataset available in the EBI-ENA database includes ITS1 raw sequences from both healthy subjects and patients with inflammatory bowel disease. For the computational analysis of this study, only healthy subjects belonging to families were considered.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn Burkina Faso, the study was carried out in accordance with the recommendations of the National Ethics Committee of Burkina Faso that granted ethical clearance (reference number 2018-8-104). For the Italian families, the study was approved by the Ethics Committee of Meyer Children Hospital, Florence, Italy (reference number 187/2018).\u003c/p\u003e\n\u003cp\u003eAdult participants gave their informed consent. Parents or primary caregivers signed off on children under the age of 18. Confidentiality was maintained for data and sample collection by assigning each participant an identifying code.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSample collection and DNA extractio\u003c/em\u003en\u003c/p\u003e\n\u003cp\u003eFor each participant, a stool sample was collected by a commercial sterile collection tube supplied with RNAlater (Thermo Fisher Scientific), then stored at -80\u0026deg;C in order to preserve nucleic acids. The DNeasy PowerSoil Pro Kit (Qiagen) was used to extract total DNA from 250 mg (wet weight) of each fecal sample according to the manufacturer\u0026apos;s instructions. The Qubit 4 Fluorometer (Thermo Fisher Scientific) and the 1x dsDNA High Sensitivity Kit were used to assess DNA concentration before the downstream analyses.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eITS1 amplification, library preparation and sequencing\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor each DNA sample, the fungal internal transcribed spacer 1 (ITS1) was amplified using a specific primer set for the ITS1 rDNA region (ITS1f: 5\u0026prime;‐CTTGGTCATTTAGAGGAAGTAA‐3\u0026prime; and ITS2r: 5\u0026prime;‐GCTGCGTTCTTCATCGATGC‐3\u0026prime;) [35], with overhang Illumina adapters. Library preparation was performed according to the Fungal Metagenomic Sequencing Demonstrated Protocol (Document # 1000000064940 v01).\u003c/p\u003e\n\u003cp\u003eSequencing was performed on the Illumina MiSeq platform (Illumina) with the V3 chemistry 600 cycle PE300 protocol at the Department of Biology, University of Florence, Italy.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAmplicon sequence variance assemblage\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBoth primer sequences were removed by using cutadapt version 1.15 [36] in paired-end mode. If a primer sequence was not found, the entire sequence was discarded along with its pair to reduce possible contamination. The raw sequences were processed using DADA2 pipeline version 1.14.1 [37] to infer amplicon sequence variants (ASVs). The low quality reads were filtered using the \u0026ldquo;filterAndTrim\u0026rdquo; function with a maximum number of expected error thresholds of 2 for forward and reverse read pairs and a minimum cut-off length of 70bp. Error rate estimation (\u0026ldquo;learnErrors\u0026rdquo; function) and denoising (\u0026ldquo;dada\u0026rdquo; function) with default parameters were performed. Denoised reads were merged using the \u0026ldquo;mergePairs\u0026rdquo; function, discarding those with any mismatches and/or an overlap length shorter than 20 bp. Chimeric sequences were also removed (\u0026ldquo;removeBimeraDenovo\u0026rdquo; function) and taxonomic classification was produced by using DECIPHER package version 2.14.0 against the Warcup database for fungal ITS1 [38]. All sequence variants not classified as Fungi and/or with no fungal counts were removed from the dataset to properly perform the downstream analyses.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed in R environment version 4.2.2 [39].\u003c/p\u003e\n\u003cp\u003eSequencing depth was depicted by rarefaction curves generated by using the \u0026ldquo;ggrare\u0026rdquo; function of \u0026ldquo;ranacapa\u0026rdquo; package version 0.1.0 [40]. Mean relative abundance was calculated using \u0026ldquo;microbiomeutilities\u0026rdquo; package version 1.0.17 [41]. Variations in fungal diversity (beta diversity analysis) were inspected using \u0026ldquo;vegan\u0026rdquo; package version 2.6.4 [42]. In detail, samples distribution was displayed by principal coordinate analysis (PCoA) using the \u0026ldquo;cmdscale\u0026rdquo; function of the \u0026ldquo;stat\u0026rdquo; package performed on distance matrices based on Bray-Curtis diversity index and Jaccard similarity coefficient to infer quantitative and qualitative differences, respectively. The data were normalized before multidimensional analysis removing counts present less than once within the sample dataset, and then transformed in relative abundances to reduce coverage bias among samples. Permutational multivariate analysis of variance using distance matrices (adonis PERMANOVA) was performed to inspect differences between sample groups by using the \u0026ldquo;adonis2\u0026rdquo; function of \u0026ldquo;vegan\u0026rdquo; package. Adonis PERMANOVA was tested on a multiple factor formula (formula = \u0026sim;Area * Family + Age + Sex) considering the interaction between area and family degree due to the crossed design experiment. Pairwise comparisons on variance among geographical areas were assessed using pairwise adonis PERMANOVA by using the \u0026ldquo;pairwise.adonis\u0026rdquo; function from \u0026ldquo;pairwiseAdonis\u0026rdquo; package version 0.4 [43] adjusting the resulting p-values with Holm correction method. The same model formula was used to inspect differences in alpha diversity metrics, first fitted the model formula using linear model (\u0026ldquo;lm\u0026rdquo; function of \u0026ldquo;stats\u0026rdquo; R package) then inspected by analysis of variance ANOVA type II from \u0026ldquo;car\u0026rdquo; package version 3.1.0 [44]. Covariation between nutritional data (amounts of macronutrients represented by the percentages of carbohydrates, simple sugar, total protein, animal protein, vegetable protein, fats, and gr/1000 Kcal of fibers) and beta diversity was assessed by fitting the nutritional parameters onto the principal components analysis (PCA) by using the \u0026lsquo;envfit\u0026rsquo; function in \u0026lsquo;vegan\u0026rsquo; package setting a number of permutations of 1000. PCA was built on VST (variance stabilizing transformation) scaled abundance matrix by using DESeq2 package version 1.38.3 [45] after single counts removal and estimated using the \u0026ldquo;rda\u0026rdquo; function of the \u0026ldquo;vegan\u0026rdquo; package.\u003c/p\u003e\n\u003cp\u003eDifferences in nutritional parameters among sample groups were assessed by Wilcoxon test by using the \u0026ldquo;wilcox_test\u0026rdquo; function from the \u0026ldquo;rstatix\u0026rdquo; package version 0.7.2 adjusting the resulting p-values with Benjamini-Hochberg correction method [46]. To assess differences on variance related to the family degree only, thus excluding the geographic area effect, the adonis PERMANOVA was also performed on multiple factors formula (formula = \u0026sim;Family member + Age range + Sex) after separating the dataset according to four geographical areas. All multivariate analyses were conducted with a number of permutations of 1000 and the r-squared values were used to inspect the amount of variance explained. The dispersion among sample group centroids was computed using the \u0026rdquo;betadisper\u0026rdquo; function of \u0026ldquo;vegan\u0026rdquo; package and the differences in dispersion were tested using the analysis of variance (ANOVA) and Tukey post-hoc test (TukeyHSD function from \u0026ldquo;stats\u0026rdquo; R package) to inspect significant pairwise contrasts. After singleton removal, the differential abundance analysis was performed using the likelihood-ratio test (LRT) method by the \u0026ldquo;DESeq\u0026rdquo; function from the \u0026ldquo;DESeq2\u0026rdquo; package. LRT was used to test significance of change in deviance between a full model (Area + Family) and reduced model (Family) provided in the model formula. Statistically significant counts (alpha = 0.05) were scaled using variance stabilizing transformation (VST) then clustered using \u0026ldquo;pheatmap\u0026rdquo; package version 1.0.12 [47]. Differences in alpha diversity metrics and taxa relative abundances were assessed using the \u0026ldquo;ggpubr\u0026rdquo; package [46]. Figures were produced by using \u0026ldquo;ggplot2\u0026rdquo; package version 3.4.2 [48] and edited using open-source graphics editor Inkscape (http://inkscape.org/).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eChanges in fungal diversity depending on geographical area and family member\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBeta diversity analysis was assessed to describe distribution and similarity between sample groups according to different geographical areas and family structure. The sample distribution of African and Italian family cohorts was observed in Principal Coordinates Analysis (PCoA) performed on distance matrices achieved by quantitative (Bray-Curtis) and qualitative (Jaccard) indexes (Fig. 1a and Fig. S6a). Both multidimensional analyses showed a clear separation of the Italian families cohort from the three African cohorts, regardless of the different level of urbanization of \u0026nbsp; \u0026nbsp; \u0026nbsp;Burkinab\u0026egrave; households (Fig. 1a Fig. S6a).\u003c/p\u003e\n\u003cp\u003eDifferences in beta diversity were then validated by multivariate permutational analysis of variance (adonis PERMANOVA) (see material and methods for additional details), demonstrating that the \u0026ldquo;area\u0026rdquo; factor (i.e., Italy, Ouagadougou, Nanoro and Rural villages) and the interaction between \u0026ldquo;area\u0026rdquo; and \u0026ldquo;family member\u0026rdquo; (i.e., father, mother, or child) were the only statistically significant factors affecting the fungal diversity (Table S4). This means that the mycobiota switches first in the four different areas and secondly in the households, but only as a function of the area of origin. In fact, the households showed significant variations in mycobiota only in Nanoro and the rural villages areas. The sample dispersion tested considering the aforementioned factors showed statistical significance only for the \u0026ldquo;area\u0026rdquo; and the interaction between \u0026ldquo;area\u0026rdquo; and \u0026ldquo;family member\u0026rdquo; (ANOVA, p\u0026lt; 0.001 for both Bray-Curtis and Jaccard indexes). However, pairwise tests (Tukey post-hoc) on all 66 pair combinations showed that this significance was driven solely by 17 and 14 significant comparisons for the Bray-Curtis and Jaccard distances, respectively (\u0026ldquo;Area:Family\u0026rdquo; for Bray-Curtis and Jaccard indexes in Table S5), indicating that the lack of homogeneity was due to few comparisons compared to the total samples present in the dataset.\u003c/p\u003e\n\u003cp\u003ePairwise adonis PERMANOVA was performed to highlight differences in fungal diversity between each area showing that the mycobiota varied between all areas except for Ouagadougou vs Nanoro comparison (Fig. 1c and Fig. S6c). The highest value of explained variance was observed between rural villages and Italian cohorts comparison (Fig. 1c and Fig. S6c), highlighting the greater degree of dissimilarity in their mycobiota. Subsequently, to evaluate the impact of the \u0026ldquo;family member\u0026rdquo; factor on sample distribution in each family cohort, the dataset was analyzed dividing it according to the four different areas. We did not find significant differences in fungal diversity within the family members of the Italian cohort and that of Ouagadougou capital city, as depicted by the adonis PERMANOVA (Table S7) and highlighted in multidimensional analysis (PCoA based on Bray-Curtis distance in Fig. 1b). Differently from Italy and Ouagadougou, the family members of the households from rural villages and the semi-urbanized area of Nanoro showed significant variations in fungal diversity (Table S7). The adonis pairwise PERMANOVA analysis identified the family member pairs that significantly influenced the multivariate analysis, i.e. children vs mothers (R2: 0.06, Holm p-adj: 0.02) in the Nanoro cohort, and fathers vs mothers (R2: 0.07, Holm p-adj: 0.03) in the rural villages cohort.\u003c/p\u003e\n\u003cp\u003eDifferences in alpha diversity measures (Observed, Shannon index and Inverse Simpson index) among different areas were also inspected. Analysis of variance (ANOVA type II) showed that all diversity metrics were significantly influenced by the area variable (Table S8). \u0026nbsp;The pairwise comparisons (Wilcoxon test with Benjamini-Hochberg correction method) showed that the alpha diversity metrics were overall higher in the cohort with the lowest level of urbanization (i.e. households from rural villages), as shown by Wilcoxon test (Fig. 1d and Table S9). Pairwise comparison showed significant differences in the total number of observed ASVs among all cohorts, except for the comparison between cohorts from Ouagadougou and Nanoro (Fig. 1d and Table S9). Shannon index metrics showed significant differences in fungal diversity between Italian cohorts and the three African cohorts, and between rural villages and families from Ouagadougou capital city (Fig. 1d and Table S9). Moreover, by using the Inverse Simpson index, we found statistically significant differences in fungal diversity between the Italian cohort and rural villages, and between the Italian cohort and the Nanoro cohort, respectively (Fig. 1d and Table S9). These results highlight the impact of urbanization on fungal diversity metrics indicating the presence of a gradient with increasing values in accordance with the reduction of the level of urbanization.\u003c/p\u003e\n\u003cp\u003eEach geographical cohort presented different dietary habit, which implied a different intake of the main nutrients considered, i.e., total kcal per day, fiber intake (grams per 1000 kcal), percentages of animal proteins, vegetable proteins, total proteins, total carbohydrates, total sugars, and total fats (specific comparisons between cohorts are reported in Fig. S1 and Fig. S2). Therefore, we performed environmental fitting analysis (\u003cem\u003eenvfit\u003c/em\u003e) to assess whether the nutritional parameters described above drive variation in the fungal communities. The analysis showed that changes in gut mycobiota were significantly associated with seven of eight nutritional parameters tested (Fig. 2a and Fig.2b), this means that changes in the fungal community associated with the geographical areas were also associated with variations in macronutrients intake. Based on the square correlation coefficient, the intake of different proportions of protein sources (animal and vegetable proteins) represents the factors that mainly affect fungal distribution. The distribution of daily macronutrient intake values significantly depicted by the environmental fitting analysis is represented in Fig. 3c for each geographical area. The distribution of values was consistent with the environmental fitting results. Environmental fitting on principal component analysis (PCA) showed a significant correlation with fungal distribution of the Italian cohort and the increase in percentage consumption of total protein, animal protein, sugars, and fats, whereas the increased consumption of grams of fiber per 1000 kcal and the percentage of vegetable proteins and carbohydrates were depicted (Fig. 2a).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDifferences among cohorts in fungal abundances follow a gradient dependent on rural-to urban transition\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe performed prevalence-abundance estimation to depict the most representative ASVs within the four different cohorts. To deepen the taxonomic variability associated with each cohort, we highlighted the most representative ASVs following prevalence and occupancy\u003cem\u003e\u0026nbsp;\u003c/em\u003ethresholds (see supplementary materials and methods for additional details).\u003cem\u003e\u0026nbsp;\u003c/em\u003eIn accordance with the threshold\u003cem\u003e\u0026nbsp;\u003c/em\u003eparameters, the analysis showed that ASV_1 \u003cem\u003e(Candida sp VVT 2012)\u0026nbsp;\u003c/em\u003ewas a part\u003cem\u003e\u0026nbsp;\u003c/em\u003eof the most\u003cem\u003e\u0026nbsp;\u003c/em\u003erepresentative core membership of Italian and Ouagadougou cohorts, whereas\u003cem\u003e\u0026nbsp;\u003c/em\u003eASV_2 (\u003cem\u003ePichia\u003c/em\u003e) was representative\u003cem\u003e\u0026nbsp;\u003c/em\u003eof core membership the African cohorts only and ASV_6 (\u003cem\u003eEpicoccum sorghi\u003c/em\u003e) as core memberships of Nanoro and Rural villages\u003cem\u003e\u0026nbsp;\u003c/em\u003ecohorts (Fig. S4 and Table S2). This highlighted how the specific and wide representative ASVs within the dataset were also shared among cohorts in accordance with the level of urbanization.\u003c/p\u003e\n\u003cp\u003eTo inspect differences in fungal distribution within sample groups, we performed a log-likelihood ratio test (LRT) using DESeq2. The analysis showed a total of 33 ASVs differentially abundant among the family cohorts (Table S10). These ASVs represented 1.99% of the total ASVs profiled in the whole fungal communities (33 out of the 1655 identified ASVs), and 59.9% of the total fungal abundance (mean abundance within the sample dataset: 50.5% and standard error: 1.83%).\u003c/p\u003e\n\u003cp\u003eSequence variant clustering (by Ward D2 Linkage method) was performed to observe the distribution of the 33 identified ASVs among the four cohorts. A pattern of 8 ASVs (Fig. 3) was referred to the Italian cohort that clustered separately from the African cohorts. The Italian pattern includes 3 ASVs assigned to 3 different fungal species (ASV_29: \u003cem\u003eRhodotorula mucilaginosa\u003c/em\u003e, ASV_71: \u003cem\u003eCandida sake\u003c/em\u003e, ASV_239: \u003cem\u003eVerticillium leptobactrum\u003c/em\u003e) and 5 ASVs assigned to 4 fungal genera (ASV_88 and ASV_154: \u003cem\u003eEupenicillum\u003c/em\u003e, ASV_77: \u003cem\u003eDebaryomyces\u003c/em\u003e, ASV_55: \u003cem\u003ePenicillium\u003c/em\u003e, ASV_58: \u003cem\u003eEurotium\u003c/em\u003e). African areas depicted 25 different ASVs, which included 9 fungal species (ASV_6: \u003cem\u003eEpicoccum sorghi\u003c/em\u003e, ASV_10: \u003cem\u003eCandida mesorugosa\u003c/em\u003e, ASV_12: \u003cem\u003eCandida tropicalis\u003c/em\u003e, ASV_33: \u003cem\u003eAspergillus flavus\u003c/em\u003e, ASV_34: \u003cem\u003eKluyveromyces marxianus, ASV_70: Cryptococcus flavus, ASV_83: Aspergillus penicillioides, ASV_99: Malassezia restricta, ASV_134: Cyberlindnera fabianii\u003c/em\u003e) and 10 fungal genera (ASV_2, ASV_7 and ASV_18: \u003cem\u003ePichia, ASV_3: Candida, ASV_9: Eurotium, ASV_20: Trichosporon, ASV_30: Davidiella, ASV_50 and ASV_82: Aspergillus, ASV_74: Gibberella, ASV_480: Rhizopus, ASV_646: Plectosphaerella\u003c/em\u003e). Other ASVs with different taxonomic assignments were also depicted (Fig. S7 and Table S10). All significant ASVs and the related taxonomic annotations were reported in Table S10.\u003c/p\u003e\n\u003cp\u003ePairwise comparison by using the Wilcoxon test (Benjamini-Hochberg correction method) (Fig. 4 and Fig. S7; Table S11 and Table S12) showed that the relative abundance of the ASVs selected by LRT analysis, follows a gradient according to the rural-to-urban transition pattern. In particular, the species level variants \u003cem\u003eCandida mesorugosa\u003c/em\u003e, \u003cem\u003eCryptococcus flavus\u003c/em\u003e, \u003cem\u003eCyberlindnera fabianii\u003c/em\u003e, \u003cem\u003eEpicoccum sorghi\u003c/em\u003e, \u003cem\u003eKluyveromyces marxianus\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003eand \u003cem\u003eMalassezia restricta\u003c/em\u003e were enriched in the rural villages cohort, and their abundance progressively decrease according to the increase in the \u0026nbsp; \u0026nbsp; \u0026nbsp;urbanization level (Fig. 4). We also identified fungal species that are more, or almost exclusively, enriched in the Italian cohorts compared to the Africans, such as \u003cem\u003eCandida sake, Rhodotorula mucilaginosa\u0026nbsp;\u003c/em\u003eand \u003cem\u003eVerticilium leptobactrum\u0026nbsp;\u003c/em\u003e(Fig. 4). Variations in fungal relative abundances as a function of the rural-to-urban transition were also visible in the non-species assigned ASVs identified by LRT and reported in Fig. S7. Some of these, such as the fungal genera \u003cem\u003eAspergillus\u003c/em\u003e, \u003cem\u003eCandida\u0026nbsp;\u003c/em\u003eand \u003cem\u003ePichia\u003c/em\u003e, showed significantly higher relative abundances in the African cohorts than in the Italian one (Fig. S7). Conversely, the genera \u003cem\u003ePenicillium\u0026nbsp;\u003c/em\u003eand \u003cem\u003eEupenicillium\u0026nbsp;\u003c/em\u003ewere markedly associated with the Italian cohort and almost absent in the African cohorts (Fig. S7).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUrbanization triggers cultural, socio-economic, and lifestyle changes, including the globalization of diets. These shifts, alongside environmental agents and hygiene practices, alter the gut microbiome and contribute to an increase in non-communicable diseases (NCDs), like inflammatory bowel disease, arthritis, asthma, diabetes, obesity, and respiratory and cardiovascular conditions [49].\u003c/p\u003e\n\u003cp\u003ePrevious studies with similar conditions associate urbanization in sub-Saharan African countries with a heightened prevalence of NCDs, surpassing infectious diseases in terms of occurrence [50,51]. Human socio-cultural adaptation is an ongoing process influenced by interactions with the environment. For example, the diverse vegetation and climate variations across Africa dictate specific agricultural methods, food preservation techniques, and consequently, dietary patterns. These aspects are closely linked to urbanization levels, which bring about substantial shifts in nutrition, hygiene practices, and medication usage. \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Our past study on lifestyle and dietary changes among rural, semi-urbanized and urban households in Burkina Faso underlined the connection between the rural-to-urban transition, obesity and the \u0026ldquo;double burden malnutrition\u0026rdquo; phenomenon [34].\u003c/p\u003e\n\u003cp\u003eThe present study investigates shifts in mycobiota composition within three cohorts from Burkina Faso, all belonging to the same ethnic group. These established cohorts represent the transition from rural to urban living, where both adults and children share similar lifestyles and dietary habits within qualitatively defined environmental and household settings [34]. Comparison of these African cohorts to urban Italian families allows us to assess how differences in geography, environment, diet, and lifestyle contribute to variations in the yeast gut mycobiota.\u003c/p\u003e\n\u003cp\u003eStudies on gut mycobiota [52] of healthy individuals following Western, animal-based, or plant-based diets, reveal potential associations between macronutrients (such as carbohydrates or saturated fatty acids) and specific fungal taxa. In a recent extensive study [53] mycobiota composition and variation of gut fungal communities in a European population were found to be significantly determined by 10 factors, mostly related to diet.\u003c/p\u003e\n\u003cp\u003eOur analysis reveals a distinguishable shift in gut fungal diversity along an urbanization gradient. Consistent distinction between Italian and rural cohorts, affirm that a decrease in richness and biodiversity result from an increase in urbanization levels. Notably, diet emerged as a significant factor in determining fungal diversity, particularly in relation to different protein sources. Rural and semi-urbanized families displayed higher fungal diversity, potentially influenced by environmental exposure, poor sanitation, beside dietary habits. Conversely, urban families, with limited environmental exposure and processed food consumption, exhibited lower fungal diversity. Taking into account family effect, we observed higher fungal diversity among family members of rural and semi-urbanized households compared to urban families from the capital city and Italy. This result may indicate rural and semi-urbanized settings as a considerable driver of differences in fungal diversity. Differences in gut fungal composition were especially apparent in semi-rural and rural areas between mother to child and mother to father. An absence of this phenomena in urban areas, may be attributed to a lack of polygamy, differences in occupation, as well as shifts in family roles and activities within households [34]. Furthermore, differential abundance analysis identified several fungal amplicon sequence variants (ASVs) significantly associated with different geographical areas. \u003cem\u003eA. flavus, A. penicillioides\u003c/em\u003e, and ASVs related to the Aspergillus genus were significantly enriched within the African groups. The decrease in abundance of \u003cem\u003eAspergillus\u003c/em\u003e in \u003cem\u003eClostridium difficile\u003c/em\u003e infections (CDI) has suggested it\u0026rsquo;s beneficial role in decreasing the risk of CDI [54]. Therefore, the association of \u003cem\u003eA. penicillioides\u003c/em\u003e with healthy individuals rather than CDI was also corroborated because its abundance increased following fecal transplantation in CDI subjects [55]. \u003cem\u003eC. tropicalis\u003c/em\u003e, here prevalent in the African cohorts, is often associated with traditional sub-Saharan fermented foods, especially in the cereal-based ones, which are also characterized by the presence of anti-nutritional factors such as phytate. \u003cem\u003eC. tropicalis\u003c/em\u003e isolated from different fermented beverages were found to have high extracellular phytase activity [56], suggesting a possible role of the yeast in promoting nutrients bioavailability. However, despite the positive contribution that this yeast can make within fermentation, the pathogenic potential found in clinical isolates of \u003cem\u003eC. tropicalis\u003c/em\u003e, as well as the opportunistic nature of yeasts belonging to the \u003cem\u003eCandida\u003c/em\u003e genus, must be considered [57]. In particular, the species level variants \u003cem\u003eCandida mesorugosa, Cryptococcus flavus, Cyberlindnera fabianii, Epicoccum sorghi, Kluyveromyces marxianus\u003c/em\u003e, and \u003cem\u003eMalassezia restricta\u003c/em\u003e were enriched in the rural villages cohort, and their abundance progressively decrease according to the increase of the level of urbanization. \u003cem\u003eC. mesorugosa\u003c/em\u003e has already been described as a pathogenic fungus causing several infections and was isolated from clinical samples in Ghana [58] and Brazil [59]. \u003cem\u003eC. \u0026nbsp;flavus\u003c/em\u003e is a basidiomycetous yeast often associated with the phyllosphere, and it has been isolated as an endophyte from rice leaves [60,61]. This yeast has shown interesting enzymatic activities such as amylase, carboxymethyl cellulase (CMCase), and xylanase activities [62,63]. \u003cem\u003eC. fabianii\u003c/em\u003e, a rare opportunistic pathogen primarily isolated from the fermentation of alcoholic beverages and sugarcane [64-67], has also been implicated in pathogenesis in several case studies involving pediatric subjects [68-71]. The prevalence-abundance analysis in accordance with the LRT analysis corroborated the role of \u003cem\u003eE. sorghi\u003c/em\u003e as a representative species of areas with a lower degree of urbanization (Nanoro and Rural villages). The wide occurrence of \u003cem\u003eE. sorghi\u003c/em\u003e within low urbanized areas can be explained by contamination mechanisms; in fact, a natural fungal contamination in food consumed in Durban, South Africa was described by Olagunju et al. 2018 [72]. Additionally, the significant prevalence of \u003cem\u003eK. marxianus\u003c/em\u003e along the rural gradient can be attributed to its widespread presence in various African fermented products [73]. Notably, \u003cem\u003eK. marxianus\u003c/em\u003e is recognized for its immunoregulatory activity; in particular, it exerts the activation of Foxp3+ Treg cells, thus reducing the levels of inflammation and potentially preventing the development of chronic inflammatory disease in the long term [74]. \u003cem\u003eM. restricta\u003c/em\u003e, classically associated with the skin microbiota, is also widely described as a component of the intestinal microbiota [75,76]. We hypothesize that the higher abundance of this fungal species in subjects from rural villages may be attributable to their lifestyle, for example by the absence of sanitization methods during food preservation and handling. Furthermore, \u003cem\u003eM. restricta\u003c/em\u003e has been demonstrated to play a regulatory role in the immune system; Kesavan and collaborators reported a significant reduction in pro-inflammatory cytokines associated with Malassezia [77]. Thus, the presence of \u003cem\u003eK. marxianus, C. tropicalis\u003c/em\u003e, and \u003cem\u003eC. fabianii\u003c/em\u003e in association with African cohorts and, in some cases, in enrichment in the context of rural villages is easily traceable to the composition of the traditional Burkinab\u0026egrave; diet. We also identified prevalent environmental fungal species like \u003cem\u003eRhodotorula mucilaginosa\u003c/em\u003e, which is almost exclusively enriched in the Italian cohorts compared to the African cohorts. In humans, \u003cem\u003eR. mucilaginosa\u003c/em\u003e is described as a transient commensal microorganism found on the skin, nails, and in the gastrointestinal, urinary, and respiratory tracts [78]. \u0026nbsp;Yeasts of this genus are deemed opportunistic pathogens, primarily impacting immunocompromised individuals [79].\u003c/p\u003e\n\u003cp\u003eThe interaction between the human immune system and fungi involves a finely tuned mechanism that responds to opportunistic pathogenic fungi while maintaining tolerance to commensal ones. In turn, fungi can modulate host immunity, enhancing defenses against pathogenic infections, with a mechanism that is conserved across kingdoms, from invertebrates to mammals [80,81]. This immune equilibrium develops in the first 18 months of life, and the lack of early exposure to fungal antigens potentially correlates with an increase in risk for allergies and asthma [82]. Traditional lifestyles often involve greater exposure to environmental microbes and allergens due to factors such as living in rural areas, close contact with animals, and the consumption of unprocessed foods. These environmental exposures may play a role in shaping the immune system and reducing the risk of allergies. It\u0026rsquo;s plausible that the co-occurrence of fungal species with potentially immunogenic profiles in non-immunocompromised subjects (healthy donors) could trigger a synergistic effect that, over time, mitigates the onset of chronic inflammatory diseases. A reduction in early life exposure to yeasts and fungi in urban settings may thus have significant implications for the development of host immune recognition and tolerance, and potentially contribute to the onset of immune-related disorders in urbanized regions worldwide.\u003c/p\u003e\n\u003cp\u003eOur hypothesis suggests that urbanization and the resulting decline in certain fungal species play a significant role in the onset of NCDs, especially chronic inflammatory disorders linked to Western lifestyles. Therefore, our results underscore the need for thorough exploration of both environmental and gut fungal communities in Africa and non-globalized regions to uncover the mechanisms underlying the connection between fungi and NCDs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ITS1 raw sequences data has been deposited to the European Nucleotide Archive (ENA) under the accession code PRJEB59322. All results from statistical analyses were mentioned by writing in the main text, represented as figures in the main text or reported as supplementary tables and figures. Further information can be provided upon request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the study participants in Burkina Faso: all households of the rural villages of Boulpon, Godo and Poessi, the households of the town of Nanoro and the capital city of Ouagadougou. A special thanks to the field staff of the IRSS-URCN-Nanoro (BF) to Dieudonn\u0026egrave; Sorgo from St. Camille Hospital-Nanoro (BF) and Maria Jos\u0026eacute; Caldes, Global Health Center Regione Toscana-Italy. We would also thank the study participants in Italy, the medical and nursing staff of the AOU Meyer children hospital of Florence that supported the activity of sample collection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Joint Programming Initiative, Eranet Cofound, a Healthy Diet for a Healthy Life (JPI-HDHL), TRANSMIC project (grant number 529051018); Bando Salute 2018 RISKCROHNBIOM project (grant number G84I18000160002) and Consorzio Interuniversitario per le Biotecnologie 2023.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSonia Renzi and Niccol\u0026ograve; Meriggi contributed equally to this work as they are co-first authors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors and Affiliations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Biology, University of Florence, Sesto Fiorentino, 50019 Florence, Italy\u003c/p\u003e\n\u003cp\u003eSonia Renzi, Niccol\u0026ograve; Meriggi, Giovanni Bacci, Monica Di Paola, Benedetta Cerasuolo, Agnese Gori \u0026amp; Duccio Cavalieri\u003c/p\u003e\n\u003cp\u003eMeyer Children\u0026apos;s Hospital IRCCS, Gastroenterology and Nutrition Unit, 50139 Florence, Italy\u003c/p\u003e\n\u003cp\u003eSilene Casari, Alessia de Blasi \u0026amp; Paolo Lionetti\u003c/p\u003e\n\u003cp\u003eMeyer Children\u0026apos;s Hospital IRCCS, UP Dietetica, 50139 Florence, Italy\u003c/p\u003e\n\u003cp\u003eElena Banci\u003c/p\u003e\n\u003cp\u003eInstitut de Recherche en Sciences de la Sant\u0026eacute;-Clinical Research Unit of Nanoro (IRSS-URCN), Nanoro 18, Burkina Faso\u003c/p\u003e\n\u003cp\u003eSalou Diallo, Berenger Kabor\u0026egrave;, Karim Derra \u0026amp; Halidou Tinto\u003c/p\u003e\n\u003cp\u003eNational Research Council, Institute of Agricultural Biology and Biotechnology, Pisa, Italy\u003c/p\u003e\n\u003cp\u003eCarlotta De Filippo\u003c/p\u003e\n\u003cp\u003eDepartment of Internal Medicine, Radboud University Medical Center, Nijmegen, the Netherlands\u003c/p\u003e\n\u003cp\u003eQuirjin de Mast\u003c/p\u003e\n\u003cp\u003eDepartment of Neurology, Pharmacology, Psychology and Child Health (NEUROFARBA), University of Florence, 50139 Florence, Italy\u003c/p\u003e\n\u003cp\u003ePaolo Lionetti\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eContributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eD.C. and P.L. conceived the work; S.R., N.M., M.D.P. and C.D.F. wrote the manuscript; S.R. and B.C. produced the metagenomic libraries and sequencing; N.M. performed amplicon sequence variants inference and statistical analysis; G.B. contributed to amplicon sequence variants inference; A.G., S.C., E.B., and A.D.B. managed and analyzed nutritional diaries data; S.D., B.K., K.D., and H.T. coordinated the enrollment and sample collection in Burkina Faso. Q.D.M., D.C., P.L., S.R., N.M., M.D.P., C.D.F., B.C., G.B., A.G., S.C., E.B., A.D.B., S.D., B.K., K.D., and H.T. revised and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCorresponding author\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Duccio Cavalieri, [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn Burkina Faso, the study was carried out in accordance with the recommendations of the National Ethics Committee of Burkina Faso that granted ethical clearance (reference number 2018-8-104). For the Italian families, the study was approved by the Ethics Committee of Meyer Children Hospital, Florence, Italy (reference number 187/2018).\u003c/p\u003e\n\u003cp\u003eAdult participants gave their informed consent. Parents or primary caregivers signed off on children under the age of 18. Confidentiality was maintained for data and sample collection by assigning each participant an identifying code.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGilbert JA, Blaser MJ, Caporaso JG, Jansson JK, Lynch SV, Knight R. Current understanding of the human microbiome. Nat Med. 2018;24:392\u0026ndash;400.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKho ZY, Lal SK. The Human Gut Microbiome - A Potential Controller of Wellness and Disease. Front Microbiol. 2018;9:1835.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Filippo C, Cavalieri D, Di Paola M, Ramazzotti M, Poullet JB, Massart S, et al. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc Natl Acad Sci U S A. 2010;107:14691\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Filippo C, Di Paola M, Ramazzotti M, Albanese D, Pieraccini G, Banci E, et al. Diet, Environments, and Gut Microbiota. A Preliminary Investigation in Children Living in Rural and Urban Burkina Faso and Italy. Front Microbiol. 2017;8:1979.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKabwe MH, Vikram S, Mulaudzi K, Jansson JK, Makhalanyane TP. The gut mycobiota of rural and urban individuals is shaped by geography. BMC Microbiol. 2020;20:257.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuo T, Ng SC. The Gut Microbiota in the Pathogenesis and Therapeutics of Inflammatory Bowel Disease. Front Microbiol. 2018;9:2247.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMart\u0026iacute;nez I, Stegen JC, Maldonado-G\u0026oacute;mez MX, Eren AM, Siba PM, Greenhill AR, et al. The gut microbiota of rural papua new guineans: composition, diversity patterns, and ecological processes. Cell Rep. 2015;11:527\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlaser MJ. The theory of disappearing microbiota and the epidemics of chronic diseases. Nat Rev Immunol. 2017;17:461\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVangay P, Johnson AJ, Ward TL, Al-Ghalith GA, Shields-Cutler RR, Hillmann BM, et al. US Immigration Westernizes the Human Gut Microbiome. Cell. 2018;175:962\u0026ndash;e97210.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrachan DP. Hay fever, hygiene, and household size. BMJ. 1989;299:1259\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlaser MJ, Falkow S. What are the consequences of the disappearing human microbiota? Nat Rev Microbiol. 2009;7:887\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGow NAR, Netea MG. Medical mycology and fungal immunology: new research perspectives addressing a major world health challenge. Philos Trans R Soc Lond B Biol Sci. 2016;371:20150462.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFiers WD, Leonardi I, Iliev ID. From Birth and Throughout Life: Fungal Microbiota in Nutrition and Metabolic Health. Annu Rev Nutr. 2020;40:323\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrati F, Di Paola M, Stefanini I, Albanese D, Rizzetto L, Lionetti P et al. Age and Gender Affect the Composition of Fungal Population of the Human Gastrointestinal Tract. Front Microbiol. 2016;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.frontiersin.org/articles/\u003c/span\u003e\u003cspan address=\"https://www.frontiersin.org/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmicb.2016.01227\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2016.01227\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrati F, Cavalieri D, Albanese D, De Felice C, Donati C, Hayek J, et al. Altered gut microbiota in Rett syndrome. Microbiome. 2016;4:41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrati F, Cavalieri D, Albanese D, De Felice C, Donati C, Hayek J, et al. New evidences on the altered gut microbiota in autism spectrum disorders. Microbiome. 2017;5:24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBliss JM, Basavegowda KP, Watson WJ, Sheikh AU, Ryan RM. Vertical and horizontal transmission of Candida albicans in very low birth weight infants using DNA fingerprinting techniques. Pediatr Infect Dis J. 2008;27:231\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoix-Amor\u0026oacute;s A, Martinez-Costa C, Querol A, Collado MC, Mira A. Multiple Approaches Detect the Presence of Fungi in Human Breastmilk Samples from Healthy Mothers. Sci Rep. 2017;7:13016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoix-Amor\u0026oacute;s A, Puente-S\u0026aacute;nchez F, du Toit E, Linderborg KM, Zhang Y, Yang B, et al. Mycobiome Profiles in Breast Milk from Healthy Women Depend on Mode of Delivery, Geographic Location, and Interaction with Bacteria. Appl Environ Microbiol. 2019;85:e02994\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollado MC, Isolauri E, Laitinen K, Salminen S. Effect of mother\u0026rsquo;s weight on infant\u0026rsquo;s microbiota acquisition, composition, and activity during early infancy: a prospective follow-up study initiated in early pregnancy. Am J Clin Nutr. 2010;92:1023\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWampach L, Heintz-Buschart A, Hogan A, Muller EEL, Narayanasamy S, Laczny CC, et al. Colonization and Succession within the Human Gut Microbiome by Archaea, Bacteria, and Microeukaryotes during the First Year of Life. Front Microbiol. 2017;8:738.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRook GA. Regulation of the immune system by biodiversity from the natural environment: an ecosystem service essential to health. Proc Natl Acad Sci U S A. 2013;110:18360\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSz\u0026oacute;stak N, Handschuh L, Samelak-Czajka A, Tomela K, Schmidt M, Pruss Ł, Milanowska-Zabel K, Kozlowski P, Philips A. Host Factors Associated with Gut Mycobiome Structure. mSystems. 2023;8(2):e0098622. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/msystems.00986-22\u003c/span\u003e\u003cspan address=\"10.1128/msystems.00986-22\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2023 Feb 14. PMID: 36786595; PMCID: PMC10134842.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShuai M, Fu Y, Zhong HL, Gou W, Jiang Z, Liang Y, Miao Z, Xu JJ, Huynh T, Wahlqvist ML, Chen YM, Zheng JS. Mapping the human gut mycobiome in middle-aged and elderly adults: multiomics insights and implications for host metabolic health. Gut. 2022;71(9):1812\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/gutjnl-2021-326298\u003c/span\u003e\u003cspan address=\"10.1136/gutjnl-2021-326298\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2022 Jan 11. PMID: 35017200; PMCID: PMC9380515.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun Y, Zuo T, Cheung CP, Gu W, Wan Y, Zhang F, Chen N, Zhan H, Yeoh YK, Niu J, Du Y, Zhang F, Wen Y, Yu J, Sung JJY, Chan PKS, Chan FKL, Wang K, Ng SC, Miao Y. Population-level configurations of gut mycobiome across 6 ethnicities in urban and rural China. Gastroenterology. 2021;160:272\u0026ndash;e28611. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1053/j.gastro.2020.09.014\u003c/span\u003e\u003cspan address=\"10.1053/j.gastro.2020.09.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVonaesch P, Billy V, Mann AE, Morien E, Habib A, Collard JM, D\u0026eacute;d\u0026eacute; M, Kapel N, Sansonetti PJ, Parfrey LW,Afribiota Investigators. The eukaryome of African children is influenced by geographic location, gut biogeography, and nutritional status. microLife. 2023;4:uqad033. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/femsml/uqad033\u003c/span\u003e\u003cspan address=\"10.1093/femsml/uqad033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJespersen L. Occurrence and taxonomic characteristics of strains of Saccharomyces cerevisiae predominant in African indigenous fermented foods and beverages. FEMS Yeast Res. 2003;3:191\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaschali A, Tsakalidou E, Kyriacou A, Karavasiloglou N, Matalas A-L. Traditional low-alcoholic and non-alcoholic fermented beverages consumed in European countries: a neglected food group. Nutr Res Rev. 2017;30:1\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohansen PG, Owusu-Kwarteng J, Parkouda C, Padonou SW, Jespersen L. Occurrence and Importance of Yeasts in Indigenous Fermented Food and Beverages Produced in Sub-Saharan Africa. Front Microbiol. 2019;10:1789. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2019.01789\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2019.01789\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuncharoen N, Techo S, Savarajara A, et al. Dentification and lipolytic activity of yeasts isolated from foods and wastes. Mycol Int J Fugal Biol. 2020;11:279\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/21501203.2020.1745922\u003c/span\u003e\u003cspan address=\"10.1080/21501203.2020.1745922\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFiers WD, Gao IH, Iliev ID. Gut mycobiota under scrutiny: fungal symbionts or environmental transients? Curr Opin Microbiol. 2019;50:79\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.mib.2019.09.010\u003c/span\u003e\u003cspan address=\"10.1016/j.mib.2019.09.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNenciarini S, Renzi S, di Paola M, Meriggi N, Cavalieri D. (2023). The yeast-human coevolution: Fungal transition from passengers, colonizers, and invaders. WIREs mechanisms of disease, e1639. Advance online publication. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/wsbm.1639\u003c/span\u003e\u003cspan address=\"10.1002/wsbm.1639\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBosu WK. An overview of the nutrition transition in West Africa: implications for non-communicable diseases. Proc Nutr Soc. 2015;74:466\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasari S, Di Paola M, Banci E, Diallo S, Scarallo L, Renzo S, et al. Changing Dietary Habits: The Impact of Urbanization and Rising Socio-Economic Status in Families from Burkina Faso in Sub-Saharan Africa. Nutrients. 2022;14:1782.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhite TJ, Bruns T, Lee S, Taylor J. Amplification and direct sequencing of fungal ribosomal RNA Genes for phylogenetics. PCR Protoc Guide Methods Appl. San Diego: Academic; 1990. pp. 315\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin M. CUTADAPT removes adapter sequences from high-throughput sequencing reads. EMBnet J 2011;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCallahan 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\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeshpande V, Wang Q, Greenfield P, Charleston M, Porras-Alfaro A, Kuske CR, et al. Fungal identification using a Bayesian classifier and the Warcup training set of internal transcribed spacer sequences. Mycologia. 2016;108:1\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org\u003c/span\u003e\u003cspan address=\"https://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKandlikar GS. ranacapa: Utility Functions and shiny App for Simple Environmental DNA Visualizations and Analyses. R package version 0.1. 2021. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShetty SA, Lahti L, microbiomeutilities. Utilities for Microbiome Analytics. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://microbiome.github.com/microbiome\u003c/span\u003e\u003cspan address=\"http://microbiome.github.com/microbiome\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 2020. https://microsud.github.io/microbiomeutilities/.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOksanen J, Simpson GL, Blanchet FG, Kindt R, Legendre P, Minchin PR et al. vegan: Community Ecology Package. 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=vegan\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=vegan\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartinez Arbizu P, pairwiseAdonis. Pairwise multilevel comparison using adonis. R package version 0.4. 2020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rdrr.io/github/nuriamw/micro4all/man/PairwiseAdonisFun.html\u003c/span\u003e\u003cspan address=\"https://rdrr.io/github/nuriamw/micro4all/man/PairwiseAdonisFun.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFox J, Weisberg S. An {R} Companion to Applied Regression, Third Edition [Internet]. 3rd ed. SAGE Publications; 2018. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://socialsciences.mcmaster.ca/jfox/Books/Companion/\u003c/span\u003e\u003cspan address=\"https://socialsciences.mcmaster.ca/jfox/Books/Companion/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLove MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKassambara A. rstatix: Pipe-Friendly Framework for Basic Statistical Tests [Internet]. 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=rstatix\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=rstatix\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKolde R. pheatmap: Pretty Heatmaps [Internet]. 2019. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=pheatmap\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=pheatmap\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWickham H, Chang W. ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics \u0026mdash; ggplot2-package. 2016 [cited 2023 Oct 8]; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ggplot2.tidyverse.org/reference/ggplot2-package.html\u003c/span\u003e\u003cspan address=\"https://ggplot2.tidyverse.org/reference/ggplot2-package.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSonnenburg ED, Sonnenburg JL. The ancestral and industrialized gut microbiota and implications for human health. Nat Rev Microbiol. 2019;17:383\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGouda HN, Charlson F, Sorsdahl K, Ahmadzada S, Ferrari AJ, Erskine H, et al. Burden of non-communicable diseases in sub-Saharan Africa, 1990\u0026ndash;2017: results from the Global Burden of Disease Study 2017. Lancet Glob Health. 2019;7:e1375\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMelaku YA, Gill TK, Taylor AW, Appleton SL, Gonzalez-Chica D, Adams R, et al. Trends of mortality attributable to child and maternal undernutrition, overweight/obesity and dietary risk factors of non-communicable diseases in sub-Saharan Africa, 1990\u0026ndash;2015: findings from the Global Burden of Disease Study 2015. Public Health Nutr. 2019;22:827\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavid 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\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSz\u0026oacute;stak N, Handschuh L, Samelak-Czajka A, Tomela K, Schmidt M, Pruss Ł, et al. Host Factors Associated with Gut Mycobiome Structure. mSystems. 2023;8:e0098622.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao Y, Wang L, Ke S, Villafuerte G\u0026aacute;lvez JA, Pollock NR, Barrett C, et al. Fecal Mycobiota Combined With Host Immune Factors Distinguish Clostridioides difficile Infection From Asymptomatic Carriage. Gastroenterology. 2021;160:2328\u0026ndash;e23396.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuo T, Wong SH, Cheung CP, Lam K, Lui R, Cheung K, et al. Gut fungal dysbiosis correlates with reduced efficacy of fecal microbiota transplantation in Clostridium difficile infection. Nat Commun. 2018;9:3663.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgunremi OR, Sanni AI, Agrawal R. Probiotic potentials of yeasts isolated from some cereal-based Nigerian traditional fermented food products. J Appl Microbiol. 2015;119:797\u0026ndash;808. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jam.12875\u003c/span\u003e\u003cspan address=\"10.1111/jam.12875\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePfaller MA, Castanheira M. Nosocomial candidiasis: antifungal stewardship and the importance of rapid diagnosis. Med Mycol. 2016;54:myv076. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/mmy/myv076\u003c/span\u003e\u003cspan address=\"10.1093/mmy/myv076\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdjapong G, Bartlett M, Hale M, Garrill A. The isolation of Candida rugosa and Candida mesorugosa from clinical samples in Ghana. Med Mycol. 2016;54:322\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChaves GM, Ter\u0026ccedil;arioli GR, Padovan ACB, Rosas RC, Ferreira RC, Melo ASA, et al. Candida mesorugosa sp. nov., a novel yeast species similar to Candida rugosa, isolated from a tertiary hospital in Brazil. Med Mycol. 2013;51:231\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNasanit R, Tangwong-O-Thai A, Tantirungkij M, Limtong S. The assessment of epiphytic yeast diversity in sugarcane phyllosphere in Thailand by culture-independent method. Fungal biology. 2015;119(12):1145\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.funbio.2015.08.021\u003c/span\u003e\u003cspan address=\"10.1016/j.funbio.2015.08.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhunnamwong P, Jindamorakot S, Limtong S. Endophytic yeast diversity in leaf tissue of rice, corn and sugarcane cultivated in Thailand assessed by a culture-dependent approach. Fungal biology. 2018;122(8):785\u0026ndash;99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.funbio.2018.04.006\u003c/span\u003e\u003cspan address=\"10.1016/j.funbio.2018.04.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParachin NS, Siqueira S, de Faria FP, Torres FAG, de Moraes LMP. Xylanases from Cryptococcus flavus 563 isolate I-11: Enzymatic profile, isolation and heterologous expression of CfXYN1 in Saccharomyces 564 cerevisiae. J Mol Catal B: Enzymatic. 2009;59:52\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWanderley KJ, Torres FAG, Moraes LMP, Ulhoa CJ. Biochemical characterization of α-amylase from the 607 yeast Cryptococcus flavus. FEMS Microbiol Lett. 2004;231:165\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJindal N, Arora S, Dhuria N, Arora D. Cyberlindnera (Pichia) fabianii infection in a neutropenic child: importance of molecular identification. JMM Case Rep. 2015;1:2015\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNyanga LK, Nout MJ, Smid EJ, Boekhout T, Zwietering MH. Fermentation characteristics of yeasts isolated from traditionally fermented masau (Ziziphus mauritiana) fruits. Int J Food Microbiol. 2013;166:426\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijfoodmicro.2013.08.003\u003c/span\u003e\u003cspan address=\"10.1016/j.ijfoodmicro.2013.08.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMukisa IM, Porcellato D, Byaruhanga YB, Muyanja CM, Rudi K, Langsrud T, Narvhus JA. The dominant microbial community associated with fermentation of Obushera (sorghum and millet beverages) determined by culture-dependent and culture-independent methods. Int J Food Microbiol. 2012;160:1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijfoodmicro.2012.09.023\u003c/span\u003e\u003cspan address=\"10.1016/j.ijfoodmicro.2012.09.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLimtong S, Kaewwichian R, Yongmanitchai W, Kawasaki H. Diversity of culturable yeasts in phylloplane of sugarcane in Thailand and their capability to produce indole-3-acetic acid. World J Microbiol Biotechnol. 2014;30:1785\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11274-014-1602-7\u003c/span\u003e\u003cspan address=\"10.1007/s11274-014-1602-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Sweih N, Ahmad S, Khan S, Joseph L, Asadzadeh M, Khan Z. Cyberlindnera fabianii fungaemia outbreak in preterm neonates in Kuwait and literature review. Mycoses. 2019;62(1):51\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJindal N, Arora S, Dhuria N, Arora D. (2015). Cyberlindnera (Pichia) fabianii infection in a neutropenic child: importance of molecular identification. JMM case Rep, 2(4), e000033.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHof H, Amann V, Tauber C, Paulun A. Peritonitis in a neonate due to Cyberlindnera fabianii, an ascomycetic yeast. Infection. 2017;45(6):921\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandey N, Paul P, Kumar D, Tilak R. (2020). Case report of a rare yeast Cyberlindnera fabianii fungemia in preterm twin neonates from North India: Diagnostic and therapeutic challenge. Indian J Case Rep, 563\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlagunju O, Mchunu N, Venter S, Guibert B, Durand N, Metayer I, Ijabadeniyi O. (2018). Fungal contamination of food commodities in Durban, South Africa. J Food Saf, 38(6), e12515.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMotey GA, Johansen PG, Owusu-Kwarteng J, Ofori LA, Obiri-Danso K, Siegumfeldt H, et al. Probiotic potential of Saccharomyces cerevisiae and Kluyveromyces marxianus isolated from West African spontaneously fermented cereal and milk products. Yeast Chichester Engl. 2020;37:403\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith IM, Baker A, Christensen JE, Boekhout T, Fr\u0026oslash;ki\u0026aelig;r H, Arneborg N, et al. Kluyveromyces marxianus and Saccharomyces boulardii Induce Distinct Levels of Dendritic Cell Cytokine Secretion and Significantly Different T Cell Responses In Vitro. PLoS ONE. 2016;11:e0167410.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpatz M, Richard ML. Overview of the Potential Role of Malassezia in Gut Health and Disease. Front Cell Infect Microbiol. 2020;10:201. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcimb.2020.00201\u003c/span\u003e\u003cspan address=\"10.3389/fcimb.2020.00201\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 32528901; PMCID: PMC7265801.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVictora CG, Bahl R, Barros AJD, Fran\u0026ccedil;a GVA, Horton S, Krasevec J, et al. Breastfeeding in the 21st century: epidemiology, mechanisms, and lifelong effect. Lancet. 2016;387:475\u0026ndash;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(15)01024-7\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(15)01024-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKesavan S, Walters CE, Holland KT, Ingham E. The effects of Malassezia on pro-inflammatory cytokine production by human peripheral blood mononuclear cells in vitro. Med Mycol. 1998;36:97\u0026ndash;106.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaimondi S, Amaretti A, Gozzoli C, Simone M, Righini L, Candeliere F, Brun P, Ardizzoni A, Colombari B, Paulone S, Castagliuolo I, Cavalieri D, Blasi E, Rossi M, Peppoloni S. Longitudinal Survey of Fungi in the Human Gut: ITS Profiling, Phenotyping, and Colonization. Front Microbiol. 2019;10:1575. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmicb.2019.01575\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2019.01575\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 31354669; PMCID: PMC6636193.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHof H. Rhodotorula spp. in the gut - foe or friend? GMS Infect Dis. 2019;7:Doc02. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3205/id000042\u003c/span\u003e\u003cspan address=\"10.3205/id000042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 31538040; PMCID: PMC6734584.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRizzetto L, Ifrim DC, Moretti S, Tocci N, Cheng S-C, Quintin J, et al. Fungal Chitin Induces Trained Immunity in Human Monocytes during Cross-talk of the Host with Saccharomyces cerevisiae. J Biol Chem. 2016;291:7961\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeriggi N, Di Paola M, Vitali F, Rivero D, Cappa F, Turillazzi F, et al. Saccharomyces cerevisiae Induces Immune Enhancing and Shapes Gut Microbiota in Social Wasps. Front Microbiol. 2019;10:2320.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoussel S, Sudre B, Reboux G, Waser M, Buchele G, Vacheyrou M, et al. Exposure to moulds and actinomycetes in Alpine farms: a nested environmental study of the PASTURE cohort. Environ Res. 2011;111:744\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4073876/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4073876/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHow the evolution of dietary habits has impacted the yeasts associated with our gut is largely unknown. The impact of urbanization and globalization on human nutrition and the composition of gut microbial communities are considered driving forces behind the rise in non-communicable diseases. While previous studies in developing countries have investigated changes in the bacterial component of the gut microbiota during the transition from rural to urban areas, the modifications in the intestinal fungal communities are completely unexplored. In this study, we examined the impact of urbanization and dietary shifts on the composition of the gut mycobiota in families residing in rural, semi-urbanized, and urban areas in Burkina Faso. We compared these findings with families living in the urban area of Florence (Italy) as a reference for a globalized lifestyle.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur research revealed a significant reduction in the alpha diversity of the intestinal mycobiota as individuals transitioned from rural to urban areas. Members of rural households exhibited greater fungal richness and biodiversity compared to those in urban households, including affluent families in the capital city, Ouagadougou. We observed that the fungal diversity varied in households as a function of the rural-to-urban transition gradient, and we identified 33 fungal amplicon sequence variants (ASVs), including 12 fungal species, as associated with distinct areas with specific lifestyle and dietary patterns as indicators of the rural-to-urban transition.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003e The household-level survey of rural and urban communities in Burkina Faso highlighted the effect of urbanization on the lifestyle and subsequent composition of the participants' intestinal mycobiota. A greater diversity of fungal taxa emerged in the rural cohort, along with the presence of distinct species with potential pathogenic traits. This finding suggests that the continuous exposure to pathogenic fungi and the ensuing interaction with the immune system may contribute to the maintenance of lower incidence and severity of non-communicable diseases (NCDs) in non-globalized communities. In agreement with the \u0026ldquo;hygiene hypothesis\u0026rdquo;, the lack of yeast diversity could provide a potential explanation for the higher prevalence of inflammatory and immune-related disorders in urbanized regions across the world.\u003c/p\u003e","manuscriptTitle":"Exploring the Influence of Urbanization on Gut Mycobiota through Dietary Changes in Burkina Faso","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-29 18:54:59","doi":"10.21203/rs.3.rs-4073876/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-08T10:38:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-02T20:30:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102006102331987393058469923092744547977","date":"2024-04-29T06:47:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98cd5ea1-32da-44a5-8c64-b569285e1f6c","date":"2024-04-19T14:32:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-18T00:15:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"56149bf2-ae7f-47e8-ad86-4ebe8b38b92f","date":"2024-03-29T01:29:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"bcb0f546-ab6e-4f31-a48a-e2b4301e0d17","date":"2024-03-27T02:59:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-27T02:33:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-25T19:50:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-25T19:39:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2024-03-11T11:06:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aab99923-c7c0-4259-a371-60cededf2d4f","owner":[],"postedDate":"March 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-18T16:02:59+00:00","versionOfRecord":{"articleIdentity":"rs-4073876","link":"https://doi.org/10.1186/s12866-025-04278-9","journal":{"identity":"bmc-microbiology","isVorOnly":false,"title":"BMC Microbiology"},"publishedOn":"2025-08-16 15:57:58","publishedOnDateReadable":"August 16th, 2025"},"versionCreatedAt":"2024-03-29 18:54:59","video":"","vorDoi":"10.1186/s12866-025-04278-9","vorDoiUrl":"https://doi.org/10.1186/s12866-025-04278-9","workflowStages":[]},"version":"v1","identity":"rs-4073876","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4073876","identity":"rs-4073876","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}