Gut microbiome structure in asylum seekers newly arrived in Italy from Africa

Gut microbiome structure in asylum seekers newly arrived in Italy from Africa | 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 Article Gut microbiome structure in asylum seekers newly arrived in Italy from Africa Giorgia Palladino, Marianna Marangi, Daniel Scicchitano, Silvia Turroni, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5929447/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The global landscape of migration has evolved significantly, with international migrants tripling since 1970, reaching approximately 281 million by 2020. This rise includes a notable surge in forcibly displaced individuals due to conflicts, wars, and human rights violations. Additionally, climate change is reshaping migration patterns by environmental degradation and extreme weather events, with projections indicating that 143 million individuals may be uprooted by climate catastrophes over the next three decades. In this context, migrants experience chronic stress due to the uncertainties of their journey, exposure to trauma, and changes in living conditions, possibly exacerbating health issues, including through impairment of the gut microbiome. Our study focuses on the characterization – by 16S rRNA gene amplicon sequencing – of intestinal microbiome in 79 asylum seekers newly arrived in Italy from African countries through their comparison with publicly available datasets of worldwide populations encompassing different origin and lifestyle. This microbiological surveillance, conducted as cross-sectional sampling over one year, aimed to assess how the forced migration journey and the associated stressors affect refugees’ gut health. Our findings suggest significant deviations in the gut microbiome composition of refugees compared to traditional rural populations, possibly driven by stressors such a psychological trauma and dietary changes. The loss of microbial diversity may increase susceptibility to health issues, highlighting the need for targeted public health strategies for refugee populations. Biological sciences/Microbiology Biological sciences/Molecular biology Health sciences/Health care/Public health refugees gut microbiome VANISH taxa dysbiosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction According to the World Migration Report, the number of migrants has increased more than three times since 1970. In 2020, it was estimated that one in 30 people worldwide lived in a different country than the one of birth, for a total of about 281 million international migrants worldwide. In particular, the number of forcibly displaced individuals due to conflict, war, and human rights violations, has increased in recent decades. As of May 2022, the United Nations High Commissioner for Refugees (UNHCR) reported 100 million global forced travelers and refugees [1]. In addition to global conflicts, climate and global changes are significantly contributing to reshaping the patterns and magnitude of migration worldwide, through a combination of environmental degradation, extreme weather events and gradual climatic shifts [2]. According to the UNHCR, an annual average of 21.5 million people has been forcibly displaced each year since 2008 by sudden weather-related hazards – such as floods, storms, wildfires, and extreme temperature. Over the next 30 years, 143 million people are predicted to be uprooted by rising seas, drought, searing temperatures and other climate catastrophes in Central and South America, sub-Saharan Africa and South Asia, according to the 2022 U.N.’s Intergovernmental Panel on Climate Change report (https://www.ipcc.ch/report/ar6/wg2/). In this scenario, it is crucial to assess the impact of migration events on the health and wellbeing of the people involved. Migrants frequently experience chronic stress due to the uncertainties of their journey, exposure to trauma, and changes in their living conditions [3]. Among others, migration fluxes are associated with a massive impact on the gut-associated microbiome, a key modulator of human health, due to complex and multi-factorial causes. In particular, chronic exposure to migration-related stressors, psychological trauma, and changes in food supply and dietary habits have been reported to disrupt gut microbiota balance, possibly exacerbating health issues in the short term [1,4-7]. These changes might compromise key gut health-promoting functions, for example by reducing short-chain fatty acid (SCFA) production and/or increasing the susceptibility to infectious diseases, also through a compromised barrier effect [8]. On the long run, it has been shown that changes in lifestyle, food supply, exposure to medications, access to health care services, and cultural and socio-economic conditions lead to a progressive change in gut microbiome composition and function, with consequent transitioning towards a more westernized configuration within the first year after migration [9,10]. However, studies of migrant populations worldwide remain limited, particularly for refugees. Like Europe, and especially the Mediterranean countries, Italy is currently facing a substantial refugee movement, particularly from several African countries with low socioeconomic levels in search of better work opportunities. Data indicate that, in 2024, Italy welcomed the highest number of sea arrivals in Southern Europe, with a total of 41,617 people arriving by sea (https://www.unhcr.org/europe/sites/europe/files/2024-10/bi-annual-fact-sheet-2024-09-italy.pdf). However, due to limited access to official health data, information on the health status of migrant populations arriving in Italy is still scarce [11]. In this context, here we aimed to provide some glimpses on the gut microbiome structure of apparently healthy asylum seekers newly arrived in Italy from African countries through a one-year cross-sectional microbiological surveillance study conducted during 2022-2023. In particular, we investigated deviations in the gut microbiome composition of refugees compared to traditional rural populations, resulting in the loss of sensitive microbial taxa prevalent in non-industrialized populations that might increase susceptibility to health issues. Our study thus provided new insights into how migration journey and related stressors affect refugees’ gut health, which could inform public health strategies for refugee populations. 2. Ethical statement The present study was performed following the guidelines of the Declaration of Helsinki in 1975, revised in 2013 and all the procedures performed in this study meet the national and international guidelines. A written informed consent was obtained from every patient before the study and patients were completely anonymized by the researchers. Ethical approval was approved with the number 85-CE-2024 by Policlinico Foggia Ethical Committee. Included samples were obtained according to standard diagnostic and therapeutic protocols for the management of gastrointestinal infections. All the authors ensure that this study is HIPAA (Health Insurance Portability and Accountability Act, 1996) compliant. The researchers followed every mandatory (health and safety) procedure. 3. Materials and methods 3.1 Sample collection, processing and 16S rRNA gene amplification and sequencing Seventy-nine individuals arrived during the period April 2022-May 2023 and temporarily admitted to asylum sicker Borgo Mezzanone, Foggia, Italy, were selected for the gut microbiome profiling using 16S rRNA gene amplicon sequencing. Microbiological screening was free offered to all consecutive newly arrived asylum seekers arrived in Italy crossing the Mediterranean Sea within the previous 7 days. Stool samples were collected during routine health examinations and immediately sent to the Microbiology and Virology Unit of Foggia Policlinic, Foggia, Italy, to be processed as described below. According to internal procedures, each individual was interviewed with the support of mediators speaking the refugee’s native language in order to collect personal data (age and gender), when possible. While we aimed to sample the largest possible number of newly arrived refugees, no formal sample size calculation was performed. This decision was influenced by the significant cultural barriers and extreme difficulty encountered in obtaining fecal samples from this population. Consequently, we collected a single specimen from each participant. Microbial DNA was extracted from faecal samples (approximately 200 mg) using the QIAamp DNA Stool Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions and then eluted in 200 μL of TE buffer. Extracted DNA was processed for 16S rRNA gene amplification and sequencing for prokaryotic community characterization. Library preparation was performed following the Illumina 16S Metagenomic Sequencing Library Preparation protocol (Illumina, San Diego, CA, USA). The V3–V4 hypervariable regions of the 16S rRNA gene were PCR amplified in a 50μL final volume containing 25 ng of microbial DNA, 2X KAPA HiFi HotStart ReadyMix (Roche, Basel, Switzerland) and 200 nmol/L of 341F (5’- CCTACGGGNGGCWGCAG-3′) and 785R (5′-GACTACHVGGGTATCTAATCC-3′) primers with Illumina adapter overhang sequences [12]. The PCR thermocycle consisted of 3 min at 95 ◦C, 25 cycles of 30 s at 95°C, 30 s at 55°C and 30 s at 72°C, and a final 5-min extension step at 72°C [13]. PCR products were then purified with Agencourt AMPure XP magnetic beads (Beckman Coulter, Brea, CA, USA). Indexed libraries were prepared by limited-cycle PCR, using Nextera technology (Illumina), and cleaned-up as described above. Libraries were quantified using the Qubit 3.0 fluorimeter (Invitrogen, Waltham, MA, USA), normalised to 4 nM and pooled. The sample pool was denatured with 0.2 N NaOH and diluted to a final concentration of 4.5 pM with a 20 % PhiX control. Sequencing was performed on an Illumina MiSeq platform using a 2 × 250 bp paired-end protocol, according to the manufacturer's instructions (Illumina). 3.2 Bioinformatics and statistical analysis For 16S rRNA gene analysis, raw sequences from all individuals were processed using a pipeline combining PANDAseq [14] and QIIME 2 [15]. High-quality reads were retained using the “fastq filter” function of the Usearch11 algorithm [16] and clustered into amplicon sequence variants (ASVs) using DADA2 [17]. Taxonomy was assigned using the VSEARCH classifier [18] and the SILVA database (2020, v138.1 release) as a reference [19]. All sequences assigned to eukaryotes or unassigned were discarded. Overall, a total of 637,063 high-quality reads (mean ± standard deviation: 8,382 ± 2,621) was obtained, resulting in a total of 3,401 amplicon sequence variants (ASVs). 16S rRNA gene sequences of the gut microbiome associated with worldwide populations, characterized by different subsistence strategies (i.e., rural and urban), were downloaded from publicly available datasets [20-25]. The 16S data from the samples analyzed in Rampelli et al. [26] were produced in the context of the “HARVEST” project and published at PRJEB85423. Sequences were subjected to direct read mapping with Kraken2 (v2.1.2) [27] and the resulting report was processed through Bracken (v 2.6.2) [28] pipeline in order to obtain taxonomic tables at the genus level. Both Kraken2 and Bracken were used with SILVA database (2020, v138.1 release) as a reference [19] with default options. Bracken outputs were then merged to obtain a single taxonomic table, which was subsequently merged with the genus-level taxonomic table of the original refugee dataset produced in this study. All statistical analyses were performed using R software (R Core Team; www.r-project.org), v. 4.3.2, with the package “vegan” (https://CRAN.R-project.org/package=vegan) [29]. Alpha diversity was calculated at the genus level using the function “diversity” of the vegan package, with the metrics “Shannon index”, “Simpson”, “Inverted Simpson” and “Observed genera”. The Wilcoxon rank-sum test was used to assess significant differences in alpha diversity and genus-level relative abundance between groups. Beta diversity was estimated by computing Bray-Curtis distances at the genus level, and data separation in the Principal Coordinates Analysis (PCoA) was tested using a permutation test with pseudo-F ratio (function “adonis” in the vegan package). P -values, when necessary, were corrected for multiple testing using the Benjamin–Hochberg method, with a false discovery rate (FDR) ≤ 0.05 considered statistically significant. Linear discriminant analysis (LDA) Effect Size (LEfSe) [30] was performed to identify discriminant bacterial genera. Only microbial taxa with LDA score threshold of ±2 (on a log10 scale) and p -value ≤ 0.05 were retained. 4. Results 4.1 Gut microbiome profile of refugees in the global scenario A total of 79 refugees (74 males and 5 females, mean age 26 ± 7 years) were profiled for the gut microbiome using 16S rRNA gene amplicon sequencing. A total of 637,063 sequences (mean ± standard deviation: 8,382 ± 2,621) and 3,401 ASVs were obtained. The gut microbiome composition of refugees was compared with publicly available sequences from worldwide populations encompassing different subsistence strategies (rural and urban) [20-26]. Specifically, we selected 15 Nigerian individuals, of which three rural and 12 urban [25], 18 rural Nzime, 26 rural Baka [26] and 8 rural Pigmy individuals [21] from Cameroon, 29 urban Canadian and 24 rural Inuit Canadian [24], 24 rural Tunapuco and 20 rural Matses individuals from Peru [22], 42 urban USA individuals from Norman and 38 urban Native Americans [23], 27 rural Hadza from Tanzania and 16 urban Italians [20]. As expected, subsistence type was a major driver of the gut microbiome variation, with individual populations significantly segregating in the PCoA plot (permutation test with pseudo-F ratio, p-value ≤ 0.001) (Figure 1A). When considering the population distribution, we observed a significant segregation according to lifestyle, namely rural populations (150 subjetcs) and urbanized populations (137 subjects), and refugee status (79 subjects), regardless of geographical origin (p-value ≤ 0.001), with the centroid of the refugees’ group clustering closer to the rural group (Figure 1B). In contrast, the alpha diversity of the refugees’ gut microbiome was lower than that of rural populations (Wilcoxon rank-sum test corrected with FDR, p-values ≤ 0.05 for all metrics), but mostly comparable to that of urban populations (p-values ≤ 0.05 for Simpson, Inverted Simpson and Observed genera, but p-value ≥ 0.05 for Shannon metric) (Figure 2). We identified significantly discriminant genera amongst the three groups taken into account (i.e. rural, urban, refugee) through LEfSe analysis (LDA score threshold of ± 2 and p-value ≤ 0.05) (Figure 3). The urban group was mainly discriminated by Faecalibacterium , Blautia , Bifidobacterium , Bacteroides and Akkermansia (LDA score > 4); the rural group by Treponema , Bacillus , Ruminobacter , Butyrivibrio , and Roseburia (LDA score > 3); the refugee group by Prevotella , Escherichia-Shigella and Alloprevotella (LDA score > 4), but also Klebsiella (LDA score > 3). Interestingly, some compositional signatures of the refugee gut microbiome, particularly Prevotella and Alloprevotella , but also Succinivibrio , Streptococcus , and Ruminococcus , were shared with the gut microbiome of rural populations (Wilcoxon rank-sum test corrected with FDR, p-values > 0.05) (Supplementary Table1). 5.2 Gut microbiome profile of refugees compared to the Italian cohort Finally, we specifically compared the gut microbiome of refugees with that of the population of relocation, i.e. Italians. The Italian cohort comprised 16 urban living Italian adults from Bologna, Italy (5 males and 11 females, mean age 32 ± 5 years) [20]. The two groups significantly segregated in the PCoA plot (permutation test with pseudo-F ratio, p-value ≤ 0.001) (Figure 4A) and differed in alpha diversity, which was lower in the refugees (Wilcoxon rank-sum test corrected with FDR, p-values ≤ 0.01 for all the metrics) (Figure 4B). Confirming the previous findings, the refugee group exhibited a significantly higher relative abundance of Prevotella and Alloprevotella (LDA score > 3 and p-value ≤ 0.05) (Figure 5). On the other hand, the Italian cohort was characterized by significant higher levels of Faecalibacterium , Bifidobacterium and Blautia as observed in the urban group above. 5. Discussion and conclusions In the present study we provide insights into the potential impact of the challenging journey experienced by newly arrived African asylum seekers on their gut microbiome health, highlighting concerns about the risks to gut health in this vulnerable group. Through a one-year cross-sectional microbiological surveillance conducted from 2022 to 2023, we analyzed the gut microbiomes of 79 individuals using 16S rRNA gene amplicon sequencing and compared them to those of rural and urban populations worldwide and to the population of relocation (i.e. Italy). Our results showed refugees gut microbiome segregating distinctly from the one of rural and urban populations, and with refugees’ alpha diversity being significantly lower than that of rural populations, showing a peculiar configuration in in opportunistic taxa increase. Refugees from African countries experience a range of perilous challenges, stressors, and events during their migration journey, influenced by drastic socio-economic and political factors [31]. Many refugees flee from conflict zones or oppressive regimes, resulting in racial discrimination, genocidal repression, or violence related to civil wars [32]. Other refugees come from areas experiencing severe humanitarian crises due to famine or environmental disasters, seeking safety and sustenance in a new country [33]. The journey faced by refugees encompasses crossing the Sahara Desert an across Libya, where they often face threats from human traffickers and the risk of being sold into slavery or forced labor [34,35]. Furthermore, migrants who are able to attempt to cross the Mediterranean Sea to Italy add another layer of risk to their flight, with many perishing during these attempts [36,37]. Here, we showed that the gut microbiome configuration of the refugees differed from that of rural populations worldwide, despite sharing some compositional features. Given the known migration routes from Africa to Europe, which involve individuals from Sub-Saharan Africa (ISTAT https://www.istat.it/wp-content/uploads/2024/10/REPORT-CITTADINI-NON-COMUNITARI_Anno-2023.pdf; UNHCR https://data.unhcr.org/en/situations/europe-sea-arrivals/location/24521; ISPI https://www.ispionline.it/it/pubblicazione/africa-occidentale-le-migrazioni-infra-ed-extra-regionali-135619), where the majority of the population resides in rural areas (IPCC report 2022, https://doi.org/10.1017/9781009325844.011), it is plausible to speculate that a significant portion of the analyzed refugee population may have originated from rural settings. This speculation is supported by the fact that many migrants from Sub-Saharan Africa are likely to have been exposed to similar environmental and lifestyle factors associated with rural living before their migration. In particular, the refugee gut microbiome retained taxa that are typically prevalent in non-industrialized populations and are diminished or absent in industrialized societies (i.e., VANISH taxa). These include Prevotella , Alloprevotella , Succinivibrio , Streptococcus , and Ruminococcus , which are often associated with high-fiber diets [25,38-40]. On the other hand, the refugee gut microbiome was depleted of other VANISH taxa typical of rural populations, such as Treponema , which aids in the breakdown of complex plant polysaccharides [20], Ruminobacter , able to thrive on fibrous plant material [1], and Butyrivibrio and Roseburia , known for the production of SCFAs, particularly butyrate, which is linked to beneficial anti-inflammatory effects [41,42]. This loss of VANISH taxa in the gut microbial composition of the refugee cohort compared to rural populations might be an early indicator of the very strong impact of migration-related stressors and potentially impact refugees’ health. Indeed, migration-related stressors have been shown to lead to dysbiosis, characterized by a decrease in alpha diversity and an increase in potentially harmful bacteria [1]. Supporting this, we also observed that the refugee group was discriminated by opportunistic pathogens, such as Escherichia-Shigella and Klebsiella , which could increase systematic inflammation and susceptibility to infections [43-47]. In conclusion, our research revealed a significant deviation of the refugee gut microbiome from that of rural populations, with a loss of diversity of particularly sensitive VANISH taxa and an enrichment in pathobionts. Given that refugees were likely originating from rural areas, this alteration was possibly driven by stressors related to the migration journey, including chronic stress, psychological trauma, and shifts in food supply and dietary habits. These migration-related compositional changes could potentially increase the risk of dysbiosis, with an emergence of pathogenic taxa emergence, thus exacerbating the already precarious health conditions of asylum seekers individuals. Declarations Data availability High-quality reads from the present study were deposited in the European Nucleotide Archive (ENA) under the project accession number PRJEB85422. Funding The authors declare that no specific funding was used for this research. Author contributions Giorgia Palladino : Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft. Marianna Marangi: Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review & editing. Daniel Scicchitano : Writing – Review & Editing; Silvia Turroni : Writing – Review & Editing. Simone Rampelli : Data curation, Writing – Review and Editing; Marco Candela : Resources, Writing – Review and Editing, Supervision. Declaration of competing interest The authors declare no competing interests. Acknowledgements This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. We are very grateful to all the staff of the Microbiology and Virology Unit of Foggia Policlinic, Foggia, Italy for their collaboration in collecting the samples. References Parizadeh, M., & Arrieta, M. C. (2023). The global human gut microbiome: genes, lifestyles, and diet. Trends in Molecular Medicine. Upadhyay, R. K. (2020). Markers for global climate change and its impact on social, biological and ecological systems: A review. American Journal of Climate Change , 9 (03), 159. Ryan, D., Dooley, B., & Benson, C. (2008). Theoretical perspectives on post-migration adaptation and psychological well-being among refugees: Towards a resource-based model. Journal of refugee studies , 21 (1), 1-18. Fuzail, M. A., Ahmed, B., Burke, D. L., Spjeldnæs, A. H., Horino, M., Ching, C., & Zaman, M. H. (2021). Microbiome research potential for developing holistic approaches to improve refugee health. Journal of Global Health Reports , 5 , e2021094. Kang, J., Moser, D. K., Lennie, T. A., Chung, M. L., Thomas, D. T., & Biddle, M. J. (2024). Diet Quality Mediates the Relationship Between Chronic Stress and Inflammation in Patients With Metabolic Syndrome. Journal of Cardiovascular Nursing, 10-1097. Yi, B., Rykova, M., Jäger, G., Feuerecker, M., Hörl, M., Matzel, S., ... & Choukèr, A. (2015). Influences of large sets of environmental exposures on immune responses in healthy adult men. Scientific Reports, 5(1), 13367. Beurel, E. (2024). Stress in the microbiome-immune crosstalk. Gut microbes, 16(1), 2327409. Karl, J. P., Hatch, A. M., Arcidiacono, S. M., Pearce, S. C., Pantoja-Feliciano, I. G., & Soares, J. W. (2018). Effects of psychological, environmental and physical stressors on the gut microbiota. Frontiers in microbiology , 9 , 372026. Vangay, P., Johnson, A. J., Ward, T. L., Al-Ghalith, G. A., Shields-Cutler, R. R., Hillmann, B. M., ... & Knights, D. (2018). US immigration westernizes the human gut microbiome. Cell , 175 (4), 962-972. Shanahan, F., Ghosh, T. S., & O’Toole, P. W. (2023). Human microbiome variance is underestimated. Current Opinion in Microbiology , 73 , 102288. Sulekova, L. F., Ceccarelli, G., Pombi, M., Esvan, R., Lopalco, M., Vita, S., ... & Walter, I. (2019). Occurrence of intestinal parasites among asylum seekers in Italy: a cross-sectional study. Travel Medicine and Infectious Disease , 27 , 46-52. Klindworth, A., Pruesse, E., Schweer, T., Peplies, J., Quast, C., Horn, M., & Glöckner, F. O. (2013). Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic acids research , 41 (1), e1-e1. Turroni, S., Fiori, J., Rampelli, S., Schnorr, S. L., Consolandi, C., Barone, M., ... & Candela, M. (2016). Fecal metabolome of the Hadza hunter-gatherers: a host-microbiome integrative view. Scientific reports , 6 (1), 32826. Masella, A. P., Bartram, A. K., Truszkowski, J. M., Brown, D. G., & Neufeld, J. D. (2012). PANDAseq: paired-end assembler for illumina sequences. BMC bioinformatics , 13 , 1-7. Bolyen, E., Rideout, J. R., Dillon, M. R., Bokulich, N. A., Abnet, C. C., Al-Ghalith, G. A., ... & Caporaso, J. G. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature biotechnology , 37 (8), 852-857. Edgar, R. C. (2010). Search and clustering orders of magnitude faster than BLAST. Bioinformatics , 26 (19), 2460-2461. Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature methods , 13 (7), 581-583. Rognes, T., Flouri, T., Nichols, B., Quince, C., & Mahé, F. (2016). VSEARCH: a versatile open source tool for metagenomics. PeerJ , 4 , e2584. Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., ... & Glöckner, F. O. (2012). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic acids research , 41 (D1), D590-D596. Schnorr, S. L., Candela, M., Rampelli, S., Centanni, M., Consolandi, C., Basaglia, G., ... & Crittenden, A. N. (2014). Gut microbiome of the Hadza hunter-gatherers. Nature communications , 5 (1), 3654. Morton, E. R., Lynch, J., Froment, A., Lafosse, S., Heyer, E., Przeworski, M., ... & Segurel, L. (2015). Variation in rural African gut microbiota is strongly correlated with colonization by Entamoeba and subsistence. PLoS genetics , 11 (11), e1005658. Obregon-Tito, A. J., Tito, R. Y., Metcalf, J., Sankaranarayanan, K., Clemente, J. C., Ursell, L. K., ... & Lewis, C. M. (2015). Subsistence strategies in traditional societies distinguish gut microbiomes. Nature communications , 6 (1), 6505. Sankaranarayanan, K., Ozga, A. T., Warinner, C., Tito, R. Y., Obregon-Tito, A. J., Xu, J., ... & Lewis, C. M. (2015). Gut microbiome diversity among Cheyenne and Arapaho individuals from western Oklahoma. Current Biology , 25 (24), 3161-3169. Girard, C., Tromas, N., Amyot, M., & Shapiro, B. J. (2017). Gut microbiome of the canadian arctic Inuit. Msphere , 2 (1), 10-1128. Ayeni, F. A., Biagi, E., Rampelli, S., Fiori, J., Soverini, M., Audu, H. J., ... & Turroni, S. (2018). Infant and adult gut microbiome and metabolome in rural Bassa and urban settlers from Nigeria. Cell reports , 23 (10), 3056-3067. Rampelli, S., Gallois, S., D’Amico, F., Turroni, S., Fabbrini, M., Scicchitano, D., ... & Henry, A. (2024). The gut microbiome of Baka forager-horticulturalists from Cameroon is optimized for wild plant foods. Iscience, 27(3). Wood, D. E., Lu, J., & Langmead, B. (2019). Improved metagenomic analysis with Kraken 2. Genome biology, 20, 1-13. Lu, J., Breitwieser, F. P., Thielen, P., & Salzberg, S. L. (2017). Bracken: estimating species abundance in metagenomics data. PeerJ Computer Science, 3, e104. Oksanen, J. (2013). Vegan: ecological diversity. R project , 368 , 1-11. Segata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett, W. S., & Huttenhower, C. (2011). Metagenomic biomarker discovery and explanation. Genome biology , 12 , 1-18. Idemudia, E., & Boehnke, K. (2020). Psychosocial experiences of African migrants in six European countries: A mixed method study (p. 237). Springer Nature. Chikanda, A. (2019). An analysis of forced migration from Zimbabwe. migration Studies , 7 (1), 59-82. Zickgraf, C. (2019). Climate change and migration crisis in Africa. The Oxford handbook of migration crises , 347. Adepoju, A. (2005). Review of research and data on human trafficking in sub-Saharan Africa. International Migration , 43 (1-2), 75-98. Bello, P. O., & Olutola, A. A. (2020). The conundrum of human trafficking in Africa. Modern slavery and human trafficking , 15-27. Kassar, H., & Dourgnon, P. (2014). The big crossing: illegal boat migrants in the Mediterranean. The European Journal of Public Health , 24 (suppl_1), 11-15. McMahon, S., & Sigona, N. (2021). Death and migration: Migrant journeys and the governance of migration during Europe’s “migration crisis”. International Migration Review , 55 (2), 605-628. De Filippo, C., Cavalieri, D., Di Paola, M., Ramazzotti, M., Poullet, J. B., Massart, S., ... & Lionetti, P. (2010). Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proceedings of the National Academy of Sciences, 107(33), 14691-14696. Sonnenburg, J. L., & Sonnenburg, E. D. (2019). Vulnerability of the industrialized microbiota. Science , 366 (6464), eaaw9255. Carter, M. M., Olm, M. R., Merrill, B. D., Dahan, D., Tripathi, S., Spencer, S. P., ... & Sonnenburg, J. L. (2023). Ultra-deep sequencing of Hadza hunter-gatherers recovers vanishing gut microbes. Cell , 186 (14), 3111-3124. Cresci, G. A., & Bawden, E. (2015). Gut microbiome: what we do and don't know. Nutrition in Clinical Practice, 30(6), 734-746. Dehingia, M., Thangjam Devi, K., Talukdar, N. C., Talukdar, R., Reddy, N., Mande, S. S., ... & Khan, M. R. (2015). Gut bacterial diversity of the tribes of India and comparison with the worldwide data. Scientific reports, 5(1), 18563. Hu, J., Cheng, S., Yao, J., Lin, X., Li, Y., Wang, W., ... & Zhi, M. (2022). Correlation between altered gut microbiota and elevated inflammation markers in patients with Crohn’s disease. Frontiers in Immunology , 13 , 947313. Zhao, J., Bai, M., Ning, X., Qin, Y., Wang, Y., Yu, Z., ... & Sun, S. (2022). Expansion of Escherichia-Shigella in gut is associated with the onset and response to immunosuppressive therapy of IgA nephropathy. Journal of the American Society of Nephrology , 33 (12), 2276-2292. Calderon-Gonzalez, R., Lee, A., Lopez-Campos, G., Hancock, S. J., Sa-Pessoa, J., Dumigan, A., ... & Bengoechea, J. A. (2023). Modelling the gastrointestinal carriage of Klebsiella pneumoniae infections. Mbio , 14 (1), e03121-22. Singh, S. B., Carroll-Portillo, A., & Lin, H. C. (2023). Desulfovibrio in the gut: the enemy within?. Microorganisms , 11 (7), 1772. Vornhagen, J., Rao, K., & Bachman, M. A. (2024). Gut community structure as a risk factor for infection in Klebsiella pneumoniae-colonized patients. Msystems , 9 (8), e00786-24. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.pdf Cite Share Download PDF Status: Published Journal Publication published 18 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Jul, 2025 Editor assigned by journal 15 Jul, 2025 Editor invited by journal 15 Jul, 2025 Reviews received at journal 24 Jun, 2025 Reviewers agreed at journal 10 Jun, 2025 Reviews received at journal 13 May, 2025 Reviewers agreed at journal 01 May, 2025 Reviewers invited by journal 29 Apr, 2025 Submission checks completed at journal 09 Apr, 2025 First submitted to journal 28 Mar, 2025 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. 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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-5929447","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":449551764,"identity":"28c4ea80-91eb-4330-a6ff-90755df4a919","order_by":0,"name":"Giorgia Palladino","email":"","orcid":"","institution":"Alma Mater Studiorum - University of Bologna","correspondingAuthor":false,"prefix":"","firstName":"Giorgia","middleName":"","lastName":"Palladino","suffix":""},{"id":449551766,"identity":"652c9bf6-a784-45bc-a38d-79828555dae5","order_by":1,"name":"Marianna Marangi","email":"","orcid":"","institution":"University of Foggia","correspondingAuthor":false,"prefix":"","firstName":"Marianna","middleName":"","lastName":"Marangi","suffix":""},{"id":449551768,"identity":"7c74d074-79e4-4f4f-aef2-5b263baff2e6","order_by":2,"name":"Daniel Scicchitano","email":"","orcid":"","institution":"Alma Mater Studiorum - University of Bologna","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Scicchitano","suffix":""},{"id":449551770,"identity":"e0612294-54fe-4b0c-9163-72b7167469bc","order_by":3,"name":"Silvia Turroni","email":"","orcid":"","institution":"Alma Mater Studiorum - University of Bologna","correspondingAuthor":false,"prefix":"","firstName":"Silvia","middleName":"","lastName":"Turroni","suffix":""},{"id":449551771,"identity":"41a8b542-03a5-4ef3-ae8c-d59684f47b4b","order_by":4,"name":"Simone Rampelli","email":"","orcid":"","institution":"Alma Mater Studiorum - University of Bologna","correspondingAuthor":false,"prefix":"","firstName":"Simone","middleName":"","lastName":"Rampelli","suffix":""},{"id":449551772,"identity":"9ae07fcd-217b-4c37-885f-588a163b502b","order_by":5,"name":"Marco Candela","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYHCDBMYDDBUMDGwSIDaRWhgOMJyBaSFOD1ALYxuQlmDAb41u+9mHHz4w2MmbsycfOPBz3uE8PunmAwwPf+DWYnYm3VhyBkOy4c6eZwkHe7cdLmaTOZaA12FmB9LYmHmATtpwI8fgAO+2w4ltEjkG+LWcfwbWYr/hRv6Hg3/nEKPlBsSWRKAtDId5G4jS8oxZcoZBcvKGM88MDsscS09sA/rlQEIaPoelMX74UGFnu+F48sOHb2qsE+fPbj748IcNbi0QYIDGP0BIwygYBaNgFIwC/AAAPBZXKqng8VwAAAAASUVORK5CYII=","orcid":"","institution":"Alma Mater Studiorum - University of Bologna","correspondingAuthor":true,"prefix":"","firstName":"Marco","middleName":"","lastName":"Candela","suffix":""}],"badges":[],"createdAt":"2025-01-30 10:23:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5929447/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5929447/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-24250-x","type":"published","date":"2025-11-18T15:57:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81672060,"identity":"c755cdfe-b0e6-4f2b-8b05-36e55a497470","added_by":"auto","created_at":"2025-04-30 06:09:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1934471,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eGut microbiome structure of different populations worldwide, including refugees.\u003c/u\u003e a) A significant segregation was found in the Principal Coordinates Analysis (PCoA) based on Bray-Curtis distances between the gut microbiome profiles of individual populations worldwide, including urban and rural populations whose sequences are publicly available [17-23], and refugees from the present study (permutation test with pseudo-F ratio, p-value ≤ 0.001). Abbreviations: C\u0026amp;A = Cheyenne and Arapaho. b) Same populations as in panel “a”, grouped by subsistence type, namely rural populations, urban populations, and refugees. A significant segregation as found in the Bray-Curtis-based PCoA regardless of geographical origin (p-value ≤ 0.001). The first and second principal components (PCoA1 and PCoA2) are plotted and the percentage of variance in the dataset explained by each axis is shown. Color legend in the figure.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5929447/v1/0f9818804727bbce5b5357bd.png"},{"id":81672062,"identity":"a73ff225-e108-4408-a397-20146e3edfce","added_by":"auto","created_at":"2025-04-30 06:09:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":293944,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eGut microbiome diversity of refugees compared to urban and rural populations worldwide. \u003c/u\u003eBoxplots of alpha diversity calculated at the genus level using the metrics “Shannon”, “Simpson”, “Inverted Simpson” and “Observed genera” in the three groups (rural populations, urban populations, refugees). Significant p-values are indicated in the figure (p-values ≤ 0.001***, p-values ≤ 0.05*) (Wilcoxon rank-sum test corrected with FDR).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5929447/v1/e5f5cc1c71bef2f8d6604eb0.png"},{"id":81672063,"identity":"52b8826b-d060-499e-b8f2-b99be5ca3e22","added_by":"auto","created_at":"2025-04-30 06:09:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1157242,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eDiscriminant genera of the gut microbiome of refugees, rural and urban populations.\u003c/u\u003e\u003cem\u003e \u003c/em\u003eLinear discriminant analysis (LDA) Effect Size (LEfSe) score of discriminating gut genera of the gut microbiome of the three groups (rural populations, urban populations, refugees). Only taxa with LDA score threshold of ± 2 and p-value ≤ 0.05 are shown.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5929447/v1/51ca64d0d767fb0963420ce4.png"},{"id":81672059,"identity":"1440ebb4-f991-4c66-a31d-9d0222a02acc","added_by":"auto","created_at":"2025-04-30 06:09:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":682005,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eGut microbiome structure of refugees compared to the Italian cohort.\u003c/u\u003e\u003cem\u003e \u003c/em\u003ea) A significantly segregation was found in the Principal Coordinates Analysis (PCoA) based on Bray-Curtis distances between the gut microbiome profiles of refugees and Italians [17] (permutation test with pseudo-F ratio, p-value ≤ 0.001). The first and second principal components (PCoA1 and PCoA2) are plotted and the percentage of variance in the dataset explained by each axis is shown. Color legend in figure. b) Boxplots of alpha diversity calculated at the genus level using the metrics “Shannon”, “Simpson”, “Inverted Simpson” and “Observed genera”. Significant p-values are indicated in the figure (p-values ≤ 0.001***, p-values ≤ 0.01**) (Wilcoxon rank-sum test corrected with FDR).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5929447/v1/9707c37d7c0872881bcacdbd.png"},{"id":81672121,"identity":"7a7b463c-5533-499c-ac3a-5bae36bd5a97","added_by":"auto","created_at":"2025-04-30 06:17:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1697431,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eDiscriminant genera of the gut microbiome of refugees compared to Italian cohort.\u003c/u\u003e\u003cem\u003e \u003c/em\u003eLinear discriminant analysis (LDA) Effect Size (LEfSe) score of discriminating taxa of the gut microbiome of refugees and Italians. Only taxa with LDA score threshold of ± 2 and p-value ≤ 0.05 are shown.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5929447/v1/dc818a55646a76c3012bc901.png"},{"id":96650095,"identity":"17466508-37ef-4846-b72a-d4a052f95566","added_by":"auto","created_at":"2025-11-24 16:07:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5754898,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5929447/v1/266f62c9-2802-4193-b4c3-23b1041bc8e3.pdf"},{"id":81672058,"identity":"54de64c0-4327-4205-9b90-fb8dfca80640","added_by":"auto","created_at":"2025-04-30 06:09:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":69511,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5929447/v1/db63a6073ad24be8e7299a33.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gut microbiome structure in asylum seekers newly arrived in Italy from Africa","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAccording to the World Migration Report, the number of migrants has increased more than three times since 1970. In 2020, it was estimated that one in 30 people worldwide lived in a different country than the one of birth, for a total of about 281 million international migrants worldwide. In particular, the number of forcibly displaced individuals due to conflict, war, and human rights violations, has increased in recent decades. As of May 2022, the United Nations High Commissioner for Refugees (UNHCR) reported 100 million global forced travelers and refugees [1]. In addition to global conflicts, climate and global changes are significantly contributing to reshaping the patterns and magnitude of migration worldwide, through a combination of environmental degradation, extreme weather events and gradual climatic shifts [2]. According to the UNHCR, an annual average of 21.5 million people has been forcibly displaced each year since 2008 by sudden weather-related hazards \u0026ndash; such as floods, storms, wildfires, and extreme temperature. Over the next 30 years, 143 million people are predicted to be uprooted by rising seas, drought, searing temperatures and other climate catastrophes in Central and South America, sub-Saharan Africa and South Asia, according to the 2022 U.N.\u0026rsquo;s Intergovernmental Panel on Climate Change report (https://www.ipcc.ch/report/ar6/wg2/).\u003c/p\u003e\n\u003cp\u003eIn this scenario, it is crucial to assess the impact of migration events on the health and wellbeing of the people involved. Migrants frequently experience chronic stress due to the uncertainties of their journey, exposure to trauma, and changes in their living conditions [3]. Among others, migration fluxes are associated with a massive impact on the gut-associated microbiome, a key modulator of human health, due to complex and multi-factorial causes. In particular, chronic exposure to migration-related stressors, psychological trauma, and changes in food supply and dietary habits have been reported to disrupt gut microbiota balance, possibly exacerbating health issues in the short term [1,4-7]. These changes might compromise key gut health-promoting functions, for example by reducing short-chain fatty acid (SCFA) production and/or increasing the susceptibility to infectious diseases, also through a compromised barrier effect [8]. On the long run, it has been shown that changes in lifestyle, food supply, exposure to medications, access to health care services, and cultural and socio-economic conditions lead to a progressive change in gut microbiome composition and function, with consequent transitioning towards a more westernized configuration within the first year after migration [9,10]. However, studies of migrant populations worldwide remain limited, particularly for refugees.\u003c/p\u003e\n\u003cp\u003eLike Europe, and especially the Mediterranean countries, Italy is currently facing a substantial refugee movement, particularly from several African countries with low socioeconomic levels in search of better work opportunities. Data indicate that, in 2024, Italy welcomed the highest number of sea arrivals in Southern Europe, with a total of 41,617 people arriving by sea (https://www.unhcr.org/europe/sites/europe/files/2024-10/bi-annual-fact-sheet-2024-09-italy.pdf). However, due to limited access to official health data, information on the health status of migrant populations arriving in Italy is still scarce [11].\u003c/p\u003e\n\u003cp\u003eIn this context, here we aimed to provide some glimpses on the gut microbiome structure of apparently healthy asylum seekers newly arrived in Italy from African countries through a one-year cross-sectional microbiological surveillance study conducted during 2022-2023. In particular, we investigated deviations in the gut microbiome composition of refugees compared to traditional rural populations, resulting in the loss of sensitive microbial taxa prevalent in non-industrialized populations that might increase susceptibility to health issues. Our study thus provided new insights into how migration journey and related stressors affect refugees\u0026rsquo; gut health, which could inform public health strategies for refugee populations.\u003c/p\u003e"},{"header":"2.\tEthical statement","content":"\u003cp\u003eThe present study was performed following the guidelines of the Declaration of Helsinki in 1975, revised in 2013 and all the procedures performed in this study meet the national and international guidelines. A written informed consent was obtained from every patient before the study and patients were completely anonymized by the researchers. Ethical approval was approved with the number 85-CE-2024 by Policlinico Foggia Ethical Committee. Included samples were obtained according to standard diagnostic and therapeutic protocols for the management of gastrointestinal infections. All the authors ensure that this study is HIPAA (Health Insurance Portability and Accountability Act, 1996) compliant. The researchers followed every mandatory (health and safety) procedure.\u003c/p\u003e"},{"header":"3.\tMaterials and methods","content":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003e3.1 Sample collection, processing and\u0026nbsp;\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e16S rRNA gene amplification and sequencing\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSeventy-nine\u0026nbsp;individuals arrived\u0026nbsp;during the period April 2022-May 2023 and\u0026nbsp;temporarily admitted to asylum sicker Borgo Mezzanone, Foggia, Italy, were selected for the gut microbiome profiling using 16S rRNA gene amplicon sequencing.\u003c/p\u003e\n\u003cp\u003eMicrobiological screening was free offered to all consecutive newly arrived asylum seekers arrived in Italy crossing the Mediterranean Sea within the previous 7 days. Stool samples\u0026nbsp;were collected during routine health examinations and immediately sent to the Microbiology and Virology Unit of Foggia Policlinic, Foggia, Italy, to be processed as described below.\u0026nbsp;According to internal procedures, each individual was interviewed with the support of mediators speaking the refugee\u0026rsquo;s native language in order to collect\u0026nbsp;personal data (age and gender), when possible. While we aimed to sample the largest possible number of newly arrived refugees, no formal sample size calculation was performed. This decision was influenced by the significant cultural barriers and extreme difficulty encountered in obtaining fecal samples from this population. Consequently, we collected a single specimen from each participant.\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMicrobial DNA was extracted from faecal samples (approximately 200 mg) using the QIAamp DNA Stool Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer\u0026apos;s instructions and then eluted in 200 \u0026mu;L of TE buffer. Extracted DNA was processed for 16S rRNA gene amplification and sequencing for prokaryotic community characterization. Library preparation was performed following the Illumina 16S Metagenomic Sequencing Library Preparation protocol (Illumina, San Diego, CA, USA). The V3\u0026ndash;V4 hypervariable regions of the 16S rRNA gene were PCR amplified in a 50\u0026mu;L final volume containing 25 ng of microbial DNA, 2X KAPA HiFi HotStart ReadyMix (Roche, Basel, Switzerland) and 200 nmol/L of 341F (5\u0026rsquo;- CCTACGGGNGGCWGCAG-3\u0026prime;) and 785R (5\u0026prime;-GACTACHVGGGTATCTAATCC-3\u0026prime;) primers with Illumina adapter overhang sequences [12]. The PCR thermocycle consisted of 3 min at 95 ◦C, 25 cycles of 30 s at 95\u0026deg;C, 30 s at 55\u0026deg;C and 30 s at 72\u0026deg;C, and a final 5-min extension step at 72\u0026deg;C [13]. PCR products were then purified with Agencourt AMPure XP magnetic beads (Beckman Coulter, Brea, CA, USA). Indexed libraries were prepared by limited-cycle PCR, using Nextera technology (Illumina), and cleaned-up as described above. Libraries were quantified using the Qubit 3.0 fluorimeter (Invitrogen, Waltham, MA, USA), normalised to 4 nM and pooled. The sample pool was denatured with 0.2 N NaOH and diluted to a final concentration of 4.5 pM with a 20 % PhiX control. Sequencing was performed on an Illumina MiSeq platform using a 2 \u0026times; 250 bp paired-end protocol, according to the manufacturer\u0026apos;s instructions (Illumina).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003e3.2 Bioinformatics and statistical analysis\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor 16S rRNA gene analysis, raw sequences from all individuals were processed using a pipeline combining PANDAseq [14] and QIIME 2 [15]. High-quality reads were retained using the \u0026ldquo;fastq filter\u0026rdquo; function of the Usearch11 algorithm [16] and clustered into amplicon sequence variants (ASVs) using DADA2 [17]. Taxonomy was assigned using the VSEARCH classifier [18] and the SILVA database (2020, v138.1 release) as a reference [19]. All sequences assigned to eukaryotes or unassigned were discarded. Overall, a total of\u0026nbsp;637,063 high-quality reads (mean \u0026plusmn; standard deviation: 8,382 \u0026plusmn; 2,621)\u0026nbsp;was obtained, resulting in a total of\u0026nbsp;3,401 amplicon sequence variants (ASVs).\u003c/p\u003e\n\u003cp\u003e16S rRNA gene sequences of the gut microbiome associated with worldwide populations, characterized by different subsistence strategies (i.e., rural and urban), were downloaded from\u0026nbsp;publicly available datasets [20-25]. The 16S data from the samples analyzed in Rampelli et al. [26] were produced in the context of the \u0026ldquo;HARVEST\u0026rdquo; project and published at PRJEB85423. Sequences were subjected to direct read mapping with Kraken2 (v2.1.2) [27] and the resulting report was processed through Bracken (v 2.6.2) [28] pipeline in order to obtain taxonomic tables at the genus level. Both Kraken2 and Bracken were used with SILVA database (2020, v138.1 release) as a reference [19] with default options. Bracken outputs were then merged to obtain a single taxonomic table, which was subsequently merged with the genus-level taxonomic table of the original refugee dataset produced in this study.\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using R software (R Core Team; www.r-project.org), v. 4.3.2, with the package \u0026ldquo;vegan\u0026rdquo; (https://CRAN.R-project.org/package=vegan) [29]. Alpha diversity was calculated at the genus level using the function \u0026ldquo;diversity\u0026rdquo; of the vegan package, with the metrics \u0026ldquo;Shannon index\u0026rdquo;, \u0026ldquo;Simpson\u0026rdquo;, \u0026ldquo;Inverted Simpson\u0026rdquo; and \u0026ldquo;Observed genera\u0026rdquo;. The Wilcoxon rank-sum test was used to assess significant differences in alpha diversity and genus-level relative abundance between groups. Beta diversity was estimated by computing Bray-Curtis distances at the genus level, and data separation in the Principal Coordinates Analysis (PCoA) was tested using a permutation test with pseudo-F ratio (function \u0026ldquo;adonis\u0026rdquo; in the vegan package). \u003cem\u003eP\u003c/em\u003e-values, when necessary, were corrected for multiple testing using the Benjamin\u0026ndash;Hochberg method, with a false discovery rate (FDR)\u0026nbsp;\u0026le;\u0026nbsp;0.05 considered statistically significant.\u003c/p\u003e\n\u003cp\u003eLinear discriminant analysis (LDA) Effect Size (LEfSe) [30] was performed to identify discriminant bacterial genera. Only microbial taxa with LDA score threshold of \u0026plusmn;2 (on a log10 scale) and \u003cem\u003ep\u003c/em\u003e-value \u0026le; 0.05 were retained.\u003c/p\u003e"},{"header":"4.\tResults","content":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003e4.1 Gut microbiome profile of refugees in the global scenario\u0026nbsp;\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 79 refugees (74 males and 5 females, mean age 26 \u0026plusmn; 7 years) were profiled for the gut microbiome using 16S rRNA gene amplicon sequencing. A total of 637,063 sequences (mean \u0026plusmn; standard deviation: 8,382 \u0026plusmn; 2,621) and 3,401 ASVs were obtained.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe gut microbiome composition of refugees was compared with publicly available sequences from worldwide populations encompassing different subsistence strategies (rural and urban) [20-26]. Specifically, we selected 15 Nigerian individuals, of which three rural and 12 urban [25], 18 rural Nzime, 26 rural Baka [26] and 8 rural Pigmy individuals [21] from Cameroon, 29 urban Canadian and 24 rural Inuit Canadian [24], 24 rural Tunapuco and 20 rural Matses individuals from Peru [22], 42 urban USA individuals from Norman and 38 urban Native Americans [23], 27 rural Hadza from Tanzania and 16 urban Italians [20]. As expected, subsistence type was a major driver of the gut microbiome variation, with individual populations significantly segregating in the PCoA plot (permutation test with pseudo-F ratio, p-value \u0026le; 0.001) (Figure 1A). When considering the population distribution, we observed a significant segregation according to lifestyle, namely rural populations (150 subjetcs) and urbanized populations (137 subjects), and refugee status (79 subjects), regardless of geographical origin (p-value \u0026le; 0.001), with the centroid of the refugees\u0026rsquo; group clustering closer to the rural group (Figure 1B). In contrast, the alpha diversity of the refugees\u0026rsquo; gut microbiome was lower than that of rural populations (Wilcoxon rank-sum test corrected with FDR, p-values \u0026le; 0.05 for all metrics), but mostly comparable to that of urban populations (p-values \u0026le; 0.05 for Simpson, Inverted Simpson and Observed genera, but p-value \u0026ge; 0.05 for Shannon metric) (Figure 2).\u003c/p\u003e\n\u003cp\u003eWe identified significantly discriminant genera amongst the three groups taken into account (i.e. rural, urban, refugee) through LEfSe analysis (LDA score threshold of \u0026plusmn; 2 and p-value \u0026le; 0.05) (Figure 3). The urban group was mainly discriminated by \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e and \u003cem\u003eAkkermansia\u003c/em\u003e (LDA score \u0026gt; 4); the rural group by \u003cem\u003eTreponema\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eRuminobacter\u003c/em\u003e, \u003cem\u003eButyrivibrio\u003c/em\u003e, and \u003cem\u003eRoseburia\u003c/em\u003e (LDA score \u0026gt; 3); the refugee group by \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eEscherichia-Shigella\u003c/em\u003e and \u003cem\u003eAlloprevotella\u003c/em\u003e (LDA score \u0026gt; 4), but also \u003cem\u003eKlebsiella\u003c/em\u003e (LDA score \u0026gt; 3). Interestingly, some compositional signatures of the refugee gut microbiome, particularly \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eAlloprevotella\u003c/em\u003e, but also \u003cem\u003eSuccinivibrio\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, and \u003cem\u003eRuminococcus\u003c/em\u003e, were shared with the gut microbiome of rural populations (Wilcoxon rank-sum test corrected with FDR, p-values \u0026gt; 0.05) (Supplementary Table1).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003e5.2 Gut microbiome profile of refugees compared to the Italian cohort \u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFinally, we specifically compared the gut microbiome of refugees with that of the population of relocation, i.e. Italians. The Italian cohort comprised 16 urban living Italian adults from Bologna, Italy (5 males and 11 females, mean age 32\u0026nbsp;\u0026plusmn; 5 years) [20]. The two groups significantly segregated in the PCoA plot (permutation test with pseudo-F ratio, p-value \u0026le; 0.001) (Figure 4A) and differed in alpha diversity, which was lower in the refugees (Wilcoxon rank-sum test corrected with FDR, p-values \u0026le; 0.01 for all the metrics) (Figure 4B).\u003c/p\u003e\n\u003cp\u003eConfirming the previous findings, the refugee group exhibited a significantly higher relative abundance of \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eAlloprevotella\u003c/em\u003e (LDA score \u0026gt; 3 and p-value \u0026le; 0.05) (Figure 5). On the other hand, the Italian cohort was characterized by significant higher levels of \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e and \u003cem\u003eBlautia\u003c/em\u003e as observed in the urban group above.\u003c/p\u003e"},{"header":"5. Discussion and conclusions","content":"\u003cp\u003eIn the present study we provide insights into the potential impact of the challenging journey experienced by newly arrived African asylum seekers on their gut microbiome health, highlighting concerns about the risks to gut health in this vulnerable group. Through a one-year cross-sectional microbiological surveillance conducted from 2022 to 2023, we analyzed the gut microbiomes of 79 individuals using 16S rRNA gene amplicon sequencing and compared them to those of rural and urban populations worldwide and to the population of relocation (i.e. Italy). Our results showed \u0026nbsp;refugees gut microbiome segregating distinctly from the one of rural and urban populations, and with refugees\u0026rsquo; alpha diversity being significantly lower than that of rural populations, showing a peculiar configuration in in opportunistic taxa increase.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRefugees from African countries experience a range of perilous challenges, stressors, and events during their migration journey, influenced by drastic socio-economic and political factors [31]. Many refugees flee from conflict zones or oppressive regimes, resulting in racial discrimination, genocidal repression, or violence related to civil wars [32]. Other refugees come from areas experiencing severe humanitarian crises due to famine or environmental disasters, seeking safety and sustenance in a new country [33]. The journey faced by refugees encompasses crossing the Sahara Desert an across Libya, where they often face threats from human traffickers and the risk of being sold into slavery or forced labor [34,35]. Furthermore, migrants who are able to attempt to cross the Mediterranean Sea to Italy add another layer of risk to their flight, with many perishing during these attempts [36,37].\u003c/p\u003e\n\u003cp\u003eHere, we showed that the gut microbiome configuration of the refugees differed from that of rural populations worldwide, despite sharing some compositional features. Given the known migration routes from Africa to Europe, which involve individuals from Sub-Saharan Africa (ISTAT https://www.istat.it/wp-content/uploads/2024/10/REPORT-CITTADINI-NON-COMUNITARI_Anno-2023.pdf; \u0026nbsp;UNHCR https://data.unhcr.org/en/situations/europe-sea-arrivals/location/24521; ISPI https://www.ispionline.it/it/pubblicazione/africa-occidentale-le-migrazioni-infra-ed-extra-regionali-135619), where the majority of the population resides in rural areas (IPCC report 2022, https://doi.org/10.1017/9781009325844.011), it is plausible to speculate that a significant portion of the analyzed refugee population may have originated from rural settings. This speculation is supported by the fact that many migrants from Sub-Saharan Africa are likely to have been exposed to similar environmental and lifestyle factors associated with rural living before their migration. In particular, the refugee gut microbiome retained taxa that are typically prevalent in non-industrialized populations and are diminished or absent in industrialized societies (i.e., VANISH taxa). These include \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eAlloprevotella\u003c/em\u003e, \u003cem\u003eSuccinivibrio\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, and \u003cem\u003eRuminococcus\u003c/em\u003e, which are often associated with high-fiber diets [25,38-40]. On the other hand, the refugee gut microbiome was depleted of other VANISH taxa typical of rural populations, such as \u003cem\u003eTreponema\u003c/em\u003e, which aids in the breakdown of complex plant polysaccharides [20], \u003cem\u003eRuminobacter\u003c/em\u003e, able to thrive on fibrous plant material [1], and \u003cem\u003eButyrivibrio\u003c/em\u003e and \u003cem\u003eRoseburia\u003c/em\u003e, known for the production of SCFAs, particularly butyrate, which is linked to beneficial anti-inflammatory effects [41,42]. This loss of VANISH taxa in the gut microbial composition of the refugee cohort compared to rural populations might be an early indicator of the very strong impact of migration-related stressors and potentially impact refugees\u0026rsquo; health. Indeed, migration-related stressors have been shown to lead to dysbiosis, characterized by a decrease in alpha diversity and an increase in potentially harmful bacteria [1]. Supporting this, we also observed that the refugee group was discriminated by opportunistic pathogens, such as \u003cem\u003eEscherichia-Shigella\u003c/em\u003e and \u003cem\u003eKlebsiella\u003c/em\u003e, which could increase systematic inflammation and susceptibility to infections [43-47].\u003c/p\u003e\n\u003cp\u003eIn conclusion, our research revealed a significant deviation of the refugee gut microbiome from that of rural populations, with a loss of diversity of particularly sensitive VANISH taxa and an enrichment in pathobionts. Given that refugees were likely originating from rural areas, this alteration was possibly driven by stressors related to the migration journey, including chronic stress, psychological trauma, and shifts in food supply and dietary habits. These migration-related compositional changes could potentially increase the risk of dysbiosis, with an emergence of pathogenic taxa emergence, thus exacerbating the already precarious health conditions of asylum seekers individuals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh-quality reads from the present study were deposited in the European Nucleotide Archive (ENA) under the project accession number PRJEB85422.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no specific funding was used for this research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGiorgia Palladino\u003c/strong\u003e: Conceptualization, Data curation, Formal analysis, Methodology, Writing \u0026ndash; original draft.\u003cstrong\u003e\u0026nbsp;Marianna Marangi:\u0026nbsp;\u003c/strong\u003eConceptualization, Investigation, Methodology, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eDaniel Scicchitano\u003c/strong\u003e: Writing \u0026ndash; Review \u0026amp; Editing; \u003cstrong\u003eSilvia Turroni\u003c/strong\u003e: Writing \u0026ndash; Review \u0026amp; Editing.\u0026nbsp;\u003cstrong\u003eSimone Rampelli\u003c/strong\u003e: Data curation, Writing \u0026ndash; Review and Editing; \u003cstrong\u003eMarco Candela\u003c/strong\u003e: Resources, Writing \u0026ndash; Review and Editing, Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. We are very grateful to all the staff of the Microbiology and Virology Unit of Foggia Policlinic, Foggia, Italy for their collaboration in collecting the samples.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eParizadeh, M., \u0026amp; Arrieta, M. C. (2023). The global human gut microbiome: genes, lifestyles, and diet. Trends in Molecular Medicine.\u003c/li\u003e\n\u003cli\u003eUpadhyay, R. K. (2020). Markers for global climate change and its impact on social, biological and ecological systems: A review. \u003cem\u003eAmerican Journal of Climate Change\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(03), 159.\u003c/li\u003e\n\u003cli\u003eRyan, D., Dooley, B., \u0026amp; Benson, C. (2008). Theoretical perspectives on post-migration adaptation and psychological well-being among refugees: Towards a resource-based model. \u003cem\u003eJournal of refugee studies\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(1), 1-18.\u003c/li\u003e\n\u003cli\u003eFuzail, M. A., Ahmed, B., Burke, D. L., Spjeldn\u0026aelig;s, A. H., Horino, M., Ching, C., \u0026amp; Zaman, M. H. (2021). Microbiome research potential for developing holistic approaches to improve refugee health. \u003cem\u003eJournal of Global Health Reports\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e, e2021094. \u003c/li\u003e\n\u003cli\u003eKang, J., Moser, D. K., Lennie, T. A., Chung, M. L., Thomas, D. T., \u0026amp; Biddle, M. J. (2024). Diet Quality Mediates the Relationship Between Chronic Stress and Inflammation in Patients With Metabolic Syndrome. Journal of Cardiovascular Nursing, 10-1097.\u003c/li\u003e\n\u003cli\u003eYi, B., Rykova, M., J\u0026auml;ger, G., Feuerecker, M., H\u0026ouml;rl, M., Matzel, S., ... \u0026amp; Chouk\u0026egrave;r, A. (2015). Influences of large sets of environmental exposures on immune responses in healthy adult men. Scientific Reports, 5(1), 13367.\u003c/li\u003e\n\u003cli\u003eBeurel, E. (2024). Stress in the microbiome-immune crosstalk. Gut microbes, 16(1), 2327409.\u003c/li\u003e\n\u003cli\u003eKarl, J. P., Hatch, A. M., Arcidiacono, S. M., Pearce, S. C., Pantoja-Feliciano, I. G., \u0026amp; Soares, J. W. (2018). Effects of psychological, environmental and physical stressors on the gut microbiota. \u003cem\u003eFrontiers in microbiology\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e, 372026.\u003c/li\u003e\n\u003cli\u003eVangay, P., Johnson, A. J., Ward, T. L., Al-Ghalith, G. A., Shields-Cutler, R. R., Hillmann, B. M., ... \u0026amp; Knights, D. (2018). US immigration westernizes the human gut microbiome. \u003cem\u003eCell\u003c/em\u003e, \u003cem\u003e175\u003c/em\u003e(4), 962-972.\u003c/li\u003e\n\u003cli\u003eShanahan, F., Ghosh, T. S., \u0026amp; O\u0026rsquo;Toole, P. W. (2023). Human microbiome variance is underestimated. \u003cem\u003eCurrent Opinion in Microbiology\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e, 102288.\u003c/li\u003e\n\u003cli\u003eSulekova, L. F., Ceccarelli, G., Pombi, M., Esvan, R., Lopalco, M., Vita, S., ... \u0026amp; Walter, I. (2019). Occurrence of intestinal parasites among asylum seekers in Italy: a cross-sectional study. \u003cem\u003eTravel Medicine and Infectious Disease\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e, 46-52.\u003c/li\u003e\n\u003cli\u003eKlindworth, A., Pruesse, E., Schweer, T., Peplies, J., Quast, C., Horn, M., \u0026amp; Gl\u0026ouml;ckner, F. O. (2013). Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. \u003cem\u003eNucleic acids research\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(1), e1-e1.\u003c/li\u003e\n\u003cli\u003eTurroni, S., Fiori, J., Rampelli, S., Schnorr, S. L., Consolandi, C., Barone, M., ... \u0026amp; Candela, M. (2016). Fecal metabolome of the Hadza hunter-gatherers: a host-microbiome integrative view. \u003cem\u003eScientific reports\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 32826.\u003c/li\u003e\n\u003cli\u003eMasella, A. P., Bartram, A. K., Truszkowski, J. M., Brown, D. G., \u0026amp; Neufeld, J. D. (2012). PANDAseq: paired-end assembler for illumina sequences. \u003cem\u003eBMC bioinformatics\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, 1-7.\u003c/li\u003e\n\u003cli\u003eBolyen, E., Rideout, J. R., Dillon, M. R., Bokulich, N. A., Abnet, C. C., Al-Ghalith, G. A., ... \u0026amp; Caporaso, J. G. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. \u003cem\u003eNature biotechnology\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(8), 852-857.\u003c/li\u003e\n\u003cli\u003eEdgar, R. C. (2010). Search and clustering orders of magnitude faster than BLAST. \u003cem\u003eBioinformatics\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(19), 2460-2461.\u003c/li\u003e\n\u003cli\u003eCallahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., \u0026amp; Holmes, S. P. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. \u003cem\u003eNature methods\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(7), 581-583.\u003c/li\u003e\n\u003cli\u003eRognes, T., Flouri, T., Nichols, B., Quince, C., \u0026amp; Mah\u0026eacute;, F. (2016). VSEARCH: a versatile open source tool for metagenomics. \u003cem\u003ePeerJ\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e, e2584.\u003c/li\u003e\n\u003cli\u003eQuast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., ... \u0026amp; Gl\u0026ouml;ckner, F. O. (2012). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. \u003cem\u003eNucleic acids research\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(D1), D590-D596.\u003c/li\u003e\n\u003cli\u003eSchnorr, S. L., Candela, M., Rampelli, S., Centanni, M., Consolandi, C., Basaglia, G., ... \u0026amp; Crittenden, A. N. (2014). Gut microbiome of the Hadza hunter-gatherers. \u003cem\u003eNature communications\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(1), 3654.\u003c/li\u003e\n\u003cli\u003eMorton, E. R., Lynch, J., Froment, A., Lafosse, S., Heyer, E., Przeworski, M., ... \u0026amp; Segurel, L. (2015). Variation in rural African gut microbiota is strongly correlated with colonization by Entamoeba and subsistence. \u003cem\u003ePLoS genetics\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(11), e1005658.\u003c/li\u003e\n\u003cli\u003eObregon-Tito, A. J., Tito, R. Y., Metcalf, J., Sankaranarayanan, K., Clemente, J. C., Ursell, L. K., ... \u0026amp; Lewis, C. M. (2015). Subsistence strategies in traditional societies distinguish gut microbiomes. \u003cem\u003eNature communications\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 6505.\u003c/li\u003e\n\u003cli\u003eSankaranarayanan, K., Ozga, A. T., Warinner, C., Tito, R. Y., Obregon-Tito, A. J., Xu, J., ... \u0026amp; Lewis, C. M. (2015). Gut microbiome diversity among Cheyenne and Arapaho individuals from western Oklahoma. \u003cem\u003eCurrent Biology\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(24), 3161-3169.\u003c/li\u003e\n\u003cli\u003eGirard, C., Tromas, N., Amyot, M., \u0026amp; Shapiro, B. J. (2017). Gut microbiome of the canadian arctic Inuit. \u003cem\u003eMsphere\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(1), 10-1128.\u003c/li\u003e\n\u003cli\u003eAyeni, F. A., Biagi, E., Rampelli, S., Fiori, J., Soverini, M., Audu, H. J., ... \u0026amp; Turroni, S. (2018). Infant and adult gut microbiome and metabolome in rural Bassa and urban settlers from Nigeria. \u003cem\u003eCell reports\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(10), 3056-3067.\u003c/li\u003e\n\u003cli\u003eRampelli, S., Gallois, S., D\u0026rsquo;Amico, F., Turroni, S., Fabbrini, M., Scicchitano, D., ... \u0026amp; Henry, A. (2024). The gut microbiome of Baka forager-horticulturalists from Cameroon is optimized for wild plant foods. Iscience, 27(3).\u003c/li\u003e\n\u003cli\u003eWood, D. E., Lu, J., \u0026amp; Langmead, B. (2019). Improved metagenomic analysis with Kraken 2. Genome biology, 20, 1-13.\u003c/li\u003e\n\u003cli\u003eLu, J., Breitwieser, F. P., Thielen, P., \u0026amp; Salzberg, S. L. (2017). Bracken: estimating species abundance in metagenomics data. PeerJ Computer Science, 3, e104.\u003c/li\u003e\n\u003cli\u003eOksanen, J. (2013). Vegan: ecological diversity. \u003cem\u003eR project\u003c/em\u003e, \u003cem\u003e368\u003c/em\u003e, 1-11.\u003c/li\u003e\n\u003cli\u003eSegata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett, W. S., \u0026amp; Huttenhower, C. (2011). Metagenomic biomarker discovery and explanation. \u003cem\u003eGenome biology\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, 1-18. \u003c/li\u003e\n\u003cli\u003eIdemudia, E., \u0026amp; Boehnke, K. (2020). \u003cem\u003ePsychosocial experiences of African migrants in six European countries: A mixed method study\u003c/em\u003e (p. 237). Springer Nature.\u003c/li\u003e\n\u003cli\u003eChikanda, A. (2019). An analysis of forced migration from Zimbabwe. \u003cem\u003emigration Studies\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(1), 59-82.\u003c/li\u003e\n\u003cli\u003eZickgraf, C. (2019). Climate change and migration crisis in Africa. \u003cem\u003eThe Oxford handbook of migration crises\u003c/em\u003e, 347.\u003c/li\u003e\n\u003cli\u003eAdepoju, A. (2005). Review of research and data on human trafficking in sub-Saharan Africa. \u003cem\u003eInternational Migration\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(1-2), 75-98.\u003c/li\u003e\n\u003cli\u003eBello, P. O., \u0026amp; Olutola, A. A. (2020). The conundrum of human trafficking in Africa. \u003cem\u003eModern slavery and human trafficking\u003c/em\u003e, 15-27.\u003c/li\u003e\n\u003cli\u003eKassar, H., \u0026amp; Dourgnon, P. (2014). The big crossing: illegal boat migrants in the Mediterranean. \u003cem\u003eThe European Journal of Public Health\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(suppl_1), 11-15.\u003c/li\u003e\n\u003cli\u003eMcMahon, S., \u0026amp; Sigona, N. (2021). Death and migration: Migrant journeys and the governance of migration during Europe\u0026rsquo;s \u0026ldquo;migration crisis\u0026rdquo;. \u003cem\u003eInternational Migration Review\u003c/em\u003e, \u003cem\u003e55\u003c/em\u003e(2), 605-628.\u003c/li\u003e\n\u003cli\u003eDe Filippo, C., Cavalieri, D., Di Paola, M., Ramazzotti, M., Poullet, J. B., Massart, S., ... \u0026amp; Lionetti, P. (2010). Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proceedings of the National Academy of Sciences, 107(33), 14691-14696.\u003c/li\u003e\n\u003cli\u003eSonnenburg, J. L., \u0026amp; Sonnenburg, E. D. (2019). Vulnerability of the industrialized microbiota. \u003cem\u003eScience\u003c/em\u003e, \u003cem\u003e366\u003c/em\u003e(6464), eaaw9255.\u003c/li\u003e\n\u003cli\u003eCarter, M. M., Olm, M. R., Merrill, B. D., Dahan, D., Tripathi, S., Spencer, S. P., ... \u0026amp; Sonnenburg, J. L. (2023). Ultra-deep sequencing of Hadza hunter-gatherers recovers vanishing gut microbes. \u003cem\u003eCell\u003c/em\u003e, \u003cem\u003e186\u003c/em\u003e(14), 3111-3124.\u003c/li\u003e\n\u003cli\u003eCresci, G. A., \u0026amp; Bawden, E. (2015). Gut microbiome: what we do and don\u0026apos;t know. Nutrition in Clinical Practice, 30(6), 734-746.\u003c/li\u003e\n\u003cli\u003eDehingia, M., Thangjam Devi, K., Talukdar, N. C., Talukdar, R., Reddy, N., Mande, S. S., ... \u0026amp; Khan, M. R. (2015). Gut bacterial diversity of the tribes of India and comparison with the worldwide data. Scientific reports, 5(1), 18563.\u003c/li\u003e\n\u003cli\u003eHu, J., Cheng, S., Yao, J., Lin, X., Li, Y., Wang, W., ... \u0026amp; Zhi, M. (2022). Correlation between altered gut microbiota and elevated inflammation markers in patients with Crohn\u0026rsquo;s disease. \u003cem\u003eFrontiers in Immunology\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, 947313.\u003c/li\u003e\n\u003cli\u003eZhao, J., Bai, M., Ning, X., Qin, Y., Wang, Y., Yu, Z., ... \u0026amp; Sun, S. (2022). Expansion of Escherichia-Shigella in gut is associated with the onset and response to immunosuppressive therapy of IgA nephropathy. \u003cem\u003eJournal of the American Society of Nephrology\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(12), 2276-2292.\u003c/li\u003e\n\u003cli\u003eCalderon-Gonzalez, R., Lee, A., Lopez-Campos, G., Hancock, S. J., Sa-Pessoa, J., Dumigan, A., ... \u0026amp; Bengoechea, J. A. (2023). Modelling the gastrointestinal carriage of Klebsiella pneumoniae infections. \u003cem\u003eMbio\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), e03121-22.\u003c/li\u003e\n\u003cli\u003eSingh, S. B., Carroll-Portillo, A., \u0026amp; Lin, H. C. (2023). Desulfovibrio in the gut: the enemy within?. \u003cem\u003eMicroorganisms\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(7), 1772.\u003c/li\u003e\n\u003cli\u003eVornhagen, J., Rao, K., \u0026amp; Bachman, M. A. (2024). Gut community structure as a risk factor for infection in Klebsiella pneumoniae-colonized patients. \u003cem\u003eMsystems\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(8), e00786-24.\u003c/li\u003e\n\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"refugees, gut microbiome, VANISH taxa, dysbiosis","lastPublishedDoi":"10.21203/rs.3.rs-5929447/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5929447/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe global landscape of migration has evolved significantly, with international migrants tripling since 1970, reaching approximately 281 million by 2020. This rise includes a notable surge in forcibly displaced individuals due to conflicts, wars, and human rights violations. Additionally, climate change is reshaping migration patterns by environmental degradation and extreme weather events, with projections indicating that 143 million individuals may be uprooted by climate catastrophes over the next three decades.\u003c/p\u003e\n\u003cp\u003eIn this context, migrants experience chronic stress due to the uncertainties of their journey, exposure to trauma, and changes in living conditions, possibly exacerbating health issues, including through impairment of the gut microbiome. Our study focuses on the characterization – by 16S rRNA gene amplicon sequencing – of intestinal microbiome in 79 asylum seekers newly arrived in Italy from African countries through their comparison with publicly available datasets of worldwide populations encompassing different origin and lifestyle. This microbiological surveillance, conducted as cross-sectional sampling over one year, aimed to assess how the forced migration journey and the associated stressors affect refugees’ gut health. Our findings suggest significant deviations in the gut microbiome composition of refugees compared to traditional rural populations, possibly driven by stressors such a psychological trauma and dietary changes. The loss of microbial diversity may increase susceptibility to health issues, highlighting the need for targeted public health strategies for refugee populations.\u003c/p\u003e","manuscriptTitle":"Gut microbiome structure in asylum seekers newly arrived in Italy from Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-30 06:09:14","doi":"10.21203/rs.3.rs-5929447/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-18T06:14:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-15T10:57:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-15T08:19:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-24T23:24:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117414939289912022608408023805203309440","date":"2025-06-10T15:06:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-13T18:31:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213384146832213603698640097082256795635","date":"2025-05-01T14:02:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-29T06:50:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-09T11:45:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-28T08:00:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cae8f409-9a19-4587-a3de-882d551f1e31","owner":[],"postedDate":"April 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47836527,"name":"Biological sciences/Microbiology"},{"id":47836528,"name":"Biological sciences/Molecular biology"},{"id":47836529,"name":"Health sciences/Health care/Public health"}],"tags":[],"updatedAt":"2025-11-24T16:00:57+00:00","versionOfRecord":{"articleIdentity":"rs-5929447","link":"https://doi.org/10.1038/s41598-025-24250-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-18 15:57:33","publishedOnDateReadable":"November 18th, 2025"},"versionCreatedAt":"2025-04-30 06:09:14","video":"","vorDoi":"10.1038/s41598-025-24250-x","vorDoiUrl":"https://doi.org/10.1038/s41598-025-24250-x","workflowStages":[]},"version":"v1","identity":"rs-5929447","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5929447","identity":"rs-5929447","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}