The gut bacterial microbiome of Nile tilapia (Oreochromis niloticus) from lakes across an altitudinal gradient

The gut bacterial microbiome of Nile tilapia (Oreochromis niloticus) from lakes across an altitudinal gradient | 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 The gut bacterial microbiome of Nile tilapia (Oreochromis niloticus) from lakes across an altitudinal gradient Negash Kabtimer Bereded, Getachew Beneberu Abebe, Solomon Workneh Fanta, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-956338/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Apr, 2022 Read the published version in BMC Microbiology → Version 1 posted 1 You are reading this latest preprint version Abstract Background Microorganisms inhabiting the gut play a significant role in supporting fundamental physiological processes of the host, which contributes to their survival in varied environments. Several studies have shown that altitude affects the composition and diversity of intestinal microbial communities in terrestrial animals. However, little is known about the impact of altitude on the gut microbiota of aquatic animals. The current study examined the variations in the gut microbiota of Nile tilapia ( Oreochromis niloticus ) from four lakes along an altitudinal gradient in Ethiopia by using 16S rDNA Illumina MiSeq high-throughput sequencing. Results The results indicated that low-altitude samples typically displayed greater alpha diversity. The results of principal coordinate analysis (PCoA) showed significant differences across samples from different lakes. Firmicutes was the most abundant phylum in the Lake Awassa and Lake Chamo samples whereas Fusobacteriota was the dominant phylum in samples from Lake Hashengie and Lake Tana. The ratio of Firmicutes to Bacteroidota in the high-altitude sample (Lake Hashengie, altitude 2440 m) was much higher than the ratio of Firmicutes to Bacteroidota in the low altitude population (Lake Chamo, altitude 1235 m). We found that the relative abundances of Actinobacteriota, Chloroflexi, Cyanobacteria, and Firmicutes were negatively correlated with altitude, while Fusobacteriota showed a positive association with altitude. Despite variability in the abundance of the gut microbiota across the lakes, some shared microbial communities were detected. Conclusions In summary, this study showed the indirect influence of altitude on gut microbiota. Altitude has the potential to modulate the gut microbiota composition and diversity of Nile tilapia. Future work will be needed to elucidate the functional significance of gut microbiota variations based on the geographical environment. Significance and Impact of the Study : Our study determined the composition and diversity of the gut microbiota in Nile tilapia collected from lakes across an altitude gradient. Our findings greatly extend the baseline knowledge of fish gut microbiota in Ethiopian lakes that plays an important role in this species sustainable aquaculture activities and conservation. altitude diversity fish gut microbiota lake 16S rDNA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Natural environmental conditions may have a notable effect on the organism as well as on overall biological characteristics. Animals are subject to environmentally possible adaptive selection in diverse habitats [ 1 , 2 ]. Among the most extreme conditions, high-altitude environments are intriguing habitats for a large number of animals. Atmospheric pressure declines significantly with increasing altitude, causing hypobaric hypoxia [ 3 ]. These changes can affect the main energy production process by suppressing aerobic metabolism. To adapt to different geographical environments particularly to high altitudes, animals have evolved many fascinating ways of dealing with life in these environments. To overcome harsh environmental conditions, animals inhabiting high-altitude areas evolved morphological, physiological, and genetic adaptations, such as changing body masses, raising metabolic rates, and genetic modification [ 4 – 6 ]. For example, the Lake Titicaca frog ( Telmatobius culeus ) has behavioural, morphological, and physiological adaptations that allow aquatic animals to survive at high altitudes (3812 m) [ 7 ]. Moreover, to deal with low temperatures cold-adapted animals have antifreeze proteins [ 8 ]. Antifreezing proteins, which include specific proteins, glycopeptides, and peptides, are produced by different organisms for cold adaptation. Antifreezing proteins protect cells and body fluids from freezing by reducing the freezing point of water and inhibiting the growth of ice crystals [ 9 ]. In animals, numerous microorganisms inhabit the gut and create an intricate intestinal microbiota. These gut microbes live in a kind of symbiotic association with the host organism. Intestinal microbial communities play a critical role in integrating the main essential functions in the host, for instance, nutrient absorption, hampering the colonization of pathogenic organisms, and maintaining normal mucosal immunity, while hosts provide a living environment for the gut microbiota [ 10 ]. The reason for structural differences in intestinal microbial communities is reported to be the strong selection and coevolution of the host and its environment [ 11 ]. Some studies have reported that the gut microbiota of animals may support host adaptation to different environments [ 12 , 13 ]. Ruminants in high altitude environments have a gut microbiome with changes in energy-metabolism-related genes [ 14 ]. Due to this adaptation, intestinal digesta from sheep and yaks inhabiting high-altitude environments consist of more methane and volatile fatty acids than their low-altitude relatives [ 14 ]. Gut microbial communities have a robust fermentation ability and are equipped with more genes involved in the production of volatile fatty acids [ 14 ]. From Tibetan and Han populations living at different altitudes, more energy-efficient microbial communities were obtained in samples from those living at higher altitudes [ 12 ]. The functions of gut microbial communities depend on their structure, which is influenced by several host-associated elements, such as genetic makeup, season, stress, and geographical location [ 15 – 17 ]. The availability of food resources in different geographical environments is variable and hence significantly affects the diversity and composition of the host gut microbiota [ 18 ]. Due to exposure to different geographic environments, the gut microbiota of chickens has changed [ 13 ]. Altitude is the main determinant that can modulate the structure and diversity of intestinal microbial communities of animals [ 12 , 19 ]. Moreover, the impact of high altitude and low oxygen concentration on the gut microbiota of mice was reported recently, which supports the notion of modulation of the gut microbiota composition and metabolic processes due to environmental change [ 3 ]. For animals inhabiting aquatic environments at high altitude, low atmospheric pressure, low temperature, and high radiation conditions are some of the factors affecting their physiology [ 20 ]. The composition and diversity of fish intestinal microbial communities may be affected by environmental factors such as temperature and salinity, which affect metabolic activity and hence the health status of the host [ 21 ]. Nile tilapia ( Oreochromis niloticus ) is a commonly farmed freshwater fish in the world [ 22 ]. Nile tilapia is characterized by its capability to endure a broad range of biotic and abiotic stresses, a rapid growth rate, and an omnivorous mode of feeding [ 23 – 26 ]. As a consequence of these qualities, Nile tilapia is an ideal freshwater fish model. Variations in the availability of food resources are the most direct factor modulating the composition of intestinal microbial communities [ 17 ]. Nile tilapia feeds on phytoplankton, macrophytes, insects, detritus, and zooplankton [ 27 ] depending on its life stages. However, the availability of these food resources differs substantially in several geographical areas [ 28 , 29 ]. The studies done thus far have emphasized mainly compositional variations and associated adaptive mechanisms of gut microbiota of terrestrial animals inhabiting distinctive geographical locations [ 3 , 30 , 31 ]. However, there is a shortage of studies on the adaptive mechanisms of aquatic animals in water environments. Nile tilapia is a good model for assessing the association of gut microbiota composition and geographical variation due to its adaptation to a multitude of aquatic environments. Investigations on the gut microbial community profile of Nile tilapia inhabiting lakes with distinct altitude environments are greatly lacking. In this study, we aspired to reveal the variations in the gut microbiota profile of Nile tilapia from lakes at distinctive altitudes to provide a deeper understanding of the gut microbiota composition and diversity of the populations of this fish inhabiting differing altitudes. We hypothesized that environmental factor such as altitude would be strongly linked with modulation of the gut microbiota of Nile tilapia. This study contributes to our understanding of the gut microbiota of Nile tilapia from lakes with altitude gradients and provides new insights into this species adaptive mechanisms. Results Sequencing profiles The Illumina MiSeq 16S rDNA sequencing data of 39 samples were examined for gut microbiota. After conducting a series of quality filter processes, a total of 88,203 read counts were recovered, with an average of 2261 reads per sample, ranging from 849 to 3357 ( Fig. S1 ). The rarefaction curves had attained a plateau ( Fig. S2 ), suggesting that accurate microbial groups within each sample were demonstrated. Moreover, sequence integrity was assessed using Good’s coverage. The Good’s coverage estimators for all samples in our study were greater than 99, indicating that the majority of microbial communities in our samples were fully identified. Good’s coverage and alpha diversity indices of the gut microbial communities are summarized in Table S3. Alpha diversity of gut bacterial communities among the studied lakes Alpha diversity of gut microbiota was examined by Chao1, observed, Accumulated Cyclone Index (ACE), Shannon, Simpson, and Fisher indices (Fig. 1 , Fig. S3 ). Lake Chamo, with a 1235 m altitude, showed the highest value for all indices assessed. Moreover, Lake Chamo samples showed significantly higher alpha diversity than Lake Tana and Lake Hashengie populations in all indices analysed (two-tailed t-test, p<0.05 ). The alpha diversity of Lake Awassa was found to be significantly higher than the alpha diversity of Lake Hashengie and Lake Tana, particularly for the Chao1 and ACE indices. Lake Tana and Lake Hashengie showed similar alpha diversity (two-tailed t-test, p>0.05 ). The results of the present study suggested that low-altitude samples typically displayed greater alpha diversity. Beta Diversity Of Gut Microbiota To analyse the intestinal microbiota composition of Nile tilapia from lakes of different altitudes, the beta diversity index was used. To take abundance alteration and phylogenetic association into consideration unweighted UniFrac distance and weighted UniFrac distance were selected as signals of beta diversity. Principal coordinate analysis (PCoA) indicated that substantial variations were observed across samples from different lakes (p < 0.001) (Fig. 2 ). Based on our results, for unweighted UniFrac distance, Axis 1 accounted for 27.2% of the total difference, while Axis 2 accounted for 19.1%, and for weighted UniFrac distance, Axis 1 accounted for 74.4% of the total disparity, while Axis 2 rated 8.1%. We performed an analysis of similarity (ANOSIM) on both unweighted and weighted UniFrac distance results to substantiate this dissimilarity. The ANOSIM outcome indicated that there were significant differences between lakes of different altitudes (unweighted R: 0.72842; p value < 0.001; weighted R: 0.58415; p value < 0.001). We also carried out a permutational multivariate analysis of variance (PERMANOVA); the PERMANOVA results were concurrent with those of ANOSIM (unweighted R-squared: 0.45044; p value < 0.001; weighted R-squared: 0.58502; p value < 0.001). Bacterial Community Structure The bacterial phyla recovered from all samples included Bacteroidota (1.8%), Bdellovibrionota (1.2%), Cyanobacteria (0.6%), Firmicutes (52.8%), Fusobacteriota (35.6%), Proteobacteria (6.9%), Chloroflexi (0.3%), Actinobacteriota (0.7%) and Dependentiae (0.1%). However, the proportions of the same bacteria in samples from different lakes were different at the phylum level (Fig. 3 , Table 1 ). There were significant differences in all phyla detected except Bacteroidota and Dependentiae (t-test, two-tailed, p value<0.01). The relative abundance of Bacteroidota in Lake Chamo (0.0434±0.0236) was highest among all groups, followed by Lake Tana (0.0161±0.0048), Lake Awassa (0.0093±0.0085), and Lake Hashengie (0.0004±0.0003). Bdellovibrionota was detected only in Lake Tana and Lake Awassa. The highest Cyanobacteria abundance was recorded in Lake Awassa (0.0211±0.0053). The relative abundance of Firmicutes in the samples of Lake Awassa (0.8941±0.0259) and Lake Chamo (0.6563±0.0670) was much higher than the relative abundance of Firmicutes in the samples from the other two lakes. However, Lake Hashengie and Lake Tana samples showed higher Fusobacteriota abundance than the other lakes. Proteobacteria was highest in Lake Chamo (0.1333±0.0389). Dependentiae was detected only in Lake Awassa (Table 1 ). The relative abundances of Actinobacteriota, Chloroflexi, Firmicutes, and Fusobacteriota in the Lake Tana samples were significantly different (t-test, two-tailed, p value<0.05 ) from the relative abundances of Actinobacteriota, Chloroflexi, Firmicutes, and Fusobacteriota in the samples of Lake Chamo and Lake Awassa. Moreover, Lake Tana samples showed significant variation with Lake Chamo in Bdellovibrionota and Proteobacteria (t-test, two-tailed, p value<0.05 ). The ratio of Firmicutes to Bacteroidota in high-altitude populations (Lake Hashengie) was more than the ratio of Firmicutes to Bacteroidota in low-altitude populations (Lake Chamo) by many fold. The Firmicutes to Bacteroidota ratios were found to be 15.13 and 1122.33 in the Lake Chamo and Lake Hashengie samples, respectively. Table 1 Relative abundance at the phylum level presented as % (out of 100). The results are expressed as the mean ± standard error of the mean (SEM). Phyla Mean±SEM Lake Awassa Lake Chamo Lake Hashengie Lake Tana Actinobacteriota 0.75±0.002 1.15±0.004 1.16±0.010 0.01±0.0001 Bacteroidota 0.93±0.009 4.34±0.024 0.04±0.0003 1.61±0.005 Bdellovibrionota 0.74±0.003 0 0 3.53±0.012 Chloroflexi 0.34±0.001 0.54±0.002 0.17±0.001 0 Cyanobacteria 2.11±0.005 0.45±0.002 0 0.12±0.001 Dependentiae 0.25±0.001 0 0 0 Firmicutes 89.41±0.026 65.63±0.067 44.22±0.112 24.24±0.046 Fusobacteriota 1.55±0.008 14.56±0.043 47.52±0.108 66.92±0.056 Proteobacteria 3.86±0.012 13.33±0.039 6.90±0.022 3.58±0.012 Investigation of the structure of bacterial communities at the family level resulted in 39 families from all samples ( Table S1 ). Among these families, 27 varied significantly in all samples (two-tailed t-test p< 0.05 ). The dominant families obtained were Clostridiaceae, Erysipelotrichaceae, Fusobacteriaceae, and Peptostreptococcaceae. The relative abundance of Clostridiaceae in Lake Awassa (0.3930±0.0774) and Lake Chamo (0.2413±0.0381) specimens was greater than the relative abundance of Clostridiaceae in Lake Tana and Lake Hashengie. The relative abundance of Erysipelotrichaceae in Lake Awassa (0.1473±0.0293) was highest among all groups. Lake Tana and Lake Hashengie showed higher Fusobacteriaceae abundances than the two lakes, with relative abundances of 0.6692±0.0563 and 0.4752±0.1075, respectively. The relative abundance of Peptostreptococcaceae in Lake Chamo (0.3901±0.0688) was highest among all groups, followed by Lake Awassa (0.3509±0.0661), Lake Hashengie (0.2540±0.0809), and Lake Tana (0.1447±0.0197). Cyanobiaceae, Microcystaceae, and Rickettsiaceae were not detected in the Lake Hashengie samples. Moreover, some families were detected in only one lake, such as Microbacteriaceae and Solirubrobacteraceae in Lake Hashengie; Microtrichaceae, Ruminococcaceae, and UBA12409 in Lake Awassa; and Silvanigrellaceae in Lake Tana. Five families, namely, Acetobacteraceae, Comamonadaceae, Erysipelotrichaceae, Fusobacteriaceae, and Mycobacteriaceae significantly differed (two-tailed t-test p< 0.05 ) between Lake Chamo and Lake Hashengie. Fourteen families showed a significant difference between Lake Tana and Lake Awassa, and 16 families showed a significant difference between Lake Tana and Lake Chamo ( Table S5 ). In total, 27 taxa were identified at the genus level from all samples ( Table S2 ). The majority of these genera (18 genera) varied significantly across sampling lakes at different altitudes (two-tailed t-test p< 0.05 ). The dominant genera detected were Cetobacterium , Clostridium_sensu_stricto_1 , Turicibacter , and Romboutsia from nearly all samples. The relative abundance of Cetobacterium was found to be higher in Lake Tana (0.6628±0.0580) and Lake Hashengie (0.4750±0.1074) samples than in the other lakes. The relative abundance of Clostridium_sensu_stricto_1 was highest in Lake Awassa (0.1983±0.0622), followed by Lake Hashengie (0.1388±0.0362)0. Moreover, in Lake Hashengie, which is the lake highest in altitude, the lowest relative abundances of Hyphomicrobium , Macellibacteroides , Turicibacter , and Uncultured were obtained compared to the other lakes. In contrast, the relative abundances of Romboutsia and Plesiomonas were found to be the highest in Lake Hashengie. Some microbial communities were found to be unique for particular lakes only, such as Silvanigrella in Lake Tana, Aurantimicrobium in Lake Hashengie, Candidatus_Soleaferrea in Lake Awassa, and Nocardioides in Lake Chamo. Cetobacterium , Nocardioides , Turicibacter , and Uncultured significantly differed (two-tailed t-test p< 0.05 ) between Lake Chamo and Lake Hashengie. The composition of Lake Tana samples was unique since more genera were significantly different (two-tailed t-test p< 0.05 ) from Lake Awassa (i.e., Cetobacterium , Cyanobium_PCC_6307 , Microcystis_PCC_7914 , Romboutsia , Silvanigrella , Turicibacter , and V2 ) and Lake Chamo (i.e. Cetobacterium , Nocardioides , Romboutsia , Silvanigrella , Roseomonas , and V2 ). Bacterial Signatures In Different Samples Linear discriminant analysis effect size (LEfSe) was performed to detect the microbial signature in every lake. Signature gut microbial communities at the genus level comprised Clostridium_sensu_stricto_1 , Cyanobium_PCC_6307 , Microcystis_PCC_7914 , Turicibacter and V3 in the Lake Awassa sample; Macellibacteroides , Clostridium_sensu_stricto_13 and Uncultured in the Lake Chamo sample; Cetobacterium and Silvanigrella in the Lake Tana sample; and Romboutsia , Legionella , Epulopiscium , Methylocystis and Aeromonas in the Lake Hashengie sample (Fig. 4 ). At the phylum level, Firmicutes, Cyanobacteria, Dependentiae, and Patescibacteria in Lake Awassa; Proteobacteria, Chloroflexi, and Bacteroidota in Lake Chamo; Fusobacteriota and Bdellovibrionota in Lake Tana; and Actinobacteriota in Lake Hashengie were found to be important taxa. Unique and shared bacteria in the gut of Nile tilapia A Venn diagram was made to assess the distribution of amplicon sequence variants (ASVs) among different samples collected from lakes located at different altitudes. The results showed that five ASVs (ASV13, ASV16, ASV2, ASV1, and ASV3) were shared by all lakes. ASV12, ASV110, ASV81, and ASV133 were shared between Lake Hashengie and Lake Chamo. Moreover, six ASVs (ASV47, ASV145, ASV113, ASV44, ASV7, and ASV55) were shared by Lake Awassa and Chamo. However, some ASVs were peculiar to some lakes only, e.g., 12 ASVs in Lake Hashengie, two ASVs (ASV19 and ASV78) in Lake Tana, 12 ASVs in Lake Awassa, and 33 ASVs in Lake Chamo (Fig. 5 ). Correlation Between Gut Microbiota And Altitude To determine which bacterial communities were associated with altitude, Spearman correlation analysis was employed. We found that the relative abundances of Actinobacteriota (Spearman correlation 0.388, p value <0.05), Chloroflexi (Spearman correlation 0.396, p value <0.05), Cyanobacteria (Spearman correlation 0.503, p value <0.01) and Firmicutes (Spearman correlation 0.464, p value <0.01) were negatively correlated with altitude, while Fusobacteriota (Spearman correlation 0.561, p value <0.01) showed a positive association with altitude (Table 2 ). At the genus level, altitude was positively correlated with Aurantimicrobium , Legionella , and Cetobacterium . However, 11 genera, Clostridium_sensu_stricto_13 , Hyphomicrobium , Macellibacteroides , Methyloparacoccus , Microcystis_PCC_7914 , V2 , Nocardioides , Roseomonas , Shewanella , uncultured and Turicibacter , showed negative correlations with altitude ( Table S4 ). Table 2 Spearman correlation between the relative abundances of gut microbial communities at the phylum level and altitude. **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). Taxa Correlation Coefficient Sig. (2-tailed) Actinobacteriota -0.388* 0.015 Bacteroidota -0.295 0.068 Bdellovibrionota 0.068 0.681 Chloroflexi -0.396* 0.013 Cyanobacteria -0.503** 0.001 Dependentiae -0.215 0.189 Firmicutes -0.464** 0.003 Fusobacteriota 0.561** 0.000 Others -0.171 0.298 Proteobacteria -0.103 0.533 Discussion Various studies have shown that the composition of the gut microbiota of animals is affected by altitude [ 3 , 30 , 31 , 33 ]. Animals inhabiting high-altitude environments are characterized by unique gut microbiota compared to those living at low altitudes. There is a shortage of investigations on the intestinal microbial communities of fish species from lakes located at different altitudes. Our study investigated the effect of altitude on gut-associated microbial communities of Nile tilapia from four Ethiopian lakes located at different altitudes. This study included Lake Hashengie, a high-altitude lake in Ethiopia, and this is the first investigation of the intestinal microbial communities of Nile tilapia in Lake Hashengie using high-throughput sequencing. In our study, the question of how the gut microbial communities were affected with respect to altitude was addressed. From the studied lakes, Nile tilapia are not native to Lake Hashengie, the lake located at an altitude of 2440 m. The Nile tilapia in Lake Hashengie are believed to have been introduced and adapted to the high-altitude environment [ 34 ]. At high altitudes aquatic organisms face physiological challenges as a response to lower levels of nutrients, buffering capacity, atmospheric pressure, and temperatures [ 20 ]. Moreover, high-altitude lakes are characterized by strong ultraviolet radiation and low primary productivity [ 20 , 35 ]. The low air pressure at high altitudes could affect the amount of dissolved oxygen in lakes and therefore the microbiome community. The decrease in temperature with altitude can have similar effects. A previous study suggested that water temperature may affect the intestinal microbial communities of fishes [ 36 ]. The composition and diversity of gut microbiota are reported to take part in a crucial role in host environmental adaptation [ 33 , 37 ]. In the present investigation, we showed that Nile tilapia inhabiting lakes with dissimilar altitudes have comparatively different gut microbiota and corroborated that altitudinal differences were a determinant that formed the composition of gut microbial communities in Nile tilapia on Ethiopian lakes. Our findings were consistent with prior studies that showed the effect of altitude on gut microbiota in different animals [ 3 , 30 , 31 , 33 ]. In high-altitude mice, hypoxia was found to be one of the key factors that caused modification of the gut microbiota [ 3 ]. As reported in high-altitude mice, hypoxia might be the factor for variation of the gut microbiota in the Nile tilapia along the altitude gradient. Lake Chamo (lowest altitude) samples demonstrated the topmost values on the global alpha-diversity assessments, which were calculated on the rarefied ASVs. The occurrence of a more diversified gut microbiota is believed to be evidence of a robust microbiota [ 38 ]. A great alpha diversity attests to a vast number of microbial communities, which may help the host assimilate different diets. Thus, Lake Chamo Nile tilapia samples with enormous diversity may have a greater potential to use a variety of food sources and support fulfillment of their dietary demand in this particular habitat. Beta diversity assessment showed significant differences between gut-related microbial communities of Nile tilapia. Both the unweighted UniFrac and weighted UniFrac approaches revealed clear significant clustering by the source of the samples in the PCoA plot (Fig. 2 ), suggesting that altitude may modulate the abundance of different microbial communities in the gut. In our study, Firmicutes was the most abundant phylum in the Lake Awassa and Lake Chamo samples, whereas Fusobacteriota was the dominant phylum in the samples of Lake Hashengie and Lake Tana. Previous studies on the intestinal microbial communities of Nile tilapia from Ethiopian lakes showed dominance by the phylum Firmicutes followed by Fusobacteriota [ 15 , 39 ]. Fusobacteriota was also reported as a dominant phylum from the intestine of Nile tilapia of Lake Nasser in Egypt [ 40 ] and captive Nile tilapia [ 41 ]. Clostridiaceae and Peptostreptococcaceae were the predominant families from samples of Lake Awassa and Chamo, while Fusobacteriaceae was the most dominant in Lake Hashengie and Lake Tana. Ideally, we should have more low- and high-altitude lakes to ensure that the patterns we find are indeed a result of altitude change and not other factors. However, Lake Tana and Lake Hashengie (the lakes located at higher altitudes) show some commonalities that may be evidence that altitude plays a role. In an earlier study, Clostridiaceae played essential roles in carbohydrate degradation in the gut [ 42 ]. Peptostreptococcaceae is reported to be involved in the fermentative type of metabolism of proteinаceous substrates and carbohydrates [ 43 ]. Importantly, the relative abundances of Fusobacteriaceae, Beijerinckiaceae, Lachnospiraceae, and Vibrionaceae clearly showed higher values at Lake Hashengie than at Lake Chamo, demonstrating that these microorganisms may help the host adapt well to high-altitude environments. To the best of our knowledge, phyla such as Dependentiae, Patescibacteria, and Bdellovibrionota were identified for the first time from the gut of Nile tilapia. These results imply that the intestine of Nile tilapia might harbour more diversified microbial communities in addition to the microbial communities reported thus far. Patescibacteria and Dependentiae dominated the pupfish gut [ 44 ]. Patescibacteria is associated with biodegradable plastics such as polylactic acid [ 45 ], and has also been reported as a potential microbial bioindicator of phosphorus mining [ 46 ]. The phylum Bdellovibrionota has many predatory species, that employ a range of strategies to attack their bacterial prey [ 47 ]. The relative abundance of Firmicutes varied significantly among the studied Nile tilapia samples. Firmicutes can produce several enzymes for the degradation of dietary nutrients, thus assisting their hosts in the digestion and absorption of nutrients [ 48 ]. Since the gut microbiota of Nile tilapia comprised a large proportion of Firmicutes, we can deduce that the host might be effective in obtaining energy from dietary nutrients. In addition, we also found that the high-altitude samples (Lake Hashengie) had an increased Firmicutes:Bacteroidetes ratio compared with the Firmicutes:Bacteroidetes ratio of the low-altitude samples (Lake Chamo). The higher ratio of Firmicutes to Bacteroidetes in the gut microbiota is reported to be associated with the efficient absorption of food energy [ 49 ]. Moreover, the increase in the ratio of Firmicutes to Bacteroidetes was associated with better herbage energy utilization ability and increased resistance to cold stress in the gut of mammals inhabiting high altitudes [ 30 ]. In our study, the increase in the ratio of Firmicutes to Bacteroidetes in Lake Hashengie samples indicates that high-altitude Nile tilapia may have efficiency in harvesting energy and may also help them adapt to the environment. In terrestrial animals, consumption of meat and dairy products is associated with a higher Firmicutes:Bacteroidetes ratio and, in contrast, low Firmicutes: Bacteroidetes ratios related to consumption of fruits and vegetables [ 31 , 50 , 51 ]. Lake Hashengie harbours a higher diversity of zooplankton [ 52 ]. Moreover, zooplankton were found to be the dominant food source for Nile tilapia in Lake Hashengie [ 27 ]. Therefore, the high Firmicutes:Bacteroidetes ratio in Lake Hashengie might help Nile tilapia consume zooplankton. A higher Firmicutes:Bacteroidetes ratio was also associated with the gut of obese animals and humans compared with normal-weight individuals [ 53 , 54 ]. Fish from Lake Hashengie were extremely fatty, most likely due to the dominance of zooplankton in their food [ 52 ] and the high Firmicutes:Bacteroidetes ratio in Lake Hashengie confirmed that the association with obesity also works for Nile tilapia. Fusobacteriota was found to be positively correlated with altitude, which was principally ascribed to the substantial increasing abundance of the genus Cetobacterium. In contrast, a negative correlation between Actinobacteriota, Chloroflexi, Cyanobacteria, and Firmicutes with altitude was observed. Lower microbial taxon richness in high-altitude lakes compared to reference lakes located at lower elevations has been reported previously [ 35 ]. In contrast to our study, high-altitude lakes were reported to have a higher abundance of Cyanobacteria [ 55 , 56 ]. At the genus level, Cetobacterium , Turicibacter , and Nocardioides were revealed as the genera that varied significantly between the low elevation (Lake Chamo) and high elevation (Lake Hashengie) samples. The genus Cetobacterium , which was found in the family Fusobacteriaceae, was one of the bacterial genera positively correlated with altitude. Cetobacterium isolated from freshwater fish produces vitamin B-12 [ 57 ]. Turicibacter species are involved in the modulation of bacterial colonization in the gut, regulation of host energy metabolism, and host immunity [ 58 – 60 ]. Nocardioides were detected in the gut of shrimp [ 61 ] and a masculinization pond of Nile tilapia fry [ 62 ]. Nocardioides were reported to be involved in the degradation of steroids and latex [ 62 , 63 ]. Romboutsia , Legionella , Epulopiscium , Methylocystis , and Aeromonas are unique and signature gut bacteria in Lake Hashengie, demonstrating that they might perform a crucial function in adaptation to high-altitude habitats. Despite the variability in the abundance of the gut microbiota across the sampling lakes, a number of shared microbial communities, including Proteobacteria, Firmicutes, and Fusobacteria, were detected. These microbial communities may be critical for the assembly and function of a gut microbiome, which may highlight their importance to host performance [ 64 ]. The shared microbial communities within the gut of Nile tilapia might be due to the availability of specific microorganisms in the water of the lake that are capable of colonizing the gut and due to selective pressures within the gut habitat. Conclusions In conclusion, the gut microbiota of Nile tilapia from lakes at different altitudes in Ethiopia was successfully characterized by Illumina MiSeq sequencing. Our results showed that the composition and diversity of gut microbial communities between lakes were different. These variations in intestinal microbiota are possibly due to a consequence of various selection pressures occurring in these habitats, mainly altitude. Our results also highlight the dominance of Firmicutes in the Lake Awassa and Lake Chamo samples and Fusobacteriota in the Lake Hashengie and Lake Tana samples. The ratio of Firmicutes to Bacteroidota in high-altitude samples (Lake Hashengie) was many-fold higher than the ratio of Firmicutes to Bacteroidota in low-altitude populations (Lake Chamo), suggesting similarity with terrestrial animals. This study sheds new light on the associations between altitude and gut microbiota in aquatic animals and provides a perspective on fish ecological adaptation. Furthermore, this study provides valuable information to establish sustainable growth for the aquaculture sector in different geographical locations. Nevertheless, further work needs to be done to better understand how altitude is affecting Nile tilapia’s gut microbiome. First, functional significance of gut microbiota variations based on the geographical environment should be explored in future studies by adding more populations and larger sampling sizes. Second, previous study showed that seasonality influences the microbiota of gut content of Nile tilapia [ 15 ]. This effect may be different in different altitudes, which needs to be further explored. Third, the differences found might be a consequence of the microbiome composition of the lakes and not necessarily an adaptation of Nile tilapia. Therefore, a comparison between the microbial communities in the water and the fish gut is needed. Methods And Materials Description of the sampling sites The samples were collected in July and August 2018 from four lakes with different altitudes, i.e., Lake Awassa, Lake Chamo, Lake Tana, and Lake Hashengie (Fig. 6 , Table 3 ). Lake Awassa and Lake Chamo are located in the Great Rift Valley whereas Lake Tana and Lake Hashengie are not. Table 3 Morphometric characteristics of the four sampling lakes. Lakes Altitude (m.a.s.l) * Location Mean depth (m) Latitudes Longitudes Awassa 1685 06 0 58’ to 07 0 14’ N 38 0 22’ to 38 0 28’ E 11 Chamo 1235 5 0 50’ to 5 0 83’ N 37 0 33’ to 37 0 55’ E 10 Tana 1800 10 0 95′ and 12 0 78′ N 36 0 89′ to 38 0 25′ E 8 Hashengie 2440 12 0 31'9`` N 39 0 30'50`` E 14 *Meters above sea level Specimen Handling And Processing Nile tilapia ( Oreochromis niloticus ) samples were purchased at the landing site of the sampling lakes from the fishermen. Sample collection was performed from June 30, 2018 to August 8, 2018. In this study, only adult male fish samples showing no gross or clinical signs of diseases were included. Males were selected due to their larger size. A total of 39 samples (12 from Lake Tana, 10 from Lake Chamo, 9 from Lake Hashengie and 8 from Lake Awassa) were collected and treated as reported previously [ 39 ]. All samples were sacrificed using high doses of clove oil [ 65 ]. Ethanol (70%) was applied to the body surface of the fish, and instruments were used for dissection. The intestine was dissected, and the luminal contents from the posterior region were collected in sterile screw-cap tubes filled with phosphate buffered saline and glycerol (50:50, v/v) and stored at -20°C until further processing [ 66 ]. Microbial Dna Extraction, Pcr Amplification, And Sequencing Microbial DNA extraction, polymerase chain reaction (PCR) amplification, PCR product purification, and amplicon sequencing processes were performed as previously described [ 39 ]. Briefly, genomic DNA extraction was carried out using the PowerFecal® DNA Isolation Kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions with some modifications. The Illumina MiSeq paired-end sequencing system (Illumina, San Diego, CA, USA) was used to sequence the V3-V4 region of the microbial 16S rRNA gene. Two-step PCR was performed to prepare DNA sequencing libraries using the dual index approach following Shokralla et al [ 67 ]. The first PCR was conducted with the primers 347F and 803R from Nossa et al. [ 68 ] extended with the Illumina adapter sequences. Briefly, 4 µL of genomic DNA was used as a template for the first PCR (95°C for 15 min; 30 cycles of 95°C for 30 s, 55°C for 1 min, and 72°C for 1 min; and a final extension at 72°C for 10 min). For index PCR, TrueSeq adapter sequences were used. The reaction was carried out with the purified PCR product as a template, after an initial denaturation and activation at 95°C for 15 min, using 10 cycles of 95°C for 30 s, 58°C for 60 s, and 72°C for 60 s. Both PCRs were performed in 10 µL reaction volumes amplifying with 5 µL of QIAGEN Multiplex PCR Master Mix (Qiagen, Hilden, Germany). The PCR product of each sample was purified by the magnetic bead extraction method. The 16S rRNA genes were sequenced at the Genomics Service Unit, Ludwig‐Maximilian’s‐Universität München, Germany. Analysis Of Sequence Data Regions with low sequence quality, adapter, and primer sequences were trimmed with Cutadapt v. 0.11.1 software as described previously [ 39 , 69 ]. Paired-end reads were merged with PEAR v. 0.9.4 [ 39 , 70 ]. Chimeric sequences were detected with USEARCH 6.0 based on the RDP pipeline [ 71 ]. High-quality read sequences were dereplicated using USEARCH and finally denoised to produce amplicon sequence variants (ASVs) using the USEARCH - unoise3 command [ 72 ]. After mapping the reads for each sample to the list of ASVs, an “OTU” table was made using the ‘otutab’ command in USEARCH. For the classification of the generated ASVs and construction of the phylogenetic tree, the curated SILVA taxonomy was used [ 73 ]. Furthermore, to augment the downstream statistical analysis, low-quality features were removed using minimum counts of 2 and 10% prevalence in samples on MicrobiomeAnalyst [ 32 ]. Moreover, ASVs assigned as chloroplasts and mitochondria were removed before downstream analysis. To solve problems of the variability in sampling depth, data rarefaction to a minimum library size was carried out. Moreover, sequence reads were normalized by total sum scaling approaches. Data Analysis Alpha diversity and beta diversity analyses was done using the phyloseq package as implemented in MicrobiomeAnalyst [ 32 ]. The alpha diversity of each sample was examined using the Chao1, observed, ACE, Shannon, Simpson, and Fisher indices. For the beta diversity, the dissimilarity matrix was measured using Unweighted Unifrac distance and weighted Unifrac distance method and visualized by principal coordinate analysis (PCoA). Permutational multivariate analysis of variance (PERMANOVA) and analysis of group similarities (ANOSIM) were employed to assess the statistical significance of the clustering pattern in ordination plots. Linear discriminant analysis effect size (LEfSe) were employed to identify the taxa with substantially different relative abundance across all samples [ 74 ]. Hierarchical clustering is performed with the function hclust in the package stat [ 32 ]. The numbers of shared and unique ASVs are presented in Venn diagrams ( http://bioinformatics.psb.ugent.be/webtools/Venn/ ). Only ASVs present in at least 40% of the samples were included for constructing the Venn diagram. Independent t-tests were used to examine the variations in microbial abundance and alpha indices. Spearman's rank correlation test was used to identify which microbial communities were significantly associated with altitude. Statistical analysis was done with SPSS 21 for Windows. All statistical analyses were performed with a significance level of α = 0.05 ( p < 0.05) unless otherwise stated. Abbreviations ANOSIM Analysis of group similarities ASV Amplicon sequence variant LEfSe Linear discriminant analysis effect size OTU Operational taxonomic unit PCoA Principal coordinate analysis PCR Polymerase chain reaction PERMANOVA Permutational multivariate analysis of variance Declarations Ethics approval The experimental procedures performed in this study were authorized by ethical clearance committee of the College of Science at Bahir Dar University, Ethiopia. The ethical approval Reference number is PGRCSVD/G1/2012. Consent for publication Not Applicable Availability of data The raw sequences have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA), under the BioProject ID PRJNA763202 accession numbers SRX12193142 to SRX12193150, and some of the samples were found under Bioproject IDs PRJNA705209 and PRJNA637890. Competing interests The authors declare that they have no competing interests. Funding The grant has been awarded to NKB within the framework of EU Erasmus + Key Action 1 International Credit Mobility Project. (http://www.bildung.erasmusplus.at/hochschulbildung/mobilitaet/internationale_mobilitaet/) Authors' contributions NKB , KJD, HM, HW, and GBA conceptualization; NKB performed laboratory activities, NKB , GBA and SWF coordination in selecting field sampling sites and sample collection; NKB , MC, SWF formal analysis; MC and NKB data curation; KJD, HM, and HW supervision; KJD, HM, SWF resources; NKB wrote the original draft. All authors revised and approved the manuscript. Acknowledgements The authors acknowledge support from the IPGL team (Institute of Hydrobiology and Aquatic Ecosystem Management, BOKU, Vienna), particularly Gerold Winkler, Nina Haslinger and Marlene Randle. We thank Misganaw Liyew, Melkamu Andargie, Kibret Ayuba, Kidane and Birhanu Gebo for their assistance in sample collection and Workie Worie for his help in making the map for this study. References McMahon RF. Evolutionary and physiological adaptations of aquatic invasive animals: r selection versus resistance. Can J Fish Aquat Sci. 2002;59:1235–44. Rago A, Kouvaris K, Uller T, Watson R. How adaptive plasticity evolves when selected against. PLOS Comput Biol. 2019;15:e1006260. Zhang W, Jiao L, Liu R, Zhang Y, Ji Q, Zhang H, et al. The effect of exposure to high altitude and low oxygen on intestinal microbial communities in mice. PLOS ONE. 2018;13:e0203701. Ding XZ, Liang CN, Guo X, Wu XY, Wang HB, Johnson KA, et al. Physiological insight into the high-altitude adaptations in domesticated yaks (Bos grunniens) along the Qinghai-Tibetan Plateau altitudinal gradient. Livest Sci. 2014;162:233–9. Williams CM, Szejner-Sigal A, Morgan TJ, Edison AS, Allison DB, Hahn DA. Adaptation to Low Temperature Exposure Increases Metabolic Rates Independently of Growth Rates. Integr Comp Biol. 2016;56:62–72. Xiao S, Mou Z, Fan D, Zhou H, Zou M, Zou Y, et al. Genome of Tetraploid Fish Schizothorax o’connori Provides Insights into Early Re-diploidization and High-Altitude Adaptation. iScience. 2020;23:101497. Hutchison VH, Haines HB, Engbretson G. Aquatic life at high altitude: Respiratory adaptations in the lake titicaca frog, Telmatobius culeus. Respir Physiol. 1976;27:115–29. Jacobsen D, Dangles O. Strategies and adaptations to aquatic life at high altitude. Oxford University Press; 2017. Kandaswamy KK, Chou K-C, Martinetz T, Möller S, Suganthan PN, Sridharan S, et al. AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties. J Theor Biol. 2011;270:56–62. Banerjee G, Ray AK. Bacterial symbiosis in the fish gut and its role in health and metabolism. Symbiosis. 2017;72:1–11. Shapira M. Gut Microbiotas and Host Evolution: Scaling Up Symbiosis. Trends Ecol Evol. 2016;31:539–49. Li K, Dan Z, Gesang L, Wang H, Zhou Y, Du Y, et al. Comparative Analysis of Gut Microbiota of Native Tibetan and Han Populations Living at Different Altitudes. PLOS ONE. 2016;11:e0155863. Zhou X, Jiang X, Yang C, Ma B, Lei C, Xu C, et al. Cecal microbiota of Tibetan Chickens from five geographic regions were determined by 16S rRNA sequencing. MicrobiologyOpen. 2016;5:753–62. Zhang Z, Xu D, Wang L, Hao J, Wang J, Zhou X, et al. Convergent Evolution of Rumen Microbiomes in High-Altitude Mammals. Curr Biol. 2016;26:1873–9. Bereded NK, Abebe GB, Fanta SW, Curto M, Waidbacher H, Meimberg H, et al. The Impact of Sampling Season and Catching Site (Wild and Aquaculture) on Gut Microbiota Composition and Diversity of Nile Tilapia (Oreochromis niloticus). Biology. 2021;10:180. Li W, Liu J, Tan H, Yang C, Ren L, Liu Q, et al. Genetic Effects on the Gut Microbiota Assemblages of Hybrid Fish From Parents With Different Feeding Habits. Front Microbiol. 2018;9:2972. Senghor B, Sokhna C, Ruimy R, Lagier J-C. Gut microbiota diversity according to dietary habits and geographical provenance. Hum Microbiome J. 2018;7–8:1–9. Sun B, Wang X, Bernstein S, Huffman MA, Xia D-P, Gu Z, et al. Marked variation between winter and spring gut microbiota in free-ranging Tibetan Macaques (Macaca thibetana). Sci Rep. 2016;6:26035. Zhao J, Yao Y, Li D, Xu H, Wu J, Wen A, et al. Characterization of the Gut Microbiota in Six Geographical Populations of Chinese Rhesus Macaques (Macaca mulatta), Implying an Adaptation to High-Altitude Environment. Microb Ecol. 2018;76:565–77. Catalan J. High mountain lakes: extreme habitats and witnesses of environmental changes. Limnetica. 2006;25:551–84. Ghanbari M, Kneifel W, Domig KJ. A new view of the fish gut microbiome: Advances from next-generation sequencing. Aquaculture. 2015;448:464–75. Prabu E, Rajagopalsamy CBT, Ahilan B, Jeevagan IJMA, Renuhadevi M. Tilapia – An Excellent Candidate Species for World Aquaculture: A Review. Annu Res Rev Biol. 2019;:1–14. Azaza MS, Dhraïef MN, Kraïem MM. Effects of water temperature on growth and sex ratio of juvenile Nile tilapia Oreochromis niloticus (Linnaeus) reared in geothermal waters in southern Tunisia. J Therm Biol. 2008;33:98–105. Njiru M, Ojuok JE, Okeyo-Owuor JB, Muchiri M, Ntiba MJ, Cowx IG. Some biological aspects and life history strategies of Nile tilapia Oreochromis niloticus (L.) in Lake Victoria, Kenya: Biology of Nile tilapia in Lake Victoria. Afr J Ecol. 2006;44:30–7. Rebouças VT, Lima FR dos, Cavalcante S. DDH, Sá MV do C e. Tolerance of Nile tilapia juveniles to highly acidic rearing water . Acta Sci Anim Sci. 2015;37:227. Tesfahun A. Food and feeding habits of Nile tilapia Oreochromis niloticus (L.) in Ethiopian water bodies: A review. 2018. Teame T, Natarajan P. Analysis of Diet and Biochemical Composition of Nile Tilapia (O. niloticus) from Tekeze Reservoir and Lake Hashenge, Ethiopia. J Fish Livest Prod. 2016;4. Green J. Zooplankton associations in some Ethiopian crater lakes. Freshw Biol. 1986;16:495–9. Vijverberg J, Dejen E, Getahun A, Nagelkerke LAJ. Zooplankton, fish communities and the role of planktivory in nine Ethiopian lakes. Hydrobiologia. 2014;722:45–60. Fan Q, Wanapat M, Yan T, Hou F. Altitude influences microbial diversity and herbage fermentation in the rumen of yaks. BMC Microbiol. 2020;20:370. Wu Y, Yao Y, Dong M, Xia T, Li D, Xie M, et al. Characterisation of the gut microbial community of rhesus macaques in high-altitude environments. BMC Microbiol. 2020;20:68. Chong J, Liu P, Zhou G, Xia J. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat Protoc. 2020;15:799–821. Ma Y, Ma S, Chang L, Wang H, Ga Q, Ma L, et al. Gut microbiota adaptation to high altitude in indigenous animals. Biochem Biophys Res Commun. 2019;516:120–6. Tsegay T. Characteristics and Status of the Highland Lake Hashenge Fishery, Tigray, Northern Ethiopia. Int J Aquac Fish Sci. 2017;:071–6. Xing P, Hahn MW, Wu QL. Low Taxon Richness of Bacterioplankton in High-Altitude Lakes of the Eastern Tibetan Plateau, with a Predominance of Bacteroidetes and Synechococcus spp. Appl Environ Microbiol. 2009;75:7017–25. Neuman C, Hatje E, Zarkasi KZ, Smullen R, Bowman JP, Katouli M. The effect of diet and environmental temperature on the faecal microbiota of farmed Tasmanian Atlantic Salmon ( Salmo salar L.). Aquac Res. 2016;47:660–72. Giatsis C, Sipkema D, Smidt H, Heilig H, Benvenuti G, Verreth J, et al. The impact of rearing environment on the development of gut microbiota in tilapia larvae. Sci Rep. 2015;5:18206. Petersen C, Round JL. Defining dysbiosis and its influence on host immunity and disease. Cell Microbiol. 2014;16:1024–33. Bereded NK, Curto M, Domig KJ, Abebe GB, Fanta SW, Waidbacher H, et al. Metabarcoding Analyses of Gut Microbiota of Nile Tilapia (Oreochromis niloticus) from Lake Awassa and Lake Chamo. Ethiopia Microorganisms. 2020;8:1040. Elsaied HE, Soliman T, Abu-Taleb HT, Goto H, Jenke-Kodam H. Phylogenetic characterization of eukaryotic and prokaryotic gut flora of Nile tilapia, Oreochromis niloticus, along niches of Lake Nasser, Egypt, based on rRNA gene high-throughput sequences. Ecol Genet Genomics. 2019;11:100037. Ray C, Bujan N, Tarnecki A, Davis DA, Cr CB. A. Analysis of the Gut Microbiome of Nile Tilapia Oreochromis Niloticus L. Fed Diets Supplemented with Previda® and Saponin. J Fish. 2017;11. Wüst PK, Horn MA, Drake HL. Clostridiaceae and Enterobacteriaceae as active fermenters in earthworm gut content. ISME J. 2011;5:92–106. Slobodkin A. The Family Peptostreptococcaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F, editors. The Prokaryotes. Berlin: Springer Berlin Heidelberg; 2014. pp. 291–302. Bhute SS, Escobedo B, Haider M, Mekonen Y, Ferrer D, Hillyard SD, et al. The gut microbiome and its potential role in paradoxical anaerobism in pupfishes of the Mojave Desert. Anim Microbiome. 2020;2:20. Rüthi J, Bölsterli D, Pardi-Comensoli L, Brunner I, Frey B. The “Plastisphere” of Biodegradable Plastics Is Characterized by Specific Microbial Taxa of Alpine and Arctic Soils. Front Environ Sci. 2020;8:562263. Mason LM, Eagar A, Patel P, Blackwood CB, DeForest JL. Potential microbial bioindicators of phosphorus mining in a temperate deciduous forest. J Appl Microbiol. 2021;130:109–22. Waite DW, Chuvochina M, Pelikan C, Parks DH, Yilmaz P, Wagner M, et al. Proposal to reclassify the proteobacterial classes Deltaproteobacteria and Oligoflexia, and the phylum Thermodesulfobacteria into four phyla reflecting major functional capabilities. Int J Syst Evol Microbiol. 2020;70:5972–6016. Colston TJ, Jackson CR. Microbiome evolution along divergent branches of the vertebrate tree of life: what is known and unknown. Mol Ecol. 2016;25:3776–800. Murphy EF, Cotter PD, Healy S, Marques TM, O’Sullivan O, Fouhy F, et al. Composition and energy harvesting capacity of the gut microbiota: relationship to diet, obesity and time in mouse models. Gut. 2010;59:1635–42. Springer A, Fichtel C, Al-Ghalith GA, Koch F, Amato KR, Clayton JB, et al. Patterns of seasonality and group membership characterize the gut microbiota in a longitudinal study of wild Verreaux’s sifakas ( Propithecus verreauxi ). Ecol Evol. 2017;7:5732–45. Zhang W, Li N, Tang X, Liu N, Zhao W. Changes in intestinal microbiota across an altitudinal gradient in the lizard Phrynocephalus vlangalii . Ecol Evol. 2018;8:4695–703. Tadesse Z. Diet composition impacts the fatty acid contents of Nile tilapia, Oreochromis niloticus L., in Ethiopian highland lakes. SIL Proc 1922-2010. 2010;30:1363–8. Ley RE, Backhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI. Obesity alters gut microbial ecology. Proc Natl Acad Sci. 2005;102:11070–5. Ley RE, Turnbaugh PJ, Klein S, Gordon JI. Human gut microbes associated with obesity. Nature. 2006;444:1022–3. Singh Y, Khattar JIS, Singh DP, Rahi P, Gulati A. Limnology and cyanobacterial diversity of high altitude lakes of Lahaul-Spiti in Himachal Pradesh, India. J Biosci. 2014;39:643–57. Vincent WF, Quesada A. Cyanobacteria in High Latitude Lakes, Rivers and Seas. In: Whitton BA, editor. Ecology of Cyanobacteria II. Dordrecht: Springer Netherlands; 2012. pp. 371–85. Tsuchiya C, Sakata T, Sugita H. Novel ecological niche of Cetobacterium somerae, an anaerobic bacterium in the intestinal tracts of freshwater fish. Lett Appl Microbiol. 2007;0:071018031740002-???. Dimitriu PA, Boyce G, Samarakoon A, Hartmann M, Johnson P, Mohn WW. Temporal stability of the mouse gut microbiota in relation to innate and adaptive immunity: Mouse gut microbiota dynamics. Environ Microbiol Rep. 2013;5:200–10. Fung TC, Vuong HE, Luna CDG, Pronovost GN, Aleksandrova AA, Riley NG, et al. Intestinal serotonin and fluoxetine exposure modulate bacterial colonization in the gut. Nat Microbiol. 2019;4:2064–73. Gao H, Jiang Q, Ji H, Ning J, Li C, Zheng H. Type 1 diabetes induces cognitive dysfunction in rats associated with alterations of the gut microbiome and metabolomes in serum and hippocampus. Biochim Biophys Acta BBA - Mol Basis Dis. 2019;1865:165541. Deng Y, Xu X, Yin X, Lu H, Chen G, Yu J, et al. Effect of stock density on the microbial community in biofloc water and Pacific white shrimp (Litopenaeus vannamei) gut microbiota. Appl Microbiol Biotechnol. 2019;103:4241–52. Homklin S, Ong SK, Limpiyakorn T. Degradation of 17α-methyltestosterone by Rhodococcus sp. and Nocardioides sp. isolated from a masculinizing pond of Nile tilapia fry. J Hazard Mater. 2012;221–222:35–44. Vilanova C, Baixeras J, Latorre A, Porcar M. The Generalist Inside the Specialist: Gut Bacterial Communities of Two Insect Species Feeding on Toxic Plants Are Dominated by Enterococcus sp. Front Microbiol. 2016;7. Turnbaugh PJ, Gordon JI. The core gut microbiome, energy balance and obesity: The core gut microbiome, energy balance and obesity. J Physiol. 2009;587:4153–8. Deriggi Pisani G, Inoue L, Moraes G. Stress responses to handling in Nile tilapia (Oreochromis niloticus Linnaeus): Assessment of eugenol as an alternative anesthetic. Acta Sci - Biol Sci. 2006;28:269–74. Ghanbari M, Shahraki H, Kneifel W, Domig KJ. A first insight into the intestinal microbiota of snow trout (Schizothorax zarudnyi). Symbiosis. 2017;72:183–93. Shokralla S, Hellberg RS, Handy SM, King I, Hajibabaei M. A DNA Mini-Barcoding System for Authentication of Processed Fish Products. Sci Rep. 2015;5:15894. Nossa CW. Design of 16S rRNA gene primers for 454 pyrosequencing of the human foregut microbiome. World J Gastroenterol. 2010;16:4135. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetjournal. 2011;17:10. Zhang J, Kobert K, Flouri T, Stamatakis A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics. 2014;30:614–20. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27:2194–200. Edgar RC. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. preprint. Bioinformatics; 2016. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590–6. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60. Supplementary Files AuthorChecklistE10only.pdf Supplementarymaterials.docx Cite Share Download PDF Status: Published Journal Publication published 04 Apr, 2022 Read the published version in BMC Microbiology → Version 1 posted First submitted to journal 31 Jan, 2022 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. 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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-956338","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":85354884,"identity":"549bd4a8-4cf5-4ee2-b934-bee15a025d59","order_by":0,"name":"Negash Kabtimer Bereded","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYLACxoYEBgMgCWTWyIEEDjwgSgsbWMsxY7CWBOK0gJnMiSCNDPi0mLO3X/z4c0eavLl8c+uGj3vY0ueHHX4ItMVOTrcBuxbLnjPF0rxncgx3tjG23ZzxTCZ34+00A6CWZGOzA9i1GNzISZBmbKtg3HCMse02zwG23I2zE0BaDiRuw6Xl/pvknz/bKuzBWv4cYE43nJ3+Ab+WG+zHJHjbchLBWhgOMCfIS+cQsOVMDps1b1ta8s62xLabPQeOGW6Qzik4kGCAxy/Hjz+++bMt2XY78/FnN34cqJGXn52++cOHCjs5XFoYGHgM0AwBqzTAohIO2B+g8uUb8KkeBaNgFIyCkQgAt9xtsYiRUCsAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-1122-9452","institution":"BOKU DLWT: Universitat fur Bodenkultur Wien Department fur Lebensmittelwissenschaften und Lebensmitteltechnologie","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Negash","middleName":"Kabtimer","lastName":"Bereded","suffix":""},{"id":85354885,"identity":"cf0fe52a-6480-43c0-ae6a-1b9c1fa7e694","order_by":1,"name":"Getachew Beneberu Abebe","email":"","orcid":"","institution":"Bahir Dar University Department of Biology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Getachew","middleName":"Beneberu","lastName":"Abebe","suffix":""},{"id":85354886,"identity":"107c7319-8830-48a0-9b91-00ec95d23d3d","order_by":2,"name":"Solomon Workneh Fanta","email":"","orcid":"","institution":"Bahir Dar University Institute of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Solomon","middleName":"Workneh","lastName":"Fanta","suffix":""},{"id":85354887,"identity":"36296f96-4f62-4db9-a7d8-78c6e87118ff","order_by":3,"name":"Manuel Curto","email":"","orcid":"","institution":"Universitat fur Bodenkultur Wien Department fur Integrative Biologie und Biodiversitatsforschung","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Manuel","middleName":"","lastName":"Curto","suffix":""},{"id":85354888,"identity":"eb2f03b1-3439-4de1-ad65-023b10aac473","order_by":4,"name":"Herwig Waidbacher","email":"","orcid":"","institution":"Universitat fur Bodenkultur Wien Department Wasser-Atmosphare-Umwelt","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Herwig","middleName":"","lastName":"Waidbacher","suffix":""},{"id":85354889,"identity":"bcca71f2-52b0-4aa6-9462-2fef54cf7a32","order_by":5,"name":"Harald Meimberg","email":"","orcid":"","institution":"Universitat fur Bodenkultur Wien Department fur Integrative Biologie und Biodiversitatsforschung","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Harald","middleName":"","lastName":"Meimberg","suffix":""},{"id":85354890,"identity":"e8df5ae2-4bc8-4c9b-bed2-1192356022ea","order_by":6,"name":"Konrad J Domig","email":"","orcid":"","institution":"BOKU DLWT: Universitat fur Bodenkultur Wien Department fur Lebensmittelwissenschaften und Lebensmitteltechnologie","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Konrad","middleName":"J","lastName":"Domig","suffix":""}],"badges":[],"createdAt":"2021-10-05 11:21:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-956338/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-956338/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12866-022-02496-z","type":"published","date":"2022-04-04T04:49:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":18534186,"identity":"d8422104-c98a-41d6-afd2-59391212bc8c","added_by":"auto","created_at":"2022-02-23 17:37:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":81309,"visible":true,"origin":"","legend":"\u003cp\u003eAlpha diversity indices based on sampling lakes. Independent \u003cem\u003et-\u003c/em\u003e tests was employed to examine the differences between lakes. The significance is shown by small letters a, b, and c. Boxes with different letters show significant differences (\u003cem\u003ep \u0026lt;0.05\u003c/em\u003e).\u0026nbsp;\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-956338/v1/e4f5bf281ec43e74df8970ff.jpg"},{"id":18534187,"identity":"8a3521e0-bff7-4c0f-9ada-38a7947ac6a9","added_by":"auto","created_at":"2022-02-23 17:37:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":30503,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cspan class=\"ql-cursor\"\u003e\u003c/span\u003ePrincipal coordinate analysis based on unweighted UniFrac distance (a) and weighted UniFrac distance(b) of the microbial community recovered from the gut of Nile tilapia collected from four lakes in Ethiopia located at different altitudes. The explained variances are indicated in brackets.\u003c/p\u003e","description":"","filename":"Fig.2revised.jpg","url":"https://assets-eu.researchsquare.com/files/rs-956338/v1/a481112fde8324d4d5521de8.jpg"},{"id":18534222,"identity":"13412e5d-0543-46fa-880e-6dbd65cc8c8a","added_by":"auto","created_at":"2022-02-23 17:40:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":139210,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap showing phyla distribution from the sampling lakes. Clustering was performed based on distance measures using Euclidean and clustering algorithms using Ward at the phylum level.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-956338/v1/08f749a0bb341b063ba82271.png"},{"id":18534191,"identity":"7243ab5a-c38b-470c-bd53-1cb210db6e17","added_by":"auto","created_at":"2022-02-23 17:37:48","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":54502,"visible":true,"origin":"","legend":"\u003cp\u003eLEfSe results [32]. This figure indicates the microbial communities that were different in abundance between the four sampling lakes. The\u0026nbsp;length\u0026nbsp;of\u0026nbsp;the\u0026nbsp;bar\u0026nbsp;column\u0026nbsp;shows\u0026nbsp;the\u0026nbsp;linear discriminant analysis (LDA)\u0026nbsp;score.\u0026nbsp;(a) at phylum level, and (b) at genus level.\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-956338/v1/ba9b543ab95e05163c6aa616.jpg"},{"id":18534337,"identity":"ede78d10-e134-44e3-970e-159de019134f","added_by":"auto","created_at":"2022-02-23 17:43:48","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":18416,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram showing the total number of unique\u0026nbsp;and\u0026nbsp;shared ASV numbers in the four sampling lakes. \u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-956338/v1/25e5669521fcd40988939a0f.jpg"},{"id":18534189,"identity":"65a347bb-414f-4555-ad39-9c2e1b78385f","added_by":"auto","created_at":"2022-02-23 17:37:48","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":94632,"visible":true,"origin":"","legend":"\u003cp\u003eA map showing the location of the four sampling lakes.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Fig.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-956338/v1/2779241237cc4342bfee9c38.jpg"},{"id":19916074,"identity":"70472650-84c3-42ef-b746-3027fdf5978f","added_by":"auto","created_at":"2022-04-04 04:49:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":810806,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-956338/v1/1eb562e1-43f6-4dd3-ad9a-24cc5e1555bd.pdf"},{"id":18534338,"identity":"395012c4-853b-4f34-afdf-f969d426db9d","added_by":"auto","created_at":"2022-02-23 17:43:48","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":125152,"visible":true,"origin":"","legend":"","description":"","filename":"AuthorChecklistE10only.pdf","url":"https://assets-eu.researchsquare.com/files/rs-956338/v1/67afd54e89a78df1f89c1751.pdf"},{"id":18534224,"identity":"f92ddb5f-a3f6-4d04-aad3-993dd314c496","added_by":"auto","created_at":"2022-02-23 17:40:48","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":333230,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-956338/v1/242da69fe435c5978a6e988f.docx"}],"financialInterests":"","formattedTitle":"The gut bacterial microbiome of Nile tilapia (Oreochromis niloticus) from lakes across an altitudinal gradient","fulltext":[{"header":"Background","content":"\u003cp\u003eNatural environmental conditions may have a notable effect on the organism as well as on overall biological characteristics. Animals are subject to environmentally possible adaptive selection in diverse habitats [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Among the most extreme conditions, high-altitude environments are intriguing habitats for a large number of animals. Atmospheric pressure declines significantly with increasing altitude, causing hypobaric hypoxia [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These changes can affect the main energy production process by suppressing aerobic metabolism. To adapt to different geographical environments particularly to high altitudes, animals have evolved many fascinating ways of dealing with life in these environments. To overcome harsh environmental conditions, animals inhabiting high-altitude areas evolved morphological, physiological, and genetic adaptations, such as changing body masses, raising metabolic rates, and genetic modification [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For example, the Lake Titicaca frog (\u003cem\u003eTelmatobius culeus\u003c/em\u003e) has behavioural, morphological, and physiological adaptations that allow aquatic animals to survive at high altitudes (3812 m) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Moreover, to deal with low temperatures cold-adapted animals have antifreeze proteins [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Antifreezing proteins, which include specific proteins, glycopeptides, and peptides, are produced by different organisms for cold adaptation. Antifreezing proteins protect cells and body fluids from freezing by reducing the freezing point of water and inhibiting the growth of ice crystals [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn animals, numerous microorganisms inhabit the gut and create an intricate intestinal microbiota. These gut microbes live in a kind of symbiotic association with the host organism. Intestinal microbial communities play a critical role in integrating the main essential functions in the host, for instance, nutrient absorption, hampering the colonization of pathogenic organisms, and maintaining normal mucosal immunity, while hosts provide a living environment for the gut microbiota [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The reason for structural differences in intestinal microbial communities is reported to be the strong selection and coevolution of the host and its environment [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Some studies have reported that the gut microbiota of animals may support host adaptation to different environments [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Ruminants in high altitude environments have a gut microbiome with changes in energy-metabolism-related genes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Due to this adaptation, intestinal digesta from sheep and yaks inhabiting high-altitude environments consist of more methane and volatile fatty acids than their low-altitude relatives [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Gut microbial communities have a robust fermentation ability and are equipped with more genes involved in the production of volatile fatty acids [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. From Tibetan and Han populations living at different altitudes, more energy-efficient microbial communities were obtained in samples from those living at higher altitudes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe functions of gut microbial communities depend on their structure, which is influenced by several host-associated elements, such as genetic makeup, season, stress, and geographical location [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The availability of food resources in different geographical environments is variable and hence significantly affects the diversity and composition of the host gut microbiota [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Due to exposure to different geographic environments, the gut microbiota of chickens has changed [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Altitude is the main determinant that can modulate the structure and diversity of intestinal microbial communities of animals [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, the impact of high altitude and low oxygen concentration on the gut microbiota of mice was reported recently, which supports the notion of modulation of the gut microbiota composition and metabolic processes due to environmental change [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. For animals inhabiting aquatic environments at high altitude, low atmospheric pressure, low temperature, and high radiation conditions are some of the factors affecting their physiology [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The composition and diversity of fish intestinal microbial communities may be affected by environmental factors such as temperature and salinity, which affect metabolic activity and hence the health status of the host [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNile tilapia (\u003cem\u003eOreochromis niloticus\u003c/em\u003e) is a commonly farmed freshwater fish in the world [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Nile tilapia is characterized by its capability to endure a broad range of biotic and abiotic stresses, a rapid growth rate, and an omnivorous mode of feeding [\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. As a consequence of these qualities, Nile tilapia is an ideal freshwater fish model. Variations in the availability of food resources are the most direct factor modulating the composition of intestinal microbial communities [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Nile tilapia feeds on phytoplankton, macrophytes, insects, detritus, and zooplankton [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] depending on its life stages. However, the availability of these food resources differs substantially in several geographical areas [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The studies done thus far have emphasized mainly compositional variations and associated adaptive mechanisms of gut microbiota of terrestrial animals inhabiting distinctive geographical locations [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, there is a shortage of studies on the adaptive mechanisms of aquatic animals in water environments. Nile tilapia is a good model for assessing the association of gut microbiota composition and geographical variation due to its adaptation to a multitude of aquatic environments. Investigations on the gut microbial community profile of Nile tilapia inhabiting lakes with distinct altitude environments are greatly lacking. In this study, we aspired to reveal the variations in the gut microbiota profile of Nile tilapia from lakes at distinctive altitudes to provide a deeper understanding of the gut microbiota composition and diversity of the populations of this fish inhabiting differing altitudes. We hypothesized that environmental factor such as altitude would be strongly linked with modulation of the gut microbiota of Nile tilapia. This study contributes to our understanding of the gut microbiota of Nile tilapia from lakes with altitude gradients and provides new insights into this species adaptive mechanisms.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSequencing profiles\u003c/h2\u003e \u003cp\u003eThe Illumina MiSeq 16S rDNA sequencing data of 39 samples were examined for gut microbiota. After conducting a series of quality filter processes, a total of 88,203 read counts were recovered, with an average of 2261 reads per sample, ranging from 849 to 3357 (\u003cb\u003eFig. S1\u003c/b\u003e). The rarefaction curves had attained a plateau (\u003cb\u003eFig. S2\u003c/b\u003e), suggesting that accurate microbial groups within each sample were demonstrated. Moreover, sequence integrity was assessed using Good\u0026rsquo;s coverage. The Good\u0026rsquo;s coverage estimators for all samples in our study were greater than 99, indicating that the majority of microbial communities in our samples were fully identified. Good\u0026rsquo;s coverage and alpha diversity indices of the gut microbial communities are summarized in \u003cb\u003eTable S3.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eAlpha diversity of gut bacterial communities among the studied lakes\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAlpha diversity of gut microbiota was examined by Chao1, observed, Accumulated Cyclone Index (ACE), Shannon, Simpson, and Fisher indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cb\u003eFig. S3\u003c/b\u003e). Lake Chamo, with a 1235 m altitude, showed the highest value for all indices assessed. Moreover, Lake Chamo samples showed significantly higher alpha diversity than Lake Tana and Lake Hashengie populations in all indices analysed (two-tailed t-test, \u003cem\u003ep\u0026lt;0.05\u003c/em\u003e). The alpha diversity of Lake Awassa was found to be significantly higher than the alpha diversity of Lake Hashengie and Lake Tana, particularly for the Chao1 and ACE indices. Lake Tana and Lake Hashengie showed similar alpha diversity (two-tailed t-test, \u003cem\u003ep\u0026gt;0.05\u003c/em\u003e). The results of the present study suggested that low-altitude samples typically displayed greater alpha diversity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003eBeta Diversity Of Gut Microbiota\u003c/h2\u003e\n\u003cp\u003eTo analyse the intestinal microbiota composition of Nile tilapia from lakes of different altitudes, the beta diversity index was used. To take abundance alteration and phylogenetic association into consideration unweighted UniFrac distance and weighted UniFrac distance were selected as signals of beta diversity. Principal coordinate analysis (PCoA) indicated that substantial variations were observed across samples from different lakes (p \u0026lt; 0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Based on our results, for unweighted UniFrac distance, Axis 1 accounted for 27.2% of the total difference, while Axis 2 accounted for 19.1%, and for weighted UniFrac distance, Axis 1 accounted for 74.4% of the total disparity, while Axis 2 rated 8.1%. We performed an analysis of similarity (ANOSIM) on both unweighted and weighted UniFrac distance results to substantiate this dissimilarity. The ANOSIM outcome indicated that there were significant differences between lakes of different altitudes (unweighted R: 0.72842; p value \u0026lt; 0.001; weighted R: 0.58415; p value \u0026lt; 0.001). We also carried out a permutational multivariate analysis of variance (PERMANOVA); the PERMANOVA results were concurrent with those of ANOSIM (unweighted R-squared: 0.45044; p value \u0026lt; 0.001; weighted R-squared: 0.58502; p value \u0026lt; 0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch2\u003eBacterial Community Structure\u003c/h2\u003e\n\u003cp\u003eThe bacterial phyla recovered from all samples included Bacteroidota (1.8%), Bdellovibrionota (1.2%), Cyanobacteria (0.6%), Firmicutes (52.8%), Fusobacteriota (35.6%), Proteobacteria (6.9%), Chloroflexi (0.3%), Actinobacteriota (0.7%) and Dependentiae (0.1%). However, the proportions of the same bacteria in samples from different lakes were different at the phylum level (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). There were significant differences in all phyla detected except Bacteroidota and Dependentiae (t-test, two-tailed, \u003cem\u003ep\u003c/em\u003e value\u0026lt;0.01). The relative abundance of Bacteroidota in Lake Chamo (0.0434\u0026plusmn;0.0236) was highest among all groups, followed by Lake Tana (0.0161\u0026plusmn;0.0048), Lake Awassa (0.0093\u0026plusmn;0.0085), and Lake Hashengie (0.0004\u0026plusmn;0.0003). Bdellovibrionota was detected only in Lake Tana and Lake Awassa. The highest Cyanobacteria abundance was recorded in Lake Awassa (0.0211\u0026plusmn;0.0053). The relative abundance of Firmicutes in the samples of Lake Awassa (0.8941\u0026plusmn;0.0259) and Lake Chamo (0.6563\u0026plusmn;0.0670) was much higher than the relative abundance of Firmicutes in the samples from the other two lakes. However, Lake Hashengie and Lake Tana samples showed higher Fusobacteriota abundance than the other lakes. Proteobacteria was highest in Lake Chamo (0.1333\u0026plusmn;0.0389). Dependentiae was detected only in Lake Awassa (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe relative abundances of Actinobacteriota, Chloroflexi, Firmicutes, and Fusobacteriota in the Lake Tana samples were significantly different (t-test, two-tailed, \u003cem\u003ep value\u0026lt;0.05\u003c/em\u003e) from the relative abundances of Actinobacteriota, Chloroflexi, Firmicutes, and Fusobacteriota in the samples of Lake Chamo and Lake Awassa. Moreover, Lake Tana samples showed significant variation with Lake Chamo in Bdellovibrionota and Proteobacteria (t-test, two-tailed, \u003cem\u003ep value\u0026lt;0.05\u003c/em\u003e). The ratio of Firmicutes to Bacteroidota in high-altitude populations (Lake Hashengie) was more than the ratio of Firmicutes to Bacteroidota in low-altitude populations (Lake Chamo) by many fold. The Firmicutes to Bacteroidota ratios were found to be 15.13 and 1122.33 in the Lake Chamo and Lake Hashengie samples, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelative abundance at the phylum level presented as % (out of 100). The results are expressed as the mean \u0026plusmn; standard error of the mean (SEM).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhyla\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eMean\u0026plusmn;SEM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLake Awassa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLake Chamo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLake Hashengie\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLake Tana\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eActinobacteriota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75\u0026plusmn;0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15\u0026plusmn;0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16\u0026plusmn;0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u0026plusmn;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBacteroidota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u0026plusmn;0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.34\u0026plusmn;0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u0026plusmn;0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.61\u0026plusmn;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBdellovibrionota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74\u0026plusmn;0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.53\u0026plusmn;0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eChloroflexi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u0026plusmn;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54\u0026plusmn;0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u0026plusmn;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCyanobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.11\u0026plusmn;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45\u0026plusmn;0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12\u0026plusmn;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDependentiae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u0026plusmn;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFirmicutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.41\u0026plusmn;0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.63\u0026plusmn;0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.22\u0026plusmn;0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.24\u0026plusmn;0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFusobacteriota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55\u0026plusmn;0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.56\u0026plusmn;0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.52\u0026plusmn;0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.92\u0026plusmn;0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eProteobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.86\u0026plusmn;0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.33\u0026plusmn;0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.90\u0026plusmn;0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.58\u0026plusmn;0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eInvestigation of the structure of bacterial communities at the family level resulted in 39 families from all samples (\u003cb\u003eTable S1\u003c/b\u003e). Among these families, 27 varied significantly in all samples (two-tailed t-test \u003cem\u003ep\u0026lt; 0.05\u003c/em\u003e). The dominant families obtained were Clostridiaceae, Erysipelotrichaceae, Fusobacteriaceae, and Peptostreptococcaceae. The relative abundance of Clostridiaceae in Lake Awassa (0.3930\u0026plusmn;0.0774) and Lake Chamo (0.2413\u0026plusmn;0.0381) specimens was greater than the relative abundance of Clostridiaceae in Lake Tana and Lake Hashengie. The relative abundance of Erysipelotrichaceae in Lake Awassa (0.1473\u0026plusmn;0.0293) was highest among all groups. Lake Tana and Lake Hashengie showed higher Fusobacteriaceae abundances than the two lakes, with relative abundances of 0.6692\u0026plusmn;0.0563 and 0.4752\u0026plusmn;0.1075, respectively. The relative abundance of Peptostreptococcaceae in Lake Chamo (0.3901\u0026plusmn;0.0688) was highest among all groups, followed by Lake Awassa (0.3509\u0026plusmn;0.0661), Lake Hashengie (0.2540\u0026plusmn;0.0809), and Lake Tana (0.1447\u0026plusmn;0.0197). Cyanobiaceae, Microcystaceae, and Rickettsiaceae were not detected in the Lake Hashengie samples. Moreover, some families were detected in only one lake, such as Microbacteriaceae and Solirubrobacteraceae in Lake Hashengie; Microtrichaceae, Ruminococcaceae, and UBA12409 in Lake Awassa; and Silvanigrellaceae in Lake Tana. Five families, namely, Acetobacteraceae, Comamonadaceae, Erysipelotrichaceae, Fusobacteriaceae, and Mycobacteriaceae significantly differed (two-tailed t-test \u003cem\u003ep\u0026lt; 0.05\u003c/em\u003e) between Lake Chamo and Lake Hashengie. Fourteen families showed a significant difference between Lake Tana and Lake Awassa, and 16 families showed a significant difference between Lake Tana and Lake Chamo (\u003cb\u003eTable S5\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eIn total, 27 taxa were identified at the genus level from all samples (\u003cb\u003eTable S2\u003c/b\u003e). The majority of these genera (18 genera) varied significantly across sampling lakes at different altitudes (two-tailed t-test \u003cem\u003ep\u0026lt; 0.05\u003c/em\u003e). The dominant genera detected were \u003cem\u003eCetobacterium\u003c/em\u003e, \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e, \u003cem\u003eTuricibacter\u003c/em\u003e, and \u003cem\u003eRomboutsia\u003c/em\u003e from nearly all samples. The relative abundance of \u003cem\u003eCetobacterium\u003c/em\u003e was found to be higher in Lake Tana (0.6628\u0026plusmn;0.0580) and Lake Hashengie (0.4750\u0026plusmn;0.1074) samples than in the other lakes. The relative abundance of \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e was highest in Lake Awassa (0.1983\u0026plusmn;0.0622), followed by Lake Hashengie (0.1388\u0026plusmn;0.0362)0. Moreover, in Lake Hashengie, which is the lake highest in altitude, the lowest relative abundances of \u003cem\u003eHyphomicrobium\u003c/em\u003e, \u003cem\u003eMacellibacteroides\u003c/em\u003e, \u003cem\u003eTuricibacter\u003c/em\u003e, and \u003cem\u003eUncultured\u003c/em\u003e were obtained compared to the other lakes. In contrast, the relative abundances of \u003cem\u003eRomboutsia\u003c/em\u003e and \u003cem\u003ePlesiomonas\u003c/em\u003e were found to be the highest in Lake Hashengie. Some microbial communities were found to be unique for particular lakes only, such as \u003cem\u003eSilvanigrella\u003c/em\u003e in Lake Tana, \u003cem\u003eAurantimicrobium\u003c/em\u003e in Lake Hashengie, \u003cem\u003eCandidatus_Soleaferrea\u003c/em\u003e in Lake Awassa, and \u003cem\u003eNocardioides\u003c/em\u003e in Lake Chamo. \u003cem\u003eCetobacterium\u003c/em\u003e, \u003cem\u003eNocardioides\u003c/em\u003e, \u003cem\u003eTuricibacter\u003c/em\u003e, and \u003cem\u003eUncultured\u003c/em\u003e significantly differed (two-tailed t-test \u003cem\u003ep\u0026lt; 0.05\u003c/em\u003e) between Lake Chamo and Lake Hashengie. The composition of Lake Tana samples was unique since more genera were significantly different (two-tailed t-test \u003cem\u003ep\u0026lt; 0.05\u003c/em\u003e) from Lake Awassa (i.e., \u003cem\u003eCetobacterium\u003c/em\u003e, \u003cem\u003eCyanobium_PCC_6307\u003c/em\u003e, \u003cem\u003eMicrocystis_PCC_7914\u003c/em\u003e, \u003cem\u003eRomboutsia\u003c/em\u003e, \u003cem\u003eSilvanigrella\u003c/em\u003e, \u003cem\u003eTuricibacter\u003c/em\u003e, and \u003cem\u003eV2\u003c/em\u003e) and Lake Chamo (i.e. \u003cem\u003eCetobacterium\u003c/em\u003e, \u003cem\u003eNocardioides\u003c/em\u003e, \u003cem\u003eRomboutsia\u003c/em\u003e, \u003cem\u003eSilvanigrella\u003c/em\u003e, \u003cem\u003eRoseomonas\u003c/em\u003e, and \u003cem\u003eV2\u003c/em\u003e).\u003c/p\u003e\n\u003ch2\u003eBacterial Signatures In Different Samples\u003c/h2\u003e\n\u003cp\u003eLinear discriminant analysis effect size (LEfSe) was performed to detect the microbial signature in every lake. Signature gut microbial communities at the genus level comprised \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e, \u003cem\u003eCyanobium_PCC_6307\u003c/em\u003e, \u003cem\u003eMicrocystis_PCC_7914\u003c/em\u003e, \u003cem\u003eTuricibacter\u003c/em\u003e and \u003cem\u003eV3\u003c/em\u003e in the Lake Awassa sample; \u003cem\u003eMacellibacteroides\u003c/em\u003e, \u003cem\u003eClostridium_sensu_stricto_13\u003c/em\u003e and \u003cem\u003eUncultured\u003c/em\u003e in the Lake Chamo sample; \u003cem\u003eCetobacterium\u003c/em\u003e and \u003cem\u003eSilvanigrella\u003c/em\u003e in the Lake Tana sample; and \u003cem\u003eRomboutsia\u003c/em\u003e, \u003cem\u003eLegionella\u003c/em\u003e, \u003cem\u003eEpulopiscium\u003c/em\u003e, \u003cem\u003eMethylocystis\u003c/em\u003e and \u003cem\u003eAeromonas\u003c/em\u003e in the Lake Hashengie sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). At the phylum level, Firmicutes, Cyanobacteria, Dependentiae, and Patescibacteria in Lake Awassa; Proteobacteria, Chloroflexi, and Bacteroidota in Lake Chamo; Fusobacteriota and Bdellovibrionota in Lake Tana; and Actinobacteriota in Lake Hashengie were found to be important taxa.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eUnique and shared bacteria in the gut of Nile tilapia\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA Venn diagram was made to assess the distribution of amplicon sequence variants (ASVs) among different samples collected from lakes located at different altitudes. The results showed that five ASVs (ASV13, ASV16, ASV2, ASV1, and ASV3) were shared by all lakes. ASV12, ASV110, ASV81, and ASV133 were shared between Lake Hashengie and Lake Chamo. Moreover, six ASVs (ASV47, ASV145, ASV113, ASV44, ASV7, and ASV55) were shared by Lake Awassa and Chamo. However, some ASVs were peculiar to some lakes only, e.g., 12 ASVs in Lake Hashengie, two ASVs (ASV19 and ASV78) in Lake Tana, 12 ASVs in Lake Awassa, and 33 ASVs in Lake Chamo (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch2\u003eCorrelation Between Gut Microbiota And Altitude\u003c/h2\u003e\n\u003cp\u003eTo determine which bacterial communities were associated with altitude, Spearman correlation analysis was employed. We found that the relative abundances of Actinobacteriota (Spearman correlation 0.388, p value \u0026lt;0.05), Chloroflexi (Spearman correlation 0.396, p value \u0026lt;0.05), Cyanobacteria (Spearman correlation 0.503, p value \u0026lt;0.01) and Firmicutes (Spearman correlation 0.464, p value \u0026lt;0.01) were negatively correlated with altitude, while Fusobacteriota (Spearman correlation 0.561, p value \u0026lt;0.01) showed a positive association with altitude (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). At the genus level, altitude was positively correlated with \u003cem\u003eAurantimicrobium\u003c/em\u003e, \u003cem\u003eLegionella\u003c/em\u003e, and \u003cem\u003eCetobacterium\u003c/em\u003e. However, 11 genera, \u003cem\u003eClostridium_sensu_stricto_13\u003c/em\u003e, \u003cem\u003eHyphomicrobium\u003c/em\u003e, \u003cem\u003eMacellibacteroides\u003c/em\u003e, \u003cem\u003eMethyloparacoccus\u003c/em\u003e, \u003cem\u003eMicrocystis_PCC_7914\u003c/em\u003e, \u003cem\u003eV2\u003c/em\u003e, \u003cem\u003eNocardioides\u003c/em\u003e, \u003cem\u003eRoseomonas\u003c/em\u003e, \u003cem\u003eShewanella\u003c/em\u003e, \u003cem\u003euncultured\u003c/em\u003e and \u003cem\u003eTuricibacter\u003c/em\u003e, showed negative correlations with altitude (\u003cb\u003eTable S4\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpearman correlation between the relative abundances of gut microbial communities at the phylum level and altitude. **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaxa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrelation Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSig. (2-tailed)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActinobacteriota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.388*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteroidota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBdellovibrionota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChloroflexi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.396*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyanobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.503**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependentiae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirmicutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.464**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFusobacteriota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.561**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eVarious studies have shown that the composition of the gut microbiota of animals is affected by altitude [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Animals inhabiting high-altitude environments are characterized by unique gut microbiota compared to those living at low altitudes. There is a shortage of investigations on the intestinal microbial communities of fish species from lakes located at different altitudes. Our study investigated the effect of altitude on gut-associated microbial communities of Nile tilapia from four Ethiopian lakes located at different altitudes. This study included Lake Hashengie, a high-altitude lake in Ethiopia, and this is the first investigation of the intestinal microbial communities of Nile tilapia in Lake Hashengie using high-throughput sequencing. In our study, the question of how the gut microbial communities were affected with respect to altitude was addressed.\u003c/p\u003e \u003cp\u003eFrom the studied lakes, Nile tilapia are not native to Lake Hashengie, the lake located at an altitude of 2440 m. The Nile tilapia in Lake Hashengie are believed to have been introduced and adapted to the high-altitude environment [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. At high altitudes aquatic organisms face physiological challenges as a response to lower levels of nutrients, buffering capacity, atmospheric pressure, and temperatures [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Moreover, high-altitude lakes are characterized by strong ultraviolet radiation and low primary productivity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The low air pressure at high altitudes could affect the amount of dissolved oxygen in lakes and therefore the microbiome community. The decrease in temperature with altitude can have similar effects. A previous study suggested that water temperature may affect the intestinal microbial communities of fishes [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The composition and diversity of gut microbiota are reported to take part in a crucial role in host environmental adaptation [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In the present investigation, we showed that Nile tilapia inhabiting lakes with dissimilar altitudes have comparatively different gut microbiota and corroborated that altitudinal differences were a determinant that formed the composition of gut microbial communities in Nile tilapia on Ethiopian lakes. Our findings were consistent with prior studies that showed the effect of altitude on gut microbiota in different animals [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In high-altitude mice, hypoxia was found to be one of the key factors that caused modification of the gut microbiota [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As reported in high-altitude mice, hypoxia might be the factor for variation of the gut microbiota in the Nile tilapia along the altitude gradient.\u003c/p\u003e \u003cp\u003eLake Chamo (lowest altitude) samples demonstrated the topmost values on the global alpha-diversity assessments, which were calculated on the rarefied ASVs. The occurrence of a more diversified gut microbiota is believed to be evidence of a robust microbiota [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. A great alpha diversity attests to a vast number of microbial communities, which may help the host assimilate different diets. Thus, Lake Chamo Nile tilapia samples with enormous diversity may have a greater potential to use a variety of food sources and support fulfillment of their dietary demand in this particular habitat. Beta diversity assessment showed significant differences between gut-related microbial communities of Nile tilapia. Both the unweighted UniFrac and weighted UniFrac approaches revealed clear significant clustering by the source of the samples in the PCoA plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), suggesting that altitude may modulate the abundance of different microbial communities in the gut.\u003c/p\u003e \u003cp\u003eIn our study, Firmicutes was the most abundant phylum in the Lake Awassa and Lake Chamo samples, whereas Fusobacteriota was the dominant phylum in the samples of Lake Hashengie and Lake Tana. Previous studies on the intestinal microbial communities of Nile tilapia from Ethiopian lakes showed dominance by the phylum Firmicutes followed by Fusobacteriota [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Fusobacteriota was also reported as a dominant phylum from the intestine of Nile tilapia of Lake Nasser in Egypt [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and captive Nile tilapia [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Clostridiaceae and Peptostreptococcaceae were the predominant families from samples of Lake Awassa and Chamo, while Fusobacteriaceae was the most dominant in Lake Hashengie and Lake Tana. Ideally, we should have more low- and high-altitude lakes to ensure that the patterns we find are indeed a result of altitude change and not other factors. However, Lake Tana and Lake Hashengie (the lakes located at higher altitudes) show some commonalities that may be evidence that altitude plays a role. In an earlier study, Clostridiaceae played essential roles in carbohydrate degradation in the gut [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Peptostreptococcaceae is reported to be involved in the fermentative type of metabolism of proteinаceous substrates and carbohydrates [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Importantly, the relative abundances of Fusobacteriaceae, Beijerinckiaceae, Lachnospiraceae, and Vibrionaceae clearly showed higher values at Lake Hashengie than at Lake Chamo, demonstrating that these microorganisms may help the host adapt well to high-altitude environments. To the best of our knowledge, phyla such as Dependentiae, Patescibacteria, and Bdellovibrionota were identified for the first time from the gut of Nile tilapia. These results imply that the intestine of Nile tilapia might harbour more diversified microbial communities in addition to the microbial communities reported thus far. Patescibacteria and Dependentiae dominated the pupfish gut [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Patescibacteria is associated with biodegradable plastics such as polylactic acid [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], and has also been reported as a potential microbial bioindicator of phosphorus mining [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The phylum Bdellovibrionota has many predatory species, that employ a range of strategies to attack their bacterial prey [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe relative abundance of Firmicutes varied significantly among the studied Nile tilapia samples. Firmicutes can produce several enzymes for the degradation of dietary nutrients, thus assisting their hosts in the digestion and absorption of nutrients [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Since the gut microbiota of Nile tilapia comprised a large proportion of Firmicutes, we can deduce that the host might be effective in obtaining energy from dietary nutrients. In addition, we also found that the high-altitude samples (Lake Hashengie) had an increased Firmicutes:Bacteroidetes ratio compared with the Firmicutes:Bacteroidetes ratio of the low-altitude samples (Lake Chamo). The higher ratio of Firmicutes to Bacteroidetes in the gut microbiota is reported to be associated with the efficient absorption of food energy [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Moreover, the increase in the ratio of Firmicutes to Bacteroidetes was associated with better herbage energy utilization ability and increased resistance to cold stress in the gut of mammals inhabiting high altitudes [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In our study, the increase in the ratio of Firmicutes to Bacteroidetes in Lake Hashengie samples indicates that high-altitude Nile tilapia may have efficiency in harvesting energy and may also help them adapt to the environment. In terrestrial animals, consumption of meat and dairy products is associated with a higher Firmicutes:Bacteroidetes ratio and, in contrast, low Firmicutes: Bacteroidetes ratios related to consumption of fruits and vegetables [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Lake Hashengie harbours a higher diversity of zooplankton [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Moreover, zooplankton were found to be the dominant food source for Nile tilapia in Lake Hashengie [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, the high Firmicutes:Bacteroidetes ratio in Lake Hashengie might help Nile tilapia consume zooplankton. A higher Firmicutes:Bacteroidetes ratio was also associated with the gut of obese animals and humans compared with normal-weight individuals [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Fish from Lake Hashengie were extremely fatty, most likely due to the dominance of zooplankton in their food [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] and the high Firmicutes:Bacteroidetes ratio in Lake Hashengie confirmed that the association with obesity also works for Nile tilapia.\u003c/p\u003e \u003cp\u003eFusobacteriota was found to be positively correlated with altitude, which was principally ascribed to the substantial increasing abundance of the genus \u003cem\u003eCetobacterium.\u003c/em\u003e In contrast, a negative correlation between Actinobacteriota, Chloroflexi, Cyanobacteria, and Firmicutes with altitude was observed. Lower microbial taxon richness in high-altitude lakes compared to reference lakes located at lower elevations has been reported previously [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In contrast to our study, high-altitude lakes were reported to have a higher abundance of Cyanobacteria [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt the genus level, \u003cem\u003eCetobacterium\u003c/em\u003e, \u003cem\u003eTuricibacter\u003c/em\u003e, and \u003cem\u003eNocardioides\u003c/em\u003e were revealed as the genera that varied significantly between the low elevation (Lake Chamo) and high elevation (Lake Hashengie) samples. The genus \u003cem\u003eCetobacterium\u003c/em\u003e, which was found in the family Fusobacteriaceae, was one of the bacterial genera positively correlated with altitude. Cetobacterium isolated from freshwater fish produces vitamin B-12 [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. \u003cem\u003eTuricibacter\u003c/em\u003e species are involved in the modulation of bacterial colonization in the gut, regulation of host energy metabolism, and host immunity [\u003cspan additionalcitationids=\"CR59\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. \u003cem\u003eNocardioides\u003c/em\u003e were detected in the gut of shrimp [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] and a masculinization pond of Nile tilapia fry [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. \u003cem\u003eNocardioides\u003c/em\u003e were reported to be involved in the degradation of steroids and latex [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. \u003cem\u003eRomboutsia\u003c/em\u003e, \u003cem\u003eLegionella\u003c/em\u003e, \u003cem\u003eEpulopiscium\u003c/em\u003e, \u003cem\u003eMethylocystis\u003c/em\u003e, and \u003cem\u003eAeromonas\u003c/em\u003e are unique and signature gut bacteria in Lake Hashengie, demonstrating that they might perform a crucial function in adaptation to high-altitude habitats.\u003c/p\u003e \u003cp\u003eDespite the variability in the abundance of the gut microbiota across the sampling lakes, a number of shared microbial communities, including Proteobacteria, Firmicutes, and Fusobacteria, were detected. These microbial communities may be critical for the assembly and function of a gut microbiome, which may highlight their importance to host performance [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. The shared microbial communities within the gut of Nile tilapia might be due to the availability of specific microorganisms in the water of the lake that are capable of colonizing the gut and due to selective pressures within the gut habitat.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, the gut microbiota of Nile tilapia from lakes at different altitudes in Ethiopia was successfully characterized by Illumina MiSeq sequencing. Our results showed that the composition and diversity of gut microbial communities between lakes were different. These variations in intestinal microbiota are possibly due to a consequence of various selection pressures occurring in these habitats, mainly altitude. Our results also highlight the dominance of Firmicutes in the Lake Awassa and Lake Chamo samples and Fusobacteriota in the Lake Hashengie and Lake Tana samples. The ratio of Firmicutes to Bacteroidota in high-altitude samples (Lake Hashengie) was many-fold higher than the ratio of Firmicutes to Bacteroidota in low-altitude populations (Lake Chamo), suggesting similarity with terrestrial animals. This study sheds new light on the associations between altitude and gut microbiota in aquatic animals and provides a perspective on fish ecological adaptation. Furthermore, this study provides valuable information to establish sustainable growth for the aquaculture sector in different geographical locations. Nevertheless, further work needs to be done to better understand how altitude is affecting Nile tilapia\u0026rsquo;s gut microbiome. First, functional significance of gut microbiota variations based on the geographical environment should be explored in future studies by adding more populations and larger sampling sizes. Second, previous study showed that seasonality influences the microbiota of gut content of Nile tilapia [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This effect may be different in different altitudes, which needs to be further explored. Third, the differences found might be a consequence of the microbiome composition of the lakes and not necessarily an adaptation of Nile tilapia. Therefore, a comparison between the microbial communities in the water and the fish gut is needed.\u003c/p\u003e"},{"header":"Methods And Materials","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDescription of the sampling sites\u003c/h2\u003e \u003cp\u003eThe samples were collected in July and August 2018 from four lakes with different altitudes, i.e., Lake Awassa, Lake Chamo, Lake Tana, and Lake Hashengie (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Lake Awassa and Lake Chamo are located in the Great Rift Valley whereas Lake Tana and Lake Hashengie are not.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMorphometric characteristics of the four sampling lakes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLakes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAltitude (m.a.s.l) *\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean depth (m)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eLatitudes\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLongitudes\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAwassa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e06\u003csup\u003e0\u003c/sup\u003e58\u0026rsquo; to 07\u003csup\u003e0\u003c/sup\u003e14\u0026rsquo; N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u003csup\u003e0\u003c/sup\u003e22\u0026rsquo; to 38\u003csup\u003e0\u003c/sup\u003e28\u0026rsquo; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChamo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003csup\u003e0\u003c/sup\u003e50\u0026rsquo; to 5\u003csup\u003e0\u003c/sup\u003e83\u0026rsquo; N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37\u003csup\u003e0\u003c/sup\u003e33\u0026rsquo; to 37\u003csup\u003e0\u003c/sup\u003e55\u0026rsquo; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003csup\u003e0\u003c/sup\u003e95\u0026prime; and 12\u003csup\u003e0\u003c/sup\u003e78\u0026prime; N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36\u003csup\u003e0\u003c/sup\u003e89\u0026prime; to 38\u003csup\u003e0\u003c/sup\u003e25\u0026prime; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHashengie\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003csup\u003e0\u003c/sup\u003e31'9`` N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39\u003csup\u003e0\u003c/sup\u003e30'50`` E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Meters above sea level\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003eSpecimen Handling And Processing\u003c/h2\u003e\n\u003cp\u003eNile tilapia (\u003cem\u003eOreochromis niloticus\u003c/em\u003e) samples were purchased at the landing site of the sampling lakes from the fishermen. Sample collection was performed from June 30, 2018 to August 8, 2018. In this study, only adult male fish samples showing no gross or clinical signs of diseases were included. Males were selected due to their larger size. A total of 39 samples (12 from Lake Tana, 10 from Lake Chamo, 9 from Lake Hashengie and 8 from Lake Awassa) were collected and treated as reported previously [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. All samples were sacrificed using high doses of clove oil [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Ethanol (70%) was applied to the body surface of the fish, and instruments were used for dissection. The intestine was dissected, and the luminal contents from the posterior region were collected in sterile screw-cap tubes filled with phosphate buffered saline and glycerol (50:50, v/v) and stored at -20\u0026deg;C until further processing [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e].\u003c/p\u003e\n\u003ch2\u003eMicrobial Dna Extraction, Pcr Amplification, And Sequencing\u003c/h2\u003e\n\u003cp\u003eMicrobial DNA extraction, polymerase chain reaction (PCR) amplification, PCR product purification, and amplicon sequencing processes were performed as previously described [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Briefly, genomic DNA extraction was carried out using the PowerFecal\u0026reg; DNA Isolation Kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions with some modifications. The Illumina MiSeq paired-end sequencing system (Illumina, San Diego, CA, USA) was used to sequence the V3-V4 region of the microbial 16S rRNA gene. Two-step PCR was performed to prepare DNA sequencing libraries using the dual index approach following Shokralla et al [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. The first PCR was conducted with the primers 347F and 803R from Nossa et al. [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] extended with the Illumina adapter sequences. Briefly, 4 \u0026micro;L of genomic DNA was used as a template for the first PCR (95\u0026deg;C for 15 min; 30 cycles of 95\u0026deg;C for 30 s, 55\u0026deg;C for 1 min, and 72\u0026deg;C for 1 min; and a final extension at 72\u0026deg;C for 10 min). For index PCR, TrueSeq adapter sequences were used. The reaction was carried out with the purified PCR product as a template, after an initial denaturation and activation at 95\u0026deg;C for 15 min, using 10 cycles of 95\u0026deg;C for 30 s, 58\u0026deg;C for 60 s, and 72\u0026deg;C for 60 s. Both PCRs were performed in 10 \u0026micro;L reaction volumes amplifying with 5 \u0026micro;L of QIAGEN Multiplex PCR Master Mix (Qiagen, Hilden, Germany). The PCR product of each sample was purified by the magnetic bead extraction method. The 16S rRNA genes were sequenced at the Genomics Service Unit, Ludwig‐Maximilian\u0026rsquo;s‐Universit\u0026auml;t M\u0026uuml;nchen, Germany.\u003c/p\u003e\n\u003ch2\u003eAnalysis Of Sequence Data\u003c/h2\u003e\n\u003cp\u003eRegions with low sequence quality, adapter, and primer sequences were trimmed with Cutadapt v. 0.11.1 software as described previously [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Paired-end reads were merged with PEAR v. 0.9.4 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Chimeric sequences were detected with USEARCH 6.0 based on the RDP pipeline [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. High-quality read sequences were dereplicated using USEARCH and finally denoised to produce amplicon sequence variants (ASVs) using the USEARCH \u003cem\u003e-\u003c/em\u003eunoise3 command [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. After mapping the reads for each sample to the list of ASVs, an \u0026ldquo;OTU\u0026rdquo; table was made using the \u0026lsquo;otutab\u0026rsquo; command in USEARCH. For the classification of the generated ASVs and construction of the phylogenetic tree, the curated SILVA taxonomy was used [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Furthermore, to augment the downstream statistical analysis, low-quality features were removed using minimum counts of 2 and 10% prevalence in samples on MicrobiomeAnalyst [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Moreover, ASVs assigned as chloroplasts and mitochondria were removed before downstream analysis. To solve problems of the variability in sampling depth, data rarefaction to a minimum library size was carried out. Moreover, sequence reads were normalized by total sum scaling approaches.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eAlpha diversity and beta diversity analyses was done using the phyloseq package as implemented in MicrobiomeAnalyst [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The alpha diversity of each sample was examined using the Chao1, observed, ACE, Shannon, Simpson, and Fisher indices. For the beta diversity, the dissimilarity matrix was measured using Unweighted Unifrac distance and weighted Unifrac distance method and visualized by principal coordinate analysis (PCoA). Permutational multivariate analysis of variance (PERMANOVA) and analysis of group similarities (ANOSIM) were employed to assess the statistical significance of the clustering pattern in ordination plots. Linear discriminant analysis effect size (LEfSe) were employed to identify the taxa with substantially different relative abundance across all samples [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Hierarchical clustering is performed with the function hclust in the package stat [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The numbers of shared and unique ASVs are presented in Venn diagrams (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.psb.ugent.be/webtools/Venn/\u003c/span\u003e\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e Only ASVs present in at least 40% of the samples were included for constructing the Venn diagram. Independent \u003cem\u003et-tests\u003c/em\u003e were used to examine the variations in microbial abundance and alpha indices. Spearman's rank correlation test was used to identify which microbial communities were significantly associated with altitude. Statistical analysis was done with SPSS 21 for Windows. All statistical analyses were performed with a significance level of α\u0026thinsp;=\u0026thinsp;0.05 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) unless otherwise stated.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eANOSIM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalysis of group similarities\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eASV\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmplicon sequence variant\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLEfSe\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLinear discriminant analysis effect size\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOTU\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOperational taxonomic unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePCoA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal coordinate analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePCR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolymerase chain reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePERMANOVA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePermutational multivariate analysis of variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch4\u003eEthics approval\u003c/h4\u003e\n\u003cp\u003eThe experimental procedures performed in this study were authorized by ethical clearance\u003c/p\u003e\n\u003cp\u003ecommittee of the College of Science at Bahir Dar University, Ethiopia. The ethical approval\u003c/p\u003e\n\u003cp\u003eReference number is PGRCSVD/G1/2012.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003ch4\u003eAvailability of data\u0026nbsp;\u003c/h4\u003e\n\u003cp\u003eThe raw sequences have been deposited in the\u0026nbsp;National Center for Biotechnology Information\u0026nbsp;(NCBI) Sequence Read Archive (SRA), under the BioProject ID PRJNA763202 accession numbers SRX12193142 to SRX12193150, and some of the samples were found under Bioproject IDs PRJNA705209 and PRJNA637890. \u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eCompeting interests\u0026nbsp;\u003c/h4\u003e\n\u003ch4\u003eThe authors declare that they have no competing interests.\u003c/h4\u003e\n\u003ch4\u003eFunding\u003c/h4\u003e\n\u003cp\u003eThe grant has been awarded to NKB within the framework of EU Erasmus + Key Action 1\u003c/p\u003e\n\u003cp\u003eInternational Credit Mobility Project.\u003c/p\u003e\n\u003cp\u003e(http://www.bildung.erasmusplus.at/hochschulbildung/mobilitaet/internationale_mobilitaet/)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNKB\u003c/strong\u003e, KJD, HM, HW, and GBA conceptualization; \u003cstrong\u003eNKB\u003c/strong\u003e performed laboratory activities, \u003cstrong\u003eNKB\u003c/strong\u003e, GBA and SWF coordination in selecting field sampling sites and sample collection; \u003cstrong\u003eNKB\u003c/strong\u003e, MC, SWF formal analysis; MC and \u003cstrong\u003eNKB\u003c/strong\u003e data curation; KJD, HM, and HW supervision; KJD, HM, SWF resources; \u003cstrong\u003eNKB\u003c/strong\u003e wrote the\u0026nbsp;original draft. All authors revised and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge support from the IPGL team (Institute of Hydrobiology and Aquatic Ecosystem Management, BOKU, Vienna), particularly Gerold Winkler, Nina Haslinger and Marlene Randle. We thank Misganaw Liyew, Melkamu Andargie, Kibret Ayuba, Kidane and Birhanu Gebo for their assistance in sample collection and Workie Worie for his help in making the map for this study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMcMahon RF. Evolutionary and physiological adaptations of aquatic invasive animals: \u003cem\u003er\u003c/em\u003e selection versus resistance. Can J Fish Aquat Sci. 2002;59:1235\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRago A, Kouvaris K, Uller T, Watson R. How adaptive plasticity evolves when selected against. PLOS Comput Biol. 2019;15:e1006260.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang W, Jiao L, Liu R, Zhang Y, Ji Q, Zhang H, et al. The effect of exposure to high altitude and low oxygen on intestinal microbial communities in mice. PLOS ONE. 2018;13:e0203701.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing XZ, Liang CN, Guo X, Wu XY, Wang HB, Johnson KA, et al. Physiological insight into the high-altitude adaptations in domesticated yaks (Bos grunniens) along the Qinghai-Tibetan Plateau altitudinal gradient. Livest Sci. 2014;162:233\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams CM, Szejner-Sigal A, Morgan TJ, Edison AS, Allison DB, Hahn DA. Adaptation to Low Temperature Exposure Increases Metabolic Rates Independently of Growth Rates. Integr Comp Biol. 2016;56:62\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao S, Mou Z, Fan D, Zhou H, Zou M, Zou Y, et al. Genome of Tetraploid Fish Schizothorax o\u0026rsquo;connori Provides Insights into Early Re-diploidization and High-Altitude Adaptation. iScience. 2020;23:101497.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHutchison VH, Haines HB, Engbretson G. Aquatic life at high altitude: Respiratory adaptations in the lake titicaca frog, Telmatobius culeus. Respir Physiol. 1976;27:115\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJacobsen D, Dangles O. Strategies and adaptations to aquatic life at high altitude. Oxford University Press; 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKandaswamy KK, Chou K-C, Martinetz T, M\u0026ouml;ller S, Suganthan PN, Sridharan S, et al. AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties. J Theor Biol. 2011;270:56\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBanerjee G, Ray AK. Bacterial symbiosis in the fish gut and its role in health and metabolism. Symbiosis. 2017;72:1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShapira M. Gut Microbiotas and Host Evolution: Scaling Up Symbiosis. Trends Ecol Evol. 2016;31:539\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi K, Dan Z, Gesang L, Wang H, Zhou Y, Du Y, et al. Comparative Analysis of Gut Microbiota of Native Tibetan and Han Populations Living at Different Altitudes. PLOS ONE. 2016;11:e0155863.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou X, Jiang X, Yang C, Ma B, Lei C, Xu C, et al. Cecal microbiota of Tibetan Chickens from five geographic regions were determined by 16S rRNA sequencing. MicrobiologyOpen. 2016;5:753\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z, Xu D, Wang L, Hao J, Wang J, Zhou X, et al. Convergent Evolution of Rumen Microbiomes in High-Altitude Mammals. Curr Biol. 2016;26:1873\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBereded NK, Abebe GB, Fanta SW, Curto M, Waidbacher H, Meimberg H, et al. The Impact of Sampling Season and Catching Site (Wild and Aquaculture) on Gut Microbiota Composition and Diversity of Nile Tilapia (Oreochromis niloticus). Biology. 2021;10:180.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi W, Liu J, Tan H, Yang C, Ren L, Liu Q, et al. Genetic Effects on the Gut Microbiota Assemblages of Hybrid Fish From Parents With Different Feeding Habits. Front Microbiol. 2018;9:2972.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSenghor B, Sokhna C, Ruimy R, Lagier J-C. Gut microbiota diversity according to dietary habits and geographical provenance. Hum Microbiome J. 2018;7\u0026ndash;8:1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun B, Wang X, Bernstein S, Huffman MA, Xia D-P, Gu Z, et al. Marked variation between winter and spring gut microbiota in free-ranging Tibetan Macaques (Macaca thibetana). Sci Rep. 2016;6:26035.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao J, Yao Y, Li D, Xu H, Wu J, Wen A, et al. Characterization of the Gut Microbiota in Six Geographical Populations of Chinese Rhesus Macaques (Macaca mulatta), Implying an Adaptation to High-Altitude Environment. Microb Ecol. 2018;76:565\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCatalan J. High mountain lakes: extreme habitats and witnesses of environmental changes. Limnetica. 2006;25:551\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhanbari M, Kneifel W, Domig KJ. A new view of the fish gut microbiome: Advances from next-generation sequencing. Aquaculture. 2015;448:464\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrabu E, Rajagopalsamy CBT, Ahilan B, Jeevagan IJMA, Renuhadevi M. Tilapia \u0026ndash; An Excellent Candidate Species for World Aquaculture: A Review. Annu Res Rev Biol. 2019;:1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzaza MS, Dhra\u0026iuml;ef MN, Kra\u0026iuml;em MM. Effects of water temperature on growth and sex ratio of juvenile Nile tilapia Oreochromis niloticus (Linnaeus) reared in geothermal waters in southern Tunisia. J Therm Biol. 2008;33:98\u0026ndash;105.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNjiru M, Ojuok JE, Okeyo-Owuor JB, Muchiri M, Ntiba MJ, Cowx IG. Some biological aspects and life history strategies of Nile tilapia Oreochromis niloticus (L.) in Lake Victoria, Kenya: Biology of Nile tilapia in Lake Victoria. Afr J Ecol. 2006;44:30\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRebou\u0026ccedil;as VT, Lima FR dos, Cavalcante S. DDH, S\u0026aacute; MV do C e. \u003cb\u003eTolerance of Nile tilapia juveniles to highly acidic rearing water\u003c/b\u003e. Acta Sci Anim Sci. 2015;37:227.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTesfahun A. Food and feeding habits of Nile tilapia Oreochromis niloticus (L.) in Ethiopian water bodies: A review. 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeame T, Natarajan P. Analysis of Diet and Biochemical Composition of Nile Tilapia (O. niloticus) from Tekeze Reservoir and Lake Hashenge, Ethiopia. J Fish Livest Prod. 2016;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreen J. Zooplankton associations in some Ethiopian crater lakes. Freshw Biol. 1986;16:495\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVijverberg J, Dejen E, Getahun A, Nagelkerke LAJ. Zooplankton, fish communities and the role of planktivory in nine Ethiopian lakes. Hydrobiologia. 2014;722:45\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan Q, Wanapat M, Yan T, Hou F. Altitude influences microbial diversity and herbage fermentation in the rumen of yaks. BMC Microbiol. 2020;20:370.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Y, Yao Y, Dong M, Xia T, Li D, Xie M, et al. Characterisation of the gut microbial community of rhesus macaques in high-altitude environments. BMC Microbiol. 2020;20:68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChong J, Liu P, Zhou G, Xia J. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat Protoc. 2020;15:799\u0026ndash;821.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa Y, Ma S, Chang L, Wang H, Ga Q, Ma L, et al. Gut microbiota adaptation to high altitude in indigenous animals. Biochem Biophys Res Commun. 2019;516:120\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsegay T. Characteristics and Status of the Highland Lake Hashenge Fishery, Tigray, Northern Ethiopia. Int J Aquac Fish Sci. 2017;:071\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXing P, Hahn MW, Wu QL. Low Taxon Richness of Bacterioplankton in High-Altitude Lakes of the Eastern Tibetan Plateau, with a Predominance of Bacteroidetes and Synechococcus spp. Appl Environ Microbiol. 2009;75:7017\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeuman C, Hatje E, Zarkasi KZ, Smullen R, Bowman JP, Katouli M. The effect of diet and environmental temperature on the faecal microbiota of farmed Tasmanian Atlantic Salmon (\u003cem\u003eSalmo salar\u003c/em\u003e L.). Aquac Res. 2016;47:660\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiatsis C, Sipkema D, Smidt H, Heilig H, Benvenuti G, Verreth J, et al. The impact of rearing environment on the development of gut microbiota in tilapia larvae. Sci Rep. 2015;5:18206.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetersen C, Round JL. Defining dysbiosis and its influence on host immunity and disease. Cell Microbiol. 2014;16:1024\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBereded NK, Curto M, Domig KJ, Abebe GB, Fanta SW, Waidbacher H, et al. Metabarcoding Analyses of Gut Microbiota of Nile Tilapia (Oreochromis niloticus) from Lake Awassa and Lake Chamo. Ethiopia Microorganisms. 2020;8:1040.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElsaied HE, Soliman T, Abu-Taleb HT, Goto H, Jenke-Kodam H. Phylogenetic characterization of eukaryotic and prokaryotic gut flora of Nile tilapia, Oreochromis niloticus, along niches of Lake Nasser, Egypt, based on rRNA gene high-throughput sequences. Ecol Genet Genomics. 2019;11:100037.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRay C, Bujan N, Tarnecki A, Davis DA, Cr CB. A. Analysis of the Gut Microbiome of Nile Tilapia Oreochromis Niloticus L. Fed Diets Supplemented with Previda\u0026reg; and Saponin. J Fish. 2017;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eW\u0026uuml;st PK, Horn MA, Drake HL. Clostridiaceae and Enterobacteriaceae as active fermenters in earthworm gut content. ISME J. 2011;5:92\u0026ndash;106.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlobodkin A. The Family Peptostreptococcaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F, editors. The Prokaryotes. Berlin: Springer Berlin Heidelberg; 2014. pp.\u0026nbsp;291\u0026ndash;302.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhute SS, Escobedo B, Haider M, Mekonen Y, Ferrer D, Hillyard SD, et al. The gut microbiome and its potential role in paradoxical anaerobism in pupfishes of the Mojave Desert. Anim Microbiome. 2020;2:20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR\u0026uuml;thi J, B\u0026ouml;lsterli D, Pardi-Comensoli L, Brunner I, Frey B. The \u0026ldquo;Plastisphere\u0026rdquo; of Biodegradable Plastics Is Characterized by Specific Microbial Taxa of Alpine and Arctic Soils. Front Environ Sci. 2020;8:562263.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMason LM, Eagar A, Patel P, Blackwood CB, DeForest JL. Potential microbial bioindicators of phosphorus mining in a temperate deciduous forest. J Appl Microbiol. 2021;130:109\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaite DW, Chuvochina M, Pelikan C, Parks DH, Yilmaz P, Wagner M, et al. Proposal to reclassify the proteobacterial classes Deltaproteobacteria and Oligoflexia, and the phylum Thermodesulfobacteria into four phyla reflecting major functional capabilities. Int J Syst Evol Microbiol. 2020;70:5972\u0026ndash;6016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColston TJ, Jackson CR. Microbiome evolution along divergent branches of the vertebrate tree of life: what is known and unknown. Mol Ecol. 2016;25:3776\u0026ndash;800.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurphy EF, Cotter PD, Healy S, Marques TM, O\u0026rsquo;Sullivan O, Fouhy F, et al. Composition and energy harvesting capacity of the gut microbiota: relationship to diet, obesity and time in mouse models. Gut. 2010;59:1635\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpringer A, Fichtel C, Al-Ghalith GA, Koch F, Amato KR, Clayton JB, et al. Patterns of seasonality and group membership characterize the gut microbiota in a longitudinal study of wild Verreaux\u0026rsquo;s sifakas (\u003cem\u003ePropithecus verreauxi\u003c/em\u003e). Ecol Evol. 2017;7:5732\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang W, Li N, Tang X, Liu N, Zhao W. Changes in intestinal microbiota across an altitudinal gradient in the lizard \u003cem\u003ePhrynocephalus vlangalii\u003c/em\u003e. Ecol Evol. 2018;8:4695\u0026ndash;703.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTadesse Z. Diet composition impacts the fatty acid contents of Nile tilapia, \u003cem\u003eOreochromis niloticus\u003c/em\u003e L., in Ethiopian highland lakes. SIL Proc 1922-2010. 2010;30:1363\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLey RE, Backhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI. Obesity alters gut microbial ecology. Proc Natl Acad Sci. 2005;102:11070\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLey RE, Turnbaugh PJ, Klein S, Gordon JI. Human gut microbes associated with obesity. Nature. 2006;444:1022\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh Y, Khattar JIS, Singh DP, Rahi P, Gulati A. Limnology and cyanobacterial diversity of high altitude lakes of Lahaul-Spiti in Himachal Pradesh, India. J Biosci. 2014;39:643\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVincent WF, Quesada A. Cyanobacteria in High Latitude Lakes, Rivers and Seas. In: Whitton BA, editor. Ecology of Cyanobacteria II. Dordrecht: Springer Netherlands; 2012. pp.\u0026nbsp;371\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsuchiya C, Sakata T, Sugita H. Novel ecological niche of Cetobacterium somerae, an anaerobic bacterium in the intestinal tracts of freshwater fish. Lett Appl Microbiol. 2007;0:071018031740002-???.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDimitriu PA, Boyce G, Samarakoon A, Hartmann M, Johnson P, Mohn WW. Temporal stability of the mouse gut microbiota in relation to innate and adaptive immunity: Mouse gut microbiota dynamics. Environ Microbiol Rep. 2013;5:200\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFung TC, Vuong HE, Luna CDG, Pronovost GN, Aleksandrova AA, Riley NG, et al. Intestinal serotonin and fluoxetine exposure modulate bacterial colonization in the gut. Nat Microbiol. 2019;4:2064\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao H, Jiang Q, Ji H, Ning J, Li C, Zheng H. Type 1 diabetes induces cognitive dysfunction in rats associated with alterations of the gut microbiome and metabolomes in serum and hippocampus. Biochim Biophys Acta BBA - Mol Basis Dis. 2019;1865:165541.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng Y, Xu X, Yin X, Lu H, Chen G, Yu J, et al. Effect of stock density on the microbial community in biofloc water and Pacific white shrimp (Litopenaeus vannamei) gut microbiota. Appl Microbiol Biotechnol. 2019;103:4241\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHomklin S, Ong SK, Limpiyakorn T. Degradation of 17α-methyltestosterone by Rhodococcus sp. and Nocardioides sp. isolated from a masculinizing pond of Nile tilapia fry. J Hazard Mater. 2012;221\u0026ndash;222:35\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVilanova C, Baixeras J, Latorre A, Porcar M. The Generalist Inside the Specialist: Gut Bacterial Communities of Two Insect Species Feeding on Toxic Plants Are Dominated by Enterococcus sp. Front Microbiol. 2016;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurnbaugh PJ, Gordon JI. The core gut microbiome, energy balance and obesity: The core gut microbiome, energy balance and obesity. J Physiol. 2009;587:4153\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeriggi Pisani G, Inoue L, Moraes G. Stress responses to handling in Nile tilapia (Oreochromis niloticus Linnaeus): Assessment of eugenol as an alternative anesthetic. Acta Sci - Biol Sci. 2006;28:269\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhanbari M, Shahraki H, Kneifel W, Domig KJ. A first insight into the intestinal microbiota of snow trout (Schizothorax zarudnyi). Symbiosis. 2017;72:183\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShokralla S, Hellberg RS, Handy SM, King I, Hajibabaei M. A DNA Mini-Barcoding System for Authentication of Processed Fish Products. Sci Rep. 2015;5:15894.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNossa CW. Design of 16S rRNA gene primers for 454 pyrosequencing of the human foregut microbiome. World J Gastroenterol. 2010;16:4135.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetjournal. 2011;17:10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Kobert K, Flouri T, Stamatakis A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics. 2014;30:614\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27:2194\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdgar RC. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. preprint. Bioinformatics; 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSegata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60.\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":"altitude, diversity, fish, gut microbiota, lake, 16S rDNA","lastPublishedDoi":"10.21203/rs.3.rs-956338/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-956338/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eMicroorganisms inhabiting the gut play a significant role in supporting fundamental physiological processes of the host, which contributes to their survival in varied environments. Several studies have shown that altitude affects the composition and diversity of intestinal microbial communities in terrestrial animals. However, little is known about the impact of altitude on the gut microbiota of aquatic animals. The current study examined the variations in the gut microbiota of Nile tilapia (\u003cem\u003eOreochromis niloticus\u003c/em\u003e) from four lakes along an altitudinal gradient in Ethiopia by using 16S rDNA Illumina MiSeq high-throughput sequencing. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe results indicated that low-altitude samples typically displayed greater alpha diversity. The results of principal coordinate analysis (PCoA) showed significant differences across samples from different lakes. Firmicutes\u003cem\u003e \u003c/em\u003ewas the most abundant phylum in the Lake Awassa and Lake Chamo samples whereas Fusobacteriota\u003cem\u003e \u003c/em\u003ewas the dominant phylum in samples from Lake Hashengie and Lake Tana. The ratio of Firmicutes to Bacteroidota in the high-altitude sample (Lake Hashengie, altitude 2440 m) was much higher than the ratio of Firmicutes to Bacteroidota in the low altitude population (Lake Chamo, altitude 1235 m). We found that the relative abundances of Actinobacteriota, Chloroflexi, Cyanobacteria, and Firmicutes were negatively correlated with altitude, while \u003cem\u003eFusobacteriota\u003c/em\u003e showed a positive association with altitude. Despite variability in the abundance of the gut microbiota across the lakes, some shared microbial communities were detected. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eIn summary, this study showed the indirect influence of altitude on gut microbiota. Altitude has the potential to modulate the gut microbiota composition and diversity of Nile tilapia. Future work will be needed to elucidate the functional significance of gut microbiota variations based on the geographical environment. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSignificance and Impact of the Study\u003c/strong\u003e: Our study determined the composition and diversity of the gut microbiota in Nile tilapia collected from lakes across an altitude gradient. Our findings greatly extend the baseline knowledge of fish gut microbiota in Ethiopian lakes that plays an important role in this species sustainable aquaculture activities and conservation.\u003c/p\u003e","manuscriptTitle":"The gut bacterial microbiome of Nile tilapia (Oreochromis niloticus) from lakes across an altitudinal gradient","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-02-23 17:37:46","doi":"10.21203/rs.3.rs-956338/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"submitted","content":"BMC Microbiology","date":"2022-01-31T07:48:19+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":"424d69b8-0407-463f-8d20-1a02b0d02817","owner":[],"postedDate":"February 23rd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2022-04-04T04:49:23+00:00","versionOfRecord":{"articleIdentity":"rs-956338","link":"https://doi.org/10.1186/s12866-022-02496-z","journal":{"identity":"bmc-microbiology","isVorOnly":false,"title":"BMC Microbiology"},"publishedOn":"2022-04-04 04:49:22","publishedOnDateReadable":"April 4th, 2022"},"versionCreatedAt":"2022-02-23 17:37:46","video":"","vorDoi":"10.1186/s12866-022-02496-z","vorDoiUrl":"https://doi.org/10.1186/s12866-022-02496-z","workflowStages":[]},"version":"v1","identity":"rs-956338","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-956338","identity":"rs-956338","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}