Next Article in Journal
New Report of Zu cristatus (Bonelli, 1819) in the Ionian Sea with an In-Depth Morphometrical Comparison with All Mediterranean Records
Next Article in Special Issue
Effects of Dietary Tannic Acid on Growth, Digestion, Immunity and Resistance to Ammonia Stress, and Intestinal Microbial Community in Pacific White Shrimp (Litopenaeus vannamei)
Previous Article in Journal
Tenacibaculum ovolyticum 16S rDNA Quantitative-PCR Assay Development and Field Testing
Previous Article in Special Issue
First Evaluation of Associated Gut Microbiota in Wild Thick-Lipped Grey Mullets (Chelon labrosus, Risso 1827)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Fishes
Volume 7
Issue 6
10.3390/fishes7060304
fishes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Differential Study of Microbiota in the Gill and Intestine of Silver Carp (Hypophthalmichthys molitrix) from the Algae-Dominated and Hydrophyte-Dominated Areas of Taihu Lake, China

by
Dan Zhou
1,2,†,
Ting Zhang
1,†,
Long Ren
1,
Di-An Fang
1 and
Dong-Po Xu
1,2,*
1
Key Laboratory of Freshwater Fisheries and Germplasm Resources Utilization, Ministry of Agriculture and Rural aAffairs, Freshwater Fisheries Research Center, Chinese Academy of Fishery Sciences, Wuxi 214081, China
2
National Demonstration Center for Experimental Fisheries Science Education, Shanghai Ocean University, Shanghai 201306, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Fishes 2022, 7(6), 304; https://doi.org/10.3390/fishes7060304
Submission received: 25 August 2022 / Revised: 27 September 2022 / Accepted: 30 September 2022 / Published: 25 October 2022
(This article belongs to the Special Issue Gut Microbiota in Fish and Shellfish)
Browse Figures
Review Reports Versions Notes

Abstract

:
Both fish gills and guts can support lots of microbiota that play important roles in the health and growth of hosts. Although the microbiota of silver carp has been widely studied, the data on microbial variation according to fish tissues and local habitats are lacking. In this study, the microbes in the guts and gills of silver carp (Hypophthalmichthys molitrix) from the hydrophyte-dominated region (zone H) and the algae-dominated region (zone A) of Taihu Lake in autumn were analyzed. Proteobacteria, Cyanobacteria, and Firmicutes were the dominant bacteria in silver carp. The microbial diversity was higher in the gills than that in the intestines, and higher in fish from zone H than that from zone A. Beta diversity analysis revealed significant differences in microbial community structures between gill and guts, and between fish from the two habitats. Gills had a higher abundance of phyla Actinobacteria, Bacteroidetes, and Deinococcus-Thermus, and a lower abundance of verrucomicrobia than the intestine. Both tissues possessed indicator taxa, while many indicator taxa in the gill were conditional pathogens. Compared to fish from zone H, fish from zone A had more abundant Cyanobacteria, and less abundant Proteobacteria and Bacteroidetes. PICRUSt2 analysis revealed that fish microbial functions were mainly associated with metabolism, replication, repair, folding, sorting, and degradation. These results showed that the microbial community of silver carp from Taihu Lake varied according to tissues and habitats.

1. Introduction

Many fish tissues can harbor high concentrations of microbes, forming a complex community of fish microbiota that exhibits important roles in the host’s digestion, metabolism, growth, and health [1,2]. The fish intestine, an important digestive organ, is a typical site for microbial colonization. The intestinal microbes can promote digestion and nutrient absorption and enhance the immune functions of host fish [2,3]. In the past decades, there have been hundreds of studies into fish microbiota. Some described the signatures of gut microbes of various fish species [4,5,6], and some identified the potential factors affecting fish gut microbes, such as the species [7], diet [8], developmental stage [9], and gender of the host [10], as well as physicochemical parameters and bacterioplankton in local habitats [11,12].
The gill is the main respiratory organ of fish and other aquatic animals and is in constant contact with the aquatic medium. Like fish guts, the gill can also support high concentrations of microorganisms [13]. As a part of the natural physical and chemical barrier to pathogens, fish gills, and their associated microbiota, exert vital roles in maintaining the host’s health [14,15]. Except for several recent studies that have explored the gill microbial signature of some fish species [16,17], there is still a gap in the research on fish gill microbiota.
Silver carp (Hypophthalmichthys molitrix) is one of the economically important fish among the “Four Major Domesticated Fish” in China [18]. This fish species is a filter feeder and has been used to control cyanobacteria blooms in eutrophic water bodies, such as Yangcheng Lake and Taihu Lake [19,20]. So far, some studies have investigated the microbial signatures of its gills and guts [18,21], but the differences in microbiota between its gills and guts, as well as the effects of different habitats on the microbes, have not been fully characterized.
Taihu Lake is located in the Yangtze River Delta (30°55′40″–31°32′58″ N, 119°52′32″–120°36′10″ E). According to previous studies, the water quality and habitats of this lake exhibit significant spatial differences, ranging from the eastern area to the other regions [22]. The eastern area is a hydrophyte-type region with a high hydrophyte coverage and lower eutrophication, while the northern, northwestern, southern, and central regions, are algae-dominated zones and suffer from severe cyanobacteria blooms and eutrophication [23,24]. To this day, many studies have reported the differences in physicochemical parameters, plankton, and bacterioplankton, between these two habitats [25,26]. Nevertheless, little data is available on the differences in fish microbiota between the two habitats.
In this study, the characteristics of the gill and gut of silver carp from algae-type and hydrophyte-type areas of Taihu Lake were detected, aiming to investigate the differences in microbiota between fish intestines and gills, and the effect of local habitats on fish microbes.

2. Materials and Methods

2.1. Ethics Statement

This study was approved by the Care and Use Committee of the Ministry of Freshwater Fisheries Research Center of the Chinese Academy of Fishery Sciences. All experimental animal procedures followed the Guideline for the Care and Use of Laboratory Animals in China.

2.2. Sample Collection

The experimental samples were collected in September 2020 (autumn) in Taihu Lake. Seven silver carp were collected from zone A and zone H, respectively (Figure S1). Zone A (31°26′ 11.13″ N, 120°4′28.34″ E) is located in Zhushan Bay, the northwestern region of Taihu Lake, with a depth of 1.9 m. It is a typical algae-dominated region with high pollutant flux into the lake, high nitrogen, and phosphorus nutrient loads, and frequent cyanobacterial blooms. The sediment type in this zone is mainly silt. Zone H (31°16′51.09″ N, 120°22′63.49″ E) is located in Tandong Bay, the eastern region of Taihu Lake, with a depth of 1.8 m. This zone is a typical hydrophyte-dominated region with highly abundant aquatic plants and less nutrient levels. The sediment type in this region is a hard clay layer with stable horizons [23,24,27]. Three water samples (2 L each sample) were collected from 30 cm below the water surface at each sampling site. The collected fish and water samples were carried back to the laboratory under low-temperature conditions. After measuring their body length and weight, fish were dissected under aseptic conditions. The gill filaments in the middle of the left branchial arch were collected. Fish intestines were isolated and the intestinal contents were extruded into 2 mL lyophilized tubes. One liter of each water sample was used to measure hydrochemical parameters and the remaining liter of water was filtered through an organic microporous membrane (0.22 μm in pore size and 50 mm in diameter; Sangon Biotech, Shanghai, China). The samples of gills, guts, and filter membranes, were rapidly frozen in liquid nitrogen and subsequently kept at −80 °C for further experiments.

2.3. Measurements of Physicochemical Parameters

The temperature, pH, and dissolved oxygen (DO), were measured using a water quality analyzer (YSI 6600, YSI, Yellow Springs, OH, USA). The concentrations of total nitrogen (TN) and total dissolved nitrogen (TDN) were determined by the spectrophotometric method with alkaline potassium persulfate. The concentration of nitrite nitrogen (NO2-N) was detected by the spectrophotometry method with aniline A naphthol, and the concentration of ammonium (NH4+-N), was detected by the spectrophotometric method with Nessler’s reagent. The concentrations of total phosphorus (TP), total dissolved phosphorus (TDP), and orthophosphate (PO43−-P), were determined by the molybdenum blue colorimetry method. Chlorophyll A (Chla) was extracted with acetone and detected by the spectrophotometry method. The chemical oxygen demand (COD) was determined by the dichromate method.

2.4. DNA Extraction, Polymerase Chain Reaction (PCR) Amplification, and Sequencing

All samples were subjected to genomic DNA extraction according to the instructions of the MiniBEST Bacteria Genomic DNA Extraction Kit (Takara Biotechnology, Dalian, China). The DNA integrity and purity were measured by 1% agarose gel electrophoresis. The DNA concentration and purity were detected by a spectrophotometer (Thermo Fisher Scientific, USA). After qualitative and quantitative analysis, genomic DNA templates and a primer set (515F: 5′-GTGCCAGCMGCCGCGGTAA-3′; 806R: 5′-GGACTACHVGGGTWTCTAAT-3′) [28] were used to amplify the V4 region of the 16SrRNA gene according to the following amplification procedures: 94 °C for 5 min; 30 cycles at 94 °C for 30 s, 52 °C for 30 s, and 72 °C for 30 s (30 cycles); and 72 °C for 10 min. The amplification experiments were performed with 3 replicates per sample. PCR fragments were examined with 1% agarose gel electrophoresis to select bands in the normal range (290–310 bp) for further experimentation. The PCR products were then purified using the E.Z.N.A.® Gel Extraction Kit (Omega Bio-Tek, Norcross, GA, USA) according to the manufacturer’s instructions. The pe250 sublibrary sequence was constructed according to the standard process of NEBNext® Ultra™ II DNA Library Prep Kit for Illumina® (New England Biolabs Inc., Ipswich, MA, USA). The library pool sequencing was performed at an Illumina Nova 6000 platform (Guangdong Mackin Biotechnology Co. Guangzhou, China.)

2.5. Bioinformatic Data Analysis

The paired-end raw reads obtained by Illumina Miseq sequencing were filtered to get clean raw reads using the Fastp and Cutadapt software. The clean raw reads were assembled to get raw tags using the Usearch-Fastq_Mergepairs software. The raw tags were then filtered using the Fastp software and clean tags were obtained. The valid sequences were clustered into operational taxonomic units (OTUs) by both the UPARSE and UCLIUST methods according to a sequence similarity of 97%. The OTU category was assigned using the RDP classifier Bayesian algorithm and the GenBank database (Rockville PikeBethesda, MD, USA). A confidence threshold of 0.5 was used for classification. The representative OUTs were annotated by comparing them with multiple databases (SILVA, RDP, and Greengens). The predictive functions were analyzed using the PICRUSt 2.0 software (Cambridge, MA, USA).

2.6. Statistical Analyses

The Chao1, Simpson, and Shannon indexes, were calculated using QIIME 2 and utilized to evaluate microbial alpha diversity [29]. The differences in Chao1, Simpson, and Shannon indexes, between the two groups, were compared using the Wilcox rank sum test [18]. Non-metric multidimensional scaling (NMDS), and an analysis of similarity (ANOSIM) based on the Bray-Curtis distance algorithm, were employed to assess microbial beta diversity. Linear discriminant analysis (LDA) effect size (LefSe) analysis was used to identify indicator taxa with an LDA score >3. The predictive microbial functions were analyzed using the PICRUSt2 software (USA) from the KEGG pathways. The Stamp software was used to compare the differences in abundances (>1%) of microbiota between groups. The data analysis of hydrochemical parameters was performed by the Graphpad Prism 7.0 software (Graphpad, Santiago, Chile). The difference in hydrochemical parameters between the two groups was compared using a t-test. A p-value less than 0.05 indicated statistical significance.

3. Results

3.1. Morphological Characterization of Fish and Physicochemical Parameters of Water

The body length of silver carp from zone A ranged from 175.33 mm to 195.15 mm, and the body weight ranged from 119.50–150.40 g. The range of the body length of fish from zone H was 174.06–198.85 mm, and the range of the body weight was 127.80–156.60 g. There were no significant differences in the body length and body weight between fish from zone A and zone H (Figure S2).
As shown in Figure 1, there were no significant differences in the pH and temperature of water between the two zones. The concentration of DO was significantly higher in water from zone H than that from zone A. However, the contents of TN, dissolved TDN, NH4+-N, NO2--N, TP, TP, PO43--P, Chla, and COD, were significantly higher in water samples from the algae-dominated region than in water samples from zone H. This result is indicative of a higher eutrophication level in zone A.

3.2. High-Throughput Sequencing Data and Microbial Composition in Fish and Water

The high-throughput sequencing data of 14 fish samples and six water samples are shown in Table S1. The ranges of the raw total reads and clean total tags were 47,994–130,363 and 23,207–118,496, respectively. The Q20 ranged from 97.4% to 98.6% and Q30 ranged from 92.0% to 94.7%. The mean GC content was 53.9%. The rarefaction curves of the Chao1 index plateaued in all samples (Figure S3), indicating that the sequencing data of all samples was sufficient.

3.3. Diversity Analysis

The Wilcox rank-sum test showed that there was no significant difference in the microbial richness between gills and intestines, whereas gills had a significantly higher Shannon index and lower Simpson index than intestines (Figure 2A–C). No significant difference in the Chao1 index was found in fish from the two zones. Compared to fish from zone A, fish from zone H showed significantly higher Shannon index and lower Simpson index (Figure 2D,E).
The NMSD analysis revealed that the gill and intestine samples were independently clustered, and somewhat overlapped (Figure 3A). The Anosim analysis further confirmed a clear distinction in the microbiota structure between fish gills and guts (Figure 3C). Figure 3B illustrated that fish samples from zone A and zone H were independently clustered, and slightly overlapped. The Anosim analysis verified a dissimilarity in the microbiota structure of fish from the two sampling sites (Figure 3D). Moreover, the R-value of the Anosim analysis based on different tissues (R-value: 0.515) was lower than that based on different habitats (R-value: 0.634), indicating habitats contributes more to the variations of fish microbiota than body niches (Figure 3C,D).

3.4. Composition Analysis

The Venn diagram showed that fish guts and gills shared 1631 OTUs. Fish gills had more unique OUTs than intestines (Figure S4A). At the phylum level, the top three dominant bacterial phyla in the gills and guts were the same, mainly including Proteobacteria, Cyanobacteria, and Firmicutes (Figure 4A). Even so, significant differences in the abundances of other bacterial taxa between the two body niches of fish were confirmed by Welch’s t-test. As shown in Figure 5A, the abundances of the phyla of Actinobacteria, Bacteroidetes, and Deinococcus-Thermus, were significantly higher in the gills than in the guts, while the abundance of verrucomicrobia followed the opposite tendency. At the genus level, the most abundant genera in the gills and guts were Microcystis_PCC-7914 and Cyanobium_PCC-6307 (Figure 4B). In comparison to the gills, guts had higher abundances of LD29, Legionella, and Phreatobacter, while they had a lower abundance of Pseudomonas (Figure 5B).
The predominated bacterial phyla in water were Actinobacteria, Proteobacteria, Cyanobacteria, and Bacteroidetes, at both sampling sites. In zone A, the predominated bacterial genera were CL500-29_marine_group, hgcl_clade, and Microcystis_PCC-7914. In zone H, the predominated bacterial genera were CL500-29_marine_group, Acinetobacter, and hgcl_clade (Figure 4A,B). A total of 2,065 shared OTUs were found in fish from the two zones. Fish from zone A had more OUTs than that from zone H (Figure 4B). In general, the microbiota in fish from zone A and zone H were dominated by the phyla of Proteobacteria, Cyanobacteria, and Firmicutes (Figure 4A). The dominant bacterial genera in fish from zone A were Microcystis_PCC-7914, Phreatobacter and Cetobacterium, while the top three abundant genera in fish from zone H were Cyanobium_PCC-6307, Enterobacter, and Pseudomonas (Figure 5B). At the phylum level, Cyanobacteria and Deinococcus-Thermus were more abundant in fish from zone A, whereas, Proteobacteria, Bacteroidetes, and Chlamydiae, were more abundant in fish from zone H (Figure 5C). At the genus level, the abundances of Microcystis_PCC-7914 and Phreatobacter were higher in fish from zone A than that from zone H, while the percentage of Cyanobium_PCC-6307, Enterobacter, and Pseudomonas, and Legionella, were less in fish from zone A than that from zone H (Figure 5D).
LEfSe analysis identified 37 indicator taxa at the genus level between gills and guts. Among these genera, 12 were the indicator taxa in the guts, such as Caldalkalibacillus, Lysobacter, and Legionella. For gills, 25 indicator taxa were identified, including multiple conditional pathogens, such as Flavobacterium, Clostridium_sensu_stricto_1, Arcobacter, Neorickettsia, Bacteroides, and Legionella (Figure 6A). Moreover, the LDA score bar plots displayed 22 indicator taxa in fish at the genus level between the two zones. The indicator taxa in fish from zone A included some anaerobic bacteria belonging to Firmicute, Clostridia, such as Clostridium_sensu_stricto_10, Clostridium_sensu_stricto_14, Clostridium_sensu_stricto_12, and Lachnoclostridium (Figure 6B).

3.5. Function Analysis

Furthermore, the microbial functions were predicted by PICRUSt analysis. As shown in Figure 7, at the KEGG-level-2, the microbial functions were mainly associated with the metabolism of xenobiotics, lipids, amino acids, and carbohydrates. In fish, the microbial functions were mainly related to transcription, folding, sorting and degradation, signal transduction, as well as metabolisms of terpenoids and polyketides, and glycan.

4. Discussion

It is well known that fish microbiota exhibits vital roles in the host’s health and growth [1]. Although some studies have elucidated the microbial characteristics of many fish species and explored the influencing factors of fish microbiota, the available data about fish microbiota is still lacking compared to land vertebrates. In the present study, we identified the microbial distinctions between the guts and gills of a filter-feeding fish species, silver carp. Additionally, we compared the microbial difference of silver carp in algae-type and hydrophyte-type areas of Taihu Lake, elucidating the effects of different habitats on fish microbiota.
Previous research found that Proteobacteria and Firmicutes are the predominant bacteria in many fish species, such as Coilia nasus, Carassius auratus, and Cyprinus carpio [10,21]. Consistent with pats previous research, Proteobacteria and Firmicutes were also the most common bacteria in the gills and guts of silver carp. Additionally, Cyanobacteria, a vital food source for fish [18], was the second most abundant bacteria in the gills and intestines of silver carp. This occurrence could be associated with the fact that silver carp is a filter-feeding fish species, feeding mainly on phytoplankton [30,31].
Many studies reported that fish microbiota displays tissue specificity [16,32]. Compared to fish guts, fish gills contact the surrounding aquatic medium, therefore they are subjected to environmental changes [30,32]. In this study, we found that the gills and guts of silver carp shared 1631 OTUs and had the same dominant bacteria. Even so, the microbes in the gills were able to be distinguishable from those in the guts. First, our analysis of Shannon and Simpson indexes revealed that microbial diversity in the gills was higher than that in the guts. Besides, the microbiota structures varied between gills and guts. These results are similar to a previous study on silver carp and bighead carp [18]. In terms of microbial composition, Actinobacteria, Bacteroidetes, and Deinococcus-Thermus, were more abundant in the gills than in the guts. Most Actinobacteria are saprophytes and able to decompose various kinds of organic substances [33]. Bacteroidetes play an important role in carbohydrate transport and protein metabolism [34]. Likewise, relatively high abundances of these three bacterial phyla were found in water samples. This might mean that these microorganisms derive from the environment but did not enter the gut in large numbers. Moreover, the abundance of Verrucomicrobia, which plays an important role in polysaccharide degradation [35], was higher in guts than in gills, suggesting an important role of Verrucomicrobia in intestinal polysaccharide degradation. At the genus level, Microcystis_PCC-7914 and Cyanobium_PCC-6307 were the dominant bacterial genera in the gills and guts. This may be due to the feeding of silver carp in these two genera. In comparison to the gills, guts had higher abundances of LD29, Legionella, and Phreatobacter, while they had a lower abundance of Pseudomonas. The possible explanation for the difference may be that the gut of fish may have a certain filtering effect on the environment or gill microbiota. Among these four genera, only Pseudomonas is reported as the potentially pathogenic bacteria of fish [36]. Additionally, a previous study revealed that the abundance of Pseudomonas in the skin of flag cichlid was higher than that in the gut, which was similar to this study [7]. LD29, Legionella, and Phreatobacter, are bacterial genera that mainly exist in various aquatic ecosystems and are rarely found in the tissues of fish or other aquatic animals [37,38,39]. The LEfSe analysis identified many indicator taxa between fish guts and gills. In the gills, many indicator genera were conditional pathogens, such as Flavobacterium, Clostridium_sensu_stricto_1, Arcobacter, Neorickettsia, and Bacteroides [40,41]. The abundance of these conditional pathogens in gills indicated that this organ acts as a barrier for pathogens, and can defend guts against possible infections. Additionally, Gleocapsa and Limnothrix were also the indicator genera in the gills. This indicated that although silver carp feed on phytoplankton, they feed on phytoplankton filtered through gills. In conclusion, these data demonstrated that fish microbiota varied between gills and guts, which may be appropriate for the function of each tissue.
Due to their habitat characteristics, the microbiota of fish is affected by their local habitats [42]. Previous works of literature have shown significant differences in fish microbial diversity and structure between different habitats [7,18]. Despite both sampling sites in this study pertaining to Taihu Lake, the physiochemical conditions of the two regions are different. The habitat of the algae-dominated region suffers from severe cyanobacteria blooms and eutrophication, while the habitat of the hydrophyte-type region has a dense coverage of aquatic plants [23,24]. Fish from the hydrophyte-dominated region showed higher microbial diversity than fish from the algae-dominated region. Differences in fish microbial community structure were found between the two sampling sites. It has been previously explained that many environmental factors can impact the hosts’ microbiota, such as pH, temperature, and other nutrient indexes [43,44]. Interestingly, this study found that the concentrations of DO, TN, TDN, NH4+-N, NO2--N, TP, TDP, PO43--P, Chla, and COD, were significantly higher in water samples from zone A compared to zone H. The effects of ammonia and nitrite nitrogen in water on the gut microbiota have been confirmed in many aquatic animals, including fish, shrimp, and shellfish [45,46,47]. A previous study reported the impacts of DO fish microbiota [48]. Thus, we speculate that these environmental characteristics might be one of the main factors driving microbial differences in fish from the two regions. Moreover, according to previous measurements of microcystin in Taihu Lake, microcystin was higher in the algal-type area than in the hydrophyte-type region [49]. There is supporting evidence indicating that microcystin exposure can alter the gut microbiota of fish [39,50]. We hypothesize that microcystin is an additional important factor in the differences observed in fish microbiota in this study.
The dominant bacteria phylum of Cyanobacteria was more abundant in fish from zone A than from zone H, causing the silver carp living in the algae-dominated region to ingest more Cyanobacteria. In contrast, the phyla of Proteobacteria and Bacteroidetes were more abundant in fish from zone H than in fish from zone A. Proteobacteria can reflect the host’s health condition, and thus it can be used as a potential marker of disorders and diseases [51]. The abundance of such bacteria can be decreased by hypoxia stress [48], suggesting that the low abundance of Proteobacteria in fish from zone A might be related to low DO. As previously mentioned, Bacteroidetes play an important role in carbohydrate transport and protein metabolism [34], and microcystin treatment can reduce its abundance in BALB/c mice [52]. This evidence hinted that the low abundance of Bacteroidetes in the fish from zone A might be caused by high concentrations of microcystin. In comparison to silver carp from zone H, silver carp from zone A had more abundance of Cyanobium_PCC-6307 and less abundance of Microcystis_PCC-7914. Similar results were found in the bacterioplankton, implying that silver carp passively ingested phytoplankton from the environment. The abundance of Legionella can be reduced by microcystin exposure in fish guts [39]. Thus, the lower abundance of Legionella in silver carp from zone A was probably the response to higher concentrations of microcystin. Furthermore, in this study, the discriminative genera in fish from zone A included many anaerobic bacteria belonging to class Clostridia, such as Clostridium_sensu_stricto_14 and Lachnoclostridium. According to previous research, the abundance of Clostridia in the guts of Lateolabrax maculatus was higher in the aquatic environment with lower DO [48]. Overall, our results demonstrate the effects of habitats on fish microbiota and reveal that its effects might be regulated by environmental factors.
A previous study found that the dominant bacteria in Taihu Lake were the above four phyla, but the abundance of these microbiotas varied in different habitats [22]. Similarly, in other lakes, such as East Lake, Hulun Lake, Shahu Lake, and Neisha Lake, the main microorganisms in water bodies are Actinobacteria, Proteobacteria, Cyanobacteria, and Bacteroidetes [53,54]. In this study, the predominated bacterial phyla in water were also Actinobacteria, Proteobacteria, Cyanobacteria, and Bacteroidetes, at both sampling sites. Many studies have shown that fish-related microbiota is largely influenced by their environment microbiota [7,10]. In this study, we found that water and fish had certain shared dominant bacteria, such as Proteobacteria, Cyanobacteria, and Actinobacteria, indicating a similarity between related microbiota and water microbiota. It was worth noting that the changes of some specific microbiota in the water environment and fish were consistent. For instance, the abundance of Cyanobacteria and Microcystis_PCC-7914 in water from zone A was higher than from zone H, as well as in fish, both in gills and gut. The abundance of Acinetobacter and Cyanobium_PCC_6307 in water in water from zone H was high than from zone H, and so it was in fish gills and guts. Moreover, water from zone H had a higher abundance of Proteobacteria than from zone H, and a similar result was found in fish gills. Firmicutes showed a higher abundance in both fish guts and water from zone H than those from zone A. These data indicated the impacts of microbiota in water on fish-associated microbiota.

5. Conclusions

In conclusion, this study characterized the microbiota in the gills and guts of silver carp from the algae-type and hydrophyte-type areas of Taihu Lake, in autumn, by 16S sequencing. The predominated bacterial phyla in water were Actinobacteria, Proteobacteria, Cyanobacteria, and Bacteroidetes, at both sampling sites, and the dominant bacteria in fish were Proteobacteria, Cyanobacteria, and Firmicutes, both in gill and guts. Fish gills had higher microbial diversity than guts. Although the composition of the dominant microbiota was similar in the gut and gills, the abundance of some microbiota was different between the two tissues. Fish from the hydrophyte-type region showed higher microbial diversity than from the algae-type area and had more abundance of Cyanobacteria. Compared to the algae-type region, fish from the algae-type areas had more Microcystis_PCC_794 and less Cyanobium_PCC_6307. Beta diversity analysis confirmed the effects of host body niches and local habitats on fish microbial structures, and indicated that the local habitats contributed more to the variation of fish microbiota than host body niches. The effects of habitats on fish microbiota may be related to environmental factors and microbiota composition in water.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/fishes7060304/s1, Figure S1: Schematic diagram of sampling sites; Figure S2: The body length and body weight of 14 fish. Each dot on box plot represented one fish; Figure S3: The rarefaction curve of the Chao1 index in all samples; Figure S4: The Venn diagrams show the numbers of shared and unique OTUs; Table S1: The high-throughput sequencing data of all samples.

Author Contributions

Conceptualization, D.X. and D.Z.; methodology, D.Z. and T.Z.; validation, D.Z., T.Z. and L.R.; formal analysis, D.Z. and T.Z.; investigation, D.Z., T.Z., and L.R; data curation, D.Z., T.Z. and. D.-a.F.; writing—original draft preparation, D.Z. and T.Z.; writing—review and editing, D.Z., T.Z and D.X.; funding acquisition, D.X. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the fundings of the Central Public-interest Scientific Institution Basal Research Fund, CAFS (2020XT13), the National Key R&D Program of China (2020YFD0900500), and Central Public-interest Scientific Institution Basal Research Fund, CAFS (2020TD61; 2021JBFM16).

Institutional Review Board Statement

This study was approved by the Care and Use Committee of the Ministry of Freshwater Fisheries Research Center of the Chinese Academy of Fishery Sciences. All animal experimental procedures followed the Guideline for the Care and Use of Laboratory Animals in China.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data generated or analyzed during this study are included in this article.

Conflicts of Interest

We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

References

  1. Van Kessel, M.A.; Dutilh, B.E.; Neveling, K.; Kwint, M.P.; Veltman, J.A.; Flik, G.; Jetten, M.S.; Klaren, P.H.; Op den Camp, H.J. Pyrosequencing of 16S rRNA gene amplicons to study the microbiota in the gastrointestinal tract of carp (Cyprinus carpio L.). AMB Express 2011, 1, 41. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Banerjee, G.; Ray, A.K. Bacterial symbiosis in the fish gut and its role in health and metabolism. Symbiosis 2017, 72, 1–11. [Google Scholar] [CrossRef]
  3. Nayak, S.K. Role of gastrointestinal microbiota in fish. Aquac. Res. 2010, 41, 1553–1573. [Google Scholar] [CrossRef]
  4. Larsen, A.M.; Mohammed, H.H.; Arias, C.R. Characterization of the gut microbiota of three commercially valuable warmwater fish species. J. Appl. Microbiol. 2014, 116, 1396–1404. [Google Scholar] [CrossRef]
  5. Le, D.; Nguyen, P.; Nguyen, D.; Dierckens, K.; Boon, N.; Lacoere, T.; Kerckhof, F.M.; De Vrieze, J.; Vadstein, O.; Bossier, P. Gut Microbiota of Migrating Wild Rabbit Fish (Siganus guttatus) Larvae Have Low Spatial and Temporal Variability. Microb. Ecol. 2020, 79, 539–551. [Google Scholar] [CrossRef] [Green Version]
  6. Xiao, F.S.; Zhu, W.G.; Yu, Y.H.; He, Z.L.; Wu, B.; Wang, C.; Shu, L.F.; Li, X.H.; Yin, H.Q.; Wang, J.J.; et al. Host development overwhelms environmental dispersal in governing the ecological succession of zebrafish gut microbiota. Npj Biofilms Microbiomes 2021, 7, 5. [Google Scholar] [CrossRef]
  7. Sylvain, F.-É.; Holland, A.; Bouslama, S.; Audet-Gilbert, É.; Lavoie, C.; Val, A.L.; Derome, N. Fish Skin and Gut Microbiomes Show Contrasting Signatures of Host Species and Habitat. Appl. Environ. Microbiol. 2020, 86, e00789-20. [Google Scholar] [CrossRef]
  8. Wu, S.; Wang, G.; Angert, E.R.; Wang, W.; Li, W.; Zou, H. Composition, diversity, and origin of the bacterial community in grass carp intestine. PLoS ONE 2012, 7, e30440. [Google Scholar] [CrossRef] [Green Version]
  9. Briones, J.C.; Tsai, C.H.; Nakazawa, T.; Sakai, Y.; Papa, R.D.; Hsieh, C.H.; Okuda, N. Long-term changes in the diet of Gymnogobius isaza from Lake Biwa, Japan: Effects of body size and environmental prey availability. PLoS ONE 2012, 7, e53167. [Google Scholar] [CrossRef]
  10. Jiang, M.; Xu, M.; Ying, C.; Yin, D.; Dai, P.; Yang, Y.; Ye, K.; Liu, K. The intestinal microbiota of lake anchovy varies according to sex, body size, and local habitat in Taihu Lake, China. Microbiologyopen 2020, 9, e00955. [Google Scholar] [CrossRef]
  11. Wong, S.; Rawls, J.F. Intestinal microbiota composition in fishes is influenced by host ecology and environment. Mol. Ecol. 2012, 21, 3100–3102. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Sun, F.; Wang, Y.; Wang, C.; Zhang, L.; Zheng, Z. Insights into the intestinal microbiota of several aquatic organisms and association with the surrounding environment. Aquaculture 2019, 507, 196–202. [Google Scholar] [CrossRef]
  13. Llewellyn, M.S.; Boutin, S.; Hoseinifar, S.H.; Derome, N. Teleost microbiomes: The state of the art in their characterization, manipulation and importance in aquaculture and fisheries. Front. Microbiol. 2014, 5, 207. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Rosado, D.; Pérez-Losada, M.; Pereira, A.; Severino, R.; Xavier, R. Effects of aging on the skin and gill microbiota of farmed seabass and seabream. Anim. Microbiome 2021, 3, 10. [Google Scholar] [CrossRef] [PubMed]
  15. Legrand, T.; Catalano, S.R.; Wos-Oxley, M.L.; Stephens, F.; Landos, M.; Bansemer, M.S.; Stone, D.A.J.; Qin, J.G.; Oxley, A.P.A. The Inner Workings of the Outer Surface: Skin and Gill Microbiota as Indicators of Changing Gut Health in Yellowtail Kingfish. Front. Microbiol. 2018, 8, 2664. [Google Scholar] [CrossRef]
  16. Wu, Z.B.; Zhang, Q.Q.; Lin, Y.Y.; Hao, J.W.; Wang, S.Y.; Zhang, J.Y.; Li, A.H. Taxonomic and Functional Characteristics of the Gill and Gastrointestinal Microbiota and Its Correlation with Intestinal Metabolites in NEW GIFT Strain of Farmed Adult Nile Tilapia (Oreochromis niloticus). Microorganisms 2021, 9, 617. [Google Scholar] [CrossRef]
  17. Guivier, E.; Pech, N.; Chappaz, R.; Gilles, A. Microbiota associated with the skin, gills, and gut of the fish Parachondrostoma toxostoma from the Rhone basin. Freshw. Biol. 2020, 65, 446–459. [Google Scholar] [CrossRef]
  18. Kuang, T.; He, A.; Lin, Y.; Huang, X.; Zhou, L. Comparative analysis of microbial communities associated with the gill, gut, and habitat of two filter-feeding fish. Aquac. Rep. 2020, 18, 100501. [Google Scholar] [CrossRef]
  19. Chen, W.; Zhang, J.; Cai, C.; He, J.; Song, X.; Xu, G.; Zhou, J. A comparative study of the algae control effect of bighead carp in three lakes of Yangcheng Lake. J. Water Ecol. 2020, 41, 73–80. [Google Scholar]
  20. Feng, C.; Xu, D.; Chen, Y.; Xu, R.; Shan, J. Effects of bighead carp release on phytoplankton in the Sanguocheng waters of Taihu Lake. J. Dalian Ocean Univ. 2018, 33, 666–673. [Google Scholar]
  21. Li, J.; Ni, J.; Li, J.; Wang, C.; Li, X.; Wu, S.; Zhang, T.; Yu, Y.; Yan, Q. Comparative study on gastrointestinal microbiota of eight fish species with different feeding habits. J. Appl. Microbiol. 2014, 117, 1750–1760. [Google Scholar] [CrossRef] [PubMed]
  22. Chang, W.; Sun, J.; Pang, Y. Effects of different habitats on the bacterial community composition in the water and sediments of Lake Taihu, China. Environ. Sci. Pollut. Res. 2020, 27, 44983–44994. [Google Scholar] [CrossRef] [PubMed]
  23. Fang, C. Eutrophication and Aquatic Community Structure in Taihu Lake. Master’s Thesis, Soochow University, Suzhou, China, 2010. [Google Scholar]
  24. Liu, H.; Fang, Z.; Meng, Y.; Yuan, Z. Analysis of the current pollution situation of water bodies around Taihu Lake. J. Ecol. Environ. 2020, 29, 2262–2269. [Google Scholar]
  25. Wu, Q.L.; Zwart, G.; Wu, J.; Kamst-van Agterveld, M.P.; Liu, S.; Hahn, M.W. Submersed macrophytes play a key role in structuring bacterioplankton community composition in the large, shallow, subtropical Taihu Lake, China. Environ. Microbiol. 2007, 9, 2765–2774. [Google Scholar] [CrossRef]
  26. Wu, D.; Jia, G.; Wu, H. Chlorophyll-a concentration variation characteristics of the alage-dominated and macrophyte-dominant ares in Taihu and its driving factors, 2007–2019. J. Lake Sci. 2021, 33, 1364–1375. [Google Scholar]
  27. Zheng, X.L.; Wen, S.L.; Li, X.; Gong, W.Q.; Liu, D.H.; Zhong, J.C. Characteristics of N2O release and influencing factors in grass-type and algea-type zones of taihu lake during summer. Environ. Sci. 2018, 39, 2306–2315. [Google Scholar]
  28. Walters, W.; Hyde, E.R.; Berg-Lyons, D.; Ackermann, G.; Humphrey, G.; Parada, A.; Gilbert, J.A.; Jansson, J.K.; Caporaso, J.G.; Fuhrman, J.A.; et al. Improved Bacterial 16S rRNA Gene (V4 and V4-5) and Fungal Internal Transcribed Spacer Marker Gene Primers for Microbial Community Surveys. mSystems 2015, 1, e00009-15. [Google Scholar] [CrossRef] [Green Version]
  29. Caporaso, J.G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F.D.; Costello, E.K.; Fierer, N.; Peña, A.G.; Goodrich, J.K.; Gordon, J.I.; et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 2010, 7, 335–336. [Google Scholar] [CrossRef] [Green Version]
  30. Croft, M.T.; Lawrence, A.D.; Raux-Deery, E.; Warren, M.J.; Smith, A.G. Algae acquire vitamin B12 through a symbiotic relationship with bacteria. Nature 2005, 438, 90–103. [Google Scholar] [CrossRef]
  31. Liu, Q.G.; Yong, C.; Li, J.L.; Chen, L.Q. The food web structure and ecosystem properties of a filter-feeding carps dominated deep reservoir ecosystem. Ecol. Model. 2007, 203, 279–289. [Google Scholar] [CrossRef]
  32. Tarnecki, A.M.; Brennan, N.P.; Schloesser, R.W.; Rhody, N.R. Shifts in the Skin-Associated Microbiota of Hatchery-Reared Common Snook Centropomus undecimalis during Acclimation to the Wild. Microb. Ecol. 2019, 77, 770–781. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Puttaswamygowda, G.H.; Olakkaran, S.; Antony, A.; Kizhakke Purayil, A. Chapter 22—Present Status and Future Perspectives of Marine Actinobacterial Metabolites. In Recent Developments in Applied Microbiology and Biochemistry; Buddolla, V., Ed.; Academic Press: Cambridge, MA, USA, 2019; pp. 307–319. [Google Scholar]
  34. Karlsson, F.H.; Ussery, D.W.; Nielsen, J.; Nookaew, I. A closer look at bacteroides: Phylogenetic relationship and genomic implications of a life in the human gut. Microb. Ecol. 2011, 61, 473–485. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Cardman, Z.; Arnosti, C.; Durbin, A.; Ziervogel, K.; Cox, C.; Steen, A.D.; Teske, A. Verrucomicrobia are candidates for polysaccharide-degrading bacterioplankton in an arctic fjord of Svalbard. Appl. Environ. Microbiol. 2014, 80, 3749–3756. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Duman, M.; Mulet, M.; Altun, S.; Saticioglu, I.B.; Gomila, M.; Lalucat, J.; García-Valdés, E. Pseudomonas anatoliensis sp. nov and Pseudomonas iridis sp. nov. isolated from fish. Syst. Appl. Microbiol. 2021, 44, 126198. [Google Scholar] [CrossRef]
  37. Neupane, S.; Modry, D.; Pafčo, B.; Zurek, L. Bacterial Community of the Digestive Tract of the European Medicinal Leech (Hirudo verbana) from the Danube River. Invertebr. Microbiol. 2019, 77, 1082–1090. [Google Scholar]
  38. Liao, Q.Z.; Zhen, Y.T.; Qin, Y.; Jiang, Q.; Lan, T.C.; Huang, L.D.; Shen, P.H. Effects of dietary Metschnikowia sp. GXUS03 on growth, immunity, gut microbiota and Streptococcus agalactiae resistance of Nile tilapia (Oreochromis niloticus). Aquac. Res. 2022, 53, 1918–1928. [Google Scholar] [CrossRef]
  39. Duperron, S.; Halary, S.; Habiballah, M.; Gallet, A.; Huet, H.; Duval, C.; Bernard, C.; Marie, B. Response of Fish Gut Microbiota to Toxin-Containing Cyanobacterial Extracts: A Microcosm Study on the Medaka (Oryzias latipes). Environ. Sci. Technol. Lett. 2019, 6, 341–347. [Google Scholar] [CrossRef] [Green Version]
  40. Kong, C.; Gao, R.; Yan, X.; Huang, L.; Qin, H. Probiotics improve gut microbiota dysbiosis in obese mice fed a high-fat or high-sucrose diet. Nutrition 2019, 60, 175–184. [Google Scholar] [CrossRef]
  41. Dabadé, D.S.; Wolkers-Rooijackers, J.C.; Azokpota, P.; Hounhouigan, D.J.; Zwietering, M.H.; Nout, M.J.; den Besten, H.M. Bacterial concentration and diversity in fresh tropical shrimps (Penaeus notialis) and the surrounding brackish waters and sediment. Int. J. Food Microbiol. 2016, 218, 96–104. [Google Scholar] [CrossRef]
  42. Hassenruck, C.; Reinwald, H.; Kunzmann, A.; Tiedemann, I.; Gardes, A. Effects of Thermal Stress on the Gut Microbiome of Juvenile Milkfish (Chanos chanos). Microorganisms 2021, 9, 5. [Google Scholar] [CrossRef]
  43. Sylvain, F.; Cheaib, B.; Llewellyn, M.; Gabriel Correia, T.; Barros Fagundes, D.; Luis Val, A.; Derome, N. pH drop impacts differentially skin and gut microbiota of the Amazonian fish tambaqui (Colossoma macropomum). Sci. Rep. 2016, 6, 32032. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Duan, Y.; Liu, Q.; Wang, Y.; Zhang, J.; Xiong, D. Impairment of the intestine barrier function in Litopenaeus vannamei exposed to ammonia and nitrite stress. Fish Shellfish. Immunol. 2018, 78, 279–288. [Google Scholar] [CrossRef] [PubMed]
  45. Zhang, T.; Zhang, Y.; Xu, J.; Yan, Z.; Sun, Q.; Huang, Y.; Wang, S.; Li, S.; Sun, B. Toxic effects of ammonia on the intestine of the Asian clam (Corbicula fluminea). Environ. Pollut. 2021, 287, 117617. [Google Scholar] [CrossRef]
  46. Qi, X.Z.; Xue, M.Y.; Yang, S.B.; Zha, J.W.; Wang, G.X.; Ling, F. Ammonia exposure alters the expression of immune-related and antioxidant enzymes-related genes and the gut microbial community of crucian carp (Carassius auratus). Fish Shellfish Immunol. 2017, 70, 485–492. [Google Scholar] [CrossRef] [PubMed]
  47. Sun, Y.X.; Dong, H.B.; Wang, W.H.; Cao, M.; Duan, Y.F.; Li, H.; Liu, Q.S.; Zhang, J.S. Effects of periodic hypoxia stress on intestinal microflora structure of Lateolabrax maculatus. South China Fish. Sci. 2019, 15, 46–52. [Google Scholar]
  48. Su, X.; Xue, Q.; Steinman, A.D.; Zhao, Y.; Xie, L. Spatiotemporal Dynamics of Microcystin Variants and Relationships with Environmental Parameters in Lake Taihu, China. Toxins 2015, 7, 3224–3244. [Google Scholar] [CrossRef]
  49. Zhang, Y.; Li, Z.Y.; Kholodkevich, S.; Sharov, A.; Feng, Y.J.; Ren, N.Q.; Sun, K. Microcystin-LR-induced changes of hepatopancreatic transcriptome, intestinal microbiota, and histopathology of freshwater crayfish (Procambarus clarkii). Sci. Total Environ. 2020, 711, 134549. [Google Scholar] [CrossRef]
  50. Blandford, M.I.; Taylor-Brown, A.; Schlacher, T.A.; Nowak, B.; Polkinghorne, A. Epitheliocystis in fish: An emerging aquaculture disease with a global impact. Transbound. Emerg. Dis. 2018, 65, 1436–1446. [Google Scholar] [CrossRef]
  51. Zhang, G.; Zhang, P.; Li, W.; Hao, F.; Chen, F.; Yang, Y.; Hou, Y.; Tao, Y. Reduction of gut microbial diversity and short chain fatty acids in BALB/c mice exposure to microcystin-LR. Ecotoxicology 2020, 29, 1347–1357. [Google Scholar]
  52. Ji, B.; Liang, J.C.; Ma, Y.Q.; Zhu, L.; Liu, Y. Bacterial community and eutrophic index analysis of the East Lake. Environ. Pollut. 2019, 252, 682–688. [Google Scholar] [CrossRef]
  53. Shang, Y.Q.; Wu, X.Y.; Wang, X.B.; Wei, Q.G.; Ma, S.C.; Sun, G.L.; Zhang, H.X.; Wang, L.D.; Dou, H.S.; Zhang, H.Z. Factors affecting seasonal variation of microbial community structure in Hulun Lake, China. Sci. Total Environ. 2022, 805, 150294. [Google Scholar] [CrossRef] [PubMed]
  54. Guo, D.; Liang, J.C.; Chen, W.; Wang, J.; Ji, B.; Luo, S.Y. Bacterial Community Analysis of Two Neighboring Freshwater Lakes Originating from One Lake. Pol. J. Environ. Stud. 2021, 30, 111–117. [Google Scholar] [CrossRef]
Fishes 07 00304 g001 550
Figure 1. The physicochemical parameters of water. The difference between the two groups was compared using a t-test. “*” indicated p < 0.05. DO: dissolved oxygen; TN: total nitrogen; TDN: total dissolved nitrogen; NH4+-N: ammonium; NO2−-N: nitrite nitrogen; TP: total phosphorus; TDP: total dissolved phosphorus; PO43−-P: orthophosphate; Chla: chlorophyll A; COD: chemical oxygen demand.
Figure 1. The physicochemical parameters of water. The difference between the two groups was compared using a t-test. “*” indicated p < 0.05. DO: dissolved oxygen; TN: total nitrogen; TDN: total dissolved nitrogen; NH4+-N: ammonium; NO2−-N: nitrite nitrogen; TP: total phosphorus; TDP: total dissolved phosphorus; PO43−-P: orthophosphate; Chla: chlorophyll A; COD: chemical oxygen demand.
Fishes 07 00304 g001
Fishes 07 00304 g002 550
Figure 2. The Alpha diversity analysis of microbiota at the OUT level. (A–C) Chao1, Shannon, and Simpson indexes, analysis based on different tissues. The red dots on the box plot represented a sum of intestine samples from two zones. The green dots represented a sum of gill samples from two zones. (D–F) Chao1, Shannon, and Simpson indexes, analysis based on different zones. The orange dots on the box plot represented a sum of gill and intestine samples from zone A. The blue dots represented a sum of gill and intestine samples from zone H. “*” indicated p < 0.05 and “**” indicated p < 0.01.
Figure 2. The Alpha diversity analysis of microbiota at the OUT level. (A–C) Chao1, Shannon, and Simpson indexes, analysis based on different tissues. The red dots on the box plot represented a sum of intestine samples from two zones. The green dots represented a sum of gill samples from two zones. (D–F) Chao1, Shannon, and Simpson indexes, analysis based on different zones. The orange dots on the box plot represented a sum of gill and intestine samples from zone A. The blue dots represented a sum of gill and intestine samples from zone H. “*” indicated p < 0.05 and “**” indicated p < 0.01.
Fishes 07 00304 g002
Fishes 07 00304 g003 550
Figure 3. The beta diversity analysis of microbiota at the OUT level. (A,B) The NMDS analysis; (C,D) Anosim analysis. All analyses were based on the Bray-Curtis distance algorithm.
Figure 3. The beta diversity analysis of microbiota at the OUT level. (A,B) The NMDS analysis; (C,D) Anosim analysis. All analyses were based on the Bray-Curtis distance algorithm.
Fishes 07 00304 g003
Fishes 07 00304 g004 550
Figure 4. The microbial composition in all samples. (A) The abundances of top 10 phyla; (B) the abundances of top 10 genera. AI: intestinal samples of fish from zone A; AG: gill samples of fish from zone A; HI: intestine of fish from zone H; HG: gill of fish from zone H; AW: water samples from zone A; HW: water samples from zone H.
Figure 4. The microbial composition in all samples. (A) The abundances of top 10 phyla; (B) the abundances of top 10 genera. AI: intestinal samples of fish from zone A; AG: gill samples of fish from zone A; HI: intestine of fish from zone H; HG: gill of fish from zone H; AW: water samples from zone A; HW: water samples from zone H.
Fishes 07 00304 g004
Fishes 07 00304 g005 550
Figure 5. The difference in microbial composition between groups. (A,B) The microbial phyla (A) and genera (B) with significantly different abundances (>1%) between gills and intestines; (C,D) the microbial phyla (C) and genera (D) with different abundances (>1%) of fish from different habitats. The difference in abundances between the two groups was compared using Welch’s t-test.
Figure 5. The difference in microbial composition between groups. (A,B) The microbial phyla (A) and genera (B) with significantly different abundances (>1%) between gills and intestines; (C,D) the microbial phyla (C) and genera (D) with different abundances (>1%) of fish from different habitats. The difference in abundances between the two groups was compared using Welch’s t-test.
Fishes 07 00304 g005
Fishes 07 00304 g006 550
Figure 6. LDA score bar plots showed discriminative taxa (LDA score >3, p < 0.05) of microbiota at the genus level. (A) LDA score bar plots displayed discriminative microbial genera between fish gills and intestines; (B) LDA score bar plots displayed discriminative microbial genera between fish from two zones.
Figure 6. LDA score bar plots showed discriminative taxa (LDA score >3, p < 0.05) of microbiota at the genus level. (A) LDA score bar plots displayed discriminative microbial genera between fish gills and intestines; (B) LDA score bar plots displayed discriminative microbial genera between fish from two zones.
Fishes 07 00304 g006
Fishes 07 00304 g007 550
Figure 7. Heatmap showed the top 20 microbial functions of all fish samples at KEGG-2-level. AI: intestinal samples of fish from zone A; AG: gill samples of fish from zone A; HI: intestine of fish from zone H; HG: gill of fish from zone H; AW: water samples from zone A; HW: water samples from zone H.
Figure 7. Heatmap showed the top 20 microbial functions of all fish samples at KEGG-2-level. AI: intestinal samples of fish from zone A; AG: gill samples of fish from zone A; HI: intestine of fish from zone H; HG: gill of fish from zone H; AW: water samples from zone A; HW: water samples from zone H.
Fishes 07 00304 g007
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Zhou, D.; Zhang, T.; Ren, L.; Fang, D.-A.; Xu, D.-P. Differential Study of Microbiota in the Gill and Intestine of Silver Carp (Hypophthalmichthys molitrix) from the Algae-Dominated and Hydrophyte-Dominated Areas of Taihu Lake, China. Fishes 2022, 7, 304. https://doi.org/10.3390/fishes7060304

AMA Style

Zhou D, Zhang T, Ren L, Fang D-A, Xu D-P. Differential Study of Microbiota in the Gill and Intestine of Silver Carp (Hypophthalmichthys molitrix) from the Algae-Dominated and Hydrophyte-Dominated Areas of Taihu Lake, China. Fishes. 2022; 7(6):304. https://doi.org/10.3390/fishes7060304

Chicago/Turabian Style

Zhou, Dan, Ting Zhang, Long Ren, Di-An Fang, and Dong-Po Xu. 2022. "Differential Study of Microbiota in the Gill and Intestine of Silver Carp (Hypophthalmichthys molitrix) from the Algae-Dominated and Hydrophyte-Dominated Areas of Taihu Lake, China" Fishes 7, no. 6: 304. https://doi.org/10.3390/fishes7060304

APA Style

Zhou, D., Zhang, T., Ren, L., Fang, D. -A., & Xu, D. -P. (2022). Differential Study of Microbiota in the Gill and Intestine of Silver Carp (Hypophthalmichthys molitrix) from the Algae-Dominated and Hydrophyte-Dominated Areas of Taihu Lake, China. Fishes, 7(6), 304. https://doi.org/10.3390/fishes7060304

Article Metrics

Back to TopTop