Bacterial communities along a 4500-meter elevation gradient in the sediment of the Yangtze River: what are the driving factors?

W. Zhang et al. / Desalination and Water Treatment 177 (2020) 109–130 110 within the interaction of biogeochemical nutrients and the transformation and temporary storage of terrestrially-derived organic matter (OM) [2,5]. These are based on the function of the microbial community in riverbed sediment [6,7]. Microbial communities have been suggested to play central roles in aquatic ecosystems and biogeochemistry cycles [8,9]. Moreover, bacterial assemblages quickly respond to abrupt environmental perturbation and are sensitive to anthropogenic disturbances [1]. Recently, two types of processes (deterministic processes and stochastic processes) have been applied to explain the response of microbial community variations to environmental disturbance [10]. Although stochastic processes, related to neutral theory, based on the diffusion and migration of bacterial communities as a result of the water flow, had been established, a multitude of microbial communities were structured by their local environment [11]. Considering special geographical characteristics, such as elevation and rapid development of the Yangtze basin, deterministic processes, driven by species competition, selection, and niche differentiation, were considered to determine the mechanisms of bacterial distribution and succession. Previous studies indicated that microbial communities could be largely structured by deterministic effects, such as niche differentiation [12]. Thus, deterministic processes, that assess the interaction between natural selection and anthropogenic selection effects, may cause variations in response to altitude. With regard to the effects of natural selection, altitudinal patterns of diversity and bacterial community functional traits had been proved in previous studies [12,13]. However, the bacterial diversity and community functional traits along large elevation gradients in river sediment have not been studied in depth, particularly in the Yangtze river. Elevation gradient, as a typical geographical factor, was suggested to cause strong variances in climate and biotic features over short geographic distances [14]. Bryant et al. [15] reported that altitudinal gradients correlate with the taxonomic changes of microorganisms and macroorganisms. Previous investigations considered mountainous systems [14,16,17] and glacial ecosystems [18] to analyze the effects of altitudinal patterns of microbial communities. Geographical variation trends were observed in bacterial diversity along elevation gradients based on various ecosystems. Wilhelm et al. [12] documented that the microbial community compositions of the biofilm in alpine streams changed along the elevation gradient. Hence, whether the altitudinal gradient is one of the key factors affecting microbial diversity and community compositions in stream and river ecosystems should be investigated. It is of great importance to further determine how altitudinal gradients affect bacterial communities in the sediment of the Yangtze River continuum. The Yangtze River closely link Eurasia with the marine ecosystem, and is the third longest river in the world and the longest among all rivers in Asia [19]. Its water quality is significantly correlated to global water environmental safety. Except for the characteristic altitude gradients of the Yangtze River, anthropogenic selection effects should also be considered. The Yangtze River watershed is one of the most economically developed basins in China. Its urban agglomeration has become a remarkable phenomenon, and undoubtedly caused underlying ecological destruction driven by human activities [20]. Anthropogenic inputs (e.g., OM) affect bacterial communities and their function in the sediment of the Yangtze River and have inevitably become a key problem that disturbs the stability and security of this ecosystem. Yang et al. [21] reported that the Yangtze River faces unprecedented ecological risk caused by allochthonous inputs. However, previous studies mainly focused on functional bacteria and the microbial distribution in small specific areas or overlying water bodies [22–24]. The bacterial communities in the sediment of the Yangtze River under the influence of elevation gradients, as well as the anthropogenic interferences have not been investigated so far. Hence, knowledge about the driving factors, elevation gradients, or anthropogenic inputs, that result in the variance of microbial community compositions in the sediment of the Yangtze River is of great importance. In this study, high-throughput sequencing technology was applied to explore the phylogenetic diversity patterns of bacteria and relative environmental variables along the elevation gradient in the sediment of the Yangtze River. The objectives of the study were (1) to explore the biogeographical patterns and driving factors for the bacterial composition of the sediment of the Yangtze River; (2) to identify the most significant driving factors, elevation gradients, or anthropogenic inputs (e.g., OM) that cause the deterministic process related to niche differentiation; (3) to further determine the key variables among anthropogenic selection processes if the dominant drivers were anthropogenic inputs only. To do so, statistical methods were used to detect the distribution pattern based on operational taxonomic units (OTUs) and environmental factors, that is, non-metric multidimensional scaling analysis (NMDS), canonical correspondence analysis (CCA), and redundancy analysis (RDA). Accordingly, the study provides theoretical support for the initiation of a water environmental evaluation index system toward the repair of the water quality of the Yangtze River. 2. Materials and methods 2.1. Study site and sample collection Surveys were carried out at eight state-controlled sections along the Yangtze River (Fig. 1). Each section set three sampling sites, located in the upstream of city, downstream of city and inner city, respectively, so that there were 24 sampling sites in total. All sediment samples were collected using mud bucket in July 2014 and January 2015, respectively. Triplicate sediments were collected at 20 m intervals. Each sample was made by mixing three triplicates randomly and saved in dryice box immediately after removing visible plant particles and transported to the laboratory as soon as possible. Each sample was split into two parts, with one stored at –4°C for physicochemical properties analysis and the other was stored at –80°C in the laboratory until bacterial molecular genomic determination. 2.2. Environmental physicochemical properties analysis Water temperature was measured using a multiparameter water quality analyzer (HQ30d, Hach, USA) in situ. Soil pH (water:soil ratio of 2.5:1) was determined by a 111 W. Zhang et al. / Desalination and Water Treatment 177 (2020) 109–130 digital pH meter. Total nitrogen (TN) was determined by an element analyzer (VarioEL cube, Elementar, Germany) and total phosphorus (TP) was measured by spectrophotometry after digestion. The sediment organic matter (OM) content was analyzed using the method provided in previous study [25]. Copper (Cu), lead (Pb), cadmium (Cd) [5] and arsenic (As) were determined by inductively coupled plasma atomic emission spectrometry (ICP-AES) as previous study introduced [26] and hydrargyrum (Hg) was analyzed by Mercury Analyzer (MA-800, Taiwan). The physicochemical properties of the aquatic environment and sediment collected are summarized (Table 1), as well as the geographical characteristics and urban development level of typical cities (Table 2). 2.3. DNA extraction and high-throughput sequencing Bacterial DNA was extracted from sediments using the FastDNA spin kit (MP Biomedicals, USA) according to the manufacturer’s instructions. The extracted DNA was examined with 3 μL in 1% agarose gels by electrophoresis. Other extracted DNA were sent to Shanghai Majorbio for high-throughput sequencing on an Illumina MiSeq instrument (San Diego CA, USA) using a pairedend 150 bp sequence read run. A set of primers, 338F (5’-ACTCCTRCGGGAGGCAGCAG-3’) and 806R (5’-GGACT ACCVGGGTATCTAAT-3’), was applied to amplify the V3-V4 hypervariable region of bacterial gene. A sample-specific barcode was added to reverse primer for identifying individual samples in a mixture within a single pyrosequencing [27]. A 20 μL reaction system was applied to amplify the bacterial DNA using the following protocol: 95°C for 3 min, 27 cycles of 30 s at 95°C, 30 s at 55°C, and 45 s at 72°C, and a final extension at 72°C for 10 min. 2.4. Sequence analysis The 16S rRNA-based Illumina Miseq sequencing data were analyzed using Mothur software package and SILVA database [27,28]. Raw sequences were removed if any sequences length was <50 bp or if they had even a single base bias. PCR chimeras were checked and removed using the chimera.uchime command implemented in mothur. The remaining sequences were aligned to the RDP Classifier database to determine their phylogenetic assignments, with a confidence threshold of 80%. A clustering threshold of 97% similarity could be adopted to establish an OTU table in Mothur [27]. 2.5. Statistical analyses The α-diversity indices (e.g., OTU numbers, Shannon index) for each sample were calculated using vegan package in R (v. 3.12; http://www.r-project.org/). Community analysis was conducted based on phylum and class levels, and comparisons of relative abundance of bacterial community genus among samples were performed using pheatmap package and package gplots. NMDS was carried out employing the software Past, which is based on Bray–Curtis distances, to illuminate the similarity between different samples. A Pearson correlation analysis using SPSS 20.0 to understand the linkage between bacterial α-diversity and environmental variables, and an RDA was applied to study the correlations between the microbial community compositions and environmental variables with 999 permutations in R. 2.6. Nucleotide sequen