Microbial community structure in soils around salt lakes
Microbial community composition was investigated by high-throughput Illumina sequencing. The number of bacterial and archaeal sequences in the five samples were 205,563 and 283,308, respectively. A total of 643 operational taxonomic units (OTUs) were recovered comprising of 611 and 32 bacterial and archaeal OTUs, respectively. The rarefaction curves of all samples were flat, indicating that the amount of sequencing data was sufficient (See Supplementary Fig. S1).
The bacterial domain was divided into 18 phyla, 42 classes, 66 orders, 115 families, and 195 genera. The dominant bacterial phyla (relative abundance >10%) in the five samples belonged to Proteobacteria (85.08%), followed by Bacteroidetes (10.37%) and Firmicutes (2.99%), these three bacterial phyla constituted more than 98% of all reads (Fig. 1A). The major classes were Betaproteobacteria (66.65%), Alphaproteobacteria (16.01%), Sphingobacteriia (5.17%), Bacteroidia (4.24%) and Gammaproteobacteria (2.18%), which were among the top five of the total bacterial classes (Fig. 1B). At the order level, Burkholderiales (66.56%) were found to be the most dominant, followed by Caulobacterales (9.75%), Rhizobiales (5.61%), Sphingobacteriales (5.17%), and Bacteroidales (4.24%) in total abundance (Fig. 1C). At the family level, Burkholderiaceae (60.76%) was dominant among all bacterial families (Fig. 1D). Of these, several genera were frequently dominant, with proportions in total sequences of more than 1% (Fig. 1E). Among the dominant genera, Burkholderia was found to be the most abundant (1 OTU, 50.77% of total sequences), followed by Phenylobacterium (1 OTU, 9.64%), Ralstonia (2 OTUs, 8.47%), Herbaspirillum (1 OTU, 5.43%), Prevotella (80 OTUs, 3.41%), Chitinophaga (1 OTU, 2.92%), Bradyrhizobium (1 OTU, 2.49%), Mesorhizobium (1 OTU, 2.17%), Sediminibacterium (1 OTU, 2.16%), and Cupriavidus (1 OTU, 1.52%) (Fig. 1E). These ten dominant genera accounted for 88.98% of the total classified sequences.
All the archaea detected belonged to the phylum Euryarchaeota, including 3 classes, 6 orders, 7 families, and 15 genera. Of these 3 classes, Halobacteria was the most abundant, accounting for 90.63% of the total 32 OTUs, covering 223,081 sequences (78.74% of total 283,308 reads), followed by Methanomicrobia (2 OTUs, 40,511 sequences (14.30%)) and Methanobacteria (1 OTU, 19,716 sequences (6.96%), Fig. 1F). Halobacteriales (51.30%) dominated among all bacterial orders (Fig. 1G) and Halobacteriaceae (51.30%) dominated among all bacterial families (Fig. 1H). At the genus level, the dominant archaeal genera (relative abundance > 10%) were unclassified_Halobacteriaceae, unclassified_Halobacteria, and Methanomicrobium, each with a widely varying abundance. The subdominant genera (1–10% relative abundance) consisted of Halorussus, Halovivax, Methanobrevibacter, Halalkalicoccus, unclassified_Methanoregulaceae, Salinarchaeum, unclassified_Natrialbaceae, and unclassified_Haloferacaceae. Other minor genera included Halomicrobium, Natronoarchaeum, Halorubellus, and Natronomonas, which constituted small percentages of community abundance (<1%). (Fig. 1I)
Alpha diversity analysis revealed that bacterial and archaeal community richness (Chao1), diversity (Shannon and Simpson), and evenness (Shannoneven) varied widely among the samples (Table 1). In particular, the lowest bacterial richness, diversity, and evenness were samples from QSG4, and the highest richness was QSB, with the highest diversity and evenness being QSG1. For archaea, the lowest richness and diversity were samples from QSG1, the highest were samples from QSG2, the lowest evenness was QSB, and the highest was QSG1.
Table 1
Statistical analysis of microbial diversity in the soil around the Qarhan Salt Lake on the Qinghai—Tibet plateau
Classification
|
Sample
|
Sequence number
|
OTUs
|
Chao
|
Shannon
|
Simpson
|
Coverage
|
Shannoneven
|
Bacteria
|
QSB
|
42785
|
283
|
284.909
|
2.535
|
0.273
|
0.99984
|
0.449
|
QSG1
|
39352
|
266
|
266.857
|
2.690
|
0.216
|
0.99990
|
0.482
|
QSG2
|
44574
|
161
|
161.000
|
2.133
|
0.284
|
0.99998
|
0.420
|
QSG3
|
41922
|
272
|
272.500
|
2.529
|
0.239
|
0.99995
|
0.451
|
QSG4
|
36930
|
117
|
117.250
|
1.661
|
0.427
|
0.99995
|
0.349
|
Archaea
|
QSB
|
54738
|
10
|
10.000
|
1.647
|
0.240
|
1.00000
|
0.715
|
QSG1
|
54344
|
2
|
2.000
|
0.690
|
0.504
|
1.00000
|
0.995
|
QSG2
|
58870
|
20
|
20.000
|
2.570
|
0.095
|
0.99998
|
0.858
|
QSG3
|
52600
|
6
|
6.000
|
1.473
|
0.275
|
1.00000
|
0.822
|
QSG4
|
62756
|
7
|
7.000
|
1.798
|
0.176
|
1.00000
|
0.924
|
Alkaline saline soil prokaryotic β-diversity
Unweighted UniFrac distance metrics were used to estimate bacterial and archaeal β-diversity and to identify dissimilarities between the different samples. The principal coordinate analysis (PCoA) plot illustrated the dissimilarity of OTU composition; the first two principal components explained 79.18% (PCoA 1 + PCoA 2; bacteria) and 79.18% (PCoA 1 + PCoA 2; archaea) of the total variation (Fig. 2). For the analysis of multivariate homogeneity among groups, the analysis of similarities (ANOSIM) test was performed, and the results showed that there were no significant differences between the bare land and the grassland (p > 0.05).
Bacteria from bare land and grassland shared 187 OTUs (Fig. 3A), and more unique OTUs (102) were recovered from QSG3, a number that exceeded the unique OTUs found in bare land QSB (96) (Fig. 3B). For archaea, bare land and grassland shared seven OTUs (Fig. 3C), more unique OTUs (15) were recovered from QSG2, a number that also exceeded the unique OTUs found in bare land QSB (3) (Fig. 3D).
Potential correlations between microbial communities and soil variables
RDA was performed to reveal the relationship between microbial community structures and the soil variables. The first two RDA axes explained 60.38% and 64.8% of the bacterial and archaeal community variations, respectively (See Supplementary Fig. S2).
Spearman’s rank correlation test was performed to clarify the relationship between environmental factors and prokaryotic composition (relative abundance at the genus level) (Fig. 4). For bacteria, Ralstonia and Cupriavidus were positively correlated with Mg2+ and K+, but Mesorhizobium, Escherichia_Shigella, and Bradyrhizobium were negatively correlated with Mg2+ and K+; Burkholderia was negatively correlated with Na, but Chitinophaga, Phenylobacterium and Mesorhizobium were positively correlated with the Na, and Phenylobacterium and Mesorhizobium were negatively correlated with P (Fig. 4A). For archaea, Halovivax was positively correlated with Mg2+ and K+, Halomicrobium and Methanobrevibacter were negatively correlated with Na, but positively correlated with P; Methanomicrobium was positively correlated with Na (Fig. 4B). These findings suggest that soil variables are important contributing factors for the regulation of soil prokaryotes.
Co‑occurrence network of dominant taxa among prokaryotic microorganisms
A co‑occurrence network was constructed to identify the possible assemblages existing among prokaryotic microorganism OTUs in alkaline saline soil. The core dominant taxa in the cluster were strongly correlated with each other (∣R∣> 0.8, p < 0.05). Notably, the network depicted several keystone OTUs that were assigned to the phyla Bacteroidetes, genus Prevotella (OTU19, and OTU13), Proteobacteria (OTU9 - Cupriavidus, OTU3 - Ralstonia, OTU1 - Burkholderia, OTU5 - Mesorhizobium, OTU7 - Herbaspirillum, and OTU2 - Phenylobacterium) (Fig. 5A). For archaeal taxa, including Halobacteria (OTU20, OTU8, and OTU17), Halobacteriaceae (OTU18, OTU29, OTU12, OTU25, OTU7, and OTU4), Haloferacaceae (OTU19), Natrialbaceae (OTU38, OTU24, OTU16, and OTU26), Methanoregulaceae (OTU9), Halorussus (OTU13 and OTU5), Halorubellus (OTU23), Salinarchaeum (OTU27), Halovivax (OTU3), Methanobrevibacter (OTU6), Halomicrobium (OTU21), and Natronoarchaeum (OTU22) (Fig. 5B).
The co-occurrence network is an effective way to reveal the relationship between individual group members and the entire ecosystem 19,20. The co-occurrence network clusters suggest that core bacterial and archaeal taxa in alkaline saline soil are likely to collaborate with each other and play a role in key metabolic steps in response to environmental changes. (Fig. 5). Thus, study of physiological and metabolic characteristics belonging to these key species can help us understand the mechanisms of microbial adaptation to the environment.
Prediction of ecological function of prokaryotic microorganisms
To gain insight into the ecological function of bacteria and archaea to alkaline saline soil, the prediction tools PICRUSt and FAPROTAX were used to determine the functional characteristics of the prokaryotic communities in the soil. Table 2 presents the number of sequence reads of the predicted genes involved in adaptation to a high-salt environment.
Table 2
Metabolic enzymes for which cellular abundance was related to adaptation to high-salt conditions.
Taxa
|
Enzyme No.
|
KEGG No.
|
Type of enzyme
|
Abundance
|
QSB
|
QSG1
|
QSG2
|
QSG3
|
QSG4
|
Bacteria
|
1.4.1.13/1.4.1.14
|
K00266
|
glutamate synthase (NADPH/NADH) small chain
|
30397
|
30001
|
33352
|
31344
|
26406
|
6.3.1.2
|
K01915
|
glutamine synthetase
|
26337
|
26850
|
31833
|
27808
|
23459
|
1.2.1.8
|
K00130
|
betaine-aldehyde dehydrogenase
|
24450
|
19588
|
25485
|
22474
|
25803
|
1.5.3.1
|
K00303
|
sarcosine oxidase, subunit beta
|
10861
|
8942
|
12185
|
10113
|
10499
|
1.5.1.2
|
K00286
|
pyrroline-5-carboxylate reductase
|
10282
|
10506
|
11645
|
10995
|
8691
|
1.4.1.13/1.4.1.14
|
K00265
|
glutamate synthase (NADPH/NADH) large chain
|
9881
|
10661
|
11912
|
11643
|
7185
|
2.7.7.42
|
K00982
|
glutamate-ammonia-ligase adenylyltransferase
|
8380
|
7815
|
9945
|
8467
|
7853
|
1.4.1.2
|
K00260
|
glutamate dehydrogenase
|
6967
|
6655
|
8671
|
7069
|
6739
|
1.4.1.3
|
K00261
|
glutamate dehydrogenase (NAD(P)+)
|
7014
|
6237
|
7835
|
7698
|
6566
|
1.5.3.1
|
K00302
|
sarcosine oxidase, subunit alpha
|
6707
|
5610
|
7793
|
6285
|
6074
|
1.5.3.1
|
K00304
|
sarcosine oxidase, subunit delta
|
6678
|
5590
|
7723
|
6244
|
6078
|
1.5.3.1
|
K00305
|
sarcosine oxidase, subunit gamma
|
6224
|
5161
|
7112
|
5809
|
5746
|
3.6.3.32
|
K02000
|
glycine betaine/proline transport system ATP-binding protein
|
5905
|
4772
|
6228
|
5528
|
5424
|
3.1.3.12
|
K01087
|
trehalose-phosphatase
|
5640
|
4492
|
6090
|
5153
|
5230
|
3.1.6.6
|
K01133
|
choline-sulfatase
|
4959
|
4072
|
5275
|
4672
|
4815
|
1.4.7.1
|
K00284
|
glutamate synthase (ferredoxin)
|
3810
|
3187
|
4049
|
3581
|
4117
|
1.5.3.1
|
K00301
|
sarcosine oxidase
|
2319
|
2942
|
3882
|
2762
|
2178
|
1.4.1.4
|
K00262
|
glutamate dehydrogenase (NADP+)
|
2850
|
2865
|
1420
|
2223
|
869
|
1.14.11.-
|
K00674
|
ectoine hydroxylase
|
227
|
508
|
704
|
647
|
205
|
3.2.1.93
|
K01226
|
trehalose-6-phosphate hydrolase
|
150
|
96
|
48
|
35
|
5
|
4.2.1.108
|
K06720
|
L-ectoine synthase
|
107
|
38
|
37
|
71
|
64
|
2.3.1.178
|
K06718
|
L-2,4-diaminobutyric acid acetyltransferase
|
103
|
38
|
37
|
55
|
61
|
1.5.3.1/1.5.3.7
|
K00306
|
sarcosine oxidase / L-pipecolate oxidase
|
0
|
0
|
0
|
12
|
0
|
Archaea
|
1.4.1.3
|
K00261
|
glutamate dehydrogenase (NAD(P)+)
|
35517
|
44521
|
82617
|
112244
|
57820
|
6.3.1.2
|
K01915
|
glutamine synthetase
|
35133
|
59573
|
35278
|
50496
|
34394
|
1.4.1.13/1.4.1.14
|
K00265
|
glutamate synthase (NADPH/NADH) large chain
|
26151
|
19646
|
62308
|
53274
|
49897
|
1.5.3.1
|
K00303
|
sarcosine oxidase, subunit beta
|
20100
|
9823
|
36957
|
61074
|
24948
|
1.5.1.2
|
K00286
|
pyrroline-5-carboxylate reductase
|
20592
|
34698
|
17267
|
14484
|
17282
|
3.1.6.6
|
K01133
|
choline-sulfatase
|
15417
|
9823
|
20790
|
31252
|
17282
|
1.4.1.13/1.4.1.14
|
K00266
|
glutamate synthase (NADPH/NADH) small chain
|
0
|
49750
|
0
|
12380
|
9446
|
3.1.3.12
|
K01087
|
trehalose-phosphatase
|
4683
|
0
|
14914
|
29822
|
7667
|
1.2.1.8
|
K00130
|
betaine-aldehyde dehydrogenase
|
4683
|
0
|
12724
|
29822
|
7667
|
3.6.3.32
|
K02000
|
glycine betaine/proline transport system ATP-binding protein
|
0
|
24875
|
0
|
6190
|
4723
|
1.5.3.1
|
K00301
|
sarcosine oxidase
|
0
|
0
|
12265
|
6864
|
7667
|
1.4.1.4
|
K00262
|
glutamate dehydrogenase (NADP+)
|
9858
|
0
|
0
|
0
|
0
|
1.4.1.2
|
K00260
|
glutamate dehydrogenase
|
0
|
0
|
459
|
0
|
256
|
The OTUs detected in all samples were compared with FAPROTAX annotation rule in an automated manner; however, most OTUs could not be assigned to any functional group. Thus, only those OTUs that were successfully annotated were analyzed. Chemoheterotrophy, aerobic chemoheterotrophy, nitrogen fixation, ureolysis, nitrate reduction, fermentation, predatory or exoparasitic were the most abundance bacterial functional groups (Fig. 6A). Methanogenesis, hydrogenotrophic methanogenesis, methanogenesis by CO2 reduction with H2, chemoheterotrophy, methanogenesis using formate, dark hydrogen oxidation, nitrate reduction, and aerobic chemoheterotrophy were the most abundance archaeal functional groups (Fig. 6B). These functional groups provide directions for understanding the mechanisms of adaptation of prokaryotes to high salinity environments.
The metabolic pathways of microbial consortia predicted by PICRUSt were further analyzed. Metabolic pathways were identified at three levels. Functions of bacteria and archaea related to high-salt environment in level 1 include cellular processes (4.19–4.31%, 1.78–3.99%), environmental information processing (15.87–17.12%, 10.74–12.55%), genetic information processing (13.44–14.32%, 17.18–18.99%), and metabolism (49.29–49.62%, 46.69–52.02%). The distribution of bacterial and archaeal functions at level 2 was further analyzed. For bacteria, the relative abundances of membrane transport, amino acid metabolism, carbohydrate metabolism, and replication and repair were enriched in the alkaline saline soil, and there was not much difference between the samples (Fig. 7A). However, for archaea, the relative abundances of amino acid metabolism, carbohydrate metabolism, membrane transport, energy metabolism, and translation were enriched in alkaline saline soil, and there was a great deal of variation among samples (Fig. 7B). It is reasonable that bacteria and archaea may adopt different strategies when coping with extreme environments, and the bacterial community is relatively stable, while the archaea community is quite different.