Skip to main content
PMC home page
Scientific Data logoLink to Scientific Data
. 2024 Aug 9;11:856. doi: 10.1038/s41597-024-03711-4

The first large scale rDNA amplicon database of soil microbiomes from Pamir Plateau, China

Jing Zhu 1,#, Xiang Sun 2,#, Qi-Yong Tang 1, Mei-Ying Gu 1, Zhi-Dong Zhang 1,✉, Jian-Wei Chen 3,4,✉
PMCID: PMC11316072  PMID: 39122795

Abstract

The Eastern Pamir, distinguished with high altitude, extremely arid and cold climate, limited nutrients and sparse vegetation, is a unique ecological reservoir. Microbial communities play a central role in maintaining Eastern Pamir’s ecosystem functioning. Despite the ecological significance, due to the difficulty of sample collection and microbial isolation, the microbial diversity and its functionality at the Pamir Plateau have been rarely documented. To fill this gap, 80 soil samples from 17 sites across different elevations were collected, performed the rDNA amplicon sequencing to present the first large-scale overview of bacterial, archaeal, and fungal communities in the Eastern Pamir. Microbiome analysis revealed that the bacteria Actinobacteria, Alphaproteobacteria and Bacteroidia, alongside such as archaea Nitrososphaeria and Halobacteria, and fungi including Dothideomycetes, Sordariomycetes and Eurotiomycetes were dominant lineages at class level in soil microbial communities. The community structure and biodiversity of soil microorganisms provided by this dataset would be pivotal for future studies aimed at understanding the biogeographical distribution, ecological functions and environmental responses of microbial communities of the Pamir Plateau.

Subject terms: Microbiology, Soil microbiology

Background & Summary

The plateau ecosystems, characterized by their cold climates and extensive expanses of tundra, lakes, and glaciers, are integral to the terrestrial ecosystems. These high-latitude and high-elevation ecosystems are particularly susceptible to global climate change, as evidenced by Johnson et al.1. Microorganisms play a pivotal role in the biogeochemical cycling of carbon and nitrogen in such extreme environments, marked by such as high altitude, low temperatures, aridity, and low oxygen levels (only about 70% of that in the plains). Furthermore, microorganisms are essential in shaping and maintaining ecosystems under these harsh conditions2.

The Pamir Plateau is the second largest plateau in the world, extending across southwestern Xinjiang in China, southeastern Tajikistan, and northeastern Afghanistan, covering an area of approximately 100,000 square kilometers with an average elevation of 4,500 meters above sea level3,4. The vast mountains, elevated terrain, and varying topography in the region foster an extraordinary arid and cold climate with severe temperature fluctuations5,6. The region also encompasses diverse natural habitats like lakes, water systems, and glaciers, which collectively shape a distinctive heterogeneous zone7,8. The region is known for its abundant and unique microbial resources, making it a significant constitution to the high-altitude bioresource repository and global genetic diversity and becoming a key zone for biodiversity conservation9,10. However, the region confronts challenges due to anthropogenic disturbances, natural disasters, and climate change, which have accelerated the loss and extinction of microbial species in fragile ecosystems11. Soil microbial diversity and community functionality are particularly vulnerable to these extreme environmental changes.

Soil microorganisms constitute one of the most diverse groups of organisms on Earth. They play a crucial role in soil biogeochemical processes, participate in almost all material transformations in the soil, and have a significant impact on ecosystems such as forests, grasslands, wetlands, and arable lands12,13. The extreme aridity, nutrient scarcity, diurnal temperature fluctuations, and high ultraviolet radiation in the Pamir Plateau present a challenging environment. These conditions disrupt soil productivity and ecosystem functioning14,15. To date, prior studies on microorganisms in plateau regions have focused on the Tibetan, Loess, and Mongolian Plateaus. These studies have involved variations in soil enzyme activities and microbial communities along elevation gradients16, reflections of functional microorganisms in soil to natural factors such as nitrogen deposition and precipitation17,18, or different responses of soil microorganisms from different plant communities to changes in environmental stresses19.

There were sporadic reports on the microorganisms of the Pamir Plateau. As early as the 1960s, researchers studied soil actinomycetes and their antagonistic properties on the Pamir Plateau20,21. Aksenov et al.22 studied the adaptive mechanisms of Cryptococcus in the Pamir region under very low humidity conditions. In the 1970s, Szymon et al.23 isolated 16 species of fungi from 12 species of endemic plants of the Pamir Aly Mountains, which were found to be predominantly cotyledonous mycorrhizal fungi. Nataliia et al.24,25 carried out studies on microbial diversity and colonization strategies in rock of cold desert ecosystems in the Eastern Pamir Mountains of Tajikistan, and Bu et al.26 isolated numerous cold-adapted bacteria as well as archaeal resources from the Pamir Plateau. However, a systematic investigation of the microbial resources of the Pamir Plateau, comprehensive data on the microbial resources of the Pamir region, including the distribution, composition, and function of the microbial communities is still an uncharted area. Therefore, research into soil microorganisms and the data collection on microbial resources in the Pamir Plateau are of great importance for understanding regional and global climatic and environmental changes, biodiversity, and carbon cycling.

In this study, we collected 85 soil samples from 17 sites along the G314 highway, stretching approximately 300 kilometers from Kashgar City to Tashkurgan County, with altitudes ranging from 1715 meters to 4069 meters (Table 1 and Fig. 1). Since five of the soil samples were unsuccessful in DNA extraction, resulting in a final dataset of 80 soil samples. Amplicon sequencing using Illumina NovaSeq sequencing platform yielded 7,644,450, 6,821,012, and 6,627,693 amplicon reads for soil archaeal, bacterial, and fungal datasets, respectively. Subsequent analysis yielded a count of 480 taxa in the archaeal dataset, 9,829 taxa in the bacterial dataset, and 1,778 taxa in the fungal dataset across all soil samples (Fig. 2 and Table 2). In Fig. 2, the rarefaction curves of all samples exhibit a plateauing trend, suggesting that the current sampling strategy is adequate to capture all the microbial taxa in soil communities. However, it is noteworthy that the species richness levels for archaea and fungi in this study were relatively low.

Table 1.

Geographic location of sampling sites and environmental conditions.

Locations Location Abbreviation Latitude Longitude Altitude (m) Moisture (%) Annual Mean Air Temperature (°C) Annual Precipitation (mm) Annual Average Solar Radiation (kJ/m·day)
Oytagh Bridge 1st OytBrdg1 39.02049 75.55944 1715 37.6 10.2 85.3 15995.26
Oytagh Glacier Park (sunny slope) OytGlaSun 38.98163 75.52304 1772 37.7 9.7 84.4 15963.7
Oytagh Glacier Park (shady slope) OytGlaShade 38.98283 75.48941 1845 27 9.1 88.2 15902.47
Akto County (sunny slope) AktSun 38.84969 75.47602 2024 33.9 7.9 82.5 15820
Akto County (shady slope) AktShade 38.8075 75.3985 2192 29.2 6.2 82 15756.35
Gez River site 2 Gez2 38.77064 75.1986 2743 36.5 2.1 94 15110.25
Gez River site 1 Gez1 38.74822 75.14589 3025 31.1 0.3 84.5 15219.63
Bulungkol Lake BulLake 38.73108 75.01833 3311 32.6 1.0 79.8 15287.22
Subashi Bridge 8th SbsBrdg8 38.07994 75.00631 3488 33.5 1.6 65.5 15248.35
Subashi Bridge 7th SbsBrdg7 38.14237 74.97312 3493 20.3 0.3 67.75 15184.54
Kongur Tiube KongT 38.4779 75.04658 3635 35.3 0.2 83 14910.54
Sate Baile Dikule Lake SBDLake 38.47334 75.04553 3648 21.6 0.2 82.5 14910.58
Muztagata Camp Muztagata 38.35576 74.96696 3743 35.4 −0.7 80.8 14968.42
Kulma Pass Kulma 38.19964 74.92149 3792 31.4 −0.6 74.7 15061.81
Subashi Pass (shady slope) SbsShade 38.28943 74.93091 3828 22.4 −1.3 76.6 15026.77
Subashi Pass (sunny slope) SbsSun 38.26913 74.9166 4063 31.2 −2.2 86 14898.46
Ulugjewat Pass Ulug 38.27456 74.91571 4069 36.8 −2.1 84.3 14923.88
Open in a new tab

Fig. 1.

Fig. 1

Open in a new tab

Contour map of sampling site distribution in the Eastern Pamir.

Fig. 2.

Fig. 2

Open in a new tab

Microbial diversity of the soil microbiota profile in Eastern Pamir. (a) Rarefaction curves of the bacterial (purple), archaeal (blue) and fungal (red) communities. (b–d) The richness of the archaeal (b), bacterial (c), and fungal (d) diversity index for each sampling site in Pamir, respectively.

Table 2.

Sequence data statistics of each sample.

No. Location Abbreviation Bacteria Fungi Archaea
Raw Data Clean Data Taxa Raw Data Clean Data Taxa Raw Data Clean Data Taxa
1.1 OytBrdg1 81127 37046 624 106181 70228 243 71994 29606 52
1.2 NA NA NA 97441 71222 187 102508 72556 59
1.3 96763 38291 684 101993 78320 201 102342 69549 29
1.4 NA NA NA NA NA NA 113473 44395 109
1.5 93155 42075 608 96983 69924 188 109918 74512 29
2.1 OytGlaSun 96041 43972 583 105809 77964 177 NA NA NA
2.2 66457 45946 155 107575 70399 146 104308 37907 87
2.3 56148 31365 116 102689 73311 220 105817 81798 34
2.4 NA NA NA NA NA NA 100273 49003 55
2.5 82945 29755 592 98953 38008 300 NA NA NA
3.1 AktSun 98450 39646 653 NA NA NA 68061 35078 18
3.2 93462 34872 619 99320 72083 202 96392 46233 23
3.3 86179 40949 692 102975 42904 189 83731 57515 19
3.4 82393 36739 677 101004 66329 207 60065 38912 20
3.5 80986 35024 698 100784 62676 124 85668 39382 17
4.1 AktShade 86603 38797 685 109611 57296 202 80458 32611 14
4.2 99861 42097 848 96145 32663 208 106091 41123 17
4.3 93110 39756 771 105440 40561 242 98180 61756 18
4.4 85457 37454 776 101892 64562 230 102724 42016 21
4.5 92872 38900 695 108790 56616 307 93930 59586 19
5.1 Gez1 80538 35217 757 87137 64059 293 68965 18251 24
5.2 92557 38848 804 104914 71405 251 100995 31464 28
5.3 93009 39541 794 99152 55096 191 103662 29404 19
5.4 95288 41661 834 73897 58164 207 106732 25386 22
5.5 89905 39038 822 103324 67032 361 99264 32307 19
6.1 95153 39902 820 101409 61367 324 102757 33989 13
6.2 Gez2 98946 40289 828 59501 40570 282 97811 27883 18
6.3 92736 41161 791 100780 66384 276 108241 37623 27
6.4 92707 40660 890 101765 77743 260 96429 21677 22
6.5 NA NA NA NA NA NA NA NA NA
7.1 BulLake 82784 39317 707 103654 84889 177 104962 52355 16
7.2 93309 37702 739 100586 73873 192 99953 34696 25
7.3 93138 42157 767 108176 78538 237 99110 35857 28
7.4 83447 38237 771 97369 65186 220 57988 19234 19
7.5 92861 39680 660 107833 75591 263 100077 42338 35
8.1 KongT 94350 42409 816 99669 59198 247 94400 28866 32
8.2 97770 42533 731 103514 77261 179 107780 40015 28
8.3 97851 44258 855 93623 61889 211 105292 54314 20
8.4 84483 34393 650 108747 81320 192 109687 61840 19
8.5 95077 42218 786 106769 83772 308 106241 65936 28
9.1 Ulug 91544 37740 712 107448 57594 267 107204 24581 20
9.2 99665 43802 825 96619 54878 247 100230 40095 21
9.3 83700 32439 702 94006 39803 260 104717 32030 33
9.4 87298 38941 759 102264 71696 299 103261 44939 31
9.5 86640 39360 760 105541 67208 269 105980 37566 30
10.1 SbsBrdg8 98940 42530 469 101126 36841 102 76088 28679 85
10.2 87046 28168 385 96806 68489 165 111395 18499 97
10.3 87918 32005 307 59710 26443 165 101113 20049 130
10.4 90073 37236 514 105560 75376 288 108576 30421 125
10.5 87358 37206 405 96344 33724 116 99615 31571 109
11.1 SbsBrdg7 96804 43791 781 88225 56071 252 82555 52902 17
11.2 81827 39433 742 78325 51099 120 106978 57662 32
11.3 86957 39381 746 90041 65233 121 106383 62481 37
11.4 99407 45173 730 96509 8598 93 101142 48389 48
11.5 84119 40198 467 108047 69131 269 100687 41758 35
12.1 Kulma 89756 39844 737 91067 75556 166 100238 70058 23
12.2 95343 42014 697 98032 75644 150 106011 34652 37
12.3 90373 40396 732 106359 83818 169 102570 62457 23
12.4 92810 38061 738 107623 85373 174 102845 68556 22
12.5 98819 42569 730 110523 90111 156 100170 58957 22
13.1 SbsSun 82955 36927 782 73772 52785 170 105102 67569 26
13.2 90166 42547 898 70773 23683 206 89428 47278 22
13.3 92805 40953 802 72543 29191 211 109099 55110 27
13.4 80138 36949 821 108335 59513 215 101583 35751 22
13.5 94503 40283 756 72819 60415 160 102685 38785 20
14.1 SbsShade 84621 37152 665 86146 70877 140 95770 57832 21
14.2 94208 39175 733 82297 38227 199 96050 45042 17
14.3 87595 36151 713 89026 69658 157 98091 55849 15
14.4 89134 38332 659 76132 52794 184 103492 38897 15
14.5 88583 39214 624 66874 49040 121 106644 48336 42
15.1 Muztagata 99451 40691 789 81664 51021 232 96985 46916 55
15.2 93884 39904 770 67192 41985 188 107963 49119 23
15.3 93292 39845 831 54698 40413 224 104981 53142 34
15.4 83925 36503 780 102541 62155 270 78994 32384 35
15.5 81575 35545 727 95595 66562 237 98689 68570 53
16.1 SBDLake 90060 35091 571 92143 69039 216 93060 46678 67
16.2 91537 61700 58 NA NA NA NA NA NA
16.3 NA NA NA 99829 69259 316 NA NA NA
16.4 93617 41625 634 96269 66037 207 109626 61580 17
16.5 83779 31905 550 90873 65003 102 98892 53283 51
17.1 OytGlaShade 78399 56105 205 101311 78789 147 104636 75109 21
17.2 92113 44251 705 104515 34438 115 100544 70146 18
17.3 89096 43143 787 105164 77054 143 105325 76928 23
17.4 53678 45397 63 105793 31322 142 102962 77373 28
17.5 92456 41945 838 108489 49105 195 106569 44224 25
Open in a new tab

Note: NA indicates that soil sample DNA could not be extracted and sequencing results could not be obtained.

In addition, 224 taxa could not be taxonomically classified within the fungal community at the phylum level, representing 12.60% of the overall detected fungal taxa with the cumulative relative abundance. Similarly, 422 bacterial taxa could not be classified at the phylum level, comprising 4.29% of the total detected bacterial taxa. Moreover, all archaeal taxa were annotated into 14 families, but 191 archaeal taxa remained unclassified at the genus level, accounting for a substantial 39.79% of the total detected archaeal taxa. This suggests that there may be a considerable volume of unrecognized microbial resources in this region waiting for further exploration.

The analysis of the bacterial communities revealed that the Actinobacteria (15.61%) and Alphaproteobacteria (11.99%) emerged as the dominant groups, alongside notable prevalence of Bacteroidia (8.72%) and Gammaproteobacteria (7.01%) (Fig. 3a). Fungal communities were dominantly represented by Dothideomycetes (32.06%), Sordariomycetes (23.66%), and Eurotiomycetes (9.35%) (Fig. 3b). In archaeal communities, Nitrososphaeria class was predominant, representing 70.46% of the relative abundance, followed by Halobacteria at 27.90% (Fig. 4a). Further analysis of the composition of archaeal community at the genus level showed that Candidatus Nitrososphaera (26.48%) was the predominant genus, followed by Candidatus Nitrocosmicus (25.9%), Nitrososphaeraceae (18.08%), Haloferacaceae (8.58%), and Halalkalicoccus (3.74%) (Fig. 4b). Notably, the highest species richness for archaea is observed at Subashi Bridge 8th (SbsBrdg8), whereas for bacteria and fungi, it was at Gez River site 2 (Gez2) (Table 3). The results of the amplicon data provided insights into the composition of the microbial community and its spatial distribution patterns in the region.

Fig. 3.

Fig. 3

Open in a new tab

Soil microbial community structures in Pamir. The bar plots show the taxonomic distribution of the bacterial community (a) and fungal community (b) at the class level.

Fig. 4.

Fig. 4

Open in a new tab

Average relative abundance of the predominant archaea at the class level (a) and genus level (b) in Pamir.

Table 3.

Soil microbial alpha diversity indices at sampling sites on the Pamir Plateau.

Group Archaea Bacteria Fungi
Shannon Richness Fisher’s α Simpson Shannon Richness Fisher’s α Simpson Shannon Richness Fisher’s α Simpson
OytBrdg1 3.47 ± 0.57b 55.60 ± 32.75bc 8.93 ± 5.67b 0.95 ± 0.02ab 6.32 ± 0.07a 638.67 ± 40.07abcd 123.06 ± 8.11abcd 1.00 ± 0.00a 4.98 ± 0.20ab 204.75 ± 26.29abc 36.52 ± 5.19abc 0.99 ± 0.01a
OytGlaSun 3.45 ± 0.75b 58.67 ± 26.69b 9.44 ± 4.46b 0.93 ± 0.06ab 5.34 ± 1.04a 361.50 ± 261.47e 68.20 ± 51.06e 0.99 ± 0.01a 4.92 ± 0.38ab 210.75 ± 66.79abc 38.68 ± 14.16abc 0.99 ± 0.01a
OytGlaShade 2.50 ± 0.29c 23.00 ± 3.81d 3.40 ± 0.57c 0.88 ± 0.04bc 5.45 ± 1.66a 519.60 ± 358.72bcde 101.18 ± 70.25bcde 0.96 ± 0.07a 4.44 ± 0.19b 148.40 ± 28.98c 26.69 ± 5.90c 0.98 ± 0.01a
AktSun 2.15 ± 0.12c 19.40 ± 2.30d 2.93 ± 0.43c 0.80 ± 0.03c 6.37 ± 0.05a 667.80 ± 32.34abc 128.94 ± 7.06abc 1.00 ± 0.00a 4.56 ± 0.40ab 180.50 ± 38.42abc 33.49 ± 7.22abc 0.98 ± 0.01a
AktShade 2.25 ± 0.19c 17.80 ± 2.59d 2.57 ± 0.40c 0.85 ± 0.02bc 6.50 ± 0.10a 755.00 ± 66.79ab 147.57 ± 14.12ab 1.00 ± 0.00a 4.92 ± 0.30ab 237.80 ± 41.94abc 45.33 ± 7.27abc 0.98 ± 0.01a
Gez2 2.50 ± 0.22c 20.00 ± 5.94d 2.91 ± 0.98c 0.88 ± 0.02bc 6.62 ± 0.05a 832.25 ± 41.65a 163.88 ± 9.02a 1.00 ± 0.00a 5.22 ± 0.15a 285.50 ± 27.29a 53.84 ± 5.52a 0.99 ± 0.01a
Gez1 2.72 ± 0.12c 22.40 ± 3.78d 3.21 ± 0.61c 0.92 ± 0.01ab 6.58 ± 0.04a 802.20 ± 29.65ab 157.32 ± 6.40ab 1.00 ± 0.00a 5.09 ± 0.34ab 260.60 ± 68.81abc 48.86 ± 12.92ab 0.99 ± 0.01a
BulLake 2.51 ± 0.29c 24.60 ± 7.50d 3.73 ± 1.30c 0.87 ± 0.04bc 6.45 ± 0.09a 728.80 ± 46.24abc 142.39 ± 9.07ab 1.00 ± 0.00a 4.84 ± 0.20ab 217.80 ± 34.45abc 41.19 ± 6.52abc 0.98 ± 0.01a
SbsBrdg8 4.40 ± 0.26a 109.20 ± 18.82a 18.12 ± 3.19a 0.98 ± 0.01a 5.79 ± 0.22a 416.00 ± 79.65de 78.40 ± 16.27de 1.00 ± 0.00a 4.65 ± 0.54ab 167.20 ± 73.27bc 29.63 ± 13.56bc 0.98 ± 0.01a
SbsBrdg7 2.65 ± 0.32c 33.80 ± 11.17 cd 5.49 ± 2.08bc 0.86 ± 0.03bc 6.38 ± 0.25a 693.20 ± 127.87abc 135.06 ± 26.42abc 1.00 ± 0.00a 4.56 ± 0.56ab 171.00 ± 82.69bc 31.22 ± 16.20bc 0.98 ± 0.01a
KongT 2.33 ± 0.45c 25.40 ± 5.64d 3.94 ± 0.87c 0.80 ± 0.10c 6.52 ± 0.12a 767.60 ± 79.83ab 150.19 ± 17.11ab 1.00 ± 0.00a 4.85 ± 0.27ab 227.40 ± 51.83abc 43.68 ± 11.29abc 0.98 ± 0.00a
SBDLake 2.81 ± 0.96c 45.00 ± 25.53bcd 7.53 ± 4.61bc 0.85 ± 0.14bc 5.51 ± 1.39a 453.25 ± 265.91cde 86.55 ± 51.90cde 0.98 ± 0.03a 4.74 ± 0.66ab 210.25 ± 87.45abc 39.17 ± 16.22abc 0.98 ± 0.02a
Muztagata 2.91 ± 0.32bc 40.00 ± 13.64bcd 6.43 ± 2.50bc 0.91 ± 0.03ab 6.56 ± 0.05a 779.40 ± 37.38ab 151.99 ± 8.17ab 1.00 ± 0.00a 4.96 ± 0.15ab 230.20 ± 29.38abc 42.85 ± 6.16abc 0.99 ± 0.00a
Kulma 2.49 ± 0.32c 25.40 ± 6.50d 3.84 ± 1.03c 0.87 ± 0.05bc 6.47 ± 0.04a 726.80 ± 16.99abc 141.45 ± 3.00ab 1.00 ± 0.00a 4.44 ± 0.06b 163.00 ± 9.80bc 30.17 ± 2.12bc 0.98 ± 0.00a
SbsShade 2.41 ± 0.36c 22.00 ± 11.45d 3.27 ± 1.93c 0.87 ± 0.04bc 6.39 ± 0.07a 678.80 ± 43.85abc 131.21 ± 9.27abc 1.00 ± 0.00a 4.59 ± 0.18ab 160.20 ± 31.73bc 28.78 ± 6.83bc 0.98 ± 0.01a
SbsSun 2.71 ± 0.15c 23.40 ± 2.97d 3.39 ± 0.94c 0.91 ± 0.01ab 6.59 ± 0.07a 811.80 ± 53.88 159.53 ± 11.77ab 1.00 ± 0.00a 4.77 ± 0.17ab 192.40 ± 25.46abc 35.28 ± 4.82abc 0.98 ± 0.01a
Ulug 2.78 ± 0.15c 27.00 ± 6.04 4.02 ± 1.02c 0.91 ± 0.01ab 6.51 ± 0.07a 751.60 ± 48.84ab 146.27 ± 10.44ab 1.00 ± 0.00a 5.19 ± 0.08ab 268.40 ± 19.15ab 49.90 ± 3.97ab 0.99 ± 0.00a
Open in a new tab

The above data represent the mean ± standard deviation (n ≥ 3). Different letters (a, b, c, d) indicate significant differences between the data (Duncan test, p < 0.05).

Multiple Regression on Distance Matrices (MRM) analysis explored the influence of environmental factors on the composition of the three types of microbial communities (Table 4). It also reveals that site geographical distances significantly influenced the compositions of all three microbial communities. Archaeal communities are best explained by the environmental factors (R² = 0.4121), followed by fungal (R² = 0.2517) and bacterial communities (R² = 0.1446). Soluble salt emerges as the most influential factor for archaeal communities, followed by total nitrogen, pH, precipitation seasonality, and available nitrogen. For bacterial communities, precipitation seasonality and soil moisture were significantly influential, while total nitrogen, moisture, total organic matter, soluble salt, and available nitrogen were key factors shaping fungal communities. Annual average solar radiation, annual mean temperature, and temperature seasonality strongly correlate with altitude, suggesting elevation is a key determinant of temperature and solar radiation in this region (Fig. 5).

Table 4.

The key environmental factors influential to microbial communities revealed with MRM.

Archaea p Bacteria p Fungi p
Distances among sites −0.1719 ** −0.2672 *** −0.1311 ***
Moisture −0.0053 ns −0.1217 * −0.1043 ***
Total organic matter −0.0280 ns 0.0828 ns 0.103 *
Total nitrogen −0.1973 ** −0.1969 ns −0.122 **
Available nitrogen 0.0580 * 0.0566 ns 0.0506 **
Soluble salt −0.4375 *** −0.1004 ns −0.0528 *
pH 0.0765 * 0.0006 ns −0.0089 ns
Precipitation seasonality 0.0673 * 0.1508 ** 0.0179 ns
Open in a new tab

The star symbol indicated significant correlations between the data. ns represented p > 0.05, *represented 0.01 < p ≤ 0.05, **represented 0.001 < p ≤ 0.01, ***represented p ≤ 0.001.

Fig. 5.

Fig. 5

Open in a new tab

Kendall correlation indicated self-correlated environmental factors in the present study.

Methods

Sampling

In July 2020, 85 soil samples were collected from 17 sites along the eastern slope of the Eastern Pamir Plateau in Xinjiang, China (Fig. 1 and Table 1). At each site, five soil samples were taken as replicates. These replicates were collected from 5 m × 5 m plots using a five-point sampling strategy. The 2 cm of topsoil was removed to discard litters, and approximately 1 kg of soil was collected from depths of 2–20 cm and sieved to remove rocks and debris. The soil samples were immediately transported to the laboratory in an ice box. For subsequent DNA extraction, 50 g of soil from each sample was stored at −80 °C, and the remainder was stored under cool, dry conditions for geochemical analysis. The contour map of sampling sites was drawn using ArcGIS mapping software (Arc Geographic Information System, Environmental Systems Research Institute, Inc. USA) and downloading 30 m resolution DEM data from the Geospatial Data Cloud website (https://www.gscloud.cn/), contour data is generated through the splicing, cropping, and contour tools in the ArcGIS toolbox. Smooth the generated contour data and add a grid, legend, compass, scale bar, and drawing name.

DNA extraction and PCR amplification

Total genomic DNA was extracted from 5 g of each soil sample using CTAB/SDS method. DNA concentration and quality was assessed on 1% agarose gels. Due to the failure to extract DNA from 5 soil samples, data from 80 soil samples were finally obtained. DNA was then diluted to 1 ng/µL using sterile deionized water. The specific amplification process employed barcode-attached universal primers targeting the 16S rRNA V4-V5 region for archaea (Arch519F/Arch915R)27, the 16S rRNA V4 region for bacteria (515 F/806 R)28, and the ITS1 region for fungi (ITS1-1F-F/ITS1-1F-R)29. All PCR reactions were carried out with Phusion® High-Fidelity PCR Master Mix (New England Biolabs). All PCR reactions were performed using 15 µL systems, composing of Phusion® High-Fidelity PCR Master Mix (New England Biolabs), 0.2 µM of both forward and reverse primers, and about 10 ng DNA templates. The thermal cycling consisted of an initial denaturation at 98 °C for 1 min, followed by 30 cycles of denaturation at 98 °C for 10 s, annealing at 50 °C for 30 s, and elongation at 72 °C for 30 s, with a final elongation 72 °C for 5 min. PCR products were mixed with 1X loading buffer (contained SYB green) and subjected to electrophoresis on a 2% agarose gel for quality assessment and purified with GeneJETTM Gel Extraction Kit (Thermo Scientific).

Library preparation and sequencing

The sequencing libraries were generated using TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, USA) following the manufacturer’s recommendations. The library quality was assessed on the Qubit@ 2.0 Fluorometer (Thermo Scientific) and Agilent Bioanalyzer 2100 system. The qualified libraries were mixed in equal amounts and then sequenced on an Illumina NovaSeq platform with a 250 bp paired-end model. The DNA extraction, Amplicon library preparation, and sequencing services were provided by Novogene Co., Ltd. (Beijing, China).

Bioinformatics analysis

The forward and reverse amplicon reads acquired through the Illumina NovaSeq sequencer were demultiplexed utilizing the QIIME2 plugins (version 2018.11). Primer trimming was performed with Cutadapt tool30. The subsequent quality filtering, denoising, paired-end merging, and dereplication were conducted via the DADA2 workflow31. Chimeric sequences were identified and removed using the removeBimeradenovo function (Fig. 6).

Fig. 6.

Fig. 6

Open in a new tab

The Schematic overview of sampling and details of the workflow and tools used for data processing and analysis performed in this study.

Taxonomic assignment for archaeal and bacterial ASVs was conducted using the Naïve Bayes approach (with a minimum of 75 bootstrap cutoff value) following the DADA2 workflow31, in reference to the SILVA training set (version 138.1)32. Fungal ASVs were classified against the UNITE Fungi database (version 10.05.2021_dev)33. The taxonomically annotated ASVs were subsequently agglomerated at the species level using the tax_glom function in the “phyloseq” package31,34. ASVs not assignable at the species level were clustered into operational taxonomic units (OTUs) based on 97% similarity using the OTU function in the “kmer” package35. Representative sequences for these OTUs were classified with the Naïve Bayes approach. In each microbial dataset, singletons, doubletons, and samples with fewer than 1,000 sequences were excluded. Taxonomic assignment reads among the samples were Hellinger transformed using the decostand function in the R “vegan” package36, and these transformed values were subsequently considered as abundance measures for statistical analyses. Figure 6 depicts the overall course of the production of all datasets.

Environmental factors analysis

A standard soil test series (NY/T 1121) was conducted. Organic matter (OM) was determined using the K2Cr2O7 oxidation method. The total nitrogen (TN) was measured using the Kjeldahl method. Available nitrogen (AN) was determined using the sulfate extraction method. Available phosphorus (AP) was detected using the hydrochloric acid–ammonium fluoride extraction–molybdenum antimony colorimetric method. Available potassium (AK) was detected using the ammonium acetate extraction–flame photometric method. Soluble salt (Salt) was detected using the mass method. The pH was determined using a potentiometric method37. Kendall’s τ statistic was employed to estimate a rank-based measure of associations between environmental factors, utilizing pairs function within the “graphics” package and panel-related functions in the “MESS” package38. Multiple Regression on Distance Matrices (MRM) analysis was conducted to test the principal environmental factors shaping microbial communities, using the MRM function in the “ecodist” package39. MRM analysis employed the 1-distanceBray-Curtis similarity measure to represent the microbial community composition.

Data Records

The processed data along with metadata have been deposited in the Sequence Read Archive (SRA) database of the National Center for Biotechnology Information (NCBI) under the BioProject IDs PRJNA1032247 (https://identifiers.org/ncbi/insdc.sra:SRP468803)40. The ASV tables, fasta sequences, and taxonomy data for archaea, bacteria, and fungi were respectively uploaded to Figshare repository, resulting in distinct links for each dataset (10.6084/m9.figshare.2608753341; 10.6084/m9.figshare.2608755442; 10.6084/m9.figshare.2608756643).

Technical Validation

Sampling procedure

Strict aseptic procedures were implemented during soil sample collection to prevent contamination from the human body or between samples. The shoves and sieves were sprayed and wiped with 75% ethanol before and after every sampling, and latex gloves worn by collectors were changed for every sampling. Plastic bags and containers were newly opened or sterilized with ethanol before taking soil samples.

Qualification strategy

Eighty soil samples for each microbial dataset were successfully sequenced, yielding 98,765.09, 89,198.56, and 95,629.65 raw reads per sample in average in archaeal, bacterial, and fungal communities, respectively. The observed and estimated error rates were evaluated after error learning, to determine the optimal quality control strategy. In our research, sequencing quality thresholds for archaeal and bacterial raw reads were expected errors lower than 2 for both pair ends, trimming forward reads to 180 bp and reverse reads to 160 bp, truncating at the first instance of a quality score less than or equal to 2, and removing the reads of final lengths shorter than 100 bp; quality thresholds for fungal raw reads were expected errors at 2 and lengths trimmed to 210 bp for both pair ends, with the same quality truncating and final lengths criteria. The quality filtration, error denoising, pair-ends merging, and chimera removal yielded 56,988.48, 65,828.96, and 71,913.94 clean reads per sample on average in archaeal, bacterial, and fungal communities, respectively, which were subjected to subsequent analysis.

Taxonomy annotation

In order to take into account both ASV-based high taxonomy resolution and diversity evaluation at species level, this study employed a two-step approach for taxonomic annotation. ASVs assigned with species names were agglomerated and thus were not analogous to remaining ASVs at the taxonomic hierarchy. Consequently, the remaining ASVs that were unable to be annotated at species level, were clustered into OTUs based on 97% similarity, and annotated at genus level or above. The microbial diversity and community composition were assessed at species level with species agglomerated from ASVs and OTUs clustered from ASVs. The threshold of bootstrap value was set to 75 rather than the default 50.

Acknowledgements

This work was supported by the Project of Fund for Stable Support to Agricultural Sci-Tech Renovation (xjnkywdzc-2023005).

Author contributions

J.Z., X.S., Z.D.Z. and J.W.C. designed the study. J.Z., Q.Y.T., M.Y.G. and Z.D.Z. collected the samples. X.S. and J.Z. performed the analysis. J.Z. and X.S. wrote the paper and prepared the figure and tables. All co-authors commented on the final manuscript.

Code availability

R codes applied in the present study are available at: https://github.com/xnus/PamirSoilMicrobes.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Jing Zhu, Xiang Sun.

Contributor Information

Zhi-Dong Zhang, Email: zhangzheedong@qq.com.

Jian-Wei Chen, Email: chenjianwei@genomics.cn.

References

  • 1.Johnson, D., Campbell, C. D., Lee, J. A., Callaghan, T. V. & Gwynn-Jones, D. Arctic microorganisms respond more to elevated UV-B radiation than CO2. Nature.416, 82–83 (2002). 10.1038/416082a [DOI] [PubMed] [Google Scholar]
  • 2.Kong, W. D. A review of microbial diversity in polar terrestrial environments. Biodiversity Science.21, 456–467 (2013). [Google Scholar]
  • 3.Yang, S. P., Jiang, J. & Yan, P. Eco-geographic distribution of rare and endangered plants and endemism in the Pamirs Region of China. Journal of Arid Land Resources and Environment.32, 115–120 (2018). [Google Scholar]
  • 4.Mętrak, M., Sulwiński, M., Chachulski, Ł., Wilk, M.B., Suskamalawska, M. Creeping environmental problems in the pamir mountains: Landscape conditions, climate change, wise use and threats. In Climate Change Impacts on High-Altitude Ecosystems. Springer Science and Business Media LLC: Berlin, Germany. 665–694 (2015).
  • 5.Yao, T. et al. Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings. Nat. Clim. Change.2(9), 663–667 (2012). 10.1038/nclimate1580 [DOI] [Google Scholar]
  • 6.Mętrak, M. et al. Nature’s Patchwork: How water sources and soil salinity determine the distribution and structure of halophytic plant communities in arid environments of the Eastern Pamir. PLoS ONE12, e0174496 (2017). 10.1371/journal.pone.0174496 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Xiang, S. R., Yao, T. D., An, L. Z., Xu, B. L. & Wang, J. X. 16S rRNA Sequences and Differences in Bacteria Isolated from the Muztag Ata Glacier at Increasing Depths. Applied and Environmental Microbiology.71, 4619–4627 (2005). 10.1128/AEM.71.8.4619-4627.2005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Singh, V.P., Singh, P., Haritashya, U.K. (Eds.). Pamirs. Encyclopedia of Snow, Ice and Glaciers. 13–815 Springer, Netherlands (2011).
  • 9.Mölg, N., Bolch, T., Rastner, P., Strozzi, T. & Paul, F. A consistent glacier inventory for Karakoram and Pamir derived from Landsat data: distribution of debris cover and mapping challenges. Earth Syst. Sci. Data10, 1807–1827 (2018). 10.5194/essd-10-1807-2018 [DOI] [Google Scholar]
  • 10.Barandun, M. et al. Hot Spots of glacier mass balance variability in central Asia. Geophysical Research Letters48, e2020GL092084 (2021). 10.1029/2020GL092084 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Monika, M. et al. Morphology and distribution of biological soil crusts and their potential role in soil-forming processes under dry high-altitude periglacial conditions (Eastern Pamir, Tajikistan). Geoderma Regional.33, e00636 (2023). 10.1016/j.geodrs.2023.e00636 [DOI] [Google Scholar]
  • 12.Zhang, Q. & Duan, K. Q. Characteristics of precipitation in the Pamirs in 2017 based on WRF simulation. Arid Land Geography44, 1707–1716 (2021). [Google Scholar]
  • 13.Veresoglou, S. D., Halley, J. M. & Rillig, M. C. Extinction risk of soil biota. Nature Communications.6, 8862–8871 (2015). 10.1038/ncomms9862 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Ma, B., Wang, H., Dsouza, M. & Xu, J. Geographic patterns of co-occurrence network topological features for soil microbiota at continental scale in eastern China. ISME Journal.10, 1891–1901 (2016). 10.1038/ismej.2015.261 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Li, M. Y., Wang, J. L. & Gulbahar, S. Culturable bacterial diversity in snow, ice and melt water of theYangbark Glacier, Muztag Ata. Jourmal of Glaciology and Geocryology.37, 1634–1641 (2015). [Google Scholar]
  • 16.Fan, S. Y. et al. Variations in Soil Enzyme Activities and Microbial Communities along an Altitudinal Gradient on the Eastern Qinghai–Tibetan Plateau. Forests12, 681–681 (2021). 10.3390/f12060681 [DOI] [Google Scholar]
  • 17.Li, Y. M. et al. Function and distribution of nitrogen-cycling microbial communities in the Napahai plateau wetland. Archives of Microbiology205, 357–357 (2023). 10.1007/s00203-023-03695-6 [DOI] [PubMed] [Google Scholar]
  • 18.Xu, M. J. et al. Sensitivity of soil nitrifying and denitrifying microorganisms to nitrogen deposition on the Qinghai-Tibetan plateau. Annals of Microbiology volume71, 6 (2021). 10.1186/s13213-020-01619-z [DOI] [Google Scholar]
  • 19.Xu, Y. T., Sun, R., Yan, W. M. & Zhong, Y. Q. W. Divergent response of soil microbes to environmental stress change under different plant communities in the Loess Plateau. Catena230, 107240 (2023).
  • 20.Agre, N. S. & Orleanskii, V. K. Thermophil actinomycetes in Pamir and their antagonistic properties. Mon Bull Minist Health Public Health Lab Serv.31, 95–102 (1962). [PubMed] [Google Scholar]
  • 21.KuznetsovM, V. D. Actinomycetes of various Pamir soils and their antagonistic properties. Mikrobiologiia29, 563–570 (1969). [PubMed] [Google Scholar]
  • 22.Aksenov, S. I., Babyeva, I. P. & Golubev, V. I. On the mechanism of adaptation of microorganisms to conditions of extreme low humidity. Life Sci Space Res.11, 55–61 (1973). [PubMed] [Google Scholar]
  • 23.Szymon, Z., Marcin, N., Janusz, B., Piotr, M. & Arkadiusz, N. Fungal root endophyte associations of plants endemic to the Pamir Alay Mountains of Central Asia. Symbiosis54, 139–149 (2011). 10.1007/s13199-011-0137-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Khomutovska, N. et al. Life in extreme habitats: diversity of endolithic microorganisms from cold desert ecosystems of eastern Pamir. Polish Journal of Ecology65, 303–319 (2017). 10.3161/15052249PJE2017.65.4.001 [DOI] [Google Scholar]
  • 25.Khomutovska, N., de los Ríos, A. & Jasser, I. Diversity and Colonization Strategies of Endolithic Cyanobacteria in the Cold Mountain Desert of Pamir. Microorganisms9, 6 (2021). 10.3390/microorganisms9010006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Bu, X. Y., Ren, M., Wan, C. X. & Zhang, L. L. Diversity of aerobic cold-adapted bacteria and archaea isolated from the Pamir Plateau. Acta Microbiologica Sinica.62, 2568–2581 (2022). [Google Scholar]
  • 27.Wei, S. et al. Comparative evaluation of three archaeal primer pairs for exploring archaeal communities in deep-sea sediments and permafrost soils. Extremophiles23, 747–757 (2019). 10.1007/s00792-019-01128-1 [DOI] [PubMed] [Google Scholar]
  • 28.Walters, W. et al. Improved Bacterial 16S rRNA Gene (V4 and V4-5) and Fungal Internal Transcribed Spacer Marker Gene Primers for Microbial Community Surveys. mSystems22, e00009–15 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Usyk, M., Zolnik, C. P., Patel, H., Levi, M. H. & Burk, R. D. Novel ITS1 Fungal Primers for Characterization of the Mycobiome. mSphere13, e00488–17 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetjournal.17, 10–2 (2011). [Google Scholar]
  • 31.Callahan, B. J., Sankaran, K., Fukuyama, J. A., McMurdie, P. J. & Holmes, S. P. Bioconductor workflow for microbiome data analysis: From raw reads to community analyses. F1000Research.5, 1492 (2016). 10.12688/f1000research.8986.1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Yilmaz, P. et al. The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Research.42, D643–D8 (2014). 10.1093/nar/gkt1209 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Nilsson, R. H. et al. K. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res.47, D259–D64 (2018). 10.1093/nar/gky1022 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.McMurdie, P. J. & Holmes, S. phyloseq: An r package for reproducible interactive analysis and graphics of microbiome census data. PLOS ONE.8, e61217 (2013). 10.1371/journal.pone.0061217 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Wilkinson, S. kmer: an R package for fast alignment-free clustering of biological sequences. https://cran.r-project.org/package=kmer Accessed (2018).
  • 36.Oksanen, J. et al vegan: Community Ecology Package. R package version 2.5-6. (2019).
  • 37.Zhu, J., Wang, H. N., Tang, Q. Y., Gu, M. Y. & Zhang, Z. D. Composition and distribution of bacterial communities and potential radiation-resistant bacteria at different elevations in the eastern Pamirs. Front. Microbiol.15, 1427806 (2024). 10.3389/fmicb.2024.1427806 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Ekstrøm, C. MESS: Miscellaneous Esoteric Statistical Scripts. CRAN.R-project.org/package=MESS. Accessed (2022).
  • 39.Goslee, S. C. & Urban, D. L. The ecodist package for dissimilarity-based analysis of ecological data. Journal of Statistical Software22, 1–19 (2007). 10.18637/jss.v022.i07 [DOI] [Google Scholar]
  • 40.NCBI Sequence Read Archive.https://identifiers.org/ncbi/insdc.sra:SRP468803 (2023).
  • 41.Zhu, J. Amplicon data of 80 soil archaea from the Pamir Plateau. figshare10.6084/m9.figshare.26087533 (2024). 10.6084/m9.figshare.26087533 [DOI]
  • 42.Zhu, J. Amplicon data of 80 soil bacteria from the Pamir Plateau. figshare10.6084/m9.figshare.26087554 (2024). 10.6084/m9.figshare.26087554 [DOI]
  • 43.Zhu, J. Amplicon data of 80 soil fungi from the Pamir Plateau. figshare10.6084/m9.figshare.26087566 (2024). 10.6084/m9.figshare.26087566 [DOI]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

  1. NCBI Sequence Read Archive.https://identifiers.org/ncbi/insdc.sra:SRP468803 (2023).
  2. Zhu, J. Amplicon data of 80 soil archaea from the Pamir Plateau. figshare10.6084/m9.figshare.26087533 (2024). 10.6084/m9.figshare.26087533 [DOI]
  3. Zhu, J. Amplicon data of 80 soil bacteria from the Pamir Plateau. figshare10.6084/m9.figshare.26087554 (2024). 10.6084/m9.figshare.26087554 [DOI]
  4. Zhu, J. Amplicon data of 80 soil fungi from the Pamir Plateau. figshare10.6084/m9.figshare.26087566 (2024). 10.6084/m9.figshare.26087566 [DOI]

Data Availability Statement

R codes applied in the present study are available at: https://github.com/xnus/PamirSoilMicrobes.

Articles from Scientific Data are provided here courtesy of Nature Publishing Group

RESOURCES