Microbial diversity in hummock and hollow soils of three wetlands on the Qinghai-Tibetan plateau revealed by 16S rRNA pyrosequencing

The wetlands of the Qinghai-Tibetan Plateau are believed to play an important role in global nutrient cycling, but the composition and diversity of microorganisms in this ecosystem are poorly characterized. An understanding of the effects of geography and microtopography on microbial populations will provide clues to the underlying mechanisms that structure microbial communities. In this study, we used pyrosequencing-based analysis of 16S rRNA gene sequences to assess and compare the composition of soil microbial communities present in hummock and hollow soils from three wetlands (Dangxiong, Hongyuan and Maduo) on the Qinghai-Tibetan Plateau, the world’s highest plateau. A total of 36 bacterial phyla were detected. Proteobacteria (34.5% average relative abundance), Actinobacteria (17.3%) and Bacteroidetes (11%) had the highest relative abundances across all sites. Chloroflexi, Acidobacteria, Verrucomicrobia, Firmicutes, and Planctomycetes were also relatively abundant (1–10%). In addition, archaeal sequences belonging to Euryarchaea, Crenarchaea and Thaumarchaea were detected. Alphaproteobacteria sequences, especially of the order Rhodospirillales, were significantly more abundant in Maduo than Hongyuan and Dangxiong wetlands. Compared with Hongyuan soils, Dangxiong and Maduo had significantly higher relative abundances of Gammaproteobacteria sequences (mainly order Xanthomonadales). Hongyuan wetland had a relatively high abundance of methanogens (mainly genera Methanobacterium, Methanosarcina and Methanosaeta) and methanotrophs (mainly Methylocystis) compared with the other two wetlands. Principal coordinate analysis (PCoA) indicated that the microbial community structure differed between locations and microtopographies and canonical correspondence analysis indicated an association between microbial community structure and soil properties or geography. These insights into the microbial community structure and the main controlling factors in wetlands of the Qinghai-Tibetan Plateau provide a valuable background for further studies on biogeochemical processes in this distinct ecosystem

A sequence-based genetic linkage map as a reference for Brassica rapa pseudochromosome assembly

<p>Abstract</p> <p>Background</p> <p><it>Brassica rapa </it>is an economically important crop and a model plant for studies concerning polyploidization and the evolution of extreme morphology. The multinational <it>B. rapa </it>Genome Sequencing Project (BrGSP) was launched in 2003. In 2008, next generation sequencing technology was used to sequence the <it>B. rapa </it>genome. Several maps concerning <it>B. rapa </it>pseudochromosome assembly have been published but their coverage of the genome is incomplete, anchoring approximately 73.6% of the scaffolds on to chromosomes. Therefore, a new genetic map to aid pseudochromosome assembly is required.</p> <p>Results</p> <p>This study concerns the construction of a reference genetic linkage map for <it>Brassica rapa</it>, forming the backbone for anchoring sequence scaffolds of the <it>B. rapa </it>genome resulting from recent sequencing efforts. One hundred and nineteen doubled haploid (DH) lines derived from microspore cultures of an F1 cross between a Chinese cabbage (<it>B. rapa </it>ssp. <it>pekinensis</it>) DH line (Z16) and a rapid cycling inbred line (L144) were used to construct the linkage map. PCR-based insertion/deletion (InDel) markers were developed by re-sequencing the two parental lines. The map comprises a total of 507 markers including 415 InDels and 92 SSRs. Alignment and orientation using SSR markers in common with existing <it>B. rapa </it>linkage maps allowed ten linkage groups to be identified, designated A01-A10. The total length of the linkage map was 1234.2 cM, with an average distance of 2.43 cM between adjacent marker loci. The lengths of linkage groups ranged from 71.5 cM to 188.5 cM for A08 and A09, respectively. Using the developed linkage map, 152 scaffolds were anchored on to the chromosomes, encompassing more than 82.9% of the <it>B. rapa </it>genome. Taken together with the previously available linkage maps, 183 scaffolds were anchored on to the chromosomes and the total coverage of the genome was 88.9%.</p> <p>Conclusions</p> <p>The development of this linkage map is vital for the integration of genome sequences and genetic information, and provides a useful resource for the international <it>Brassica </it>research community.</p

Identification of active aerobic methanotrophs in plateau wetlands using DNA stable isotope probing

Sedge-dominated wetlands on the Qinghai–Tibetan Plateau are methane emission centers. Methanotrophs at these sites play a role in reducing methane emissions, but relatively little is known about the composition of active methanotrophs in these wetlands. Here, we used DNA stable isotope probing to identify the key active aerobic methanotrophs in three sedge-dominated wetlands on the plateau. We found that Methylocystis species were active in two peatlands, Hongyuan and Dangxiong. Methylobacter species were found to be active only in Dangxiong peat. Hongyuan peat had the highest methane oxidation rate, and cross-feeding of carbon from methanotrophs to methylotrophic Hyphomicrobium species was observed. Owing to a low methane oxidation rate during the incubation, the labeling of methanotrophs in Maduo wetland samples was not detected. Our results indicate that there are large differences in the activity of methanotrophs in the wetlands of this region

Aerobic methanotroph diversity in Riganqiao peatlands on the Qinghai-Tibetan Plateau

The Zoige Plateau is characterized by its high altitude, low latitude and low annual mean temperature of approximately 1°C and is a major source of atmospheric methane in the Qinghai–Tibetan Plateau. Methanotrophs play an important role in the global cycling of CH4, but the diversity, identity and activity of methanotrophs in this region are poorly characterized. Soils were collected from hummocks and hollows in the Riganqiao peatland and the methanotroph community was analysed by qPCR and sequencing methane monooxygenase (pmoA and mmoX) genes. The pmoA genes ranged between 107 and 108?copies?g?1 fresh soil, with a somewhat greater abundance in hummocks than hollows. The pmoA genes were analysed by amplicon pyrosequencing and the mmoX genes by cloning and sequencing. Methylocystis species were found to be the most abundant methanotrophs, but numerous clades were present including three novel pmoA and three novel mmoX clusters. There were differences between the methanotroph communities in the hummocks and hollows, with the most significant being an increased abundance of uncultivated type Ib methanotrophs in the hollows. The results indicate that aerobic methanotrophs are abundant in Riganqiao peatland and include previously undetected clades in this geographically isolated and distinctive environmen

Salinity affects the composition of the aerobic methanotroph community in alkaline lake sediments from the Tibetan Plateau

Lakes are widely distributed on the Tibetan Plateau, which plays an important role in natural methane emission. Aerobic methanotrophs in lake sediments reduce the amount of methane released into the atmosphere. However, no study to date has analyzed the methanotroph community composition and their driving factors in sediments of these high-altitude lakes (&gt;4000 m). To provide new insights on this aspect, the abundance and composition in the sediments of six high-altitude alkaline lakes (including both freshwater and saline lakes) on the Tibetan Plateau were studied. The quantitative PCR, terminal restriction fragment length polymorphism and 454- pyrosequencing methods were used to target the pmoA genes. The pmoA gene copies ranged 10? -10? per gram fresh sediment. Type I methanotrophs predominated in Tibetan lake sediments, with Methylobacter and uncultivated type Ib methanotrophs being dominant in freshwater lakes and Methylomicrobium in saline lakes. Combining the pmoA-pyrosequencing data from Tibetan lakes with other published pmoAsequencing data from lake sediments of other regions, a significant salinity and alkalinity effect (P=0.001) was detected, especially salinity, explained ~25% of methanotroph community variability. The main effect was Methylomicrobium being dominant (up to 100%) in saline lakes only. In freshwater lakes, however, methanotroph composition was relatively diverse, including Methylobacter, Methylocystis, and uncultured type Ib clusters. This study provides the first methanotroph data for high-altitude lake sediments (&gt;4000 m) and shows that salinity is a driving factor for the community composition of aerobic methanotrophs

Methylococcaceae are the dominant active aerobic methanotrophs in a Chinese tidal marsh

Although coastal marshes are net carbon sinks, they are net methane sources. Aerobic methanotrophs in coastal marsh soils are important methane consumers, but their activity and populations are poorly characterized. DNA stable-isotope probing followed by sequencing was used to determine how active methanotrophic populations differed in the main habitats of a Chinese coastal marsh. These habitats included mudflat, native plant-dominated, and alien plant-dominated habitats. Methylococcaceae was the most active methanotroph family across four habitats. Abundant methylotroph sequences, including methanotrophs and non-methane-oxidizing methylotrophs (Methylotenera and Methylophaga), constituted 50–70% of the 16S rRNA genes detected in the labeled native plant-dominated and mudflat soils. Methylotrophs were less abundant (~ 20%) in labeled alien plant-dominated soil, suggesting less methane assimilation into the target community or a different extent of carbon cross-feeding. Canonical correspondence analysis indicated a significant correlation between the active bacterial communities and soil properties (salinity, organic carbon, total nitrogen, pH, and available phosphorus). Importantly, these results highlight how changing vegetation or soil features in coastal marshes may change their resident active methanotrophic populations, which will further influence methane cycling.</p

Drug Repositioning by Kernel-Based Integration of Molecular Structure, Molecular Activity, and Phenotype Data

<div><p>Computational inference of novel therapeutic values for existing drugs, i.e., drug repositioning, offers the great prospect for faster and low-risk drug development. Previous researches have indicated that chemical structures, target proteins, and side-effects could provide rich information in drug similarity assessment and further disease similarity. However, each single data source is important in its own way and data integration holds the great promise to reposition drug more accurately. Here, we propose a new method for drug repositioning, PreDR (<b>Pre</b>dict <b>D</b>rug <b>R</b>epositioning), to integrate molecular structure, molecular activity, and phenotype data. Specifically, we characterize drug by profiling in chemical structure, target protein, and side-effects space, and define a kernel function to correlate drugs with diseases. Then we train a support vector machine (SVM) to computationally predict novel drug-disease interactions. PreDR is validated on a well-established drug-disease network with 1,933 interactions among 593 drugs and 313 diseases. By cross-validation, we find that chemical structure, drug target, and side-effects information are all predictive for drug-disease relationships. More experimentally observed drug-disease interactions can be revealed by integrating these three data sources. Comparison with existing methods demonstrates that PreDR is competitive both in accuracy and coverage. Follow-up database search and pathway analysis indicate that our new predictions are worthy of further experimental validation. Particularly several novel predictions are supported by clinical trials databases and this shows the significant prospects of PreDR in future drug treatment. In conclusion, our new method, PreDR, can serve as a useful tool in drug discovery to efficiently identify novel drug-disease interactions. In addition, our heterogeneous data integration framework can be applied to other problems.</p></div