Steeper spatial scaling patterns of subsoil microbiota are shaped by deterministic assembly process.

Although many studies have investigated the spatial scaling of microbial communities living in surface soils, very little is known about the patterns within deeper strata, nor is the mechanism behind them. Here, we systematically assessed spatial scaling of prokaryotic biodiversity within three different strata (Upper: 0-20 cm, Middle: 20-40 cm, and Substratum: 40-100 cm) in a typical grassland by examining both distance-decay (DDRs) and species-area relationships (SARs), taxonomically and phylogenetically, as well as community assembly processes. Each layer exhibited significant biogeographic patterns in both DDR and SAR (p < .05), with taxonomic turnover rates higher than phylogenetic ones. Specifically, the spatial turnover rates, ß and z values, respectively, ranged from 0.016 ± 0.005 to 0.023 ± 0.005 and 0.065 ± 0.002 to 0.077 ± 0.004 across soil strata, and both increased with depth. Moreover, the prokaryotic community in grassland soils assembled mainly according to deterministic rather than stochastic mechanisms. By using normalized stochasticity ratio (NST) based on null model, the relative importance of deterministic ratios increased from 48.0 to 63.3% from Upper to Substratum, meanwhile a phylogenetic based method revealed average ßNTI also increased with depth, from -5.29 to 19.5. Using variation partitioning and distance approaches, both geographic distance and soil properties were found to strongly affect biodiversity structure, the proportions increasing with depth, but spatial distance was always the main underlying factor. These indicated increasingly deterministic proportions in accelerating turnover rates for spatial assembly of prokaryotic biodiversity. Our study provided new insights on biogeography in different strata, revealing importance of assembly patterns and mechanisms of prokaryote communities in below-surface soils.

Niche width of above- and below-ground organisms varied in predicting biodiversity profiling along a latitudinal gradient.

Biodiversity is the foundation of all ecosystems across the planet, and having a better understanding of its global distribution mechanism could be important for biodiversity conservation under global change. A niche width model, combined with metabolic theory, has successfully predicted the increase of α-diversity and decrease of ß-diversity in the below-ground microbial community along an altitudinal mountain gradient. In this study, we evaluated this niche width model of above-ground plants (mainly trees and shrubs) and below-ground bulk soil microbial communities (i.e., bacteria and archaea) along a latitudinal gradient of forests in China. The niche widths of both plants and microbes increased with increasing temperature and precipitation, and with proximity to circumneutral pH. However, the α- and ß-diversities (observed richness and Bray-Curtis dissimilarity, respectively) could not be accurately predicted by a single niche width model alone, either temperature, precipitation or pH. Considering the interactions among different niche width models, all three niche width models were combined to predict biodiversity at the community level using structural equation modelling. The results showed that the niche width model of circumneutral pH was most important in predicting diversity profiling (i.e., α- and ß-diversity) for both plants and microbes, while niche width of precipitation and temperature showed both direct and indirect importance for microbe and plant biodiversity, respectively. Because the current niche width model neglects several scenarios related to taxon and environmental attributes, it still needs to be treated with caution in predicting biodiversity trends.

DARHD: A sequence database for aromatic ring-hydroxylating dioxygenase analysis and primer evaluation.

Biodegradation of aromatic compounds is ubiquitous in the environment and important for controlling organic pollutants. Aromatic ring-hydroxylating dioxygenases (ARHDs) are responsible for the first and rate-limiting step of aerobic biodegradation of aromatic compounds. The ARHD α subunit is a good biomarker for studying functional microorganisms in the environment, however their diversity and corresponding primer coverage are unclear, both of which require a comprehensive sequence database for the ARHD α subunit. Here amino acid sequences of the ARHD α subunit were collected, and a total of 103 sequences were selected as seed sequences that were distributed in 72 bacterial genera with 34 gene names. Based on both homolog search and keyword confirmation against the GenBank, a sequence database of ARHD (DARHD) has been established and 6367 highly credible sequences were retrieved. DARHD contained 407 bacterial genera capable of degrading 38 aromatic substrates, and intricate relationships among the gene name, aromatic substrate and microbial taxa were observed. Thereafter, a total of 136 pairs of primers were collected and assessed. Results showed coverages of most published primers were low. Our research provides new insights for understanding the diversity of ARHD α subunit, and gives guidance on the design and application of primers in the future.

Immune cell profiling of preeclamptic pregnant and postpartum women by single-cell RNA sequencing.

Preeclampsia (PE), a leading cause of maternal and fetal morbidity and mortality, is closely related to the immune system alterations. However, little is known about the landscape and heterogeneity of maternal immune system at single-cell level among PE patients. In this study, peripheral blood mononuclear cells (PBMCs) were isolated from three early-onset preeclamptic pregnant women and two healthy control, respectively. Single-cell RNA sequencing was performed on 10× genomics platform and single-cell transcriptomes were obtained to characterize immune cell subgroups at the pregnant and postpartum stages. In total, 80,429 single-cell transcriptomes were obtained. 19 cellular compositions were identified, which were categorized into six cell types including T cells, natural killer (NK) cells, B cells, monocytes, plasmacytoid dendritic cells and conventional dendritic cells. There were excessive activation of B cells, monocytes and NK cells in PE patients at the pregnant stage based on comparative analysis. Lower immune response activation was noticed in CD4+ and CD8+ T cells in PE patients, especially the low-activation of memory T cells at the pregnant and postpartum stages. PE patients showed high activation of B cells in pregnancy persisted postpartum and lower activation of memory T cells, indicating their persistent effects on the pathogenesis and recurrence risk of PE. This study provide a broad characterization of the single-cell transcriptome of PBMCs in PE, which contributes to identification of immune imbalance for its monitoring and treatment.

Preeclampsia (PE) is characterized by de novo onset of proteinuria or other maternal organ dysfunction and hypertension at gestational age of 20 weeks or more. Its pathogenesis is associated with the abnormal placental development caused by insufficient trophoblast invasion and impaired spiral artery remodeling, and consequent maternal-inflammatory response and endovascular dysfunction. Peripheral immune response may play a crucial role in the pathogenesis of PE. Besides, lower activation of CD4⁺ memory T cells was related to the onset of PE. However, the roles of the immune cells in PE recurrence are still not well defined. Single-cell RNA sequencing was performed to immune cells in PE patients, which confirmed an excessive inflammatory state in monocytes, NK cells and B cells and lower activation of memory T cells in PE women. Our data showed that PE was associated with the immune imbalance, which may provide potential therapeutic strategies for its treatment.

Sampling cores and sequencing depths affected the measurement of microbial diversity in soil quadrats.

Due to the massive quantity and broad phylogeny, an accurate measurement of microbial diversity is highly challenging in soil ecosystems. Initially, the deviation caused by sampling should be adequately considered. Here, we attempted to uncover the effect of different sampling strategies on α diversity measurement of soil prokaryotes. Four 1 m2 sampling quadrats in a typical grassland were thoroughly surveyed through deep 16S rRNA gene sequencing (over 11 million reads per quadrat) with numerous replicates (33 soil sampling cores with total 141 replicates per quadrat). We found the difference in diversity was relatively small when pooling soil cores before and after DNA extraction and sequencing, but they were both superior to a non-pooling strategy. Pooling a small number of soil cores (i.e., 5 or 9) combined with several technical replicates is sufficient to estimate diversities for soil prokaryotes, and there is great flexibility in pooling original samples or data at different experimental steps. Additionally, the distribution of local α diversity varies with sampling core number, sequencing depth, and abundance distribution of the community, especially for high orders of Hill diversity index (i.e., Shannon entropy and inverse Simpson index). For each grassland soil quadrat (1 m2), retaining 100,000 reads after taxonomic clustering might be a realistic option, as these number of reads can efficiently cover the majority of common species in this area. Our findings provide important guidance for soil sampling strategy, and the general results can serve as a basis for further studies.

ARDEP, a Rapid Degenerate Primer Design Pipeline Based on k-mers for Amplicon Microbiome Studies.

The survey of microbial diversity in various environments has relied upon the widespread use of well-evaluated amplification primers for taxonomic marker genes (e.g., prokaryotic 16S and fungal ITS). However, it is urgent to develop a fast and accurate bioinformatic program to design primers for microbial functional genes to explore more mechanisms in the microbial community. Here, we provide a rapid degenerate primer design pipeline (ARDEP) based on the k-mer algorithm, which can bypass the time-consuming step of sequence alignment to greatly reduce run times while ensuring accuracy. In addition, we developed an open-access platform for the implementation of primer design projects that could also calculate the amplification product length, GC content, Annealing Temperature (Tm), and ΔG of primer self-folding, and identify covered species and functional groups. Using this new platform, we designed primers for several functional genes in the nitrogen cycle, including napA and amoA. Our newly designed primers achieved higher coverage than the commonly used primers for all tested genes. The program and the associated platform that applied the k-mer algorithm could greatly enhance the design and evaluation of primers for environmental microbiome studies.

Adaptive shifts of bacterioplankton communities in response to nitrogen enrichment in a highly polluted river.

Anthropogenic activity-mediated nutrient pollution, especially nitrogen enrichment, poses one of the major threats to river ecosystems. However, it remains unclear how and to which extent it affects aquatic microbial communities, especially in heavily polluted rivers. In this study, a significant environmental gradient, particularly nitrogen gradient, was observed along a wastewater receiving river, the North Canal River (NCR). The pollution level was highest, moderate, and lowest in the up-, middle, and down-streams, respectively. The community composition of bacterioplankton transitioned from being Betaproteobacteria-dominated upstream to Gammaproteobacteria-dominated downstream. Copiotrophic groups, such as Polynucleobacter (Betaproteobacteria) and Hydrogenophaga (Betaproteobacteria), were dominant in the upstream. Multiple statistical analyses indicated that total nitrogen (TN) was the most important factor driving the adaptive shifts of community structure. Analyses of co-occurrence networks showed that the complexity of networks was disrupted in the up- and middle streams, while enhanced in the downstream. Our findings here suggested that microbial interactions were reduced in response to the aggravation of nutrient pollution. Similar to these changes, we observed significant dissimilarity of composition of functional groups, with highest abundance of nitrogen metabolism members under the highest level of nitrogen enrichment. Further analyses indicated that most of these functional groups belonged to Betaproteobacteria, suggesting the potential coupling of community composition and function diversity. In summary, adaptive shifts of bacterioplankton community composition, as well as species interactions, occurred in response to nutrient pollution in highly polluted water bodies.

ARGA, a pipeline for primer evaluation on antibiotic resistance genes.

Molecular biology techniques have assisted in the investigation of antibiotic resistance genes (ARGs) from various environments. However, their accuracy relies on primer quality and data interpretation, both of which require a full-coverage sequence database for ARGs. Here, based upon the abandoned Antibiotic Resistance Genes Database (ARDB), we created an updated sequence database of antibiotic resistance genes (SDARG). A total of 1,260,069 protein sequences and 1,164,479 nucleotide sequences, 56 times more sequences than ARDB, from 448 types of ARGs (enabling resistance to 18 categories of antibiotics) were collected and integrated with different hierarchical credibility and full-scale taxonomic information. Based on this comprehensive sequence database, an online pipeline - ARG analyzer (ARGA, http://mem.rcees.ac.cn:8083/) was developed to assess current ARGs primers, as well as annotate ARGs from environmental metagenomes. Thereafter, a list of 658 published primer pairs, targeting 173 ARGs, was evaluated using ARGA and integrated in ARGA as ARGs primer database. The results showed that 65.05% primers are of high specificity (≥90%), while only 29.79% primers cover >50% of targeted sequences, indicating a divergence in the quality of current ARG primers. Hence, primer assessment or redesign is highly recommended to improve the accuracy of ARGs studies. ARGs primer database was attached in ARGA to provide researchers alternatives to better survey ARGs in the environment.

Habitat environments impacted the gut microbiome of long-distance migratory swan geese but central species conserved.

The gut microbime plays an important role in the health of wild animals. This microbial community could be altered by habitat pollution and other human activities that threaten the host organisms. Here, we satellite-tracked a flock of swan geese (Anser cygnoides) migrating from their breeding area (Khukh Lake, Mongolia), with low levels of human activity, to their wintering area (Poyang Lake, China) which has been heavily impacted by human activities. Twenty fecal samples were collected from each site. High-throughput sequencing of 16S and ITS was employed to explore bacterial and fungal composition and diversity of their gut microbiome. Although general composition, alpha-diversity, functional prediction, and the central taxa in the phylogenetic networks showed some similarities between the two habitats, significant divergences were detected in terms of beta-diversity, species abundances, and interaction network topologies. In addition, disease-related and xenobiotic biodegradation pathways, and pathogenic bacteria were significantly increased in bacterial communities from samples at Poyang Lake. Our results reveal that the gut microbiome of swan geese, while somewhat altered after long-distance migration, still maintained a core group of species. We also show that habitat environmental stress could impact these gut microbial communities, suggesting that habitat pollution could indirectly threaten wild animals by altering their gut microbiome.