Soil pH is an important determinant of microbial community composition and diversity, yet few studies have characterized the specific effects of pH on individual bacterial taxa within bacterial communities, both abundant and rare. We collected composite soil samples over 2 years from an experimentally maintained pH gradient ranging from 4.5 to 7.5 from the Craibstone Experimental Farm (Craibstone, Scotland). Extracted nucleic acids were characterized by bacterial and group-specific denaturing gradient gel electrophoresis and next-generation sequencing of bacterial 16S rRNA genes. Both methods demonstrated comparable and reproducible shifts within higher taxonomic bacterial groups (e.g. Acidobacteria, Alphaproteobacteria, Verrucomicrobia, and Gammaproteobacteria) across the pH gradient. In addition, we used non-negative matrix factorization (NMF) for the first time on 16S rRNA gene data to identify positively interacting (i.e. co-occurring) operational taxonomic unit (OTU) clusters (i.e. 'components'), with abundances that correlated strongly with pH, and sample year to a lesser extent. All OTUs identified by NMF were visualized within principle coordinate analyses of UNIFRAC distances and subjected to taxonomic network analysis (SSUnique), which plotted OTU abundance and similarity against established taxonomies. Most pH-dependent OTUs identified here would not have been identified by previous methodologies for microbial community profiling and were unrelated to known lineages.

Sequence clustering is a basic bioinformatics task that is attracting renewed attention with the development of metagenomics and microbiomics. The latest sequencing techniques have decreased costs and as a result, massive amounts of DNA/RNA sequences are being produced. The challenge is to cluster the sequence data using stable, quick and accurate methods. For microbiome sequencing data, 16S ribosomal RNA operational taxonomic units are typically used. However, there is often a gap between algorithm developers and bioinformatics users. Different software tools can produce diverse results and users can find them difficult to analyze. Understanding the different clustering mechanisms is crucial to understanding the results that they produce. In this review, we selected several popular clustering tools, briefly explained the key computing principles, analyzed their characters and compared them using two independent benchmark datasets. Our aim is to assist bioinformatics users in employing suitable clustering tools effectively to analyze big sequencing data. Related data, codes and software tools were accessible at the link http://lab.malab.cn/∼lg/clustering/.

Preeclampsia (PE) is one of the pregnancy metabolic diseases. Since Gut microbiota play important roles in the hosts' metabolism, it is necessary to investigate the gut microbiota in PE patients, so that some intestinal dysbiosis might be detected as a biomarker for PE early diagnosis or as a target for intervention. One hundred subjects were categorized into four groups: 26 PE patients in late pregnancy, healthy individuals in early, middle, and late pregnancy (26/24/24 women). Gut microbiota were analyzed by sequencing the V4 region of the 16S rDNA gene using Illuminal MiSeq. Data were analyzed by multivariate statistics. Bacteroidetes was the dominant bacterium (47.57-52.35%) in the pregnant women in South China. Tenericutes increased while Verrucomicrobia almost disappeared in late pregnancy. In the PE patients, there was an overall increase in pathogenic bacteria, Clostridium perfringens (p = 0.03) and Bulleidia moorei (p = 0.00) but a reduction in probiotic bacteria Coprococcus catus (p = 0.03). Our research suggests that there is a significant structural shift of the gut microbiota in PE patients, which might be associated with the occurrence and development of the disease. However, further studies are required to understand the underlying mechanisms.

Our meta-analysis suggests a significant association of the MAOA gene with major depressive disorder and BPD within specific groups, indicating that these three polymorphisms of the MAOA gene may be associated with mood disorders by sex and ethnicity. Moreover, our systematic meta-analysis has revealed that although MAOA may be a common candidate gene for mood disorders, different polymorphisms and alleles appear to play different roles in major depressive disorder and BPD.