ASAP 2: a pipeline and web server to analyze marker gene amplicon sequencing data automatically and consistently

Re-vitalizing Wastewater: Nutrient Recovery and carbon capture through Microbe-Algae synergy using Omics-Biology

Increasing amounts of wastewater is the most pervasive and challenging environmental problem globally. Conventional treatment methods are costly and entail huge energy and carbon consumption and greenhouse gas emissions. Owing to their unique ability of carbon capturing and resource recovery, microalgae-microbiome based treatment is a potential approach and is widely used for carbon-neutral wastewater treatment. Microalgae-bacteria synergy (i.e., the functionally beneficial microbial synthetic communities) performs better and enhances carbon-sequestration and nutrient recovery from wastewater treatment plants. This review presents a comprehensive information regarding the potential of microalgae-microbiome as a sustainable agent for wastewater and discusses synergistic approaches for effective nutrient removal. Moreover, this review discusses, the role of omics-biology and Insilco approaches in unravelling and understanding the algae-microbe synergism and their response toward wastewater treatment. Finally, it discusses various microbiome engineering approaches for developing the effective microalgae-bacteria partners for carbon sequestration and nutrient recovery from wastewater, and summarizes future research perspectives on microalgae-microbiome based bioremediation.

The Maleth program: Malta's first space mission discoveries on the microbiome of diabetic foot ulcers

The purpose of the Maleth Program, also known as Project Maleth, is Malta's first space program to evaluate human skin tissue microbiome changes in type 2 diabetes mellitus (T2DM) patients afflicted with diabetic foot ulcers (DFU). This was carried out in both ground-based models and spaceflight. The first mission (Maleth I) under this program was carried out to uncover the effects of spaceflight, microgravity and radiation on human skin tissue microbiome samples from six T2DM patients recruited into the study. Each patient human skin tissue sample was split in three, with one section processed immediately for genomic profiling by 16S typing and the rest were processed for longer term ground-control and spaceflight experiments. Ground-control and spaceflight human skin tissue samples were also processed for genomic profiling upon mission re-entry and completion. Maleth I's overall objective was achieved, as human skin tissue samples with their microbiomes travelled to space and back yielding positive results by both standard microbiology techniques and genetic typing using 16S rRNA amplicon sequencing. Preliminary findings of this mission are discussed in light of its innovative approach at DFU microbiome research, and the clinical implications that may emerge from this and other future similar studies.

Tourmaline: A containerized workflow for rapid and iterable amplicon sequence analysis using QIIME 2 and Snakemake

Background

Amplicon sequencing (metabarcoding) is a common method to survey diversity of environmental communities whereby a single genetic locus is amplified and sequenced from the DNA of whole or partial organisms, organismal traces (e.g., skin, mucus, feces), or microbes in an environmental sample. Several software packages exist for analyzing amplicon data, among which QIIME 2 has emerged as a popular option because of its broad functionality, plugin architecture, provenance tracking, and interactive visualizations. However, each new analysis requires the user to keep track of input and output file names, parameters, and commands; this lack of automation and standardization is inefficient and creates barriers to meta-analysis and sharing of results.

Findings

We developed Tourmaline, a Python-based workflow that implements QIIME 2 and is built using the Snakemake workflow management system. Starting from a configuration file that defines parameters and input files—a reference database, a sample metadata file, and a manifest or archive of FASTQ sequences—it uses QIIME 2 to run either the DADA2 or Deblur denoising algorithm; assigns taxonomy to the resulting representative sequences; performs analyses of taxonomic, alpha, and beta diversity; and generates an HTML report summarizing and linking to the output files. Features include support for multiple cores, automatic determination of trimming parameters using quality scores, representative sequence filtering (taxonomy, length, abundance, prevalence, or ID), support for multiple taxonomic classification and sequence alignment methods, outlier detection, and automated initialization of a new analysis using previous settings. The workflow runs natively on Linux and macOS or via a Docker container. We ran Tourmaline on a 16S ribosomal RNA amplicon data set from Lake Erie surface water, showing its utility for parameter optimization and the ability to easily view interactive visualizations through the HTML report, QIIME 2 viewer, and R- and Python-based Jupyter notebooks.

Conclusion

Automated workflows like Tourmaline enable rapid analysis of environmental amplicon data, decreasing the time from data generation to actionable results. Tourmaline is available for download at github.com/aomlomics/tourmaline.

Microbial Richness of Marine Biofilms Revealed by Sequencing Full-Length 16S rRNA Genes

Marine biofilms are a collective of microbes that can grow on many different surfaces immersed in marine environments. Estimating the microbial richness and specificity of a marine biofilm community is a challenging task due to the high complexity in comparison with seawater. Here, we compared the resolution of full-length 16S rRNA gene sequencing technique of a PacBio platform for microbe identification in marine biofilms with the results of partial 16S rRNA gene sequencing of traditional Illumina PE250 platform. At the same time, the microbial richness, diversity, and composition of adjacent seawater communities in the same batch of samples were analyzed. Both techniques revealed higher species richness, as reflected by the Chao1 index, in the biofilms than that in the seawater communities. Moreover, compared with Illumina sequencing, PacBio sequencing detected more specific species for biofilms and less specific species for seawater. Members of Vibrio, Arcobacter, Photobacterium, Pseudoalteromonas, and Thalassomonas were significantly enriched in the biofilms, which is consistent with the previous understanding of species adapted to a surface-associated lifestyle and validates the taxonomic analyses in the current study. To conclude, the full-length sequencing of 16S rRNA genes has probably a stronger ability to analyze more complex microbial communities, such as marine biofilms, the species richness of which has probably been under-estimated in previous studies.