First shotgun metagenomics study of Juan de Fuca deep-sea sediments reveals distinct microbial communities above, within, between, and below sulfate methane transition zones.

The marine deep subsurface is home to a vast microbial ecosystem, affecting biogeochemical cycles on a global scale. One of the better-studied deep biospheres is the Juan de Fuca (JdF) Ridge, where hydrothermal fluid introduces oxidants into the sediment from below, resulting in two sulfate methane transition zones (SMTZs). In this study, we present the first shotgun metagenomics study of unamplified DNA from sediment samples from different depths in this stratified environment. Bioinformatic analyses showed a shift from a heterotrophic, Chloroflexota-dominated community above the upper SMTZ to a chemolithoautotrophic Proteobacteria-dominated community below the secondary SMTZ. The reintroduction of sulfate likely enables respiration and boosts active cells that oxidize acetate, iron, and complex carbohydrates to degrade dead biomass in this low-abundance, low-diversity environment. In addition, analyses showed many proteins of unknown function as well as novel metagenome-assembled genomes (MAGs). The study provides new insights into microbial communities in this habitat, enabled by an improved DNA extraction protocol that allows a less biased view of taxonomic composition and metabolic activities, as well as uncovering novel taxa. Our approach presents the first successful attempt at unamplified shotgun sequencing samples from beyond 50 meters below the seafloor and opens new ways for capturing the true diversity and functional potential of deep-sea sediments.

Elucidating the functional roles of prokaryotic proteins using big data and artificial intelligence.

Annotating protein sequences according to their biological functions is one of the key steps in understanding microbial diversity, metabolic potentials, and evolutionary histories. However, even in the best-studied prokaryotic genomes, not all proteins can be characterized by classical in vivo, in vitro, and/or in silico methods-a challenge rapidly growing alongside the advent of next-generation sequencing technologies and their enormous extension of 'omics' data in public databases. These so-called hypothetical proteins (HPs) represent a huge knowledge gap and hidden potential for biotechnological applications. Opportunities for leveraging the available 'Big Data' have recently proliferated with the use of artificial intelligence (AI). Here, we review the aims and methods of protein annotation and explain the different principles behind machine and deep learning algorithms including recent research examples, in order to assist both biologists wishing to apply AI tools in developing comprehensive genome annotations and computer scientists who want to contribute to this leading edge of biological research.

Macroporous Silicone Chips for Decoding Microbial Dark Matter in Environmental Microbiomes.

Natural evolution has produced an almost infinite variety of microorganisms that can colonize almost any conceivable habitat. Since the vast majority of these microbial consortia are still unknown, there is a great need to elucidate this "microbial dark matter" (MDM) to enable exploitation in biotechnology. We report the fabrication and application of a novel device that integrates a matrix of macroporous elastomeric silicone foam (MESIF) into an easily fabricated and scalable chip design that can be used for decoding MDM in environmental microbiomes. Technical validation, performed with the model organism Escherichia coli expressing a fluorescent protein, showed that this low-cost, bioinert, and widely modifiable chip is rapidly colonized by microorganisms. The biological potential of the chip was then illustrated through targeted sampling and enrichment of microbiomes in a variety of habitats ranging from wet, turbulent moving bed biofilters and wastewater treatment plants to dry air-based environments. Sequencing analyses consistently showed that MESIF chips are not only suitable for sampling with high robustness but also that the material can be used to detect a broad cross section of microorganisms present in the habitat in a short time span of a few days. For example, results from the biofilter habitat showed efficient enrichment of microorganisms belonging to the enigmatic Candidate Phyla Radiation, which comprise ∼70% of the MDM. From dry air, the MESIF chip was able to enrich a variety of members of Actinobacteriota, which is known to produce specific secondary metabolites. Targeted sampling from a wastewater treatment plant where the herbicide glyphosate was added to the chip's reservoir resulted in enrichment of Cyanobacteria and Desulfobacteria, previously associated with glyphosate degradation. These initial case studies suggest that this chip is very well suited for the systematic study of MDM and opens opportunities for the cultivation of previously unculturable microorganisms.

How clear is our current view on microbial dark matter? (Re-)assessing public MAG & SAG datasets with MDMcleaner.

As of today, the majority of environmental microorganisms remain uncultured and is therefore referred to as 'microbial dark matter' (MDM). Hence, genomic insights into these organisms are limited to cultivation-independent approaches such as single-cell- and metagenomics. However, without access to cultured representatives for verifying correct taxon-assignments, MDM genomes may cause potentially misleading conclusions based on misclassified or contaminant contigs, thereby obfuscating our view on the uncultured microbial majority. Moreover, gradual database contaminations by past genome submissions can cause error propagations which affect present as well as future comparative genome analyses. Consequently, strict contamination detection and filtering need to be applied, especially in the case of uncultured MDM genomes. Current genome reporting standards, however, emphasize completeness over purity and the de facto gold standard genome assessment tool, checkM, discriminates against uncultured taxa and fragmented genomes. To tackle these issues, we present a novel contig classification, screening, and filtering workflow and corresponding open-source python implementation called MDMcleaner, which was tested and compared to other tools on mock and real datasets. MDMcleaner revealed substantial contaminations overlooked by current screening approaches and sensitively detects misattributed contigs in both novel genomes and the underlying reference databases, thereby greatly improving our view on 'microbial dark matter'.