Ecological and Genomic Attributes of Novel Bacterial Taxa That Thrive in Subsurface Soil Horizons.

While most bacterial and archaeal taxa living in surface soils remain undescribed, this problem is exacerbated in deeper soils, owing to the unique oligotrophic conditions found in the subsurface. Additionally, previous studies of soil microbiomes have focused almost exclusively on surface soils, even though the microbes living in deeper soils also play critical roles in a wide range of biogeochemical processes. We examined soils collected from 20 distinct profiles across the United States to characterize the bacterial and archaeal communities that live in subsurface soils and to determine whether there are consistent changes in soil microbial communities with depth across a wide range of soil and environmental conditions. We found that bacterial and archaeal diversity generally decreased with depth, as did the degree of similarity of microbial communities to those found in surface horizons. We observed five phyla that consistently increased in relative abundance with depth across our soil profiles: Chloroflexi, Nitrospirae, Euryarchaeota, and candidate phyla GAL15 and Dormibacteraeota (formerly AD3). Leveraging the unusually high abundance of Dormibacteraeota at depth, we assembled genomes representative of this candidate phylum and identified traits that are likely to be beneficial in low-nutrient environments, including the synthesis and storage of carbohydrates, the potential to use carbon monoxide (CO) as a supplemental energy source, and the ability to form spores. Together these attributes likely allow members of the candidate phylum Dormibacteraeota to flourish in deeper soils and provide insight into the survival and growth strategies employed by the microbes that thrive in oligotrophic soil environments.IMPORTANCE Soil profiles are rarely homogeneous. Resource availability and microbial abundances typically decrease with soil depth, but microbes found in deeper horizons are still important components of terrestrial ecosystems. By studying 20 soil profiles across the United States, we documented consistent changes in soil bacterial and archaeal communities with depth. Deeper soils harbored communities distinct from those of the more commonly studied surface horizons. Most notably, we found that the candidate phylum Dormibacteraeota (formerly AD3) was often dominant in subsurface soils, and we used genomes from uncultivated members of this group to identify why these taxa are able to thrive in such resource-limited environments. Simply digging deeper into soil can reveal a surprising number of novel microbes with unique adaptations to oligotrophic subsurface conditions

Prediction of microbial communities for urban metagenomics using neural network approach.

BACKGROUND:Microbes are greatly associated with human health and disease, especially in densely populated cities. It is essential to understand the microbial ecosystem in an urban environment for cities to monitor the transmission of infectious diseases and detect potentially urgent threats. To achieve this goal, the DNA sample collection and analysis have been conducted at subway stations in major cities. However, city-scale sampling with the fine-grained geo-spatial resolution is expensive and laborious. In this paper, we introduce MetaMLAnn, a neural network based approach to infer microbial communities at unsampled locations given information reflecting different factors, including subway line networks, sampling material types, and microbial composition patterns. RESULTS:We evaluate the effectiveness of MetaMLAnn based on the public metagenomics dataset collected from multiple locations in the New York and Boston subway systems. The experimental results suggest that MetaMLAnn consistently performs better than other five conventional classifiers under different taxonomic ranks. At genus level, MetaMLAnn can achieve F1 scores of 0.63 and 0.72 on the New York and the Boston datasets, respectively. CONCLUSIONS:By exploiting heterogeneous features, MetaMLAnn captures the hidden interactions between microbial compositions and the urban environment, which enables precise predictions of microbial communities at unmeasured locations

Recent Advances in the Phylogenetic Analysis to Study Rumen Microbiome

Background: Recent rumen microbiome studies are progressive due to the advent of next-generation sequencing technologies, computational models, and gene referencing databases. Rumen metagenomics enables the linking of the genetic structure and composition of the rumen microbial community to the functional role it plays in the ecosystem. Systematic investigations of the rumen microbiome, including its composition in cattle, have revealed the importance of microbiota in rumen functions. Various research studies have identified different types of microbiome species that reside within the rumen and their relationships, leading to a greater understanding of their functional contribution. Objective: The objective of this scoping review was to highlight the role of the phylogenetic and functional composition of the microbiome in cattle functions. It is driven by a natural assumption that closely related microbial genes/operational taxonomical units (OTUs)/amplicon sequence variants (ASVs) by phylogeny are highly correlated and tend to have similar functional traits. Methods: PRISMA approach has been used to conduct the current scoping review providing state-of-the-art studies for a comprehensive understanding of microbial genes’ phylogeny in the rumen microbiome and their functional capacity. Results: 44 studies have been included in the review, which has facilitated phylogenetic advancement in studying important cattle functions and identifying key microbiota. Microbial genes and their inter-relations have the potential to accurately predict the phenotypes linked to ruminants, such as feed efficiency, milk production, and high/low methane emissions. In this review, a variety of cattle have been considered, ranging from cows, buffaloes, lambs, Angus Bulls, etc. Also, results from the reviewed literature indicate that metabolic pathways in microbiome genomic groupings result in better carbon channeling, thereby affecting methane production by ruminants. Conclusion: The mechanistic understanding of the phylogeny of the rumen microbiome could lead to a better understanding of ruminant functions. The composition of the rumen microbiome is crucial for the understanding of dynamics within the rumen environment. The integration of biological domain knowledge with functional gene activity, metabolic pathways, and rumen metabolites could lead to a better understanding of the rumen system