An Improved Approach to Identify Bacterial Pathogens to Human in Environmental Metagenome

Beyond water and soil: Air emerges as a major reservoir of human pathogens

Assessing the risk of human pathogens in the environment is crucial for controlling the spread of diseases and safeguarding human health. However, conducting a thorough assessment of low-abundance pathogens in highly complex environmental microbial communities remains challenging. This study compiled a comprehensive catalog of 247 human-pathogenic bacterial taxa from global biosafety agencies and identified more than 78 million genome-specific markers (GSMs) from their 17,470 sequenced genomes. Subsequently, we analyzed these pathogens' types, abundance, and diversity within 474 shotgun metagenomic sequences obtained from diverse environmental sources. The results revealed that among the four habitats studied (air, water, soil, and sediment), the detection rate, diversity, and abundance of detectable pathogens in the air all exceeded those in the other three habitats. Air, sediment, and water environments exhibited identical dominant taxa, indicating that these human pathogens may have unique environmental vectors for their transmission or survival. Furthermore, we observed the impact of human activities on the environmental risk posed by these pathogens, where greater amounts of human activities significantly increased the abundance of human pathogenic bacteria, especially in water and air. These findings have remarkable implications for the environmental risk assessment of human pathogens, providing valuable insights into their presence and distribution across different habitats.

MetagenomicKG: a knowledge graph for metagenomic applications

Motivation

The sheer volume and variety of genomic content within microbial communities makes metagenomics a field rich in biomedical knowledge. To traverse these complex communities and their vast unknowns, metagenomic studies often depend on distinct reference databases, such as the Genome Taxonomy Database (GTDB), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Bacterial and Viral Bioinformatics Resource Center (BV-BRC), for various analytical purposes. These databases are crucial for genetic and functional annotation of microbial communities. Nevertheless, the inconsistent nomenclature or identifiers of these databases present challenges for effective integration, representation, and utilization. Knowledge graphs (KGs) offer an appropriate solution by organizing biological entities and their interrelations into a cohesive network. The graph structure not only facilitates the unveiling of hidden patterns but also enriches our biological understanding with deeper insights. Despite KGs having shown potential in various biomedical fields, their application in metagenomics remains underexplored.

Results

We present MetagenomicKG, a novel knowledge graph specifically tailored for metagenomic analysis. MetagenomicKG integrates taxonomic, functional, and pathogenesis-related information from widely used databases, and further links these with established biomedical knowledge graphs to expand biological connections. Through several use cases, we demonstrate its utility in enabling hypothesis generation regarding the relationships between microbes and diseases, generating sample-specific graph embeddings, and providing robust pathogen prediction.

Availability and Implementation

The source code and technical details for constructing the MetagenomicKG and reproducing all analyses are available at Github: https://github.com/KoslickiLab/MetagenomicKG. We also host a Neo4j instance: http://mkg.cse.psu.edu:7474 for accessing and querying this graph.

Alteration of Colonic Bacterial and Fungal Composition and Their Inter- and Intra-Kingdom Interaction in Patients with Adenomas with Low-Grade Dysplasia

Colorectal cancer (CRC) develops from pre-cancerous cellular lesions in the gut epithelium and mainly originates from specific types of colonic adenomas with dysplasia. However, gut microbiota signatures among sampling sites in patients with colorectal adenomas with low-grade dysplasia (ALGD) and normal control (NC) remain uncharacterized. To characterize gut microbial and fungal profiles in ALGD and normal colorectal mucosa tissues. We used 16S and ITS1-2 rRNA gene sequencing and bioinformatics analysis on the microbiota of ALGD and normal colorectal mucosa from 40 subjects. Bacterial sequences in the ALGD group showed an increase in Rhodobacterales, Thermales, Thermaceae, Rhodobacteraceae, and several genera, including Thermus, Paracoccus, Sphingobium, and Pseudomonas, compared to the NC group. Fungal sequences in the ALGD group showed an increase in Helotiales, Leotiomycetes, and Basidiomycota, while several orders, families, and genera, including Verrucariales, Russulales, and Trichosporonales, were decreased. The study found various interactions between intestinal bacteria and fungi. The bacterial functional analysis showed increased glycogen and vanillin degradation pathways in the ALGD group. Meanwhile, the fungal functional analysis showed a decrease in pathways related to the biosynthesis of gondoate and stearate, as well as degradation of glucose, starch, glycogen, sucrose, L-tryptophan, and pantothenate, and an increase in the octane oxidation pathway in the ALGD group. The mucosal microbiota in ALGD exhibits altered fungal and microbial composition compared to the NC mucosa, potentially contributing to the development of intestinal cancer by regulating specific metabolic pathways. Therefore, these changes in microbiota and metabolic pathways may be potential markers for diagnosing and treating colorectal adenoma and carcinoma.