Investigating the impact of database choice on the accuracy of metagenomic read classification for the rumen microbiome

Metagenomic unveils the promotion of mainstream PD-anammox process at lower nZVI concentration and inhibition at higher dosage

The partial-denitrification-anammox (PdNA) process exhibits great potential in enabling the simultaneous removal of NO3--N and NH4+-N. This study delved into the impact of exogenous nano zero-valent iron (nZVI) on the PdNA process. Adding 10 mg L-1 of nZVI increased nitrogen removal efficiency up to 83.12 % and maintained higher relative abundances of certain beneficial bacteria. The maximum relative abundance of Candidatus Brocadia (1.6 %), Candidatus Kuenenia (1.5 %), Ignavibacterium (1.3 %), and Azospira (1.2 %) was observed at 10 mg L-1 of nZVI. However, the greatest relative abundance of Thauera (1.3 %) was recorded under 50 mg L-1. Moreover, applying nZVI selectively enhanced the abundance of NO3--N reductase genes. So, keeping the nZVI concentration at 10 mg L-1 or below is advisable to ensure a stable PdNA process in mainstream conditions. Considering nitrogen removal efficiency, using nZVI in the PD-anammox process could be more cost-effective in enhancing its adoption in industrial and mainstream settings.

Using a multi-omics approach to explore potential associations with rumen content and serum of cows with different milk production levels based on genomic predicted transmitting ability for milk and phenotypic milk production

This study aims to compare rumen microbiome and metabolites between second lactation dairy cows in the 75th percentile (n = 12; 57.2 ± 5.08 kg/d) of production according to genomic predicted transmitting ability for milk (GPTAM) and their counterparts in the 25th percentile (n = 12; 47.2 ± 8.61 kg/d). It was hypothesized that the metagenome and metabolome would differ between production levels. Cows were matched by days in milk (DIM), sire, occurrence of disease, and days open in previous lactation. For an additional comparison, the cows were also divided by phenotype into high (n = 6; 61.3 ± 2.8 kg/d), medium (n = 10; 55 ± 1.2 kg/d), and low (n = 8; 41.9 ± 5.6 kg/d) based on their milk production. Samples were collected 65 ± 14 DIM. Rumen content was collected using an oro-gastric tube and serum samples were collected from the coccygeal vessels. High-resolution liquid chromatography-mass spectrometry (LC-MS) was used for rumen and serum metabolite profiling. Shotgun metagenomics was used for rumen microbiome profiling. Microbiome sample richness and diversity were used to determine alpha and Bray-Curtis dissimilarity index was used to estimate beta diversity. Differences in metabolites were determined using t-tests or ANOVA. Pearson correlations were used to consider associations between serum metabolites and milk production. There was no evidence of a difference in rumen metabolites or microbial communities by GPTAM or phenotype. Cows in the phenotypic low group had greater serum acetate to propionate ratio and acetate proportion compared to the cows in the phenotypic medium group. Likewise, serum propionate proportion was greater in the medium compared to the low phenotypic group. Serum acetate, butyrate, and propionate concentrations had a weak positive correlation with milk production. When investigating associations between rumen environment and milk production, future studies must consider the impact of the ruminal epithelium absorption and post-absorption processes in relation to milk production.

Fecal microbiota transplantation: current challenges and future landscapes

SUMMARYGiven the importance of gut microbial homeostasis in maintaining health, there has been considerable interest in developing innovative therapeutic strategies for restoring gut microbiota. One such approach, fecal microbiota transplantation (FMT), is the main "whole gut microbiome replacement" strategy and has been integrated into clinical practice guidelines for treating recurrent Clostridioides difficile infection (rCDI). Furthermore, the potential application of FMT in other indications such as inflammatory bowel disease (IBD), metabolic syndrome, and solid tumor malignancies is an area of intense interest and active research. However, the complex and variable nature of FMT makes it challenging to address its precise functionality and to assess clinical efficacy and safety in different disease contexts. In this review, we outline clinical applications, efficacy, durability, and safety of FMT and provide a comprehensive assessment of its procedural and administration aspects. The clinical applications of FMT in children and cancer immunotherapy are also described. We focus on data from human studies in IBD in contrast with rCDI to delineate the putative mechanisms of this treatment in IBD as a model, including colonization resistance and functional restoration through bacterial engraftment, modulating effects of virome/phageome, gut metabolome and host interactions, and immunoregulatory actions of FMT. Furthermore, we comprehensively review omics technologies, metagenomic approaches, and bioinformatics pipelines to characterize complex microbial communities and discuss their limitations. FMT regulatory challenges, ethical considerations, and pharmacomicrobiomics are also highlighted to shed light on future development of tailored microbiome-based therapeutics.

Towards facilitated interpretation of shotgun metagenomics long-read sequencing data analyzed with KMA for the detection of bacterial pathogens and their antimicrobial resistance genes

Metagenomic sequencing is a promising method that has the potential to revolutionize the world of pathogen detection and antimicrobial resistance (AMR) surveillance in food-producing environments. However, the analysis of the huge amount of data obtained requires performant bioinformatics tools and databases, with intuitive and straightforward interpretation. In this study, based on long-read metagenomics data of chicken fecal samples with a spike-in mock community, we proposed confidence levels for taxonomic identification and AMR gene detection, with interpretation guidelines, to help with the analysis of the output data generated by KMA, a popular k-mer read alignment tool. Additionally, we demonstrated that the completeness and diversity of the genomes present in the reference databases are key parameters for accurate and easy interpretation of the sequencing data. Finally, we explored whether KMA, in a two-step procedure, can be used to link the detected AMR genes to their bacterial host chromosome, both detected within the same long-reads. The confidence levels were successfully tested on 28 metagenomics datasets which were obtained with sequencing of real and spiked samples from fecal (chicken, pig, and buffalo) or food (minced beef and food enzyme products) origin. The methodology proposed in this study will facilitate the analysis of metagenomics sequencing datasets for KMA users. Ultimately, this will contribute to improvements in the rapid diagnosis and surveillance of pathogens and AMR genes in food-producing environments, as prioritized by the EU.

Deep learning methods in metagenomics: a review

The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome's key role in our health.

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.

HiTaxon: a hierarchical ensemble framework for taxonomic classification of short reads

Motivation

Whole microbiome DNA and RNA sequencing (metagenomics and metatranscriptomics) are pivotal to determining the functional roles of microbial communities. A key challenge in analyzing these complex datasets, typically composed of tens of millions of short reads, is accurately classifying reads to their taxa of origin. While still performing worse relative to reference-based short-read tools in species classification, ML algorithms have shown promising results in taxonomic classification at higher ranks. A recent approach exploited to enhance the performance of ML tools, which can be translated to reference-dependent classifiers, has been to integrate the hierarchical structure of taxonomy within the tool's predictive algorithm.

Results

Here, we introduce HiTaxon, an end-to-end hierarchical ensemble framework for taxonomic classification. HiTaxon facilitates data collection and processing, reference database construction and optional training of ML models to streamline ensemble creation. We show that databases created by HiTaxon improve the species-level performance of reference-dependent classifiers, while reducing their computational overhead. In addition, through exploring hierarchical methods for HiTaxon, we highlight that our custom approach to hierarchical ensembling improves species-level classification relative to traditional strategies. Finally, we demonstrate the improved performance of our hierarchical ensembles over current state-of-the-art classifiers in species classification using datasets comprised of either simulated or experimentally derived reads.

Availability and implementation

HiTaxon is available at: https://github.com/ParkinsonLab/HiTaxon.

Rumen and fecal microbiomes are related to diet and production traits in Bos indicus beef cattle

Background

Ruminants harbor a complex microbial community within their gastrointestinal tract, which plays major roles in their health and physiology. Brazil is one of the largest producers of beef in the world and more than 90% of the beef cattle herds are composed of pure and crossbred Nelore (Bos indicus). Despite its importance to the Brazilian economy and human feeding, few studies have characterized the Nelore microbiome. Therefore, using shotgun metagenomics, we investigated the impact of diet on the composition and functionality of the Nelore microbiome, and explored the associations between specific microbial taxa and their functionality with feed efficiency and methane emission.

Results

The ruminal microbiome exhibited significantly higher microbial diversity, distinctive taxonomic profile and variations in microbial functionality compared to the fecal microbiome, highlighting the distinct contributions of the microbiomes of these environments. Animals subjected to different dietary treatments exhibited significant differences in their microbiomes' archaeal diversity and in the abundance of 89 genera, as well as in the functions associated with the metabolism of components of each diet. Moreover, depending on the diet, feed-efficient animals and low methane emitters displayed higher microbial diversity in their fecal microbiome. Multiple genera were associated with an increase or decrease of the phenotypes. Upon analyzing the functions attributed to these taxa, we observed significant differences on the ruminal taxa associated with feed efficient and inefficient cattle. The ruminal taxa that characterized feed efficient cattle stood out for having significantly more functions related to carbohydrate metabolism, such as monosaccharides, di-/oligosaccharides and amino acids. The taxa associated with methane emission had functions associated with methanogenesis and the production of substrates that may influence methane production, such as hydrogen and formate.

Conclusion

Our findings highlight the significant role of diet in shaping Nelore microbiomes and how its composition and functionality may affect production traits such as feed efficiency and methane emission. These insights provide valuable support for the implementation of novel feeding and biotechnological strategies.

Effect of castration timing and weaning strategy on the taxonomic and functional profile of ruminal bacteria and archaea of beef calves

BACKGROUND

Beef cattle experience several management challenges across their lifecycle. Castration and weaning, two major interventions in the early life of beef cattle, can have a substantial impact on animal performance. Despite the key role of the rumen microbiome on productive traits of beef cattle, the effect of castration timing and weaning strategy on this microbial community has not been formally described. We assessed the effect of four castration time windows (at birth, turnout, pre-weaning and weaning) and two weaning strategies (fence-line and truck transportation) on the rumen microbiome in a randomized controlled study with 32 male calves across 3 collection days (i.e., time points). Ruminal fluid samples were submitted to shotgun metagenomic sequencing and changes in the taxonomic (microbiota) and functional profile (metagenome) of the rumen microbiome were described.

RESULTS

Using a comprehensive yet stringent taxonomic classification approach, we identified 10,238 unique taxa classified under 40 bacterial and 7 archaeal phyla across all samples. Castration timing had a limited long-term impact on the rumen microbiota and was not associated with changes in alpha and beta diversity. The interaction of collection day and weaning strategy was associated with changes in the rumen microbiota, which experienced a significant decrease in alpha diversity and shifts in beta diversity within 48 h post-weaning, especially in calves abruptly weaned by truck transportation. Calves weaned using a fence-line weaning strategy had lower relative abundance of Bacteroides, Lachnospira, Fibrobacter and Ruminococcus genera compared to calves weaned by truck transportation. Some genes involved in the hydrogenotrophic methanogenesis pathway (fwdB and fwdF) had higher relative abundance in fence-line-weaned calves post-weaning. The antimicrobial resistance gene tetW consistently represented more than 50% of the resistome across time, weaning and castration groups, without significant changes in relative abundance.

CONCLUSIONS

Within the context of this study, castration timing had limited long-term effects on the rumen microbiota, while weaning strategy had short-term effects on the rumen microbiota and methane-associated metagenome, but not on the rumen resistome.

Comparison of databases useful for the analysis of vaginal microbiota in Japanese women using next-generation sequencing data (QIIME 2 software)

AIMS

Approximately 10% of children are born prematurely, and bacterial vaginosis during pregnancy is associated with preterm delivery. Highly accurate species-level vaginal microflora analysis helps control bacteria-induced preterm birth. Therefore, we aimed to conduct a bioinformatic analysis of gene sequences using 16S databases and compare their efficacy in comprehensively identifying potentially pathogenic vaginal microbiota in Japanese women.

METHODS AND RESULTS

The 16 s rRNA databases, Silva, Greengenes, and the basic local alignment search tool (BLAST) were compared to determine whether the classification quality could be improved using the V3-V4 region next-generation sequencing (NGS) sequences. It was found that NGS data were aligned using the BLAST database with the QIIME 2 platform, whose classification quality was higher than that of Silva, and the combined Silva and Greengenes databases based on the mutual complementarity of the two databases.

CONCLUSIONS

The reference database selected during the bioinformatic processing influenced the recognized sequence percentage, taxonomic rankings, and accuracy. This study showed that the BLAST database was the best choice for NGS data analysis of Japanese women's vaginal microbiota.