Abundance Tracking by Long-Read Nanopore Sequencing of Complex Microbial Communities in Samples from 20 Different Biogas/Wastewater Plants

Metataxonomic Studies to Evaluate the Beneficial Effect of Enzymatic Pretreatment on the Anaerobic Digestion of Waste Generated in Turkey Farming

Turkey litter waste is lignocellulosic and keratinous, requiring prior enzymatic treatment to facilitate fiber hydrolysis and utilization by microorganisms in anaerobic digestion (AD) process. The understanding of the performance of microorganisms in AD can be facilitated through molecular biology and bioinformatics tools. This study aimed to determine the taxonomic profile and functional prediction of microbial communities in the AD of turkey litter waste subjected to enzymatic pretreatment and correlate it with operational parameters. The tests involved the use of turkey litter (T) at 25 g L-1 of volatile solids, a granular inoculum (S) (10% m/v), and the addition of cellulase (C), and pectinase (P) enzymes at four concentrations. The use of enzymes increased methane production by 19% (turkey litter, inoculum, and cellulase-TSC4) and 15% (turkey litter, inoculum, and enzymatic pectinase-TSP4) compared to the control (turkey litter and inoculum-TS), being more effective in TSC4 (667.52 mLCH4), where there was consumption of acetic, butyric, and propionic acids. The pectinase assay (TSP4) showed a methane production of 648 mLCH4 and there was the accumulation of metabolites. Cellulolytic microorganisms Bacteroides, Ruminofilibacter, Lachnospiraceae, Ruminococcaceae, and Methanosaeta were favored in TSC4. In TSP4, the predominant genus was Macellibacteroides and Methanosarcina, and genes involved in methylotrophic methanogenesis were also found (mtaB, mtmB, and mtbB). Enzymes involved in hydrogenotrophic methanogenesis were identified in both assays (TSC4 and TSP4). Molecular tools helped to understand the metabolic routes involved in AD with enzymatic treatment, allowing the elaboration of strategies to improve the sustainable degradation of turkey litter waste.

Sensitivity and consistency of long- and short-read metagenomics and epicPCR for the detection of antibiotic resistance genes and their bacterial hosts in wastewater

Wastewater surveillance is a powerful tool to assess the risks associated with antibiotic resistance in communities. One challenge is selecting which analytical tool to deploy to measure risk indicators, such as antibiotic resistance genes (ARGs) and their respective bacterial hosts. Although metagenomics is frequently used for analyzing ARGs, few studies have compared the performance of long-read and short-read metagenomics in identifying which bacteria harbor ARGs in wastewater. Furthermore, for ARG host detection, untargeted metagenomics has not been compared to targeted methods such as epicPCR. Here, we 1) evaluated long-read and short-read metagenomics as well as epicPCR for detecting ARG hosts in wastewater, and 2) investigated the host range of ARGs across the wastewater treatment plant (WWTP) to evaluate host proliferation. Results highlighted long-read revealed a wider range of ARG hosts compared to short-read metagenomics. Nonetheless, the ARG host range detected by long-read metagenomics only represented a subset of the hosts detected by epicPCR. The ARG-host linkages across the influent and effluent of the WWTP were characterized. Results showed the ARG-host phylum linkages were relatively consistent across the WWTP, whereas new ARG-host species linkages appeared in the WWTP effluent. The ARG-host linkages of several clinically relevant species found in the effluent were identified.

Current status and future developments of assessing microbiome composition and dynamics in anaerobic digestion systems using metagenomic approaches

The metagenomic approach stands as a powerful technique for examining the composition of microbial communities and their involvement in various anaerobic digestion (AD) systems. Understanding the structure, function, and dynamics of microbial communities becomes pivotal for optimizing the biogas process, enhancing its stability and improving overall performance. Currently, taxonomic profiling of biogas-producing communities relies mainly on high-throughput 16S rRNA sequencing, offering insights into the bacterial and archaeal structures of AD assemblages and their correlations with fed substrates and process parameters. To delve even deeper, shotgun and genome-centric metagenomic approaches are employed to recover individual genomes from the metagenome. This provides a nuanced understanding of collective functionalities, interspecies interactions, and microbial associations with abiotic factors. The application of OMICs in AD systems holds the potential to revolutionize the field, leading to more efficient and sustainable waste management practices particularly through the implementation of precision anaerobic digestion systems. As ongoing research in this area progresses, anticipations are high for further exciting developments in the future. This review serves to explore the current landscape of metagenomic analyses, with focus on advancing our comprehension and critically evaluating biases and recommendations in the analysis of microbial communities in anaerobic digesters. Its objective is to explore how contemporary metagenomic approaches can be effectively applied to enhance our understanding and contribute to the refinement of the AD process. This marks a substantial stride towards achieving a more comprehensive understanding of anaerobic digestion systems.

Long-read MinION™ sequencing of 16S and 16S-ITS-23S rRNA genes provides species-level resolution of Lactobacillaceae in mixed communities

The Lactobacillaceae are lactic acid bacteria harnessed to deliver important outcomes across numerous industries, and their unambiguous, species-level identification from mixed community environments is an important endeavor. Amplicon-based metataxonomics using short-read sequencing of partial 16S rRNA gene regions is widely used to support this, however, the high genetic similarity among Lactobacillaceae species restricts our ability to confidently describe these communities even at genus level. Long-read sequencing (LRS) of the whole 16S rRNA gene or the near complete rRNA operon (16S-ITS-23S) has the potential to improve this. We explored species ambiguity amongst Lactobacillaceae using in-silico tool RibDif2, which identified allele overlap when various partial and complete 16S rRNA gene and 16S-ITS-23S rRNA regions were amplified. We subsequently implemented LRS by MinION™ to compare the capacity of V3-V4, 16S and 16S-ITS-23S rRNA amplicons to accurately describe the diversity of a 20-species Lactobacillaceae mock community in practice. In-silico analysis identified more instances of allele/species overlap with V3-V4 amplicons (n = 43) compared to the 16S rRNA gene (n = 11) and partial (n = up to 15) or complete (n = 0) 16S-ITS-23S rRNA amplicons. With subsequent LRS of a DNA mock community, 80% of target species were identified using V3-V4 amplicons whilst the 16S rRNA gene and 16S-ITS-23S rRNA region amplicons resulted in 95 and 100% of target species being identified. A considerable reduction in false-positive identifications was also seen with 16S rRNA gene (n = 3) and 16S-ITS-23S rRNA region (n = 9) amplicons compared with V3-V4 amplicons (n = 43). Whilst the target species affected by allele overlap in V3-V4 and 16S rRNA gene sequenced mock communities were predicted by RibDif2, unpredicted species ambiguity was observed in 16S-ITS-23S rRNA sequenced communities. Considering the average nucleotide identity (ANI) between ambiguous species (~97%) and the basecall accuracy of our MinION™ sequencing protocol (96.4%), the misassignment of reads between closely related taxa is to be expected. With basecall accuracy exceeding 99% for recent MinION™ releases, the increased species-level differentiating power promised by longer amplicons like the 16S-ITS-23S rRNA region, may soon be fully realized.

Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks

Taxonomic classification, that is, the assignment to biological clades with shared ancestry, is a common task in genetics, mainly based on a genome similarity search of large genome databases. The classification quality depends heavily on the database, since representative relatives must be present. Many genomic sequences cannot be classified at all or only with a high misclassification rate. Here we present BERTax, a deep neural network program based on natural language processing to precisely classify the superkingdom and phylum of DNA sequences taxonomically without the need for a known representative relative from a database. We show BERTax to be at least on par with the state-of-the-art approaches when taxonomically similar species are part of the training data. For novel organisms, however, BERTax clearly outperforms any existing approach. Finally, we show that BERTax can also be combined with database approaches to further increase the prediction quality in almost all cases. Since BERTax is not based on similar entries in databases, it allows precise taxonomic classification of a broader range of genomic sequences, thus increasing the overall information gain.

Cultured and uncultured microbial community associated with biogas production in anaerobic digestion processes

The search for sustainable development has increased interest in the improvement of technologies that use renewable energy sources. One of the alternatives in the production of renewable energy comes from the use of waste including urban solids, animal excrement from livestock, and biomass residues from agro-industrial plants. These materials may be used in the production of biogas, making its production highly sustainable and environmentally friendly. The present study aimed to evaluate the cultivated and uncultivated microbial community from a substrate (starter) used as an adapter for biogas production in anaerobic digestion processes. 16S rDNA metabarcoding revealed the domain of bacteria belonging to the phyla Firmicutes, Bacteroidota, Chloroflexi and Synergistota. The methanogenic group was represented by the phyla Halobacterota and Euryarchaeota. Through 16S rRNA sequencing of isolates recovered from the starter culture, the genera Rhodococcus (Actinobacteria phylum), Vagococcus, Lysinibacillus, Niallia, Priestia, Robertmurraya, Proteiniclasticum (Firmicutes phylum), and Luteimonas (Proteobacteria phylum) were identified, genera that were not observed in the metabarcoding data. The volatile solids, volatile organic acids, and total inorganic carbon reached 659.10 g kg^-1, 717.70 g kg^-1, 70,005.0 g kg^-1, respectively. The cultured groups are involved in the metabolism of sugars and other compounds derived from lignocellulosic material, as well as in anaerobic methane production processes. The results demonstrate that culture-dependent approaches, such as isolation and sequencing, and culture-independent studies, such as the Metabarcoding approach, are complementary methodologies that, when integrated provide robust and comprehensive information about the microbial communities involved in processes of the production of biogas in anaerobic digestion processes.

Near Chromosome-Level Genome Assembly and Annotation of Rhodotorula babjevae Strains Reveals High Intraspecific Divergence

The genus Rhodotorula includes basidiomycetous oleaginous yeast species. Rhodotorula babjevae can produce compounds of biotechnological interest such as lipids, carotenoids, and biosurfactants from low value substrates such as lignocellulose hydrolysate. High-quality genome assemblies are needed to develop genetic tools and to understand fungal evolution and genetics. Here, we combined short- and long-read sequencing to resolve the genomes of two R. babjevae strains, CBS 7808 (type strain) and DBVPG 8058, at chromosomal level. Both genomes are 21 Mbp in size and have a GC content of 68.2%. Allele frequency analysis indicates that both strains are tetraploid. The genomes consist of a maximum of 21 chromosomes with a size of 0.4 to 2.4 Mbp. In both assemblies, the mitochondrial genome was recovered in a single contig, that shared 97% pairwise identity. Pairwise identity between most chromosomes ranges from 82 to 87%. We also found indications for strain-specific extrachromosomal endogenous DNA. A total of 7591 and 7481 protein-coding genes were annotated in CBS 7808 and DBVPG 8058, respectively. CBS 7808 accumulated a higher number of tandem duplications than DBVPG 8058. We identified large translocation events between putative chromosomes. Genome divergence values between the two strains indicate that they may belong to different species.

Evaluation of microbiome enrichment and host DNA depletion in human vaginal samples using Oxford Nanopore's adaptive sequencing

Metagenomic sequencing is promising for clinical applications to study microbial composition concerning disease or patient outcomes. Alterations of the vaginal microbiome are associated with adverse pregnancy outcomes, like preterm premature rupture of membranes and preterm birth. Methodologically these samples often have to deal with low relative amounts of prokaryotic DNA and high amounts of host DNA (> 90%), decreasing the overall microbial resolution. Nanopore's adaptive sampling method offers selective DNA depletion or target enrichment to directly reject or accept DNA molecules during sequencing without specialized sample preparation. Here, we demonstrate how selective 'human host depletion' resulted in a 1.70 fold (± 0.27 fold) increase in total sequencing depth, providing higher taxonomic profiling sensitivity. At the same time, the microbial composition remains consistent with the control experiments. The complete removal of all human host sequences is not yet possible and should be considered as an ethical approval statement might still be necessary. Adaptive sampling increased microbial sequencing yield in all 15 sequenced clinical routine vaginal samples, making it a valuable tool for clinical surveillance and medical-based research, which can be used in addition to other host depletion methods before sequencing.

Phage Genome Diversity in a Biogas-Producing Microbiome Analyzed by Illumina and Nanopore GridION Sequencing

The microbial biogas network is complex and intertwined, and therefore relatively stable in its overall functionality. However, if key functional groups of microorganisms are affected by biotic or abiotic factors, the entire efficacy may be impaired. Bacteriophages are hypothesized to alter the steering process of the microbial network. In this study, an enriched fraction of virus-like particles was extracted from a mesophilic biogas reactor and sequenced on the Illumina MiSeq and Nanopore GridION sequencing platforms. Metagenome data analysis resulted in identifying 375 metagenome-assembled viral genomes (MAVGs). Two-thirds of the classified sequences were only assigned to the superkingdom Viruses and the remaining third to the family Siphoviridae, followed by Myoviridae, Podoviridae, Tectiviridae, and Inoviridae. The metavirome showed a close relationship to the phage genomes that infect members of the classes Clostridia and Bacilli. Using publicly available biogas metagenomic data, a fragment recruitment approach showed the widespread distribution of the MAVGs studied in other biogas microbiomes. In particular, phage sequences from mesophilic microbiomes were highly similar to the phage sequences of this study. Accordingly, the virus particle enrichment approach and metavirome sequencing provided additional genome sequence information for novel virome members, thus expanding the current knowledge of viral genetic diversity in biogas reactors.

Fungal and ciliate protozoa are the main rumen microbes associated with methane emissions in dairy cattle

Background

Mitigating the effects of global warming has become the main challenge for humanity in recent decades. Livestock farming contributes to greenhouse gas emissions, with an important output of methane from enteric fermentation processes, mostly in ruminants. Because ruminal microbiota is directly involved in digestive fermentation processes and methane biosynthesis, understanding the ecological relationships between rumen microorganisms and their active metabolic pathways is essential for reducing emissions. This study analysed whole rumen metagenome using long reads and considering its compositional nature in order to disentangle the role of rumen microbes in methane emissions.

Results

The β-diversity analyses suggested a subtle association between methane production and overall microbiota composition (0.01 < R² < 0.02). Differential abundance analysis identified 36 genera and 279 KEGGs as significantly associated with methane production (P_adj < 0.05). Those genera associated with high methane production were Eukaryota from Alveolata and Fungi clades, while Bacteria were associated with low methane emissions. The genus-level association network showed 2 clusters grouping Eukaryota and Bacteria, respectively. Regarding microbial gene functions, 41 KEGGs were found to be differentially abundant between low- and high-emission animals and were mainly involved in metabolic pathways. No KEGGs included in the methane metabolism pathway (ko00680) were detected as associated with high methane emissions. The KEGG network showed 3 clusters grouping KEGGs associated with high emissions, low emissions, and not differentially abundant in either. A deeper analysis of the differentially abundant KEGGs revealed that genes related with anaerobic respiration through nitrate degradation were more abundant in low-emission animals.

Conclusions

Methane emissions are largely associated with the relative abundance of ciliates and fungi. The role of nitrate electron acceptors can be particularly important because this respiration mechanism directly competes with methanogenesis. Whole metagenome sequencing is necessary to jointly consider the relative abundance of Bacteria, Archaea, and Eukaryota in the statistical analyses. Nutritional and genetic strategies to reduce CH₄ emissions should focus on reducing the relative abundance of Alveolata and Fungi in the rumen. This experiment has generated the largest ONT ruminal metagenomic dataset currently available.

Chromosome-level genome assembly and transcriptome-based annotation of the oleaginous yeast Rhodotorula toruloides CBS 14

Rhodotorula toruloides is an oleaginous yeast with high biotechnological potential. In order to understand the molecular physiology of lipid synthesis in R. toruloides and to advance metabolic engineering, a high-resolution genome is required. We constructed a genome draft of R. toruloides CBS 14, using a hybrid assembly approach, consisting of short and long reads generated by Illumina and Nanopore sequencing, respectively. The genome draft consists of 23 contigs and 3 scaffolds, with a N50 length of 1,529,952 bp, thus largely representing chromosomal organization. The total size of the genome is 20,534,857 bp and the overall GC content is 61.83%. Transcriptomic data from different growth conditions was used to aid species-specific gene annotation. We annotated 9464 genes and identified 11,691 transcripts. Furthermore, we demonstrated the presence of a potential plasmid, an extrachromosomal circular structure of about 11 kb with a copy number about three times as high as the other chromosomes.

Nanopore Technology and Its Applications in Gene Sequencing

In recent years, nanopore technology has become increasingly important in the field of life science and biomedical research. By embedding a nano-scale hole in a thin membrane and measuring the electrochemical signal, nanopore technology can be used to investigate the nucleic acids and other biomacromolecules. One of the most successful applications of nanopore technology, the Oxford Nanopore Technology, marks the beginning of the fourth generation of gene sequencing technology. In this review, the operational principle and the technology for signal processing of the nanopore gene sequencing are documented. Moreover, this review focuses on the applications using nanopore gene sequencing technology, including the diagnosis of cancer, detection of viruses and other microbes, and the assembly of genomes. These applications show that nanopore technology is promising in the field of biological and biomedical sensing.

Metagenomics workflow for hybrid assembly, differential coverage binning, metatranscriptomics and pathway analysis (MUFFIN)

Metagenomics has redefined many areas of microbiology. However, metagenome-assembled genomes (MAGs) are often fragmented, primarily when sequencing was performed with short reads. Recent long-read sequencing technologies promise to improve genome reconstruction. However, the integration of two different sequencing modalities makes downstream analyses complex. We, therefore, developed MUFFIN, a complete metagenomic workflow that uses short and long reads to produce high-quality bins and their annotations. The workflow is written by using Nextflow, a workflow orchestration software, to achieve high reproducibility and fast and straightforward use. This workflow also produces the taxonomic classification and KEGG pathways of the bins and can be further used for quantification and annotation by providing RNA-Seq data (optionally). We tested the workflow using twenty biogas reactor samples and assessed the capacity of MUFFIN to process and output relevant files needed to analyze the microbial community and their function. MUFFIN produces functional pathway predictions and, if provided de novo metatranscript annotations across the metagenomic sample and for each bin. MUFFIN is available on github under GNUv3 licence: https://github.com/RVanDamme/MUFFIN.

The Role of Petrimonas mucosa ING2-E5AT in Mesophilic Biogas Reactor Systems as Deduced from Multiomics Analyses

Members of the genera Proteiniphilum and Petrimonas were speculated to represent indicators reflecting process instability within anaerobic digestion (AD) microbiomes. Therefore, Petrimonas mucosa ING2-E5AT was isolated from a biogas reactor sample and sequenced on the PacBio RSII and Illumina MiSeq sequencers. Phylogenetic classification positioned the strain ING2-E5AT in close proximity to Fermentimonas and Proteiniphilum species (family Dysgonomonadaceae). ING2-E5AT encodes a number of genes for glycosyl-hydrolyses (GH) which are organized in Polysaccharide Utilization Loci (PUL) comprising tandem susCD-like genes for a TonB-dependent outer-membrane transporter and a cell surface glycan-binding protein. Different GHs encoded in PUL are involved in pectin degradation, reflecting a pronounced specialization of the ING2-E5AT PUL systems regarding the decomposition of this polysaccharide. Genes encoding enzymes participating in amino acids fermentation were also identified. Fragment recruitments with the ING2-E5AT genome as a template and publicly available metagenomes of AD microbiomes revealed that Petrimonas species are present in 146 out of 257 datasets supporting their importance in AD microbiomes. Metatranscriptome analyses of AD microbiomes uncovered active sugar and amino acid fermentation pathways for Petrimonas species. Likewise, screening of metaproteome datasets demonstrated expression of the Petrimonas PUL-specific component SusC providing further evidence that PUL play a central role for the lifestyle of Petrimonas species.