Imputing Metagenomic Hi-C Contacts Facilitates the Integrative Contig Binning Through Constrained Random Walk with Restart.

Metagenomic Hi-C (metaHi-C) has shown remarkable potential for retrieving high-quality metagenome-assembled genomes from complex microbial communities. Nevertheless, existing metaHi-C-based contig binning methods solely rely on Hi-C interactions between contigs, disregarding crucial biological information such as the presence of single-copy marker genes. To overcome this limitation, we introduce ImputeCC, an integrative contig binning tool optimized for metaHi-C datasets. ImputeCC integrates both Hi-C interactions and the discriminative power of single-copy marker genes to group marker-gene-containing contigs into preliminary bins. It also introduces a novel constrained random walk with restart algorithm to enhance Hi-C connectivity among contigs. Comprehensive assessments using both mock and real metaHi-C datasets from diverse environments demonstrate that ImputeCC consistently outperforms other Hi-C-based contig binning tools. A genus-level analysis of the sheep gut microbiota reconstructed by ImputeCC underlines its capability to recover key species from dominant genera and identify previously unknown genera.

Metagenomic insights into nitrogen-cycling microbial communities and their relationships with nitrogen removal potential in the Yangtze River.

Nitrogen (N) pollution is a major threat to river ecosystems worldwide. Elucidating the community structure of N-cycling microorganisms in rivers is essential to understanding how ecosystem processes and functions will respond to increasing N inputs. However, previous studies generally focus on limited functional genes through amplicon sequencing or quantitative PCR techniques and cannot cover all N-cycling microorganisms. Here, metagenomic sequencing and genome binning were used to determine N-cycling genes in water, channel sediments, and riparian soils of the Yangtze River, which has been heavily polluted by N. Additionally, the denitrification and anaerobic ammonium oxidation (anammox) rates that reflect N removal potential were measured using 15N isotope pairing technique. Results showed that functional genes involved in organic N metabolism (i.e., organic degradation and synthesis) and nitrate reduction pathways (i.e., dissimilatory and assimilatory nitrate reduction to ammonium and denitrification) were more abundant and diverse than other N-cycling genes. A total of 121 metagenome-assembled genomes (MAGs) were identified to be involved in N-cycling processes, and the key MAGs were mainly taxonomically classified as Alphaproteobacteria and Gammaproteobacteria. The abundance and diversity of most N-cycling genes were higher in soils and sediments than in water, as well as higher in downstream and midstream than in upstream sites. These spatial variations were explained not only by local environment and vegetation but also by geographical and climatic factors. N removal process (i.e., denitrification and anammox) rates were significantly related to the abundance or diversity of several N-cycling genes, and climate and edaphic factors could regulate denitrification and anammox rates directly and indirectly through their effects on functional genes. Overall, these results provide a new avenue for further understanding the biogeographic patterns and environmental drivers of N-cycling microorganisms in rivers from the metagenomic perspective.

Fairy: fast approximate coverage for multi-sample metagenomic binning.

BACKGROUND: Metagenomic binning, the clustering of assembled contigs that belong to the same genome, is a crucial step for recovering metagenome-assembled genomes (MAGs). Contigs are linked by exploiting consistent signatures along a genome, such as read coverage patterns. Using coverage from multiple samples leads to higher-quality MAGs; however, standard pipelines require all-to-all read alignments for multiple samples to compute coverage, becoming a key computational bottleneck. RESULTS: We present fairy ( https://github.com/bluenote-1577/fairy ), an approximate coverage calculation method for metagenomic binning. Fairy is a fast k-mer-based alignment-free method. For multi-sample binning, fairy can be > 250 × faster than read alignment and accurate enough for binning. Fairy is compatible with several existing binners on host and non-host-associated datasets. Using MetaBAT2, fairy recovers 98.5 % of MAGs with > 50 % completeness and < 5 % contamination relative to alignment with BWA. Notably, multi-sample binning with fairy is always better than single-sample binning using BWA ( > 1.5 × more > 50 % complete MAGs on average) while still being faster. For a public sediment metagenome project, we demonstrate that multi-sample binning recovers higher quality Asgard archaea MAGs than single-sample binning and that fairy's results are indistinguishable from read alignment. CONCLUSIONS: Fairy is a new tool for approximately and quickly calculating multi-sample coverage for binning, resolving a computational bottleneck for metagenomics. Video Abstract.

Composition and metabolic flexibility of hydrocarbon-degrading consortia in oil reservoirs.

Hydrocarbon-degrading consortia (HDC) play an important role in petroleum exploitation. However, the real composition and metabolic mechanism of HDC in the microbial enhanced oil recovery (MEOR) process remain unclear. By combining 13C-DNA stable isotope probing microcosms with metagenomics, some newly reported phyla, including Chloroflexi, Synergistetes, Thermotogae, and Planctomycetes, dominated the HDC in the oil reservoirs. In the field trials, the HDC in the aerobic-facultative-anaerobic stage of oilfields jointly promoted the MEOR process, with monthly oil increments of up to 189 tons. Pseudomonas can improve oil recovery by producing rhamnolipid in the facultative condition. Roseovarius was the novel taxa potentially oxidizing alkane and producing acetate to improve oil porosity and permeability in the aerobic condition. Ca. Bacteroidia were the new members potentially degrading hydrocarbons by fumarate addition in the anaerobic environment. Comprehensive identification of the active HDC in oil reservoirs provides a novel theoretical basis for oilfield regulatory scheme.

Copper and cadmium co-contamination increases the risk of nitrogen loss in red paddy soils.

The microbiome plays a crucial role in soil nitrogen (N) cycling and in regulating its bioavailability. However, the functional and genomic information of microorganisms encoding N cycling in response to copper (Cu) and cadmium (Cd) contamination is largely unknown. Here, metagenomics and genome binning were used to examine microbial N cycling in Cu and Cd co-contaminated red paddy soils collected from a polluted watershed in southern China. The results showed that soil Cu and Cd concentrations induced more drastic changes in microbial N functional and taxonomic traits than soil general properties. Soil Cu and Cd co-contamination stimulated microbial nitrification, denitrification, and dissimilatory nitrate reduction processes mainly by increasing the abundance of Nitrospira (phylum Nitrospirota), while inhibiting N fixation by decreasing the abundance of Desulfobacca. These contrasting changes in microbial N cycling processes suggested a potential risk of N loss in paddy soils. A high-quality genome was identified as belonging to Nitrospirota with the highest abundance in heavily contaminated soils. This novel Nitrospirota strain possessed metabolic capacities for N transformation and metal resistance. These findings elucidate the genetic mechanisms underlying soil N bioavailability under long-term Cu and Cd contamination, which is essential for maintaining agricultural productivity and controlling heavy metal pollution.

Decomposing a San Francisco estuary microbiome using long-read metagenomics reveals species- and strain-level dominance from picoeukaryotes to viruses.

Although long-read sequencing has enabled obtaining high-quality and complete genomes from metagenomes, many challenges still remain to completely decompose a metagenome into its constituent prokaryotic and viral genomes. This study focuses on decomposing an estuarine metagenome to obtain a more accurate estimate of microbial diversity. To achieve this, we developed a new bead-based DNA extraction method, a novel bin refinement method, and obtained 150 Gbp of Nanopore sequencing. We estimate that there are ~500 bacterial and archaeal species in our sample and obtained 68 high-quality bins (>90% complete, <5% contamination, ≤5 contigs, contig length of >100 kbp, and all ribosomal and tRNA genes). We also obtained many contigs of picoeukaryotes, environmental DNA of larger eukaryotes such as mammals, and complete mitochondrial and chloroplast genomes and detected ~40,000 viral populations. Our analysis indicates that there are only a few strains that comprise most of the species abundances. IMPORTANCE: Ocean and estuarine microbiomes play critical roles in global element cycling and ecosystem function. Despite the importance of these microbial communities, many species still have not been cultured in the lab. Environmental sequencing is the primary way the function and population dynamics of these communities can be studied. Long-read sequencing provides an avenue to overcome limitations of short-read technologies to obtain complete microbial genomes but comes with its own technical challenges, such as needed sequencing depth and obtaining high-quality DNA. We present here new sampling and bioinformatics methods to attempt decomposing an estuarine microbiome into its constituent genomes. Our results suggest there are only a few strains that comprise most of the species abundances from viruses to picoeukaryotes, and to fully decompose a metagenome of this diversity requires 1 Tbp of long-read sequencing. We anticipate that as long-read sequencing technologies continue to improve, less sequencing will be needed.

AFITbin: a metagenomic contig binning method using aggregate l-mer frequency based on initial and terminal nucleotides.

BACKGROUND: Using next-generation sequencing technologies, scientists can sequence complex microbial communities directly from the environment. Significant insights into the structure, diversity, and ecology of microbial communities have resulted from the study of metagenomics. The assembly of reads into longer contigs, which are then binned into groups of contigs that correspond to different species in the metagenomic sample, is a crucial step in the analysis of metagenomics. It is necessary to organize these contigs into operational taxonomic units (OTUs) for further taxonomic profiling and functional analysis. For binning, which is synonymous with the clustering of OTUs, the tetra-nucleotide frequency (TNF) is typically utilized as a compositional feature for each OTU. RESULTS: In this paper, we present AFIT, a new l-mer statistic vector for each contig, and AFITBin, a novel method for metagenomic binning based on AFIT and a matrix factorization method. To evaluate the performance of the AFIT vector, the t-SNE algorithm is used to compare species clustering based on AFIT and TNF information. In addition, the efficacy of AFITBin is demonstrated on both simulated and real datasets in comparison to state-of-the-art binning methods such as MetaBAT 2, MaxBin 2.0, CONCOT, MetaCon, SolidBin, BusyBee Web, and MetaBinner. To further analyze the performance of the purposed AFIT vector, we compare the barcodes of the AFIT vector and the TNF vector. CONCLUSION: The results demonstrate that AFITBin shows superior performance in taxonomic identification compared to existing methods, leveraging the AFIT vector for improved results in metagenomic binning. This approach holds promise for advancing the analysis of metagenomic data, providing more reliable insights into microbial community composition and function. AVAILABILITY: A python package is available at: https://github.com/SayehSobhani/AFITBin .

Optimizing anaerobic digestion: Benefits of mild temperature transition from thermophilic to mesophilic conditions.

Anaerobic digestion (AD) plays a significant role in renewable energy recovery. Upgrading AD from thermophilic (50-57 °C) to mesophilic (30-38 °C) conditions to enhance process stability and reduce energy input remains challenging due to the high sensitivity of thermophilic microbiomes to temperature fluctuations. Here we compare the effects of two decreasing-temperature modes from 55 to 35 °C on cell viability, microbial dynamics, and interspecies interactions. A sharp transition (ST) is a one-step transition by 20 °C d-1, while a mild transition (MT) is a stepwise transition by 1 °C d-1. We find a greater decrease in methane production with ST (88.8%) compared to MT (38.9%) during the transition period. ST mode overproduced reactive oxygen species by 1.6-fold, increased membrane permeability by 2.2-fold, and downregulated microbial energy metabolism by 25.1%, leading to increased apoptosis of anaerobes by 1.9-fold and release of intracellular substances by 2.9-fold, further constraining methanogenesis. The higher (1.6 vs. 1.1 copies per gyrA) metabolic activity of acetate-dependent methanogenesis implied more efficient methane production in a steady mesophilic, MT-mediated system. Metagenomic binning and network analyses indicated that ST induced dysbiosis in keystone species and greatly enhanced microbial functional redundancy, causing loss of microbial syntrophic interactions and redundant metabolic pathways. In contrast, the greater microbial interconnections (average degrees 44.9 vs. 22.1) in MT at a steady mesophilic state suggested that MT could better maintain necessary system functionality and stability through microbial syntrophy or specialized pathways. Adopting MT to transform thermophilic digesters into mesophilic digesters is feasible and could potentially enhance the further optimization and broader application of practical anaerobic engineering.

Solving genomic puzzles: computational methods for metagenomic binning.

Metagenomics involves the study of genetic material obtained directly from communities of microorganisms living in natural environments. The field of metagenomics has provided valuable insights into the structure, diversity and ecology of microbial communities. Once an environmental sample is sequenced and processed, metagenomic binning clusters the sequences into bins representing different taxonomic groups such as species, genera, or higher levels. Several computational tools have been developed to automate the process of metagenomic binning. These tools have enabled the recovery of novel draft genomes of microorganisms allowing us to study their behaviors and functions within microbial communities. This review classifies and analyzes different approaches of metagenomic binning and different refinement, visualization, and evaluation techniques used by these methods. Furthermore, the review highlights the current challenges and areas of improvement present within the field of research.

Antimicrobial resistance and its risks evaluation in wetlands on the Qinghai-Tibetan Plateau.

Amidst the global antimicrobial resistance (AMR) crisis, antibiotic resistance has permeated even the most remote environments. To understand the dissemination and evolution of AMR in minimally impacted ecosystems, the resistome and mobilome of wetlands across the Qinghai-Tibetan Plateau and its marginal regions were scrutinized using metagenomic sequencing techniques. The composition of wetland microbiomes exhibits significant variability, with dominant phyla including Proteobacteria, Actinobacteria, Bacteroidetes, and Verrucomicrobia. Notably, a substantial abundance of Antibiotic Resistance Genes (ARGs) and Mobile Genetic Elements (MGEs) was detected, encompassing 17 ARG types, 132 ARG subtypes, and 5 types of MGEs (Insertion Sequences, Insertions Sequences, Genomic Islands, Transposons, and Integrative Conjugative Elements). No significant variance was observed in the prevalence of resistome and mobilome across different wetland types (i.e., the Yellow River, other rivers, lakes, and marshes) (R=-0.5882, P=0.607). The co-occurrence of 74 ARG subtypes and 22 MGEs was identified, underscoring the pivotal role of MGEs in shaping ARG pools within the Qinghai-Tibetan Plateau wetlands. Metagenomic binning and analysis of assembled genomes (MAGs) revealed that 93 out of 206 MAGs harbored ARGs (45.15 %). Predominantly, Burkholderiales, Pseudomonadales, and Enterobacterales were identified as the primary hosts of these ARGs, many of which represent novel species. Notably, a substantial proportion of ARG-carrying MAGs also contained MGEs, reaffirming the significance of MGEs in AMR dissemination. Furthermore, utilizing the arg_ranker framework for risk assessment unveiled severe contamination of high-risk ARGs across most plateau wetlands. Moreover, some prevalent human pathogens were identified as potential hosts for these high-risk ARGs, posing substantial transmission risks. This study aims to investigate the prevalence of resistome and mobilome in wetlands, along with evaluating the risk posed by high-risk ARGs. Such insights are crucial for informing environmental protection strategies and facilitating the management of water resources on the Qinghai-Tibetan Plateau.