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.

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.

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.

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.

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.

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.

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.

Integrative metagenomic dissection of last-resort antibiotic resistance genes and mobile genetic elements in hospital wastewaters.

Hospital wastewater is a critical source of antimicrobial resistance (AMR), which facilitates the proliferation and spread of clinically significant antimicrobial resistance genes (ARGs) and pathogenic bacteria. This study utilized metagenomic approaches, including advanced binning techniques, such as MetaBAT2, MaxBin2, and CONCOCT, which offer significant improvements in accuracy and completeness over traditional binning methods. These methods were used to comprehensively assess the dynamics and composition of resistomes and mobilomes in untreated wastewater samples taken from two general hospitals and one cancer hospital. This study revealed a diverse bacterial landscape, largely consisting of Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria, with notable variations in microbial composition among hospitals. Analysis of the top 15 genera showed unique microbial pattern distribution in each hospital: Aeromonas was predominant in 1stHWTS (49.39 %), Acidovorax in the CAHWTS at 16.85 %, and Escherichia and Bacteroides in the 2ndHWTS at 11.44 % and 11.33 %, respectively. A total of 114 pathogenic bacteria were identified, with drug-resistant Aeromonas caviae and Escherichia coli being the most prevalent. The study identified 34 types and 1660 subtypes of ARGs, including important last-resort antibiotic resistance genes (LARGs), such as blaNDM, mcr, and tet(X). Using metagenomic binning, this study uncovered distinct patterns of host-resistance associations, particularly with Proteobacteria and Firmicutes. Network analysis highlighted the complex interactions among ARGs, mobile genetic elements (MGEs), and bacterial species, all contributing to the dissemination of AMR. These findings emphasize the intricate nature of AMR in hospital wastewater and the influence of hospital-specific factors on microbial resistance patterns. This study provides support for implementing integrated management strategies, including robust surveillance, advanced wastewater treatment, and strict antibiotic stewardship, to control the dissemination of AMR. Understanding the interplay among bacterial communities, ARGs, and MGEs is important for developing effective public health measures against AMR.

Quantifying abnormal alveolar microstructure in cystic fibrosis lung disease via hyperpolarized 129Xe diffusion MRI.

RATIONALE: Cystic Fibrosis (CF) progresses through recurrent infection and inflammation, causing permanent lung function loss and airway remodeling. CT scans reveal abnormally low-density lung parenchyma in CF, but its microstructural nature remains insufficiently explored due to clinical CT limitations. To this end, diffusion-weighted 129Xe MRI is a non-invasive and validated measure of lung microstructure. In this work, we investigate microstructural changes in people with CF (pwCF) relative to age-matched, healthy subjects using comprehensive imaging and analysis involving pulmonary-function tests (PFTs), and 129Xe MRI. METHODS: 38 healthy subjects (age 6-40; 17.2 ± 9.5 years) and 39 pwCF (age 6-40; 15.6 ± 8.0 years) underwent 129Xe-diffusion MRI and PFTs. The distribution of diffusion measurements (i.e., apparent diffusion coefficients (ADC) and morphometric parameters) was assessed via linear binning (LB). The resulting volume percentages of bins were compared between controls and pwCF. Mean ADC and morphometric parameters were also correlated with PFTs. RESULTS: Mean whole-lung ADC correlated significantly with age (P < 0.001) for both controls and CF, and with PFTs (P < 0.05) specifically for pwCF. Although there was no significant difference in mean ADC between controls and pwCF (P = 0.334), age-adjusted LB indicated significant voxel-level diffusion (i.e., ADC and morphometric parameters) differences in pwCF compared to controls (P < 0.05). CONCLUSIONS: 129Xe diffusion MRI revealed microstructural abnormalities in CF lung disease. Smaller microstructural size may reflect compression from overall higher lung density due to interstitial inflammation, fibrosis, or other pathological changes. While elevated microstructural size may indicate emphysema-like remodeling due to chronic inflammation and infection.

Chemical fertilizers promote dissemination of ARGs in maize rhizosphere: An overlooked risk revealed after 37-year traditional agriculture practice.

Bacterial communities in soil and rhizosphere maintain a large collection of antibiotic resistance genes (ARGs). However, few of these ARGs and antibiotic resistant bacteria (ARB) are well-characterized under traditional farming practices. Here we compared the ARG profiles of maize rhizosphere and their bulk soils using metagenomic analysis to identify the ARG dissemination and explored the potential impact of chemical fertilization on ARB. Results showed a relatively lower abundance but higher diversity of ARGs under fertilization than straw-return. Moreover, the abundance and diversity of MGEs were significantly promoted by chemical fertilizer inputs in the rhizosphere compared to bulk soil. Machine learning and bipartite networks identified three bacterial genera (Pseudomonas, Bacillus and Streptomyces) as biomarkers for ARG accumulation. Thus we cultured 509 isolates belonging to these three genera from the rhizosphere and tested their antimicrobial susceptibility, and found that multi-resistance was frequently observed among Pseudomonas isolates. Assembly-based tracking explained that ARGs and four class I integrons (LR134330, LS998783, CP065848, LT883143) were co-occurred among contigs from Pseudomonas sp. Chemical fertilizers may shape the resistomes of maize rhizosphere, highlighting that rhizosphere carried multidrug-resistant Pseudomonas isolates, which may pose a risk to animal and human health. This study adds knowledge of long-term chemical fertilization on ARG dissemination in farmland systems and provides information for decision-making in agricultural production and monitoring.

FFT-Based Probability Density Imaging of Euler Solutions.

When using traditional Euler deconvolution optimization strategies, it is difficult to distinguish between anomalies and their corresponding Euler tails (those solutions are often distributed outside the anomaly source, forming "tail"-shaped spurious solutions, i.e., misplaced Euler solutions, which must be removed or marked) with only the structural index. The nonparametric estimation method based on the normalized B-spline probability density (BSS) is used to separate the Euler solution clusters and mark different anomaly sources according to the similarity and density characteristics of the Euler solutions. For display purposes, the BSS needs to map the samples onto the estimation grid at the points where density will be estimated in order to obtain the probability density distribution. However, if the size of the samples or the estimation grid is too large, this process can lead to high levels of memory consumption and excessive computation times. To address this issue, a fast linear binning approximation algorithm is introduced in the BSS to speed up the computation process and save time. Subsequently, the sample data are quickly projected onto the estimation grid to facilitate the discrete convolution between the grid and the density function using a fast Fourier transform. A method involving multivariate B-spline probability density estimation based on the FFT (BSSFFT), in conjunction with fast linear binning appropriation, is proposed in this paper. The results of two random normal distributions show the correctness of the BSS and BSSFFT algorithms, which is verified via a comparison with the true probability density function (pdf) and Gaussian kernel smoothing estimation algorithms. Then, the Euler solutions of the two synthetic models are analyzed using the BSS and BSSFFT algorithms. The results are consistent with their theoretical values, which verify their correctness regarding Euler solutions. Finally, the BSSFFT is applied to Bishop 5X data, and the numerical results show that the comprehensive analysis of the 3D probability density distributions using the BSSFFT algorithm, derived from the Euler solution subset of x0,y0,z0, can effectively separate and locate adjacent anomaly sources, demonstrating strong adaptability.

Combined environmental pressure induces unique assembly patterns of micro-plastisphere biofilm microbial communities in constructed wetlands.

The characteristics and dynamics of micro-plastisphere biofilm on the surface of microplastics (MPs) within artificial ecosystems, such as constructed wetlands (CWs), remain unclear, despite these ecosystems' potential to serve as sinks for MPs. This study investigates the dynamic evolution of micro-plastisphere biofilm in CWs, utilizing simulated wastewater containing sulfamethoxazole and humic acid, through physicochemical characterization and metagenomic analysis. Two different types of commercial plastics, including non-degradable polyethylene and degradable polylactic acid, were shredded into MPs and studied. The findings reveal that the types, shape and incubation time of MPs, along with humic acid content in wastewater, affected the quantity and quality of biofilms, such as the biofilm composition, spatial structure and microbial communities. After just 15 days into incubation, numerous microbials were observed on MP samples, with increases in biofilms content and enhanced humification of extracellular polymeric substances over time. Additionally, microbial communities on polylactic acid MPs, or those incubated for longer time, exhibit higher diversity, connectivity and stability, along with reduced vulnerability. Conversely, biofilms on polyethylene MPs were thicker, with higher potential for greenhouse gas emission and increased risk of antibiotic resistance genes. The addition of humic acid demonstrated opposite effects on biofilms across environmental interfaces, possibly due to its dual potential to produce light-induced free radicals and serve as a carbon source. Binning analysis further uncovered a unique assembly pattern of nutrients cycle genes and antibiotic resistance genes, significantly correlated within micro-plastisphere microbial communities, under the combined stress of nutrition and sulfamethoxazole. These results emphasize the shaping of micro-plastisphere biofilm characteristics by unique environmental conditions in artificial ecosystems, and the need to understand how DOM and other pollutants covary with MP pollution.

Co-occurrence of dominant bacteria and methanogenic archaea and their metabolic traits in a thermophilic anaerobic digester.

Thermophilic anaerobic digestion (TAD) represents a promising biotechnology for both methane energy production and waste stream treatment. However, numerous critical microorganisms and their metabolic characteristics involved in this process remain unidentified due to the limitations of culturable isolates. This study investigated the phylogenetic composition and potential metabolic traits of bacteria and methanogenic archaea in a TAD system using culture-independent metagenomics. Predominant microorganisms identified in the stable phase of TAD included hydrogenotrophic methanogens (Methanothermobacter and Methanosarcina) and hydrogen-producing bacteria (Coprothermobacter, Acetomicrobium, and Defluviitoga). Nine major metagenome-assembled genomes (MAGs) associated with the dominant genera were selected to infer their metabolic potentials. Genes related to thermal resistance were widely found in all nine major MAGs, such as the molecular chaperone genes, Clp protease gene, and RNA polymerase genes, which may contribute to their predominance under thermophilic condition. Thermophilic temperatures may increase the hydrogen partial pressure of Coprothermobacter, Acetomicrobium, and Defluviitoga, subsequently altering the primary methanogenesis pathway from acetoclastic pathway to hydrogenotrophic pathway in the TAD. Consequently, genes encoding the hydrogenotrophic methanogenesis pathway were the most abundant in the recovered archaeal MAGs. The potential interaction between hydrogen-producing bacteria and hydrogenotrophic methanogens may play critical roles in TAD processes.

On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data.

Using information-theoretic quantities in practical applications with continuous data is often hindered by the fact that probability density functions need to be estimated in higher dimensions, which can become unreliable or even computationally unfeasible. To make these useful quantities more accessible, alternative approaches such as binned frequencies using histograms and k-nearest neighbors (k-NN) have been proposed. However, a systematic comparison of the applicability of these methods has been lacking. We wish to fill this gap by comparing kernel-density-based estimation (KDE) with these two alternatives in carefully designed synthetic test cases. Specifically, we wish to estimate the information-theoretic quantities: entropy, Kullback-Leibler divergence, and mutual information, from sample data. As a reference, the results are compared to closed-form solutions or numerical integrals. We generate samples from distributions of various shapes in dimensions ranging from one to ten. We evaluate the estimators' performance as a function of sample size, distribution characteristics, and chosen hyperparameters. We further compare the required computation time and specific implementation challenges. Notably, k-NN estimation tends to outperform other methods, considering algorithmic implementation, computational efficiency, and estimation accuracy, especially with sufficient data. This study provides valuable insights into the strengths and limitations of the different estimation methods for information-theoretic quantities. It also highlights the significance of considering the characteristics of the data, as well as the targeted information-theoretic quantity when selecting an appropriate estimation technique. These findings will assist scientists and practitioners in choosing the most suitable method, considering their specific application and available data. We have collected the compared estimation methods in a ready-to-use open-source Python 3 toolbox and, thereby, hope to promote the use of information-theoretic quantities by researchers and practitioners to evaluate the information in data and models in various disciplines.

Microbial diversity and metabolic pathways linked to benzene degradation in petrochemical-polluted groundwater.

The rapid advance in shotgun metagenome sequencing has enabled us to identify uncultivated functional microorganisms in polluted environments. While aerobic petrochemical-degrading pathways have been extensively studied, the anaerobic mechanisms remain less explored. Here, we conducted a study at a petrochemical-polluted groundwater site in Henan Province, Central China. A total of twelve groundwater monitoring wells were installed to collect groundwater samples. Benzene appeared to be the predominant pollutant, detected in 10 out of 12 samples, with concentrations ranging from 1.4 µg/L to 5,280 µg/L. Due to the low aquifer permeability, pollutant migration occurred slowly, resulting in relatively low benzene concentrations downstream within the heavily polluted area. Deep metagenome sequencing revealed Proteobacteria as the dominant phylum, accounting for over 63 % of total abundances. Microbial α-diversity was low in heavily polluted samples, with community compositions substantially differing from those in lightly polluted samples. dmpK encoding the phenol/toluene 2-monooxygenase was detected across all samples, while the dioxygenase bedC1 was not detected, suggesting that aerobic benzene degradation might occur through monooxygenation. Sequence assembly and binning yielded 350 high-quality metagenome-assembled genomes (MAGs), with 30 MAGs harboring functional genes associated with aerobic or anaerobic benzene degradation. About 80 % of MAGs harboring functional genes associated with anaerobic benzene degradation remained taxonomically unclassified at the genus level, suggesting that our current database coverage of anaerobic benzene-degrading microorganisms is very limited. Furthermore, two genes integral to anaerobic benzene metabolism, i.e, benzoyl-CoA reductase (bamB) and glutaryl-CoA dehydrogenase (acd), were not annotated by metagenome functional analyses but were identified within the MAGs, signifying the importance of integrating both contig-based and MAG-based approaches. Together, our efforts of functional annotation and metagenome binning generate a robust blueprint of microbial functional potentials in petrochemical-polluted groundwater, which is crucial for designing proficient bioremediation strategies.

Evaluating and improving the representation of bacterial contents in long-read metagenome assemblies.

BACKGROUND: In the metagenomic assembly of a microbial community, abundant species are often thought to assemble well given their deeper sequencing coverage. This conjuncture is rarely tested or evaluated in practice. We often do not know how many abundant species are missing and do not have an approach to recover them. RESULTS: Here, we propose k-mer based and 16S RNA based methods to measure the completeness of metagenome assembly. We show that even with PacBio high-fidelity (HiFi) reads, abundant species are often not assembled, as high strain diversity may lead to fragmented contigs. We develop a novel reference-free algorithm to recover abundant metagenome-assembled genomes (MAGs) by identifying circular assembly subgraphs. Complemented with a reference-free genome binning heuristics based on dimension reduction, the proposed method rescues many abundant species that would be missing with existing methods and produces competitive results compared to those state-of-the-art binners in terms of total number of near-complete genome bins. CONCLUSIONS: Our work emphasizes the importance of metagenome completeness, which has often been overlooked. Our algorithm generates more circular MAGs and moves a step closer to the complete representation of microbial communities.