Motivation: Imbalances in the human gut microbiome have been linked to various conditions, including inflammatory bowel disease (IBD), diabetes, and mental health disorders. While metagenomics and amplicon sequencing are the most commonly used technologies to characterize microbial communities, they do not capture all layers of functional activity of the microbiome. Unfortunately, data from other meta-omics modalities is generally difficult to obtain, due to high costs and error-prone technologies, among other issues. The growing availability of paired meta-omics data offers an opportunity to develop machine learning models that can infer connections between metagenomics data and other forms of meta-omics data. The aim is to enable the prediction of these other forms of meta-omics data from metagenomics data. To that end, we evaluated several machine learning model architectures on the task of predicting meta-omics features from various meta-omics inputs, and analyzed the robustness of these models, as well as potential use cases of artificially generated microbiome data.

Results: Machine learning models, in particular simpler architectures such as elastic net regression models and random forests, generated reliable predictions of transcript and metabolite abundances, with correlations of up to 0.77 and 0.74, respectively, but predicting protein profiles proved more difficult, with correlations of at most 0.42. We also identified a core set of well-predicted features for each meta-omics output type, and showed that multi-output regression neural networks performed similarly when trained using fewer output features. Lastly, our experiments demonstrated that predicted features can be used for the downstream task of IBD prediction. For instance, accuracy obtained using predicted metabolite abundances was 77%, compared to the 80% accuracy achieved using real metabolomics data.

Yeast metabolism can be engineered to produce xenobiotic compounds, such as cannabinoids, the principal isoprenoids of the plant Cannabis sativa, through heterologous metabolic pathways. However, yeast cell factories continue to have low cannabinoid production. This study employed an integrated omics approach to investigate the physiological effects of cannabidiol on S. cerevisiae CENPK2-1C yeast cultures. We treated the experimental group with 0.5 mM CBD and monitored CENPK2-1C cultures. We observed a latent-stationary phase post-diauxic shift in the experimental group and harvested samples in the inflection point of this growth phase for transcriptomic and metabolomic analysis. We compared the transcriptomes of the CBD-treated yeast and the positive control, identifying eight significantly overexpressed genes with a log fold change of at least 1.5 and a significant adjusted p-value. Three notable genes were PDR5 (an ABC-steroid and cation transporter), CIS1, and YGR035C. These genes are all regulated by pleiotropic drug resistance linked promoters. Knockout and rescue of PDR5 showed that it is a causal factor in the post-diauxic shift phenotype. Metabolomic analysis revealed 48 significant spectra associated with CBD-fed cell pellets, 20 of which were identifiable as non-CBD compounds, including fatty acids, glycerophospholipids, and phosphate-salvage indicators. Our results suggest that mitochondrial regulation and lipidomic remodeling play a role in yeast’s response to CBD, which are employed in tandem with pleiotropic drug resistance (PDR). We conclude that bioengineers should account for off-target product C-flux, energy use from ABC-transport, and post-stationary phase cell growth when developing cannabinoid-biosynthetic yeast strains.@en

Background: Assembly algorithm choice should be a deliberate, well-justified decision when researchers create genome assemblies for eukaryotic organisms from third-generation sequencing technologies. While third-generation sequencing by Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) has overcome the disadvantages of short read lengths specific to next-generation sequencing (NGS), third-generation sequencers are known to produce more error-prone reads, thereby generating a new set of challenges for assembly algorithms and pipelines. However, the introduction of HiFi reads, which offer substantially reduced error rates, has provided a promising solution for more accurate assembly outcomes. Since the introduction of third-generation sequencing technologies, many tools have been developed that aim to take advantage of the longer reads, and researchers need to choose the correct assembler for their projects. Results: We benchmarked state-of-the-art long-read de novo assemblers to help readers make a balanced choice for the assembly of eukaryotes. To this end, we used 12 real and 64 simulated datasets from different eukaryotic genomes, with different read length distributions, imitating PacBio continuous long-read (CLR), PacBio high-fidelity (HiFi), and ONT sequencing to evaluate the assemblers. We include 5 commonly used long-read assemblers in our benchmark: Canu, Flye, Miniasm, Raven, and wtdbg2 for ONT and PacBio CLR reads. For PacBio HiFi reads, we include 5 state-of-the-art HiFi assemblers: HiCanu, Flye, Hifiasm, LJA, and MBG. Evaluation categories address the following metrics: reference-based metrics, assembly statistics, misassembly count, BUSCO completeness, runtime, and RAM usage. Additionally, we investigated the effect of increased read length on the quality of the assemblies and report that read length can, but does not always, positively impact assembly quality. Conclusions: Our benchmark concludes that there is no assembler that performs the best in all the evaluation categories. However, our results show that overall Flye is the best-performing assembler for PacBio CLR and ONT reads, both on real and simulated data. Meanwhile, best-performing PacBio HiFi assemblers are Hifiasm and LJA. Next, the benchmarking using longer reads shows that the increased read length improves assembly quality, but the extent to which that can be achieved depends on the size and complexity of the reference genome.

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Motivation: Plasmids are carriers for antimicrobial resistance (AMR) genes and can exchange genetic material with other structures, contributing to the spread of AMR. There is no reliable approach to identify the transfer of AMR genes across plasmids. This is mainly due to the absence of a method to assess the phylogenetic distance of plasmids, as they show large DNA sequence variability. Identifying and quantifying such transfer can provide novel insight into the role of small mobile elements and resistant plasmid regions in the spread of AMR. Results: We developed SHIP, a novel method to quantify plasmid similarity based on the dynamics of plasmid evolution. This allowed us to find conserved fragments containing AMR genes in structurally different and phylogenetically distant plasmids, which is evidence for lateral transfer. Our results show that regions carrying AMR genes are highly mobilizable between plasmids through transposons, integrons, and recombination events, and contribute to the spread of AMR. Identified transferred fragments include a multi-resistant complex class 1 integron in Escherichia coli and Klebsiella pneumoniae, and a region encoding tetracycline resistance transferred through recombination in Enterococcus faecalis.

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Horizontal gene transfer (HGT) trough plasmids is one of the main contributors to the rapid increase of antimicrobial resistance (AMR). Studying wastewater from wastewater treatment plants (WWTPs) allows us new insights into HGT as bacteria from different sources come together. Currently the analysis of HGT is limited, as plasmids cannot be linked to their host species with only metagenomic samples, however when combined with Hi-C sequencing data, sequences from the same cell can be linked together.
We developed a method that uses metagenomic Hi-C data to link bacterial genera together with detected plasmid consensus clusters and resistance genes. Using this method, we analysed datasets from two sources: activated sludge put into a reactor with an antibiotic pressure and a WWTP entrance. The activated sludge dataset was sequenced at two timepoints with an increasing antibiotic concentration. This allowed us to compare degrees of antibiotic resistance in different antibiotic pressures as well as detect broad host-range resistant plasmids. We detected an increase in acquired resistance in environments with a higher antibiotic pressure and detected a resistant plasmid in both locations, linked to both pathogenic as well as bacteria found in active sludge.

Application of biochar to landfill cover soils can purportedly improve methane (CH4) oxidation rates, but understanding the combined effects of soil texture, compaction, and biochar on the activity and composition of the methanotrophs is limited. The amendment of wood biochar on two differently textured landfill cover soils at three compaction levels of the Proctor density was explored by analyzing changes in soil physical properties relevant to methane oxidation, the effects on CH4 oxidation rates, and the composition of the methanotrophic community. Loose soils with and without biochar were pre-incubated to equally elevate the CH4 oxidation rates. Hereafter, soils were compacted and re-incubated. Methane oxidation rates, gas diffusivity, water retention characteristics, and pore size distribution were analyzed on the compacted soils. The relative abundance of methanotrophic bacteria (MOB) was determined at the end of both the pre-incubation and incubation tests of the packed samples. Biochar significantly increased porosity at all compaction levels, enhancing diffusion coefficients. Also, a re-distribution in pore sizes was observed. Increased gas diffusivity from low compaction and amendment of biochar, though, did not reflect higher methane oxidation rates due to high diffusive oxygen fluxes over the limited height of the compacted soil specimens. All soils, with and without biochar, were strongly dominated by Type II methanotrophs. In the sandy soil, biochar amendment strongly increased MOB abundance, which could be attributed to a corresponding increase in the relative abundance of Methylocystis species, while no such response was observed in the clayey soil. Compaction did not change the community composition in either soil. Fir-wood biochar addition to landfill cover soils may not always enhance methanotrophic activity and hence reduce fugitive methane emissions, with the effect being soil-specific. However, especially in finer and more compacted soils, biochar amendment can maintain soil diffusivity above a critical level, preventing the collapse of methanotrophy.@en

The success of antibiotics as a therapeutic agent has led to their ineffectiveness. The continuous use and misuse in clinical and non-clinical areas have led to the emergence and spread of antibiotic-resistant bacteria and its genetic determinants. This is a multi-dimensional problem that has now become a global health crisis. Antibiotic resistance research has primarily focused on the clinical healthcare sectors while overlooking the non-clinical sectors. The increasing antibiotic usage in the environment – including animals, plants, soil, and water – are drivers of antibiotic resistance and function as a transmission route for antibiotic resistant pathogens and is a source for resistance genes. These natural compartments are interconnected with each other and humans, allowing the spread of antibiotic resistance via horizontal gene transfer between commensal and pathogenic bacteria. Identifying and understanding genetic exchange within and between natural compartments can provide insight into the transmission, dissemination, and emergence mechanisms. The development of high-throughput DNA sequencing technologies has made antibiotic resistance research more accessible and feasible. In particular, the combination of metagenomics and powerful bioinformatic tools and platforms have facilitated the identification of microbial communities and has allowed access to genomic data by bypassing the need for isolating and culturing microorganisms. This review aimed to reflect on the different sequencing techniques, metagenomic approaches, and bioinformatics tools and pipelines with their respective advantages and limitations for antibiotic resistance research. These approaches can provide insight into resistance mechanisms, the microbial population, emerging pathogens, resistance genes, and their dissemination. This information can influence policies, develop preventative measures and alleviate the burden caused by antibiotic resistance.@en