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Phage-plasmids are unique mobile genetic elements that function as plasmids and temperate phages. While it has been observed that such elements often encode antibiotic resistance genes and defense system genes, little else is known about other functional traits they encode. Further, no study to date has documented their environmental distribution and prevalence. Here, we performed genome sequence mining of public databases of phages and plasmids utilizing a random forest classifier to identify phage-plasmids. We recovered 5,712 unique phage-plasmid-like genomes from a remarkable array of disparate environments, including human, animal, plant, fungi, soil, sediment, freshwater, wastewater, and saltwater environments. The resulting genomes were used in a comparative sequence analysis, revealing functional traits/accessory genes associated with specific environments. Host-associated elements contained the most defense systems (including CRISPR and anti-CRISPR systems) as well as antibiotic resistance genes, while other environments, such as freshwater and saltwater systems, tended to encode components of various biosynthetic pathways. Interestingly, we identified genes encoding for certain functional traits, including anti-CRISPR systems and specific antibiotic resistance genes, that were enriched in phage-plasmid-like elements relative to both plasmids and phages. Our results highlight that phage-plasmid-like elements are found across a wide-array of environments and likely play a role in shaping microbial ecology in a multitude of niches. 

Antibiotic resistance (AR) presents a global health challenge, necessitating an improved understanding of the ecology, evolution, and dissemination of antibiotic resistance genes (ARGs). Several tools, databases, and algorithms are now available to facilitate the identification of ARGs in metagenomic sequencing data; however, direct annotation of short-read data provides limited contextual information. Knowledge of whether an ARG is carried in the chromosome or on a specific mobile genetic element (MGE) is critical to understanding mobility, persistence, and potential for co-selection. Here we developed ARGContextProfiler, a pipeline designed to extract and visualize ARG genomic contexts. By leveraging the assembly graph for genomic neighborhood extraction and validating contexts through read mapping, ARGContextProfiler minimizes chimeric errors that are a common artifact of assembly outputs. Testing on real, synthetic, and semi-synthetic data, including long-read sequencing data from environmental samples, demonstrated that ARGContextProfiler offers superior accuracy, precision, and sensitivity compared to conventional assembly-based methods. ARGContextProfiler thus provides a powerful tool for uncovering the genomic context of ARGs in metagenomic sequencing data, which can be of value to both fundamental and applied research aimed at understanding and stemming the spread of AR. The source code of ARGContextProfiler is publicly available atGitHub. 

Despite the recent surge of viral metagenomic studies, it remains a significant challenge to recover complete virus genomes from metagenomic data. The majority of viral contigs generated from de novo assembly programs are highly fragmented, presenting significant challenges to downstream analysis and inference. To address this issue, we have developed Virseqimprover, a computational pipeline that can extend assembled contigs to complete or nearly complete genomes while maintaining extension quality. Virseqimprover first examines whether there is any chimeric sequence based on read coverage, breaks the sequence into segments if there is, then extends the longest segment with uniform depth of coverage, and repeats these procedures until the sequence cannot be extended. Finally, Virseqimprover annotates the gene content of the resulting sequence. Results show that Virseqimprover has good performances on correcting and extending viral contigs to their full lengths, hence can be a useful tool to improve the completeness and minimize the assembly errors of viral contigs. Both a web server and a conda package for Virseqimprover are provided to the research community free of charge. 

We study in detail the influence of different chemical potentials (baryon, electric charge, strange, and neutrino) on how and how fast a free gas of quarks in the zero-temperature limit reaches the conformal limit. We discuss the influence of non-zero masses, the inclusion of leptons, and different constraints, such as charge neutrality, zero-net strangeness, and fixed lepton fraction. We also investigate for the first time how the symmetry energy of the system under some of these conditions approaches the conformal limit. We find that the inclusion of all quark masses (even the light ones) can produce different results depending on the chemical potential values or constraints assumed. A positive or negative deviation of 10% from the pressure of free massless quarks with the same chemical potential was found to take place as low as μB=77 to as high as 48,897 MeV. This illustrates the fact that the “free” or conformal limit is not a unique description. Finally, we briefly discuss what kind of corrections are expected from perturbative QCD as one goes away from the conformal limit. 

Abstract While numerous environmental factors contribute to the spread of antibiotic resistance genes (ARGs), quantifying their relative contributions remains a fundamental challenge. Similarly, it is important to differentiate acute human health risks from environmental exposure, versus broader ecological risk of ARG evolution and spread across microbial taxa. Recent studies have proposed various methods for achieving such aims. Here, we introduce MetaCompare 2.0, which improves upon original MetaCompare pipeline by differentiating indicators of human health resistome risk (potential for human pathogens of acute resistance concern to acquire ARGs) from ecological resistome risk (overall mobility of ARGs and potential for pathogen acquisition). The updated pipeline's sensitivity was demonstrated by analyzing diverse publicly-available metagenomes from wastewater, surface water, soil, sediment, human gut, and synthetic microbial communities. MetaCompare 2.0 provided distinct rankings of the metagenomes according to both human health resistome risk and ecological resistome risk, with both scores trending higher when influenced by anthropogenic impact or other stress. We evaluated the robustness of the pipeline to sequence assembly methods, sequencing depth, contig count, and metagenomic library coverage bias. The risk scores were remarkably consistent despite variations in these technological aspects. We packaged the improved pipeline into a publicly-available web service (http://metacompare.cs.vt.edu/) that provides an easy-to-use interface for computing resistome risk scores and visualizing results. 

ML Framework for PPCPs fate in WWTPs. 

Abstract Activated sludge is the centerpiece of biological wastewater treatment, as it facilitates removal of sewage-associated pollutants, fecal bacteria, and pathogens from wastewater through semi-controlled microbial ecology. It has been hypothesized that horizontal gene transfer facilitates the spread of antibiotic resistance genes within the wastewater treatment plant, in part because of the presence of residual antibiotics in sewage. However, there has been surprisingly little evidence to suggest that sewage-associated antibiotics select for resistance at wastewater treatment plants via horizontal gene transfer or otherwise. We addressed the role of sewage-associated antibiotics in promoting antibiotic resistance using lab-scale sequencing batch reactors fed field-collected wastewater, metagenomic sequencing, and our recently developed bioinformatic tool Kairos. Here, we found confirmatory evidence that fluctuating levels of antibiotics in sewage are associated with horizontal gene transfer of antibiotic resistance genes, microbial ecology, and microdiversity-level differences in resistance gene fate in activated sludge.