ABSTRACT For the past 25 years, flow cytometry (FCM) has been a gold standard for the direct measurement of virus-like particles (VLPs) in aquatic ecosystems. Flow cytometry allows for higher throughput and costs less than alternative enumeration methods, leading to its broad usage in aquatic viral ecology. A major challenge associated with flow cytometry is the degradation of VLPs over time, making the use of high-throughput plates not possible, thus lowering the overall throughput. It has also been difficult to maintain a method with low contamination, high signal-to-noise ratios, and observations of real VLPs vs fake particles. For these reasons, the use of flow cytometry has rapidly declined over the years due to the advent of massively parallel sequencing. Here, we describe a high-throughput method (HTM) in a 96-well plate format that provides a hands-free approach to viral enumeration. In a standard run, 60 samples can be measured for VLPs within 2.25 h, which is ~1 h faster than the standard single-tube approach and ~1.5 h faster than epifluorescence microscopy (EFM). Direct measurement of VLPs still provides a window into the viral-host interactions within aquatic ecosystems, which can be rapidly measured and resolved in a high-throughput manner.IMPORTANCEViruses represent the most numerically abundant biological entity on planet Earth. Enumeration of viruses within aquatic ecosystems (e.g., lakes and oceans) is essential to viral ecology and our understanding of their role within ecosystems. However, reliable and time-efficient high-throughput methods (HTM) for their enumeration are still needed. Here, we present a time saving HTM to count virus-like particles (VLPs) in a consistent and robust manner.

Abstract We now exist in the era of massive datasets from genomics, large language models, and all the known knowledge of humanity right at our fingertips. Much of this data is becoming more accessible; however, processing such data remains an ongoing issue across systems including high performance computing (HPC) infrastructures. Massively parallel computing (MPP) has solved this using a divide and conquer approach by splitting workloads across independent nodes (i.e., central processing units (CPU) allowing for higher scaling of data). The main engine for this in python is Ray; however, it has many issues including a large code space, security issues, debugging opacity, and memory management issues. Here, we present HydraMPP, a lightweight, ease of use and utilization, with high auditability, and with SLURM ergonomics.

Viruses represent the most numerous’biological entities’on Earth; but the direct quantification of viruses within ecosystems is a significant challenge. The classical method of epifluorescence microscopy (EFM) remains the gold standard measurement of viral-like particles (VLPs) within ecosystems. Quantifying VLPs in epifluorescence microscopy is burdened by ongoing challenges that include manual human counting, an absence of accurate morphological sizing, and the range of viral sizes falling below the diffraction limit of light microscopy. EpiVirQuant utilizes epifluorescence microscopy images of VLPs from environmental and/or clinical samples then quantifies VLP particle size and abundance using computational image analysis. The software analyses VLP images in four steps: (i) optimization box selection, (ii) blind deconvolution, (iii) derivation of a size-correction factor, and (iv) enumeration and sizing of VLPs. Sizing occurs by the spiked-in of DAPI beads of a known size via a calibrate image and by deriving a tunable point-spread function (tPSF). Due to microscope and user variation the calibration image choice substantially impacts the derived tPSF and resulting size estimates. This image should be very high quality, lacking blur, with clear DAPI beads and VLPs present. tPSF was benchmarked against commonly used blind-deconvolution PSFs (e.g. Gaussian PSF). tPSF outperformed other PSFs converging to the target microsphere diameter fastest while maintaining stability over increasing Richardson–Lucy iterations. Using the selected tPSF and calibration image, automated counts from EpiVirQuant closely matched expert human counts, with only 2

Megaphages are bacteriophages (i.e., phages) with exceptionally large genomes that have been discovered computationally across the globe. To date, all have evaded cultivation except phage G, which we examined with multiomics and artificial intelligence (AI) to resolve its 50-year cultivated history. Phage G is one of the largest phages with a size of >0.6 µm, about half the width of the host cell, and a 499 kbp, non-permuted, linear genome that has, uniquely among known phages, two pairs of ends. Phage G has >650 protein-coding open reading frames (ORFs), with >65% being hypothetical proteins, with the expansive repertoire of auxiliary metabolic genes (AMGs) acquired from its bacterial host, manipulate host sporulation (sspD, RsfA, spoK), and antiviral escape genes (e.g., anti-CBass nuclease and Anti-Pycsar protein). Our study represents a doorway into the complexity of the genomic repertoire of the only cultivated megaphage.

BACKGROUND:The United States Department of Agriculture has recently released reports that show samples collected from 2022 to 2025 of highly pathogenic avian influenza (H5N1) have been detected in mammals and birds. Up to February 2025, the United States Centres for Disease Control and Prevention reports that there have been 67 humans infected with H5N1 since 2024 with 1 death. The broader potential impact on human health remains unclear. METHODS:In this study, we computationally model 1804 protein complexes consisting of various H5 isolates from 1959 to 2024 against 11 haemagglutinin domain 1 (HA1)-neutralising antibodies. This was performed using AI-based protein folding and physics-based simulations of the antibody-antigen interactions. We analysed binding affinity changes over time and across various antibodies using multiple biochemical and biophysical binding metrics. FINDINGS:This study shows a trend of weakening binding affinity of existing antibodies against H5 isolates over time, indicating that the H5N1 virus is evolving immune escape from our therapeutic and immunological defences. We also found that based on the wide variety of host species and geographic locations in which H5N1 was observed to have been transmitted from birds to mammals, there is not a single central reservoir host species or location associated with H5N1's spread. INTERPRETATION:These results indicate that the virus has potential to move from epidemic to pandemic status. This study illustrates the value of high-performance computing to rapidly model protein-protein interactions and viral genomic sequence data at-scale for functional insights into medical preparedness. FUNDING:No external funding was used in this study.

Endosymbiotic bacteria extensively impact phenotypes of their eukaryotic hosts, while experiencing dramatic changes to their own genome as they become more host-restricted in lifestyle. Understanding the trajectory of such a genome has largely been done through the study of animal-associated bacteria, especially insect endosymbionts. Yet, endofungal bacteria provide another natural experimental model for investigating how microbial genomes change when living inside of a host cell. Mycetohabitans spp. are culturable bacterial endosymbionts of the Mucoromycota fungus Rhizopus microsporus. To investigate the genome dynamics resulting from the endohyphal nature of this emerging model genus, we long-read sequenced and assembled new complete genomes to combine with previous assemblies, creating a global dataset of 28 complete Mycetohabitans genomes. All genomes were between 3.3 and 3.9 Mbp in size and were multipartite, structured into two conserved replicons with some strains having an additional plasmid. Based on evolutionary rate and gene content analysis of the different replicons, we termed the two major ones a chromosome and chromid. The differential presence of a third, mobilome-rich plasmid in some strains and the proliferation of transposable elements provide putative mechanisms for recombination or gene loss. The conservation of intact prophage and putative toxin-antitoxin systems and extensive enrichment of secondary metabolite clusters in the Mycetohabitans genomes highlight the dynamic nature of this reducing genome. With fungal-bacterial symbioses becoming increasingly apparent phenomena, lessons learned from this symbiosis will inform our understanding of bacterial adaptation to novel hosts and the process of microbe-microbe coevolution.

We present draft genomes of two Paraburkholderia strains and two Burkholderia strains. Three strains were isolated from surrounding soils and one from roots of switchgrass (Panicum virgatum) in Michigan, USA.

Resistance to antibiotics is approaching crisis levels for organisms such as the ESKAPEE pathogens (includes Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter spp., and Escherichia coli) that often are acquired in hospitals. These organisms sometimes have acquired plasmids that confer resistance to most if not all beta-lactam antibiotics. We have been developing alternative means for dealing with antibiotic resistant microbes that cause infections in humans by developing viruses (bacteriophages) that attack and kill them. One of these pathogens, K. pneumoniae, has one of the highest propensities for antimicrobial resistance. We identified many phages that have lytic capacity against limited numbers of clinical isolates, and through experimental evolution over the course of 30 days, were able to vastly expand the host ranges of these phages to kill a broader range of clinical K. pneumoniae isolates including MDR (multi-drug resistant) and XDR (extensively-drug resistant) isolates. Most interestingly, they were capable of inhibiting growth of clinical isolates both on solid and in liquid medium over extended periods. That we were able to extend the host ranges of multiple naïve antibiotic resistant K. pneumoniae through experimental phage evolution suggests that such a technique may be applicable to other antibiotic-resistant organisms to help stem the tide of antibiotic resistance and offer further options for medical treatments.

Past anti-bacterial use of bacteriophages (phage therapy) is already well reviewed as a potential therapeutic response to the emergence of multidrug-resistant, pathogenic bacteria. Phage therapy has been limited by the following. (1) The success rate is too low for routine use and Food and Drug Administration (FDA) approval. (2) Current strategies of routine phage characterization do not sufficiently improve the success rate of phage therapy. (3) The stability of many phages at ambient temperature is not high enough to routinely store and transport phages at ambient temperature. In the present communication, we present new and previous data that we interpret as introductory to biophysically and efficiently transforming phage therapy to the needed level of effectiveness. Included are (1) procedure and preliminary data for the use of native gel electrophoresis (a low-cost procedure) for projecting the therapy effectiveness of a newly isolated phage, (2) data that suggest a way to achieve stabilizing of dried, ambient-temperature phages via polymer embedding, and (3) data that suggest means to increase the blood persistence, and therefore the therapy effectiveness, of what would otherwise be a relatively low-persistence phage.

Motivation:Polymerase chain reaction (PCR) is the world's most important molecular diagnostic with applications ranging from medicine to ecology. PCR can fail because of poor primer design. The nearest-neighbor thermodynamic properties, picking conserved regions, and filtration via penalty of oligonucleotides form the basis for good primer design.Results:DeGenPrime is a console-based high-quality PCR primer design tool that can utilize MSA formats and degenerate bases expanding the target range for a single primer set. Our software utilizes thermodynamic properties, filtration metrics, penalty scoring, and conserved region finding of any proposed primer. It has degeneracy, repeated k-mers, relative GC content, and temperature range filters. Minimal penalty scoring is included according to secondary structure self-dimerization metrics, GC clamping, tri- and tetra-loop hairpins, and internal repetition. We compared PrimerDesign-M, DegePrime, ConsensusPrimer, and DeGenPrime on acceptable primer yield. PrimerDesign-M, DegePrime, and ConsensusPrimer provided 0%, 11%, and 17% yield, respectively, for the alternative iron nitrogenase (anfD) gene target. DeGenPrime successfully identified quality primers within the conserved regions of the T4-like phage major capsid protein (g23), conserved regions of molybdenum-based nitrogenase (nif), and its alternatives vanadium (vnf) and iron (anf) nitrogenase. DeGenPrime provides a universal and scalable primer design tool for the entire tree of life.Availability and implementation:DeGenPrime is written in C++ and distributed under a BSD-3-Clause license. The source code for DeGenPrime is freely available on www.github.com/raw-lab/degenprime.

The soil microbiome is recognized as an essential component of healthy soils. Viruses are also diverse and abundant in soils, but their roles in soil systems remain unclear. Here we argue for the consideration of viruses in soil microbial food webs and describe the impact of viruses on soil biogeochemistry. The soil food web is an intricate series of trophic levels that span from autotrophic microorganisms to plants and animals. Each soil system encompasses contrasting and dynamic physicochemical conditions, with labyrinthine habitats composed of particles. Conditions are prone to shifts in space and time, and this variability can obstruct or facilitate interactions of microorganisms and viruses. Because viruses can infect all domains of life, they must be considered as key regulators of soil food web dynamics and biogeochemical cycling. We highlight future research avenues that will enable a more robust understanding of the roles of viruses in soil function and health.

Motivation MerCat2 ("Mer-Catenate2") is a versatile, parallel, scalable and modular property software package for robustly analyzing features in omics data. Using massively parallel sequencing raw reads, assembled contigs, and protein sequences from any platform as input, MerCat2 performs k-mer counting of any length k, resulting in feature abundance counts tables, quality control reports, protein feature metrics, and graphical representation (i.e. principal component analysis (PCA)).Results MerCat2 allows for direct analysis of data properties in a database-independent manner that initializes all data, which other profilers and assembly-based methods cannot perform. MerCat2 represents an integrated tool to illuminate omics data within a sample for rapid cross-examination and comparisons.Availability and implementation MerCat2 is written in Python and distributed under a BSD-3 license. The source code of MerCat2 is freely available at https://github.com/raw-lab/mercat2. MerCat2 is compatible with Python 3 on Mac OS X and Linux. MerCat2 can also be easily installed using bioconda: mamba create -n mercat2 -c conda-forge -c bioconda mercat2

Biological nitrogen fixation is a fundamental biogeochemical process that transforms molecular nitrogen into biologically available nitrogen via diazotrophic microbes. Diazotrophs anaerobically fix nitrogen using the nitrogenase enzyme which is arranged in three different gene clusters: (i) molybdenum nitrogenase (nifHDK) is the most abundant, followed by it's alternatives, (ii) vanadium nitrogenase (vnfHDK) and (iii) iron nitrogenase (anfHDK). Multiple databases have been constructed as resources for diazotrophic 'omics analysis; however, an integrated database based on whole genome references does not exist. Here, we present NFixDB (Nitrogen Fixation DataBase), a comprehensive integrated whole genome based database for diazotrophs, which includes all nitrogenases (nifHDK, vnfHDK, anfHDK) and nitrogenase-like enzymes (e.g. nflHD) linked to ribosomal RNA operons (16S-5S-23S). NFixDB was computed using Hidden Markov Models (HMMs) against the entire whole genome based Genome Taxonomy Database (GTDB R214), providing searchable reference HMMs for all nitrogenase and nitrogenase-like genes, complete ribosomal RNA operons, both GTDB and NCBI/RefSeq taxonomy, and an SQL database for querying matches. We compared NFixDB to nifH databases from Buckley, Zehr, Mise and FunGene finding extensive evidence of nifH, in addition to vnfH and nflH. NFixDB contains >4000 verified nifHDK sequences contained on 50 unique phyla of bacteria and archaea. NFixDB provides the first comprehensive nitrogenase database available to researchers unlocking diazotrophic microbial potential.

MOTIVATION:MetaCerberus is a massively parallel, fast, low memory, scalable annotation tool for inference gene function across genomes to metacommunities. MetaCerberus provides an elusive HMM/HMMER-based tool at a rapid scale with low memory. It offers scalable gene elucidation to major public databases, including KEGG (KO), COGs, CAZy, FOAM, and specific databases for viruses, including VOGs and PHROGs, from single genomes to metacommunities. RESULTS:MetaCerberus is 1.3× as fast on a single node than eggNOG-mapper v2 on 5× less memory using an exclusively HMM/HMMER mode. In a direct comparison, MetaCerberus provides better annotation of viruses, phages, and archaeal viruses than DRAM, Prokka, or InterProScan. MetaCerberus annotates more KOs across domains when compared to DRAM, with a 186× smaller database, and with 63× less memory. MetaCerberus is fully integrated for automatic analysis of statistics and pathways using differential statistic tools (i.e. DESeq2 and edgeR), pathway enrichment (GAGE R), and pathview R. MetaCerberus provides a novel tool for unlocking the biosphere across the tree of life at scale. AVAILABILITY AND IMPLEMENTATION:MetaCerberus is written in Python and distributed under a BSD-3 license. The source code of MetaCerberus is freely available at https://github.com/raw-lab/metacerberus compatible with Python 3 and works on both Mac OS X and Linux. MetaCerberus can also be easily installed using bioconda: mamba create -n metacerberus -c bioconda -c conda-forge metacerberus.

Epifluorescence microscopy (EFM) has been the gold standard method for environmental viral enumeration for over 25 years. Currently, standard EFM methods using the Anodisc filters are no longer cost-effective (>$15 per slide). We present a cost-effective method for environmental viral enumeration from aquatic samples, microbial mats, and exopolymeric substances (EPSs) within modern microbialites using EFM. Our integrated approach, which includes filtration, differential centrifugation, chloroform treatment, glutaraldehyde fixation, benzonase nuclease treatment, probe sonication (EPS and mat only), SYBR Gold staining, wet mounting, and imaging, provides a robust method for modern microbialites and aquatic samples. Our method provides a robust and cost-effective (~$0.75 per sample) viral enumeration within modern microbialites and aquatic ecosystems.

Protists are less studied for their role and diversity in ecosystems. Notably, protists have played and still play an important role in microbialites. Microbialites, or lithified microbial mats, represent the oldest evidence of fossil biofilms (~3.5 Gyr). Modern microbialites may offer a unique proxy to study the potential role of protists within a geological context. We examined protist diversity in freshwater (Kelly and Pavilion Lake in British Columbia, Canada) and marine (Highborne Cay, Bahamas) to hypersaline (Shark Bay, Australia) microbialites to decipher their geomicrobiological role. The freshwater microbialite communities were clearly distinct from their marine and hypersaline counterparts. Chlorophytes had higher numerical abundance in freshwater microbialites; whereas pennate diatoms dominated numerically in marine microbialites. Despite the differences, protists across ecosystems may have adopted similar roles and functions. We suggest a consistent biogeochemical role of protists across microbialites globally; but that salinity may shape protist composition and evolution in these ecosystems.

Plant-specific microbial diversity reflecting host-microbe coevolution was frequently shown at the structural level but less on the functional scale. We studied the microbiome of three compartments at the soil root interface (root endosphere, rhizosphere, bulk soil) of medicinal plants cultivated under organic management in Egypt. The study aimed to examine the impact of the rhizosphere on microbial community composition and diversity in desert agricultural soil, as well as to identify specific functions associated with the rhizosphere. The microbiome community structure, diversity, and microbial functioning were evaluated through the utilization of 16S rRNA gene amplicon and shotgun metagenome sequencing. We found the typical rhizosphere effect and plant-species-specific enrichment of bacterial diversity. The annual plants Calendula officinalis and Matricaria chamomilla (Asteraceae) were more similar than the perennial Solanum distichum (Solanaceae). Altogether, plant species explained 50.5

Switchgrass (Panicum virgatum L.) remains the preeminent American perennial (C4) bioenergy crop for cellulosic ethanol, that could help displace over a quarter of the US current petroleum consumption. Intriguingly, there is often little response to nitrogen fertilizer once stands are established. The rhizosphere microbiome plays a critical role in nitrogen cycling and overall plant nutrient uptake. We used high-throughput metagenomic sequencing to characterize the switchgrass rhizosphere microbial community before and after a nitrogen fertilization event for established stands on marginal land. We examined community structure and bulk metabolic potential, and resolved 29 individual bacteria genomes via metagenomic de novo assembly. Community structure and diversity were not significantly different before and after fertilization; however, the bulk metabolic potential of carbohydrate-active enzymes was depleted after fertilization. We resolved 29 metagenomic assembled genomes, including some from the 'most wanted' soil taxa such as Verrucomicrobia, Candidate phyla UBA10199, Acidobacteria (rare subgroup 23), Dormibacterota, and the very rare Candidatus Eisenbacteria. The Dormibacterota (formally candidate division AD3) we identified have the potential for autotrophic CO utilization, which may impact carbon partitioning and storage. Our study also suggests that the rhizosphere microbiome may be involved in providing associative nitrogen fixation (ANF) via the novel diazotroph Janthinobacterium to switchgrass.

Soil viral ecology is a growing research field; however, the state of knowledge still lags behind that of aquatic systems. Therefore, to facilitate progress, the first Soil Viral Workshop was held to encourage international scientific discussion and collaboration, suggest guidelines for future research, and establish soil viral research as a concrete research area. The workshop took place at Søminestationen, Denmark, between 15 and 17th of June 2022. The meeting was primarily held in person, but the sessions were also streamed online. The workshop was attended by 23 researchers from ten different countries and from a wide range of subfields and career stages. Eleven talks were presented, followed by discussions revolving around three major topics: viral genomics, virus-host interactions, and viruses in the soil food web. The main take-home messages and suggestions from the discussions are summarized in this report.

Early detection of causal pathogens is important to prevent crop loss from diseases. However, some diseases, such as soilborne diseases, are difficult to diagnose due to the absence of visible or characteristic symptoms. In the present study, the use of the Oxford Nanopore MinION sequencer as a molecular diagnostic tool was assessed due to its long-read sequencing capabilities and portability. Nucleotide samples (DNA or RNA) from potato field soils were sequenced and analyzed using a locally curated pathogen database, followed by identification via sequence mapping. We performed computational speed tests of three commonly used mapping/annotation tools (BLAST, BWA-BLAST, and BWA-GraphMap) and found BWA-GraphMap to be the fastest tool for local searching against our curated pathogen database. The data collected demonstrate the high potential of Nanopore sequencing as a minimally biased diagnostic tool for comprehensive pathogen detection in soil from potato fields. Our GraphMap-based MinION sequencing method could be useful as a predictive approach for disease management by identifying pathogens present in field soil prior to planting. Although this method still needs further experimentation with a larger sample size for practical use, the data analysis pipeline presented can be applied to other cropping systems and diagnostics for detecting multiple pathogens.