Combining compositional data sets introduces error in covariance network reconstruction.

Microbial communities are diverse biological systems that include taxa from across multiple kingdoms of life. Notably, interactions between bacteria and fungi play a significant role in determining community structure. However, these statistical associations across kingdoms are more difficult to infer than intra-kingdom associations due to the nature of the data involved using standard network inference techniques. We quantify the challenges of cross-kingdom network inference from both theoretical and practical points of view using synthetic and real-world microbiome data. We detail the theoretical issue presented by combining compositional data sets drawn from the same environment, e.g. 16S and ITS sequencing of a single set of samples, and we survey common network inference techniques for their ability to handle this error. We then test these techniques for the accuracy and usefulness of their intra- and inter-kingdom associations by inferring networks from a set of simulated samples for which a ground-truth set of associations is known. We show that while the two methods mitigate the error of cross-kingdom inference, there is little difference between techniques for key practical applications including identification of strong correlations and identification of possible keystone taxa (i.e. hub nodes in the network). Furthermore, we identify a signature of the error caused by transkingdom network inference and demonstrate that it appears in networks constructed using real-world environmental microbiome data.

The complete genome sequence of probiotic Lactobacillus acidophilus ATCC 9224 isolated from sour milk.

We present the complete genome sequence of the probiotic strain Lactobacillus acidophilus ATCC 9224. The genome sequence provides a valuable resource for investigating the phylogenetic evolution of this lineage and conducting comparative genomics with other Lactobacillus strains and species.

Fabricated devices for performing bacterial-fungal interaction experiments across scales.

Diverse and complex microbiomes are found in virtually every environment on Earth. Bacteria and fungi often co-dominate environmental microbiomes, and there is growing recognition that bacterial-fungal interactions (BFI) have significant impacts on the functioning of their associated microbiomes, environments, and hosts. Investigating BFI in vitro remains a challenge, particularly when attempting to examine interactions at multiple scales of system complexity. Fabricated devices can provide control over both biotic composition and abiotic factors within an experiment to enable the characterization of diverse BFI phenotypes such as modulation of growth rate, production of biomolecules, and alterations to physical movements. Engineered devices ranging from microfluidic chips to simulated rhizosphere systems have been and will continue to be invaluable to BFI research, and it is anticipated that such devices will continue to be developed for diverse applications in the field. This will allow researchers to address specific questions regarding the nature of BFI and how they impact larger microbiome and environmental processes such as biogeochemical cycles, plant productivity, and overall ecosystem resilience. Devices that are currently used for experimental investigations of bacteria, fungi, and BFI are discussed herein along with some of the associated challenges and several recommendations for future device design and applications.

Host and nonhost bacteria support bacteriophage dissemination along mycelia and abiotic dispersal networks.

Bacteriophages play a crucial role in shaping bacterial communities, yet the mechanisms by which nonmotile bacteriophages interact with their hosts remain poorly understood. This knowledge gap is especially pronounced in structured environments like soil, where spatial constraints and air-filled zones hinder aqueous diffusion. In soil, hyphae of filamentous microorganisms form a network of 'fungal highways' (FHs) that facilitate the dispersal of other microorganisms. We propose that FHs also promote bacteriophage dissemination. Viral particles can diffuse in liquid films surrounding hyphae or be transported by infectable (host) or uninfectable (nonhost) bacterial carriers coexisting on FH networks. To test this, two bacteriophages that infect Pseudomonas putida DSM291 (host) but not KT2440 (nonhost) were used. In the absence of carriers, bacteriophages showed limited diffusion on 3D-printed abiotic networks, but diffusion was significantly improved in Pythium ultimum-formed FHs when the number of connecting hyphae exceeded 20. Transport by both host and nonhost carriers enhanced bacteriophage dissemination. Host carriers were five times more effective in transporting bacteriophages, particularly in FHs with over 30 connecting hyphae. This study enhances our understanding of bacteriophage dissemination in nonsaturated environments like soils, highlighting the importance of biotic networks and bacterial hosts in facilitating this process.

Recommendations for Uniform Variant Calling of SARS-CoV-2 Genome Sequence across Bioinformatic Workflows.

Genomic sequencing of clinical samples to identify emerging variants of SARS-CoV-2 has been a key public health tool for curbing the spread of the virus. As a result, an unprecedented number of SARS-CoV-2 genomes were sequenced during the COVID-19 pandemic, which allowed for rapid identification of genetic variants, enabling the timely design and testing of therapies and deployment of new vaccine formulations to combat the new variants. However, despite the technological advances of deep sequencing, the analysis of the raw sequence data generated globally is neither standardized nor consistent, leading to vastly disparate sequences that may impact identification of variants. Here, we show that for both Illumina and Oxford Nanopore sequencing platforms, downstream bioinformatic protocols used by industry, government, and academic groups resulted in different virus sequences from same sample. These bioinformatic workflows produced consensus genomes with differences in single nucleotide polymorphisms, inclusion and exclusion of insertions, and/or deletions, despite using the same raw sequence as input datasets. Here, we compared and characterized such discrepancies and propose a specific suite of parameters and protocols that should be adopted across the field. Consistent results from bioinformatic workflows are fundamental to SARS-CoV-2 and future pathogen surveillance efforts, including pandemic preparation, to allow for a data-driven and timely public health response.

A Practical Approach to Using the Genomic Standards Consortium MIxS Reporting Standard for Comparative Genomics and Metagenomics.

Comparative analysis of (meta)genomes necessitates aggregation, integration, and synthesis of well-annotated data using standards. The Genomic Standards Consortium (GSC) collaborates with the research community to develop and maintain the Minimum Information about any (x) Sequence (MIxS) reporting standard for genomic data. To facilitate the use of the GSC's MIxS reporting standard, we provide a description of the structure and terminology, how to navigate ontologies for required terms in MIxS, and demonstrate practical usage through a soil metagenome example.

Associated bacterial communities, confrontation studies, and comparative genomics reveal important interactions between Morchella with Pseudomonas spp.

Members of the fungal genus Morchella are widely known for their important ecological roles and significant economic value. In this study, we used amplicon and genome sequencing to characterize bacterial communities associated with sexual fruiting bodies from wild specimens, as well as vegetative mycelium and sclerotia obtained from Morchella isolates grown in vitro. These investigations included diverse representatives from both Elata and Esculenta Morchella clades. Unique bacterial community compositions were observed across the various structures examined, both within and across individual Morchella isolates or specimens. However, specific bacterial taxa were frequently detected in association with certain structures, providing support for an associated core bacterial community. Bacteria from the genus Pseudomonas and Ralstonia constituted the core bacterial associates of Morchella mycelia and sclerotia, while other genera (e.g., Pedobacter spp., Deviosa spp., and Bradyrhizobium spp.) constituted the core bacterial community of fruiting bodies. Furthermore, the importance of Pseudomonas as a key member of the bacteriome was supported by the isolation of several Pseudomonas strains from mycelia during in vitro cultivation. Four of the six mycelial-derived Pseudomonas isolates shared 16S rDNA sequence identity with amplicon sequences recovered directly from the examined fungal structures. Distinct interaction phenotypes (antagonistic or neutral) were observed in confrontation assays between these bacteria and various Morchella isolates. Genome sequences obtained from these Pseudomonas isolates revealed intriguing differences in gene content and annotated functions, specifically with respect to toxin-antitoxin systems, cell adhesion, chitinases, and insecticidal toxins. These genetic differences correlated with the interaction phenotypes. This study provides evidence that Pseudomonas spp. are frequently associated with Morchella and these associations may greatly impact fungal physiology.