Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2022.10.12.504627

CRISPR-Cas phage defense systems and prophages in Candidatus Accumulibacter

Deng, X.·Yuan, J.·Chen, L.·Chen, H.·Wei, C.·Nielsen, P. H.·Wuertz, S.·Qiu, G.
View original source ↗Export metadata ↓

Abstract

Candidatus Accumulibacter is a key genus of polyphosphate-accumulating organisms (PAOs) found in laboratory- and full-scale wastewater treatment systems, mediating enhanced biological phosphorus removal (EBPR). However, little is known about their ability to resist phage infection. We conducted a systematic analysis of the occurrence and characteristics of clustered regularly interspaced short palindromic repeats and associated proteins (CRISPR-Cas) systems and prophages in diverse Ca. Accumulibacter taxa (43 in total, including 10 newly recovered genomes). Fourty complete CRISPR loci were identified in 28 genomes, falling into 6 subtypes. The occurrence and distribution of CRISPR-Cas systems did not follow a vertical evolutionary relationship. Phylogenetic analyses of the cas genes and direct repeats (DRs) suggested that the CRISPR-Cas systems were likely acquired via horizontal gene transfer, with acquisition rates higher than those of speciation, rendering different Ca. Accumulibacter distinct adaptivity to phage predations. 2448 spacers were identified, 67 of them matched to known phages. Major differences were observed among the numbers of spacers for different Ca. Accumulibacter, showing unique phages that could be resisted by different members. A comparison of the spacers in genomes having the same cas gene but from distinct geographical locations indicated that habitat isolation may have resulted in the acquisition of different spacers by different Ca. Accumulibacter. Metagenomic analysis allowed the identification of 26 viral contigs (18 are Caudovirales members) in 6 metagenomic datasets from three lab-scale enrichment reactors, matching to 73 spacers in 10 Ca. Accumulibacter genomes in these reactors, showing the specific immunity of these Ca. Accumulibacter. Metatranscriptomic analyses showed the activity of the CRISPR-Cas system under both anaerobic and aerobic conditions. Extra efforts were made to identify prophages in Ca. Accumulibacter genomes. In total, 133 prophage regions were identified. Twenty-seven of them were classified as potentially active prophages. Three prophages (all are Caudovirales members, in DS2011, SCELSE-7IIH and SCELSE-5IIH, respectively) are readily activable. Differences in the occurrence of CRISPR-Cas systems and prophages in Ca. Accumulibacter will lead to their distinct responses under phage predation. This study represents the first systematic analysis of CRISPR-Cas systems and prophages with combined experimental and bioinformatic methods in the Ca. Accumulibacter lineage, providing new perspectives on the potential impacts of phages on Ca. Accumulibacter and EBPR systems.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Deng, X., Yuan, J., Chen, L., Chen, H., Wei, C., Nielsen, P. H., Wuertz, S., Qiu, G.. 2022-10-13. CRISPR-Cas phage defense systems and prophages in Candidatus Accumulibacter. https://doi.org/10.1101/2022.10.12.504627

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗
bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗
bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗