Molecular prediction of lytic vs lysogenic states for Microcystis phage: Metatranscriptomic evidence of lysogeny during large bloom events

Microcystis aeruginosa is a freshwater bloom-forming cyanobacterium capable of producing the potent hepatotoxin, microcystin. Despite increased interest in this organism, little is known about the viruses that infect it and drive nutrient mobilization and transfer of genetic material between organisms. The genomic complement of sequenced phage suggests these viruses are capable of integrating into the host genome, though this activity has not been observed in the laboratory. While analyzing RNA-sequence data obtained from Microcystis blooms in Lake Tai (Taihu, China), we observed that a series of lysogeny-associated genes were highly expressed when genes involved in lytic infection were down-regulated. This pattern was consistent, though not always statistically significant, across multiple spatial and temporally distinct samples. For example, samples from Lake Tai (2014) showed a predominance of lytic virus activity from late July through October, while genes associated with lysogeny were strongly expressed in the early months (June–July) and toward the end of bloom season (October). Analyses of whole phage genome expression shows that transcription patterns are shared across sampling locations and that genes consistently clustered by co-expression into lytic and lysogenic groups. Expression of lytic-cycle associated genes was positively correlated to total dissolved nitrogen, ammonium concentration, and salinity. Lysogeny-associated gene expression was positively correlated with pH and total dissolved phosphorous. Our results suggest that lysogeny may be prevalent in Microcystis blooms and support the hypothesis that environmental conditions drive switching between temperate and lytic life cycles during bloom proliferation

Metatranscriptomic Analyses of Diel Metabolic Functions During a Microcystis Bloom in Western Lake Erie (United States)

This study examined diel shifts in metabolic functions of spp. during a 48-h Lagrangian survey of a toxin-producing cyanobacterial bloom in western Lake Erie in the aftermath of the 2014 Toledo Water Crisis. Transcripts mapped to the genomes of recently sequenced lower Great Lakes isolates showed distinct patterns of gene expression between samples collected across day (10:00 h, 16:00 h) and night (22:00 h, 04:00 h). Daytime transcripts were enriched in functions related to Photosystem II (e.g., ), nitrogen and phosphate acquisition, cell division (), heat shock response (, ), and uptake of inorganic carbon (, ). Genes transcribed during nighttime included those involved in phycobilisome protein synthesis and Photosystem I core subunits. Hierarchical clustering and principal component analysis (PCA) showed a tightly clustered group of nighttime expressed genes, whereas daytime transcripts were separated from each other over the 48-h duration. Lack of uniform clustering within the daytime transcripts suggested that the partitioning of gene expression in is dependent on both circadian regulation and physicochemical changes within the environment

Genome and Environmental Activity of a Chrysochromulina parva Virus and Its Virophages

Some giant viruses are ecological agents that are predicted to be involved in the top-down control of single-celled eukaryotic algae populations in aquatic ecosystems. Despite an increased interest in giant viruses since the discovery and characterization of Mimivirus and other viral giants, little is known about their physiology and ecology. In this study, we characterized the genome and functional potential of a giant virus that infects the freshwater haptophyte Chrysochromulina parva, originally isolated from Lake Ontario. This virus, CpV-BQ2, is a member of the nucleo-cytoplasmic large DNA virus (NCLDV) group and possesses a 437 kb genome encoding 503 ORFs with a GC content of 25%. Phylogenetic analyses of core NCLDV genes place CpV-BQ2 amongst the emerging group of algae-infecting Mimiviruses informally referred to as the “extended Mimiviridae,” making it the first virus of this group to be isolated from a freshwater ecosystem. During genome analyses, we also captured and described the genomes of three distinct virophages that co-occurred with CpV-BQ2 and likely exploit CpV for their own replication. These virophages belong to the polinton-like viruses (PLV) group and encompass 19–23 predicted genes, including all of the core PLV genes as well as several genes implicated in genome modifications. We used the CpV-BQ2 and virophage reference sequences to recruit reads from available environmental metatranscriptomic data to estimate their activity in fresh waters. We observed moderate recruitment of both virus and virophage transcripts in samples obtained during Microcystis aeruginosa blooms in Lake Erie and Lake Tai, China in 2013, with a spike in activity in one sample. Virophage transcript abundance for two of the three isolates strongly correlated with that of the CpV-BQ2. Together, the results highlight the importance of giant viruses in the environment and establish a foundation for future research on the physiology and ecology CpV-BQ2 as a model system for algal Mimivirus dynamics in freshwaters

Histogram Statistics of Local Image Regions for Object Segmentation

Abstract. We present a novel approach, based on local image histograms, for statistically characterizing the appearance of deformable models. In deformable model segmentation, appearance models measure the likelihood of an object given a target image. To determine this likelihood we compute pixel intensity histograms of local object-relative image regions from a 3D image volume near the object boundary. We use a Gaussian model to statistically characterize the variation of non-parametric histograms mapped to Euclidean space using the Earth Mover’s Distance. The new method is illustrated and evaluated in a deformable model segmentation study on CT images of the human bladder, prostate, and rectum. Results show improvement over a previous profile based appearance model, out-performance of statistically modeled histograms over simple histogram measurements, and advantages of local image regions over global regions.