Microbial genome collection of aerobic granular sludge cultivated in sequencing batch reactors using different carbon source mixtures.

Aerobic granular sludge (AGS) consists of a microbial consortium that has an important role in wastewater treatment. This study investigates AGS microorganisms cultivated in a laboratory-scale sequencing batch reactor. Metagenomic sequencing was conducted using PacBio and Illumina, resulting in 759 metagenome-assembled genomes, 331 of which remained after dereplication.

Benchmarking informatics approaches for virus discovery: caution is needed when combining in silico identification methods.

Understanding the ecological impacts of viruses on natural and engineered ecosystems relies on the accurate identification of viral sequences from community sequencing data. To maximize viral recovery from metagenomes, researchers frequently combine viral identification tools. However, the effectiveness of this strategy is unknown. Here, we benchmarked combinations of six widely used informatics tools for viral identification and analysis (VirSorter, VirSorter2, VIBRANT, DeepVirFinder, CheckV, and Kaiju), called "rulesets." Rulesets were tested against mock metagenomes composed of taxonomically diverse sequence types and diverse aquatic metagenomes to assess the effects of the degree of viral enrichment and habitat on tool performance. We found that six rulesets achieved equivalent accuracy [Matthews Correlation Coefficient (MCC) = 0.77, Padj ≥ 0.05]. Each contained VirSorter2, and five used our "tuning removal" rule designed to remove non-viral contamination. While DeepVirFinder, VIBRANT, and VirSorter were each found once in these high-accuracy rulesets, they were not found in combination with each other: combining tools does not lead to optimal performance. Our validation suggests that the MCC plateau at 0.77 is partly caused by inaccurate labeling within reference sequence databases. In aquatic metagenomes, our highest MCC ruleset identified more viral sequences in virus-enriched (44%-46%) than in cellular metagenomes (7%-19%). While improved algorithms may lead to more accurate viral identification tools, this should be done in tandem with careful curation of sequence databases. We recommend using the VirSorter2 ruleset and our empirically derived tuning removal rule. Our analysis provides insight into methods for in silico viral identification and will enable more robust viral identification from metagenomic data sets. IMPORTANCE: The identification of viruses from environmental metagenomes using informatics tools has offered critical insights in microbial ecology. However, it remains difficult for researchers to know which tools optimize viral recovery for their specific study. In an attempt to recover more viruses, studies are increasingly combining the outputs from multiple tools without validating this approach. After benchmarking combinations of six viral identification tools against mock metagenomes and environmental samples, we found that these tools should only be combined cautiously. Two to four tool combinations maximized viral recovery and minimized non-viral contamination compared with either the single-tool or the five- to six-tool ones. By providing a rigorous overview of the behavior of in silico viral identification strategies and a pipeline to replicate our process, our findings guide the use of existing viral identification tools and offer a blueprint for feature engineering of new tools that will lead to higher-confidence viral discovery in microbiome studies.

Genomic insights into the coupling of a Chlorella-like microeukaryote and sulfur bacteria in the chemocline of permanently stratified Lake Cadagno.

Meromictic Lake Cadagno is a permanently stratified system with a persistent microbial bloom within the oxic-anoxic boundary called the chemocline. The association between oxygenic and anoxygenic photosynthesis within the chemocline has been known for at least two decades. Although anoxygenic purple and green sulfur bacteria have been well studied, reports on oxygenic phytoplankton have remained sparse since their discovery in the 1920s. Nearly a century later, this study presents the first near-complete genome of a photosynthetic microbial eukaryote from the chemocline of Lake Cadagno, provisionally named Chlorella-like MAG. The 18.9 Mbp nuclear genome displays a high GC content (71.5%), and the phylogenetic placement suggests that it is a novel species of the genus Chlorella of Chlorophytes. Functional annotation of the Chlorella-like metagenome-assembled genome predicted 10,732 protein-coding genes, with an approximate 0.6% proportion potentially involved in carbon, sulfur, and nitrogen (C, N, and S) metabolism. In addition to C4 photosynthesis, this study detected genes for heat shock proteins (HSPs) in the Chlorella-like algae, consistent with the other Chlorella species. Altogether, the genomic insights in this study suggest the cooperation of photosynthetic algae with phototrophic sulfur bacteria via C, N, and S metabolism, which may aid their collective persistence in the Lake Cadagno chemocline. Furthermore, this work additionally presents the chloroplast genome of Cryptomonas-like species, which was likely to be presumed as cyanobacteria in previous studies because of the presence of phycobilisomes.

Bacterial, Phytoplankton, and Viral Distributions and Their Biogeochemical Contexts in Meromictic Lake Cadagno Offer Insights into the Proterozoic Ocean Microbial Loop.

Lake Cadagno, a permanently stratified high-alpine lake with a persistent microbial bloom in its chemocline, has long been considered a model for the low-oxygen, high-sulfide Proterozoic ocean. Although the lake has been studied for over 25 years, the absence of concerted study of the bacteria, phytoplankton, and viruses, together with primary and secondary production, has hindered a comprehensive understanding of its microbial food web. Here, the identities, abundances, and productivity of microbes were evaluated in the context of Lake Cadagno biogeochemistry. Photosynthetic pigments together with 16S rRNA gene phylogenies suggest the prominence of eukaryotic phytoplankton chloroplasts, primarily chlorophytes. Chloroplasts closely related to those of high-alpine-adapted Ankyra judayi persisted with oxygen in the mixolimnion, where photosynthetic efficiency was high, while chloroplasts of Closteriopsis-related chlorophytes peaked in the chemocline and monimolimnion. The anoxygenic phototrophic sulfur bacterium Chromatium dominated the chemocline along with Lentimicrobium, a genus of known fermenters. Secondary production peaked in the chemocline, which suggested that anoxygenic primary producers depended on heterotrophic nutrient remineralization. The virus-to-microbe ratio peaked with phytoplankton abundances in the mixolimnion and were at a minimum where Chromatium abundance was highest, trends that suggest that viruses may play a role in the modulation of primary production. Through the combined analysis of bacterial, eukaryotic, viral, and biogeochemical spatial dynamics, we provide a comprehensive synthesis of the Lake Cadagno microbial loop. This study offers a new ecological perspective on how biological and geochemical connections may have occurred in the chemocline of the Proterozoic ocean, where eukaryotic microbial life is thought to have evolved. IMPORTANCE As a window into the past, this study offers insights into the potential role that microbial guilds may have played in the production and recycling of organic matter in ancient Proterozoic ocean chemoclines. The new observations described here suggest that chloroplasts of eukaryotic algae were persistent in the low-oxygen upper chemocline along with the purple and green sulfur bacteria known to dominate the lower half of the chemocline. This study provides the first insights into Lake Cadagno's viral ecology. High viral abundances suggested that viruses may be essential components of the chemocline, where their activity may result in the release and recycling of organic matter. The integration of diverse geochemical and biological data types provides a framework that lays the foundation to quantitatively resolve the processes performed by the discrete populations that comprise the microbial loop in this early anoxic ocean analogue.