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Objectives We aim to estimate geographic variability in total numbers of infections and infection fatality ratios (IFR; the number of deaths caused by an infection per 1,000 infected people) when the availability and quality of data on disease burden are limited during an epidemic. Methods We develop a noncentral hypergeometric framework that accounts for differential probabilities of positive tests and reflects the fact that symptomatic people are more likely to seek testing. We demonstrate the robustness, accuracy, and precision of this framework, and apply it to the United States (U.S.) COVID-19 pandemic to estimate county-level SARS-CoV-2 IFRs. Results The estimators for the numbers of infections and IFRs showed high accuracy and precision; for instance, when applied to simulated validation data sets, across counties, Pearson correlation coefficients between estimator means and true values were 0.996 and 0.928, respectively, and they showed strong robustness to model misspecification. Applying the county-level estimators to the real, unsimulated COVID-19 data spanning April 1, 2020 to September 30, 2020 from across the U.S., we found that IFRs varied from 0 to 44.69, with a standard deviation of 3.55 and a median of 2.14. Conclusions The proposed estimation framework can be used to identify geographic variation in IFRs across settings. 

The aerobic hyperthermophile“Fervidibacter sacchari”catabolizes diverse polysaccharides and is the only cultivated member of the class“Fervidibacteria”within the phylumArmatimonadota. It encodes 117 putative glycoside hydrolases (GHs), including two from GH family 50 (GH50). In this study, we expressed, purified, and functionally characterized one of these GH50 enzymes, Fsa16295Glu. We show that Fsa16295Glu is a β-1,3-endoglucanase with optimal activity on carboxymethyl curdlan (CM-curdlan) and only weak agarase activity, despite most GH50 enzymes being described as β-agarases. The purified enzyme has a wide temperature range of 4–95°C (optimal 80°C), making it the first characterized hyperthermophilic representative of GH50. The enzyme is also active at a broad pH range of at least 5.5–11 (optimal 6.5–10). Fsa16295Glu possesses a relatively highk_cat/K_Mof 1.82 × 10⁷ s⁻¹M⁻¹with CM-curdlan and degrades CM-curdlan nearly completely to sugar monomers, indicating preferential hydrolysis of glucans containing β-1,3 linkages. Finally, a phylogenetic analysis of Fsa16295Glu and all other GH50 enzymes revealed that Fsa16295Glu is distant from other characterized enzymes but phylogenetically related to enzymes from thermophilic archaea that were likely acquired horizontally from“Fervidibacteria.”Given its functional and phylogenetic novelty, we propose that Fsa16295Glu represents a new enzyme subfamily, GH50_3. 

Abstract Microbial sulfur metabolism contributes to biogeochemical cycling on global scales. Sulfur metabolizing microbes are infected by phages that can encode auxiliary metabolic genes (AMGs) to alter sulfur metabolism within host cells but remain poorly characterized. Here we identified 191 phages derived from twelve environments that encoded 227 AMGs for oxidation of sulfur and thiosulfate ( dsrA , dsrC/tusE , soxC , soxD and soxYZ ). Evidence for retention of AMGs during niche-differentiation of diverse phage populations provided evidence that auxiliary metabolism imparts measurable fitness benefits to phages with ramifications for ecosystem biogeochemistry. Gene abundance and expression profiles of AMGs suggested significant contributions by phages to sulfur and thiosulfate oxidation in freshwater lakes and oceans, and a sensitive response to changing sulfur concentrations in hydrothermal environments. Overall, our study provides fundamental insights on the distribution, diversity, and ecology of phage auxiliary metabolism associated with sulfur and reinforces the necessity of incorporating viral contributions into biogeochemical configurations. 

Abstract Changes in the sequence of an organism’s genome, i.e., mutations, are the raw material of evolution. The frequency and location of mutations can be constrained by specific molecular mechanisms, such as diversity-generating retroelements (DGRs). DGRs have been characterized from cultivated bacteria and bacteriophages, and perform error-prone reverse transcription leading to mutations being introduced in specific target genes. DGR loci were also identified in several metagenomes, but the ecological roles and evolutionary drivers of these DGRs remain poorly understood. Here, we analyze a dataset of >30,000 DGRs from public metagenomes, establish six major lineages of DGRs including three primarily encoded by phages and seemingly used to diversify host attachment proteins, and demonstrate that DGRs are broadly active and responsible for >10% of all amino acid changes in some organisms. Overall, these results highlight the constraints under which DGRs evolve, and elucidate several distinct roles these elements play in natural communities. 

Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities. Exploration of this vast sequence space has been limited to a comparative analysis against reference microbial genomes and protein families derived from those genomes. Here, to examine the scale of yet untapped functional diversity beyond what is currently possible through the lens of reference genomes, we develop a computational approach to generate reference-free protein families from the sequence space in metagenomes. We analyze 26,931 metagenomes and identify 1.17 billion protein sequences longer than 35 amino acids with no similarity to any sequences from 102,491 reference genomes or the Pfam database. Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical, and gene neighborhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overall, our results uncover an enormously diverse functional space, highlighting the importance of further exploring the microbial functional dark matter.