Nitrogen sourcing during viral infection of marine cyanobacteria.

The building blocks of a virus derived from de novo biosynthesis during infection and/or catabolism of preexisting host cell biomass, and the relative contribution of these 2 sources has important consequences for understanding viral biogeochemistry. We determined the uptake of extracellular nitrogen (N) and its biosynthetic incorporation into both virus and host proteins using an isotope-labeling proteomics approach in a model marine cyanobacterium Synechococcus WH8102 infected by a lytic cyanophage S-SM1. By supplying dissolved N as 15N postinfection, we found that proteins in progeny phage particles were composed of up to 41% extracellularly derived N, while proteins of the infected host cell showed almost no isotope incorporation, demonstrating that de novo amino acid synthesis continues during infection and contributes specifically and substantially to phage replication. The source of N for phage protein synthesis shifted over the course of infection from mostly host derived in the early stages to more medium derived later on. We show that the photosystem II reaction center proteins D1 and D2, which are auxiliary metabolic genes (AMGs) in the S-SM1 genome, are made de novo during infection in an apparently light-dependent manner. We also identified a small set of host proteins that continue to be produced during infection; the majority are homologs of AMGs in S-SM1 or other viruses, suggesting selective continuation of host protein production during infection. The continued acquisition of nutrients by the infected cell and their utilization for phage replication are significant for both evolution and biogeochemical impact of viruses.

Postnovo: Postprocessing Enables Accurate and FDR-Controlled de Novo Peptide Sequencing.

De novo sequencing offers an alternative to database search methods for peptide identification from mass spectra. Since it does not rely on a predetermined database of expected or potential sequences in the sample, de novo sequencing is particularly appropriate for samples lacking a well-defined or comprehensive reference database. However, the low accuracy of many de novo sequence predictions has prevented the widespread use of the variety of sequencing tools currently available. Here, we present a new open-source tool, Postnovo, that postprocesses de novo sequence predictions to find high-accuracy results. Postnovo uses a predictive model to rescore and rerank candidate sequences in a manner akin to database search postprocessing tools such as Percolator. Postnovo leverages the output from multiple de novo sequencing tools in its own analyses, producing many times the length of amino acid sequence information (including both full- and partial-length peptide sequences) at an equivalent false discovery rate (FDR) compared to any individual tool. We present a methodology to reliably screen the sequence predictions to a desired FDR given the Postnovo sequence score. We validate Postnovo with multiple data sets and demonstrate its ability to identify proteins that are missed by database search even in samples with paired reference databases.