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Abstract Coral bleaching events from thermal stress are increasing globally in duration, frequency, and intensity. While bleaching can cause mortality, some corals survive, reacquire symbionts, and recover. We experimentally bleachedMontipora capitatato examine molecular and physiological differences between corals that recover (resilient) and those that die (susceptible). Corals were collected and monitored for eight months post-bleaching to identify genets with long-term resilience. Using an integrated systems-biology approach that included quantitative proteomics, 16S rRNA sequencing to characterize the coral microbiome, total coral lipids, symbiont community composition and density, we explored molecular-level mechanisms of tolerance in corals pre- and post-bleaching. Prior to thermal stress, resilient corals have a more diverse microbiome and abundant proteins essential for carbon acquisition, symbiont retention, and pathogen resistance. Protein signatures of susceptible corals showed early symbiont rejection and utilized urea for carbon and nitrogen. Our results reveal molecular factors for surviving bleaching events and identify diagnostic protein biomarkers for reef management and restoration. 

Abstract A critical challenge in mass spectrometry proteomics is accurately assessing error control, especially given that software tools employ distinct methods for reporting errors. Many tools are closed-source and poorly documented, leading to inconsistent validation strategies. Here we identify three prevalent methods for validating false discovery rate (FDR) control: one invalid, one providing only a lower bound, and one valid but under-powered. The result is that the proteomics community has limited insight into actual FDR control effectiveness, especially for data-independent acquisition (DIA) analyses. We propose a theoretical framework for entrapment experiments, allowing us to rigorously characterize different approaches. Moreover, we introduce a more powerful evaluation method and apply it alongside existing techniques to assess existing tools. We first validate our analysis in the better-understood data-dependent acquisition setup, and then, we analyze DIA data, where we find that no DIA search tool consistently controls the FDR, with particularly poor performance on single-cell datasets. 

Abstract Data-independent acquisition (DIA)-based mass spectrometry is becoming an increasingly popular mass spectrometry acquisition strategy for carrying out quantitative proteomics experiments. Most of the popular DIA search engines make use of in silico generated spectral libraries. However, the generation of high-quality spectral libraries for DIA data analysis remains a challenge, particularly because most such libraries are generated directly from data-dependent acquisition (DDA) data or are from in silico prediction using models trained on DDA data. In this study, we introduce Carafe, a tool that generates high-quality experiment-specific in silico spectral libraries by training deep learning models directly on DIA data. We demonstrate the performance of Carafe on a wide range of DIA datasets, where we observe improved fragment ion intensity prediction and peptide detection relative to existing pretrained DDA models. To make Carafe more accessible to the community, we integrate Carafe into the widely used Skyline tool. 

In May and June of 2021, marine microbial samples were collected for DNA sequencing in East Sound, WA, USA every 4 hours for 22 days. This high temporal resolution sampling effort captured the last 3 days of aRhizosoleniasp. bloom, the initiation and complete bloom cycle ofChaetoceros socialis(8 days), and the following bacterial bloom (2 days). Metagenomes were completed on the time series, and the dataset includes 128 size-fractionated microbial samples (0.22–1.2 µm), providing gene abundances for the dominant members of bacteria, archaea, and viruses. This dataset also has time-matched nutrient analyses, flow cytometry data, and physical parameters of the environment at a single point of sampling within a coastal ecosystem that experiences regular bloom events, facilitating a range of modeling efforts that can be leveraged to understand microbial community structure and their influences on the growth, maintenance, and senescence of phytoplankton blooms.