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Summary Homologous recombination (HR) is essential for high-fidelity DNA repair during mitotic proliferation and meiosis. Yet, context-specific modifications must tailor the recombination machinery to avoid (mitosis) or enforce (meiosis) the formation of reciprocal exchanges—crossovers—between recombining chromosomes. To obtain molecular insight into how crossover control is achieved, we affinity purified 7 DNA-processing enzymes that channel HR intermediates into crossovers or noncrossovers from vegetative cells or cells undergoing meiosis. Using mass spectrometry, we provide a global characterization of their composition and reveal mitosis- and meiosis-specific modules in the interaction networks. Functional analyses of meiosis-specific interactors of MutLγ-Exo1 identified Rtk1, Caf120, and Chd1 as regulators of crossing-over. Chd1, which transiently associates with Exo1 at the prophase-to-metaphase I transition, enables the formation of MutLγ-dependent crossovers through its conserved ability to bind and displace nucleosomes. Thus, rewiring of the HR network, coupled to chromatin remodeling, promotes context-specific control of the recombination outcome.

To a large extent functional diversity in cells is achieved by the expansion of molecular complexity beyond that of the coding genome. Various processes create multiple distinct but related proteins per coding gene – so-called proteoforms – that expand the functional capacity of a cell. Evaluating proteoforms from classical bottom-up proteomics datasets, where peptides instead of intact proteoforms are measured, has remained difficult. Here we present COPF, a tool for COrrelation-based functional ProteoForm assessment in bottom-up proteomics data. It leverages the concept of peptide correlation analysis to systematically assign peptides to co-varying proteoform groups. We show applications of COPF to protein complex co-fractionation data as well as to more typical protein abundance vs. sample data matrices, demonstrating the systematic detection of assembly- and tissue-specific proteoform groups, respectively, in either dataset. We envision that the presented approach lays the foundation for a systematic assessment of proteoforms and their functional implications directly from bottom-up proteomic datasets.

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