A moonlighting function of a chitin polysaccharide monooxygenase, CWR-1, in Neurospora crassa allorecognition
Abstract
Organisms require the ability to differentiate themselves from organisms of different or even the same species. Allorecognition processes in filamentous fungi are essential to ensure identity of an interconnected syncytial colony to protect it from exploitation and disease. Neurospora crassa has three cell fusion checkpoints controlling formation of an interconnected mycelial network. The locus that controls the second checkpoint, which allows for cell wall dissolution and subsequent fusion between cells/hyphae, cwr (cell wall remodeling), encodes two linked genes, cwr-1 and cwr-2. Previously, it was shown that cwr-1 and cwr-2 show severe linkage disequilibrium with six different haplogroups present in N. crassa populations. Isolates from an identical cwr haplogroup show robust fusion, while somatic cell fusion between isolates of different haplogroups is significantly blocked in cell wall dissolution. The cwr-1 gene encodes a putative polysaccharide monooxygenase (PMO). Herein we confirm that CWR-1 is a C1-oxidizing chitin PMO. We show that the catalytic (PMO) domain of CWR-1 was sufficient for checkpoint function and cell fusion blockage; however, through analysis of active-site, histidine-brace mutants, the catalytic activity of CWR-1 was ruled out as a major factor for allorecognition. Swapping a portion of the PMO domain (V86 to T130) did not switch cwr haplogroup specificity, but rather cells containing this chimera exhibited a novel haplogroup specificity. Allorecognition to mediate cell fusion blockage is likely occurring through a protein-protein interaction between CWR-1 with CWR-2. These data highlight a moonlighting role in allorecognition of the CWR-1 PMO domain.
Data availability
Materials AvailabilityAll strains and plasmids listed in Supplementary file 1a, b and d are available upon request or from the Fungal Genetics Stock Center (https://www.fgsc.net). Primers used in this study are listed in Supplementary file 1c. P value data for Figures 1, 3, 5, 6 and Figure 1-figure supplement 3B are provided in the Figure 1-source data 4, Figure 3-source data 1, Figure 5-source data 5, Figure 6-source data 1, Figure 6-source data 2 and Figure 1-figure supplement 3-source data 1. Data for biochemical analyses of CWR-1 are provided in Figure 1-source data 3; Figure 4-source data 4; Figure 5-source data 4 (HRP oxygen reduction assays); Figure 1-source data 2; Figure 4-source data 3; Figure 5-source data 3 (ICP); Figure 1-source data 1; Figure 4-source data 1; Figure 4-source data 2; Figure 4-source data 5; Figure 4-source data 6; Figure 4-source data 7; Figure 5-source data 1, Figure 5-source data 2, Figure-1-figure supplement 5-source data 1; Figure 1-figure supplement 5-source data 1; Figure 4-figure supplement 1-source data 1 (HPAEC-PAD traces). Data for construction of the SSN is provided in Supplementary file 1g and the raw data as Figure 1-figure supplement 2-source data 1. Original data for Figure 1-supplement 4A are provided as Figure-1-figure supplement 4-source data 1. Whole protein MS data are provided in Figure 1-figure supplement 4-source data 2, Figure 5-figure supplement 1-source data 1. EPR source data are provided in Figure 5-figure supplement 2-source data 1. Tandem MS data are provided in Figure 1-figure supplement 6-source data 1.
Article and author information
Author details
Funding
National Science Foundation (MCB 1818283)
- Tyler C Detomasi
- Adriana M Rico Ramírez
- Richard I Sayler
- Michael A Marletta
- N Louise Glass
National Science Foundation (CHE-1904540)
- Tyler C Detomasi
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2022, Detomasi et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,046
- views
-
- 277
- downloads
-
- 12
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Genetics and Genomics
Advances in single-cell sequencing technologies have provided novel insights into the dynamics of gene expression and cellular heterogeneity within tissues and have enabled the construction of transcriptomic cell atlases. However, linking anatomical information to transcriptomic data and positively identifying the cell types that correspond to gene expression clusters in single-cell sequencing data sets remains a challenge. We describe a straightforward genetic barcoding approach that takes advantage of the powerful genetic tools in Drosophila to allow in vivo tagging of defined cell populations. This method, called Targeted Genetically-Encoded Multiplexing (TaG-EM), involves inserting a DNA barcode just upstream of the polyadenylation site in a Gal4-inducible UAS-GFP construct so that the barcode sequence can be read out during single-cell sequencing, labeling a cell population of interest. By creating many such independently barcoded fly strains, TaG-EM enables positive identification of cell types in cell atlas projects, identification of multiplet droplets, and barcoding of experimental timepoints, conditions, and replicates. Furthermore, we demonstrate that TaG-EM barcodes can be read out using next-generation sequencing to facilitate population-scale behavioral measurements. Thus, TaG-EM has the potential to enable large-scale behavioral screens in addition to improving the ability to multiplex and reliably annotate single-cell transcriptomic experiments.
-
- Cell Biology
- Genetics and Genomics
Progression through the G1 phase of the cell cycle is the most highly regulated step in cellular division. We employed a chemogenetic approach to discover novel cellular networks that regulate cell cycle progression. This approach uncovered functional clusters of genes that altered sensitivity of cells to inhibitors of the G1/S transition. Mutation of components of the Polycomb Repressor Complex 2 rescued proliferation inhibition caused by the CDK4/6 inhibitor palbociclib, but not to inhibitors of S phase or mitosis. In addition to its core catalytic subunits, mutation of the PRC2.1 accessory protein MTF2, but not the PRC2.2 protein JARID2, rendered cells resistant to palbociclib treatment. We found that PRC2.1 (MTF2), but not PRC2.2 (JARID2), was critical for promoting H3K27me3 deposition at CpG islands genome-wide and in promoters. This included the CpG islands in the promoter of the CDK4/6 cyclins CCND1 and CCND2, and loss of MTF2 lead to upregulation of both CCND1 and CCND2. Our results demonstrate a role for PRC2.1, but not PRC2.2, in antagonizing G1 progression in a diversity of cell linages, including chronic myeloid leukemia (CML), breast cancer, and immortalized cell lines.