CrRLK1L receptor-like kinases HERK1 and ANJEA are female determinants of pollen tube reception.

Communication between the gametophytes is vital for angiosperm fertilisation. Multiple CrRLK1L-type receptor kinases prevent premature pollen tube burst, while another CrRLK1L protein, FERONIA (FER), is required for pollen tube reception in the female gametophyte. We report here the identification of two additional CrRLK1L homologues, HERCULES RECEPTOR KINASE 1 (HERK1) and ANJEA (ANJ), which act redundantly to promote pollen tube growth arrest at the synergid cells. HERK1 and ANJ localise to the filiform apparatus of the synergid cells in unfertilised ovules, and in herk1 anj mutants, a majority of ovules remain unfertilised due to pollen tube overgrowth, together indicating that HERK1 and ANJ act as female determinants for fertilisation. As in fer mutants, the synergid cell-specific, endomembrane protein NORTIA (NTA) is not relocalised after pollen tube reception; however, unlike fer mutants, reactive oxygen species levels are unaffected in herk1 anj double mutants. Both ANJ and HERK1 associate with FER and its proposed co-receptor LORELEI (LRE) in planta. Together, our data indicate that HERK1 and ANJ act with FER to mediate female-male gametophyte interactions during plant fertilisation.

AlphaMissense is better correlated with functional assays of missense impact than earlier prediction algorithms.

Missense variants that alter a single amino acid in the encoded protein contribute to many human disorders but pose a substantial challenge in interpretation. Though these variants can be reliably identified through sequencing, distinguishing the clinically significant ones remains difficult, such that "Variants of Unknown Significance" outnumber those classified as "Pathogenic" or "Likely Pathogenic." Numerous in silico approaches have been developed to predict the functional impact of missense variants to inform clinical interpretation, the latest being AlphaMissense, which uses artificial intelligence methods trained on predicted protein structure. To independently assess the performance of AlphaMissense and 38 other predictors of missense severity, we compared predictions to data from multiplexed assays of variant effect (MAVE). MAVE experiments generate almost every possible individual amino acid change in a gene and measure their functional impact using a high-throughput assay. Assessing 17,696 variants across five genes (DDX3X, MSH2, PTEN, KCNQ4, and BRCA1), we find that AlphaMissense is consistently one of the top five algorithms based on correlation with functional impact and is the best-correlated algorithm for two genes. We conclude that AlphaMissense represents the current best-in-class predictor by this metric; however, the improvement over other algorithms is modest. We note that multiple missense predictors, including AlphaMissense, appear to overcall variants as pathogenic despite minimal functional impact and that substantially more high-quality training data, including consistently analyzed patient cohorts and MAVE analyses, are required to improve accuracy.