The caffeoyl-CoA O-methyltransferase gene SNP replacement in Russet Burbank potato variety enhances late blight resistance through cell wall reinforcement.

KEY MESSAGE: Metabolic pathway gene editing in tetraploid potato enhanced resistance to late blight. Multiallelic mutation correction of a caffeoyl-CoA O-methyltransferase gene increased accumulation of resistance metabolites in Russet Burbank potato. Late blight of potato is a devastating disease worldwide and requires weekly applications of fungicides to manage. Genetic improvement is the best option, but the self-incompatibility and inter-specific incompatibility makes potato breeding very challenging. Immune receptor gene stacking has increased resistance, but its durability is limited. Quantitative resistance is durable, and it mainly involves secondary cell wall thickening due to several metabolites and their conjugates. Deleterious mutations in biosynthetic genes can hinder resistance metabolite biosynthesis. Here a probable resistance role of the StCCoAOMT gene was first confirmed by an in-planta transient overexpression of the functional StCCoAOMT allele in late blight susceptible Russet Burbank (RB) genotype. Following this, a precise single nucleotide polymorphism (SNP) mutation correction of the StCCoAOMT gene in RB potato was carried out using CRISPR-Cas9 mediated homology directed repair (HDR). The StCCoAOMT gene editing increased the transcript abundance of downstream biosynthetic resistance genes. Following pathogen inoculation, several phenylpropanoid pathway genes were highly expressed in the edited RB plants, as compared to the non-edited. The disease severity (fold change = 3.76) and pathogen biomass in inoculated stems of gene-edited RB significantly reduced (FC = 21.14), relative to non-edited control. The metabolic profiling revealed a significant increase in the accumulation of resistance-related metabolites in StCCoAOMT edited RB plants. Most of these metabolites are involved in suberization and lignification. The StCCoAOMT gene, if mutated, can be edited in other potato cultivars to enhance resistance to late blight, provided it is associated with other functional genes in the metabolic pathway network.

Genome-wide in silico identification of LysM-RLK genes in potato (Solanum tuberosum L.).

The receptor like kinases (RLKs) belong to the RLK/Pelle superfamily, one of the largest gene families in plants. RLKs play an important role in plant development, as well as in response to biotic and abiotic stresses. The lysine motif receptor like kinases (LysM-RLKs) are a subfamily of RLKs containing at least one lysine motif (LysM) that are involved in the perception of elicitors or pathogen-associated molecular patterns (PAMPs). In the present study, 77 putative RLKs genes and three receptor like proteins were identified in potato (Solanum tuberosum) genome, following a genome-wide search. The 77 potato RLK proteins are classified into two major phylogenetic groups based on their kinase domain amino acid sequence similarities. Out of 77 RLKs, 10 proteins had at least one LysM. Among them three RLP proteins were found in potato genome with either 2 or three tandem LysM but these lacked a cytoplasmic kinase domain. Expression analyses of a potato LysM-RLKs (StLysM-RLK05) was carried out by a Real time RT-PCR, following inoculation of potato leaves and immature tubers with late blight and common scab pathogens, respectively. The expression was significantly higher in resistant than in susceptible following S. scabies inoculation. The StLysM-RLK05 sequence was verified and it was polymorphic in scab susceptible cultivar. The present study provides an overview of the StLysM-RLKs gene family in potato genome. This information is helpful for future functional analysis of such an important protein family, in Solanaceae species.

Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms.

The development of a reference atlas of the healthy human body requires automated image segmentation of major anatomical structures across multiple organs based on spatial bioimages generated from various sources with differences in sample preparation. We present the setup and results of the "Hacking the Human Body" machine learning algorithm development competition hosted by the Human Biomolecular Atlas (HuBMAP) and the Human Protein Atlas (HPA) teams on the Kaggle platform. We showcase how 1,175 teams from 78 countries engaged in community- driven, open-science code development that resulted in machine learning models which successfully segment anatomical structures across five organs using histology images from two consortia and that will be productized in the HuBMAP data portal to process large datasets at scale in support of Human Reference Atlas construction. We discuss the benchmark data created for the competition, major challenges faced by the participants, and the winning models and strategies.

Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms.

The development of a reference atlas of the healthy human body requires automated image segmentation of major anatomical structures across multiple organs based on spatial bioimages generated from various sources with differences in sample preparation. We present the setup and results of the Hacking the Human Body machine learning algorithm development competition hosted by the Human Biomolecular Atlas (HuBMAP) and the Human Protein Atlas (HPA) teams on the Kaggle platform. We create a dataset containing 880 histology images with 12,901 segmented structures, engaging 1175 teams from 78 countries in community-driven, open-science development of machine learning models. Tissue variations in the dataset pose a major challenge to the teams which they overcome by using color normalization techniques and combining vision transformers with convolutional models. The best model will be productized in the HuBMAP portal to process tissue image datasets at scale in support of Human Reference Atlas construction.

Concordance in Breast Cancer Grading by Artificial Intelligence on Whole Slide Images Compares With a Multi-Institutional Cohort of Breast Pathologists.

CONTEXT.­: Breast carcinoma grade, as determined by the Nottingham Grading System (NGS), is an important criterion for determining prognosis. The NGS is based on 3 parameters: tubule formation (TF), nuclear pleomorphism (NP), and mitotic count (MC). The advent of digital pathology and artificial intelligence (AI) have increased interest in virtual microscopy using digital whole slide imaging (WSI) more broadly. OBJECTIVE.­: To compare concordance in breast carcinoma grading between AI and a multi-institutional group of breast pathologists using digital WSI. DESIGN.­: We have developed an automated NGS framework using deep learning. Six pathologists and AI independently reviewed a digitally scanned slide from 137 invasive carcinomas and assigned a grade based on scoring of the TF, NP, and MC. RESULTS.­: Interobserver agreement for the pathologists and AI for overall grade was moderate (κ = 0.471). Agreement was good (κ = 0.681), moderate (κ = 0.442), and fair (κ = 0.368) for grades 1, 3, and 2, respectively. Observer pair concordance for AI and individual pathologists ranged from fair to good (κ = 0.313-0.606). Perfect agreement was observed in 25 cases (27.4%). Interobserver agreement for the individual components was best for TF (κ = 0.471 each) followed by NP (κ = 0.342) and was worst for MC (κ = 0.233). There were no observed differences in concordance amongst pathologists alone versus pathologists + AI. CONCLUSIONS.­: Ours is the first study comparing concordance in breast carcinoma grading between a multi-institutional group of pathologists using virtual microscopy to a newly developed WSI AI methodology. Using explainable methods, AI demonstrated similar concordance to pathologists alone.