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[前沿资讯 ] How Plants are Learning to Spot Sneaky Bacterial Invaders 进入全文
University of California, Davis
Scientists at the University of California, Davis, used artificial intelligence to help plants recognize a wider range of bacterial threats — which may lead to new ways to protect crops like tomatoes and potatoes from devastating diseases. The study was published in Nature Plants. Plants, like animals, have immune systems. Part of their defense toolkit includes immune receptors, which give them the ability to detect bacteria and defend against it. One of those receptors, called FLS2, helps plants recognize flagellin — a protein in the tiny tails bacteria use to swim. But bacteria are sneaky and constantly evolving to avoid detection. "Bacteria are in an arms race with their plant hosts, and they can change the underlying amino acids in flagellin to evade detection," said lead author Gitta Coaker, professor in the Department of Plant Pathology. To help plants keep up, Coaker’s team turned to using natural variation coupled with artificial intelligence — specifically AlphaFold, a tool developed to predict the 3D shape of proteins and reengineered FLS2, essentially upgrading its immune system to catch more intruders. The team focused on receptors already known to recognize more bacteria, even if they weren’t found in useful crop species. By comparing them with more narrowly focused receptors, the researchers were able to identify which amino acids to change.
[学术文献 ] Generation of transchromosomic mice harboring HLA-A/B/C and human B2M via mouse artificial chromosome and triple BAC integration 进入全文
Scientific Reports
Humanized transgenic mice carrying human genes are useful for research on gene function and disease. Bacterial artificial chromosomes (BACs) that carry human genomic sequences with regulatory elements enable the expression of transgenes at physiological levels in vivo. To study complex biological phenomena involving multiple genes, techniques for co-introducing transgenes into mice have been developed; however, the introduction of multiple BACs remains laborious. The simultaneous integration of multiple gene loading vectors (SIM) system was developed to incorporate three or more gene-loading vectors (GLVs) using a mouse artificial chromosome (MAC) vector. This system allows for simultaneous site-specific incorporation of three GLVs into a single MAC with only one screening. However, the capacity for large constructs, such as BACs, has yet to be evaluated. This study is the first to demonstrate the development of multi-BAC transchromosomic (Tc) mice targeting the human leukocyte antigen (HLA) class I gene cluster (HLA-A, HLA-B, HLA-C) and beta-2-microglobulin (B2M) using the SIM system. By constructing a MAC using three BACs containing these genomic regions, we successfully generated HLA class I Tc mice. The technology to generate multi-BAC Tc mice will accelerate the analysis of complex life mechanisms involving multiple factors.
[学术文献 ] Integrated biotechnological and AI innovations for crop improvement 进入全文
Nature
Crops provide food, clothing and other important products for the global population. To meet the demands of a growing population, substantial improvements are required in crop yield, quality and production sustainability. However, these goals are constrained by various environmental factors and limited genetic resources. Overcoming these limitations requires a paradigm shift in crop improvement by fully leveraging natural genetic diversity alongside biotechnological approaches such as genome editing and the heterologous expression of designed proteins, coupled with multimodal data integration. In this Review, we provide an in-depth analysis of integrated uses of omics technologies, genome editing, protein design and high-throughput phenotyping, in crop improvement, supported by artificial intelligence-enabled tools. We discuss the emerging applications and current challenges of these technologies in crop improvement. Finally, we present a perspective on how elite alleles generated through these technologies can be incorporated into the genomes of existing and de novo domesticated crops, aided by a proposed artificial intelligence model. We suggest that integrating these technologies with agricultural practices will lead to a new revolution in crop improvement, contributing to global food security in a sustainable manner.
[学术文献 ] Delineating genotype × environment interaction for horticultural traits in tomato using GGE and AMMI biplot analysis 进入全文
Nature
The aim of this study was to identify stable tomato hybrids for cultivation in India. The genotype-by-environment (G × E) interaction analysis was performed using GGE and AMMI biplots. Fifteen hybrids including two checks were grown in nine environments during winter, summer, and rainy seasons from 2021 to 2024 at Varanasi. G × E interaction was investigated for days to first harvesting, average fruit weight, pericarp thickness, total soluble solids (TSS), yield, and resistance to tomato leaf curl virus disease (ToLCD). The GGE biplot analysis identified stable and superior hybrids as well as ideal environments, based on mean vs. stability, which-won-where, discriminativeness vs. representativeness, genotype by trait (GT), genotype by yield × trait (GYT), and multi-trait genotype-ideotype distance index (MGIDI). As a result, we identified stable hybrids such as VRTH-16-4 (G2) and VRTH-16-3 (G1) for yield; VRTH-16-70 (G5) and VRTH-19-18 (G11) for days to first harvesting; VRTH-16-5 (G3) and VRTH-16-8 (G4) for fruit weight; VRTH-16-8 (G4) and VRTH-16-5 (G3) for pericarp thickness; VRTH-16-75 (G6) and VRTH-18-26 (G8) for TSS; and VRTH-16-5 (G3) and VRTH-16-70 (G5) for ToLCD resistance. Overall, this study highlights that tomato hybrids VRTH-16-4, VRTH-16-90, and VRTH-18-46 have potential for cultivation during heat and cool weather conditions.
[学术文献 ] Regulation of MORC-1 is key to the CSR-1–mediated germline gene licensing mechanism in C. elegans 进入全文
Science Advances
The Argonaute CSR-1 is essential for germline development in C. elegans. Loss of CSR-1 leads to the down-regulation of thousands of germline-expressed genes, supporting a model in which CSR-1 “licenses” gene expression via a poorly understood mechanism. In contrast, a small subset of genes is up-regulated in csr-1 mutants, including morc-1, which encodes a conserved GHKL-type ATPase. We show that morc-1 is overexpressed in csr-1 mutants and accumulates over CSR-1 licensed targets, coinciding with aberrant gain of H3K9me3, reduced H3K36me3, and transcriptional repression. Notably, loss of morc-1 fully rescues these chromatin defects and partially restores gene expression and fertility in csr-1 mutants. Conversely, ectopic overexpression of MORC-1 in the wild-type germ line is sufficient to repress CSR-1 licensed targets and severely compromise fertility. These findings support a model in which CSR-1 prevents MORC-1 overexpression and consequent misregulation of CSR-1 licensed genes.
[前沿资讯 ] Predicting expression-altering promoter mutations with deep learning 进入全文
Science
Only a minority of patients with rare genetic diseases are presently diagnosed by exome sequencing, suggesting that additional unrecognized pathogenic variants may reside in noncoding sequence. In this work, we describe PromoterAI, a deep neural network that accurately identifies noncoding promoter variants that dysregulate gene expression. We show that promoter variants with predicted expression-altering consequences produce outlier expression at both the RNA and protein levels in thousands of individuals and that these variants experience strong negative selection in human populations. We observed that clinically relevant genes in patients with rare diseases are enriched for such variants and validated their functional impact through reporter assays. Our estimates suggest that promoter variation accounts for 6% of the genetic burden associated with rare diseases.
