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Abstract Flavonols are a subclass of flavonoids widely found in plants and typically exist in glycosylated forms, decorated with various sugars at different positions on the flavonol aglycone. The composition and abundance of flavonol glycosides vary across species and among tissues within a species. Although flavonols are collectively known for their antioxidant activity, the specific physiological functions of individual flavonol structures remain poorly understood. Here, we show that 2 flavonol glycosides, kaempferol 3-O-glucosyl(1 → 2)galactoside (K2) and quercetin 3-O-glucosyl(1 → 2)galactoside (Q2), predominantly accumulate in the pollen of Solanaceae plants. K2 is evolutionarily conserved across Solanaceae, while Q2 has been lost in species such as tomato (Solanum lycopersicum). Our transcriptome profiling and biochemical analysis revealed SlUGT78D-B (78-B) as a pollen-specific flavonol 3-O-galactosyltransferase responsible for K2 production in tomato. Disruption of 78-B abolished K2 accumulation, leading to defective pollen tube growth in our in vitro assays. Supplementation with kaempferol 3-O-galactoside (K2 precursor) restores pollen tube growth, whereas quercetin 3-O-galactoside (Q2 precursor) or flavonol aglycones do not, suggesting distinct roles for individual flavonol structures. We further show that 3 key amino acid residues of 78-B dictate its sugar specificity, favoring galactosylation over glucosylation. Substitution of any one of these residues enables 78-B to acquire glucosyltransferase activity. However, 78-B remains evolutionarily constrained from gaining this activity, suggesting selective pressure to maintain flavonol galactoside accumulation in pollen. These findings indicate that individual flavonol glycosides can have specific physiological roles beyond enhancing solubility and stability. 

Abstract  Exposure to ultraviolet (UV) rays is a known risk factor for skin cancer, which can be notably mitigated through the application of sun care products. However, escalating concerns regarding the adverse health and environmental impacts of synthetic anti-UV chemicals underscore a pressing need for the development of biodegradable and eco-friendly sunscreen ingredients. Mycosporine-like amino acids (MAAs) represent a family of water-soluble anti-UV natural products synthesized by various organisms. These compounds can provide a two-pronged strategy for sun protection as they not only exhibit a superior UV absorption profile but also possess the potential to alleviate UV-induced oxidative stresses. Nevertheless, the widespread incorporation of MAAs in sun protection products is hindered by supply constraints. Delving into the biosynthetic pathways of MAAs can offer innovative strategies to overcome this limitation. Here, we review recent progress in MAA biosynthesis, with an emphasis on key biosynthetic enzymes, including the dehydroquinate synthase homolog MysA, the adenosine triphosphate (ATP)-grasp ligases MysC and MysD, and the nonribosomal peptide synthetase (NRPS)-like enzyme MysE. Additionally, we discuss recently discovered MAA tailoring enzymes. The enhanced understanding of the MAA biosynthesis paves the way for not only facilitating the supply of MAA analogs but also for exploring the evolution of this unique family of natural sunscreens. One-Sentence SummaryThis review discusses the role of mycosporine-like amino acids (MAAs) as potent natural sunscreens and delves into recent progress in their biosynthesis. 

Abstract Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high throughput. These efforts have facilitated understanding of compound mechanism of action, drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering– and deep learning–based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field. 

Abstract Over the last 25 years, biology has entered the genomic era and is becoming a science of ‘big data’. Most interpretations of genomic analyses rely on accurate functional annotations of the proteins encoded by more than 500 000 genomes sequenced to date. By different estimates, only half the predicted sequenced proteins carry an accurate functional annotation, and this percentage varies drastically between different organismal lineages. Such a large gap in knowledge hampers all aspects of biological enterprise and, thereby, is standing in the way of genomic biology reaching its full potential. A brainstorming meeting to address this issue funded by the National Science Foundation was held during 3–4 February 2022. Bringing together data scientists, biocurators, computational biologists and experimentalists within the same venue allowed for a comprehensive assessment of the current state of functional annotations of protein families. Further, major issues that were obstructing the field were identified and discussed, which ultimately allowed for the proposal of solutions on how to move forward.