Summary: The WD40-repeat proteins are a large family of scaffold molecules that assemble complexes in various cellular processes. Obtaining their structures is the key to understanding their interaction details. We present WDSPdb 2.0, a significantly updated resource providing accurately predicted secondary and tertiary structures and featured sites annotations. Based on an optimized pipeline, WDSPdb 2.0 contains about 600 thousand entries, an increase of 10-fold, and integrates more than 37 000 variants from sources of ClinVar, Cosmic, 1000 Genomes, ExAC, IntOGen, cBioPortal and IntAct. In addition, the web site is largely improved for visualization, exploring and data downloading. Availability and implementation: http://www.wdspdb.com/wdsp/ or http://wu.scbb.pkusz.edu.cn/wdsp/. Supplementary information: Supplementary data are available at Bioinformatics online.

The rising global burden of chronic diseases highlights the limitations of traditional dietary guidelines. Precision Nutrition (PN) aims to deliver personalized dietary advice to optimize individual health, and the effective implementation of PN fundamentally relies on comprehensive and accurate dietary data. However, conventional dietary assessment methods often suffer from quantification errors and poor adaptability to dynamic changes, leading to inaccurate data and ineffective guidance. Machine learning (ML) offers a powerful suite of tools to address these limitations, enabling a paradigm shift across the nutritional management pipeline. Using dietary data as a thematic thread, this article outlines this transformation and synthesizes recent advances across dietary assessment, in-depth mining, and nutritional intervention. Additionally, current challenges and future trends in this domain are also further discussed. ML is driving a critical shift from a subjective, static mode to an objective, dynamic, and personalized paradigm, enabling a loop nutrition management framework. Precise food recognition and nutrient estimation can be implemented automatically with ML techniques like computer vision (CV) and natural language processing (NLP). Integrating with multiple data sources, ML is conducive to uncovering dietary patterns, assessing nutritional status, and deciphering intricate nutritional mechanisms. It also facilitates the development of personalized dietary intervention strategies tailored to individual needs, while enabling adaptive optimization based on users’ feedback and intervention effectiveness. Although challenges regarding data privacy and model interpretability persist, ML undeniably constitutes the vital technical support for advancing PN into practical reality.

Intrahepatic cholangiocarcinoma (ICC) is the second most common malignancy among primary liver cancers, with an increasing overall incidence and poor prognosis. The intertumoral and intratumoral heterogeneity of ICC makes it difficult to find efficient drug therapies. Therefore, it is essential to identify tumor suppressor genes and oncogenes that induce ICC formation and progression. Here, we performed CRISPR/Cas9-mediated genome-wide screening in a liver-specific Smad4/Pten knockout mouse model (Smad4^co/co;Pten^co/co;Alb-Cre, abbreviated as SPC), which normally generates ICC after 6 months, and detected that mutations in Trp53, Fbxw7, Inppl1, Tgfbr2, or Cul3 markedly accelerated ICC formation. To illustrate the potential mechanisms, we conducted transcriptome sequencing and found that multiple receptor tyrosine kinases were activated, which mainly upregulated the PI3K pathway to induce cell proliferation. Remarkably, the Cul3 mutation stimulated cancer progression mainly by altering the immune microenvironment, whereas other mutations promoted the cell cycle. Moreover, Fbxw7, Inppl1, Tgfbr2, and Trp53 also affect inflammatory responses, apelin signaling, mitotic spindles, ribosome biogenesis, and nucleocytoplasmic transport pathways, respectively. We further examined FDA-approved drugs for the treatment of liver cancer and performed high-throughput drug screening of the gene-mutant organoids. Different drug responses and promising drug therapies, including chemotherapy and targeted drugs, have been discovered for ICC.

This study employed an ultrasound-assisted deep eutectic solvent (DES) method to extract Cercis chinensis seeds polysaccharides (CCSPs). Among 19 synthesized DES, DES-9 composed of betaine and ethylene glycol (Bet-Eg) had the highest extraction efficiency during the initial screening, with a yield of 47.7 mg/g, which is 3.5 times higher than that obtained by water extraction (13.6 mg/g). A structural characterization by IR and ¹H NMR revealed a robust hydrogen bonding network in DES-9. This enhanced the solubility and extraction of CCSPs. Extraction conditions were optimized via the Response Surface Methodology (RSM), yielding an experimentally validated yield of 66.3 mg/g following optimum parameters: water content 37%, temperature 72 °C, time 61 min, solid-liquid ratio 1:25 g/mL, and ultrasonic power 240 W. After four cycles of reuse experiments, DES-9 maintained over 74% extraction efficiency, demonstrating good recyclability. CCSPs extracted by DES-9 had higher Mw (46.22 kDa), higher content of Gal, and better stability under different temperatures. At concentrations of 0.03125-1 mg/mL, CCSPs extracted by DES-9 exhibited moderate antioxidant activity and α-glucosidase inhibitory ability, indicating potential as a natural food preservative. These findings confirm that ultrasound-assisted DES extraction is an efficient and green process for extracting CCSPs, providing theoretical and technical support for the utilization of C. chinensis seed resources and the preparation of functional polysaccharides.

Despite of the lack of folded structure, intrinsically disordered regions (IDRs) of proteins play versatile roles in various biological processes, and many nonsynonymous single nucleotide variants (nsSNVs) in IDRs are associated with human diseases. The continuous accumulation of nsSNVs resulted from the wide application of NGS has driven the development of disease-association prediction methods for decades. However, their performance on nsSNVs in IDRs remains inferior, possibly due to the domination of nsSNVs from structured regions in training data. Therefore, it is highly demanding to build a disease-association predictor specifically for nsSNVs in IDRs with better performance. We present IDRMutPred, a machine learning-based tool specifically for predicting disease-associated germline nsSNVs in IDRs. Based on 17 selected optimal features that are extracted from sequence alignments, protein annotations, hydrophobicity indices and disorder scores, IDRMutPred was trained using three ensemble learning algorithms on the training dataset containing only IDR nsSNVs. The evaluation on the two testing datasets shows that all the three prediction models outperform 17 other popular general predictors significantly, achieving the ACC between 0.856 and 0.868 and MCC between 0.713 and 0.737. IDRMutPred will prioritize disease-associated IDR germline nsSNVs more reliably than general predictors. The software is freely available at http://www.wdspdb.com/IDRMutPred. Supplementary data are available at Bioinformatics online.