Alterations of Phenylpropanoid Biosynthesis Lead to the Natural Formation of Pinkish-Skinned and White-Fleshed Strawberry (Fragaria × ananassa).

Anthocyanin content is important for both the external and internal fruit quality of cultivated strawberries, but the mechanism of its accumulation in pinkish-skinned and white-fleshed strawberries is puzzling. Here, we found that the factor determining variation in the flesh color was not the FaMYB10 but the FaC4H in the cultivated strawberry Benihoppe and its white-fleshed mutant Xiaobai. Compared with Benihoppe, there was no significant difference in the coding sequence and expression level of FaMYB10 in Xiaobai's flesh. Instead, the transcription of FaC4H was dramatically inhibited. The combined analyses of transcriptomics and metabolomics showed that the differential genes and metabolites were significantly enriched in the phenylpropanoid biosynthesis pathway. Furthermore, the transient overexpression of FaC4H greatly restored anthocyanins' accumulation in Xiaobai's flesh and did not produce additional pigment species, as in Benihoppe. The transcriptional repression of FaC4H was not directly caused by promoter methylations, lncRNAs, or microRNAs. In addition, the unexpressed FaF3'H, which resulted in the loss of cyanidin 3-O-glucoside in the flesh, was not due to methylation in promoters. Our findings suggested that the repression of FaC4H was responsible for the natural formation of pinkish-skinned and white-fleshed strawberries.

Genome-Wide Identification of Strawberry C2H2-ZFP C1-2i Subclass and the Potential Function of FaZAT10 in Abiotic Stress.

C2H2-type zinc finger proteins (C2H2-ZFPs) play a key role in various plant biological processes and responses to environmental stresses. In Arabidopsisthaliana, C2H2-ZFP members with two zinc finger domains have been well-characterized in response to abiotic stresses. To date, the functions of these genes in strawberries are still uncharacterized. Here, 126 C2H2-ZFPs in cultivated strawberry were firstly identified using the recently sequenced Fragaria × ananassa genome. Among these C2H2-ZFPs, 46 members containing two zinc finger domains in cultivated strawberry were further identified as the C1-2i subclass. These genes were unevenly distributed on 21 chromosomes and classified into five groups according to the phylogenetic relationship, with similar physicochemical properties and motif compositions in the same group. Analyses of conserved domains and gene structures indicated the evolutionary conservation of the C1-2i subclass. A Ka/Ks analysis indicated that the C1-2i members were subjected to purifying selection during evolution. Furthermore, FaZAT10, a typical C2H2-ZFP, was isolated. FaZAT10 was expressed the highest in roots, and it was induced by drought, salt, low-temperature, ABA, and MeJA treatments. It was localized in the nucleus and showed no transactivation activity in yeast cells. Overall, these results provide useful information for enriching the analysis of the ZFPs gene family in strawberry, and they provide support for revealing the mechanism of FaZAT10 in the regulatory network of abiotic stress.

Attention-guided multi-scale context aggregation network for multi-modal brain glioma segmentation.

BACKGROUND: Accurate segmentation of brain glioma is a critical prerequisite for clinical diagnosis, surgical planning and treatment evaluation. In current clinical workflow, physicians typically perform delineation of brain tumor subregions slice-by-slice, which is more susceptible to variabilities in raters and also time-consuming. Besides, even though convolutional neural networks (CNNs) are driving progress, the performance of standard models still have some room for further improvement. PURPOSE: To deal with these issues, this paper proposes an attention-guided multi-scale context aggregation network (AMCA-Net) for the accurate segmentation of brain glioma in the magnetic resonance imaging (MRI) images with multi-modalities. METHODS: AMCA-Net extracts the multi-scale features from the MRI images and fuses the extracted discriminative features via a self-attention mechanism for brain glioma segmentation. The extraction is performed via a series of down-sampling, convolution layers, and the global context information guidance (GCIG) modules are developed to fuse the features extracted for contextual features. At the end of the down-sampling, a multi-scale fusion (MSF) module is designed to exploit and combine all the extracted multi-scale features. Each of the GCIG and MSF modules contain a channel attention (CA) module that can adaptively calibrate feature responses and emphasize the most relevant features. Finally, multiple predictions with different resolutions are fused through different weightings given by a multi-resolution adaptation (MRA) module instead of the use of averaging or max-pooling to improve the final segmentation results. RESULTS: Datasets used in this paper are publicly accessible, that is, the Multimodal Brain Tumor Segmentation Challenges 2018 (BraTS2018) and 2019 (BraTS2019). BraTS2018 contains 285 patient cases and BraTS2019 contains 335 cases. Simulations show that the AMCA-Net has better or comparable performance against that of the other state-of-the-art models. In terms of the Dice score and Hausdorff 95 for the BraTS2018 dataset, 90.4% and 10.2 mm for the whole tumor region (WT), 83.9% and 7.4 mm for the tumor core region (TC), 80.2% and 4.3 mm for the enhancing tumor region (ET), whereas the Dice score and Hausdorff 95 for the BraTS2019 dataset, 91.0% and 10.7 mm for the WT, 84.2% and 8.4 mm for the TC, 80.1% and 4.8 mm for the ET. CONCLUSIONS: The proposed AMCA-Net performs comparably well in comparison to several state-of-the-art neural net models in identifying the areas involving the peritumoral edema, enhancing tumor, and necrotic and non-enhancing tumor core of brain glioma, which has great potential for clinical practice. In future research, we will further explore the feasibility of applying AMCA-Net to other similar segmentation tasks.

Engineering nanoenzymes integrating Iron-based metal organic frameworks with Pt nanoparticles for enhanced Photodynamic-Ferroptosis therapy.

Photodynamic therapy (PDT), as a promising strategy in cancer treatment that utilizes photosensitizers (PSs) to produce reactive oxygen species, has been widely used for eliminating cancer cells under specific wavelength light irradiation. However, the low aqueous solubility of PSs and special tumor microenvironments (TME), such as high glutathione (GSH) and tumor hypoxia remain challenges towards PDT for hypoxic tumor treatment. To address these problems, we constructed a novel nanoenzyme for enhanced PDT-ferroptosis therapy by integrating small Pt nanoparticles (Pt NPs) and near-infrared photosensitizer CyI into iron-based metal organic frameworks (MOFs). In addition, hyaluronic acid was adhered to the surface of the nanoenzymes to enhance the targeting ability. In this design, MOFs act not only as a delivery vector for PSs, but also a ferroptosis inducer. Pt NPs stabilized by MOFs were functioned as an oxygen (O2) generator by catalyzing hydrogen peroxide into O2 to relieve tumor hypoxia and increase singlet oxygen generation. In vitro and in vivo results demonstrated that under laser irradiation, this nanoenzyme could effectively relive the tumor hypoxia and decrease the level of GSH, resulting in enhanced PDT-ferroptosis therapy against hypoxic tumor. The proposed nanoenzymes represent an important advance in altering TME for improved clinical PDT-ferroptosis therapy, as well as their potential as effective theranostic agents for hypoxic tumors.

Delineation of clinical target volume and organs at risk in cervical cancer radiotherapy by deep learning networks.

PURPOSE: Delineation of the clinical target volume (CTV) and organs-at-risk (OARs) is important in cervical cancer radiotherapy. But it is generally labor-intensive, time-consuming, and subjective. This paper proposes a parallel-path attention fusion network (PPAF-net) to overcome these disadvantages in the delineation task. METHODS: The PPAF-net utilizes both the texture and structure information of CTV and OARs by employing a U-Net network to capture the high-level texture information, and an up-sampling and down-sampling (USDS) network to capture the low-level structure information to accentuate the boundaries of CTV and OARs. Multi-level features extracted from both networks are then fused together through an attention module to generate the delineation result. RESULTS: The dataset contains 276 computed tomography (CT) scans of patients with cervical cancer of staging IB-IIA. The images are provided by the West China Hospital of Sichuan University. Simulation results demonstrate that PPAF-net performs favorably on the delineation of the CTV and OARs (e.g., rectum, bladder and etc.) and achieves the state-of-the-art delineation accuracy, respectively, for the CTV and OARs. In terms of the Dice Similarity Coefficient (DSC) and the Hausdorff Distance (HD), 88.61% and 2.25 cm for the CTV, 92.27% and 0.73 cm for the rectum, 96.74% and 0.68 cm for the bladder, 96.38% and 0.65 cm for the left kidney, 96.79% and 0.63 cm for the right kidney, 93.42% and 0.52 cm for the left femoral head, 93.69% and 0.51 cm for the right femoral head, 87.53% and 1.07 cm for the small intestine, and 91.50% and 0.84 cm for the spinal cord. CONCLUSIONS: The proposed automatic delineation network PPAF-net performs well on CTV and OARs segmentation tasks, which has great potential for reducing the burden of radiation oncologists and increasing the accuracy of delineation. In future, radiation oncologists from the West China Hospital of Sichuan University will further evaluate the results of network delineation, making this method helpful in clinical practice.

Recent Progress of Metal-Organic Framework-Based Photodynamic Therapy for Cancer Treatment.

Photodynamic therapy (PDT), combining photosensitizers (PSs) and excitation light at a specific wavelength to produce toxic reactive oxygen species, has been a novel and promising approach to cancer treatment with non-invasiveness, spatial specificity, and minimal systemic toxicity, compared with conventional cancer treatment. Recently, numerous basic research and clinical research have demonstrated the potential of PDT in the treatment of a variety of malignant tumors, such as esophageal cancer, bladder cancer, and so on. Metal-organic framework (MOF) has been developed as a new type of nanomaterial with the advantages of high porosity, large specific surface area, adjustable pore size, and easy functionalization, which could serve as carriers to load PSs or increase the accumulation of PSs in target cells during PDT. Moreover, active MOFs have the potential to construct multifunctional systems, which are conducive to refining the tumor microenvironment (TME) and implementing combination therapy to improve PDT efficacy. Hence, a comprehensive and in-depth depiction of the whole scene of the recent development of MOFs-based PDT in cancer treatment is desirable. This review summarized the recent research strategies of MOFs-based PDT in antitumor therapy from the perspective of MOFs functions, including active MOFs, inactive MOFs, and their further combination therapies in clinical antitumor treatment. In addition, the bottlenecks and obstacles in the application of MOFs in PDT are also described.

Genome-Wide Characterization of Snf1-Related Protein Kinases (SnRKs) and Expression Analysis of SnRK1.1 in Strawberry.

The plant sucrose nonfermenting 1 (SNF1)-related protein kinases (SnRKs) are key regulators in the interconnection of various signaling pathways. However, little is known about the SnRK family in strawberries. In this study, a total of 26 FvSnRKs including one FvSnRK1, nine FvSnRK2s and 16 FvSnRK3s were identified from the strawberry genome database. They were respectively designated as FvSnRK1.1, FvSnRK2.1 to FvSnRK2.9 and FvSnRK3.1 to FvSnRK3.16, according to the conserved domain of each subfamily and multiple sequence alignment with Arabidopsis. FvSnRK family members were unevenly distributed in seven chromosomes. The number of exons or introns varied among FvSnRK1s, FvSnRK2s and FvSnRK3s, but highly conserved in the same subfamily. The FvSnRK1.1 had 10 exons. Most of FvSnRK2s had nine exons or eight introns, except FvSnRK2.4, FvSnRK2.8 and FvSnRK2.9. FvSnRK3 genes were divided into intron-free and intron-harboring members, and the number of introns in intron-harboring group ranged from 11 to 15. Moreover, the phylogenetic analysis showed SnRK1, SnRK2 and SnRK3 subfamilies respectively clustered together in spite of the different species of strawberry and Arabidopsis, indicating the genes were established prior to the divergence of the corresponding taxonomic lineages. Meanwhile, conserved motif analysis showed that FvSnRK sequences that belonged to the same subgroup contained their own specific motifs. Cis-element in promoter and expression pattern analyses of FvSnRK1.1 suggested that FvSnRK1.1 was involved in cold responsiveness, light responsiveness and fruit ripening. Taken together, this comprehensive analysis will facilitate further studies of the FvSnRK family and provide a basis for the understanding of their function in strawberry.