Neoantigens play important roles in cancer immunotherapy. Current methods used for neoantigen prediction focus on the binding between human leukocyte antigens (HLAs) and peptides, which is insufficient for high-confidence neoantigen prediction. In this study, we apply deep learning techniques to predict neoantigens considering both the possibility of HLA-peptide binding (binding model) and the potential immunogenicity (immunogenicity model) of the peptide-HLA complex (pHLA). The binding model achieves comparable performance with other well-acknowledged tools on the latest Immune Epitope Database (IEDB) benchmark datasets and an independent mass spectrometry (MS) dataset. The immunogenicity model could significantly improve the prediction precision of neoantigens. The further application of our method to the mutations with pre-existing T-cell responses indicating its feasibility in clinical application. DeepHLApan is freely available at https://github.com/jiujiezz/deephlapan and http://biopharm.zju.edu.cn/deephlapan.
Background The incidence rates of cervical cancer in developing countries have been steeply increasing while the medical resources for prevention, detection, and treatment are still quite limited. Computer-based deep learning methods can achieve high-accuracy fast cancer screening. Such methods can lead to early diagnosis, effective treatment, and hopefully successful prevention of cervical cancer. In this work, we seek to construct a robust deep convolutional neural network (DCNN) model that can assist pathologists in screening cervical cancer. Methods ThinPrep cytologic test (TCT) images diagnosed by pathologists from many collaborating hospitals in different regions were collected. The images were divided into a training dataset (13,775 images), validation dataset (2301 images), and test dataset (408,030 images from 290 scanned copies) for training and effect evaluation of a faster region convolutional neural network (Faster R-CNN) system. Results The sensitivity and specificity of the proposed cervical cancer screening system was 99.4 and 34.8%, respectively, with an area under the curve (AUC) of 0.67. The model could also distinguish between negative and positive cells. The sensitivity values of the atypical squamous cells of undetermined significance (ASCUS), the low-grade squamous intraepithelial lesion (LSIL), and the high-grade squamous intraepithelial lesions (HSIL) were 89.3, 71.5, and 73.9%, respectively. This system could quickly classify the images and generate a test report in about 3 minutes. Hence, the system can reduce the burden on the pathologists and saves them valuable time to analyze more complex cases. Conclusions In our study, a CNN-based TCT cervical-cancer screening model was established through a retrospective study of multicenter TCT images. This model shows improved speed and accuracy for cervical cancer screening, and helps overcome the shortage of medical resources required for cervical cancer screening.
Nutrient element deprivation, such as iron (Fe) deficiency stress, is a major factor limiting plant survival and proliferation in marginal soils. To cope with a low Fe environment, plants have evolved elaborate mechanisms underlying Fe homeostasis via intricate transcriptional and post-transcriptional regulation. Here, we characterized the Fe deficiency-inducible MYB transcription factor MdMYB58 in apple plants. Overexpressing MdMYB58 resulted in the accumulation of Fe in the root of transgenic Arabidopsis and apple calli when they were exposed to low Fe available conditions. Further investigation revealed that MdMYB58 bound to the promoter of MdMATE43, and its homologue FRD3 in Arabidopsis. Transient expression and stable transgenic assays in apple calli indicated that MdMYB58 transcriptionally repressed MdMATE43 mRNA, as well as FRD3 in Arabidopsis. Interestingly, AtMYB58, the homologue of MdMYB58, possessed higher binding activities to MdMATE43 and FRD3, which suggests a potentially conserved feature of MYB58 binding to MATE transporters in plants. Additionally, MYB-MATE-mediated regulation of Fe homeostasis may be related to the PYE-related Fe deficiency regulatory network via MdSAT1, a member of the IVa subfamily of bHLH transcription factors. Co-overexpression of MdSAT1 competitively weakened MdMYB58-overexpression induced repression of MdMATE43 transcript abundancy by protein-protein interaction. Taken together, the newly identified MYB-bHLH transcription factor expands our understanding of multilevel molecular mechanisms that plants use to coordinate Fe demand with Fe uptake, transport, and tissue partitioning under low Fe conditions.
Background DNA-binding one zinc finger (Dof) proteins are plant-specific transcription factors important for seed development, hormone regulation, and defense against abiotic stress. Although drought stress is a key determinant of plant physiology and metabolic homeostasis, the role of Dof genes in different degrees of PEG6000-induced drought stress has received little attention. Methods Tea plants (Camellia sinensis) were exposed to mild, moderate and severe drought stress. The Tea Genome and Plant TFDB databases were used to identify Dof gene family members in the tea plant. Clustal W2.1, MEGA6.0, ScanProsite, SMART, ExPASy, GSDS, MEME and STRING were used to build a phylogenetic tree, predict the molecular masses and isoelectric points of the Dof proteins, and construct a predicted protein-protein interaction network between the CsDof TFs and proteins in the A. thaliana database. The expression patterns of Dof genes in different tissues were analyzed, and qRT-PCR was used to measure the expression of Dof genes under different degrees of drought stress in tea. Results We identified 16 Dof genes in tea (C. sinensis cv. Huangjinya) using whole-genome analysis. Through comparative analysis of tea and Arabidopsis thaliana, we divided the Dof genes into four families (A, B, C, and D). We identified 15 motifs in the amino acid sequences of the CsDof proteins. Gene sequences and motif structures were highly conserved among families, especially in the B1 and C2 subfamilies. The protein-protein interaction network indicated that multiple CsDof proteins may be involved in the response to drought stress. Real-time PCR was used to examine the tissue-specific expression patterns of the CsDof genes and to measure their responses to different levels of PEG6000-induced drought stress in mature leaves. Most CsDof genes responded to drought stress. These results provide information on the Dof gene family in tea, offer new insights into the function of CsDof genes in a perennial species, and lay the foundation for further analysis of their functions.
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