Nanoparticles-based biosensors for cancer diagnostics and drug screening: A study on tumor suppressor protein-DNA INteractions

The p53 protein, dubbed “The guardian of the Genome”, is a tumor suppressor protein that plays a central role in cancer biology. It regulates the gene expression through binding with specific DNA response elements (RE), thereby enabling many important biological functions such as DNA repair and apoptosis. About 50% of human cancers can be associated with mutated p53 proteins that do not bind with the RE. This makes mutant p53 an attractive target for reactivation (i.e., restoration of normal function) by drugs. In this study, we have developed versatile metallic nanobiosensors to detect p53-DNA binding interactions and screen reactivation compounds for mutant p53 proteins. These nanobiosensors exploit the unique light absorption and strong scattering properties of gold nanoparticles (AuNPs), which allow unprecedented sensitivity (pM) detection of sequence-specific and/or drug activated protein-DNA binding in complex biological samples. A simple colorimetric biosensor is designed based on the specific binding of wildtype p53 protein to the RE sequence in the DNA-AuNPs, which alters the interparticle-distance of RE-AuNPs, resulting in a distinct change in solution color (red-to-blue) as well as UV-vis absorption spectra. Competition assay with different free RE sequences enables the evaluation of the binding affinity of wildtype p53 to various promoter sequences. Control experiments with the mutated p53 showed no distinct color change of the DNA-AuNPs (remain red and well dispersed) in the binding buffer. The second nanobiosensor is capitalized on the large scattering dimension of AuNPs (106-fold larger than fluorescent probes) due to the LSPR effect. It involved the use of AuNP dimers assembled by DNA (p53 RE), and coupled with Dynamic Light Scattering (DLS) readout system to achieve a much lower detection limit than the colorimetric method. This unique ‘mix-and-test’ DLS-based AuNPs probes not only can allow real-time monitoring of the p53-DNA binding events, but also effectively suppress the signals arising from non-binding substances in the complex cell medium. We have successfully applied these probes for high-throughput screening of the reactivation compounds of mutant p53 proteins in cancer cell lysates. These features will expedite research in the areas of drug discovery, clinical diagnostics and fundamental cell biology

Defining Key Genes Regulating Morphogenesis of Apocrine Sweat Gland in Sheepskin

The apocrine sweat gland is a unique skin appendage in humans compared to mouse and chicken models. The absence of apocrine sweat glands in chicken and murine skin largely restrains further understanding of the complexity of human skin biology and skin diseases, like hircismus. Sheep may serve as an additional system for skin appendage investigation owing to the distributions and histological similarities between the apocrine sweat glands of sheep trunk skin and human armpit skin. To understand the molecular mechanisms underlying morphogenesis of apocrine sweat glands in sheepskin, transcriptome analyses were conducted to reveal 1631 differentially expressed genes that were mainly enriched in three functional groups (cellular component, molecular function and biological process), particularly in gland, epithelial, hair follicle and skin development. There were 7 Gene Ontology (GO) terms enriched in epithelial cell migration and morphogenesis of branching epithelium that were potentially correlated with the wool follicle peg elongation. An additional 5 GO terms were enriched in gland morphogenesis (20 genes), gland development (42 genes), salivary gland morphogenesis and development (8 genes), branching involved in salivary gland morphogenesis (6 genes) and mammary gland epithelial cell differentiation (4 genes). The enriched gland-related genes and two Kyoto Encyclopedia of Genes and Genomes pathway genes (WNT and TGF-β) were potentially involved in the induction of apocrine sweat glands. Genes named BMPR1A, BMP7, SMAD4, TGFB3, WIF1, and WNT10B were selected to validate transcript expression by qRT-PCR. Immunohistochemistry was performed to localize markers for hair follicle (SOX2), skin fibroblast (PDGFRB), stem cells (SOX9) and BMP signaling (SMAD5) in sheepskin. SOX2 and PDGFRB were absent in apocrine sweat glands. SOX9 and SMAD5 were both observed in precursor cells of apocrine sweat glands and later in gland ducts. These results combined with the upregulation of BMP signaling genes indicate that apocrine sweat glands were originated from outer root sheath of primary wool follicle and positively regulated by BMP signaling. This report established the primary network regulating early development of apocrine sweat glands in sheepskin and will facilitate the further understanding of histology and pathology of apocrine sweat glands in human and companion animal skin

Single-cell profiling reveals distinct immune response landscapes in tuberculous pleural effusion and non-TPE

BackgroundTuberculosis (TB) is caused by Mycobacterium tuberculosis (Mtb) and remains a major health threat worldwide. However, a detailed understanding of the immune cells and inflammatory mediators in Mtb-infected tissues is still lacking. Tuberculous pleural effusion (TPE), which is characterized by an influx of immune cells to the pleural space, is thus a suitable platform for dissecting complex tissue responses to Mtb infection.MethodsWe employed singe-cell RNA sequencing to 10 pleural fluid (PF) samples from 6 patients with TPE and 4 non-TPEs including 2 samples from patients with TSPE (transudative pleural effusion) and 2 samples with MPE (malignant pleural effusion).ResultCompared to TSPE and MPE, TPE displayed obvious difference in the abundance of major cell types (e.g., NK, CD4+T, Macrophages), which showed notable associations with disease type. Further analyses revealed that the CD4 lymphocyte population in TPE favored a Th1 and Th17 response. Tumor necrosis factors (TNF)-, and XIAP related factor 1 (XAF1)-pathways induced T cell apoptosis in patients with TPE. Immune exhaustion in NK cells was an important feature in TPE. Myeloid cells in TPE displayed stronger functional capacity for phagocytosis, antigen presentation and IFN-γ response, than TSPE and MPE. Systemic elevation of inflammatory response genes and pro-inflammatory cytokines were mainly driven by macrophages in patients with TPE.ConclusionWe provide a tissue immune landscape of PF immune cells, and revealed a distinct local immune response in TPE and non-TPE (TSPE and MPE). These findings will improve our understanding of local TB immunopathogenesis and provide potential targets for TB therapy

PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation

Privacy-preserving cross-domain recommendation (PPCDR) refers to preserving the privacy of users when transferring the knowledge from source domain to target domain for better performance, which is vital for the long-term development of recommender systems. Existing work on cross-domain recommendation (CDR) reaches advanced and satisfying recommendation performance, but mostly neglects preserving privacy. To fill this gap, we propose a privacy-preserving generative cross-domain recommendation (PPGenCDR) framework for PPCDR. PPGenCDR includes two main modules, i.e., stable privacy-preserving generator module, and robust cross-domain recommendation module. Specifically, the former isolates data from different domains with a generative adversarial network (GAN) based model, which stably estimates the distribution of private data in the source domain with ́Renyi differential privacy (RDP) technique. Then the latter aims to robustly leverage the perturbed but effective knowledge from the source domain with the raw data in target domain to improve recommendation performance. Three key modules, i.e., (1) selective privacy preserver, (2) GAN stabilizer, and (3) robustness conductor, guarantee the cost-effective trade-off between utility and privacy, the stability of GAN when using RDP, and the robustness of leveraging transferable knowledge accordingly. The extensive empirical studies on Douban and Amazon datasets demonstrate that PPGenCDR significantly outperforms the state-of-the-art recommendation models while preserving privacy

An efficient mobile model for insect image classification in the field pest management

Accurately recognizing insect pest in their larva phase is significant to take the early treatment on the infected crops, thus helping timely reduce the yield loss in agricultural products. The convolutional neural networks (CNNs)-based classification methods have become the most competitive methods to address many technical challenges related to image recognition in the field. Focusing on accurate and small models carried on mobile devices, this study proposed a novel pest classification method PCNet (Pest Classification Network) based on lightweight CNNs embedded attention mechanism. PCNet was designed with EfficientNet V2 as the backbone, and the coordinate attention mechanism (CA) was incorporated in this architecture to learn the inter-channel pest information and pest positional information of input images. Moreover, combining the feature maps output by mobile inverted bottleneck (MBConv) with the feature maps output by average pooling to develop the feature fusion module, which implements the feature fusion between shallow layers and deep layers to address the loss of insect pest features in the down-sampling procedures. In addition, a stochastic, pipeline-based data augmentation approach was adopted to randomly enhance data diversity and thus avoid model overfitting. The experimental results show that the PCNet model achieved recognition accuracy of 98.4 % on the self-built dataset consisting of 30 classes of larvae, which outperforms three classic CNN models (AlexNet, VGG16, and ResNet101), and four lightweight CNN models (ShuffleNet V2, MobileNet V3, EfficientNet V1 and V2). To further verify the robustness on different datasets, the proposed model was also tested on two other public datasets: IP102 and miniImageNet. The recognition accuracy of PCNet is 73.7 % on the IP102 dataset, outperforming other models and 94.0 % on miniImageNet dataset, which is only lower than that of ResNet101 and MobileNet V3. The number of PCNet parameters is 20.7 M, which is less than those of traditional classic CNN models. The satisfactory accuracy and small size of this model makes it suitable for real-time pest recognition in the field with resource constrained mobile devices. Our code will be available at https://github.com/pby521/PCNet/tree/master

Liver segmentation from CT images using a sparse priori statistical shape model (SP-SSM).

This study proposes a new liver segmentation method based on a sparse a priori statistical shape model (SP-SSM). First, mark points are selected in the liver a priori model and the original image. Then, the a priori shape and its mark points are used to obtain a dictionary for the liver boundary information. Second, the sparse coefficient is calculated based on the correspondence between mark points in the original image and those in the a priori model, and then the sparse statistical model is established by combining the sparse coefficients and the dictionary. Finally, the intensity energy and boundary energy models are built based on the intensity information and the specific boundary information of the original image. Then, the sparse matching constraint model is established based on the sparse coding theory. These models jointly drive the iterative deformation of the sparse statistical model to approximate and accurately extract the liver boundaries. This method can solve the problems of deformation model initialization and a priori method accuracy using the sparse dictionary. The SP-SSM can achieve a mean overlap error of 4.8% and a mean volume difference of 1.8%, whereas the average symmetric surface distance and the root mean square symmetric surface distance can reach 0.8 mm and 1.4 mm, respectively

Three-Component Covalent Organic Framework Nanosheets for the Detection of MicroRNAs

The development of new techniques for the detection of microRNAs (miRNAs) is highly desirable. Herein, a new crystalline three-component covalent organic framework (COF) termed EB-TAPB-TFP COF was synthesized under solvothermal conditions utilizing 1,3,5-triformylphloroglucinol, 1,3,5-tris(4-aminophenyl)benzene and ethidium bromide as monomers. Interestingly, EB-TAPB-TFP COF can be self-exfoliated into two-dimensional nanosheets (NSs) in an aqueous medium. The obtained EB-TAPB-TFP NSs exhibited a remarkable fluorescence intensity enhancement in the presence of a DNA-miRNA heteroduplex when compared to the presence of single-stranded DNA and other phosphate-based small molecules, making it promising in the detection of miRNA without tagging any fluorescent marker. Moreover, the EB-TAPB-TFP NSs can also be used as sensing material for the detection of a DNA-miRNA heteroduplex using the quartz crystal microbalance technique, which is in good agreement with the fluorescence sensing result. The exploration of COF-based sensors in this work demonstrates a new pathway for the selective detection of miRNAs

Three-Component Covalent Organic Framework Nanosheets for the Detection of MicroRNAs

The development of new techniques for the detection of microRNAs (miRNAs) is highly desirable. Herein, a new crystalline three-component covalent organic framework (COF) termed EB-TAPB-TFP COF was synthesized under solvothermal conditions utilizing 1,3,5-triformylphloroglucinol, 1,3,5-tris(4-aminophenyl)benzene and ethidium bromide as monomers. Interestingly, EB-TAPB-TFP COF can be self-exfoliated into two-dimensional nanosheets (NSs) in an aqueous medium. The obtained EB-TAPB-TFP NSs exhibited a remarkable fluorescence intensity enhancement in the presence of a DNA-miRNA heteroduplex when compared to the presence of single-stranded DNA and other phosphate-based small molecules, making it promising in the detection of miRNA without tagging any fluorescent marker. Moreover, the EB-TAPB-TFP NSs can also be used as sensing material for the detection of a DNA-miRNA heteroduplex using the quartz crystal microbalance technique, which is in good agreement with the fluorescence sensing result. The exploration of COF-based sensors in this work demonstrates a new pathway for the selective detection of miRNAs