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BACKGROUND: Bladder cancer (BC) is a very common urinary tract malignancy that has a high incidence and lethality. In this study, we identified BC biomarkers and described a new noninvasive detection method using serum and urine samples for the early detection of BC. METHODS: Serum and urine samples were retrospectively collected from patients with BC (n = 99) and healthy controls (HC) (n = 50), and the expression levels of 92 inflammation-related proteins were examined via the proximity extension analysis (PEA) technique. Differential protein expression was then evaluated by univariate analysis (p < 0.05). The expression of the selected potential marker was further verified in BC and adjacent tissues by immunohistochemistry (IHC) and single-cell sequencing. A model was constructed to differentiate BC from HC by LASSO regression and compared to the detection capability of FISH. RESULTS: The univariate analysis revealed significant differences in the expression levels of 40 proteins in the serum (p < 0.05) and 17 proteins in the urine (p < 0.05) between BC patients and HC. Six proteins (AREG, RET, WFDC2, FGFBP1, ESM-1, and PVRL4) were selected as potential BC biomarkers, and their expression was evaluated at the protein and transcriptome levels by IHC and single-cell sequencing, respectively. A diagnostic model (a signature) consisting of 14 protein markers (11 in serum and three in urine) was also established using LASSO regression to distinguish between BC patients and HC (area under the curve = 0.91, PPV = 0.91, sensitivity = 0.87, and specificity = 0.82). Our model showed better diagnostic efficacy than FISH, especially for early-stage, small, and low-grade BC. CONCLUSION: Using the PEA method, we identified a panel of potential protein markers in the serum and urine of BC patients. These proteins are associated with the development of BC. A total of 14 of these proteins can be used to detect early-stage, small, low-grade BC. Thus, these markers are promising for clinical translation to improve the prognosis of BC patients.
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Detecção Precoce de Câncer , Neoplasias da Bexiga Urinária , Humanos , Estudos Retrospectivos , Curva ROC , Detecção Precoce de Câncer/métodos , Neoplasias da Bexiga Urinária/patologia , Biomarcadores TumoraisRESUMO
BACKGROUND: Methotrexate (MTX) serves as the initial treatment for rheumatoid arthritis (RA). However, a substantial proportion of RA patients, estimated between 30% and 50%, do not respond positively to MTX. While the T-cell receptor (TCR) is crucial for the immune response during RA, its role in differentiating MTX responsiveness has not been thoroughly investigated. METHODS: This study used next-generation sequencing to analyze the TCR ß-chain complementary determining region sequences in peripheral blood mononuclear cells obtained from RA patients before MTX treatment. This study aimed to compare the characteristics of the TCR repertoire between the MTX responder and non-responder groups. RESULTS: The study identified a significant difference in the TRBV6-6 gene (p = .003) concerning MTX treatment response. Additionally, a significant difference was found in the number of "3" nucleotide deletions at the 5'Jdels site (p = .023) in the VDJ rearrangement. CONCLUSION: These findings suggest distinct TCR repertoire characteristics between MTX responder and non-responder groups among RA patients. This discovery offers new insights into understanding the variable responses of RA patients to MTX therapy.
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Machine learning has been increasingly utilized in the field of protein engineering, and research directed at predicting the effects of protein mutations has attracted increasing attention. Among them, so far, the best results have been achieved by related methods based on protein language models, which are trained on a large number of unlabeled protein sequences to capture the generally hidden evolutionary rules in protein sequences, and are therefore able to predict their fitness from protein sequences. Although numerous similar models and methods have been successfully employed in practical protein engineering processes, the majority of the studies have been limited to how to construct more complex language models to capture richer protein sequence feature information and utilize this feature information for unsupervised protein fitness prediction. There remains considerable untapped potential in these developed models, such as whether the prediction performance can be further improved by integrating different models to further improve the accuracy of prediction. Furthermore, how to utilize large-scale models for prediction methods of mutational effects on quantifiable properties of proteins due to the nonlinear relationship between protein fitness and the quantification of specific functionalities has yet to be explored thoroughly. In this study, we propose an ensemble learning approach for predicting mutational effects of proteins integrating protein sequence features extracted from multiple large protein language models, as well as evolutionarily coupled features extracted in homologous sequences, while comparing the differences between linear regression and deep learning models in mapping these features to quantifiable functional changes. We tested our approach on a dataset of 17 protein deep mutation scans and indicated that the integrated approach together with linear regression enables the models to have higher prediction accuracy and generalization. Moreover, we further illustrated the reliability of the integrated approach by exploring the differences in the predictive performance of the models across species and protein sequence lengths, as well as by visualizing clustering of ensemble and non-ensemble features.
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Aprendizado de Máquina , Proteínas , Reprodutibilidade dos Testes , Proteínas/genética , Sequência de Aminoácidos , Modelos LinearesRESUMO
Extracellular vesicles (EVs) are naturally occurring vesicles secreted by cells that can transport cargo between cells, making them promising bioactive nanomaterials. However, due to the complex and heterogeneous biological characteristics, a method for robust EV manipulation and efficient EV delivery is still lacking. Here, we developed a novel class of extracellular vesicle spherical nucleic acid (EV-SNA) nanostructures with scalability, programmability, and efficient cellular delivery. EV-SNA was constructed through the simple hydrophobic coassembly of natural EVs with cholesterol-modified oligonucleotides and can be stable for 1 month at room temperature. Based on programmable nucleic acid shells, EV-SNA can respond to AND logic gates to achieve vesicle assembly manipulation. Importantly, EV-SNA can be constructed from a wide range of biological sources EV, enhancing cellular delivery capability by nearly 10-20 times. Compared to artificial liposomal SNA, endogenous EV-SNA exhibited better biocompatibility and more effective delivery of antisense oligonucleotides in hard-to-transfect primary stem cells. Additionally, EV-SNA can deliver functional EVs for immune regulation. As a novel material form, EV-SNA may provide a modular and programmable framework paradigm for EV-based applications in drug delivery, disease treatment, nanovaccines, and other fields.
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Sunitinib (SUN) is a first-line drug for the treatment of renal clear carcinoma cells by targeting receptor tyrosine kinases (RTK) on the cell membrane. However, the effective delivery of SUN to the cell membrane remains a significant challenge. In this study, we fabricated precisely structured DNA nanotapes with strong surface SUN adhesion, enabling RTK inhibition of renal clear carcinoma cells. In our design, the precisely assembled linear topological six-helical-bundle DNA origami serves as the framework, and positively charged chitosan is adsorbed onto the DNA origami surface, thereby forming DNA nanotapes. The SUN was efficiently loaded onto the surface of the DNA nanotapes by electrostatic interaction. We found that DNA nanotapes exhibit excellent stability in serum. Importantly, DNA nanotapes carrying SUN can achieve prolonged cell membrane retention and inhibit RTK, thereby enhancing cytotoxicity toward 786-0 cells. Taken together, this study provides a promising candidate platform for the efficient delivery of cell membrane receptor inhibitors in anticancer therapy.
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Aconitum scaposum Franch 1894 belongs to the Genus Aconitum and Subgenus Lycoctonum (Ranunculaceae). It is widely distributed in China and adjacent areas, used as herbal medicine and had highy toxic components. This species has little reasearch information, especially its chloroplast (cp) genome information being unclear. Therefore, with the method of high salt and low pH to extract the cp of A. scaposum, we sequenced and assembled the complete cp genome of A. scaposum using Illumina high-throughput sequencing platform. The results showed the cp genome of A. scaposum was 157 688 bp in length, including a pair of inverted repeated regions (IRa 26 156 bp and IRb 26 232 bp, respectively), large single copy region (LSC 69 309 bp) and small single copy region (SSC 16 917 bp). And cp genome of A. scaposum consisted of 145 unique genes, 8 ribosomal RNA (rRNA) genes and 38 transfer RNA (tRNA) genes, with GC content was 38%. Meanwhile, based on the cp complete genome, we performed the phylogenetic tree of 66 species with maximum likelihood (ML) method, respectively. Among them, we selected one Delphinium species as the outgroup and the bootstrap of each braches were greater than 90%. The results indicated that the phylogenetic relationship of A. scaposum was relatively closely related to A. scaposum var. vaginatum compared to other Aconitum species.