THItoGene: a deep learning method for predicting spatial transcriptomics from histological images.

Spatial transcriptomics unveils the complex dynamics of cell regulation and transcriptomes, but it is typically cost-prohibitive. Predicting spatial gene expression from histological images via artificial intelligence offers a more affordable option, yet existing methods fall short in extracting deep-level information from pathological images. In this paper, we present THItoGene, a hybrid neural network that utilizes dynamic convolutional and capsule networks to adaptively sense potential molecular signals in histological images for exploring the relationship between high-resolution pathology image phenotypes and regulation of gene expression. A comprehensive benchmark evaluation using datasets from human breast cancer and cutaneous squamous cell carcinoma has demonstrated the superior performance of THItoGene in spatial gene expression prediction. Moreover, THItoGene has demonstrated its capacity to decipher both the spatial context and enrichment signals within specific tissue regions. THItoGene can be freely accessed at https://github.com/yrjia1015/THItoGene.

Prediction of SARS-CoV-2 Infection Phosphorylation Sites and Associations of these Modifications with Lung Cancer Development.

INTRODUCTION: Since the emergence of SARS-CoV-2 viruses, multiple mutant strains have been identified. Infection with SARS-CoV-2 virus leads to alterations in host cell phosphorylation signal, which systematically modulates the immune response. METHOD: Identification and analysis of SARS-CoV-2 virus infection phosphorylation sites enable insight into the mechanisms of viral infection and effects on host cells, providing important fundamental data for the study and development of potent drugs for the treatment of immune inflammatory diseases. In this paper, we have analyzed the SARS-CoV-2 virus-infected phosphorylation region and developed a transformer-based deep learning-assisted identification method for the specific identification of phosphorylation sites in SARS-CoV-2 virus-infected host cells. RESULT: Furthermore, through association analysis with lung cancer, we found that SARS-CoV-2 infection may affect the regulatory role of the immune system, leading to an abnormal increase or decrease in the immune inflammatory response, which may be associated with the development and progression of cancer. CONCLUSION: We anticipate that this study will provide an important reference for SARS-CoV-2 virus evolution as well as immune-related studies and provide a reliable complementary screening tool for anti-SARS-CoV-2 virus drug and vaccine design.

CRISPRCasStack: a stacking strategy-based ensemble learning framework for accurate identification of Cas proteins.

CRISPR-Cas system is an adaptive immune system widely found in most bacteria and archaea to defend against exogenous gene invasion. One of the most critical steps in the study of exploring and classifying novel CRISPR-Cas systems and their functional diversity is the identification of Cas proteins in CRISPR-Cas systems. The discovery of novel Cas proteins has also laid the foundation for technologies such as CRISPR-Cas-based gene editing and gene therapy. Currently, accurate and efficient screening of Cas proteins from metagenomic sequences and proteomic sequences remains a challenge. For Cas proteins with low sequence conservation, existing tools for Cas protein identification based on homology cannot guarantee identification accuracy and efficiency. In this paper, we have developed a novel stacking-based ensemble learning framework for Cas protein identification, called CRISPRCasStack. In particular, we applied the SHAP (SHapley Additive exPlanations) method to analyze the features used in CRISPRCasStack. Sufficient experimental validation and independent testing have demonstrated that CRISPRCasStack can address the accuracy deficiencies and inefficiencies of the existing state-of-the-art tools. We also provide a toolkit to accurately identify and analyze potential Cas proteins, Cas operons, CRISPR arrays and CRISPR-Cas locus in prokaryotic sequences. The CRISPRCasStack toolkit is available at https://github.com/yrjia1015/CRISPRCasStack.

Complex genome assembly based on long-read sequencing.

High-quality genome chromosome-scale sequences provide an important basis for genomics downstream analysis, especially the construction of haplotype-resolved and complete genomes, which plays a key role in genome annotation, mutation detection, evolutionary analysis, gene function research, comparative genomics and other aspects. However, genome-wide short-read sequencing is difficult to produce a complete genome in the face of a complex genome with high duplication and multiple heterozygosity. The emergence of long-read sequencing technology has greatly improved the integrity of complex genome assembly. We review a variety of computational methods for complex genome assembly and describe in detail the theories, innovations and shortcomings of collapsed, semi-collapsed and uncollapsed assemblers based on long reads. Among the three methods, uncollapsed assembly is the most correct and complete way to represent genomes. In addition, genome assembly is closely related to haplotype reconstruction, that is uncollapsed assembly realizes haplotype reconstruction, and haplotype reconstruction promotes uncollapsed assembly. We hope that gapless, telomere-to-telomere and accurate assembly of complex genomes can be truly routinely achieved using only a simple process or a single tool in the future.

KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest.

DNA-binding protein (DBP) is a protein with a special DNA binding domain that is associated with many important molecular biological mechanisms. Rapid development of computational methods has made it possible to predict DBP on a large scale; however, existing methods do not fully integrate DBP-related features, resulting in rough prediction results. In this article, we develop a DNA-binding protein identification method called KK-DBP. To improve prediction accuracy, we propose a feature extraction method that fuses multiple PSSM features. The experimental results show a prediction accuracy on the independent test dataset PDB186 of 81.22%, which is the highest of all existing methods.

Quality assessment and traceability study of Angelicae Sinensis Radix via binary chromatography, triple quadrupole tandem mass spectrometry, and multivariate statistical analysis.

Angelicae Sinensis Radix is a world-renowned herbal medicine originating in China. Owing to many environmental and geographical factors, Angelicae Sinensis Radix from various origins may have a difference in the content of ingredients, which made the confusion in the clinical practice and market. Herein, a binary chromatographic fingerprinting analysis method is developed via hydrophilic interaction chromatography and reversed-phase liquid chromatography to obtain more chemical information. Following that, an ultra-performance liquid chromatography with a triple quadrupole mass spectrometry method is furnished to simultaneously detect 17 ingredients of Angelicae Sinensis Radix gathered from six geographic zones in China. Eventually, the principal component analysis is successfully carried out to classify and differentiate the Angelicae Sinensis Radix from different origins, meanwhile the quantitative volcano plots was used to observe the changes of ingredient trends vividly. Accordingly, the proposed binary chromatography and triple quadrupole tandem mass spectrometry coupled with multivariate statistical analysis can be utilized as a facile and reliable method for origin tracing and quality control of Angelicae Sinensis Radix.

A hairpin DNA-fueled nanoflare for simultaneous illumination of two microRNAs in drug-induced nephrotoxic cells with target catalytic recycling amplification.

The detection of specific extracellular microRNAs (miRNAs) is beneficial for the prediction of drug-induced kidney injury. Here, a novel hairpin DNA-fueled nanoflare was developed for the simultaneous detection of drug-induced nephrotoxicity-related miRNA-21 and miRNA-200c with target catalytic recycling amplification. The nanoflare utilized gold nanoparticles (AuNPs) as the highly efficient quencher to ensure a low background signal. With the help of the fueled hairpin DNA, the miRNA targets could serve as the catalysts for the assembly of DNA duplexes. Therefore, the nanoflare could respond to the miRNAs to yield signal outputs of 1 : n (target : signal) rather than an equivalent reaction ratio of 1 : 1, achieving the signal amplified detection of low-abundant miRNAs. The targets can be concurrently detected with the detection limit of 18.1 and 21.1 pM for miRNA-21 and miRNA-200c, respectively, which are approximately 2 orders of magnitude lower than that of the non-catalytic probes. In addition, this nanoflare offered a high selectivity for determination between perfectly matched targets and single-base mismatched targets. It should be noted that the nanoflare was successfully employed to predict the drug-induced nephrotoxicity by the detection of miRNAs in culture media excreted from the drug-treated renal cells using a fluorescent microplate reader. Our hairpin DNA-fueled nanoflare could also accurately detect the divergence of miRNA-21 and miRNA-200c between drug-treated nephrotoxic cells and tumor cells, demonstrating a promising potential for exploring the pathogenesis of drugs and auxiliary diagnosis of drug-induced nephrotoxicity.

Anti-rheumatoid arthritis effects of traditional Chinese herb couple in adjuvant-induced arthritis in rats.

ETHNOPHARMACOLOGICAL RELEVANCE: Clematis chinensis Osbeck / Notopterygium incisum Ting ex H, T-Chang (CN) is a traditional Chinese herb couple with prominent efficacy. The herb couple has been commonly used for clinical treatment of arthralgia syndrome ("Bi Zheng" in Chinese) for centuries in China, including rheumatic arthritis, osteoarthritis and gout in modern medicine. AIM OF THE STUDY: To evaluate the anti-arthritic effect of CN herb couple in a rat model of rheumatoid arthritis (RA). MATERIALS AND METHODS: Rats were divided randomly into six groups with eight each. Adjuvant-induced arthritis (AIA) model was established by intradermal injection of complete Freund's adjuvant (CFA). Rats were treated orally with different dosages of CN (0.7g/kg, 2.1g/kg, 6.3g/kg) from day 16 till day 40. Ibuprofen (50.4mg/kg) served as a positive control. Spontaneous activity, body weight, paw swelling, and arthritis index (AI) were monitored throughout drug treatment. Then serum levels of tumor necrosis factor α (TNF-α), interleukin-6 (IL-6), and vascular endothelial growth factor (VEGF) were determined by enzyme linked immunosorbent assay (ELISA) kits. In addition, histopathological examination and immunohistochemistry were used to assess the severity of arthritis. RESULTS: Three dosage of CN significantly ameliorated symptoms of RA via increasing body weight as well as reducing paw swelling (at dose of 6.3g/kg, p<0.01) in AIA rats. An extremely significant reduction of AI (p<0.001) was also observed with treatment of CN (6.3g/kg) compared with model group. In parallel, treatment of CN significantly down-regulated levels of TNF-α, IL-6, and VEGF both in serum (p<0.01) and in joint synovial compared with model rats. And histopathology revealed noticeable reduction in synovial hyperplasia, cartilage damage, and inflammatory infiltration by CN treatment, especially at dose of 6.3g/kg. CONCLUSIONS: To conclude, all results suggest that CN possesses evident anti-arthritic effects in AIA rats.

Quality assessment of traditional Chinese medicine herb couple by high-performance liquid chromatography and mass spectrometry combined with chemometrics.

This study was designed to develop a simple, specific and reliable method to overall analyze the chemical constituents in clematidis radix et rhizome/notopterygii rhizome et radix herb couple using high-performance liquid chromatography coupled with tandem mass spectrometry and multiple chemometric analysis. First, the separation and qualitative analysis of herb couple was achieved on an Agilent Zorbax Eclipse Plus C18 column (250 mm × 4.6 mm, 5 µm), and 69 compounds were unambiguously or tentatively identified. Moreover, in quantitative analysis, eight ingredients including six coumarins and two triterpenoid sapogenins were quantified by high-performance liquid chromatography coupled with tandem mass spectrometry. In terms of good linearity (r(2) ≥ 0.9995) with a relatively wide concentration range, recovery (85.40-102.50%) and repeatability (0.99-4.45%), the validation results suggested the proposed method was reliable, and successfully used to analyze ten batches of herb couple samples. Then, hierarchical cluster analysis and principal component analysis were used to classify samples and search significant ingredients. The results showed that ten batches of herb couple samples were classified into three groups, and six compounds were found for its better quality control.