MCDB: A comprehensive curated mitotic catastrophe database for retrieval, protein sequence alignment, and target prediction

A comprehensive review of artificial intelligence for pharmacology research

With the innovation and advancement of artificial intelligence, more and more artificial intelligence techniques are employed in drug research, biomedical frontier research, and clinical medicine practice, especially, in the field of pharmacology research. Thus, this review focuses on the applications of artificial intelligence in drug discovery, compound pharmacokinetic prediction, and clinical pharmacology. We briefly introduced the basic knowledge and development of artificial intelligence, presented a comprehensive review, and then summarized the latest studies and discussed the strengths and limitations of artificial intelligence models. Additionally, we highlighted several important studies and pointed out possible research directions.

Predicting drug-target interactions by measuring confidence with consistent causal neighborhood interventions

Predicting drug-target interactions (DTI) is a crucial stage in drug discovery and development. Understanding the interaction between drugs and targets is essential for pinpointing the specific relationship between drug molecules and targets, akin to solving a link prediction problem using information technology. While knowledge graph (KG) and knowledge graph embedding (KGE) methods have been rapid advancements and demonstrated impressive performance in drug discovery, they often lack authenticity and accuracy in identifying DTI. This leads to increased misjudgment rates and reduced efficiency in drug development. To address these challenges, our focus lies in refining the accuracy of DTI prediction models through KGE, with a specific emphasis on causal intervention confidence measures (CI). These measures aim to assess triplet scores, enhancing the precision of the predictions. Comparative experiments conducted on three datasets and utilizing 9 KGE models reveal that our proposed confidence measure approach via causal intervention, significantly improves the accuracy of DTI link prediction compared to traditional approaches. Furthermore, our experimental analysis delves deeper into the embedding of intervention values, offering valuable insights for guiding the design and development of subsequent drug development experiments. As a result, our predicted outcomes serve as valuable guidance in the pursuit of more efficient drug development processes.

CpG Island Definition and Methylation Mapping of the T2T-YAO Genome

Precisely defining and mapping all cytosine (C) positions and their clusters, known as CpG islands (CGIs), as well as their methylation status, are pivotal for genome-wide epigenetic studies, especially when population-centric reference genomes are ready for timely application. Here, we first align the two high-quality reference genomes, T2T-YAO and T2T-CHM13, from different ethnic backgrounds in a base-by-base fashion and compute their genome-wide density-defined and position-defined CGIs. Second, by mapping some representative genome-wide methylation data from selected organs onto the two genomes, we find that there are about 4.7%-5.8% sequence divergency of variable categories depending on quality cutoffs. Genes among the divergent sequences are mostly associated with neurological functions. Moreover, CGIs associated with the divergent sequences are significantly different with respect to CpG density and observed CpG/expected CpG (O/E) ratio between the two genomes. Finally, we find that the T2T-YAO genome not only has a greater CpG coverage than that of the T2T-CHM13 genome when whole-genome bisulfite sequencing (WGBS) data from the European and American populations are mapped to each reference, but also shows more hyper-methylated CpG sites as compared to the T2T-CHM13 genome. Our study suggests that future genome-wide epigenetic studies of the Chinese populations rely on both acquisition of high-quality methylation data and subsequent precision CGI mapping based on the Chinese T2T reference.

Mitotic catastrophe heterogeneity: implications for prognosis and immunotherapy in hepatocellular carcinoma

Background and aims

The mitotic catastrophe (MC) pathway plays an important role in hepatocellular carcinoma (HCC) progression and tumor microenvironment (TME) regulation. However, the mechanisms linking MC heterogeneity to immune evasion and treatment response remain unclear.

Methods

Based on 94 previously published highly correlated genes for MC, HCC patients' data from the Cancer Genome Atlas (TCGA) and changes in immune signatures and prognostic stratification were studied. Time and spatial-specific differences for MCGs were assessed by single-cell RNA sequencing and spatial transcriptome (ST) analysis. Multiple external databases (GEO, ICGC) were employed to construct an MC-related riskscore model.

Results

Identification of two MC-related subtypes in HCC patients from TCGA, with clear differences in immune signatures and prognostic risk stratification. Spatial mapping further associates low MC tumor regions with significant immune escape-related signaling. Nomogram combining MC riskscore and traditional indicators was validated great effect for early prediction of HCC patient outcomes.

Conclusion

MC heterogeneity enables immune escape and therapy resistance in HCC. The MC gene signature serves as a reliable prognostic indicator for liver cancer. By revealing clear immune and spatial heterogeneity of HCC, our integrated approach provides contextual therapeutic strategies for optimal clinical decision-making.

A Review of the Application of Spatial Transcriptomics in Neuroscience

Since spatial transcriptomics can locate and distinguish the gene expression of functional genes in special regions and tissue, it is important for us to investigate the brain development, the development mechanism of brain diseases, and the relationship between brain structure and function in Neuroscience (or Brain science). While previous studies have introduced the crucial spatial transcriptomic techniques and data analysis methods, there are few studies to comprehensively overview the key methods, data resources, and technological applications of spatial transcriptomics in Neuroscience. For these reasons, we first investigate several common spatial transcriptomic data analysis approaches and data resources. Second, we introduce the applications of the spatial transcriptomic data analysis approaches in Neuroscience. Third, we summarize the integrating spatial transcriptomics with other technologies in Neuroscience. Finally, we discuss the challenges and future research directions of spatial transcriptomics in Neuroscience.

Design, Synthesis, and Biological Evaluation of Dual Inhibitors of EGFRL858R/T790M/ACK1 to Overcome Osimertinib Resistance in Nonsmall Cell Lung Cancers

Activation of the alternative pathways and abnormal signaling transduction are frequently observed in third-generation EGFR-TKIs (epidermal growth factor receptor tyrosine kinase inhibitors)-resistant patients. Wherein, hyperphosphorylation of ACK1 contributes to EGFR-TKIs acquired resistance. Dual inhibition of EGFRL858R/T790M and ACK1 might improve therapeutic efficacy and overcome resistance in lung cancers treatment. Here, we identified a EGFRL858R/T790M/ACK1 dual-targeting compound 21a with aminoquinazoline scaffold, which showed excellent inhibitory activities against EGFRL858R/T790M (IC50 = 23 nM) and ACK1 (IC50 = 263 nM). The cocrystal and docking analysis showed that 21a occupied the ATP binding pockets of EGFRL858R/T790M and ACK1. Moreover, 21a showed potent antiproliferative activities against the H1975 cells, MCF-7 cells and osimertinib-resistant cells AZDR. Further, 21a showed significant antitumor effects and good safety in ADZR xenograft-bearing mice. Taken together, 21a was a potent dual inhibitor of EGFRL858R/T790M/ACK1, which is deserved as a potential lead for overcoming acquired resistance to osimertinib during the EGFR-targeted therapy.

Developing a prognosis and chemotherapy evaluating model for colon adenocarcinoma based on mitotic catastrophe-related genes

Mitotic catastrophe (MC) is a novel form of cell death that plays an important role in the treatment and drug resistance of colon adenocarcinoma (COAD). However, MC related genes in COAD treatment and prognosis evaluation are rarely studied. In this study, the transcriptome data, somatic mutation and copy number variation data were obtained from The Cancer Genome Atlas (TCGA) database. The mitotic catastrophe related genes (MCRGs) were obtained from GENCARDS website. Differential gene analysis was conducted with LIMMA package. Univariate Cox regression analysis was used to identify prognostic related genes. Mutation analysis was performed and displayed by maftools package. RCircos package was used for localizing the position of genes on chromosomes. "Glmnet" R package was applied for constructing a risk model via the LASSO regression method. Consensus clustering analyses was implemented for clustering different subtypes. Functional enrichment analysis through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) methods, immune infiltration analysis via single sample gene set enrichment analysis (ssGSEA), tumor mutation burden and drug sensitivity analysis by pRRophetic R package were also carried out for risk model or molecular subtype's assessment. Additionally, the connections between the expression of hub genes and overall survival (OS) were obtained from online Human Protein Atlas (HPA) website. Real-Time Quantitative Polymerase Chain Reaction (RT‑qPCR) further validated the expression of hub genes. A total of 207 differentially expressed MCRGs were selected in the TCGA cohort, 23 of which were significantly associated with OS in COAD patients. Subsequently, we constructed risk score prognostic models with 5 hub MCRGs, including SYCE2, SERPINE1, TRIP6, LIMK1, and EEPD1. The high-risk patients suffered from poorer prognosis. Furthermore, we developed a nomogram that gathered age, sex, staging, and risk score to accurately forecast the clinical survival outcomes in 1, 3, and 5 years. The results of functional enrichment suggested a significant correlation between MCRGs characteristics and cancer progression, with important implications for the immune microenvironment. Moreover, patients who displayed high TMB and high risk score showed worse prognosis, and risk characteristics were associated with different chemotherapeutic agents. Finally, RT‑qPCR verified the increased expression of the five MCRGs in clinical samples. The five MCRGs in the prognostic signature were associated with prognosis, and could be treated as reliable prognostic biomarkers and therapeutic targets for COAD patients with distinct clinicopathological characteristics, thereby providing a foundation for the precise application of pertinent drugs in COAD patients.

Systems Biology in Cancer Diagnosis Integrating Omics Technologies and Artificial Intelligence to Support Physician Decision Making

Cancer is the second major cause of disease-related death worldwide, and its accurate early diagnosis and therapeutic intervention are fundamental for saving the patient's life. Cancer, as a complex and heterogeneous disorder, results from the disruption and alteration of a wide variety of biological entities, including genes, proteins, mRNAs, miRNAs, and metabolites, that eventually emerge as clinical symptoms. Traditionally, diagnosis is based on clinical examination, blood tests for biomarkers, the histopathology of a biopsy, and imaging (MRI, CT, PET, and US). Additionally, omics biotechnologies help to further characterize the genome, metabolome, microbiome traits of the patient that could have an impact on the prognosis and patient's response to the therapy. The integration of all these data relies on gathering of several experts and may require considerable time, and, unfortunately, it is not without the risk of error in the interpretation and therefore in the decision. Systems biology algorithms exploit Artificial Intelligence (AI) combined with omics technologies to perform a rapid and accurate analysis and integration of patient's big data, and support the physician in making diagnosis and tailoring the most appropriate therapeutic intervention. However, AI is not free from possible diagnostic and prognostic errors in the interpretation of images or biochemical-clinical data. Here, we first describe the methods used by systems biology for combining AI with omics and then discuss the potential, challenges, limitations, and critical issues in using AI in cancer research.

Perspectives and mechanisms for targeting mitotic catastrophe in cancer treatment

Mitotic catastrophe is distinct from other cell death modes due to unique nuclear alterations characterized as multi and/or micronucleation. Mitotic catastrophe is a common and virtually unavoidable consequence during cancer therapy. However, a comprehensive understanding of mitotic catastrophe remains lacking. Herein, we summarize the anticancer drugs that induce mitotic catastrophe, including microtubule-targeting agents, spindle assembly checkpoint kinase inhibitors, DNA damage agents and DNA damage response inhibitors. Based on the relationships between mitotic catastrophe and other cell death modes, we thoroughly evaluated the roles played by mitotic catastrophe in cancer treatment as well as its advantages and disadvantages. Some strategies for overcoming its shortcomings while fully utilizing its advantages are summarized and proposed in this review. We also review how mitotic catastrophe regulates cancer immunotherapy. These summarized findings suggest that the induction of mitotic catastrophe can serve as a promising new therapeutic approach for overcoming apoptosis resistance and strengthening cancer immunotherapy.

Discovering hematoma-stimulated circuits for secondary brain injury after intraventricular hemorrhage by spatial transcriptome analysis

Introduction

Central nervous system (CNS) diseases, such as neurodegenerative disorders and brain diseases caused by acute injuries, are important, yet challenging to study due to disease lesion locations and other complexities.

Methods

Utilizing the powerful method of spatial transcriptome analysis together with novel algorithms we developed for the study, we report here for the first time a 3D trajectory map of gene expression changes in the brain following acute neural injury using a mouse model of intraventricular hemorrhage (IVH). IVH is a common and representative complication after various acute brain injuries with severe mortality and mobility implications.

Results

Our data identified three main 3D global pseudospace-time trajectory bundles that represent the main neural circuits from the lateral ventricle to the hippocampus and primary cortex affected by experimental IVH stimulation. Further analysis indicated a rapid response in the primary cortex, as well as a direct and integrated effect on the hippocampus after IVH stimulation.

Discussion

These results are informative for understanding the pathophysiological changes, including the spatial and temporal patterns of gene expression changes, in IVH patients after acute brain injury, strategizing more effective clinical management regimens, and developing novel bioinformatics strategies for the study of other CNS diseases. The algorithm strategies used in this study are searchable via a web service (www.combio-lezhang.online/3dstivh/home).

Targeting ferroptosis: Paving new roads for drug design and discovery

Ferroptosis, first proposed in 2012, is an iron-dependent form of regulated cell death characterized by excessive polyunsaturated fatty acid oxidation. In the past decade, researchers have revealed the formation and mechanisms of ferroptosis. Cancer drug resistance can be reversed by ferroptosis induction, and inhibiting ferroptosis has been shown to block certain disease processes. As a result, several ferroptosis-targeting drugs have been developed. However, the first-generation ferroptosis-targeting agents remain hampered from clinical use, mainly due to poor selectivity and pharmacokinetics. The discoveries of FSP1, GCH1, and other potential ferroptosis-regulating pathways independent of Xc^--GSH-GPX4 provide novel targets for drug design. Recently, protein-targeted degradation and antibody-drug conjugate strategy show promise in future drug design. With novel targets, further optimizations, and new technologies, the next-generation ferroptosis-targeting agents show a promising future with improved selectivity and efficacy. In this review, we summarize mechanisms, target types, drug design, and novel technologies of ferroptosis, aiming to pave the way for future drug design and discovery in the next decade.

Discovery of Novel Indazole Chemotypes as Isoform-Selective JNK3 Inhibitors for the Treatment of Parkinson's Disease

c-Jun N-terminal kinases (JNKs) are involved in the pathogenesis of various diseases. In particular, JNK3 and not JNK1/2 is primarily expressed in the brain and plays a key role in mediating neurodegenerative diseases like Parkinson's disease (PD). Due to the sequence similarity of JNK isoforms, developing isoform-selective JNK3 inhibitors to evaluate their biological functions and therapeutic potential in PD has become a challenge. Herein, docking-based virtual screening and structure-activity relationship studies identified 25c with excellent inhibitory activity against JNK3 (IC₅₀ = 85.21 nM) and exhibited an over 100-fold isoform selectivity for JNK3 over JNK1/2 and remarkable kinase selectivity. 25c showed neuroprotective effects on in vitro and in vivo PD models by selectively inhibiting JNK3. Meanwhile, 25c showed an ideal blood-brain barrier permeability and low toxicity. Overall, this study provided a valuable molecular tool for investigating the role of JNK3 in PD and a solid foundation for developing JNK3-targeted drugs in PD treatment.

Spatiotemporal Transformer Neural Network for Time-Series Forecasting

Predicting high-dimensional short-term time-series is a difficult task due to the lack of sufficient information and the curse of dimensionality. To overcome these problems, this study proposes a novel spatiotemporal transformer neural network (STNN) for efficient prediction of short-term time-series with three major features. Firstly, the STNN can accurately and robustly predict a high-dimensional short-term time-series in a multi-step-ahead manner by exploiting high-dimensional/spatial information based on the spatiotemporal information (STI) transformation equation. Secondly, the continuous attention mechanism makes the prediction results more accurate than those of previous studies. Thirdly, we developed continuous spatial self-attention, temporal self-attention, and transformation attention mechanisms to create a bridge between effective spatial information and future temporal evolution information. Fourthly, we show that the STNN model can reconstruct the phase space of the dynamical system, which is explored in the time-series prediction. The experimental results demonstrate that the STNN significantly outperforms the existing methods on various benchmarks and real-world systems in the multi-step-ahead prediction of a short-term time-series.

An integrated platform for Brucella with knowledge graph technology: From genomic analysis to epidemiological projection

Motivation:Brucella, the causative agent of brucellosis, is a global zoonotic pathogen that threatens both veterinary and human health. The main sources of brucellosis are farm animals. Importantly, the bacteria can be used for biological warfare purposes, requiring source tracking and routine surveillance in an integrated manner. Additionally, brucellosis is classified among group B infectious diseases in China and has been reported in 31 Chinese provinces to varying degrees in urban areas. From a national biosecurity perspective, research on brucellosis surveillance has garnered considerable attention and requires an integrated platform to provide researchers with easy access to genomic analysis and provide policymakers with an improved understanding of both reported patients and detected cases for the purpose of precision public health interventions.

Results: For the first time in China, we have developed a comprehensive information platform for Brucella based on dynamic visualization of the incidence (reported patients) and prevalence (detected cases) of brucellosis in mainland China. Especially, our study establishes a knowledge graph for the literature sources of Brucella data so that it can be expanded, queried, and analyzed. When similar “epidemiological comprehensive platforms” are established in the distant future, we can use knowledge graph to share its information. Additionally, we propose a software package for genomic sequence analysis. This platform provides a specialized, dynamic, and visual point-and-click interface for studying brucellosis in mainland China and improving the exploration of Brucella in the fields of bioinformatics and disease prevention for both human and veterinary medicine.

iPCD: A Comprehensive Data Resource of Regulatory Proteins in Programmed Cell Death

Programmed cell death (PCD) is an essential biological process involved in many human pathologies. According to the continuous discovery of new PCD forms, a large number of proteins have been found to regulate PCD. Notably, post-translational modifications play critical roles in PCD process and the rapid advances in proteomics have facilitated the discovery of new PCD proteins. However, an integrative resource has yet to be established for maintaining these regulatory proteins. Here, we briefly summarize the mainstream PCD forms, as well as the current progress in the development of public databases to collect, curate and annotate PCD proteins. Further, we developed a comprehensive database, with integrated annotations for programmed cell death (iPCD), which contained 1,091,014 regulatory proteins involved in 30 PCD forms across 562 eukaryotic species. From the scientific literature, we manually collected 6493 experimentally identified PCD proteins, and an orthologous search was then conducted to computationally identify more potential PCD proteins. Additionally, we provided an in-depth annotation of PCD proteins in eight model organisms, by integrating the knowledge from 102 additional resources that covered 16 aspects, including post-translational modification, protein expression/proteomics, genetic variation and mutation, functional annotation, structural annotation, physicochemical property, functional domain, disease-associated information, protein–protein interaction, drug–target relation, orthologous information, biological pathway, transcriptional regulator, mRNA expression, subcellular localization and DNA and RNA element. With a data volume of 125 GB, we anticipate that iPCD can serve as a highly useful resource for further analysis of PCD in eukaryotes.

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

Discovery of Tyrosinase Inhibitors: Structure-Based Virtual Screening and Biological Evaluation

Tyrosinase (EC 1.14.18.1) plays an indispensable role in the rate-limiting steps of melanin biosynthesis, and its uncontrolled activity may result in various diseases, such as albinism, melanoma, freckles, etc. The inhibition of tyrosinase activity may provide a useful and efficient strategy to treat hyperpigmentation disorders. However, the widely used tyrosinase inhibitors, like α-arbutin, hydroquinone, and kojic acid, have many shortcomings, such as lower efficacy and much more side effects. Herein, we reported the use of homology modeling and multistep structure-based virtual screening for the discovery of novel tyrosinase inhibitors. In this study, 10 initial potential hits (compounds T1–T10) were evaluated for enzyme inhibition and kinetic study, with kojic acid being used as a control. Among them, the IC50 values of both T1 (11.56 ± 0.98 μmol/L) and T5 (18.36 ± 0.82 μmol/L) were superior to that of kojic acid (23.12 ± 1.26 μmol/L). Moreover, T1 and T5 were also identified as the effective noncompetitive tyrosinase inhibitors by the subsequent kinetic study. Above all, T1 and T5 may represent the promising drug candidates for hyperpigmentation therapy in pharmaceutical fields, as well as the effective whitening agents in cosmetic applications.

Discovery of Novel Dual-Target Inhibitor of Bromodomain-Containing Protein 4/Casein Kinase 2 Inducing Apoptosis and Autophagy-Associated Cell Death for Triple-Negative Breast Cancer Therapy

Bromodomain-containing protein 4 (BRD4) is an attractive epigenetic target in human cancers. Inhibiting the phosphorylation of BRD4 by casein kinase 2 (CK2) is a potential strategy to overcome drug resistance in cancer therapy. The present study describes the synthesis of multiple BRD4-CK2 dual inhibitors based on rational drug design, structure-activity relationship, and in vitro and in vivo evaluations, and 44e was identified to possess potent and balanced activities against BRD4 (IC₅₀ = 180 nM) and CK2 (IC₅₀ = 230 nM). In vitro experiments show that 44e could inhibit the proliferation and induce apoptosis and autophagy-associated cell death of MDA-MB-231 and MDA-MB-468 cells. In two in vivo xenograft mouse models, 44e displays potent anticancer activity without obvious toxicities. Taken together, we successfully synthesized the first highly effective BRD4-CK2 dual inhibitor, which is expected to be an attractive therapeutic strategy for triple-negative breast cancer (TNBC).

Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research

It is very important for systems biologists to predict the state of the multi-omics time series for disease occurrence and health detection. However, it is difficult to make the prediction due to the high-dimensional, nonlinear and noisy characteristics of the multi-omics time series data. For this reason, this study innovatively proposes an Embedding, Koopman and Autoencoder technologies-based multi-omics time series predictive model (EKATP) to predict the future state of a high-dimensional nonlinear multi-omics time series. We evaluate this EKATP by using a genomics time series with chaotic behavior, a proteomics time series with oscillating behavior and a metabolomics time series with flow behavior. The computational experiments demonstrate that our proposed EKATP can substantially improve the accuracy, robustness and generalizability to predict the future state of a time series for multi-omics data.