SLIVER: Unveiling large scale gene regulatory networks of single-cell transcriptomic data through causal structure learning and modules aggregation.

Prevalent Gene Regulatory Network (GRN) construction methods rely on generalized correlation analysis. However, in biological systems, regulation is essentially a causal relationship that cannot be adequately captured solely through correlation. Therefore, it is more reasonable to infer GRNs from a causal perspective. Existing causal discovery algorithms typically rely on Directed Acyclic Graphs (DAGs) to model causal relationships, but it often requires traversing the entire network, which result in computational demands skyrocketing as the number of nodes grows and make causal discovery algorithms only suitable for small networks with one or two hundred nodes or fewer. In this study, we propose the SLIVER (cauSaL dIscovery Via dimEnsionality Reduction) algorithm which integrates causal structural equation model and graph decomposition. SLIVER introduces a set of factor nodes, serving as abstractions of different functional modules to integrate the regulatory relationships between genes based on their respective functions or pathways, thus reducing the GRN to the product of two low-dimensional matrices. Subsequently, we employ the structural causal model (SCM) to learn the GRN within the gene node space, enforce the DAG constraint in the low-dimensional space, and guide each factor to aggregate various functions through cosine similarity. We evaluate the performance of the SLIVER algorithm on 12 real single cell transcriptomic datasets, and demonstrate it outperforms other 12 widely used methods both in GRN inference performance and computational resource usage. The analysis of the gene information integrated by factor nodes also demonstrate the biological explanation of factor nodes in GRNs. We apply it to scRNA-seq of Type 2 diabetes mellitus to capture the transcriptional regulatory structural changes of ß cells under high insulin demand.

A Comprehensive Comparison between Primary Liver Cancer and Liver Metastases through scRNA-Seq Data Analysis.

Metastasis is one of the leading causes of cancer-related deaths. A comprehensive comparison of the differences between primary and metastatic cancers within the same organ can aid in understanding the growth mechanisms of cancer cells at metastatic sites, thereby helping to develop more effective targeted treatment strategies. Primary liver cancer is one of the most common types of cancer, and the liver is also one of the main metastatic sites. In this paper, we utilize single-cell RNA-Seq data to compare primary liver cancer and colorectal liver metastases from multiple perspectives, including cell types and proportions, activity of various cell types, cell-cell communication, mRNA expression differences within the same types of cells, key factors associated with cell proliferation, etc. Our analysis results show the following: (i) Compared to primary tissue, metastatic tissue contains more cytotoxic T cells and exhausted T cells, and it retains some specific characteristics of the primary site. (ii) Cells of the same type exhibit functional differences between primary and metastatic cancers, with metastatic cancer cells showing lower metabolism levels and immune cells exhibiting stronger immune activity. (iii) Interactions between monocytes and hepato-associated cells are strong in primary cancer, while depleted T cells frequently communicate with hepatocytes in metastatic cancer. (iv) Proliferation-related genes in primary and metastatic cancers are mainly involved in cell energy supply and basic metabolism activity, respectively.

Pharmacological Inhibition of Ferroptosis as a Therapeutic Target for Neurodegenerative Diseases and Strokes.

Emerging evidence suggests that ferroptosis, a unique regulated cell death modality that is morphologically and mechanistically different from other forms of cell death, plays a vital role in the pathophysiological process of neurodegenerative diseases, and strokes. Accumulating evidence supports ferroptosis as a critical factor of neurodegenerative diseases and strokes, and pharmacological inhibition of ferroptosis as a therapeutic target for these diseases. In this review article, the core mechanisms of ferroptosis are overviewed and the roles of ferroptosis in neurodegenerative diseases and strokes are described. Finally, the emerging findings in treating neurodegenerative diseases and strokes through pharmacological inhibition of ferroptosis are described. This review demonstrates that pharmacological inhibition of ferroptosis by bioactive small-molecule compounds (ferroptosis inhibitors) could be effective for treatments of these diseases, and highlights a potential promising therapeutic avenue that could be used to prevent neurodegenerative diseases and strokes. This review article will shed light on developing novel therapeutic regimens by pharmacological inhibition of ferroptosis to slow down the progression of these diseases in the future.

Real-world effectiveness of Azvudine versus nirmatrelvir-ritonavir in hospitalized patients with COVID-19: A retrospective cohort study.

Chinese guidelines prioritize the use of Azvudine and nirmatrelvir-ritonavir in COVID-19 patients. Nevertheless, the real-world effectiveness of Azvudine versus nirmatrelvir-ritonavir is still lacking, despite clinical trials showing their effectiveness compared with matched controls. To compare the effectiveness of Azvudine versus nirmatrelvir-ritonavir treatments in real-world clinical practice, we identified 2118 hospitalized COVID-19 patients, with a follow-up of up to 38 days. After exclusions and propensity score matching, we included 281 Azvudine recipients and 281 nirmatrelvir-ritonavir recipients who did not receive oxygen therapy at admission. The lower crude incidence rate of composite disease progression outcome (7.83 vs. 14.83 per 1000 person-days, p = 0.026) and all-cause death (2.05 vs. 5.78 per 1000 person-days, p = 0.052) were observed among Azvudine recipients. Azvudine was associated with lower risks of composite disease progression outcome (hazard ratio [HR]: 0.55; 95% confidence interval [CI]: 0.32-0.94) and all-cause death (HR: 0.40; 95% CI: 0.16-1.04). In subgroup analyses, the results of composite outcome retained significance among patients aged <65 years, those having a history of disease, those with severe COVID-19 at admission, and those receiving antibiotics. These findings suggest that Azvudine treatment showed effectiveness in hospitalized COVID-19 patients compared with nirmatrelvir-ritonavir in terms of composite disease progression outcome.

Targeting ferroptosis as a promising therapeutic strategy to treat cardiomyopathy.

Cardiomyopathies are a clinically heterogeneous group of cardiac diseases characterized by heart muscle damage, resulting in myocardium disorders, diminished cardiac function, heart failure, and even sudden cardiac death. The molecular mechanisms underlying the damage to cardiomyocytes remain unclear. Emerging studies have demonstrated that ferroptosis, an iron-dependent non-apoptotic regulated form of cell death characterized by iron dyshomeostasis and lipid peroxidation, contributes to the development of ischemic cardiomyopathy, diabetic cardiomyopathy, doxorubicin-induced cardiomyopathy, and septic cardiomyopathy. Numerous compounds have exerted potential therapeutic effects on cardiomyopathies by inhibiting ferroptosis. In this review, we summarize the core mechanism by which ferroptosis leads to the development of these cardiomyopathies. We emphasize the emerging types of therapeutic compounds that can inhibit ferroptosis and delineate their beneficial effects in treating cardiomyopathies. This review suggests that inhibiting ferroptosis pharmacologically may be a potential therapeutic strategy for cardiomyopathy treatment.

Identification of wheat stem rust resistance genes in wheat cultivars from Hebei province, China.

Wheat stem rust is caused by Puccinia graminis f. sp. tritici. This major disease has been effectively controlled via resistance genes since the 1970s. The appearance and spread of new races of P. graminis f. sp. tritici (eg., Ug99, TKTTF, and TTRTF) have renewed the interest in identifying the resistance gene and breeding cultivars resistant to wheat stem rust. In this study, gene postulation, pedigree analysis, and molecular detection were used to determine the presence of stem rust resistance genes in 65 commercial wheat cultivars from Hebei Province. In addition, two predominant races 21C3CTHTM and 34MRGQM were used to evaluate the resistance of these cultivars at the adult-plant stage in 2021-2022. The results revealed that 6 Sr genes (namely, Sr5, Sr17, Sr24, Sr31, Sr32, Sr38, and SrTmp), either singly or in combination, were identified in 46 wheat cultivars. Overall, 37 wheat cultivars contained Sr31. Sr5 and Sr17 were present in 3 and 3 cultivars, respectively. Gao 5218 strong gluten, Jie 13-Ji 7369, and Kenong 1006 contained Sr24, Sr32, and Sr38, respectively. No wheat cultivar contained Sr25 and Sr26. In total, 50 (76.9%) wheat cultivars were resistant to all tested races of P. graminis f. sp. tritici in field test in 2021-2022. This study is important for breeding wheat cultivars with resistance to stem rust.

[Therapeutic potential of targeting SIRT1 for the treatment of Alzheimer's disease].

Silent information regulator 1 (SIRT1) is one of the seven mammalian proteins of the sirtuin family of NAD+-dependent deacetylases. SIRT1 plays a pivotal role in neuroprotection and ongoing research has uncovered a mechanism by which SIRT1 may exert a neuroprotective effect on Alzheimer's disease (AD). Growing evidence demonstrates that SIRT1 regulates many pathological processes including amyloid-ß precursor protein (APP) processing, neuroinflammation, neurodegeneration, and mitochondrial dysfunction. SIRT1 has recently received enormous attention, and pharmacological or transgenic approaches to activate the sirtuin pathway have shown promising results in the experimental models of AD. In the present review, we delineate the role of SIRT1 in AD from a disease-centered perspective and provides an up-to-date overview of the SIRT1 modulators and their potential as effective therapeutics in AD.

Identification of Cancer Driver Genes by Integrating Multiomics Data with Graph Neural Networks.

Cancer is a heterogeneous disease that is driven by the accumulation of both genetic and nongenetic alterations, so integrating multiomics data and extracting effective information from them is expected to be an effective way to predict cancer driver genes. In this paper, we first generate comprehensive instructive features for each gene from genomic, epigenomic, transcriptomic levels together with protein-protein interaction (PPI)-networks-derived attributes and then propose a novel semisupervised deep graph learning framework GGraphSAGE to predict cancer driver genes according to the impact of the alterations on a biological system. When applied to eight tumor types, experimental results suggest that GGraphSAGE outperforms several state-of-the-art computational methods for driver genes identification. Moreover, it broadens our current understanding of cancer driver genes from multiomics level and identifies driver genes specific to the tumor type rather than pan-cancer. We expect GGraphSAGE to open new avenues in precision medicine and even further predict drivers for other complex diseases.

A comprehensive tool for tumor precision medicine with pharmaco-omics data analysis.

Background: Cancer precision medicine is an effective strategy to fight cancers by bridging genomics and drug discovery to provide specific treatment for patients with different genetic characteristics. Although some public databases and modelling frameworks have been developed through studies on drug response, most of them only considered the ramifications of the drug on the cell line and the effects on the patient still require a huge amount of work to integrate data from various databases and calculations, especially concerning precision treatment. Furthermore, not only efficacy but also the adverse effects of drugs on patients should be taken into account during cancer treatment. However, the adverse effects as essential indicators of drug safety assessment are always neglected. Method: A holistic estimation explores various drugs' efficacy levels by calculating their potency both in reversing and enhancing cancer-associated gene expression change. And a method for bridging the gap between cell culture and living tissue estimates the effectiveness of a drug on individual patients through the mappings of various cell lines to each person according to their genetic mutation similarities. Result: We predicted the efficacy of FDA-recommended drugs, taking into account both efficacy and toxicity, and obtained consistent results. We also provided an intuitive and easy-to-use web server called DBPOM (http://www.dbpom.net/, a comprehensive database of pharmaco-omics for cancer precision medicine), which not only integrates the above methods but also provides calculation results on more than 10,000 small molecule compounds and drugs. As a one-stop web server, clinicians and drug researchers can also analyze the overall effect of a drug or a drug combination on cancer patients as well as the biological functions that they target. DBPOM is now public, free to use with no login requirement, and contains all the data and code. Conclusion: Both the positive and negative effects of drugs during precision treatment are essential for practical application of drugs. DBPOM based on the two effects will become a vital resource and analysis platform for drug development, drug mechanism studies and the discovery of new therapies.

BET inhibitors synergize with sunitinib in melanoma through GDF15 suppression.

Targeting bromodomain and extra-terminal domain (BET) proteins has shown a promising therapeutic effect on melanoma. The development of strategies to better kill melanoma cells with BET inhibitor treatment may provide new clinical applications. Here, we used a drug synergy screening approach to combine JQ1 with 240 antitumor drugs from the Food and Drug Administration (FDA)-approved drug library and found that sunitinib synergizes with BET inhibitors in melanoma cells. We further demonstrated that BET inhibitors synergize with sunitinib in melanoma by inducing apoptosis and cell cycle arrest. Mechanistically, BET inhibitors sensitize melanoma cells to sunitinib by inhibiting GDF15 expression. Strikingly, GDF15 is transcriptionally regulated directly by BRD4 or indirectly by the BRD4/IL6/STAT3 axis. Xenograft assays revealed that the combination of BET inhibitors with sunitinib causes melanoma suppression in vivo. Altogether, these findings suggest that BET inhibitor-mediated GDF15 inhibition plays a critical role in enhancing sunitinib sensitivity in melanoma, indicating that BET inhibitors synergize with sunitinib in melanoma.

Salsolinol Induces Parkinson's Disease Through Activating NLRP3-Dependent Pyroptosis and the Neuroprotective Effect of Acteoside.

Endogenous neurotoxin 1-methyl-6,7-dihydroxy-1,2,3,4-tetrahydroiso-quinoline (Salsolinol, SAL) is a dopamine metabolite that is toxic to dopaminergic neurons in vitro and in vivo, and is involved in the pathogenesis of Parkinson's disease (PD). However, the molecular mechanism by which SAL induces neurotoxicity in PD remains challenging for future investigations. This study found that SAL induced neurotoxicity in SH-SY5Y cells and mice. RNA sequencing (RNAseq) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to detect differentially expressed genes in SAL-treated SH-SY5Y cells. We found that NLR family pyrin domain-containing 3 (NLRP3)-dependent pyroptosis was enriched by SAL, which was validated by in vitro and in vivo SAL models. Further, NLRP3 inflammasome-related genes (ASC, NLRP3, active caspase 1, IL-1ß, and IL-18) were increased at the mRNA and protein level. Acteoside mitigates SAL-induced neurotoxicity by inhibiting NLRP3 inflammasome-related pyroptosis in in vitro and in vivo PD models. In summary, the present study suggests for the first time that NLRP3-dependent pyroptosis plays a role in the pathogenesis of SAL-induced PD, and acteoside mitigates SAL-induced pyroptosis-dependent neurotoxicity in in vitro and in vivo PD models. The present results demonstrated a new mechanism whereby SAL mediates neurotoxicity by activating NLRP3-dependent pyroptosis, further highlighting SAL-induced pyroptosis-dependent neurotoxicity as a potential therapeutic target in PD.

Virulence characteristics of Blumeria graminis f. sp. tritici and its genetic diversity by EST-SSR analyses.

Wheat powdery mildew, caused by Blumeria graminis f. sp. tritici (an obligate biotrophic pathogen) is a worldwide threat to wheat production that occurs over a wide geographic area in China. For monitoring genetic variation and virulence structure of Blumeria graminis f. sp. tritici in Liaoning, Heilongjiang, and Sichuan in 2015, 31 wheat lines with known Powdery mildew resistance genes and 2 EST-SSR markers were used to characterize the virulence and genetic diversity. Results indicated that 90% of all isolates were virulent on Pm3c, Pm3e, Pm3f, Pm4a, Pm5, Pm6 (Timgalen), Pm7, Pm16, Pm19, and Pm1 + 2 + 9 and 62.6% to 89.9% of isolates were virulent on Pm3a, Pm3b, Pm3d, Pm4b, Pm6 (Coker747), Pm8, Pm17, Pm20, Pm23, Pm30, Pm4 + 8, Pm5 + 6, Pm4b + mli, Pm2 + mld, Pm4 + 2X, Pm2 + 6. The Pm13 and PmXBD genes were effective against most collected isolates from Liaoning and Heilongjiang Provinces. Only Pm21 exhibited an immune infection response to all isolates. Furthermore, closely related isolates within each region were distinguished by cluster analyses using EST-SSR representing some gene exchanges and genetic relationships between the flora in Northeast China (Liaoning, Heilongjiang) and Sichuan. Only 45% of the isolates tested show a clear correlation between EST-SSR genetic polymorphisms and the frequency of virulence gene data. However, the EST-SSR polymorphism of isolated genes did not correspond to the virulence diversity of isolates in the single-gene lineage identification of hosts.

Targeting Ferroptosis by Ubiquitin System Enzymes: A Potential Therapeutic Strategy in Cancer.

Ferroptosis is a novel type of regulated cell death driven by the excessive accumulation of iron-dependent lipid peroxidation. Therapy-resistant tumor cells, particularly those in the mesenchymal-like state and prone to metastasis, are highly susceptible to ferroptosis, suggesting that induction of ferroptosis in tumor cells is a promising strategy for cancer therapy. Although ferroptosis is regulated at various levels, ubiquitination is key to post-translational regulation of ferroptotic cell death. E3 ubiquitin ligases (E3s) and deubiquitinating enzymes (DUBs) are the most remarkable ubiquitin system enzymes, whose dysregulation accounts for the progression of multiple cancers. E3s are involved in the attachment of ubiquitin to substrates for their degradation, and this process is reversed by DUBs. Accumulating evidence has highlighted the important role of ubiquitin system enzymes in regulating the sensitivity of ferroptosis. Herein, we will portray the regulatory networks of ferroptosis mediated by E3s or DUBs and discuss opportunities and challenges for incorporating this regulation into cancer therapy.

Identification of key somatic oncogenic mutation based on a confounder-free causal inference model.

Abnormal cell proliferation and epithelial-mesenchymal transition (EMT) are the essential events that induce cancer initiation and progression. A fundamental goal in cancer research is to develop an efficient method to detect mutational genes capable of driving cancer. Although several computational methods have been proposed to identify these key mutations, many of them focus on the association between genetic mutations and functional changes in relevant biological processes, but not their real causality. Causal effect inference provides a way to estimate the real induce effect of a certain mutation on vital biological processes of cancer initiation and progression, through addressing the confounder bias due to neutral mutations and unobserved latent variables. In this study, integrating genomic and transcriptomic data, we construct a novel causal inference model based on a deep variational autoencoder to identify key oncogenic somatic mutations. Applied to 10 cancer types, our method quantifies the causal effect of genetic mutations on cell proliferation and EMT by reducing both observed and unobserved confounding biases. The experimental results indicate that genes with higher mutation frequency do not necessarily mean they are more potent in inducing cancer and promoting cancer development. Moreover, our study fills a gap in the use of machine learning for causal inference to identify oncogenic mutations.

The risk of developing second primary malignancies among colorectal cancer patients.

BACKGROUND: The increasing number of young colorectal cancer (CRC) survivors has led to ongoing concerns about the risk of secondary primary malignancies (SPMs). Here, we intended to comprehensively explore the pooled standardized incidence rates (SIRs) for total and site-specific SPMs in CRC survivors with different restriction to lag period. METHODS: Pubmed, Embase, Cochrane Library, and Web of science databases were searched to identify any studies reporting the SIRs of SPM following CRC until August 2021. Total and site-specific SIRs with different restriction to lag period were pooled using fixed/random effect models. RESULTS: A total of 42 full-text publications with more than 1, 524, 236 CRC survivors and 166, 210 SPM patients were included in the meta-analysis. Pooled data showed an increased SIRs for all SPMs in CRC survivors with different restriction to lag period (no restriction to lag period, SIR = 1.15, 95% CI = [1.08-1.23]; 1-year lag, 1.16 [1.10-1.23]; 5-year lag, 1.18 [1.09-1.28]; 10-year lag, 1.24 [1.11-1.39]). The conclusions were consistent for neoplasms of colorectum, corpus uteri, and small intestine with different restriction to lag period. However, limited evidence was presented for associations between CRC survivors and SPM for prostate, breast (female), ovarian, stomach, urinary bladder, kidney, thyroid, bone and soft tissue. CONCLUSION: CRC survivors are associated with an increased risk of SPMs, especially neoplasms of colorectum, corpus uteri, and small intestine. Further studies should explore the risks for these neoplasms in CRC survivors, thus providing the reference for future follow-up care.

Clinical characteristics and outcomes of patients with COVID-19 and psoriasis.

To summarize the clinical characteristics and explore the role of treatment types in outcomes among psoriasis patients with coronavirus disease 2019 (COVID-19). The principal summary measures were pooled prevalence and risk ratio (RR) with 95% confidential interval (CI). R statistic software was used for all the analysis. A total of 19 studies including 4073 psoriasis patients with COVID-19 were eligible for the meta-analysis. The overall hospitalization rate is about 20.2% (95% CI: 12.7%-28.7%), and changed to be 18.0% (95% CI: 9.9%-27.6%) or 14.1% (95% CI: 5.9%-24.6%) after systemic or biologic treatment. Moreover, the overall fatality rate is 1.5% (95% CI: 0.4%-3.0%), and turned to be 0.7% (95% CI: 0%-2.0%) or 0.5% (95% CI: 0%-2.2%) after systemic or biologic therapy. Notably, a lower hospitalization RR was found in patients receiving biologic therapy than those receiving other treatments (RR = 0.62, 95% CI: 0.42-0.94). The results were consistent after sensitivity analysis and trim-and-fill analysis. Systemic, especially biologic therapy could lessen the clinical severity in psoriasis patients with COVID-19. Our finding will help to guide current recommendations and provide a reference for clinical decision-making.

Molecular Subtyping of Cancer Based on Robust Graph Neural Network and Multi-Omics Data Integration.

Accurate molecular subtypes prediction of cancer patients is significant for personalized cancer diagnosis and treatments. Large amount of multi-omics data and the advancement of data-driven methods are expected to facilitate molecular subtyping of cancer. Most existing machine learning-based methods usually classify samples according to single omics data, fail to integrate multi-omics data to learn comprehensive representations of the samples, and ignore that information transfer and aggregation among samples can better represent them and ultimately help in classification. We propose a novel framework named multi-omics graph convolutional network (M-GCN) for molecular subtyping based on robust graph convolutional networks integrating multi-omics data. We first apply the Hilbert-Schmidt independence criterion least absolute shrinkage and selection operator (HSIC Lasso) to select the molecular subtype-related transcriptomic features and then construct a sample-sample similarity graph with low noise by using these features. Next, we take the selected gene expression, single nucleotide variants (SNV), and copy number variation (CNV) data as input and learn the multi-view representations of samples. On this basis, a robust variant of graph convolutional network (GCN) model is finally developed to obtain samples' new representations by aggregating their subgraphs. Experimental results of breast and stomach cancer demonstrate that the classification performance of M-GCN is superior to other existing methods. Moreover, the identified subtype-specific biomarkers are highly consistent with current clinical understanding and promising to assist accurate diagnosis and targeted drug development.