Background The RhoA/Rho kinase pathway may participate in the pathogenesis of hypoxia and monocrotaline induced pulmonary hypertension. This study tested whether RhoA/Rho kinase pathway is involved in the pathogenesis of high flow induced pulmonary hypertension in rats. Methods Male Wistar rats (4 weeks) were randomly divided into 4 shunt groups, 4 treated groups and 4 control groups. Shunt and treated groups underwent left common carotid artery/external jugular vein shunt operation. Control groups underwent sham operation. Treated groups received fasudil treatment and the others received same dose of saline. At weeks 1, 2, 4 and 8 of the study, right ventricular systolic pressure was measured and blood gases were analysed to calculate Qp/Qs. The weight ratio of right ventricle to left ventricle plus septum and the mean percentage of medial wall thickness in moderate sized pulmonary arteries were obtained. RhoA activity in pulmonary arteries was detected using Rho activity assay reagent. Rho kinase activity was quantified by the extent of MYPT1 phosphorylation with Western blot. Proliferating cells were evaluated using proliferating cell nuclear antigen immunohistological staining. Results Carotid artery/jugular vein shunt resulted in high pulmonary blood flow, both an acute and a chronic elevation of right ventricular systolic pressure, significant medial wall thickening characterized by smooth muscle cells proliferation, right ventricular hypertrophy and increased activation of RhoA and Rho kinase. Fasudil treatment lowered pulmonary artery systolic pressure, suppressed pulmonary artery smooth muscle cells proliferation, attenuated pulmonary artery medial wall thickening and inhibited right ventricular hypertrophy together with significant suppression of Rho kinase activity but not Rho activity. Conclusions Activated RhoA/Rho kinase pathway is associated with both the acute pulmonary vasoconstriction and the chronic pulmonary artery remodelling of high flow induced pulmonary hypertension. Fasudil treatment could improve pulmonary hypertension by inhibiting Rho kinase activity. Chin Med J 2007;120(1):22–29
PIWI-interacting RNAs (piRNAs) are highly expressed in various cardiovascular diseases. However, their role in cardiomyocyte death caused by ischemia/reperfusion (I/R) injury, especially necroptosis, remains elusive. In this study, a heart necroptosis-associated piRNA (HNEAP) is found that regulates cardiomyocyte necroptosis by targeting DNA methyltransferase 1 (DNMT1)-mediated 5-methylcytosine (m
Complex signaling pathways are believed to be responsible for drug resistance. Drug combinations perturbing multiple signaling targets have the potential to reduce drug resistance. The large-scale multi-omic datasets and experimental drug combination synergistic score data are valuable resources to study mechanisms of synergy (MoS) to guide the development of precision drug combinations. However, signaling patterns of MoS are complex and remain unclear, and thus it is challenging to identify synergistic drug combinations in clinical. Herein, we proposed a novel integrative and interpretable graph AI model, DeepSignalingFlow, to uncover the MoS by integrating and mining multi-omic data. The major innovation is that we uncover MoS by modeling the signaling flow from multi-omic features of essential disease proteins to the drug targets, which has not been introduced by the existing models. The model performance was assessed utilizing four distinct drug combination synergy evaluation datasets, i.e., NCI ALMANAC, O'Neil, DrugComb, and DrugCombDB. The comparison results showed that the proposed model outperformed existing graph AI models in terms of synergy score prediction, and can interpret MoS using the core signaling flows. The code is publicly accessible via Github: https://github.com/FuhaiLiAiLab/DeepSignalingFlow
Abstract Background The recent emergence of high-throughput automated image acquisition technologies has forever changed how cell biologists collect and analyze data. Historically, the interpretation of cellular phenotypes in different experimental conditions has been dependent upon the expert opinions of well-trained biologists. Such qualitative analysis is particularly effective in detecting subtle, but important, deviations in phenotypes. However, while the rapid and continuing development of automated microscope-based technologies now facilitates the acquisition of trillions of cells in thousands of diverse experimental conditions, such as in the context of RNA interference (RNAi) or small-molecule screens, the massive size of these datasets precludes human analysis. Thus, the development of automated methods which aim to identify novel and biological relevant phenotypes online is one of the major challenges in high-throughput image-based screening. Ideally, phenotype discovery methods should be designed to utilize prior/existing information and tackle three challenging tasks, i.e. restoring pre-defined biological meaningful phenotypes, differentiating novel phenotypes from known ones and clarifying novel phenotypes from each other. Arbitrarily extracted information causes biased analysis, while combining the complete existing datasets with each new image is intractable in high-throughput screens. Results Here we present the design and implementation of a novel and robust online phenotype discovery method with broad applicability that can be used in diverse experimental contexts, especially high-throughput RNAi screens. This method features phenotype modelling and iterative cluster merging using improved gap statistics. A Gaussian Mixture Model (GMM) is employed to estimate the distribution of each existing phenotype, and then used as reference distribution in gap statistics. This method is broadly applicable to a number of different types of image-based datasets derived from a wide spectrum of experimental conditions and is suitable to adaptively process new images which are continuously added to existing datasets. Validations were carried out on different dataset, including published RNAi screening using Drosophila embryos [Additional files 1, 2], dataset for cell cycle phase identification using HeLa cells [Additional files 1, 3, 4] and synthetic dataset using polygons, our methods tackled three aforementioned tasks effectively with an accuracy range of 85%–90%. When our method is implemented in the context of a Drosophila genome-scale RNAi image-based screening of cultured cells aimed to identifying the contribution of individual genes towards the regulation of cell-shape, it efficiently discovers meaningful new phenotypes and provides novel biological insight. We also propose a two-step procedure to modify the novelty detection method based on one-class SVM, so that it can be used to online phenotype discovery. In different conditions, we compared the SVM based method with our method using various datasets and our methods consistently outperformed SVM based method in at least two of three tasks by 2% to 5%. These results demonstrate that our methods can be used to better identify novel phenotypes in image-based datasets from a wide range of conditions and organisms. Conclusion We demonstrate that our method can detect various novel phenotypes effectively in complex datasets. Experiment results also validate that our method performs consistently under different order of image input, variation of starting conditions including the number and composition of existing phenotypes, and dataset from different screens. In our findings, the proposed method is suitable for online phenotype discovery in diverse high-throughput image-based genetic and chemical screens.
Mutation of presenilin-1 (PS-1) gene is known to be closely related to the familial Alzheimer disease (FAD), whereas oxidative stress and de-regulation of calcium homeostasis are two important phases. To better understand the biological mechanisms between the two important phases, we present a computerized system of automated high content, time-lapse cellular image sequence analysis to quantify calcium signals inside the single cells with PS-1. The proposed system consists of three major components: cell segmentation, image displacement correction, and calcium signal extraction, denoising, and oscillation detection; some new algorithms are implemented. The validation indicates that the proposed system is effective and reliable. Two patterns of calcium signals: stable and temporal (oscillation) are found in this study. The experimental results confirm the hypothesis that H 2 O 2 has regulatory effect on the calcium signal mediated by PS-1.
In this editorial, we summarize the 2023 International Conference on Intelligent Biology and Medicine (ICIBM 2023) conference which was held on July 16-19, 2023 in Tampa, Florida, USA. We then briefly describe the nine research articles included in this special issue. ICIBM 2023 scientific program included four tutorials and workshops, four keynote lectures, four eminent scholars' presentations, 11 concurrent scientific sessions, and a poster session. We had total of 88 scientific oral presentations, including 62 regular oral presentations and 26 flash talks, as well as 46 poster presentations. The topics of these presentations covered artificial intelligence (AI), data sciences, bioinformatics, computational biology, genomics, biomedical informatics, among others. This special issue included nine peer reviewed manuscripts selected from those submitted to our conference. These articles cover a range of topics on the methods and applications of ML and AI models in the biomedical domain.
Recently, large-scale scRNA-seq datasets have been generated to understand the complex signaling mechanisms within the microenvironment of Alzheimer’s Disease (AD), which are critical for identifying novel therapeutic targets and precision medicine. However, the background signaling networks are highly complex and interactive. It remains challenging to infer the core intra- and inter-multi-cell signaling communication networks using scRNA-seq data. In this study, we introduced a novel graph transformer model, PathFinder, to infer multi-cell intra- and inter-cellular signaling pathways and communications among multi-cell types. Compared with existing models, the novel and unique design of PathFinder is based on the divide-and-conquer strategy. This model divides complex signaling networks into signaling paths, which are then scored and ranked using a novel graph transformer architecture to infer intra- and inter-cell signaling communications. We evaluated the performance of PathFinder using two scRNA-seq data cohorts. The first cohort is an APOE4 genotype-specific AD, and the second is a human cirrhosis cohort. The evaluation confirms the promising potential of using PathFinder as a general signaling network inference model.
The Coronavirus Disease 2019 (COVID-19) pandemic has infected >5.5 million people globally and carries a relatively high mortality rate. Currently, there is no effective vaccine or therapy approved for the treatment COVID-19, though many such therapeutics are being evaluated in clinical trials. Using the unique profiles of altered gene expression found in NHBE, A549_ACE2 and Calu3 human lung epithelial cells after Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, we conducted a computational analysis to identify dysfunctional signaling pathways within infected cells. Using these data, potential therapeutic targets were identified and matched with existing medications to develop a theoretical approach to pharmacologic repurposing and inform clinical trial decisions. Specifically, signaling pathways upregulated by SARS-CoV-2 infection and their associated gene ontologies (GOs) were extracted and clustered to generate 10 super-GOs. The genes associated with the identified signaling pathways and the super-GOs were used as gene signatures to identify drugs that can potentially inhibit these signaling cascades. In these results, interesting signaling pathways and GOs such as JAK-STAT, IRF7-NFkB signaling, and MYD88/CXCR6 immune signaling were identified. Many drugs were identified with downstream inhibition of these signaling pathways and GOs, and many of which are currently undergoing clinical trial evaluation. The analysis results can be potentially helpful to facilitate the future clinical trial design. Rigorous clinical trial and experimental validations are needed to validate these results, considering the limitation of pure computational data analysis based on the noisy and limited data. some of these drugs might have harmful effects and cautions.
The objective was to investigate the prevalence of attention-deficit hyperactivity disorder (ADHD) in children with frontal lobe epilepsy and related factors. The medical records of 190 children diagnosed with frontal lobe epilepsy at Qilu Hospital of Shandong University between 2006 and 2011 were retrospectively collected, and a follow-up analysis of the prevalence of ADHD in these children was conducted. Of the 161 children with an effective follow-up, 59.0% (95/161) with frontal lobe epilepsy suffered from ADHD as well. Analysis of epilepsy and ADHD-related factors indicated that the incidence of ADHD was 89.4% (76/85) in children with abnormal electroencephalogram (EEG) discharges on the most recent EEG, which was significantly higher than the ADHD incidence of 25% (19/76) in children with normal readings on the most recent EEG ( P < .01). Children with frontal lobe epilepsy have a high incidence of ADHD. Sustained abnormal discharge on the electroencephalogram is associated with increased comorbidity of ADHD with frontal lobe epilepsy.
Single-cell RNA sequencing (scRNA-seq) is a powerful technology to investigate the transcriptional programs in stromal, immune, and disease cells, like tumor cells or neurons within the Alzheimer’s Disease (AD) brain or tumor microenvironment (ME) or niche. Cell-cell communications within ME play important roles in disease progression and immunotherapy response and are novel and critical therapeutic targets. Though many tools of scRNA-seq analysis have been developed to investigate the heterogeneity and sub-populations of cells, few were designed for uncovering cell-cell communications of ME and predicting the potentially effective drugs to inhibit the communications. Moreover, the data analysis processes of discovering signaling communication networks and effective drugs using scRNA-seq data are complex and involve a set of critical analysis processes and external supportive data resources, which are difficult for researchers who have no strong computational background and training in scRNA-seq data analysis. To address these challenges, in this study, we developed a novel open-source computational tool, sc2MeNetDrug ( https://fuhaililab.github.io/sc2MeNetDrug/ ). It was specifically designed using scRNA-seq data to identify cell types within disease MEs, uncover the dysfunctional signaling pathways within individual cell types and interactions among different cell types, and predict effective drugs that can potentially disrupt cell-cell signaling communications. sc2MeNetDrug provided a user-friendly graphical user interface to encapsulate the data analysis modules, which can facilitate the scRNA-seq data-based discovery of novel inter-cell signaling communications and novel therapeutic regimens.
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