sc2MeNetDrug: A computational tool to uncover inter-cell signaling targets and identify relevant drugs based on single cell RNA-seq data.

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.

Factors predicting resolution of left ventricular thrombus in different time windows after myocardial infarction.

BACKGROUND: Left ventricular thrombus (LVT) is a serious complication after myocardial infarction. However, due to its asymptomatic nature, early detection is challenging. We aimed to explore the differences in clinical correlates of LVT found in acute to subacute and chronic phases of myocardial infarction. METHODS: We collected data from 153 patients who were diagnosed with LVT after myocardial infarction at the Affiliated Hospital of Qingdao University from January 2013 to December 2022. Baseline information, inflammatory markers, transthoracic echocardiograph (TTE) data and other clinical correlates were collected. Patients were categorized into acute to subacute phase group (< 30 days) and chronic phase group (30 days and after) according to the time at which echocardiograph was performed. The resolution of thrombus within 90 days is regarded as the primary endpoint event. We fitted logistic regression models to relating clinical correlates with phase-specific thrombus resolution. RESULTS: For acute to subacute phase thrombus patients: C-reactive protein levels (OR: 0.95, 95% CI: 0.918-0.983, p = 0.003) were significantly associated with thrombus resolution. For chronic phase thrombus patients: anticoagulant treatment was associated with 5.717-fold odds of thrombus resolution (OR: 5.717, 95% CI: 1.543-21.18, p = 0.009). CONCLUSIONS: Higher levels of CRP were associated with lower likelihood of LVT resolution in acute phase myocardial infarction; Anticoagulant therapy is still needed for thrombus in the chronic stage of myocardial infarction.

Circular RNA FEACR inhibits ferroptosis and alleviates myocardial ischemia/reperfusion injury by interacting with NAMPT.

BACKGROUND: Emerging research has reported that circular RNAs (circRNAs) play important roles in cardiac cell death after myocardial ischemia and reperfusion (I/R). Ferroptosis, a new form of cell death discovered in recent years, has been proven to participate in the regulation of myocardial I/R. This study used circRNA sequencing to explore the key circRNA in the regulation of cardiac ferroptosis after I/R and study the mechanisms of potential circRNA function. METHODS: We performed circRNA sequencing to explore circRNAs differentially expressed after myocardial I/R. We used quantitative polymerase chain reactions to determine the circRNA expression in different tissues and detect the circRNA subcellular localization in the cardiomyocyte. Gain- and loss-of-function experiments were aimed to examine the function of circRNAs in cardiomyocyte ferroptosis and cardiac tissue damage after myocardial I/R. RNA pull-down was applied to explore proteins interacting with circRNA. RESULTS: Here, we identified a ferroptosis-associated circRNA (FEACR) that has an underlying regulatory role in cardiomyocyte ferroptosis. FEACR overexpression suppressed I/R-induced myocardial infarction and ameliorated cardiac function. FEACR inhibition induces ferroptosis in cardiomyocytes and FEACR overexpression inhibits hypoxia and reoxygenation-induced ferroptosis. Mechanistically, FEACR directly bound to nicotinamide phosphoribosyltransferase (NAMPT) and enhanced the protein stability of NAMPT, which increased NAMPT-dependent Sirtuin1 (Sirt1) expression, which promoted the transcriptional activity of forkhead box protein O1 (FOXO1) by reducing FOXO1 acetylation levels. FOXO1 further upregulated the transcription of ferritin heavy chain 1 (Fth1), a ferroptosis suppressor, which resulted in the inhibition of cardiomyocyte ferroptosis. CONCLUSIONS: Our finding reveals that the circRNA FEACR-mediated NAMPT-Sirt1-FOXO1-FTH1 signaling axis participates in the regulation of cardiomyocyte ferroptosis and protects the heart function against I/R injury. Thus, FEACR and its downstream factors could be novel targets for alleviating ferroptosis-related myocardial injury in ischemic heart diseases.

Ablation for atrial fibrillation improves the outcomes in patients with heart failure with preserved ejection fraction.

AIMS: Patients with heart failure with preserved ejection fraction (HFpEF) and atrial fibrillation (AF) have worse clinical outcomes than those with sinus rhythm (SR). We aim to investigate whether maintaining SR in patients with HFpEF through a strategy such as AF ablation would improve outcomes. METHODS AND RESULTS: This is a cohort study that analysed 1034 patients (median age 69 [63-76] years, 46.2% [478/1034] female) with HFpEF and AF. Of these, 392 patients who underwent first-time AF ablation were assigned to the ablation group, and the remaining 642 patients, who received only medical therapy, were assigned to the no ablation group. The primary endpoint was a composite of all-cause death or rehospitalization for worsening heart failure. After a median follow-up of 39 months, the cumulative incidence of the primary endpoint was significantly lower in the ablation group compared to the no ablation group (adjusted hazard ratio [HR], 0.55 [95% CI, 0.37-0.82], P = 0.003) in the propensity score-matched model. Secondary endpoint analysis showed that the benefit of AF ablation was mainly driven by a reduction in rehospitalization for worsening heart failure (adjusted HR, 0.52 [95% CI, 0.34-0.80], P = 0.003). Patients in the ablation group showed a 33% relative decrease in atrial tachycardia/AF recurrence compared to the no ablation group (adjusted HR, 0.67 [95% CI, 0.54-0.84], P < 0.001). CONCLUSION: Among patients with HFpEF and AF, the strategy of AF ablation to maintain SR was associated with a lower risk of the composite outcome of all-cause death or rehospitalization for worsening heart failure.

Motor Assessment With the STEGA iPad App to Measure Handwriting in Children.

IMPORTANCE: Handwriting and the fine motor control (hand and fingers) underlying it are key indicators of numerous motor disorders, especially among children. However, current assessment methods are expensive, slow, and subjective, leading to a lack of knowledge about the relationship between handwriting and motor control. OBJECTIVE: To develop and validate the iPad precision drawing app Standardized Tracing Evaluation and Grapheme Assessment (STEGA) to enable rapid quantitative assessment of fine motor control and handwriting. DESIGN: Cross-sectional, single-arm observational study. SETTING: Academic research institution. PARTICIPANTS: Fifty-seven typically developing right-handed children ages 9 to 12 yr with knowledge of cursive. OUTCOMES AND MEASURES: Predicted quality, measured as the correlation between handwriting letter legibility (Evaluation Tool of Children's Handwriting-Cursive [ETCH-C]) and predicted legibility (calculated from STEGA's 120 Hz, nine-variable data). RESULTS: STEGA successfully predicted handwriting (r2 = .437, p < .001) using a support vector regression method. Angular error was the most important aspect of STEGA performance. STEGA was much faster to administer than the ETCH-C (M = 6.7 min, SD = 1.3, versus M = 19.7 min, SD = 5.2). CONCLUSIONS AND RELEVANCE: Assessment of motor control (and especially pen direction control) may provide a meaningful, objective way to assess handwriting. Future studies are needed to validate STEGA with a wider age range, but the initial results indicate that STEGA can provide the first rapid, quantitative, high-resolution, telehealth-capable assessment of the motor control that underpins handwriting. What This Article Adds: The ability to control pen direction may be the most important motor skill for successful handwriting. STEGA may provide the first criterion standard for the fine motor control skills that underpin handwriting, suitable for rehabilitation research and practice.

MicroRNA-575 acts as a novel oncogene via targeting multiple signaling pathways in glioblastoma.

PURPOSE: Glioblastoma (GBM) patients currently face poor survival outcomes with an average survival period of <15 months, while only 3-5% of patients survive longer than 36 months. Although the mechanisms of tumorigenesis are still being elucidated, miRNAs are promising candidates to explore as novel and prognostic biomarkers in GBM. In this study, we identified the association between miR-575 expression and overall survival (OS) of primary GBM patients and undertook functional studies to discern the contribution of miR-575 to GBM tumorigenesis. METHODS: Total RNAs were isolated from 254 FFPE GBM tumor samples and miR expression was assayed (simultaneously) using NanoString Technologies. To determine the association between miR-575 and patients' prognosis, Kaplan-Meier, univariable and multivariable Cox regression analyses were performed. Cell proliferation, colony formation, migration assays were conducted to investigate the function of miR-575 in vitro and in vivo. In silico target gene network analysis was performed to identify the putative targets of miR-575 in GBM, which were further verified by luciferase reporter assay, as well as qPCR and immunoblotting. RESULTS: Our clinical data (n = 254) show that miR-575 is associated with worse GBM OS by univariable analysis (UVA, HR = 1.27, p-value<0.001) and multivariable (MVA, HR = 1.23, p = 0.007) analysis incorporating critical clinical variables. Functional studies indicated that overexpression of miR-575 significantly increased cell proliferation and migration of GBM cells in vitro, as well as tumor growth in vivo. Subsequent in silico target gene network and mechanistic studies identified CDKN1B/p27 and PTEN, as potential targets of miR-575 in GBM. MicroRNA-575 can also regulate the activity of AKT and ERK pathways in GBM. CONCLUSION: miR-575 has prognostic value in GBM, with higher expression associating with worse OS of patients, and contributes to GBM tumorigenesis by regulating multiple signaling pathways in GBM.

Investigating the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model.

BACKGROUND: Survival analysis is an important part of cancer studies. In addition to the existing Cox proportional hazards model, deep learning models have recently been proposed in survival prediction, which directly integrates multi-omics data of a large number of genes using the fully connected dense deep neural network layers, which are hard to interpret. On the other hand, cancer signaling pathways are important and interpretable concepts that define the signaling cascades regulating cancer development and drug resistance. Thus, it is important to investigate potential associations between patient survival and individual signaling pathways, which can help domain experts to understand deep learning models making specific predictions. RESULTS: In this exploratory study, we proposed to investigate the relevance and influence of a set of core cancer signaling pathways in the survival analysis of cancer patients. Specifically, we built a simplified and partially biologically meaningful deep neural network, DeepSigSurvNet, for survival prediction. In the model, the gene expression and copy number data of 1967 genes from 46 major signaling pathways were integrated in the model. We applied the model to four types of cancer and investigated the influence of the 46 signaling pathways in the cancers. Interestingly, the interpretable analysis identified the distinct patterns of these signaling pathways, which are helpful in understanding the relevance of signaling pathways in terms of their application to the prediction of cancer patients' survival time. These highly relevant signaling pathways, when combined with other essential signaling pathways inhibitors, can be novel targets for drug and drug combination prediction to improve cancer patients' survival time. CONCLUSION: The proposed DeepSigSurvNet model can facilitate the understanding of the implications of signaling pathways on cancer patients' survival by integrating multi-omics data and clinical factors.

Analysis of changes in circular RNA expression and construction of ceRNA networks in human dilated cardiomyopathy.

Dilated cardiomyopathy (DCM) is a severe life-threatening disease worldwide, and the underlying mechanisms remain unclear. Circular RNAs (circRNAs) have been reported to play important roles in various cardiovascular diseases and can function as competitive endogenous RNAs (ceRNAs). However, their role in human DCM has not been fully elucidated. In the present study, heart samples from DCM patients and healthy controls were used to identify circRNAs by RNA sequencing. Real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was conducted to validate differentially expressed circRNAs and mRNAs. A total of 9585 circRNAs and 22050 mRNAs were detected in the two groups. Overall, 213 circRNAs and 617 mRNAs were significantly up-regulated in the DCM group compared with the control group. Similarly, 85 circRNAs and 1125 mRNAs were significantly down-regulated. According to the ceRNA theory, circRNAs can indirectly interact with mRNAs by directly binding to microRNAs (miRNAs), and circRNAs and mRNAs should be concurrently either up-regulated or down-regulated. Based on this theory, we constructed two circRNA-miRNA-mRNA networks by using the RNA sequencing data and prediction by proprietary software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to probe the potential functions of differentially expressed circRNAs. In conclusion, this study revealed that the expression of cardiac circRNAs was altered in human DCM and explored the potential functions of circRNAs by constructing ceRNA networks. These findings provide a foundation for future studies of circRNAs in DCM.

Increased ratio of sST2/LVMI predicted cardiovascular mortality and heart failure rehospitalization in heart failure with reduced ejection fraction patients: a prospective cohort study.

BACKGROUND: Inflammation is one of the principal triggering mechanisms for left ventricular fibrosis and remodeling in heart failure, leading to adverse clinical outcomes. Soluble suppression of tumorigenicity 2 (sST2), a member of the interleukin-1 receptor family, is assumed to play a significant role in the fibrotic response to inflammation. Left ventricular mass index (LVMI) is a parameter of the prefibrotic inflammatory phase of heart failure preceding remodeling. The present study aimed to investigate the prognostic value of the sST2/LVMI ratio in heart failure with reduced ejection fraction. METHODS: This was a prospective cohort study. A total of 45 consecutive patients with heart failure with reduced ejection fraction, treated between September 2015 and December 2016, were enrolled. The sST2/LVMI ratio was measured at baseline. The primary endpoint was a composite of cardiovascular mortality and readmission for heart failure. The prognostic impact of the sST2/LVMI ratio was evaluated using a multivariable Cox proportional hazards regression model. RESULTS: Forty-five patients were enrolled in this study. Their average age was 48 ± 14 years, and approximately 20% of them were men. Patients were followed for 9 months, during which the primary outcome occurred in 15 patients. Kaplan-Meier analysis showed that patients with a high sST2/LVMI ratio (≥ 0.39) had shorter event-free survival than those with intermediate (between 0.39 and 0.24) and low ratios (< 0.24) (log-rank, P = 0.022). The fully adjusted multivariable Cox regression analysis showed that the sST2/LVMI ratio was positively associated with the composite outcome in patients with heart failure with reduced ejection fraction after adjusting for confounders (hazard ratio 1.64, 95% confidence interval 1.06 to 2.54). By subgroup analysis, a stronger association was found with age between 40 and 55 years, systolic blood pressure < 115 or ≥ 129 mmHg, diastolic blood pressure < 74 mmHg, hematocrit < 44.5%, and interventricular septum thickness ≥ 8.5 mm. CONCLUSION: In patients with heart failure with reduced ejection fraction, the relationship between the sST2/LVMI ratio and the composite outcome was linear. A higher baseline ratio of sST2/LVMI was associated with an increased risk of cardiovascular mortality and heart failure rehospitalization in the short-term follow-up.

Epithelial-to-mesenchymal transition is not required for lung metastasis but contributes to chemoresistance.

The role of epithelial-to-mesenchymal transition (EMT) in metastasis is a longstanding source of debate, largely owing to an inability to monitor transient and reversible EMT phenotypes in vivo. Here we establish an EMT lineage-tracing system to monitor this process in mice, using a mesenchymal-specific Cre-mediated fluorescent marker switch system in spontaneous breast-to-lung metastasis models. We show that within a predominantly epithelial primary tumour, a small proportion of tumour cells undergo EMT. Notably, lung metastases mainly consist of non-EMT tumour cells that maintain their epithelial phenotype. Inhibiting EMT by overexpressing the microRNA miR-200 does not affect lung metastasis development. However, EMT cells significantly contribute to recurrent lung metastasis formation after chemotherapy. These cells survived cyclophosphamide treatment owing to reduced proliferation, apoptotic tolerance and increased expression of chemoresistance-related genes. Overexpression of miR-200 abrogated this resistance. This study suggests the potential of an EMT-targeting strategy, in conjunction with conventional chemotherapies, for breast cancer treatment.

Computational analysis to repurpose drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2.

BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic has infected over 10 million people globally with a relatively high mortality rate. There are many therapeutics undergoing clinical trials, but there is no effective vaccine or therapy for treatment thus far. After affected by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), molecular signaling pathways of host cells play critical roles during the life cycle of SARS-CoV-2. Thus, it is significant to identify the involved molecular signaling pathways within the host cells. Drugs targeting these molecular signaling pathways could be potentially effective for COVID-19 treatment. METHODS: In this study, we developed a novel integrative analysis approach to identify the related molecular signaling pathways within host cells, and repurposed drugs as potentially effective treatments for COVID-19, based on the transcriptional response of host cells. RESULTS: We identified activated signaling pathways associated with the infection caused SARS-CoV-2 in human lung epithelial cells through integrative analysis. Then, the activated gene ontologies (GOs) and super GOs were identified. Signaling pathways and GOs such as MAPK, JNK, STAT, ERK, JAK-STAT, IRF7-NFkB signaling, and MYD88/CXCR6 immune signaling were particularly activated. Based on the identified signaling pathways and GOs, a set of potentially effective drugs were repurposed by integrating the drug-target and reverse gene expression data resources. In addition to many drugs being evaluated in clinical trials, the dexamethasone was top-ranked in the prediction, which was the first reported drug to be able to significantly reduce the death rate of COVID-19 patients receiving respiratory support. CONCLUSIONS: The integrative genomics data analysis and results can be helpful to understand the associated molecular signaling pathways within host cells, and facilitate the discovery of effective drugs for COVID-19 treatment.

Functional characterization of four ATP-binding cassette transporter A3 gene (ABCA3) variants.

ABCA3 transports phospholipids across lamellar body membranes in pulmonary alveolar type II cells and is required for surfactant assembly. Rare, biallelic, pathogenic ABCA3 variants result in lethal neonatal respiratory distress syndrome and childhood interstitial lung disease. Qualitative functional characterization of ABCA3 missense variants suggests two pathogenic classes: disrupted intracellular trafficking (type I mutant) or impaired ATPase-mediated phospholipid transport into the lamellar bodies (type II mutant). We qualitatively compared wild-type (WT-ABCA3) with four uncharacterized ABCA3 variants (c.418A>C;p.Asn140His, c.3609_3611delCTT;p.Phe1203del, c.3784A>G;p.Ser1262Gly, and c.4195G>A;p.Val1399Met) in A549 cells using protein processing, colocalization with intracellular organelles, lamellar body ultrastructure, and ATPase activity. We quantitatively measured lamellar body-like vesicle diameter and intracellular ABCA3 trafficking using fluorescence-based colocalization. Three ABCA3 variants (p.Asn140His, p.Ser1262Gly, and p.Val1399Met) were processed and trafficked normally and demonstrated well-organized lamellar body-like vesicles, but had reduced ATPase activity consistent with type II mutants. P.Phe1203del was processed normally, had reduced ATPase activity, and well-organized lamellar body-like vesicles, but quantitatively colocalized with both endoplasmic reticulum and lysosomal markers, an intermediate phenotype suggesting disruption of both intracellular trafficking and phospholipid transport. All ABCA3 mutants demonstrated mean vesicle diameters smaller than WT-ABCA3. Qualitative and quantitative functional characterization of ABCA3 variants informs mechanisms of pathogenicity.

Functional Genomics of ABCA3 Variants.

Rare or private, biallelic variants in the ABCA3 (ATP-binding cassette transporter A3) gene are the most common monogenic cause of lethal neonatal respiratory failure and childhood interstitial lung disease. Functional characterization of fewer than 10% of over 200 disease-associated ABCA3 variants (majority missense) suggests either disruption of ABCA3 protein trafficking (type I) or of ATPase-mediated phospholipid transport (type II). Therapies remain limited and nonspecific. A scalable platform is required for functional characterization of ABCA3 variants and discovery of pharmacologic correctors. To address this need, we first silenced the endogenous ABCA3 locus in A549 cells with CRISPR/Cas9 genome editing. Next, to generate a parent cell line (A549/ABCA3-/-) with a single recombination target site for genomic integration and stable expression of individual ABCA3 missense variant cDNAs, we used lentiviral-mediated integration of a LoxFAS cassette, FACS, and dilutional cloning. To assess the fidelity of this cell-based model, we compared functional characterization (ABCA3 protein processing, ABCA3 immunofluorescence colocalization with intracellular markers, ultrastructural vesicle phenotype) of two individual ABCA3 mutants (type I mutant, p.L101P; type II mutant, p.E292V) in A549/ABCA3-/- cells and in both A549 cells and primary, human alveolar type II cells that transiently express each cDNA after adenoviral-mediated transduction. We also confirmed pharmacologic rescue of ABCA3 variant-encoded mistrafficking and vesicle diameter in A549/ABCA3-/- cells that express p.G1421R (type I mutant). A549/ABCA3-/- cells provide a scalable, genetically versatile, physiologically relevant functional genomics platform for discovery of variant-specific mechanisms that disrupt ABCA3 function and for screening of potential ABCA3 pharmacologic correctors.

Influence of rutin on the effects of neonatal cigarette smoke exposure-induced exacerbated MMP-9 expression, Th17 cytokines and NF-κB/iNOS-mediated inflammatory responses in asthmatic mice model.

Allergic asthma is one of the most enduring diseases of the airway. The T-helper cells and regulatory T-cells are critically involved in inflammatory responses, mucus hypersecretion, airway remodelling and in airway hyper-responsiveness. Cigarette smoke (CS) has been found to aggravate inflammatory responses in asthma. Though currently employed drugs are effective, associated side effects demand identification and development of novel drugs with negligible or no adverse effects. Rutin, plant-derived flavonoid has been found to possess antioxidant and anti-inflammatory effects. We investigated the ability of rutin to modulate T-cells and inhibit inflammation in experimentally-induced asthma in cigarette smoke exposed mice. Separate groups of neonatal mice were exposed to CS for 10 days from post-natal days 2 to 11. After 2 weeks, the mice were sensitized and challenged with ovalbumin (OVA). Treatment group were given rutin (37.5 or 75 mg/kg body weight) during OVA sensitization and challenge. Rutin treatment was found to significantly inhibit cellular infiltration in the airways and Th2 and Th17 cytokine levels as well. Flow cytometry revealed effectively raised CD4+CD25+Fox3+ Treg cells and supressed Th17 cell population on rutin treatment. Airway hyper-responsiveness observed following CS and OVA challenge were inhibited by rutin. NF-κB and iNOS, chief regulators of inflammatory responses robustly activated by CS and OVA were down-regulated by rutin. Rutin also inhibited the expression of matrix metalloproteinase 9, thereby aiding in prevention of airway remodelling in asthma thereby revealing to be a potent candidate in asthma therapy.

The Synthesis and Photoluminescent Properties of CaMoO4:Eu³âº Nanocrystals by a Soft Chemical Route.

In this paper, the CaMoO4:Eu3+ phosphors were prepared by a simple hydrothermal method assisted by the citric acid as the surfactant, and characterized by X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), and fluorescent spectrophotometry. The results of XRD show that the as-prepared samples are single phase. The process of the Ostwald ripening is controlled by the content of the citric acid in the hydrothermal reaction. The pH value of the precursor affects the shift of the charge transition band (CTB) in the excitation spectra. The reaction condition can strongly affect the luminescent intensity of the samples.

A computational method for clinically relevant cancer stratification and driver mutation module discovery using personal genomics profiles.

BACKGROUND: Personalized genomics instability, e.g., somatic mutations, is believed to contribute to the heterogeneous drug responses in patient cohorts. However, it is difficult to discover personalized driver mutations that are predictive of drug sensitivity owing to diverse and complex mutations of individual patients. To circumvent this problem, a novel computational method is presented to discover potential drug sensitivity relevant cancer subtypes and identify driver mutation modules of individual subtypes by coupling differentially expressed genes (DEGs) based subtyping analysis with the driver mutation network analysis. RESULTS: The proposed method was applied to breast cancer and lung cancer samples available from The Cancer Genome Atlas (TCGA). Cancer subtypes were uncovered with significantly different survival rates, and more interestingly, distinct driver mutation modules were also discovered among different subtypes, indicating the potential mechanism of heterogeneous drug sensitivity. CONCLUSIONS: The research findings can be used to help guide the repurposing of known drugs and their combinations in order to target these dysfunctional modules and their downstream signaling effectively for achieving personalized or precision medicine treatment.

DrugComboRanker: drug combination discovery based on target network analysis.

MOTIVATION: Currently there are no curative anticancer drugs, and drug resistance is often acquired after drug treatment. One of the reasons is that cancers are complex diseases, regulated by multiple signaling pathways and cross talks among the pathways. It is expected that drug combinations can reduce drug resistance and improve patients' outcomes. In clinical practice, the ideal and feasible drug combinations are combinations of existing Food and Drug Administration-approved drugs or bioactive compounds that are already used on patients or have entered clinical trials and passed safety tests. These drug combinations could directly be used on patients with less concern of toxic effects. However, there is so far no effective computational approach to search effective drug combinations from the enormous number of possibilities. RESULTS: In this study, we propose a novel systematic computational tool DRUGCOMBORANKER: to prioritize synergistic drug combinations and uncover their mechanisms of action. We first build a drug functional network based on their genomic profiles, and partition the network into numerous drug network communities by using a Bayesian non-negative matrix factorization approach. As drugs within overlapping community share common mechanisms of action, we next uncover potential targets of drugs by applying a recommendation system on drug communities. We meanwhile build disease-specific signaling networks based on patients' genomic profiles and interactome data. We then identify drug combinations by searching drugs whose targets are enriched in the complementary signaling modules of the disease signaling network. The novel method was evaluated on lung adenocarcinoma and endocrine receptor positive breast cancer, and compared with other drug combination approaches. These case studies discovered a set of effective drug combinations top ranked in our prediction list, and mapped the drug targets on the disease signaling network to highlight the mechanisms of action of the drug combinations. AVAILABILITY AND IMPLEMENTATION: The program is available on request.

Chapter 17: bioimage informatics for systems pharmacology.

Recent advances in automated high-resolution fluorescence microscopy and robotic handling have made the systematic and cost effective study of diverse morphological changes within a large population of cells possible under a variety of perturbations, e.g., drugs, compounds, metal catalysts, RNA interference (RNAi). Cell population-based studies deviate from conventional microscopy studies on a few cells, and could provide stronger statistical power for drawing experimental observations and conclusions. However, it is challenging to manually extract and quantify phenotypic changes from the large amounts of complex image data generated. Thus, bioimage informatics approaches are needed to rapidly and objectively quantify and analyze the image data. This paper provides an overview of the bioimage informatics challenges and approaches in image-based studies for drug and target discovery. The concepts and capabilities of image-based screening are first illustrated by a few practical examples investigating different kinds of phenotypic changes caEditorsused by drugs, compounds, or RNAi. The bioimage analysis approaches, including object detection, segmentation, and tracking, are then described. Subsequently, the quantitative features, phenotype identification, and multidimensional profile analysis for profiling the effects of drugs and targets are summarized. Moreover, a number of publicly available software packages for bioimage informatics are listed for further reference. It is expected that this review will help readers, including those without bioimage informatics expertise, understand the capabilities, approaches, and tools of bioimage informatics and apply them to advance their own studies.

An in situ repositioning bone flap in retrosigmoid craniotomy for cranial reconstruction.

PURPOSE: Osteoplastic craniotomy in retrosigmoid approaches to the cerebellopontine angle region has been suggested as an alternative to traditional osteoclastic craniectomy. It is important both for prevention of postoperative complications and for cosmetic purposes. The authors investigated a safe and effective method of cranial reconstruction by repositioning a 1-step formed bone flap without bone window extension in lateral suboccipital craniotomy. METHODS: Twenty-three cases of various cerebellopontine angle pathologies, managed at the Department of Neurosurgery of the Third Affiliated Hospital of Sun Yat-Sen University from January 2011 to November 2011, had bone-flap repositioning at the original site after craniectomy during the same operative setting. All patients had preoperative three-dimensional computed tomography reconstruction of the skull. Anatomical markers were made in three-dimensional images, and individualized bone windows were designed. During operation, a 1-step formed bone flap was made according to the preoperative simulation scheme. After the intradural procedure was completed, the bone flap was repositioned and fixed in situ. RESULTS: No complication was observed. No delayed postcraniectomy pain occurred to any patient. Postoperative computed tomography scan of the skull showed good healing and shaping of the suboccipital bone at the surgical region. CONCLUSIONS: The present report provides acceptable results both clinically and radiologically. More comparative studies are required to evaluate possible advantages of this technique over osteoclastic craniectomy.

PathFinder: a novel graph transformer model to infer multi-cell intra- and inter-cellular signaling pathways and communications.

Recently, large-scale scRNA-seq datasets have been generated to understand the complex and poorly understood signaling mechanisms within microenvironment of Alzheimer's Disease (AD), which are critical for identifying novel therapeutic targets and precision medicine. Though a set of targets have been identified, however, it remains a challenging to infer the core intra- and inter-multi-cell signaling communication networks using the scRNA-seq data, considering the complex and highly interactive background signaling network. Herein, we introduced a novel graph transformer model, PathFinder, to infer multi-cell intra- and inter-cellular signaling pathways and signaling communications among multi-cell types. Compared with existing models, the novel and unique design of PathFinder is based on the divide-and-conquer strategy, which divides the complex signaling networks into signaling paths, and then score and rank them using a novel graph transformer architecture to infer the intra- and inter-cell signaling communications. We evaluated PathFinder using scRNA-seq data of APOE4-genotype specific AD mice models and identified novel APOE4 altered intra- and inter-cell interaction networks among neurons, astrocytes, and microglia. PathFinder is a general signaling network inference model and can be applied to other omics data-driven signaling network inference.