A jump in the atrioventricular conduction curve is not caused by a switch from fast pathway to slow pathway conduction.

Background: A jump in the atrioventricular (AV) conduction curve is the current clinical criterion of dual-pathway electrophysiology. However, the assumption that a jump indicates a switch from fast pathway (FP) to slow pathway (SP) conduction remains unconfirmed. This study was carried out to investigate whether a jump indeed indicates a transition from FP to SP conduction, and if not, what the potential cause is. Methods: Eighty-one experimental records from rabbit AV nodal preparations containing the following data were analyzed: 1) had at least one AV conduction curve and 2) had recording of His electrogram alternans (a validated new index of dual-pathway conduction). Most cases also had intracellular action potential recordings from the AV nodal fibers. Results: Of the 81 preparations, 11 (13%) showed a jump in the AV conduction curve. The jumps always occurred after the FP to SP transition. The FP-SP transition occurred at prematurity at 196 ± 39 ms versus the jump at 114 ± 13 ms (p < 0.001). The beat with a jump showed an SP-FP pattern in seven and an SP-SP pattern in four preparations. The jumps were always associated with and most likely caused by the formation of intranodal/nodal-atrial reentry and its subsequent conduction, rather than a switch from FP to SP conduction. Conclusion: Contrary to what has been assumed, a transition from FP to SP conduction does not produce a jump in the AV conduction curve. A jump in the AV conduction curve is most likely caused by the formation of intranodal/nodal-atrial reentry and its subsequent conduction.

Gastrointestinal Dysmotility Predisposes to Colitis through Regulation of Gut Microbial Composition and Linoleic Acid Metabolism.

Disrupted gastrointestinal (GI) motility is highly prevalent in patients with inflammatory bowel disease (IBD), but its potential causative role remains unknown. Herein, the role and the mechanism of impaired GI motility in colitis pathogenesis are investigated. Increased colonic mucosal inflammation is found in patients with chronic constipation (CC). Mice with GI dysmotility induced by genetic mutation or chemical insult exhibit increased susceptibility to colitis, dependent on the gut microbiota. GI dysmotility markedly decreases the abundance of Lactobacillus animlalis and increases the abundance of Akkermansia muciniphila. The reduction in L. animlalis, leads to the accumulation of linoleic acid due to compromised conversion to conjugated linoleic acid. The accumulation of linoleic acid inhibits Treg cell differentiation and increases colitis susceptibility via inducing macrophage infiltration and proinflammatory cytokine expression in macrophage. Lactobacillus and A. muciniphila abnormalities are also observed in CC and IBD patients, and mice receiving fecal microbiota from CC patients displayed an increased susceptibility to colitis. These findings suggest that GI dysmotility predisposes host to colitis development by modulating the composition of microbiota and facilitating linoleic acid accumulation. Targeted modulation of microbiota and linoleic acid metabolism may be promising to protect patients with motility disorder from intestinal inflammation.

Crop-GPA: an integrated platform of crop gene-phenotype associations.

With the increasing availability of large-scale biology data in crop plants, there is an urgent demand for a versatile platform that fully mines and utilizes the data for modern molecular breeding. We present Crop-GPA ( https://crop-gpa.aielab.net ), a comprehensive and functional open-source platform for crop gene-phenotype association data. The current Crop-GPA provides well-curated information on genes, phenotypes, and their associations (GPAs) to researchers through an intuitive interface, dynamic graphical visualizations, and efficient online tools. Two computational tools, GPA-BERT and GPA-GCN, are specifically developed and integrated into Crop-GPA, facilitating the automatic extraction of gene-phenotype associations from bio-crop literature and predicting unknown relations based on known associations. Through usage examples, we demonstrate how our platform enables the exploration of complex correlations between genes and phenotypes in crop plants. In summary, Crop-GPA serves as a valuable multi-functional resource, empowering the crop research community to gain deeper insights into the biological mechanisms of interest.

Fusobacterium nucleatum promotes tumor progression in KRAS p.G12D-mutant colorectal cancer by binding to DHX15.

Fusobacterium nucleatum (F. nucleatum) promotes intestinal tumor growth and its relative abundance varies greatly among patients with CRC, suggesting the presence of unknown, individual-specific effectors in F. nucleatum-dependent carcinogenesis. Here, we identify that F. nucleatum is enriched preferentially in KRAS p.G12D mutant CRC tumor tissues and contributes to colorectal tumorigenesis in Villin-Cre/KrasG12D+/- mice. Additionally, Parabacteroides distasonis (P. distasonis) competes with F. nucleatum in the G12D mouse model and human CRC tissues with the KRAS mutation. Orally gavaged P. distasonis in mice alleviates the F. nucleatum-dependent CRC progression. F. nucleatum invades intestinal epithelial cells and binds to DHX15, a protein of RNA helicase family expressed on CRC tumor cells, mechanistically involving ERK/STAT3 signaling. Knock out of Dhx15 in Villin-Cre/KrasG12D+/- mice attenuates the CRC phenotype. These findings reveal that the oncogenic effect of F. nucleatum depends on somatic genetics and gut microbial ecology and indicate that personalized modulation of the gut microbiota may provide a more targeted strategy for CRC treatment.

ECDEP: identifying essential proteins based on evolutionary community discovery and subcellular localization.

BACKGROUND: In cellular activities, essential proteins play a vital role and are instrumental in comprehending fundamental biological necessities and identifying pathogenic genes. Current deep learning approaches for predicting essential proteins underutilize the potential of gene expression data and are inadequate for the exploration of dynamic networks with limited evaluation across diverse species. RESULTS: We introduce ECDEP, an essential protein identification model based on evolutionary community discovery. ECDEP integrates temporal gene expression data with a protein-protein interaction (PPI) network and employs the 3-Sigma rule to eliminate outliers at each time point, constructing a dynamic network. Next, we utilize edge birth and death information to establish an interaction streaming source to feed into the evolutionary community discovery algorithm and then identify overlapping communities during the evolution of the dynamic network. SVM recursive feature elimination (RFE) is applied to extract the most informative communities, which are combined with subcellular localization data for classification predictions. We assess the performance of ECDEP by comparing it against ten centrality methods, four shallow machine learning methods with RFE, and two deep learning methods that incorporate multiple biological data sources on Saccharomyces. Cerevisiae (S. cerevisiae), Homo sapiens (H. sapiens), Mus musculus, and Caenorhabditis elegans. ECDEP achieves an AP value of 0.86 on the H. sapiens dataset and the contribution ratio of community features in classification reaches 0.54 on the S. cerevisiae (Krogan) dataset. CONCLUSIONS: Our proposed method adeptly integrates network dynamics and yields outstanding results across various datasets. Furthermore, the incorporation of evolutionary community discovery algorithms amplifies the capacity of gene expression data in classification.

Named entity recognition of rice genes and phenotypes based on BiGRU neural networks.

Named Entity Recognition (NER) is a fundamental but crucial task in natural language processing (NLP) and big data analysis, with wide application range. NER for rice genes and phenotypes is a technique to identify genes and phenotypes from a large amount of text. NER for rice genes and phenotypes can facilitate the acquisition of information in the field of crops and provide references for our research on higher quality crops. At the same time, named entity recognition still faces many challenges. In this paper, we propose an improved bidirectional gated recurrent unit neural network (BI-GRU) method, which is used to automatically identify the required entities (i.e. gene names, rice phenotypes) from relevant rice literature and patents. The neural network model is combined with the Softmax function to directly output the probabilities of labels, forming the BI-GRU-SF model. With the ability of deep learning methods, the semantic information in the context can be learned without the need for feature engineering. Finally, we conducted experiments, and the results showed that our proposed model provided better performance compared to other models. All datasets and resource codes of BI-GRU-SF are available at https://github.com/qqeeqq/NER for academic use.

Targeting integrin α5 in fibroblasts potentiates colorectal cancer response to PD-L1 blockade by affecting extracellular-matrix deposition.

BACKGROUND: One reason patients with cancer cannot benefit from immunotherapy is the lack of immune cell infiltration in tumor tissues. Cancer-associated fibroblasts (CAFs) are emerging as central players in immune regulation that shapes tumor microenvironment (TME). Earlier we reported that integrin α5 was enriched in CAFs in colorectal cancer (CRC), however, its role in TME and cancer immunotherapy remains unclear. Here, we aimed to investigate the role for integrin α5 in fibroblasts in modulating antitumor immunity and therapeutic efficacy combined with checkpoint blockade in CRC. METHODS: We analyzed the CRC single-cell RNA sequencing (scRNA-seq) database to define the expression of ITGA5 in CRC tumor stroma. Experimentally, we carried out in vivo mouse tumor xenograft models to confirm the targeting efficacy of combined α5ß1 inhibition and anti-Programmed death ligand 1 (PD-L1) blockade and in vitro cell-co-culture assay to investigate the role of α5 in fibroblasts in affecting T-cell activity. Clinically, we analyzed the association between α5 expression and infiltrating T cells and evaluated their correlation with patient survival and immunotherapy prognosis in CRC. RESULTS: We revealed that ITGA5 was enriched in FAP-CAFs. Both ITGA5 knockout fibroblasts and therapeutic targeting of α5 improved response to anti-PD-L1 treatment in mouse subcutaneous tumor models. Mechanistically, these treatments led to increased tumor-infiltrating CD8+ T cells. Furthermore, we found that α5 in fibroblasts correlated with extracellular matrix (ECM)-related genes and affected ECM deposition in CRC tumor stroma. Both in vivo analysis and in vitro culture and cell killing experiment showed that ECM proteins and α5 expression in fibroblasts influence T-cell infiltration and activity. Clinically, we confirmed that high α5 expression was associated with fewer CD3+ T and CD8+ T cells, and tissues with low α5 and high CD3+ T levels correlated with better patient survival and immunotherapy response in a CRC cohort with 29 patients. CONCLUSIONS: Our study identified a role for integrin α5 in fibroblasts in modulating antitumor immunity by affecting ECM deposition and showed therapeutic efficacy for combined α5ß1 inhibition and PD-L1 blockade in CRC.

The RNA m6A-Binding Protein YTHDC1 Is Downregulated and Associated With M2 Macrophage Infiltration in Muscle-Invasive Bladder Cancer.

Background: Dysregulation of RNA N6-methyladenosine (m6A) modification is indispensable in tumorigenesis. However, in muscle-invasive bladder cancer (MIBC), the key regulators and mechanisms involved in this process remain largely unknown. This study aimed to screen the key m6A regulators and explore its possible role in MIBC. Methods: Aberrantly expressed m6A regulator genes were screened in The Cancer Genome Atlas (TCGA) MIBC cohort (n = 408) and validated using fresh-frozen and formalin-fixed paraffin-embedded (FFPE) specimens collected during this study. Clinicopathological relevance and association with tumor immune infiltration was further assessed. Results: We identified that the expression of YT521-B homology-domain-containing protein 1 (YTHDC1), an m6A RNA-binding protein, was downregulated in tumor tissues compared with adjacent noncancerous tissues in the TCGA MIBC cohort and our clinical samples. Low YTHDC1 expression correlated with short patient survival, advanced pathologic stage, lymph node metastasis, basal-squamous molecular subtype, non-papillary histological type, and certain genetic mutations important to MIBC. Remarkably, YTHDC1 expression exhibited negative association with tumor-infiltrating M2 macrophage abundance in MIBC. Conclusion: Among m6A regulators, we identified that YTHDC1 was downregulated in MIBC and might play an important role in the pathological process in MIBC, especially tumor microenvironment regulation.

Alcohol and caffeine synergistically induce spontaneous ventricular tachyarrhythmias: ameliorated with dantrolene treatment.

Background: Alcohol and caffeine are the 2 frequently consumed substances in the general population, and the 2 substances are frequently co-consumed. Both substances may increase cardiac arrhythmia risk. However, it is unknown whether alcohol and caffeine co-consumption can synergistically enhance cardiac arrhythmogenesis. Objective: The study sought to investigate whether caffeine and binge drinking synergistically affect cardiac arrhythmogenesis. Methods: A binge drinking rat model (alcohol 2 g/kg, intraperitoneal, every other day for 3 times) was used. Rats (4 months old, both sexes) were randomized into the following 4 groups: binge alcohol-only group (A) (n = 8), nonalcohol, caffeine-only (60 mg/kg, intraperitoneal) group (C) (n = 8), binge alcohol plus caffeine group (A+C) (n = 8), and binge alcohol + caffeine + dantrolene group (A+D) (n = 7, treated with dantrolene 10 mg/kg before each alcohol injection). We also investigated whether alcohol induces Ca2+ sparks and dantrolene treatment attenuates alcohol-induced Ca2+ leak in ventricular myocytes. Results: No arrhythmia was induced with caffeine alone (group C, n = 0 of 8) or alcohol alone (group A, n = 0 of 8). However, alcohol + caffeine induced spontaneous ventricular tachyarrhythmias in all rats (group A+C, n = 8 of 8; P < .001 vs group C or A). Dantrolene prevented ventricular tachyarrhythmia induction in all 7 rats (group A+D, n = 0 of 7; P < .001 vs group A+C). In isolated ventricular myocytes, alcohol significantly increased Ca2+ sparks and dantrolene treatment reduced alcohol-induced Ca2+ sparks. Conclusion: Co-consumption of caffeine and binge drinking synergistically promote spontaneous ventricular tachyarrhythmias in rats. Dantrolene treatment can decrease alcohol-enhanced Ca2+ sparks in vitro and prevented alcohol and caffeine induced ventricular tachyarrhythmias in vivo.

MFBP-UNet: A Network for Pear Leaf Disease Segmentation in Natural Agricultural Environments.

The accurate prevention and control of pear tree diseases, especially the precise segmentation of leaf diseases, poses a serious challenge to fruit farmers globally. Given the possibility of disease areas being minute with ambiguous boundaries, accurate segmentation becomes difficult. In this study, we propose a pear leaf disease segmentation model named MFBP-UNet. It is based on the UNet network architecture and integrates a Multi-scale Feature Extraction (MFE) module and a Tokenized Multilayer Perceptron (BATok-MLP) module with dynamic sparse attention. The MFE enhances the extraction of detail and semantic features, while the BATok-MLP successfully fuses regional and global attention, striking an effective balance in the extraction capabilities of both global and local information. Additionally, we pioneered the use of a diffusion model for data augmentation. By integrating and analyzing different augmentation methods, we further improved the model's training accuracy and robustness. Experimental results reveal that, compared to other segmentation networks, MFBP-UNet shows a significant improvement across all performance metrics. Specifically, MFBP-UNet achieves scores of 86.15%, 93.53%, 90.89%, and 0.922 on MIoU, MP, MPA, and Dice metrics, marking respective improvements of 5.75%, 5.79%, 1.08%, and 0.074 over the UNet model. These results demonstrate the MFBP-UNet model's superior performance and generalization capabilities in pear leaf disease segmentation and its inherent potential to address analogous challenges in natural environment segmentation tasks.

Diagnosis and application of rice diseases based on deep learning.

Background: Rice disease can significantly reduce yields, so monitoring and identifying the diseases during the growing season is crucial. Some current studies are based on images with simple backgrounds, while realistic scene settings are full of background noise, making this task challenging. Traditional artificial prevention and control methods not only have heavy workload, low efficiency, but are also haphazard, unable to achieve real-time monitoring, which seriously limits the development of modern agriculture. Therefore, using target detection algorithm to identify rice diseases is an important research direction in the agricultural field. Methods: In this article a total of 7,220 pictures of rice diseases taken in Jinzhai County, Lu'an City, Anhui Province were chosen as the research object, including rice leaf blast, bacterial blight and flax leaf spot. We propose a rice disease identification method based on the improved YOLOV5s, which reduces the computation of the backbone network, reduces the weight file of the model to 3.2MB, which is about 1/4 of the original model, and accelerates the prediction speed by three times. Results: Compared with other mainstream methods, our method achieves better performance with low computational cost. It solves the problem of slow recognition speed due to the large weight file and calculation amount of model when the model is deployed in mobile terminal.

LRP12 is an endogenous transmembrane inactivator of α4 integrins.

Dynamic regulation of integrin activation and inactivation is critical for precisely controlled cell adhesion and migration in physiological and pathological processes. The molecular basis for integrin activation has been intensively studied; however, the understanding of integrin inactivation is still limited. Here, we identify LRP12 as an endogenous transmembrane inhibitor for α4 integrin activation. The LRP12 cytoplasmic domain directly binds to the integrin α4 cytoplasmic tail and inhibits talin binding to the ß subunit, thus keeping integrin inactive. In migrating cells, LRP12-α4 interaction induces nascent adhesion (NA) turnover at the leading-edge protrusion. Knockdown of LRP12 leads to increased NAs and enhanced cell migration. Consistently, LRP12-deficient T cells show an enhanced homing capability in mice and lead to aggravated chronic colitis in a T cell-transfer colitis model. Altogether, LRP12 is a transmembrane inactivator for integrins that inhibits α4 integrin activation and controls cell migration by maintaining balanced NA dynamics.

TRSRD: a database for research on risky substances in tea using natural language processing and knowledge graph-based techniques.

During the production and processing of tea, harmful substances are often introduced. However, they have never been systematically integrated, and it is impossible to understand the harmful substances that may be introduced during tea production and their related relationships when searching for papers. To address these issues, a database on tea risk substances and their research relationships was constructed. These data were correlated by knowledge mapping techniques, and a Neo4j graph database centered on tea risk substance research was constructed, containing 4189 nodes and 9400 correlations (e.g. research category-PMID, risk substance category-PMID, and risk substance-PMID). This is the first knowledge-based graph database that is specifically designed for integrating and analyzing risk substances in tea and related research, containing nine main types of tea risk substances (including a comprehensive discussion of inclusion pollutants, heavy metals, pesticides, environmental pollutants, mycotoxins, microorganisms, radioactive isotopes, plant growth regulators, and others) and six types of tea research papers (including reviews, safety evaluations/risk assessments, prevention and control measures, detection methods, residual/pollution situations, and data analysis/data measurement). It is an essential reference for exploring the causes of the formation of risk substances in tea and the safety standards of tea in the future. Database URL http://trsrd.wpengxs.cn.

PredinID: Predicting Pathogenic Inframe Indels in Human Through Graph Convolution Neural Network With Graph Sampling Technique.

Inframe insertion/deletion (indel) variants may alter protein sequence and function, which are closely related to an extensive variety of diseases. Although recent researches have paid attention to the associations between inframe indels and diseases, modeling indels in silico and interpreting their pathogenicity remain challenging, mainly due to the lack of experimental information and computational methodologies. In this article, we propose a novel computational method named PredinID (Predictor for inframe InDels) via graph convolutional network (GCN). PredinID leverages k-nearest neighbor algorithm to construct the feature graph for aggregating more informative representation, regarding the pathogenic inframe indel prediction as a node classification task. An edge-based sampling strategy is designed for extracting information from both the potential connections of feature space and the topological structure of subgraphs. Evaluated by 5-fold cross-validations, the PredinID method achieves satisfactory performance and is superior to four classic machine learning algorithms and two GCN methods. Comprehensive experiments show that PredinID has superior performances when compared with the state-of-the-art methods on the independent test set. Moreover, we also implement a web server at http://predinid.bio.aielab.cc/, to facilitate the use of the model.

Early mean absolute lymphocyte count in acute necrotizing pancreatitis is associated with infected pancreatic necrosis.

OBJECTIVES: Peripheral absolute lymphocyte count (ALC) has the potential to predict infected pancreatic necrosis (IPN), but requires verification. This study aimed to assess whether early mean absolute lymphocyte count is associated with the development of IPN in ANP patients using pooled data from a multicenter, randomized controlled trial and a retrospective study. METHODS: The study subjects are from the TRACE trial and a single-center cohort study. ALC during the first seven days was used to define early mean ALC. The entire cohort was then divided into quartiles of early mean ALC. Multivariable Cox proportional hazards regression (MCPHR) model was used to assess the association between early mean ALC and 90-day IPN. RESULTS: A total of 660 patients (median age, 44 years; 63.8 % males) were included and 157 (23.8 %) developed IPN within a 90-day period. The median (interquartile range, IQR) of the early mean ALC is 1.07 (0.80-1.36). All the study subjects were evenly divided into 4 groups: quartile-1 (0.33-0.79*10^9/L), quartile-2 (0.80-1.06*10^9/L), quartile-3 (1.07-1.36*10^9/L) and quartile-4 (1.37-4.01*10^9/L). The incidence of 90-day IPN was 38.3 %, 25.7 %, 19.2 % and 12.2 % for each group, respectively. In the MCPHR model, the lowest early mean ALC (quartile-1) was found to be an independent risk factor of 90-day IPN with a hazard ratio (95 %CI) of 2.21 (1.28-3.81) compared to the highest mean ALC(quartile-4) group. CONCLUSION: Among patients with ANP, early mean ALC was significantly associated with the development of IPN. Preventive strategies should be considered in patients with reduced ALC.

Mast cells inhibit colorectal cancer development by inducing ER stress through secreting Cystatin C.

Mast cells (MCs) are abundantly distributed in the human intestinal mucosa and submucosa. However, their roles and mechanisms in the development of colorectal cancer (CRC) are still unclear. In the present research, we found that the infiltration density of MCs in CRC tissues was positively correlated with improved patients' prognoses. Moreover, MCs suppressed the growth and induced the apoptosis of CRC cells in vitro and in vivo but had no effect on normal colonic epithelial cells. The present study revealed that MCs specifically induced endoplasmic reticulum stress (ERS) and activated the unfolded protein response (UPR) in CRC cells but not in normal cells, which led to the suppression of CRC development in vivo. Furthermore, we found that the secreted Cystatin C protein was the key factor for the MC-induced ERS in CRC cells. This work is of significance for uncovering the antitumor function of MCs in CRC progression and identifying the potential of CRC to respond to MC-targeted immunotherapy.

A deep learning framework for identifying essential proteins based on multiple biological information.

BACKGROUND: Essential Proteins are demonstrated to exert vital functions on cellular processes and are indispensable for the survival and reproduction of the organism. Traditional centrality methods perform poorly on complex protein-protein interaction (PPI) networks. Machine learning approaches based on high-throughput data lack the exploitation of the temporal and spatial dimensions of biological information. RESULTS: We put forward a deep learning framework to predict essential proteins by integrating features obtained from the PPI network, subcellular localization, and gene expression profiles. In our model, the node2vec method is applied to learn continuous feature representations for proteins in the PPI network, which capture the diversity of connectivity patterns in the network. The concept of depthwise separable convolution is employed on gene expression profiles to extract properties and observe the trends of gene expression over time under different experimental conditions. Subcellular localization information is mapped into a long one-dimensional vector to capture its characteristics. Additionally, we use a sampling method to mitigate the impact of imbalanced learning when training the model. With experiments carried out on the data of Saccharomyces cerevisiae, results show that our model outperforms traditional centrality methods and machine learning methods. Likewise, the comparative experiments have manifested that our process of various biological information is preferable. CONCLUSIONS: Our proposed deep learning framework effectively identifies essential proteins by integrating multiple biological data, proving a broader selection of subcellular localization information significantly improves the results of prediction and depthwise separable convolution implemented on gene expression profiles enhances the performance.

Stabilizing Cardiac Ryanodine Receptor With Dantrolene Treatment Prevents Binge Alcohol-Enhanced Atrial Fibrillation in Rats.

ABSTRACT: Binge drinking is a risk factor for cardiac arrhythmias, known as the holiday heart syndrome. Atrial fibrillation (AF) is the most frequently diagnosed arrhythmia in this condition. Recent reports indicated that cardiac ryanodine receptor (RyR2) dysfunction and Ca 2+ leak contribute to alcohol-enhanced AF. In this study, we investigated whether stabilizing RyR2 with dantrolene treatment can prevent alcohol-enhanced AF in rats. A binge drinking rat model was established with alcohol (2 g /kg, IP) delivered once every other day for 4 times. The study consisted of following 3 groups: control group (n = 9), binge alcohol group (n = 10), and binge alcohol + dantrolene (A+D) group (dantrolene, 10 mg/kg, IP before each alcohol injection, n = 9). Echocardiography, left ventricular hemodynamics, in vivo atrial electrophysiology and AF inducibility test, RyR2 phosphorylation level, and blood norepinephrine level were studied 24 hours after the last injection. Ca 2+ leak in isolated atrial myocytes from control and binge alcohol rats was examined. Binge alcohol significantly increased AF inducibility (1/9 in control vs. 8/9 in binge alcohol group, P < 0.05) and AF duration. Dantrolene treatment significantly reduced both AF inducibility (2/9 in dantrolene group, P < 0.05) and AF duration. Binge alcohol significantly increased Ca 2+ leak in isolated atrial myocytes, which was reduced by dantrolene treatment. Blood norepinephrine,7 RyR2 phosphorylation level, cardiac echocardiography, and left ventricular hemodynamics were not significantly affected 24 hours after binge drinking. In conclusion, stabilizing RyR2 with dantrolene treatment significantly attenuated binge drinking-enhanced AF, suggesting that therapeutic strategies stabilizing RyR2 could be a preventive measure to blunt binge drinking-enhanced AF arrhythmogenesis.

ß-Adrenergic Receptor Desensitization/Down-Regulation in Heart Failure: A Friend or Foe?

Cardiac sympathetic activation, mediated by ß-adrenergic receptors (ß-ARs), normally increases cardiac contraction and relaxation. Accomplishing this task requires a physiological, concerted Ca2+ signaling, being able to increase Ca2+ release from sarcoplasmic reticulum (SR) in systole and speed up Ca2+ re-uptake in diastole. In heart failure (HF) myocardial ß-ARs undergo desensitization/down-regulation due to sustained sympathetic adrenergic activation. ß-AR desensitization/down-regulation diminishes adrenergic signaling and cardiac contractile reserve, and is conventionally considered to be detrimental in HF progression. Abnormal Ca2+ handling, manifested as cardiac ryanodine receptor (RyR2) dysfunction and diastolic Ca2+ leak (due to sustained adrenergic activation) also occur in HF. RyR2 dysfunction and Ca2+ leak deplete SR Ca2+ store, diminish Ca2+ release in systole and elevate Ca2+ levels in diastole, impairing both systolic and diastolic ventricular function. Moreover, elevated Ca2+ levels in diastole promote triggered activity and arrhythmogenesis. In the presence of RyR2 dysfunction and Ca2+ leak, further activation of the ß-AR signaling in HF would worsen the existing abnormal Ca2+ handling, exacerbating not only cardiac dysfunction, but also ventricular arrhythmogenesis and sudden cardiac death. Thus, we conclude that ß-AR desensitization/down-regulation may be a self-preserving, adaptive process (acting like an intrinsic ß-AR blocker) protecting the failing heart from developing lethal ventricular arrhythmias under conditions of elevated sympathetic drive and catecholamine levels in HF, rather than a conventionally considered detrimental process. This also implies that medications simply enhancing ß-AR signaling (like ß-AR agonists) may not be so beneficial unless they can also correct dysfunctional Ca2+ handling in HF.

Pharmacogenomic Cluster Analysis of Lung Cancer Cell Lines Provides Insights into Preclinical Model Selection in NSCLC.

Human lung cell lines are utilized widely for investigating tumor biology, experimental therapy, anticancer drug screening and biomarkers identification. However, the consistency of drug responses of these established cell lines and non-small cell lung cancer (NSCLC) is uncertain. In this study, we assessed the drug response consistency between lung cell lines and NSCLC tumors in The Cancer Genome Atlas by hierarchical clustering using copy number variations in driver genes, and profiled the molecular patterns and correlations in cell lines. We found that some frequently used cell lines of NSCLC subtypes were not clustered with their matched subtypes of tumor. Mutation profiles in the oxidative stress response and squamous differentiation pathway in lung cell lines were in concordance with lung squamous cell carcinoma. Furthermore, lung cell lines and tumors in the same sub-cluster had very similar responses to certain drugs but some were inconsistent, suggesting that clustering through copy number variation data could capture part of the suitability of lung cell lines. The analysis of these results could aid investigators in evaluating drug response models and eventually enabling personalized treatment recommendations for individual patients with NSCLC.