A Fusion Learning Model Based on Deep Learning for Single-Cell RNA Sequencing Data Clustering.

Single-cell RNA sequencing (scRNA-seq) technology provides a means for studying biology from a cellular perspective. The fundamental goal of scRNA-seq data analysis is to discriminate single-cell types using unsupervised clustering. Few single-cell clustering algorithms have taken into account both deep and surface information, despite the recent slew of suggestions. Consequently, this article constructs a fusion learning framework based on deep learning, namely scGASI. For learning a clustering similarity matrix, scGASI integrates data affinity recovery and deep feature embedding in a unified scheme based on various top feature sets. Next, scGASI learns the low-dimensional latent representation underlying the data using a graph autoencoder to mine the hidden information residing in the data. To efficiently merge the surface information from raw area and the deeper potential information from underlying area, we then construct a fusion learning model based on self-expression. scGASI uses this fusion learning model to learn the similarity matrix of an individual feature set as well as the clustering similarity matrix of all feature sets. Lastly, gene marker identification, visualization, and clustering are accomplished using the clustering similarity matrix. Extensive verification on actual data sets demonstrates that scGASI outperforms many widely used clustering techniques in terms of clustering accuracy.

Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence-Free Survival in Hepatocellular Carcinoma.

BACKGROUND: The metastatic vascular patterns of hepatocellular carcinoma (HCC) are mainly microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC). However, most existing VETC-related radiological studies still focus on the prediction of VETC status. PURPOSE: This study aimed to build and compare VETC-MVI related models (clinical, radiomics, and deep learning) associated with recurrence-free survival of HCC patients. STUDY TYPE: Retrospective. POPULATION: 398 HCC patients (349 male, 49 female; median age 51.7 years, and age range: 22-80 years) who underwent resection from five hospitals in China. The patients were randomly divided into training cohort (n = 358) and test cohort (n = 40). FIELD STRENGTH/SEQUENCE: 3-T, pre-contrast T1-weighted imaging spoiled gradient recalled echo (T1WI SPGR), T2-weighted imaging fast spin echo (T2WI FSE), and contrast enhanced arterial phase (AP), delay phase (DP). ASSESSMENT: Two radiologists performed the segmentation of HCC on T1WI, T2WI, AP, and DP images, from which radiomic features were extracted. The RFS related clinical characteristics (VETC, MVI, Barcelona stage, tumor maximum diameter, and alpha fetoprotein) and radiomic features were used to build the clinical model, clinical-radiomic (CR) nomogram, deep learning model. The follow-up process was done 1 month after resection, and every 3 months subsequently. The RFS was defined as the date of resection to the date of recurrence confirmed by radiology or the last follow-up. Patients were followed up until December 31, 2022. STATISTICAL TESTS: Univariate COX regression, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier curves, log-rank test, C-index, and area under the curve (AUC). P < 0.05 was considered statistically significant. RESULTS: The C-index of deep learning model achieved 0.830 in test cohort compared with CR nomogram (0.731), radiomic signature (0.707), and clinical model (0.702). The average RFS of the overall patients was 26.77 months (range 1-80 months). DATA CONCLUSION: MR deep learning model based on VETC and MVI provides a potential tool for survival assessment. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Multi-View Enhanced Tensor Nuclear Norm and Local Constraint Model for Cancer Clustering and Feature Gene Selection.

The analysis of cancer data from multi-omics can effectively promote cancer research. The main focus of this article is to cluster cancer samples and identify feature genes to reveal the correlation between cancers and genes, with the primary approach being the analysis of multi-view cancer omics data. Our proposed solution, the Multi-View Enhanced Tensor Nuclear Norm and Local Constraint (MVET-LC) model, aims to utilize the consistency and complementarity of omics data to support biological research. The model is designed to maximize the utilization of multi-view data and incorporates a nuclear norm and local constraint to achieve this goal. The first step involves introducing the concept of enhanced partial sum of tensor nuclear norm, which significantly enhances the flexibility of the tensor nuclear norm. After that, we incorporate total variation regularization into the MVET-LC model to further augment its performance. It enables MVET-LC to make use of the relationship between tensor data structures and sparse data while paying attention to the feature details of the tensor data. To tackle the iterative optimization problem of MVET-LC, the alternating direction method of multipliers is utilized. Through experimental validation, it is demonstrated that our proposed model outperforms other comparison models.

NLRRC: A Novel Clustering Method of Jointing Non-Negative LRR and Random Walk Graph Regularized NMF for Single-Cell Type Identification.

The development of single-cell RNA sequencing (scRNA-seq) technology has opened up a new perspective for us to study disease mechanisms at the single cell level. Cell clustering reveals the natural grouping of cells, which is a vital step in scRNA-seq data analysis. However, the high noise and dropout of single-cell data pose numerous challenges to cell clustering. In this study, we propose a novel matrix factorization method named NLRRC for single-cell type identification. NLRRC joins non-negative low-rank representation (LRR) and random walk graph regularized NMF (RWNMFC) to accurately reveal the natural grouping of cells. Specifically, we find the lowest rank representation of single-cell samples by non-negative LRR to reduce the difficulty of analyzing high-dimensional samples and capture the global information of the samples. Meanwhile, by using random walk graph regularization (RWGR) and NMF, RWNMFC captures manifold structure and cluster information before generating a cluster allocation matrix. The cluster assignment matrix contains cluster labels, which can be used directly to get the clustering results. The performance of NLRRC is validated on simulated and real single-cell datasets. The results of the experiments illustrate that NLRRC has a significant advantage in single-cell type identification.

BioSTD: A New Tensor Multi-View Framework via Combining Tensor Decomposition and Strong Complementarity Constraint for Analyzing Cancer Omics Data.

Advances in omics technology have enriched the understanding of the biological mechanisms of diseases, which has provided a new approach for cancer research. Multi-omics data contain different levels of cancer information, and comprehensive analysis of them has attracted wide attention. However, limited by the dimensionality of matrix models, traditional methods cannot fully use the key high-dimensional global structure of multi-omics data. Moreover, besides global information, local features within each omics are also critical. It is necessary to consider the potential local information together with the high-dimensional global information, ensuring that the shared and complementary features of the omics data are comprehensively observed. In view of the above, this article proposes a new tensor integrative framework called the strong complementarity tensor decomposition model (BioSTD) for cancer multi-omics data. It is used to identify cancer subtype specific genes and cluster subtype samples. Different from the matrix framework, BioSTD utilizes multi-view tensors to coordinate each omics to maximize high-dimensional spatial relationships, which jointly considers the different characteristics of different omics data. Meanwhile, we propose the concept of strong complementarity constraint applicable to omics data and introduce it into BioSTD. Strong complementarity is used to explore the potential local information, which can enhance the separability of different subtypes, allowing consistency and complementarity in the omics data to be fully represented. Experimental results on real cancer datasets show that our model outperforms other advanced models, which confirms its validity.

Precise microdissection of gastric mixed adeno-neuroendocrine carcinoma dissects its genomic landscape and evolutionary clonal origins.

Gastric mixed adenoneuroendocrine carcinoma (MANEC) is a clinically aggressive and heterogeneous tumor composed of adenocarcinoma (ACA) and neuroendocrine carcinoma (NEC). The genomic properties and evolutionary clonal origins of MANEC remain unclear. We conduct whole-exome and multiregional sequencing on 101 samples from 33 patients to elucidate their evolutionary paths. We identify four significantly mutated genes, TP53, RB1, APC, and CTNNB1. MANEC resembles chromosomal instability stomach adenocarcinoma in that whole-genome doubling in MANEC is predominant and occurs earlier than most copy-number losses. All tumors are of monoclonal origin, and NEC components show more aggressive genomic properties than their ACA counterparts. The phylogenetic trees show two tumor divergence patterns, including sequential and parallel divergence. Furthermore, ACA-to-NEC rather than NEC-to-ACA transition is confirmed by immunohistochemistry on 6 biomarkers in ACA- and NEC-dominant regions. These results provide insights into the clonal origin and tumor differentiation of MANEC.

Cisapride induced hypoglycemia via the KCNH6 potassium channel.

Mutations in KCNH6 has been proved to cause hypoinsulinemia and diabetes in human and mice. Cisapride is a stomach-intestinal motility drug used to treat gastrointestinal dysfunction. Cisapride has been reported to be a potential inhibitor of the KCNH family, but it remained unclear whether cisapride inhibited KCNH6. Here, we discovered the role of cisapride on glucose metabolism, focusing on the KCNH6 potassium channel protein. Cisapride reduced blood glucose level and increased serum insulin secretion in wild-type (WT) mice fed standard normal chow/a high-fat diet or in db/db mice, especially when combined with tolbutamide. This effect was much stronger after 4 weeks of intraperitoneal injection. Whole-cell patch-clamp showed that cisapride inhibited KCNH6 currents in transfected HEK293 cells in a concentration-dependent manner. Cisapride induced an increased insulin secretion through the disruption of intracellular calcium homeostasis in a rat pancreatic ß-cell line, INS-1E. Further experiments revealed that cisapride did not decrease blood glucose or increase serum insulin in KCNH6 ß-cell knockout (Kcnh6-ß-KO) mice when compared with WT mice. Cisapride also ameliorated glucose-stimulated insulin secretion (GSIS) in response to high glucose in WT but not Kcnh6-ß-KO mice. Thus, our data reveal a novel way for the effect of KCNH6 in cisapride-induced hypoglycemia.

Similarity and Dissimilarity Regularized Nonnegative Matrix Factorization for Single-Cell RNA-seq Analysis.

In traditional sequencing techniques, the different functions of cells and the different roles they play in differentiation are often ignored. With the advancement of single-cell RNA sequencing (scRNA-seq) techniques, scientists can measure the gene expression value at the single-cell level, and it is helping to understand the heterogeneity hidden in cells. One of the most powerful ways to find heterogeneity is using the unsupervised clustering method to get separate subpopulations. In this paper, we propose a novel clustering method Similarity and Dissimilarity Regularized Nonnegative Matrix Factorization (SDCNMF) that simultaneously impose similarity and dissimilarity constraints on low-dimensional representations. SDCNMF both considers the similarity of closer cells and the dissimilarity of cells that are farther away. It can not only keep the similar cells getting closer in low-dimensional space, but also can push the dissimilar cells away from each other. We test the validity of our proposed method on five scRNA-seq datasets. Clustering results show that SDCNMF is better than other comparative methods, and the gene markers we find are also consistent with previous studies. Therefore, we can conclude that SDCNMF is effective in scRNA-seq data analysis. This paper proposes a novel clustering method Similarity and Dissimilarity Regularized Nonnegative Matrix Factorization (SDCNMF) that simultaneously impose similarity and dissimilarity constraints on low-dimensional representations. SDCNMF both considers the similarity of closer cells and the dissimilarity of cells that are farther away. It can not only keep the similar cells getting closer in low-dimensional space, but also can push the dissimilar cells away from each other. Clustering results show that SDCNMF is better than other comparative methods, and the gene markers we find are also consistent with previous studies.

The clinicopathological significance and predictive value for immunotherapy of programmed death ligand-1 expression in Epstein-Barr virus-associated gastric cancer.

The effect of anti-programmed cell death 1 (PD-1) antibody in Epstein-Barr virus-associated gastric cancer (EBVaGC) was debatable, and no predictive biomarkers for efficacy have been reported. Public reports on anti-PD-1 antibody monotherapy-treated EBVaGC with available programmed death ligand-1 (PD-L1) expression status were summarized and analyzed. Relevance with clinicopathologic characteristics of PD-L1 expression by immunohistochemistry was analyzed in 159 patients diagnosed with EBVaGC. Relevance with genomic transcriptome and mutation profile of PD-L1 status in EBVaGC was assessed with three datasets, the cancer genome atlas (TCGA), Gene Expression Omnibus (GEO) GSE51575, and GSE62254. Based on the data from 8 reports, patients with positive PD-L1 expression (n = 30) had significantly superior objective response rate (ORR) than patients with negative PD-L1 expression (n = 9) (63.3% vs. 0%, P = .001) in EBVaGC receiving anti-PD-1 antibody monotherapy. PD-L1 positivity was associated with less aggressive clinicopathological characteristics and was an independent predictor for a longer disease-free survival (hazard ratio [HR] and 95% CI: 0.45 [0.22-0.92], P = .03) and overall survival (HR and 95% CI: 0.17 [0.06-0.43], P < .001). Analysis of public EBVaGC transcriptome and mutation datasets revealed enhanced immune-related signal pathways in PD-L1high EBVaGC and distinct mutation patterns in PD-L1low EBVaGC. PD-L1 positivity indicates a subtype of EBVaGC with 'hot' immune microenvironment, lower aggressiveness, better prognosis, and higher sensitivity to anti-PD-1 immunotherapy.

Joint Lp-Norm and L2,1-Norm Constrained Graph Laplacian PCA for Robust Tumor Sample Clustering and Gene Network Module Discovery.

The dimensionality reduction method accompanied by different norm constraints plays an important role in mining useful information from large-scale gene expression data. In this article, a novel method named Lp-norm and L2,1-norm constrained graph Laplacian principal component analysis (PL21GPCA) based on traditional principal component analysis (PCA) is proposed for robust tumor sample clustering and gene network module discovery. Three aspects are highlighted in the PL21GPCA method. First, to degrade the high sensitivity to outliers and noise, the non-convex proximal Lp-norm (0 < p < 1)constraint is applied on the loss function. Second, to enhance the sparsity of gene expression in cancer samples, the L2,1-norm constraint is used on one of the regularization terms. Third, to retain the geometric structure of the data, we introduce the graph Laplacian regularization item to the PL21GPCA optimization model. Extensive experiments on five gene expression datasets, including one benchmark dataset, two single-cancer datasets from The Cancer Genome Atlas (TCGA), and two integrated datasets of multiple cancers from TCGA, are performed to validate the effectiveness of our method. The experimental results demonstrate that the PL21GPCA method performs better than many other methods in terms of tumor sample clustering. Additionally, this method is used to discover the gene network modules for the purpose of finding key genes that may be associated with some cancers.

Efficacy and Mechanism of a Chinese Classic Prescription of Yueju in Treating Nonalcoholic Steatohepatitis and Protecting Hepatocytes from Apoptosis.

Yueju, a famous classic Chinese prescription, has been extensively used in treating depression syndromes for hundreds of years. Recent studies have reported that Yueju showed good effects in treating metabolic diseases, such as obesity and hyperlipidemia. Nonalcoholic steatohepatitis (NASH), which leads to cirrhosis and severe cardiovascular diseases, is closely linked to obesity and abnormal lipid metabolism. In this study, Yueju could decrease the levels of alanine aminotransferase, aspartate transaminase, triglyceride, cholesterol, and low-density lipoprotein-C but increase the high-density lipoprotein-C in the serum of the NASH rat model induced by high-fat and high-cholesterol diet. Yueju could alleviate hepatosteatosis by increasing the phosphorylation of acetyl-CoA carboxylase and inhibiting the expression of fatty acid synthase and stearoyl-CoA desaturase 1. Yueju downregulated the expression of α-smooth muscle actin and collagen type 1A1, ameliorating the liver fibrilization. Yueju could also protect the hepatocytes from apoptosis by upregulating antiapoptosis protein Bcl-2 and X-linked inhibitor of apoptosis protein and downregulating apoptotic proteins Bax and cleaved poly ADP-ribose polymerase. Thus, Yueju could improve liver function, regulate lipid metabolism, alleviate hepatosteatosis and fibrosis, and protect hepatocytes from apoptosis against NASH. Yueju may be used as an alternative effective medicine for NASH treatment.

Atonal bHLH transcription factor 1 is an important factor for maintaining the balance of cell proliferation and differentiation in tumorigenesis.

Establishing the link between cellular processes and oncogenesis may aid the elucidation of targeted and effective therapies against tumor cell proliferation and metastasis. Previous studies have investigated the mechanisms involved in maintaining the balance between cell proliferation, differentiation and migration. There is increased interest in determining the conditions that allow cancer stem cells to differentiate as well as the identification of molecules that may serve as novel drug targets. Furthermore, the study of various genes, including transcription factors, which serve a crucial role in cellular processes, may present a promising direction for future therapy. The present review described the role of the transcription factor atonal bHLH transcription factor 1 (ATOH1) in signaling pathways in tumorigenesis, particularly in cerebellar tumor medulloblastoma and colorectal cancer, where ATOH1 serves as an oncogene or tumor suppressor, respectively. Additionally, the present review summarized the associated therapeutic interventions for these two types of tumors and discussed novel clinical targets and approaches.

LncRNA-Disease Associations Prediction Using Bipartite Local Model With Nearest Profile-Based Association Inferring.

There is much evidence that long non-coding RNA (lncRNA) is associated with many diseases. However, it is time-consuming and expensive to identify meaningful lncRNA-disease associations (LDAs) through medical or biological experiments. Therefore, investigating how to identify more meaningful LDAs is necessary, and at the same time it is conducive to the prevention, diagnosis and treatment of complex diseases. Considering the limitations of some current prediction models, a novel model based on bipartite local model with nearest profile-based association inferring, BLM-NPAI, is developed for predicting LDAs. This model predicts novel LDAs from the lncRNA side and the disease side, respectively. More importantly, for some lncRNAs and diseases without any association, the model can also be predicted by their nearest neighbors. Leave-one-out cross validation (LOOCV) and 5-fold cross validation are implemented for BLM-NPAI to evaluate the performance of this model. Our model is superior to current advanced methods in most cases. In addition, to verify the validity and reliability of BLM-NPAI, three disease cases and three lncRNA cases are analyzed to further evaluate BLM-NPAI. Finally, these predicted novel LDAs are confirmed by using the LncRNA-disease database.

L2,1-GRMF: an improved graph regularized matrix factorization method to predict drug-target interactions.

BACKGROUND: Predicting drug-target interactions is time-consuming and expensive. It is important to present the accuracy of the calculation method. There are many algorithms to predict global interactions, some of which use drug-target networks for prediction (ie, a bipartite graph of bound drug pairs and targets known to interact). Although these algorithms can predict some drug-target interactions to some extent, there is little effect for some new drugs or targets that have no known interaction. RESULTS: Since the datasets are usually located at or near low-dimensional nonlinear manifolds, we propose an improved GRMF (graph regularized matrix factorization) method to learn these flow patterns in combination with the previous matrix-decomposition method. In addition, we use one of the pre-processing steps previously proposed to improve the accuracy of the prediction. CONCLUSIONS: Cross-validation is used to evaluate our method, and simulation experiments are used to predict new interactions. In most cases, our method is superior to other methods. Finally, some examples of new drugs and new targets are predicted by performing simulation experiments. And the improved GRMF method can better predict the remaining drug-target interactions.

The association of calcium channel blockers with ß-cell function in type 2 diabetic patients: A cross-sectional study.

Type 2 diabetes mellitus (T2DM) patients are often accompanied with hypertension. However, the association of antihypertensive drugs with ß-cell function has not been well studied. To investigate this question, the authors performed a cross-sectional study involving 882 hypertensive T2DM patients. To assess ß-cell function, patients were given 75g glucose orally and C-peptide levels before and 1, 2, and 3 hours after glucose intake were measured. Homa-ß was computed by Homeostasis Model Assessment model to evaluate ß-cell function using fasting C-peptide and glucose levels in the plasma. Multivariable-adjusted analysis was performed to evaluate the association of antihypertensive drugs with C-peptide levels, HbA1c, and Homa-ß. Among 882 hypertensive patients, 547 (62.0%) received antihypertensive treatment. Multivariate-adjusted analysis demonstrated that use of calcium channel blockers (CCBs) was negatively associated with HbA1c levels (CCBs: 0.95 [95% CI: 0.92-0.98], P = 0.002). Our data further illustrated that the C-peptide levels before and 1, 2, and 3 hours of OGTT were 1.10-, 1.18-, 1.19-, and 1.15-fold increase in T2DM patients taking CCBs (P = 0.084 for fasting C-peptide levels; P ≤ 0.024 for C-peptide levels at 1, 2, and 3 hours after OGTT) in comparison with non-CCB users. Nevertheless, usage of any other antihypertensive drugs did neither associated with HbA1c nor associated with C-peptide levels (P ≥ 0.11). In conclusion, CCB treatment was negatively associated with HbA1c levels but positively associated with ß-cell function in hypertensive T2DM patients, implying that CCBs could be considered to treat hypertensive T2DM patients with reduced ß-cell function.

Circulating Hematopoietic Stem/Progenitor Cells are Associated with Coronary Stenoses in Patients with Coronary Heart Disease.

Inflammatory cells in atherosclerotic plaque exclusively originate from hematopoietic stem/progenitor cells (HSPCs). In this study, we investigated whether circulating HSPCs frequency related to coronary stenosis in patients with coronary heart disease (CHD). Coronary angiography was performed in 468 participants who were recruited at Cardiology Centre in LuHe Hospital from March 2016 to May 2017. Among these subjects, 344 underwent echocardiography. Mononuclear cells isolated from peripheral blood were stained with an antibody cocktail containing anti-human CD34, anti-human lineage, anti-human CD38, and anti-human CD45RA. Lineage-CD38-CD45RAdimCD34+HSPCs were quantified by flow cytometry. CHD was defined as coronary stenosis ≥50% and the extent of CHD was further categorised by coronary stenosis ≥70%. A p < 0.0031 was regarded statistically significant by the Bonferroni correction. Circulating HSPCs frequency was 1.8-fold higher in CHD patients than non-CHD participants (p = 0.047). Multivariate-adjusted logistic analysis demonstrated that HSPCs was the only marker that was associated with the odds ratio of having mild vs. severe coronary stenosis (2.08 (95% CI, 1.35-3.21), p = 0.0009). Left ventricular ejection fraction was inversely correlated with HSPCs frequency and CRP in CHD patients (p < 0.05 for both). In conclusion, HSPCs frequency in circulation is intimately related to coronary stenoses in CHD patients.

A new method for mining information of co-expression network based on multi-cancers integrated data.

BACKGROUND: Gene co-expression network is a favorable method to reveal the nature of disease. With the development of cancer, the way to build gene co-expression networks based on cancer data has been become a hot spot. However, there are still a limited number of current node measurement methods and node mining strategies for multi-cancers network construction. METHODS: In this paper, we introduce a new method for mining information of co-expression network based on multi-cancers integrated data, named PMN. We construct the network by combining the different types of relevant measures (linear and nonlinear rules) for different nodes based on integrated gene expression data of multi-cancers from The Cancer Genome Atlas (TCGA). For mining genes, we combine different properties (local and global characteristics) of the nodes. RESULTS: We uncover more suspicious abnormally expressed genes and shared pathways of different cancers. And we have also found some proven genes and pathways; of course, there are some suspicious factors and molecules that need clinical validation. CONCLUSIONS: The results demonstrate that our method is very effective in excavating gene co-expression genes of multi-cancers.

Laplacian regularized low-rank representation for cancer samples clustering.

Cancer samples clustering based on biomolecular data has been becoming an important tool for cancer classification. The recognition of cancer types is of great importance for cancer treatment. In this paper, in order to improve the accuracy of cancer recognition, we propose to use Laplacian regularized Low-Rank Representation (LLRR) to cluster the cancer samples based on genomic data. In LLRR method, the high-dimensional genomic data are approximately treated as samples extracted from a combination of several low-rank subspaces. The purpose of LLRR method is to seek the lowest-rank representation matrix based on a dictionary. Because a Laplacian regularization based on manifold is introduced into LLRR, compared to the Low-Rank Representation (LRR) method, besides capturing the global geometric structure, LLRR can capture the intrinsic local structure of high-dimensional observation data well. And what is more, in LLRR, the original data themselves are selected as a dictionary, so the lowest-rank representation is actually a similar expression between the samples. Therefore, corresponding to the low-rank representation matrix, the samples with high similarity are considered to come from the same subspace and are grouped into a class. The experiment results on real genomic data illustrate that LLRR method, compared with LRR and MLLRR, is more robust to noise and has a better ability to learn the inherent subspace structure of data, and achieves remarkable performance in the clustering of cancer samples.

Integrative graph regularized matrix factorization for drug-pathway associations analysis.

Pathway-based drug discovery can give full consideration to the efficacy of compounds in the systemic physiological environment. The recently emerged drug-pathway association identification approaches gain popularity due to its potential to decipher the mechanism of action and the targets of compounds. In this study, we propose a novel drug-pathway association identification method: Integrative Graph regularized Matrix Factorization (IGMF). It employs graph regularization to encode data geometrical information and prevent possible overfitting in prediction. Furthermore, it achieves parts-based and sparse data representation by imposing L1-norm regularization on the objective function. Empirical studies demonstrate that IGMF has strong advantages in identifying more new drug-pathway associations compared to its peer methods. It further shows a good capability to unveil the intrinsic structures of data. As an effective drug-pathway discovery method, it will inspire new analytics methods in this subfield.

From Hyper- to Hypoinsulinemia and Diabetes: Effect of KCNH6 on Insulin Secretion.

Glucose-stimulated insulin secretion from islet ß cells is mediated by KATP channels. However, the role of non-KATP K+ channels in insulin secretion is largely unknown. Here, we show that a non-KATP K+ channel, KCNH6, plays a key role in insulin secretion and glucose hemostasis in humans and mice. KCNH6 p.P235L heterozygous mutation co-separated with diabetes in a four-generation pedigree. Kcnh6 knockout (KO) or Kcnh6 p.P235L knockin (KI) mice had a phenotype characterized by changing from hypoglycemia with hyperinsulinemia to hyperglycemia with insulin deficiency. Islets from the young KO mice had increased intracellular calcium concentration and increased insulin secretion. However, islets from the adult KO mice not only had increased intracellular calcium levels but also had remarkable ER stress and apoptosis, associated with loss of ß cell mass and decreased insulin secretion. Therefore, dysfunction of KCNH6 causes overstimulation of insulin secretion in the short term and ß cell failure in the long term.