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Structure-based lead optimization is an open challenge in drug discovery, which is still largely driven by hypotheses and depends on the experience of medicinal chemists. We here propose a pairwise binding comparison network (PBCNet) based on physics-informed graph attention mechanism, specifically tailored for ranking relative binding affinity among congeneric ligands. Benchmarking on two held-out sets (provided by Schrödinger, Inc. and Merck KGaA) containing over 460 ligands and 16 targets, PBCNet demonstrated significant advantages in terms of both prediction accuracy and computational efficiency. Equipped with a fine-tuning operation, the performance of PBCNet reaches that of Schrödinger's FEP+, which is much more computationally intensive and requires significant expert intervention. A further simulation-based experiment showed that active learning-optimized PBCNet may accelerate lead optimization campaigns by 30%. Finally, for the convenience of users, a web service (https://pbcnet.alphama.com.cn/index) for PBCNet is established to facilitate complex relative binding affinity prediction through an easy-to-operate graphical interface.

A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or off-target effects. Recently, the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology. Here, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, and the biological networks under different experiment conditions further complicate the situation, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. On a benchmark set and a large time-split validation dataset, the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map. Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound, or reversely, in finding novel inhibitors of a given target of interest. Supplementary Information The online version contains supplementary material available at 10.1007/s13238-021-00885-0.

The problem of human trust is one of the most fundamental problems in applied artificial intelligence in drug discovery. In silico models have been widely used to accelerate the process of drug discovery in recent years. However, most of these models can only give reliable predictions within a limited chemical space that the training set covers (applicability domain). Predictions of samples falling outside the applicability domain are unreliable and sometimes dangerous for the drug-design decision-making process. Uncertainty quantification accordingly has drawn great attention to enable autonomous drug designing. By quantifying the confidence level of model predictions, the reliability of the predictions can be quantitatively represented to assist researchers in their molecular reasoning and experimental design. Here we summarize the state-of-the-art approaches to uncertainty quantification and underline how they can be used for drug design and discovery projects. Furthermore, we also outline four representative application scenarios of uncertainty quantification in drug discovery.

Motivation Breast cancer is one of the leading causes of cancer deaths among women worldwide. It is necessary to develop new breast cancer drugs because of the shortcomings of existing therapies. The traditional discovery process is time-consuming and expensive. Repositioning of clinically approved drugs has emerged as a novel approach for breast cancer therapy. However, serendipitous or experiential repurposing cannot be used as a routine method. Results In this study, we proposed a graph neural network model GraphRepur based on GraphSAGE for drug repurposing against breast cancer. GraphRepur integrated two major classes of computational methods, drug network-based and drug signature-based. The differentially expressed genes of disease, drug-exposure gene expression data and the drug–drug links information were collected. By extracting the drug signatures and topological structure information contained in the drug relationships, GraphRepur can predict new drugs for breast cancer, outperforming previous state-of-the-art approaches and some classic machine learning methods. The high-ranked drugs have indeed been reported as new uses for breast cancer treatment recently. Availabilityand implementation The source code of our model and datasets are available at: https://github.com/cckamy/GraphRepur and https://figshare.com/articles/software/GraphRepur_Breast_Cancer_Drug_Repurposing/14220050. Supplementary information Supplementary data are available at Bioinformatics online.

Hematopoietic adaptor containing SH3 and SAM domains-1 (HACS1) is a signaling protein with two juxtaposed protein–protein interaction domains and an intrinsically unstructured region that spans half the sequence. Here, we describe the interaction between the HACS1 SH3 domain and a sequence near the third immunoreceptor tyrosine-based inhibition motif (ITIM3) of the paired immunoglobulin receptor B (PIRB). From surface plasmon resonance binding assays using a mouse and human PIRB ITIM3 phosphopeptides as ligands, the HACS1 SH3 domain and SHP2 N-terminal SH2 domain demonstrated comparable affinities in the micromolar range. Since the PIRB ITIM3 sequence represents an atypical ligand for an SH3 domain, we determined the NMR structure of the HACS1 SH3 domain and performed a chemical shift mapping study. This study showed that the binding site on the HACS1 SH3 domain for PIRB shares many of the same amino acids found in a canonical binding cleft normally associated with polyproline ligands. Molecular modeling suggests that the respective binding sites in PIRB ITIM3 for the HACS1 SH3 domain and the SHP2 SH2 domain are too close to permit simultaneous binding. As a result, the HACS1-PIRB partnership has the potential to amalgamate signaling pathways that influence both immune and neuronal cell fate., Kwan et al. show the interaction between the HACS1 SH3 domain and a sequence near the third immunoreceptor tyrosine-based inhibition motif of the Paired immunoglobulin receptor B (PIRB). This study suggests that the HACS1-PIRB partnership has the potential to unite signaling pathways that regulate both immune and neuronal cell fate.

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

A machine-learning model trained on the whole Cambridge Structural Database was developed to assist high-throughput cocrystal screening. With only 2D structures taken as inputs, the probability of ...

Motivation Identifying compound–protein interaction (CPI) is a crucial task in drug discovery and chemogenomics studies, and proteins without three-dimensional structure account for a large part of potential biological targets, which requires developing methods using only protein sequence information to predict CPI. However, sequence-based CPI models may face some specific pitfalls, including using inappropriate datasets, hidden ligand bias and splitting datasets inappropriately, resulting in overestimation of their prediction performance. Results To address these issues, we here constructed new datasets specific for CPI prediction, proposed a novel transformer neural network named TransformerCPI, and introduced a more rigorous label reversal experiment to test whether a model learns true interaction features. TransformerCPI achieved much improved performance on the new experiments, and it can be deconvolved to highlight important interacting regions of protein sequences and compound atoms, which may contribute chemical biology studies with useful guidance for further ligand structural optimization. Availability and implementation https://github.com/lifanchen-simm/transformerCPI.

Here we report a feasibility study of a deep learning model for exploring the optimal reaction conditions for given chemical reactions. The model was trained to learn the relationships between the chemical contexts, reaction conditions and product yields based on high-quality existing experimental data, and then extrapolate reasonably to unseen reactions by in silico exploration of accessible reaction space. This strategy was applied to the Suzuki–Miyaura cross-coupling reaction to find the best catalysts for given reactants and at the same time to discover the optimum combination of the reaction conditions. We demonstrated that the trained model was able to determine the productive catalysts as well as the most favorable catalyst loading and reaction temperature for both modeled reactions and external unseen reactions. This work aims to provide an insight into the feasibility of introducing a deep learning method in the optimization of chemical reaction conditions.

Blood–brain barrier is a pivotal factor to be considered in the process of central nervous system (CNS) drug development, and it is of great significance to rapidly explore the blood–brain barrier permeability (BBBp) of compounds in silico in early drug discovery process. Here, we focus on whether and how uncertainty estimation methods improve in silico BBBp models. We briefly surveyed the current state of in silico BBBp prediction and uncertainty estimation methods of deep learning models, and curated an independent dataset to determine the reliability of the state-of-the-art algorithms. The results exhibit that, despite the comparable performance on BBBp prediction between graph neural networks-based deep learning models and conventional physicochemical-based machine learning models, the GROVER-BBBp model shows greatly improvement when using uncertainty estimations. In particular, the strategy combined Entropy and MC-dropout can increase the accuracy of distinguishing BBB + from BBB − to above 99% by extracting predictions with high confidence level (uncertainty score

Developing new drugs remains prohibitively expensive, time-consuming, and often involves safety issues. Accurate prediction of drug-target interactions (DTIs) can guide the drug discovery process and thus facilitate drug development. Non-Euclidian data such as drug-like molecule structures, key pocket residue structures, and protein interaction networks can be represented effectively using graphs. Therefore, the emerging graph neural network has been rapidly applied to predict DTIs, and proved effective in finding repositioning drugs and accelerating drug discovery. In this review, we provide a brief overview of deep neural networks used in DTI models. Then, we summarize the database required for DTI prediction, followed by a comprehensive introduction of applications of graph neural networks for DTI prediction. We also highlight current challenges and future directions to guide the further development of this field.

Artificial intelligence (AI) models usually require large amounts of high-quality training data, which is in striking contrast to the situation of small and biased data faced by current drug discovery pipelines. The concept of federated learning has been proposed to utilize distributed data from different sources without leaking sensitive information of the data. This emerging decentralized machine learning paradigm is expected to dramatically improve the success rate of AI-powered drug discovery. Here, we simulated the federated learning process with different property and activity datasets from different sources, among which overlapping molecules with high or low biases exist in the recorded values. Beyond the benefit of gaining more data, we also demonstrated that federated training has a regularization effect superior to centralized training on the pooled datasets with high biases. Moreover, different network architectures for clients and aggregation algorithms for coordinators have been compared on the performance of federated learning, where personalized federated learning shows promising results. Our work demonstrates the applicability of federated learning in predicting drug-related properties and highlights its promising role in addressing the small and biased data dilemma in drug discovery.

Hunting for chemicals with favorable pharmacological, toxicological, and pharmacokinetic properties remains a formidable challenge for drug discovery. Deep learning provides us with powerful tools to build predictive models that are appropriate for the rising amounts of data, but the gap between what these neural networks learn and what human beings can comprehend is growing. Moreover, this gap may induce distrust and restrict deep learning applications in practice. Here, we introduce a new graph neural network architecture called Attentive FP for molecular representation that uses a graph attention mechanism to learn from relevant drug discovery data sets. We demonstrate that Attentive FP achieves state-of-the-art predictive performances on a variety of data sets and that what it learns is interpretable. The feature visualization for Attentive FP suggests that it automatically learns nonlocal intramolecular interactions from specified tasks, which can help us gain chemical insights directly from data beyond human perception.

Objectives To examine the effects and mechanisms of human neutrophil peptides in systemic infection and noninfectious inflammatory lung injury. Design Prospective experimental study. Setting University hospital-based research laboratory. Subjects In vitro human cells and in vivo mouse models. Interventions Wild-type (Friend virus B-type) and conditional leukocyte human neutrophil peptides transgenic mice were subjected to either sepsis induced by cecal ligation and puncture or acute lung injury by intratracheal instillation of hydrochloric acid followed by mechanical ventilation. Using human neutrophil peptides as bait, the basal cell adhesion molecule (CD239) and the purinergic P2Y purinoceptor 6 receptor were identified as the putative human neutrophil peptides receptor complex in human lung epithelial cells. Measurements and main results In the cecal ligation and puncture sepsis model, Friend virus B-type mice exhibited higher systemic bacterial load, cytokine production, and lung injury than human neutrophil peptides transgenic mice. Conversely, an increased lung cytokine production was seen in Friend virus B-type mice, which was further enhanced in human neutrophil peptides transgenic mice in response to two-hit lung injury induced by hydrochloric acid and mechanical ventilation. The human neutrophil peptides-mediated inflammatory response was mediated through the basal cell adhesion molecule-P2Y purinoceptor 6 receptor signal pathway in human lung epithelial cells. Conclusions Human neutrophil peptides are critical in host defense against infectious sepsis by their cationic antimicrobial properties but may exacerbate tissue injury when neutrophil-mediated inflammatory responses are excessive in noninfectious lung injury. Targeting the basal cell adhesion molecule/P2Y purinoceptor 6 signaling pathway may serve as a novel approach to attenuate the neutrophil-mediated inflammatory responses and injury while maintaining the antimicrobial function of human neutrophil peptides in critical illness.

Computational target fishing aims to investigate the mechanism of action or the side effects of bioactive small molecules. Unfortunately, conventional ligand-based computational methods only explore a confined chemical space, and structure-based methods are limited by the availability of crystal structures. Moreover, these methods cannot describe cellular context-dependent effects and are thus not useful for exploring the targets of drugs in specific cells. To address these challenges, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. Using a benchmark set, the model achieved impressive target inference results compared with previous methods such as Connectivity Map and ProTINA. More importantly, the powerful generalization ability of the model observed with the external LINCS phase II dataset suggests that the model is an efficient target fishing or repositioning tool for bioactive compounds.

The kinome-wide virtual profiling of small molecules with high-dimensional structure-activity data is a challenging task in drug discovery. Here, we present a virtual profiling model against a panel of 391 kinases based on large-scale bioactivity data and the multitask deep neural network algorithm. The obtained model yields excellent internal prediction capability with an auROC of 0.90 and consistently outperforms conventional single-task models on external tests, especially for kinases with insufficient activity data. Moreover, more rigorous experimental validations including 1410 kinase-compound pairs showed a high-quality average auROC of 0.75 and confirmed many novel predicted "off-target" activities. Given the verified generalizability, the model was further applied to various scenarios for depicting the kinome-wide selectivity and the association with certain diseases. Overall, the computational model enables us to create a comprehensive kinome interaction network for designing novel chemical modulators or drug repositioning and is of practical value for exploring previously less studied kinases.

Membrane proteins are crucial to heart function and development. Here we combine cationic silica-bead coating with shotgun proteomics to enrich for and identify plasma membrane-associated proteins from primary mouse neonatal and human fetal ventricular cardiomyocytes. We identify Tmem65 as a cardiac-enriched, intercalated disc protein that increases during development in both mouse and human hearts. Functional analysis of Tmem65 both in vitro using lentiviral shRNA-mediated knockdown in mouse cardiomyocytes and in vivo using morpholino-based knockdown in zebrafish show marked alterations in gap junction function and cardiac morphology. Molecular analyses suggest that Tmem65 interaction with connexin 43 (Cx43) is required for correct localization of Cx43 to the intercalated disc, since Tmem65 deletion results in marked internalization of Cx43, a shorter half-life through increased degradation, and loss of Cx43 function. Our data demonstrate that the membrane protein Tmem65 is an intercalated disc protein that interacts with and functionally regulates ventricular Cx43.

Specific single-nucleotide polymorphisms in intronic regions of human TCF7L2 are associated with an elevated risk of developing type 2 diabetes. Whether Tcf7l2 is expressed in pancreatic islets of rodent species at a considerable level, however, remains controversial. We used RT-PCR and quantitative RT-PCR to examine Tcf7l2 expression in rodent gut, pancreas, isolated pancreatic islets, and cultured cell lines. The expression level of Tcf7l2 was relatively lower in the pancreas compared to the gut or the pancreatic β-cell line Ins-1. Immunostaining did not detect a Tcf7l2 signal in mouse pancreatic islets. Endogenous canonical Wnt activity was not appreciable in the pancreas of TOPGAL transgenic mice. Both Tcf7 and Tcf7l1, but not Lef1, were expressed in the pancreas. The expression of the three Tcf genes (Tcf7, Tcf7l1, and Tcf7l2) in the pancreas was reduced by treatment with insulin or high-fat diet feeding, in contrast to the stimulation of Tcf7l2 expression by insulin in the gut. We suggest that hyperinsulinemia represses Tcf gene expression in the pancreas. Whether and how this reduction alters the function of pancreatic β cells during hyperinsulinemia deserves further investigation.

Hacs1, a SH3 and SAM domain-containing adaptor protein, is up-regulated by IL-4 in activated B cells and strongly expressed in dendritic cells. To elucidate the function of Hacs1 in immune regulation, we generated Hacs1(-/-) mice by deletion of the SH3 and SAM domains. Hacs1(-/-) mice were viable and fertile and had normal bone marrow B-cell development and normal splenic T- and B-cell populations. However, adult Hacs1(-/-) mice had increased peritoneal B1a cells (IgM(+)CD5(+)). On immunization with T-cell-independent antigen TNP-Ficoll, Hacs1(-/-) mice had increased production of anti-TNP IgM and IgG3. Purified splenic B cells from Hacs1(-/-) mice showed increased cell proliferation on BCR (B-cell receptor) stimulation. We further demonstrate that the Hacs1(-/-) B cells had increased global tyrosine phosphorylation, including tyrosine kinases Lyn and Akt. Both T-helper type 1 (T(h)1) and T-helper type 2 (T(h)2) humoral responses were enhanced in Hacs1(-/-) mice. In vitro bone marrow-derived Hacs1(-/-) dendritic cells showed increased IL-12 production on stimulation with ovalbumin (OVA). This study suggests that Hacs1 is an immunoinhibitory adaptor that might be a useful target for immune suppression therapy.-Wang, D., Stewart, A. K., Zhuang, L., Zhu, Y., Wang, Y., Shi, C., Keating, A., Slutsky, A., Zhang, H., Wen, X.-Y. Enhanced adaptive immunity in mice lacking the immunoinhibitory adaptor Hacs1.

The B lymphocyte-induced maturation protein-1 (Blimp-1) is a transcriptional repressor involved in B cell terminal differentiation. Recent studies suggest that it also plays roles in the differentiation of myeloid and T cells. Using reporter gene analysis, we found that the mouse Blimp-1 promoter constructs of 4.4 kb, 2.5 kb and 1.6 kb exerted similar transcription activity in vitro. To study the Blimp-1 promoter function in vivo, we generated a mouse transgenic line in which the expression of EGFP is driven by the 4.4 kb Blimp-1 promoter fragment. EGFP expression was detected in multiple hematopoietic lineages including hematopoietic progenitor cells (c-kit+), B cells at various developmental stages (B220+, IgM+, IgD+ and CD138+), myeloid cells (Gr1+ and CD11b+) and T cells (CD4+ and CD8+). Furthermore, we demonstrated, for the first time, that this Blimp-1 promoter fragment also drives EGFP expression in erythroid lineage (Ter119+) and embryonic livers. Finally, EGFP was expressed in the testes of transgenic mice in a cell-specific manner with expression weakly in primary spermatocytes and strongly in spermatids, but not in spermatogonia or spermatozoa. Our studies collectively suggest that Blimp-1 may play a general role in the development of hematopoietic lineages and a stage-specific role in spermatogenesis.

Purpose: The aim of this study is to investigate the antimyeloma activity of a novel Bcl-2 family inhibitor, ABT-737, in preclinical treatment of multiple myeloma. Experimental Design: The antimyeloma activity of ABT-737 was evaluated in cultured myeloma cell lines and patient myeloma samples, and in a xenograft mouse myeloma model. Drug combination therapy using ABT-737 with other commonly used myeloma drugs was also investigated. Results: MY5 and JJN3 cell lines exhibited the most sensitivity to ABT-737 with an EC50 of 0.2 and 0.5 μmol/L, respectively, with increased cell apoptosis and elevated activated caspase-3. We identified two distinct groups of myeloma patient samples that were either sensitive or resistant to the drug. Four of 15 patient bone marrow samples (27%) were highly sensitive to ABT-737 at doses of 0.25 and 0.5 μmol/L, which eliminated 80% to 90% of myeloma cells as a result of cellular apoptosis 3 days after drug treatment. ABT-737 showed a synergistic effect when combined with dexamethasone or melphalan in inducing myeloma cell death. Furthermore, the dexamethasone-resistant MM1(Dex)R myeloma cell line was highly sensitive to 0.2 μmol/L ABT-737. As determined by colony assay, little or no detectable toxicity to patient hematologic progenitor cells was observed at 1 μmol/L ABT-737. ABT-737 dose dependently suppressed tumor growth in a xenograft MY5 mouse model. Conclusions: These studies show substantial antimyeloma activity of ABT-737 as a single agent or in combination with dexamethasone or melphalan and suggest a rationale for future clinical trials.

Monoclonal gammopathy of undetermined significance (MGUS) is the requisite precursor to multiple myeloma (MM), a malignancy of antibody-producing plasma B-cells. The genetic basis of MGUS and its progression to MM remains poorly understood. C57BL/KaLwRij (KaLwRij) is a spontaneously-derived inbred mouse strain with a high frequency of benign idiopathic paraproteinemia (BIP), a phenotype with similarities to MGUS including progression to MM. Using mouse haplotype analysis, human MM SNP array data, and whole exome and whole genome sequencing of KaLwRij mice, we identified novel KaLwRij gene variants, including deletion of Samsn1 and deleterious point mutations in Tnfrsf22 and Tnfrsf23. These variants significantly affected multiple cell types implicated in MM pathogenesis including B-cells, macrophages, and bone marrow stromal cells. These data demonstrate that multiple cell types contribute to MM development prior to the acquisition of somatic driver mutations in KaLwRij mice, and suggest that MM may an inherently non-cell autonomous malignancy.

Apoptosis is a hallmark of multiple etiologies of heart failure, including dilated cardiomyopathy. Since microRNAs are master regulators of cardiac development and key effectors of intracellular signaling, they represent novel candidates for understanding the mechanisms driving the increased dysfunction and loss of cardiomyocytes during cardiovascular disease progression. To determine the role of cardiac miRNAs in the apoptotic response, we used microarray technology to monitor miRNA levels in a validated murine phospholambam mutant model of dilated cardiomyopathy. 24 miRNAs were found to be differentially expressed, most of which have not been previously linked to dilated cardiomyopathy. We showed that individual silencing of 7 out of 8 significantly down-regulated miRNAs (mir-1, -29c, -30c, -30d, -149, -486, -499) led to a strong apoptotic phenotype in cell culture, suggesting they repress pro-apoptotic factors. To identify putative miRNA targets most likely relevant to cell death, we computationally integrated transcriptomic, proteomic and functional annotation data. We showed the dependency of prioritized target abundance on miRNA expression using RNA interference and quantitative mass spectrometry. We concluded that down regulation of key pro-survival miRNAs causes up-regulation of apoptotic signaling effectors that contribute to cardiac cell loss, potentially leading to system decompensation and heart failure.

Background Endoplasmic reticulum ( ER ) resident protein 44 ( ER p44) is a member of the protein disulfide isomerase family, is induced during ER stress, and may be involved in regulating Ca 2+ homeostasis. However, the role of ER p44 in cardiac development and function is unknown. The aim of this study was to investigate the role of ER p44 in cardiac development and function in mice, zebrafish, and embryonic stem cell ( ESC )‐derived cardiomyocytes to determine the underlying role of ER p44. Methods and Results We generated and characterized ER p44 −/− mice, ER p44 morphant zebrafish embryos, and ER p44 −/− ESC ‐derived cardiomyocytes. Deletion of ER p44 in mouse and zebrafish caused significant embryonic lethality, abnormal heart development, altered Ca 2+ dynamics, reactive oxygen species generation, activated ER stress gene profiles, and apoptotic cell death. We also determined the cardiac phenotype in pressure overloaded, aortic‐banded ER p44 +/− mice: enhanced ER stress activation and increased mortality, as well as diastolic cardiac dysfunction with a significantly lower fractional shortening. Confocal and LacZ histochemical staining showed a significant transmural gradient for ER p44 in the adult heart, in which high expression of ER p44 was observed in the outer subepicardial region of the myocardium. Conclusions ER p44 plays a critical role in embryonic heart development and is crucial in regulating cardiac cell Ca 2+ signaling, ER stress, ROS ‐induced oxidative stress, and activation of the intrinsic mitochondrial apoptosis pathway.

The type 2 diabetes risk gene TCF7L2 is the effector of the Wnt signaling pathway. We found previously that in gut endocrine L-cell lines, TCF7L2 controls transcription of the proglucagon gene (gcg), which encodes the incretin hormone glucagon-like peptide-1 (GLP-1). Whereas peripheral GLP-1 stimulates insulin secretion, brain GLP-1 controls energy homeostasis through yet-to-be defined mechanisms. We aim to determine the metabolic effect of a functional knockdown of TCF7L2 by generating transgenic mice that express dominant-negative TCF7L2 (TCF7L2DN) specifically in gcg-expressing cells. The gcg-TCF7L2DN transgenic mice showed reduced gcg expression in their gut and brain, but not in pancreas. Defects in glucose homeostasis were observed in these mice, associated with attenuated plasma insulin levels in response to glucose challenge. The defect in glucose disposal was exacerbated with high-fat diet. Brain Wnt activity and feeding-mediated hypothalamic AMP-activated protein kinase (AMPK) repression in these mice were impaired. Peripheral injection of the cAMP-promoting agent forskolin increased brain β-cat Ser675 phosphorylation and brain gcg expression and restored feeding-mediated hypothalamic AMPK repression. We conclude that TCF7L2 and Wnt signaling control gut and brain gcg expression and glucose homeostasis and speculate that positive cross-talk between Wnt and GLP-1/cAMP signaling is an underlying mechanism for brain GLP-1 in exerting its metabolic functions.

Collagen V, broadly expressed as α1(V)2 α2(V) heterotrimers that regulate collagen fibril geometry and strength, also occurs in some tissues, such as white adipose tissue (WAT), pancreatic islets, and skeletal muscle, as the poorly characterized α1(V) α2(V) α3(V) heterotrimer. Here, we investigate the role of α3(V) collagen chains by generating mice with a null allele of the α3(V) gene Col5a3 (Col5a3–/– mice). Female Col5a3–/– mice had reduced dermal fat and were resistant to high-fat diet–induced weight gain. Male and female mutant mice were glucose intolerant, insulin-resistant, and hyperglycemic, and these metabolic defects worsened with age. Col5a3–/– mice demonstrated decreased numbers of pancreatic islets, which were more susceptible to streptozotocin-induced apoptosis, and islets isolated from mutant mice displayed blunted glucose-stimulated insulin secretion. Moreover, Col5a3–/– WAT and skeletal muscle were defective in glucose uptake and mobilization of intracellular GLUT4 glucose transporter to the plasma membrane in response to insulin. Our results underscore the emerging view of the importance of ECM to the microenvironments that inform proper development/functioning of specialized cells, such as adipocytes, β cells, and skeletal muscle.

With the rapid growth of the Internet, more and more people interact with their friends in online social networks like Facebook1. Current online social networks have designed some strategies to protect users' privacy, but they are not stringent enough. Some public information of profile or relationship can be utilized to infer users' private information. Online social networks usually contain little public available information of users (labeled data) but with a large number of hidden ones (unlabeled data). Recently, Semi-Supervised Learning (SSL), which has the advantage of utilizing fewer labeled data to achieve better performance compared to classical Supervised Learning, attracts much attention from the web research community with a massive set of unlabeled data. In our paper, we focus on the privacy issue of online social networks, which is a hot and dynamic research topic. More specifically, we propose a novel SSL framework that can be used to exploit security issues in online social networks. We first introduce the general SSL framework and outline two exploit models with associated strategies within it, e.g., graph-based models and co-training model. Finally, we conduct a series of experiments on real-world data from Facebook and StudiVZ2 to evaluate the effectiveness of this SSL exploit framework. Experimental results demonstrate that our approaches can accurately infer sensitive information of online users and more effective compared to previous models.

Recommendation, in the filed of machine learning, is known as a technique of identifying user preferences to new items with ratings from recommender systems. Recently, one novel recommendation model using Green's function treats recommendation as the process of label propagation. Although this model outperforms many standard recommendation methods, it suffers from information loss during graph construction because of data sparsity. In this paper, aiming at solving this problem and improving prediction accuracy, we propose an enhanced semi-supervised Green's function recommendation model. The main contributions are two-fold: 1) To reduce information loss, we propose a novel graph construction method with global and local consistent similarity; 2) We enhance the recommendation algorithm with the multiclass semi-supervised learning framework. Finally, experimental results on real world data demonstrate the effectiveness of our model.

Background Epidemiological and experimental evidence that support the correlation between Type 2 diabetes mellitus (T2D) and increased risks of colorectal cancer formation have led us to hypothesize the existence of molecular crosstalk between insulin and canonical Wnt signaling pathways. Insulin was shown to stimulate Wnt target gene expression, utilizing the effector of the Wnt signaling pathway. Whether insulin affects expression of components of Wnt pathway has not been extensively examined. Methods cDNA microarray was utilized to assess the effect of insulin on gene expression profile in the rat intestinal non-cancer IEC-6 cell line, followed by real-time RT-PCR, Western blotting and reporter gene analyses in intestinal cancer and non-cancer cells. Results Insulin was shown to alter the expression of a dozen of Wnt pathway related genes including TCF-4 (= TCF7L2) and frizzled- (Fzd-4). The stimulatory effect of insulin on TCF-4 expression was then confirmed by real-time RT-PCR, Western blotting and luciferase reporter analyses, while the activation on Fzd-4 was confirmed by real-time PCR. General significance Our observations suggest that insulin may crosstalk with the Wnt signaling pathway in a multi-level fashion, involving insulin regulation of the expression of Wnt target genes, a Wnt receptor, as well as mediators of the Wnt signaling pathway.

Multiple myeloma (MM) is a malignancy of plasma B-cells that is invariably preceded by monoclonal gammopathy of undetermined significance (MGUS), a pre-neoplastic plasma cell proliferative disorder. C57Bl/KaLwRij (KaLwRij) is a spontaneous inbred mouse strain predisposed to benign idiopathic paraproteinemia (BIP), a plasma cell dyscrasia that parallels many features of human pathology including progression to myeloma. In both humans and mice, the genetic basis for MGUS/MM risk is unknown. We sought to identify the genetic basis for MGUS/MM risk in KaLwRij mice to shed light on genes or pathways that mediate disease risk in humans. Using an integrative approach, we combined KaLwRij x B6 haplotype analysis (418 genes) and a MM patient association study (180 genes) to identify five candidate genes likely to contribute to both murine BIP-susceptibility and human MM risk. Surprisingly, we found KaLwRij mice had homozygous germline deletion of one of the five candidate genes, Samsn1, a putative negative regulator of B-cell activation. Consistent with the reported inhibitory function of SAMSN1 in B-cells, KaLwRij mice had enhanced naïve B-cell response in vitro following stimulation with IL4 and LPS and in vivo following immunization. We found that re-expression of Samsn1 in KaLwRij-derived myeloma cell line 5TGM1 decreased MM tumor growth in vitro, suggesting that loss of Samsn1 contributes to tumor growth in part through a cell intrinsic mechanism. Importantly, germline risk alleles may also influence cell types beyond the pre-malignant B-cells. It is established that the host environment also plays a critical role in supporting myeloma growth in KaLwRij mice. We found that Samsn1 is highly expressed in macrophages, but not in other MM-supportive cell types including osteoclasts and bone marrow stromal cells. Myeloma-associated macrophages play critical roles in supporting tumor growth and are broadly classified as anti-tumor M1 macrophages and tumor-promoting M2 macrophages. Microarray analysis of macrophages from B6 and KaLwRij mice prior to onset of BIP revealed three differentially expressed genes, but only one, Samsn1, is conserved in the human genome. We found that both unpolararized primary peritoneal and MCSF cultured bone marrow macrophages from healthy KaLwRij mice had markedly elevated expression of pro-tumorigenic M2 macrophage markers FIZZ1 and YM1 compared to B6 controls, despite the absence of activating cytokines. To confirm that loss of Samsn1 influenced macrophage activation and M2 polarization, we obtained bone marrow macrophages from mice with a targeted deletion of Samsn1 and found that unpolarized Samsn1-/- cells had similarly increased pro-tumor M2 macrophage marker expression. To evaluate the biologic significance of Samsn1 loss in macrophages on MM tumor growth, we established 5TGM1 MM tumors and injected M2 polarized macrophages from B6, KaLwRij, or Samsn1-/- mice. We found that MM tumor burden was markedly enhanced by the addition of a Samsn1-/- or KaLwRij macrophages compared to wild type macrophages. Thus, we found that loss of Samsn1 in macrophages enhances M2 polarization, prior to MGUS/MM development and can promote established myeloma tumor growth. Together, this data suggests that Samsn1 is a gene contributing to BIP-susceptibility in KaLwRij mice through mutually exclusive functions in multiple cell types including B-cells and macrophages. It is likely that SAMSN1 and its pathway members contribute to human MM progression, and may represent a point for preventative therapeutic intervention. Disclosures No relevant conflicts of interest to declare.