Systematic characterization of multi-omics landscape between gut microbial metabolites and GPCRome in Alzheimer's disease.

Shifts in the magnitude and nature of gut microbial metabolites have been implicated in Alzheimer's disease (AD), but the host receptors that sense and respond to these metabolites are largely unknown. Here, we develop a systems biology framework that integrates machine learning and multi-omics to identify molecular relationships of gut microbial metabolites with non-olfactory G-protein-coupled receptors (termed the "GPCRome"). We evaluate 1.09 million metabolite-protein pairs connecting 408 human GPCRs and 335 gut microbial metabolites. Using genetics-derived Mendelian randomization and integrative analyses of human brain transcriptomic and proteomic profiles, we identify orphan GPCRs (i.e., GPR84) as potential drug targets in AD and that triacanthine experimentally activates GPR84. We demonstrate that phenethylamine and agmatine significantly reduce tau hyperphosphorylation (p-tau181 and p-tau205) in AD patient induced pluripotent stem cell-derived neurons. This study demonstrates a systems biology framework to uncover the GPCR targets of human gut microbiota in AD and other complex diseases if broadly applied.

Network medicine informed multi-omics integration identifies drug targets and repurposable medicines for Amyotrophic Lateral Sclerosis.

Amyotrophic Lateral Sclerosis (ALS) is a devastating, immensely complex neurodegenerative disease by lack of effective treatments. To date, the challenge to establishing effective treatment for ALS remains formidable, partly due to inadequate translation of existing human genetic findings into actionable ALS-specific pathobiology for subsequent therapeutic development. This study evaluates the feasibility of network medicine methodology via integrating human brain-specific multi-omics data to prioritize drug targets and repurposable treatments for ALS. Using human brain-specific genome-wide quantitative trait loci (x-QTLs) under a network-based deep learning framework, we identified 105 putative ALS-associated genes enriched in various known ALS pathobiological pathways, including regulation of T cell activation, monocyte differentiation, and lymphocyte proliferation. Specifically, we leveraged non-coding ALS loci effects from genome-wide associated studies (GWAS) on brain-specific expression quantitative trait loci (QTL) (eQTL), protein QTLs (pQTL), splicing QTL (sQTL), methylation QTL (meQTL), and histone acetylation QTL (haQTL). Applying network proximity analysis of predicted ALS-associated gene-coding targets and existing drug-target networks under the human protein-protein interactome (PPI) model, we identified a set of potential repurposable drugs (including Diazoxide, Gefitinib, Paliperidone, and Dimethyltryptamine) for ALS. Subsequent validation established preclinical and clinical evidence for top-prioritized repurposable drugs. In summary, we presented a network-based multi-omics framework to identify potential drug targets and repurposable treatments for ALS and other neurodegenerative disease if broadly applied.

Alzheimer's disease-linked risk alleles elevate microglial cGAS-associated senescence and neurodegeneration in a tauopathy model.

The strongest risk factors for Alzheimer's disease (AD) include the χ4 allele of apolipoprotein E (APOE), the R47H variant of triggering receptor expressed on myeloid cells 2 (TREM2), and female sex. Here, we combine APOE4 and TREM2R47H ( R47H ) in female P301S tauopathy mice to identify the pathways activated when AD risk is the strongest, thereby highlighting disease-causing mechanisms. We find that the R47H variant induces neurodegeneration in female APOE4 mice without impacting hippocampal tau load. The combination of APOE4 and R47H amplified tauopathy-induced cell-autonomous microglial cGAS-STING signaling and type-I interferon response, and interferon signaling converged across glial cell types in the hippocampus. APOE4-R47H microglia displayed cGAS- and BAX-dependent upregulation of senescence, showing association between neurotoxic signatures and implicating mitochondrial permeabilization in pathogenesis. By uncovering pathways enhanced by the strongest AD risk factors, our study points to cGAS-STING signaling and associated microglial senescence as potential drivers of AD risk.

Altered gene expression in excitatory neurons is associated with Alzheimer's disease and its higher incidence in women.

Introduction: Alzheimer's disease (AD) is a neurodegenerative disorder involving interactions between different cell types in the brain. Previous single-cell and bulk expression Alzheimer's studies have reported conflicting findings about the key cell types and cellular pathways whose expression is primarily altered in this disease. We re-analyzed these data in a uniform, coherent manner aiming to resolve and extend past findings. Our analysis sheds light on the observation that females have higher AD incidence than males. Methods: We re-analyzed three single-cell transcriptomics datasets. We used the software Model-based Analysis of Single-cell Transcriptomics (MAST) to seek differentially expressed genes comparing AD cases to matched controls for both sexes together and each sex separately. We used the GOrilla software to search for enriched pathways among the differentially expressed genes. Motivated by the male/female difference in incidence, we studied genes on the X-chromosome, focusing on genes in the pseudoautosomal region (PAR) and on genes that are heterogeneous across individuals or tissues for X-inactivation. We validated findings by analyzing bulk AD datasets from the cortex in the Gene Expression Omnibus. Results: Our results resolve a contradiction in the literature, showing that by comparing AD patients to unaffected controls, excitatory neurons have more differentially expressed genes than do other cell types. Synaptic transmission and related pathways are altered in a sex-specific analysis of excitatory neurons. PAR genes and X-chromosome heterogeneous genes, including, for example, BEX1 and ELK1, may contribute to the difference in sex incidence of Alzheimer's disease. GRIN1, stood out as an overexpressed autosomal gene in cases versus controls in all three single-cell datasets and as a functional candidate gene contributing to pathways upregulated in cases. Discussion: Taken together, these results point to a potential linkage between two longstanding questions concerning AD pathogenesis, involving which cell type is the most important and why females have a higher incidence than males. Highlights: By reanalyzing three, published, single-cell RNAseq datasets, we resolved a contradiction in the literature and showed that when comparing AD patients to unaffected controls, excitatory neurons have more differentially expressed genes than do other cell types.Further analysis of the published single-cell datasets showed that synaptic transmission and related pathways are altered in a sex-specific analysis of excitatory neurons.Combining analysis of single-cell datasets and publicly available bulk transcriptomics datasets revealed that X-chromosome genes, such as BEX1, ELK1, and USP11, whose X-inactivation status is heterogeneous may contribute to the higher incidence in females of Alzheimer's disease.

Graph embedding and Gaussian mixture variational autoencoder network for end-to-end analysis of single-cell RNA sequencing data.

Single-cell RNA sequencing (scRNA-seq) is a revolutionary technology to determine the precise gene expression of individual cells and identify cell heterogeneity and subpopulations. However, technical limitations of scRNA-seq lead to heterogeneous and sparse data. Here, we present autoCell, a deep-learning approach for scRNA-seq dropout imputation and feature extraction. autoCell is a variational autoencoding network that combines graph embedding and a probabilistic depth Gaussian mixture model to infer the distribution of high-dimensional, sparse scRNA-seq data. We validate autoCell on simulated datasets and biologically relevant scRNA-seq. We show that interpolation of autoCell improves the performance of existing tools in identifying cell developmental trajectories of human preimplantation embryos. We identify disease-associated astrocytes (DAAs) and reconstruct DAA-specific molecular networks and ligand-receptor interactions involved in cell-cell communications using Alzheimer's disease as a prototypical example. autoCell provides a toolbox for end-to-end analysis of scRNA-seq data, including visualization, clustering, imputation, and disease-specific gene network identification.

Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer's disease.

Translating human genetic findings (genome-wide association studies [GWAS]) to pathobiology and therapeutic discovery remains a major challenge for Alzheimer's disease (AD). We present a network topology-based deep learning framework to identify disease-associated genes (NETTAG). We leverage non-coding GWAS loci effects on quantitative trait loci, enhancers and CpG islands, promoter regions, open chromatin, and promoter flanking regions under the protein-protein interactome. Via NETTAG, we identified 156 AD-risk genes enriched in druggable targets. Combining network-based prediction and retrospective case-control observations with 10 million individuals, we identified that usage of four drugs (ibuprofen, gemfibrozil, cholecalciferol, and ceftriaxone) is associated with reduced likelihood of AD incidence. Gemfibrozil (an approved lipid regulator) is significantly associated with 43% reduced risk of AD compared with simvastatin using an active-comparator design (95% confidence interval 0.51-0.63, p < 0.0001). In summary, NETTAG offers a deep learning methodology that utilizes GWAS and multi-genomic findings to identify pathobiology and drug repurposing in AD.

The Alzheimer's Cell Atlas (TACA): A single-cell molecular map for translational therapeutics accelerator in Alzheimer's disease.

Introduction: Recent advances in generating massive single-cell/nucleus transcriptomic data have shown great potential for facilitating the identification of cell type-specific Alzheimer's disease (AD) pathobiology and drug-target discovery for therapeutic development. Methods: We developed The Alzheimer's Cell Atlas (TACA) by compiling an AD brain cell atlas consisting of over 1.1 million cells/nuclei across 26 data sets, covering major brain regions (hippocampus, cerebellum, prefrontal cortex, and so on) and cell types (astrocyte, microglia, neuron, oligodendrocytes, and so on). We conducted nearly 1400 differential expression comparisons to identify cell type-specific molecular alterations (e.g., case vs healthy control, sex-specific, apolipoprotein E (APOE) ε4/ε4, and TREM2 mutations). Each comparison was followed by protein-protein interaction module detection, functional enrichment analysis, and omics-informed target and drug (over 700,000 perturbation profiles) screening. Over 400 cell-cell interaction analyses using 6000 ligand-receptor interactions were conducted to identify the cell-cell communication networks in AD. Results: All results are integrated into TACA (https://taca.lerner.ccf.org/), a new web portal with cell type-specific, abundant transcriptomic information, and 12 interactive visualization tools for AD. Discussion: We envision that TACA will be a highly valuable resource for both basic and translational research in AD, as it provides abundant information for AD pathobiology and actionable systems biology tools for drug discovery. Highlights: We compiled an Alzheimer's disease (AD) brain cell atlas consisting of more than 1.1 million cells/nuclei transcriptomes from 26 data sets, covering major brain regions (cortex, hippocampus, cerebellum) and cell types (e.g., neuron, oligodendrocyte, astrocyte, and microglia).We conducted over 1400 differential expression (DE) comparisons to identify cell type-specific gene expression alterations. Major comparison types are (1) AD versus healthy control; (2) sex-specific DE, (3) genotype-driven DE (i.e., apolipoprotein E [APOE] ε4/ε4 vs APOE ε3/ε3; TREM2R47H vs common variants) analysis; and (4) others. Each comparison was further followed by (1) human protein-protein interactome network module analysis, (2) pathway enrichment analysis, and (3) gene-set enrichment analysis.For drug screening, we conducted gene set enrichment analysis for all the comparisons with over 700,000 drug perturbation profiles connecting more than 10,000 human genes and 13,000 drugs/compounds.A total of over 400 analyses of cell-cell interactions against 6000 experimentally validated ligand-receptor interactions were conducted to reveal the disease-relevant cell-cell communications in AD.

Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease.

Dysregulation of gene expression in Alzheimer's disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, AD-induced regulatory changes across brain cell types remains uncharted. To address this, we integrated single-cell multi-omics datasets to predict the gene regulatory networks of four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes, in control and AD brains. Importantly, we analyzed and compared the structural and topological features of networks across cell types and examined changes in AD. Our analysis shows that hub TFs are largely common across cell types and AD-related changes are relatively more prominent in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are also highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes. The further disease-module-drug association analysis suggests cell-type candidate drugs and their potential target genes. Finally, our network-based machine learning analysis systematically prioritized cell type risk genes likely involved in AD. Our strategy is validated using an independent dataset which showed that top ranked genes can predict clinical phenotypes (e.g., cognitive impairment) of AD with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals cell-type gene dysregulation in AD.

Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer's disease.

BACKGROUND: Genome-wide association studies (GWAS) have identified numerous susceptibility loci for Alzheimer's disease (AD). However, utilizing GWAS and multi-omics data to identify high-confidence AD risk genes (ARGs) and druggable targets that can guide development of new therapeutics for patients suffering from AD has heretofore not been successful. METHODS: To address this critical problem in the field, we have developed a network-based artificial intelligence framework that is capable of integrating multi-omics data along with human protein-protein interactome networks to accurately infer accurate drug targets impacted by GWAS-identified variants to identify new therapeutics. When applied to AD, this approach integrates GWAS findings, multi-omics data from brain samples of AD patients and AD transgenic animal models, drug-target networks, and the human protein-protein interactome, along with large-scale patient database validation and in vitro mechanistic observations in human microglia cells. RESULTS: Through this approach, we identified 103 ARGs validated by various levels of pathobiological evidence in AD. Via network-based prediction and population-based validation, we then showed that three drugs (pioglitazone, febuxostat, and atenolol) are significantly associated with decreased risk of AD compared with matched control populations. Pioglitazone usage is significantly associated with decreased risk of AD (hazard ratio (HR) = 0.916, 95% confidence interval [CI] 0.861-0.974, P = 0.005) in a retrospective case-control validation. Pioglitazone is a peroxisome proliferator-activated receptor (PPAR) agonist used to treat type 2 diabetes, and propensity score matching cohort studies confirmed its association with reduced risk of AD in comparison to glipizide (HR = 0.921, 95% CI 0.862-0.984, P = 0.0159), an insulin secretagogue that is also used to treat type 2 diabetes. In vitro experiments showed that pioglitazone downregulated glycogen synthase kinase 3 beta (GSK3ß) and cyclin-dependent kinase (CDK5) in human microglia cells, supporting a possible mechanism-of-action for its beneficial effect in AD. CONCLUSIONS: In summary, we present an integrated, network-based artificial intelligence methodology to rapidly translate GWAS findings and multi-omics data to genotype-informed therapeutic discovery in AD.

Network medicine links SARS-CoV-2/COVID-19 infection to brain microvascular injury and neuroinflammation in dementia-like cognitive impairment.

BACKGROUND: Dementia-like cognitive impairment is an increasingly reported complication of SARS-CoV-2 infection. However, the underlying mechanisms responsible for this complication remain unclear. A better understanding of causative processes by which COVID-19 may lead to cognitive impairment is essential for developing preventive and therapeutic interventions. METHODS: In this study, we conducted a network-based, multimodal omics comparison of COVID-19 and neurologic complications. We constructed the SARS-CoV-2 virus-host interactome from protein-protein interaction assay and CRISPR-Cas9-based genetic assay results and compared network-based relationships therein with those of known neurological manifestations using network proximity measures. We also investigated the transcriptomic profiles (including single-cell/nuclei RNA-sequencing) of Alzheimer's disease (AD) marker genes from patients infected with COVID-19, as well as the prevalence of SARS-CoV-2 entry factors in the brains of AD patients not infected with SARS-CoV-2. RESULTS: We found significant network-based relationships between COVID-19 and neuroinflammation and brain microvascular injury pathways and processes which are implicated in AD. We also detected aberrant expression of AD biomarkers in the cerebrospinal fluid and blood of patients with COVID-19. While transcriptomic analyses showed relatively low expression of SARS-CoV-2 entry factors in human brain, neuroinflammatory changes were pronounced. In addition, single-nucleus transcriptomic analyses showed that expression of SARS-CoV-2 host factors (BSG and FURIN) and antiviral defense genes (LY6E, IFITM2, IFITM3, and IFNAR1) was elevated in brain endothelial cells of AD patients and healthy controls relative to neurons and other cell types, suggesting a possible role for brain microvascular injury in COVID-19-mediated cognitive impairment. Overall, individuals with the AD risk allele APOE E4/E4 displayed reduced expression of antiviral defense genes compared to APOE E3/E3 individuals. CONCLUSION: Our results suggest significant mechanistic overlap between AD and COVID-19, centered on neuroinflammation and microvascular injury. These results help improve our understanding of COVID-19-associated neurological manifestations and provide guidance for future development of preventive or treatment interventions, although causal relationship and mechanistic pathways between COVID-19 and AD need future investigations.

Network medicine links SARS-CoV-2/COVID-19 infection to brain microvascular injury and neuroinflammation in dementia-like cognitive impairment.

BACKGROUND: Dementia-like cognitive impairment is an increasingly reported complication of SARS-CoV-2 infection. However, the underlying mechanisms responsible for this complication remain unclear. A better understanding of causative processes by which COVID-19 may lead to cognitive impairment is essential for developing preventive interventions. METHODS: In this study, we conducted a network-based, multimodal genomics comparison of COVID-19 and neurologic complications. We constructed the SARS-CoV-2 virus-host interactome from protein-protein interaction assay and CRISPR-Cas9 based genetic assay results, and compared network-based relationships therein with those of known neurological manifestations using network proximity measures. We also investigated the transcriptomic profiles (including single-cell/nuclei RNA-sequencing) of Alzheimer's disease (AD) marker genes from patients infected with COVID-19, as well as the prevalence of SARS-CoV-2 entry factors in the brains of AD patients not infected with SARS-CoV-2. RESULTS: We found significant network-based relationships between COVID-19 and neuroinflammation and brain microvascular injury pathways and processes which are implicated in AD. We also detected aberrant expression of AD biomarkers in the cerebrospinal fluid and blood of patients with COVID-19. While transcriptomic analyses showed relatively low expression of SARS-CoV-2 entry factors in human brain, neuroinflammatory changes were pronounced. In addition, single-nucleus transcriptomic analyses showed that expression of SARS-CoV-2 host factors ( BSG and FURIN ) and antiviral defense genes ( LY6E , IFITM2 , IFITM3 , and IFNAR1 ) was significantly elevated in brain endothelial cells of AD patients and healthy controls relative to neurons and other cell types, suggesting a possible role for brain microvascular injury in COVID-19-mediated cognitive impairment. Notably, individuals with the AD risk allele APOE E4/E4 displayed reduced levels of antiviral defense genes compared to APOE E3/E3 individuals. CONCLUSION: Our results suggest significant mechanistic overlap between AD and COVID-19, strongly centered on neuroinflammation and microvascular injury. These results help improve our understanding of COVID-19-associated neurological manifestations and provide guidance for future development of preventive or treatment interventions.

TREM2 sustains macrophage-hepatocyte metabolic coordination in nonalcoholic fatty liver disease and sepsis.

Sepsis is a leading cause of death in critical illness, and its pathophysiology varies depending on preexisting medical conditions. Here we identified nonalcoholic fatty liver disease (NAFLD) as an independent risk factor for sepsis in a large clinical cohort and showed a link between mortality in NAFLD-associated sepsis and hepatic mitochondrial and energetic metabolism dysfunction. Using in vivo and in vitro models of liver lipid overload, we discovered a metabolic coordination between hepatocyte mitochondria and liver macrophages that express triggering receptor expressed on myeloid cells-2 (TREM2). Trem2-deficient macrophages released exosomes that impaired hepatocytic mitochondrial structure and energy supply because of their high content of miR-106b-5p, which blocks Mitofusin 2 (Mfn2). In a mouse model of NAFLD-associated sepsis, TREM2 deficiency accelerated the initial progression of NAFLD and subsequent susceptibility to sepsis. Conversely, overexpression of TREM2 in liver macrophages improved hepatic energy supply and sepsis outcome. This study demonstrates that NAFLD is a risk factor for sepsis, providing a basis for precision treatment, and identifies hepatocyte-macrophage metabolic coordination and TREM2 as potential targets for future clinical trials.

Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease.

Because disease-associated microglia (DAM) and disease-associated astrocytes (DAA) are involved in the pathophysiology of Alzheimer's disease (AD), we systematically identified molecular networks between DAM and DAA to uncover novel therapeutic targets for AD. Specifically, we develop a network-based methodology that leverages single-cell/nucleus RNA sequencing data from both transgenic mouse models and AD patient brains, as well as drug-target network, metabolite-enzyme associations, the human protein-protein interactome, and large-scale longitudinal patient data. Through this approach, we find both common and unique gene network regulators between DAM (i.e., PAK1, MAPK14, and CSF1R) and DAA (i.e., NFKB1, FOS, and JUN) that are significantly enriched by neuro-inflammatory pathways and well-known genetic variants (i.e., BIN1). We identify shared immune pathways between DAM and DAA, including Th17 cell differentiation and chemokine signaling. Last, integrative metabolite-enzyme network analyses suggest that fatty acids and amino acids may trigger molecular alterations in DAM and DAA. Combining network-based prediction and retrospective case-control observations with 7.2 million individuals, we identify that usage of fluticasone (an approved glucocorticoid receptor agonist) is significantly associated with a reduced incidence of AD (hazard ratio [HR] = 0.86, 95% confidence interval [CI] 0.83-0.89, P < 1.0 × 10-8). Propensity score-stratified cohort studies reveal that usage of mometasone (a stronger glucocorticoid receptor agonist) is significantly associated with a decreased risk of AD (HR = 0.74, 95% CI 0.68-0.81, P < 1.0 × 10-8) compared to fluticasone after adjusting age, gender, and disease comorbidities. In summary, we present a network-based, multimodal methodology for single-cell/nucleus genomics-informed drug discovery and have identified fluticasone and mometasone as potential treatments in AD.

Pik3c3 deficiency in myeloid cells imparts partial resistance to experimental autoimmune encephalomyelitis associated with reduced IL-1ß production.

The PIK3C3/VPS34 subunit of the class III phosphatidylinositol 3-kinase (PtdIns3K) complex plays a role in both canonical and noncanonical autophagy, key processes that control immune-cell responsiveness to a variety of stimuli. Our previous studies found that PIK3C3 is a critical regulator that controls the development, homeostasis, and function of dendritic and T cells. In this study, we investigated the role of PIK3C3 in myeloid cell biology using myeloid cell-specific Pik3c3-deficient mice. We found that Pik3c3-deficient macrophages express increased surface levels of major histocompatibility complex (MHC) class I and class II molecules. In addition, myeloid cell-specific Pik3c3 ablation in mice caused a partial impairment in the homeostatic maintenance of macrophages expressing the apoptotic cell uptake receptor TIM-4. Pik3c3 deficiency caused phenotypic changes in myeloid cells that were dependent on the early machinery (initiation/nucleation) of the classical autophagy pathway. Consequently, myeloid cell-specific Pik3c3-deficient animals showed significantly reduced severity of experimental autoimmune encephalomyelitis (EAE), a primarily CD4+ T-cell-mediated mouse model of multiple sclerosis (MS). This disease protection was associated with reduced accumulation of myelin-specific CD4+ T cells in the central nervous system and decreased myeloid cell IL-1ß production. Further, administration of SAR405, a selective PIK3C3 inhibitor, delayed disease progression. Collectively, our studies establish PIK3C3 as an important regulator of macrophage functions and myeloid cell-mediated regulation of EAE. Our findings also have important implications for the development of small-molecule inhibitors of PIK3C3 as therapeutic modulators of MS and other autoimmune diseases.

The Effect of LKB1 Activity on the Sensitivity to PI3K/mTOR Inhibition in Non-Small Cell Lung Cancer.

INTRODUCTION: Liver kinase B1 (LKB1), also called serine/threonine kinase 11 (STK11), is a tumor suppressor that functions as master regulator of cell growth, metabolism, survival, and polarity. Approximately 30% to 35% of patients with NSCLC possess inactivated liver kinase B1 gene (LKB1), and these patients respond poorly to anti-programmed cell death 1 (PD-1)/programmed death ligand 1 (PD-L1) immunotherapy. Therefore, novel therapies targeting NSCLC with LKB1 loss are needed. METHODS: We used a new in silico signaling analysis method to identify the potential therapeutic targets and reposition drugs by integrating gene expression data with the Kyoto Encyclopedia of Genes and Genomes signaling pathways. LKB1 wild-type and LKB1-deficient NSCLC cell lines, including knockout clones generated by clustered regularly interspaced short pallindromic repeats-Cas9, were treated with inhibitors of mechanistic target of rapamycin kinase (mTOR) and phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) and a dual inhibitor. RESULTS: In silico experiments showed that inhibition of both mTOR and PI3K can be synergistically effective in LKB1-deficient NSCLC. In vitro and in vivo experiments showed the synergistic effect of mTOR inhibition and PI3K inhibition in LKB1-mutant NSCLC cell lines. The sensitivity to dual inhibition of mTOR and PI3K is higher in LKB1-mutant cell lines than in wild-type cell lines. A higher compensatory increase in Akt phosphorylation after rapamycin treatment of LKB1-deficient cells than after rapamycin treatment of LKB1 wild-type cells is responsible for the synergistic effect of mTOR and PI3K inhibition. Dual inhibition of mTOR and PI3K resulted in a greater decrease in protein expression of cell cycle-regulating proteins in LKB1 knockout cells than in LKB1 wild-type cells. CONCLUSION: Dual molecular targeted therapy for mTOR and PI3K may be a promising therapeutic strategy in the specific population of patients with lung cancer with LKB1 loss.

Synergy from gene expression and network mining (SynGeNet) method predicts synergistic drug combinations for diverse melanoma genomic subtypes.

Systems biology perspectives are crucial for understanding the pathophysiology of complex diseases, and therefore hold great promise for the discovery of novel treatment strategies. Drug combinations have been shown to improve durability and reduce resistance to available first-line therapies in a variety of cancers; however, traditional drug discovery approaches are prohibitively cost and labor-intensive to evaluate large-scale matrices of potential drug combinations. Computational methods are needed to efficiently model complex interactions of drug target pathways and identify mechanisms underlying drug combination synergy. In this study, we employ a computational approach, SynGeNet (Synergy from Gene expression and Network mining), which integrates transcriptomics-based connectivity mapping and network centrality analysis to analyze disease networks and predict drug combinations. As an exemplar of a disease in which combination therapies demonstrate efficacy in genomic-specific contexts, we investigate malignant melanoma. We employed SynGeNet to generate drug combination predictions for each of the four major genomic subtypes of melanoma (BRAF, NRAS, NF1, and triple wild type) using publicly available gene expression and mutation data. We validated synergistic drug combinations predicted by our method across all genomic subtypes using results from a high-throughput drug screening study across. Finally, we prospectively validated the drug combination for BRAF-mutant melanoma that was top ranked by our approach, vemurafenib (BRAF inhibitor) + tretinoin (retinoic acid receptor agonist), using both in vitro and in vivo models of BRAF-mutant melanoma and RNA-sequencing analysis of drug-treated melanoma cells to validate the predicted mechanisms. Our approach is applicable to a wide range of disease domains, and, importantly, can model disease-relevant protein subnetworks in precision medicine contexts.

Core signaling pathways in ovarian cancer stem cell revealed by integrative analysis of multi-marker genomics data.

Tumor recurrence occurs in more than 70% of ovarian cancer patients, and the majority eventually becomes refractory to treatments. Ovarian Cancer Stem Cells (OCSCs) are believed to be responsible for the tumor relapse and drug resistance. Therefore, eliminating ovarian CSCs is important to improve the prognosis of ovarian cancer patients. However, there is a lack of effective drugs to eliminate OCSCs because the core signaling pathways regulating OCSCs remain unclear. Also it is often hard for biologists to identify a few testable targets and infer driver signaling pathways regulating CSCs from a large number of differentially expression genes in an unbiased manner. In this study, we propose a straightforward and integrative analysis to identify potential core signaling pathways of OCSCs by integrating transcriptome data of OCSCs isolated based on two distinctive markers, ALDH and side population, with regulatory network (Transcription Factor (TF) and Target Interactome) and signaling pathways. We first identify the common activated TFs in two OCSC populations integrating the gene expression and TF-target Interactome; and then uncover up-stream signaling cascades regulating the activated TFs. In specific, 22 activated TFs are identified. Through literature search validation, 15 of them have been reported in association with cancer stem cells. Additionally, 10 TFs are found in the KEGG signaling pathways, and their up-stream signaling cascades are extracted, which also provide potential treatment targets. Moreover, 40 FDA approved drugs are identified to target on the up-stream signaling cascades, and 15 of them have been reported in literatures in cancer stem cell treatment. In conclusion, the proposed approach can uncover the activated up-stream signaling, activated TFs and up-regulated target genes that constitute the potential core signaling pathways of ovarian CSC. Also drugs and drug combinations targeting on the core signaling pathways might be able to eliminate OCSCs. The proposed approach can also be applied for identifying potential activated signaling pathways of other types of cancers.

Diffusion mapping of drug targets on disease signaling network elements reveals drug combination strategies.

The emergence of drug resistance to traditional chemotherapy and newer targeted therapies in cancer patients is a major clinical challenge. Reactivation of the same or compensatory signaling pathways is a common class of drug resistance mechanisms. Employing drug combinations that inhibit multiple modules of reactivated signaling pathways is a promising strategy to overcome and prevent the onset of drug resistance. However, with thousands of available FDA-approved and investigational compounds, it is infeasible to experimentally screen millions of possible drug combinations with limited resources. Therefore, computational approaches are needed to constrain the search space and prioritize synergistic drug combinations for preclinical studies. In this study, we propose a novel approach for predicting drug combinations through investigating potential effects of drug targets on disease signaling network. We first construct a disease signaling network by integrating gene expression data with disease-associated driver genes. Individual drugs that can partially perturb the disease signaling network are then selected based on a drug-disease network "impact matrix", which is calculated using network diffusion distance from drug targets to signaling network elements. The selected drugs are subsequently clustered into communities (subgroups), which are proposed to share similar mechanisms of action. Finally, drug combinations are ranked according to maximal impact on signaling sub-networks from distinct mechanism-based communities. Our method is advantageous compared to other approaches in that it does not require large amounts drug dose response data, drug-induced "omics" profiles or clinical efficacy data, which are not often readily available. We validate our approach using a BRAF-mutant melanoma signaling network and combinatorial in vitro drug screening data, and report drug combinations with diverse mechanisms of action and opportunities for drug repositioning.

Heparan sulfate proteoglycans regulate autophagy in Drosophila.

Heparan sulfate-modified proteoglycans (HSPGs) are important regulators of signaling and molecular recognition at the cell surface and in the extracellular space. Disruption of HSPG core proteins, HS-synthesis, or HS-degradation can have profound effects on growth, patterning, and cell survival. The Drosophila neuromuscular junction provides a tractable model for understanding the activities of HSPGs at a synapse that displays developmental and activity-dependent plasticity. Muscle cell-specific knockdown of HS biosynthesis disrupted the organization of a specialized postsynaptic membrane, the subsynaptic reticulum (SSR), and affected the number and morphology of mitochondria. We provide evidence that these changes result from a dysregulation of macroautophagy (hereafter referred to as autophagy). Cellular and molecular markers of autophagy are all consistent with an increase in the levels of autophagy in the absence of normal HS-chain biosynthesis and modification. HS production is also required for normal levels of autophagy in the fat body, the central energy storage and nutritional sensing organ in Drosophila. Genetic mosaic analysis indicates that HS-dependent regulation of autophagy occurs non-cell autonomously, consistent with HSPGs influencing this cellular process via signaling in the extracellular space. These findings demonstrate that HS biosynthesis has important regulatory effects on autophagy and that autophagy is critical for normal assembly of postsynaptic membrane specializations.

iso-DGR sequences do not mediate binding of fibronectin N-terminal modules to adherent fibronectin-null fibroblasts.

Fibronectin (FN) without an RGD sequence (FN-RGE), and thus lacking the principal binding site for alpha5beta1 integrin, is deposited into the extracellular matrix of mouse embryos. Spontaneous conversion of (263)NGR and/or (501)NGR to iso-DGR possibly explains this enigma, i.e. ligation of iso-DGR by alphavbeta3 integrin may allow cells to assemble FN. Partial modification of (263)NGR to DGR or iso-DGR was detected in purified plasma FN by mass spectrometry. To test functions of the conversion, one or both NGR sequences were mutated to QGR in recombinant N-terminal 70-kDa construct of FN (70K), full-length FN, or FN-RGE. The mutations did not affect the binding of soluble 70K to already adherent fibroblasts or the ability of soluble 70K to compete with non-mutant FN or FN-RGE for binding to FN assembly sites. Non-mutant FN and FN-N263Q/N501Q with both NGRs mutated to QGRs were assembled equally well by adherent fibroblasts. FN-RGE and FN-RGE-N263Q/N501Q were also assembled equally well. Although substrate-bound 70K mediated cell adhesion in the presence of 1 mm Mn(2+) by a mechanism that was inhibited by cyclic RGD peptide, the peptide did not inhibit 70K binding to cell surface. Mutations of the NGR sequences had no effect on Mn(2+)-enhanced cell adhesion to adsorbed 70K but caused a decrease in cell adhesion to reduced and alkylated 70K. These results demonstrate that iso-DGR sequences spontaneously converted from NGR are cryptic and do not mediate the interaction of the 70K region of FN with the cell surface during FN assembly.