CD10+GPR77+ Cancer-Associated Fibroblasts Promote Cancer Formation and Chemoresistance by Sustaining Cancer Stemness

Carcinoma-associated fibroblasts (CAFs) are abundant and heterogeneous stromal cells in tumor microenvironment that are critically involved in cancer progression. Here, we demonstrate that two cell-surface molecules, CD10 and GPR77, specifically define a CAF subset correlated with chemoresistance and poor survival in multiple cohorts of breast and lung cancer patients. CD10⁺GPR77⁺ CAFs promote tumor formation and chemoresistance by providing a survival niche for cancer stem cells (CSCs). Mechanistically, CD10⁺GPR77⁺ CAFs are driven by persistent NF-κB activation via p65 phosphorylation and acetylation, which is maintained by complement signaling via GPR77, a C5a receptor. Furthermore, CD10⁺GPR77⁺ CAFs promote successful engraftment of patient-derived xenografts (PDXs), and targeting these CAFs with a neutralizing anti-GPR77 antibody abolishes tumor formation and restores tumor chemosensitivity. Our study reveals a functional CAF subset that can be defined and isolated by specific cell-surface markers and suggests that targeting the CD10⁺GPR77⁺ CAF subset could be an effective therapeutic strategy against CSC-driven solid tumors.

Large-scale proteomic analysis of Alzheimer's disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation

Our understanding of Alzheimer's disease (AD) pathophysiology remains incomplete. Here we used quantitative mass spectrometry and coexpression network analysis to conduct the largest proteomic study thus far on AD. A protein network module linked to sugar metabolism emerged as one of the modules most significantly associated with AD pathology and cognitive impairment. This module was enriched in AD genetic risk factors and in microglia and astrocyte protein markers associated with an anti-inflammatory state, suggesting that the biological functions it represents serve a protective role in AD. Proteins from this module were elevated in cerebrospinal fluid in early stages of the disease. In this study of >2,000 brains and nearly 400 cerebrospinal fluid samples by quantitative proteomics, we identify proteins and biological processes in AD brains that may serve as therapeutic targets and fluid biomarkers for the disease.

The use of classification trees for bioinformatics

Classification trees are non-parametric statistical learning methods that incorporate feature selection and interactions, possess intuitive interpretability, are efficient, and have high prediction accuracy when used in ensembles. This paper provides a brief introduction to the classification tree-based methods, a review of the recent developments, and a survey of the applications in bioinformatics and statistical genetics.

Quantifying hepatic shear modulus in vivo using acoustic radiation force

The speed at which shear waves propagate in tissue can be used to quantify the shear modulus of the tissue. As many groups have shown, shear waves can be generated within tissues using focused, impulsive, acoustic radiation force excitations, and the resulting displacement response can be ultrasonically tracked through time. The goals of the work herein are twofold: (i) to develop and validate an algorithm to quantify shear wave speed from radiation force-induced, ultrasonically-detected displacement data that is robust in the presence of poor displacement signal-to-noise ratio and (ii) to apply this algorithm to in vivo datasets acquired in human volunteers to demonstrate the clinical feasibility of using this method to quantify the shear modulus of liver tissue in longitudinal studies. The ultimate clinical application of this work is noninvasive quantification of liver stiffness in the setting of fibrosis and steatosis. In the proposed algorithm, time-to-peak displacement data in response to impulsive acoustic radiation force outside the region of excitation are used to characterize the shear wave speed of a material, which is used to reconstruct the material's shear modulus. The algorithm is developed and validated using finite element method simulations. By using this algorithm on simulated displacement fields, reconstructions for materials with shear moduli (mu) ranging from 1.3-5 kPa are accurate to within 0.3 kPa, whereas stiffer shear moduli ranging from 10-16 kPa are accurate to within 1.0 kPa. Ultrasonically tracking the displacement data, which introduces jitter in the displacement estimates, does not impede the use of this algorithm to reconstruct accurate shear moduli. By using in vivo data acquired intercostally in 20 volunteers with body mass indices ranging from normal to obese, liver shear moduli have been reconstructed between 0.9 and 3.0 kPa, with an average precision of +/-0.4 kPa. These reconstructed liver moduli are consistent with those reported in the literature (mu = 0.75-2.5 kPa) with a similar precision (+/-0.3 kPa). Repeated intercostal liver shear modulus reconstructions were performed on nine different days in two volunteers over a 105-day period, yielding an average shear modulus of 1.9 +/- 0.50 kPa (1.3-2.5 kPa) in the first volunteer and 1.8 +/- 0.4 kPa (1.1-3.0 kPa) in the second volunteer. The simulation and in vivo data to date demonstrate that this method is capable of generating accurate and repeatable liver stiffness measurements and appears promising as a clinical tool for quantifying liver stiffness.

Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer's Disease Progression

Alzheimer's disease (AD) displays a long asymptomatic stage before dementia. We characterize AD stage-associated molecular networks by profiling 14,513 proteins and 34,173 phosphosites in the human brain with mass spectrometry, highlighting 173 protein changes in 17 pathways. The altered proteins are validated in two independent cohorts, showing partial RNA dependency. Comparisons of brain tissue and cerebrospinal fluid proteomes reveal biomarker candidates. Combining with 5xFAD mouse analysis, we determine 15 Aβ-correlated proteins (e.g., MDK, NTN1, SMOC1, SLIT2, and HTRA1). 5xFAD shows a proteomic signature similar to symptomatic AD but exhibits activation of autophagy and interferon response and lacks human-specific deleterious events, such as downregulation of neurotrophic factors and synaptic proteins. Multi-omics integration prioritizes AD-related molecules and pathways, including amyloid cascade, inflammation, complement, WNT signaling, TGF-β and BMP signaling, lipid metabolism, iron homeostasis, and membrane transport. Some Aβ-correlated proteins are colocalized with amyloid plaques. Thus, the multilayer omics approach identifies protein networks during AD progression.

Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer's disease susceptibility

Here we use deep sequencing to identify sources of variation in mRNA splicing in the dorsolateral prefrontal cortex (DLPFC) of 450 subjects from two aging cohorts. Hundreds of aberrant pre-mRNA splicing events are reproducibly associated with Alzheimer's disease. We also generate a catalog of splicing quantitative trait loci (sQTL) effects: splicing of 3,006 genes is influenced by genetic variation. We report that altered splicing is the mechanism for the effects of the PICALM, CLU and PTK2B susceptibility alleles. Furthermore, we performed a transcriptome-wide association study and identified 21 genes with significant associations with Alzheimer's disease, many of which are found in known loci, whereas 8 are in novel loci. These results highlight the convergence of old and new genes associated with Alzheimer's disease in autophagy-lysosomal-related pathways. Overall, this study of the transcriptome of the aging brain provides evidence that dysregulation of mRNA splicing is a feature of Alzheimer's disease and is, in some cases, genetically driven.

The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer's disease

Alzheimer's disease (AD) affects half the US population over the age of 85 and is universally fatal following an average course of 10 years of progressive cognitive disability. Genetic and genome-wide association studies (GWAS) have identified about 33 risk factor genes for common, late-onset AD (LOAD), but these risk loci fail to account for the majority of affected cases and can neither provide clinically meaningful prediction of development of AD nor offer actionable mechanisms. This cohort study generated large-scale matched multi-Omics data in AD and control brains for exploring novel molecular underpinnings of AD. Specifically, we generated whole genome sequencing, whole exome sequencing, transcriptome sequencing and proteome profiling data from multiple regions of 364 postmortem control, mild cognitive impaired (MCI) and AD brains with rich clinical and pathophysiological data. All the data went through rigorous quality control. Both the raw and processed data are publicly available through the Synapse software platform.

An Efficient Platform for Astrocyte Differentiation from Human Induced Pluripotent Stem Cells

Growing evidence implicates the importance of glia, particularly astrocytes, in neurological and psychiatric diseases. Here, we describe a rapid and robust method for the differentiation of highly pure populations of replicative astrocytes from human induced pluripotent stem cells (hiPSCs), via a neural progenitor cell (NPC) intermediate. We evaluated this protocol across 42 NPC lines (derived from 30 individuals). Transcriptomic analysis demonstrated that hiPSC-astrocytes from four individuals are highly similar to primary human fetal astrocytes and characteristic of a non-reactive state. hiPSC-astrocytes respond to inflammatory stimulants, display phagocytic capacity, and enhance microglial phagocytosis. hiPSC-astrocytes also possess spontaneous calcium transient activity. Our protocol is a reproducible, straightforward (single medium), and rapid (<30 days) method to generate populations of hiPSC-astrocytes that can be used for neuron-astrocyte and microglia-astrocyte co-cultures for the study of neuropsychiatric disorders.

Directed Differentiation of Human Pluripotent Stem Cells to Microglia

Microglia, the immune cells of the brain, are crucial to proper development and maintenance of the CNS, and their involvement in numerous neurological disorders is increasingly being recognized. To improve our understanding of human microglial biology, we devised a chemically defined protocol to generate human microglia from pluripotent stem cells. Myeloid progenitors expressing CD14/CX3CR1 were generated within 30 days of differentiation from both embryonic and induced pluripotent stem cells (iPSCs). Further differentiation of the progenitors resulted in ramified microglia with highly motile processes, expressing typical microglial markers. Analyses of gene expression and cytokine release showed close similarities between iPSC-derived (iPSC-MG) and human primary microglia as well as clear distinctions from macrophages. iPSC-MG were able to phagocytose and responded to ADP by producing intracellular Ca²⁺ transients, whereas macrophages lacked such response. The differentiation protocol was highly reproducible across several pluripotent stem cell lines.

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Efficient protocol to generate human microglia from PSCs

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iPSC-derived microglia have ramified morphology and motile processes

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Expression and cytokine profiles of iPSC-derived microglia resemble primary microglia

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iPSC-derived microglia can phagocytose beads and respond to ADP

Circuit-wide Transcriptional Profiling Reveals Brain Region-Specific Gene Networks Regulating Depression Susceptibility

Depression is a complex, heterogeneous disorder and a leading contributor to the global burden of disease. Most previous research has focused on individual brain regions and genes contributing to depression. However, emerging evidence in humans and animal models suggests that dysregulated circuit function and gene expression across multiple brain regions drive depressive phenotypes. Here, we performed RNA sequencing on four brain regions from control animals and those susceptible or resilient to chronic social defeat stress at multiple time points. We employed an integrative network biology approach to identify transcriptional networks and key driver genes that regulate susceptibility to depressive-like symptoms. Further, we validated in vivo several key drivers and their associated transcriptional networks that regulate depression susceptibility and confirmed their functional significance at the levels of gene transcription, synaptic regulation, and behavior. Our study reveals novel transcriptional networks that control stress susceptibility and offers fundamentally new leads for antidepressant drug discovery.

Efficient Test and Visualization of Multi-Set Intersections

Identification of sets of objects with shared features is a common operation in all disciplines. Analysis of intersections among multiple sets is fundamental for in-depth understanding of their complex relationships. However, so far no method has been developed to assess statistical significance of intersections among three or more sets. Moreover, the state-of-the-art approaches for visualization of multi-set intersections are not scalable. Here, we first developed a theoretical framework for computing the statistical distributions of multi-set intersections based upon combinatorial theory, and then accordingly designed a procedure to efficiently calculate the exact probabilities of multi-set intersections. We further developed multiple efficient and scalable techniques to visualize multi-set intersections and the corresponding intersection statistics. We implemented both the theoretical framework and the visualization techniques in a unified R software package, SuperExactTest. We demonstrated the utility of SuperExactTest through an intensive simulation study and a comprehensive analysis of seven independently curated cancer gene sets as well as six disease or trait associated gene sets identified by genome-wide association studies. We expect SuperExactTest developed by this study will have a broad range of applications in scientific data analysis in many disciplines.

The innate immunity protein IFITM3 modulates γ-secretase in Alzheimer's disease

Innate immunity is associated with Alzheimer's disease1, but the influence of immune activation on the production of amyloid-β is unknown2,3. Here we identify interferon-induced transmembrane protein 3 (IFITM3) as a γ-secretase modulatory protein, and establish a mechanism by which inflammation affects the generation of amyloid-β. Inflammatory cytokines induce the expression of IFITM3 in neurons and astrocytes, which binds to γ-secretase and upregulates its activity, thereby increasing the production of amyloid-β. The expression of IFITM3 is increased with ageing and in mouse models that express familial Alzheimer's disease genes. Furthermore, knockout of IFITM3 reduces γ-secretase activity and the formation of amyloid plaques in a transgenic mouse model (5xFAD) of early amyloid deposition. IFITM3 protein is upregulated in tissue samples from a subset of patients with late-onset Alzheimer's disease that exhibit higher γ-secretase activity. The amount of IFITM3 in the γ-secretase complex has a strong and positive correlation with γ-secretase activity in samples from patients with late-onset Alzheimer's disease. These findings reveal a mechanism in which γ-secretase is modulated by neuroinflammation via IFITM3 and the risk of Alzheimer's disease is thereby increased.

Identification of the ron gene product as the receptor for the human macrophage stimulating protein

Macrophage-stimulating protein (MSP) is a member of the hepatocyte growth factor-scatter factor (HGF-SF) family. Labeled MSP bound to Madin-Darby canine kidney (MDCK) cells transfected with complementary DNA encoding Ron, a cell membrane protein tyrosine kinase. Cross-linking of 125I-labeled MSP to transfected cells (MDCK-RE7 cells) and immunoprecipitation by antibodies to Ron revealed a 220-kilodalton complex, a size consistent with that of MSP (80 kilodaltons) cross-linked to the beta chain of Ron (150 kilodaltons). The binding of 125I-labeled MSP to MDCK-RE7 cells was inhibited by unlabeled MSP, but not by HGF-SF. MSP caused phosphorylation of the beta chain of Ron and induced migration of MDCK-RE7 cells. These results establish the ron gene product as a specific cell-surface receptor for MSP.

Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimer's disease

Background

Alzheimer’s disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. However, despite extensive clinical and genomic studies, the molecular basis of AD development and progression remains elusive.

Methods

To elucidate molecular systems associated with AD, we developed a large scale gene expression dataset from 1053 postmortem brain samples across 19 cortical regions of 125 individuals with a severity spectrum of dementia and neuropathology of AD. We excluded brain specimens that evidenced neuropathology other than that characteristic of AD. For the first time, we performed a pan-cortical brain region genomic analysis, characterizing the gene expression changes associated with a measure of dementia severity and multiple measures of the severity of neuropathological lesions associated with AD (neuritic plaques and neurofibrillary tangles) and constructing region-specific co-expression networks. We rank-ordered 44,692 gene probesets, 1558 co-expressed gene modules and 19 brain regions based upon their association with the disease traits.

Results

The neurobiological pathways identified through these analyses included actin cytoskeleton, axon guidance, and nervous system development. Using public human brain single-cell RNA-sequencing data, we computed brain cell type-specific marker genes for human and determined that many of the abnormally expressed gene signatures and network modules were specific to oligodendrocytes, astrocytes, and neurons. Analysis based on disease severity suggested that: many of the gene expression changes, including those of oligodendrocytes, occurred early in the progression of disease, making them potential translational/treatment development targets and unlikely to be mere bystander result of degeneration; several modules were closely linked to cognitive compromise with lesser association with traditional measures of neuropathology. The brain regional analyses identified temporal lobe gyri as sites associated with the greatest and earliest gene expression abnormalities.

Conclusions

This transcriptomic network analysis of 19 brain regions provides a comprehensive assessment of the critical molecular pathways associated with AD pathology and offers new insights into molecular mechanisms underlying selective regional vulnerability to AD at different stages of the progression of cognitive compromise and development of the canonical neuropathological lesions of AD.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-016-0355-3) contains supplementary material, which is available to authorized users.

Meta-Analysis of the Alzheimer's Disease Human Brain Transcriptome and Functional Dissection in Mouse Models

We present a consensus atlas of the human brain transcriptome in Alzheimer's disease (AD), based on meta-analysis of differential gene expression in 2,114 postmortem samples. We discover 30 brain coexpression modules from seven regions as the major source of AD transcriptional perturbations. We next examine overlap with 251 brain differentially expressed gene sets from mouse models of AD and other neurodegenerative disorders. Human-mouse overlaps highlight responses to amyloid versus tau pathology and reveal age- and sex-dependent expression signatures for disease progression. Human coexpression modules enriched for neuronal and/or microglial genes broadly overlap with mouse models of AD, Huntington's disease, amyotrophic lateral sclerosis, and aging. Other human coexpression modules, including those implicated in proteostasis, are not activated in AD models but rather following other, unexpected genetic manipulations. Our results comprise a cross-species resource, highlighting transcriptional networks altered by human brain pathophysiology and identifying correspondences with mouse models for AD preclinical studies.

Molecular subtyping of Alzheimer's disease using RNA sequencing data reveals novel mechanisms and targets

Alzheimer's disease (AD), the most common form of dementia, is recognized as a heterogeneous disease with diverse pathophysiologic mechanisms. In this study, we interrogate the molecular heterogeneity of AD by analyzing 1543 transcriptomes across five brain regions in two AD cohorts using an integrative network approach. We identify three major molecular subtypes of AD corresponding to different combinations of multiple dysregulated pathways, such as susceptibility to tau-mediated neurodegeneration, amyloid-β neuroinflammation, synaptic signaling, immune activity, mitochondria organization, and myelination. Multiscale network analysis reveals subtype-specific drivers such as GABRB2, LRP10, MSN, PLP1, and ATP6V1A We further demonstrate that variations between existing AD mouse models recapitulate a certain degree of subtype heterogeneity, which may partially explain why a vast majority of drugs that succeeded in specific mouse models do not align with generalized human trials across all AD subtypes. Therefore, subtyping patients with AD is a critical step toward precision medicine for this devastating disease.

A targetable, injectable adenoviral vector for selective gene delivery to pulmonary endothelium in vivo

Adenoviral (Ad) vectors are promising gene therapy vehicles due to their in vivo stability and efficiency, but their potential utility is compromised by their restricted tropism. Targeting strategies have been devised to improve the efficacy of these agents, but specific targeting following in vivo systemic administration of vector has not previously been demonstrated. The distinct aim of the current study was to determine whether an Ad-targeting strategy could maintain fidelity upon systemic vascular administration. We used a bispecific antibody to target Ad infection specifically to angiotensin-converting enzyme (ACE), which is preferentially expressed on pulmonary capillary endothelium and which may thus enable gene therapy for pulmonary vascular disease. Cell-specific gene delivery to ACE-expressing cells was first confirmed in vitro. Administration of retargeted vector complex via tail vein injection into rats resulted in at least a 20-fold increase in both Ad DNA localization and luciferase transgene expression in the lungs, compared to the untargeted vector. Furthermore, targeting led to reduced transgene expression in nontarget organs, especially the liver, where the reduction was over 80%. Immunohistochemical and immunoelectron microscopy analysis confirmed that the pulmonary transgene expression was specifically localized to endothelial cells. Enhancement of transgene expression in the lungs as a result of the ACE-targeting strategy was also confirmed using a new noninvasive imaging technique. This study shows that a retargeting approach can indeed specifically modify the gene delivery properties of an Ad vector given systemically and thus has encouraging implications for the further development of targetable, injectable Ad vectors.

DGCA: A comprehensive R package for Differential Gene Correlation Analysis

BACKGROUND

Dissecting the regulatory relationships between genes is a critical step towards building accurate predictive models of biological systems. A powerful approach towards this end is to systematically study the differences in correlation between gene pairs in more than one distinct condition.

RESULTS

In this study we develop an R package, DGCA (for Differential Gene Correlation Analysis), which offers a suite of tools for computing and analyzing differential correlations between gene pairs across multiple conditions. To minimize parametric assumptions, DGCA computes empirical p-values via permutation testing. To understand differential correlations at a systems level, DGCA performs higher-order analyses such as measuring the average difference in correlation and multiscale clustering analysis of differential correlation networks. Through a simulation study, we show that the straightforward z-score based method that DGCA employs significantly outperforms the existing alternative methods for calculating differential correlation. Application of DGCA to the TCGA RNA-seq data in breast cancer not only identifies key changes in the regulatory relationships between TP53 and PTEN and their target genes in the presence of inactivating mutations, but also reveals an immune-related differential correlation module that is specific to triple negative breast cancer (TNBC).

CONCLUSIONS

DGCA is an R package for systematically assessing the difference in gene-gene regulatory relationships under different conditions. This user-friendly, effective, and comprehensive software tool will greatly facilitate the application of differential correlation analysis in many biological studies and thus will help identification of novel signaling pathways, biomarkers, and targets in complex biological systems and diseases.

Back injuries in industry: a retrospective study. II. Injury factors

To evaluate the impact of back injury on industry, the authors conducted an extensive retrospective analysis of injuries among hourly employees of The Boeing Company, the largest industrial manufacturer in western Washington. The Boeing Company provided injury information on 31,200 employees for a 15-month period from July 1, 1979 to September 30, 1980. From this information, we analyzed 4,645 injury claims filed as of February 28, 1981 by 3,958 different employees. There were 900 back injuries in this group. Claims were categorized according to total incurred cost (TIC), made up of the medical costs and indemnity costs. High-cost claims were defined as those with a TIC greater than $10,000, and low-cost claims were those with a TIC less than $10,000. Among 857 claimants with 900 back injuries, lifting or material handling was much more commonly considered the cause of injury than accidents such as slips or falls. Accidents, however, had a much greater tendency to result in an expensive claim. The authors could not make reliable conclusions regarding injuries and 32 job skill classifications. The rate of injury did not vary according to day of the week or month, but a significantly higher rate of high-cost back injuries was noted on the day shift than on the evening or night shifts.

Crystallographic structure of a small molecule SIRT1 activator-enzyme complex

SIRT1, the founding member of the mammalian family of seven NAD⁺-dependent sirtuins, is composed of 747 amino acids forming a catalytic domain and extended N- and C-terminal regions. We report the design and characterization of an engineered human SIRT1 construct (mini-hSIRT1) containing the minimal structural elements required for lysine deacetylation and catalytic activation by small molecule sirtuin-activating compounds (STACs). Using this construct, we solved the crystal structure of a mini-hSIRT1-STAC complex, which revealed the STAC-binding site within the N-terminal domain of hSIRT1. Together with hydrogen-deuterium exchange mass spectrometry (HDX-MS) and site-directed mutagenesis using full-length hSIRT1, these data establish a specific STAC-binding site and identify key intermolecular interactions with hSIRT1. The determination of the interface governing the binding of STACs with human SIRT1 facilitates greater understanding of STAC activation of this enzyme, which holds significant promise as a therapeutic target for multiple human diseases.

Sirtuins are NAD⁺-dependent deacylases implicated in the regulation of stress responses, bioenergetics and epigenetic control. Here the authors describe the crystal structure of a sirtuin-activating compounds (STAC)-sirtuin complex and begin to elucidate the mechanism of sirtuins activation by STACs.

Gene transfer to ovarian cancer versus normal tissues with fiber-modified adenoviruses

Adenovirus serotype 5 (Ad5) displays unparalleled gene transfer efficacy to cells with high coxsackie-adenovirus receptor (CAR) expression. Unfortunately, cells isolated from clinical human cancers, both ovarian and other types, express highly variable and often low levels of CAR. Fortunately, native Ad5 tropism can be modified to circumvent CAR deficiency and to enhance infectivity. Ad5/3luc1 incorporates the serotype 3 fiber knob and binds to a receptor distinct from CAR, while the fiber of Ad5lucRGD is modified with an RGD-4C motif, allowing CAR-independent binding to integrins. We studied the liver tropism and blood clearance of these viruses after intravenous (i.v.) injection, and biodistribution after intraperitoneal (i.p.) injection to tumor-bearing mice. To estimate efficacy, we assessed gene transfer to purified human primary ovarian cancer cells, and in a mouse model of ovarian cancer. Ad5/3luc1 achieved improved gene transfer over Ad5lucRGD, and both infectivity-enhanced viruses were superior to the isogenic control with an unmodified Ad5 capsid. In the presence of malignant ascites, gene transfer was improved with both Ad5/3luc1 and Ad5lucRGD. Thus, retargeting to the Ad3 receptor enhances gene transfer to clinically relevant ovarian cancer substrates, while the mouse toxicity and biodistribution profile of both fiber-modified Ad vectors is comparable to Ad5.

Enhanced therapeutic efficacy for ovarian cancer with a serotype 3 receptor-targeted oncolytic adenovirus

Oncolytic viruses that are replication competent in tumor but not in normal cells represent a novel approach for treating neoplastic diseases. However, the oncolytic potency of replicating agents is determined directly by their capability of infecting target cells. Most adenoviruses used for gene therapy or virotherapy have been based on serotype 5 (Ad5). Unfortunately, expression of the primary receptor for Ad5 (the coxsackie-adenovirus receptor, or CAR) is highly variable on ovarian and other cancer cells. By performing genetic fiber pseudotyping, we created Ad5/3-Delta24, a conditionally replicating adenovirus that does not bind CAR but facilitates entry into and killing of ovarian cancer cells. We show replication of Ad5/3-Delta24 and subsequent oncolysis of ovarian adenocarcinoma lines. Replication was also analyzed with quantitative PCR on three-dimensional primary tumor cell spheroids purified from patient samples. Moreover, in a therapeutic orthotopic model of peritoneal carcinomatosis, dramatically enhanced survival was noted. Finally, Ad5/3-Delta24 achieved a significant antitumor effect as assessed by noninvasive, in vivo bioluminescence imaging. Therefore, the preclinical therapeutic efficacy of Ad5/3-Delta24 is improved over the respective CAR- and integrin-binding controls. Taken together with promising biodistribution and toxicity data, this approach could translate into successful clinical interventions for ovarian cancer patients.

LOCSVMPSI: a web server for subcellular localization of eukaryotic proteins using SVM and profile of PSI-BLAST

Subcellular location of a protein is one of the key functional characters as proteins must be localized correctly at the subcellular level to have normal biological function. In this paper, a novel method named LOCSVMPSI has been introduced, which is based on the support vector machine (SVM) and the position-specific scoring matrix generated from profiles of PSI-BLAST. With a jackknife test on the RH2427 data set, LOCSVMPSI achieved a high overall prediction accuracy of 90.2%, which is higher than the prediction results by SubLoc and ESLpred on this data set. In addition, prediction performance of LOCSVMPSI was evaluated with 5-fold cross validation test on the PK7579 data set and the prediction results were consistently better than the previous method based on several SVMs using composition of both amino acids and amino acid pairs. Further test on the SWISSPROT new-unique data set showed that LOCSVMPSI also performed better than some widely used prediction methods, such as PSORTII, TargetP and LOCnet. All these results indicate that LOCSVMPSI is a powerful tool for the prediction of eukaryotic protein subcellular localization. An online web server (current version is 1.3) based on this method has been developed and is freely available to both academic and commercial users, which can be accessed by at .

circGSK3β promotes metastasis in esophageal squamous cell carcinoma by augmenting β-catenin signaling

Background

Circular RNAs (circRNAs), a novel class of noncoding RNAs, have recently drawn much attention in the pathogenesis of human cancers. However, the role of circRNAs in esophageal squamous cell carcinoma (ESCC) remains unclear. In this study, we aimed to identify novel circRNAs that regulate ESCC progression and explored their regulatory mechanisms and clinical significance in ESCC.

Methods

Differentially expressed circRNAs between ESCC and paired adjacent normal tissues were identified using microarrays. The effects of a specific differentially expressed circRNA (circGSK3β) on tumor progression were explored in vitro and in vivo. Plasma samples from patients with ESCC, benign lesions and healthy controls were subjected to droplet digital PCR (ddPCR) analyses for circGSK3β, and the detection rates of plasma circGSK3β for ESCC were investigated.

Results

We demonstrated that upregulated expression of circGSK3β was positively associated with advanced clinical stage and poor outcome in patients with ESCC. We further revealed that circGSK3β promoted ESCC cell migration and invasion via direct interaction with GSK3β and inhibiting GSK3β activity, providing a novel mechanism of circRNA in cancer progression. Importantly, we identified that circGSK3β expression in plasma was a biomarker for detection of ESCC and early stage of ESCC with the area under curve (AUC) of 0.782 and 0.793, respectively.

Conclusions

CircGSK3β exerts critical roles in promoting ESCC metastasis and may serve as a novel therapeutic target for ESCC patients. The plasma level of circGSK3β have potential to serve as a novel diagnostic and prognostic biomarker for ESCC detection.