Traumatic events generate some of the most enduring forms of memories. Despite the elevated lifetime prevalence of anxiety disorders, effective strategies to attenuate long-term traumatic memories are scarce. The most efficacious treatments to diminish recent (i.e., day-old) traumata capitalize on memory updating mechanisms during reconsolidation that are initiated upon memory recall. Here, we show that, in mice, successful reconsolidation-updating paradigms for recent memories fail to attenuate remote (i.e., month-old) ones. We find that, whereas recent memory recall induces a limited period of hippocampal neuroplasticity mediated, in part, by S-nitrosylation of HDAC2 and histone acetylation, such plasticity is absent for remote memories. However, by using an HDAC2-targeting inhibitor (HDACi) during reconsolidation, even remote memories can be persistently attenuated. This intervention epigenetically primes the expression of neuroplasticity-related genes, which is accompanied by higher metabolic, synaptic, and structural plasticity. Thus, applying HDACis during memory reconsolidation might constitute a treatment option for remote traumata.
Respiratory syncytial virus (RSV) is the most important pathogen for lower respiratory tract illness in children for which there is no licensed vaccine. Live-attenuated RSV vaccines are the most clinically advanced in children, but achieving an optimal balance of attenuation and immunogenicity is challenging. One way to potentially retain or enhance immunogenicity of attenuated virus is to mutate virulence genes that suppress host immune responses. The NS1 and NS2 virulence genes of the RSV A2 strain were codon deoptimized according to either human or virus codon usage bias, and the resulting recombinant viruses (dNSh and dNSv, respectively) were rescued by reverse genetics. RSV dNSh exhibited the desired phenotype of reduced NS1 and NS2 expression. RSV dNSh was attenuated in BEAS-2B and primary differentiated airway epithelial cells but not in HEp-2 or Vero cells. In BALB/c mice, RSV dNSh exhibited a lower viral load than did A2, and yet it induced slightly higher levels of RSV-neutralizing antibodies than did A2. RSV A2 and RSV dNSh induced equivalent protection against challenge strains A/1997/12-35 and A2-line19F. RSV dNSh caused less STAT2 degradation and less NF-κB activation than did A2 in vitro. Serial passage of RSV dNSh in BEAS-2B cells did not result in mutations in the deoptimized sequences. Taken together, RSV dNSh was moderately attenuated, more immunogenic, and equally protective compared to wild-type RSV and genetically stable.
Respiratory syncytial virus (RSV) is the leading cause of infant viral death in the United States and worldwide, and no vaccine is available. Live-attenuated RSV vaccines are the most studied in children but have suffered from genetic instability and low immunogenicity. In order to address both obstacles, we selectively changed the codon usage of the RSV nonstructural (NS) virulence genes NS1 and NS2 to the least-used codons in the human genome (deoptimization). Compared to parental RSV, the codon-deoptimized NS1/NS2 RSV was attenuated in vitro and in mice but induced higher levels of neutralizing antibodies and equivalent protection against challenge. We identified a new attenuating module that retains immunogenicity and is genetically stable, achieved through specific targeting of nonessential virulence genes by codon usage deoptimization.
Cardiotoxicity is an aggravating side effect of many clinical antineoplastic agents such as arsenic trioxide (As2O3), which is the first-line treatment for acute promyelocytic leukemia (APL). Clinically, drug combination strategies are widely applied for complex disease management. Here, an optimized, cardiac-friendly therapeutic strategy for APL was investigated using a combination of As2O3 and genistein or resveratrol. Potential combinations were explored with respect to their effects on mitochondrial membrane potential, reactive oxygen species, superoxide dismutase activity, autophagy, and apoptosis in both NB4 cells and neonatal rat left ventricular myocytes. All experiments consistently suggested that 5 µM resveratrol remarkably alleviates As2O3-induced cardiotoxicity. To achieve an equivalent effect, a 10-fold dosage of genistein was required, thus highlighting the dose advantage of resveratrol, as poor bioavailability is a common concern for its clinical application. Co-administration of resveratrol substantially amplified the anticancer effect of As2O3 in NB4 cells. Furthermore, resveratrol exacerbated oxidative stress, mitochondrial damage, and apoptosis, thereby reflecting its full range of synergism with As2O3. Addition of 5 µM resveratrol to the single drug formula of As2O3 also further increased the expression of LC3, a marker of cellular autophagy activity, indicating an involvement of autophagy-mediated tumor cell death in the synergistic action. Our results suggest a possible application of an As2O3 and resveratrol combination to treat APL in order to achieve superior therapeutics effects and prevent cardiotoxicity.
Motivation: Fragmented RNA immunoprecipitation combined with RNA sequencing enabled the unbiased study of RNA epigenome at a near single-base resolution; however, unique features of this new type of data call for novel computational techniques.
Result: Through examining the connections of RNA epigenome sequencing data with two well-studied data types, ChIP-Seq and RNA-Seq, we unveiled the salient characteristics of this new data type. The computational strategies were discussed accordingly, and a novel data processing pipeline was proposed that combines several existing tools with a newly developed exome-based approach ‘exomePeak’ for detecting, representing and visualizing the post-transcriptional RNA modification sites on the transcriptome.
Availability: The MATLAB package ‘exomePeak’ and additional details are available at http://compgenomics.utsa.edu/exomePeak/.
firstname.lastname@example.org or email@example.com
Supplementary data are available at Bioinformatics online.
The study was designed to evaluate the efficacy and safety of lesser trochanteric osteotomy for femoral shortening in total hip arthroplasty in treatment of 28 cases of CROWE IV developmental dysplasia of the hip (DDH).
Patients underwent progressive femoral shortening at the level of lesser trochanteric to make reduction possible into the anatomical acetabulum in all hips. The results were collected and evaluated clinically and radiographically.
The mean follow-up period was 55.3 months. The average postoperative leg length discrepancy was eight millimetres for unilateral THA patients. A modified Merle d’Aubigné scale was improved from 9.3 preoperatively to 15.9 postoperatively. Sciatic nerve palsy was confirmed in two hips which resolved completely in six months. The Trendelenburg sign was positive in two hips at the final follow-up. No revision surgery was required by the final follow-up.
Lesser trochanteric osteotomy proved to be safe and effective in femoral shortening for treatment of CROWE IV DDH without the problem of nonunion at the site of osteotomy.
Numerous drugs and compounds have been validated as protecting against myocardial ischemia (MI), a leading cause of heart failure; however, synergistic possibilities among them have not been systematically explored. Thus, there appears to be significant room for optimization in the field of drug combination therapy for MI. Here, we propose an easy approach for the identification and optimization of MI-related synergistic drug combinations via visualization of the crosstalk between networks of drug targets corresponding to different drugs (each drug has a unique network of targets). As an example, in the present study, 28 target crosstalk networks (TCNs) of random pairwise combinations of 8 MI-related drugs (curcumin, capsaicin, celecoxib, raloxifene, silibinin, sulforaphane, tacrolimus, and tamoxifen) were established to illustrate the proposed method. The TCNs revealed a high likelihood of synergy between curcumin and the other drugs, which was confirmed by in vitro experiments. Further drug combination optimization showed a synergistic protective effect of curcumin, celecoxib, and sililinin in combination against H2O2-induced ischemic injury of cardiomyocytes at a relatively low concentration of 500 nM. This result is in agreement with the earlier finding of a denser and modular functional crosstalk between their networks of targets in the regulation of cell apoptosis. Our study offers a simple approach to rapidly search for and optimize potent synergistic drug combinations, which can be used for identifying better MI therapeutic strategies. Some new light was also shed on the characteristic features of drug synergy, suggesting that it is possible to apply this method to other complex human diseases.
With the trend of an increasing aged population worldwide, Alzheimer's disease (AD), an age-related neurodegenerative disorder, as one of the major causes of dementia in elderly people is of growing concern. Despite the many hard efforts attempted during the past several decades in trying to elucidate the pathological mechanisms underlying AD and putting forward potential therapeutic strategies, there is still a lack of effective treatments for AD. The efficacy of many potential therapeutic drugs for AD is of main concern in clinical practice. For example, large bodies of evidence show that the anti-tumor histone deacetylase (HDAC) inhibitor, suberoylanilidehydroxamic acid (SAHA), may be of benefit for the treatment of AD; however, its extensive inhibition of HDACs makes it a poor therapeutic. Moreover, the natural flavonoid, curcumin, may also have a potential therapeutic benefit against AD; however, it is plagued by low bioavailability. Therefore, the integrative effects of SAHA and curcumin were investigated as a protection against amyloid-beta neurotoxicity in vitro. We hypothesized that at low doses their synergistic effect would improve therapeutic selectivity, based on experiments that showed that at low concentrations SAHA and curcumin could provide comprehensive protection against Aβ25–35-induced neuronal damage in PC12 cells, strongly implying potent synergism. Furthermore, network analysis suggested that the possible mechanism underlying their synergistic action might be derived from restoration of the damaged functional link between Akt and the CBP/p300 pathway, which plays a crucial role in the pathological development of AD. Thus, our findings provided a feasible avenue for the application of a synergistic drug combination, SAHA and curcumin, in the treatment of AD.
Regulated antisense RNA (asRNA) expression has been employed successfully in Gram-positive bacteria for genome-wide essential gene identification and drug target determination. However, there have been no published reports describing the application of asRNA gene silencing for comprehensive analyses of essential genes in Gram-negative bacteria. In this study, we report the first genome-wide identification of asRNA constructs for essential genes in Escherichia coli. We screened 250,000 library transformants for conditional growth-inhibitory recombinant clones from two shot-gun genomic libraries of E. coli using a paired-termini expression vector (pHN678). After sequencing plasmid inserts of 675 confirmed inducer-sensitive cell clones, we identified 152 separate asRNA constructs of which 134 inserts came from essential genes while 18 originated from non-essential genes (but share operons with essential genes). Among the 79 individual essential genes silenced by these asRNA constructs, 61 genes (77%) engage in processes related to protein synthesis. The cell-based assays of an asRNA clone targeting fusA (encoding elongation factor G) showed that the induced cells were sensitized 12 fold to fusidic acid, a known specific inhibitor. Our results demonstrate the utility of the paired-termini expression vector and feasibility of large-scale gene silencing in E. coli using regulated asRNA expression.
Antibiotic; antisense RNA; Escherichia coli; essential gene; operon
Cancer treatment-related bone loss has become growing problematic, especially in breast and prostate cancer treated with hormone/endocrine therapy, chemotherapy and radiotherapy. However, bone loss caused by targeted therapy in cancer patients is largely unknown yet. In present study, a kinase inhibitors screen was applied for MC3T3-E1, a murine osteoprogenitor cell line, and seven kinase inhibitors (GSK1838705A, PF-04691502, Dasatinib, Masitinib, GDC-0941, XL880 and Everolimus) were found to suppress the cell viability with dose- and time-dependent manner. The most interesting is that many kinase inhibitors (such as lapatinib, erlotinib and sunitinib) can promote MC3T3-E1 cell proliferation at 0.01 μM. 4 out of 7 inhibitors were selected to perform the functional study and found that they lead to cell cycle dysregulation, treatments of PF-04691502 (AKT inhibitor), Dasatinib (Src inhibitor) and Everolimus (mTOR inhibitor) lead to G1 arrest of MC3T3-E1 cells via downregulation of cyclin D1 and p-AKT, whereas XL880 (MET and VEGFR inhibitor) treatment results in increase of sub-G1 and G2/M phase by upregulation of p53 protein. Our work provides important indications for the comprehensive care of cancer patients treated with some targeted drugs.
Cancer treatment-related bone loss; kinases inhibitors screening; osteoprogenitor cells
The neuroimmunological and behavioral consequences of a high-fat diet (HFD) are not well delineated. This is especially true when short term (24 h) fasting is used as a physiologic stressor. In this study, we examined the impact of a HFD on learning and memory and depressive-like behaviors to understand how fasting impacts neuroimmunity and if obesity modulates the response. Mice were fed diets containing either 10% (LFD mice) or 60% (HFD mice) calories from fat for 10-12 wks. Gene transcripts for 26 pro-/anti-inflammatory cytokines and markers of macrophage activation were examined in adipose tissue and whole brain. Mouse learning and memory (spontaneous alternation, novel object) and depressive like behaviors (saccharin preference, burrowing, forced swim) were studied in the fed and fasted state as were gene transcripts for F4/80, CD11b, IL-1alpha, IL-1beta, IL-1R1, IL-1R2, IL-1RA, IL-6 and TNF-alpha in cortex, hippocampus and hypothalamus. In the fed state, HFD mice compared to LFD mice had reduced locomotor activity, were adverse to saccharin and burrowed less. After fasting, LFD mice verse HFD mice lost 18% vs 5% of their body weight, respectively. In addition, HFD mice failed to down-regulate gene transcripts for the myeloid-cell associated proteins F4/80, CD11b and IL-1alpha in the brain, failed to appropriately explore a novel object, failed to reduce locomotor activity and had increased saccharin consumption and burrowing. These data indicate that fasting induces an anti-inflammatory effect on the neuroimmune system which a HFD prevents. This breakdown appears linked to the IL-1 system because of the association of this cytokine with memory and learning.
obesity; high-fat diet; starvation; neuroimmunity; depressive like behaviors; learning; memory
DNA methylation occurs in the context of a CpG dinucleotide. It is an important epigenetic modification, which can be inherited through cell division. The two major types of methylation include hypomethylation and hypermethylation. Unique methylation patterns have been shown to exist in diseases including various types of cancer. DNA methylation analysis promises to become a powerful tool in cancer diagnosis, treatment and prognostication. Large-scale methylation arrays are now available for studying methylation genome-wide. The Illumina methylation platform simultaneously measures cytosine methylation at more than 1500 CpG sites associated with over 800 cancer-related genes. Cluster analysis is often used to identify DNA methylation subgroups for prognosis and diagnosis. However, due to the unique non-Gaussian characteristics, traditional clustering methods may not be appropriate for DNA and methylation data, and the determination of optimal cluster number is still problematic.
A Dirichlet process beta mixture model (DPBMM) is proposed that models the DNA methylation expressions as an infinite number of beta mixture distribution. The model allows automatic learning of the relevant parameters such as the cluster mixing proportion, the parameters of beta distribution for each cluster, and especially the number of potential clusters. Since the model is high dimensional and analytically intractable, we proposed a Gibbs sampling "no-gaps" solution for computing the posterior distributions, hence the estimates of the parameters.
The proposed algorithm was tested on simulated data as well as methylation data from 55 Glioblastoma multiform (GBM) brain tissue samples. To reduce the computational burden due to the high data dimensionality, a dimension reduction method is adopted. The two GBM clusters yielded by DPBMM are based on data of different number of loci (P-value < 0.1), while hierarchical clustering cannot yield statistically significant clusters.
This paper considers the problem of automatic characterization and detection of target images in a rapid serial visual presentation (RSVP) task based on EEG data. A novel method that aims to identify single-trial event-related potentials (ERPs) in time-frequency is proposed, and a robust classifier with feature clustering is developed to better utilize the correlated ERP features. The method is applied to EEG recordings of a RSVP experiment with multiple sessions and subjects.
The results show that the target image events are mainly characterized by 3 distinct patterns in the time-frequency domain, i.e., a theta band (4.3 Hz) power boosting 300–700 ms after the target image onset, an alpha band (12 Hz) power boosting 500–1000 ms after the stimulus onset, and a delta band (2 Hz) power boosting after 500 ms. The most discriminant time-frequency features are power boosting and are relatively consistent among multiple sessions and subjects.
Since the original discriminant time-frequency features are highly correlated, we constructed the uncorrelated features using hierarchical clustering for better classification of target and non-target images. With feature clustering, performance (area under ROC) improved from 0.85 to 0.89 on within-session tests, and from 0.76 to 0.84 on cross-subject tests. The constructed uncorrelated features were more robust than the original discriminant features and corresponded to a number of local regions on the time-frequency plane.
Availability: The data and code are available at: http://compgenomics.cbi.utsa.edu/rsvp/index.html
Infections by viruses are associated with approximately 12% of human cancer. Kaposi’s sarcoma-associated herpesvirus (KSHV) is causally linked to several malignancies commonly found in AIDS patients. The mechanism of KSHV-induced oncogenesis remains elusive, due in part to the lack of an adequate experimental system for cellular transformation of primary cells. Here, we report efficient infection and cellular transformation of primary rat embryonic metanephric mesenchymal precursor cells (MM cells) by KSHV. Cellular transformation occurred at as early as day 4 after infection and in nearly all infected cells. Transformed cells expressed hallmark vascular endothelial, lymphatic endothelial, and mesenchymal markers and efficiently induced tumors in nude mice. KSHV established latent infection in MM cells, and lytic induction resulted in low levels of detectable infectious virions despite robust expression of lytic genes. Most KSHV-induced tumor cells were in a latent state, although a few showed heterogeneous expression of lytic genes. This efficient system for KSHV cellular transformation of primary cells might facilitate the study of growth deregulation mechanisms resulting from KSHV infections.
Transcriptional regulation by transcription factor (TF) controls the time and abundance of mRNA transcription. Due to the limitation of current proteomics technologies, large scale measurements of protein level activities of TFs is usually infeasible, making computational reconstruction of transcriptional regulatory network a difficult task.
We proposed here a novel Bayesian non-negative factor model for TF mediated regulatory networks. Particularly, the non-negative TF activities and sample clustering effect are modeled as the factors from a Dirichlet process mixture of rectified Gaussian distributions, and the sparse regulatory coefficients are modeled as the loadings from a sparse distribution that constrains its sparsity using knowledge from database; meantime, a Gibbs sampling solution was developed to infer the underlying network structure and the unknown TF activities simultaneously. The developed approach has been applied to simulated system and breast cancer gene expression data. Result shows that, the proposed method was able to systematically uncover TF mediated transcriptional regulatory network structure, the regulatory coefficients, the TF protein level activities and the sample clustering effect. The regulation target prediction result is highly coordinated with the prior knowledge, and sample clustering result shows superior performance over previous molecular based clustering method.
The results demonstrated the validity and effectiveness of the proposed approach in reconstructing transcriptional networks mediated by TFs through simulated systems and real data.
An algorithm for the discovery of time varying modules using genome-wide expression data is present here. When applied to large-scale time serious data, our method is designed to discover not only the transcription modules but also their timing information, which is rarely annotated by the existing approaches. Rather than assuming commonly defined time constant transcription modules, a module is depicted as a set of genes that are co-regulated during a specific period of time, i.e., a time dependent transcription module (TDTM). A rigorous mathematical definition of TDTM is provided, which is serve as an objective function for retrieving modules. Based on the definition, an effective signature algorithm is proposed that iteratively searches the transcription modules from the time series data. The proposed method was tested on the simulated systems and applied to the human time series microarray data during Kaposi's sarcoma-associated herpesvirus (KSHV) infection. The result has been verified by Expression Analysis Systematic Explorer.
Bacterial fimbriae can accept foreign peptides and display them on the cell surface. A highly efficient gene replacement method was used to generate peptide vaccines based on Salmonella enterica serovar Typhimurium SL3261. The T-cell epitopes (NY-ESO-1 p157-165 and p157-167) from NY-ESO-1, which is a promising target antigen in patients for the specific immune recognition of cancer, were incorporated into the gene encoding AgfA (the major subunit protein of thin aggregative fimbriae of Salmonella) by replacing an equal length of the DNA segment. To improve cytotoxic T-lymphocyte recognition, both termini of the peptide were flanked by double alanine (AA) residues. Immunofluorescence microscopy with AgfA-specific antiserum verified the expression of chimeric AgfA, which was also proved by a Congo red binding assay. Oral immunizations of HLA-A*0201 transgenic mice with recombinant SL3261 strains encoding NY-ESO-1 p157-165 or p157-167 induced NY-ESO-1 p157-165-specific CD8+ T cells, detected by an HLA-A*0201 pentamer, and induced a T-cell response detected by an enzyme-linked immunospot assay. The Salmonella fimbrial display system was efficient at the induction of an antitumor cellular immune response in vivo, providing a new strategy for the development of efficient cancer vaccinations.
Response of cells to changing endogenous or exogenous conditions is governed by intricate molecular interactions, or regulatory networks. To lead to appropriate responses, regulatory network should be 1) context-specific, i.e., its constituents and topology depend on the phonotypical and experimental context including tissue types and cell conditions, such as damage, stress, macroenvironments of cell, etc. and 2) time varying, i.e., network elements and their regulatory roles change actively over time to control the endogenous cell states e.g. different stages in a cell cycle.
A novel network model PathRNet and a reconstruction approach PATTERN are proposed for reconstructing the context specific time varying regulatory networks by integrating microarray gene expression profiles and existing knowledge of pathways and transcription factors. The nodes of the PathRNet are Transcription Factors (TFs) and pathways, and edges represent the regulation between pathways and TFs. The reconstructed PathRNet for Kaposi's sarcoma-associated herpesvirus infection of human endothelial cells reveals the complicated dynamics of the underlying regulatory mechanisms that govern this intricate process. All the related materials including source code are available at http://compgenomics.utsa.edu/tvnet.html.
The proposed PathRNet provides a system level landscape of the dynamics of gene regulatory circuitry. The inference approach PATTERN enables robust reconstruction of the temporal dynamics of pathway-centric regulatory networks. The proposed approach for the first time provides a dynamic perspective of pathway, TF regulations, and their interaction related to specific endogenous and exogenous conditions.
Motivation: Clustering is a popular data exploration technique widely used in microarray data analysis. When dealing with time-series data, most conventional clustering algorithms, however, either use one-way clustering methods, which fail to consider the heterogeneity of temporary domain, or use two-way clustering methods that do not take into account the time dependency between samples, thus producing less informative results. Furthermore, enrichment analysis is often performed independent of and after clustering and such practice, though capable of revealing biological significant clusters, cannot guide the clustering to produce biologically significant result.
Result:We present a new enrichment constrained framework (ECF) coupled with a time-dependent iterative signature algorithm (TDISA), which, by applying a sliding time window to incorporate the time dependency of samples and imposing an enrichment constraint to parameters of clustering, allows supervised identification of temporal transcription modules (TTMs) that are biologically meaningful. Rigorous mathematical definitions of TTM as well as the enrichment constraint framework are also provided that serve as objective functions for retrieving biologically significant modules. We applied the enrichment constrained time-dependent iterative signature algorithm (ECTDISA) to human gene expression time-series data of Kaposi's sarcoma-associated herpesvirus (KSHV) infection of human primary endothelial cells; the result not only confirms known biological facts, but also reveals new insight into the molecular mechanism of KSHV infection.
Availability: Data and Matlab code are available at http://engineering.utsa.edu/∼yfhuang/ECTDISA.html
Supplementary information: Supplementary data are available at Bioinformatics online.