Identification of differentially distributed gene expression and distinct sets of cancer-related genes identified by changes in mean and variability.
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ABSTRACT:
SUBMITTER: Roberts AGK
PROVIDER: S-EPMC8759562 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Neurons in macaque cortical area MSTd are driven by visual motion and eye movement related signals. This multimodal characteristic makes MSTd an ideal system for studying the dependence of neuronal activity on different variables. Here, we analyzed the temporal structure of spiking patterns during visual motion stimulation using 2 distinct behavioral paradigms: fixation (FIX) and optokinetic response. For the FIX condition, inter- and intra-trial variability of spiking activity decreased with increasing stimulus strength, complying with a recent neurophysiological study reporting stimulus-related decline of neuronal variability. In contrast, for the optokinetic condition variability increased together with increasing eye velocity while retinal image velocity remained low. Analysis of stimulus signal variability revealed a correlation between the normalized variance of image velocity and neuronal variability, but no correlation with normalized eye velocity variance. We further show that the observed difference in neuronal variability allows classifying spike trains according to the paradigm used, even when mean firing rates (FRs) were similar. The stimulus-dependence of neuronal variability may result from the local network structure and/or the variability characteristics of the input signals, but may also reflect additional timing-based mechanisms independent of the neuron's mean FR and related to the modality driving the neuron.
| S-EPMC3698363 | biostudies-literature
Project description:PURPOSE: To identify the differentially expressed genes (DEGs) in the human keratocytes in keratoconus. METHODS: Total RNA extracted from cultured corneal stromal fibroblasts from normal and keratoconic corneas were used for the synthesis of cDNA. DEGs were screened by an annealing control primer(TM)-based PCR method using GeneFishing() DEG kits. The differentially expressed bands were sequenced and analyzed. The genes identified were further evaluated by reverse transcriptase PCR and quantitative real-time PCR. RESULTS: Overexpression of bone morphogenetic protein 4 (BMP4), cofilin 1 (CFL1), and JAW1-related protein (MRVI1) and underexpression of actin, alpha 2 (ACTA2), gene rich cluster, and C 10 gene (GRCC10), tissue inhibitor of metalloproteinase 3 (TIMP3), tissue inhibitor of metalloproteinase 1 (TIMP1), and somatostatin receptor 1 (SSTR1) were verified, and these results were confirmed by reverse transcriptase PCR and quantitative real-time PCR. CONCLUSIONS: Eight genes were identified to be differentially expressed in keratoconus and related with apoptosis, the cytoskeleton, wound healing, and nerve fibers. The genes identified may be involved in the mechanism underlying stromal thinning; thus, they could be important and deserve further investigation.
| S-EPMC2786887 | biostudies-literature
Project description:BackgroundCDK9 is the catalytic subunit of the Positive Transcription Elongation Factor b (P-TEFb), which phosphorylates the CTD of RNAPII and negative elongation factors enabling for productive elongation after initiation. CDK9 associates with T-type cyclins and cyclin K and its activity is tightly regulated in cells at different levels. CDK9 is also the catalytic subunit of TAK (Tat activating Kinase), essential for HIV1 replication. Because of CDK9's potential as a therapeutic target in AIDS, cancer, inflammation, and cardiomyophathy it is important to understand the consequences of CDK9 inhibition. A previous gene expression profiling study performed with human glioblastoma T98G cells in which CDK9 activity was inhibited either with a dominant negative mutant form of CDK9 (dnCDK9) or the pharmacological inhibitor Flavopiridol unveiled striking differences in gene expression effects. In the present report we extended these studies by (1) using both immortalized normal human fibroblasts and primary human astrocytes, (2) eliminating potential experimental variability due to transduction methodology and (3) also modulating CDK9 activity with siRNA.FindingsStriking differences in the effects on gene expression resulting from the strategy used to inhibit CDK9 activity (dnCDK9 or FVP) remain even when potential variability due to viral transduction is eliminated. siRNA mediated CDK9 knockdown in human fibroblasts and astrocytes efficiently reduced CDK9 expression and led to potent changes in gene expression that exhibit little correlation with the effects of dnCDK9 or FVP. Interestingly, HEXIM1 a validated CDK9 target gene, was found to be potently downregulated by dnCDK9, FVP and siCDK9, but the cluster of genes with expression profiles similar to HEXIM1 was small. Finally, cluster analysis of all treatments revealed higher correlation between treatments than cell type origin.ConclusionThe nature of the strategy used to inhibit CDK9 profoundly affects the patterns of gene expression resulting from CDK9 inhibition. These results suggest multiple variables that affect outcome, including kinetics of inhibition, potency, off-target effects, and selectivity issues. This is particularly important when considering CDK9 as a potential target for therapeutic intervention.
| S-EPMC4045923 | biostudies-literature
Project description:Background: Although the etiology of Hashimoto's thyroiditis (HT), a common autoimmune endocrine disease, is unknown, studies suggest a potential association with genetic factors and environmental conditions inducing excessive iodine intake. Additionally, HT patients have a high risk of papillary thyroid cancer (PTC), which is probably related to the chronic inflammation and autoimmune pathologic process occurring in HT, as it is thought to be associated with neoplastic transformation. Methods: Bioinformatics approaches can identify differentially expressed genes (DEGs) and analyze DEG functions in diseases. R software was used in this study to identify DEGs in HT and PTC using data in Gene Expression Omnibus (GEO). The online tools DAVID, Reactome, and AmiGO were employed for annotation, visualization, and integration of DEGs related to HT and PTC, and the STRING database and Cytoscape software were applied to predict and visualize protein-protein networks (PPIs) for DEG-encoded proteins. Coexpressed DEGs in HT and PTC were validated by reverse transcription PCR (RT-PCR). Results: In total, 326, 231, and 210 DEGs in HT specimens and samples of central PTC and PTC invasive areas, respectively, were detected. According to the PPI network, PTPN6, HLA-A, C3AR1, LCK and ITGB2 are hub genes among HT-DEGs, whereas FN1, CDH2, SERPINA1, and CYR61 are PTC-DEG hub genes. The shared DEGs LTF and CCL21 were validated by RT-PCR. Both bioinformatics and RT-PCR analyses showed LTF and CCL21 to be upregulated in HT tissues and downregulated in PTC tissues. Conclusions: We identified that expression of LTF and CCL21 are significantly different in HT and PTC, suggesting an underlying association between HT and PTC.
| S-EPMC8436097 | biostudies-literature
Project description:In the big data era, the generation of data presents some new characteristics, including wide distribution, high velocity, high dimensionality, and privacy concern. To address these challenges for big data analytics, we develop a privacy-preserving distributed online learning framework on the data collected from distributed data sources. Specifically, each node (i.e., data source) has the capacity of learning a model from its local dataset, and exchanges intermediate parameters with a random part of their own neighboring (logically connected) nodes. Hence, the topology of the communications in our distributed computing framework is unfixed in practice. As online learning always performs on the sensitive data, we introduce the notion of differential privacy (DP) into our distributed online learning algorithm (DOLA) to protect the data privacy during the learning, which prevents an adversary from inferring any significant sensitive information. Our model is of general value for big data analytics in the distributed setting, because it can provide rigorous and scalable privacy proof and have much less computational complexity when compared to classic schemes, e.g., secure multiparty computation (SMC). To tackle high-dimensional incoming data entries, we study a sparse version of the DOLA with novel DP techniques to save the computing resources and improve the utility. Furthermore, we present two modified private DOLAs to meet the need of practical applications. One is to convert the DOLA to distributed stochastic optimization in an offline setting, the other is to use the mini-batches approach to reduce the amount of the perturbation noise and improve the utility. We conduct experiments on real datasets in a configured distributed platform. Numerical experiment results validate the feasibility of our private DOLAs.
| S-EPMC6764830 | biostudies-literature
Project description:BackgroundTo understand the carcinogenesis caused by accumulated genetic and epigenetic alterations and seek novel biomarkers for various cancers, studying differentially expressed genes between cancerous and normal tissues is crucial. In the study, two cDNA libraries of lung cancer were constructed and screened for identification of differentially expressed genes.MethodsTwo cDNA libraries of differentially expressed genes were constructed using lung adenocarcinoma tissue and adjacent nonmalignant lung tissue by suppression subtractive hybridization. The data of the cDNA libraries were then analyzed and compared using bioinformatics analysis. Levels of mRNA and protein were measured by quantitative real-time polymerase chain reaction (q-RT-PCR) and western blot respectively, as well as expression and localization of proteins were determined by immunostaining. Gene functions were investigated using proliferation and migration assays after gene silencing and gene over-expression.ResultsTwo libraries of differentially expressed genes were obtained. The forward-subtracted library (FSL) and the reverse-subtracted library (RSL) contained 177 and 59 genes, respectively. Bioinformatic analysis demonstrated that these genes were involved in a wide range of cellular functions. The vast majority of these genes were newly identified to be abnormally expressed in lung cancer. In the first stage of the screening for 16 genes, we compared lung cancer tissues with their adjacent non-malignant tissues at the mRNA level, and found six genes (ERGIC3, DDR1, HSP90B1, SDC1, RPSA, and LPCAT1) from the FSL were significantly up-regulated while two genes (GPX3 and TIMP3) from the RSL were significantly down-regulated (P < 0.05). The ERGIC3 protein was also over-expressed in lung cancer tissues and cultured cells, and expression of ERGIC3 was correlated with the differentiated degree and histological type of lung cancer. The up-regulation of ERGIC3 could promote cellular migration and proliferation in vitro.ConclusionsThe two libraries of differentially expressed genes may provide the basis for new insights or clues for finding novel lung cancer-related genes; several genes were newly found in lung cancer with ERGIC3 seeming a novel lung cancer-related gene. ERGIC3 may play an active role in the development and progression of lung cancer.
| S-EPMC3567939 | biostudies-literature
Project description:BACKGROUND:Using next-generation sequencing technology to measure gene expression, an empirically intriguing question concerns the identification of differentially expressed genes across treatment groups. Existing methods aim to identify genes whose mean expressions differ among treatment groups by assuming equal dispersion across all groups. For syndromes, however, various combinations of gene expression alterations can result in the same disease, leading to greater heteroscedasticity in the biological replicates in the disease group compared to the normal group. Traditional methods that only consider changes in the mean will fail to fully analyze gene expression in such a scenario. In addition, sequencing technology is relatively expensive; most labs can only afford a few replicates per treatment group, which poses further challenges to reliably estimating the mean and dispersion under each treatment condition. RESULTS:We designed an empirical Bayes method and a pooled permutation test to simultaneously consider the change in mean and dispersion across treatment groups. We further computed confidence intervals based on Bayes estimates to identify differentially expressed genes that are unique to each disease sample as well as those that are common across all disease samples. We illustrated our method by applying it to gene expression data from a large offspring syndrome experiment, which motivated this study. We compared our method to competing approaches through simulation studies that mimicked the real datasets to demonstrate the effectiveness of our proposed method. CONCLUSIONS:We will show that, compared to popular methods that only aim to find the difference in the mean, our method can capture greater variation in the disease group to effectively identify differentially expressed genes for syndromes.
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Project description:Stochastic fluctuations at the transcriptional level contribute to isogenic cell-to-cell heterogeneity in mammalian cell populations. However, we still have no clear understanding of the repercussions of this heterogeneity, given the lack of tools to independently control mean expression and variability of a gene. Here, we engineer a synthetic circuit to modulate mean expression and heterogeneity of transgenes and endogenous human genes. The circuit, a Tunable Noise Rheostat (TuNR), consists of a transcriptional cascade of two inducible transcriptional activators, where the output mean and variance can be modulated by two orthogonal small molecule inputs. In this fashion, different combinations of the inputs can achieve the same mean but with different population variability. With TuNR, we achieve low basal expression, over 1000-fold expression of a transgene product, and up to 7-fold induction of the endogenous gene NGFR. Importantly, for the same mean expression level, we are able to establish varying degrees of heterogeneity in expression within an isogenic population, thereby decoupling gene expression noise from its mean. TuNR is therefore a modular tool that can be used in mammalian cells to enable direct interrogation of the implications of cell-to-cell variability.
| S-EPMC7804932 | biostudies-literature
Project description:Breast cancer patients have different responses to chemotherapeutic treatments. Genes associated with drug response can provide insight to understand the mechanisms of drug resistance, identify promising therapeutic opportunities, and facilitate personalized treatment. Estrogen receptor (ER) positive and ER negative breast cancer have distinct clinical behavior and molecular properties. However, to date, few studies have rigorously assessed drug response genes in them. In this study, our goal was to systematically identify genes associated with multidrug response in ER positive and ER negative breast cancer cell lines. We tested 27 human breast cell lines for response to seven chemotherapeutic agents (cyclophosphamide, docetaxel, doxorubicin, epirubicin, fluorouracil, gemcitabine, and paclitaxel). We integrated publicly available gene expression profiles of these cell lines with their in vitro drug response patterns, then applied meta-analysis to identify genes related to multidrug response in ER positive and ER negative cells separately. One hundred eighty-eight genes were identified as related to multidrug response in ER positive and 32 genes in ER negative breast cell lines. Of these, only three genes (DBI, TOP2A, and PMVK) were common to both cell types. TOP2A was positively associated with drug response, and DBI was negatively associated with drug response. Interestingly, PMVK was positively associated with drug response in ER positive cells and negatively in ER negative cells. Functional analysis showed that while cell cycle affects drug response in both ER positive and negative cells, most biological processes that are involved in drug response are distinct. A number of signaling pathways that are uniquely enriched in ER positive cells have complex cross talk with ER signaling, while in ER negative cells, enriched pathways are related to metabolic functions. Taken together, our analysis indicates that distinct mechanisms are involved in multidrug response in ER positive and ER negative breast cells.
| S-EPMC3397945 | biostudies-literature
Project description:BACKGROUND: The availability of genome and transcriptome sequences for a number of species permits the identification and characterization of conserved as well as divergent genes such as lineage-specific genes which have no detectable sequence similarity to genes from other lineages. While genes conserved among taxa provide insight into the core processes among species, lineage-specific genes provide insights into evolutionary processes and biological functions that are likely clade or species specific. RESULTS: Comparative analyses using the Arabidopsis thaliana genome and sequences from 178 other species within the Plant Kingdom enabled the identification of 24,624 A. thaliana genes (91.7%) that were termed Evolutionary Conserved (EC) as defined by sequence similarity to a database entry as well as two sets of lineage-specific genes within A. thaliana. One of the A. thaliana lineage-specific gene sets share sequence similarity only to sequences from species within the Brassicaceae family and are termed Conserved Brassicaceae-Specific Genes (914, 3.4%, CBSG). The other set of A. thaliana lineage-specific genes, the Arabidopsis Lineage-Specific Genes (1,324, 4.9%, ALSG), lack sequence similarity to any sequence outside A. thaliana. While many CBSGs (76.7%) and ALSGs (52.9%) are transcribed, the majority of the CBSGs (76.1%) and ALSGs (94.4%) have no annotated function. Co-expression analysis indicated significant enrichment of the CBSGs and ALSGs in multiple functional categories suggesting their involvement in a wide range of biological functions. Subcellular localization prediction revealed that the CBSGs were significantly enriched in proteins targeted to the secretory pathway (412, 45.1%). Among the 107 putatively secreted CBSGs with known functions, 67 encode a putative pollen coat protein or cysteine-rich protein with sequence similarity to the S-locus cysteine-rich protein that is the pollen determinant controlling allele specific pollen rejection in self-incompatible Brassicaceae species. Overall, the ALSGs and CBSGs were more highly methylated in floral tissue compared to the ECs. Single Nucleotide Polymorphism (SNP) analysis showed an elevated ratio of non-synonymous to synonymous SNPs within the ALSGs (1.99) and CBSGs (1.65) relative to the EC set (0.92), mainly caused by an elevated number of non-synonymous SNPs, indicating that they are fast-evolving at the protein sequence level. CONCLUSIONS: Our analyses suggest that while a significant fraction of the A. thaliana proteome is conserved within the Plant Kingdom, evolutionarily distinct sets of genes that may function in defining biological processes unique to these lineages have arisen within the Brassicaceae and A. thaliana.
| S-EPMC2829037 | biostudies-literature