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:The microRNAs, also known as miRNAs, are the class of small noncoding RNAs. They repress the expression of a gene posttranscriptionally. In effect, they regulate expression of a gene or protein. It has been observed that they play an important role in various cellular processes and thus help in carrying out normal functioning of a cell. However, dysregulation of miRNAs is found to be a major cause of a disease. Various studies have also shown the role of miRNAs in cancer and the utility of miRNAs for the diagnosis of cancer and other diseases. Unlike with mRNAs, a modest number of miRNAs might be sufficient to classify human cancers. However, the absence of a robust method to identify differentially expressed miRNAs makes this an open problem. In this regard, this paper presents a novel approach for in silico identification of differentially expressed miRNAs from microarray expression data sets. It integrates judiciously the theory of rough sets and merit of the so-called B.632+ bootstrap error estimate. While rough sets select relevant and significant miRNAs from expression data, the B.632+ error rate minimizes the variability and bias of the derived results. The effectiveness of the proposed approach, along with a comparison with other related approaches, is demonstrated on several miRNA microarray expression data sets, using the support vector machine.
| S-EPMC3790281 | 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:Neurodegenerative diseases are characterized by distinct protein aggregates, such as those of α-synuclein and tau. Lysosomal defect is a key contributor to the accumulation and propagation of aberrant protein aggregates in these diseases. The discoveries of common proteinopathies in multiple forms of lysosomal storage diseases (LSDs) and the identification of some LSD genes as susceptible genes for those proteinopathies suggest causative links between LSDs and the proteinopathies. The present study hypothesized that defects in lysosomal genes will differentially affect the propagation of α-synuclein and tau proteins, thereby determining the progression of a specific proteinopathy. We established an imaging-based high-contents screening (HCS) system in Caenorhabditis elegans (C. elegans) model, by which the propagation of α-synuclein or tau is measured by fluorescence intensity. Using this system, we performed RNA interference (RNAi) screening to induce a wide range of lysosomal malfunction through knock down of 79 LSD genes, and to obtain the candidate genes with significant change in protein propagation. While some LSD genes commonly affected both α-synuclein and tau propagation, our study identified the distinct sets of LSD genes that differentially regulate the propagation of either α-synuclein or tau. The specificity and efficacy of these LSD genes were retained in the disease-related phenotypes, such as pharyngeal pumping behavior and life span. This study suggests that distinct lysosomal genes differentially regulate the propagation of α-synuclein and tau, and offer a steppingstone to understanding disease specificity. [BMB Reports 2023; 56(12): 657-662].
| S-EPMC10761752 | 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:MOTIVATION:Cancer genomics studies frequently aim to identify genes that are differentially expressed between clinically distinct patient subgroups, generally by testing single genes one at a time. However, the results of any individual transcriptomic study are often not fully reproducible. A particular challenge impeding statistical analysis is the difficulty of distinguishing between differential expression comprising part of the genomic disease etiology and that induced by downstream effects. More robust analytical approaches that are well-powered to detect potentially causative genes, are less prone to discovering spurious associations, and can deliver reproducible findings across different studies are needed. RESULTS:We propose a set-based procedure for testing of differential expression and show that this set-based approach can produce more robust results by aggregating information across multiple, correlated genomic markers. Specifically, we adapt the Generalized Berk-Jones statistic to test for the transcription factors that may contribute to the progression of estrogen receptor positive breast cancer. We demonstrate the ability of our method to produce reproducible findings by applying the same analysis to 21 publicly available datasets, producing a similar list of significant transcription factors across most studies. Our Generalized Berk-Jones approach produces results that show improved consistency over three set-based testing algorithms: Generalized Higher Criticism, Gene Set Analysis and Gene Set Enrichment Analysis. AVAILABILITY AND IMPLEMENTATION:Data are in the MetaGxBreast R package. Code is available at github.com/ryanrsun/gaynor_sun_GBJ_breast_cancer. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
| S-EPMC6853697 | biostudies-literature
Project description:To explore the feature of cancer cells and tumor subclones, we analyzed 101,065 single-cell transcriptomes from 12 colorectal cancer (CRC) patients and 92 single cell genomes from one of these patients. We found cancer cells, endothelial cells and stromal cells in tumor tissue expressed much more genes and had stronger cell-cell interactions than their counterparts in normal tissue. We identified copy number variations (CNVs) in each cancer cell and found correlation between gene copy number and expression level in cancer cells at single cell resolution. Analysis of tumor subclones inferred by CNVs showed accumulation of mutations in each tumor subclone along lineage trajectories. We found differentially expressed genes (DEGs) between tumor subclones had two populations: DEGCNV and DEGreg. DEGCNV, showing high CNV-expression correlation and whose expression differences depend on the differences of CNV level, enriched in housekeeping genes and cell adhesion associated genes. DEGreg, showing low CNV-expression correlation and mainly in low CNV variation regions and regions without CNVs, enriched in cytokine signaling genes. Furthermore, cell-cell communication analyses showed that DEGCNV tends to involve in cell-cell contact while DEGreg tends to involve in secreted signaling, which further support that DEGCNV and DEGreg are two regulatorily and functionally distinct categories.
| S-EPMC10958426 | 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: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.
| S-EPMC6148965 | biostudies-literature
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:BackgroundThe 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.ResultsComparative 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.ConclusionsOur 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