Time-series Gene Expression Profiles Research Articles

The two-stage molecular scenery of SARS-CoV-2 infection with implications to disease severity: An in-silico quest.

The two-stage molecular profile of the progression of SARS-CoV-2 (SCOV2) infection is explored in terms of five key biological/clinical questions: (a) does SCOV2 exhibits a two-stage infection profile? (b) SARS-CoV-1 (SCOV1) vs. SCOV2: do they differ? (c) does and how SCOV2 differs from Influenza/INFL infection? (d) does low viral-load and (e) does COVID-19 early host response relate to the two-stage SCOV2 infection profile? We provide positive answers to the above questions by analyzing the time-series gene-expression profiles of preserved cell-lines infected with SCOV1/2 or, the gene-expression profiles of infected individuals with different viral-loads levels and different host-response phenotypes. Our analytical methodology follows an in-silico quest organized around an elaborate multi-step analysis pipeline including: (a) utilization of fifteen gene-expression datasets from NCBI's gene expression omnibus/GEO repository; (b) thorough designation of SCOV1/2 and INFL progression stages and COVID-19 phenotypes; (c) identification of differentially expressed genes (DEGs) and enriched biological processes and pathways that contrast and differentiate between different infection stages and phenotypes; (d) employment of a graph-based clustering process for the induction of coherent groups of networked genes as the representative core molecular fingerprints that characterize the different SCOV2 progression stages and the different COVID-19 phenotypes. In addition, relying on a sensibly selected set of induced fingerprint genes and following a Machine Learning approach, we devised and assessed the performance of different classifier models for the differentiation of acute respiratory illness/ARI caused by SCOV2 or other infections (diagnostic classifiers), as well as for the prediction of COVID-19 disease severity (prognostic classifiers), with quite encouraging results. The central finding of our experiments demonstrates the down-regulation of type-I interferon genes (IFN-1), interferon induced genes (ISGs) and fundamental innate immune and defense biological processes and molecular pathways during the early SCOV2 infection stages, with the inverse to hold during the later ones. It is highlighted that upregulation of these genes and pathways early after infection may prove beneficial in preventing subsequent uncontrolled hyperinflammatory and potentially lethal events. The basic aim of our study was to utilize in an intuitive, efficient and productive way the most relevant and state-of-the-art bioinformatics methods to reveal the core molecular mechanisms which govern the progression of SCOV2 infection and the different COVID-19 phenotypes.

The SYSCID map: a graphical and computational resource of molecular mechanisms across rheumatoid arthritis, systemic lupus erythematosus and inflammatory bowel disease.

Chronic inflammatory diseases (CIDs), including inflammatory bowel disease (IBD), rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) are thought to emerge from an impaired complex network of inter- and intracellular biochemical interactions among several proteins and small chemical compounds under strong influence of genetic and environmental factors. CIDs are characterised by shared and disease-specific processes, which is reflected by partially overlapping genetic risk maps and pathogenic cells (e.g., T cells). Their pathogenesis involves a plethora of intracellular pathways. The translation of the research findings on CIDs molecular mechanisms into effective treatments is challenging and may explain the low remission rates despite modern targeted therapies. Modelling CID-related causal interactions as networks allows us to tackle the complexity at a systems level and improve our understanding of the interplay of key pathways. Here we report the construction, description, and initial applications of the SYSCID map (https://syscid.elixir-luxembourg.org/), a mechanistic causal interaction network covering the molecular crosstalk between IBD, RA and SLE. We demonstrate that the map serves as an interactive, graphical review of IBD, RA and SLE molecular mechanisms, and helps to understand the complexity of omics data. Examples of such application are illustrated using transcriptome data from time-series gene expression profiles following anti-TNF treatment and data from genome-wide associations studies that enable us to suggest potential effects to altered pathways and propose possible mechanistic biomarkers of treatment response.

Mutual Information Based on Multiple Level Discretization Network Inference from Time Series Gene Expression Profiles

Discovering a genetic regulatory network (GRN) from time series gene expression data plays an essential role in the field of biomedical research. In its development, many methods have been proposed for inferring GRNs. Although most of them are effective, they have limitations in terms of network size and the number of regulatory genes due to high computational cost. Thus, it is necessary to develop an efficient method that can operate with large networks and provide reliable results within an acceptable run time. In this study, we propose a new method using mutual information based on multi-level discretization network inference (MIDNI) from time series gene expression profiles. The proposed method discretizes time series gene expression data to minimize information loss and computational consumption through K-means clustering. We do not fix the number of clusters, instead varying it depending on the distribution of gene expression values. We compared MIDNI with three well-known inference methods through extensive simulations on both artificial and real gene expression datasets. Our results illustrate that MIDNI significantly outperforms the alternatives in terms of dynamic accuracy. The proposed method represents an efficient and scalable tool for inferring GRNs from time series gene expression data.

Network analyses reveal new insights into the effect of multicomponent Tr14 compared to single-component diclofenac in an acute inflammation model

BackgroundModifying the acute inflammatory response has wide clinical benefits. Current options include non-steroidal anti-inflammatory drugs (NSAIDs) and therapies that may resolve inflammation. Acute inflammation involves multiple cell types and various processes. We, therefore, investigated whether an immunomodulatory drug that acts simultaneously at multiple sites shows greater potential to resolve acute inflammation more effectively and with fewer side effects than a common anti-inflammatory drug developed as a small molecule for a single target. In this work, we used time-series gene expression profiles from a wound healing mouse model to compare the effects of Traumeel (Tr14), a multicomponent natural product, to diclofenac, a single component NSAID on inflammation resolution.ResultsWe advance previous studies by mapping the data onto the “Atlas of Inflammation Resolution”, followed by in silico simulations and network analysis. We found that Tr14 acts primarily on the late phase of acute inflammation (during resolution) compared to diclofenac, which suppresses acute inflammation immediately after injury.ConclusionsOur results provide new insights how network pharmacology of multicomponent drugs may support inflammation resolution in inflammatory conditions.

TSMiner: a novel framework for generating time-specific gene regulatory networks from time-series expression profiles.

Time-series gene expression profiles are the primary source of information on complicated biological processes; however, capturing dynamic regulatory events from such data is challenging. Herein, we present a novel analytic tool, time-series miner (TSMiner), that can construct time-specific regulatory networks from time-series expression profiles using two groups of genes: (i) genes encoding transcription factors (TFs) that are activated or repressed at a specific time and (ii) genes associated with biological pathways showing significant mutual interactions with these TFs. Compared with existing methods, TSMiner demonstrated superior sensitivity and accuracy. Additionally, the application of TSMiner to a time-course RNA-seq dataset associated with mouse liver regeneration (LR) identified 389 transcriptional activators and 49 transcriptional repressors that were either activated or repressed across the LR process. TSMiner also predicted 109 and 47 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways significantly interacting with the transcriptional activators and repressors, respectively. These findings revealed the temporal dynamics of multiple critical LR-related biological processes, including cell proliferation, metabolism and the immune response. The series of evaluations and experiments demonstrated that TSMiner provides highly reliable predictions and increases the understanding of rapidly accumulating time-series omics data.

Fuzzy and Rough Set Theory Based Computational Framework for Mining Genetic Interaction Triplets From Gene Expression Profiles for Lung Adenocarcinoma.

Genetic interactions are very helpful in understanding different disease and discovering drugs for it. Compared to the gene pairs that represent the genetic interactions between two genes, the gene triplets are more informative and useful. However, existing works on genetic interactions among gene triplets have primarily focused on detecting gene triplets from time series gene expression profiles. Generating the time series gene expression profiles for humans is quite impracticable but the labeled gene expression profiles are available for different diseases in case of humans. In this paper, a computational framework has been proposed to detect gene triplets from labeled gene expression profiles. First, it employs Rough Set Theory for extracting the key genes and then designs a fuzzy inference system for generating possible gene triplets. Further, Root Mean Squared Error measure has been used to prune out the irrelevant gene triplets. In the present work, the proposed computational framework has been applied to labeled lung adenocarcinoma dataset and can be applied to any other labeled gene expression dataset. The extracted gene triplets and their functionalities have been verified with existing biological literature and benchmark databases and the results of verification signify that the proposed framework is promising in terms of finding useful genetic triplets. Further, the proposed framework has been found more efficient as compared to an existing mutual information-based technique in terms of detecting known genetic interactions.

Predicting viral exposure response from modeling the changes of co-expression networks using time series gene expression data

BackgroundDeciphering the relationship between clinical responses and gene expression profiles may shed light on the mechanisms underlying diseases. Most existing literature has focused on exploring such relationship from cross-sectional gene expression data. It is likely that the dynamic nature of time-series gene expression data is more informative in predicting clinical response and revealing the physiological process of disease development. However, it remains challenging to extract useful dynamic information from time-series gene expression data.ResultsWe propose a statistical framework built on considering co-expression network changes across time from time series gene expression data. It first detects change point for co-expression networks and then employs a Bayesian multiple kernel learning method to predict exposure response. There are two main novelties in our method: the use of change point detection to characterize the co-expression network dynamics, and the use of kernel function to measure the similarity between subjects. Our algorithm allows exposure response prediction using dynamic network information across a collection of informative gene sets. Through parameter estimations, our model has clear biological interpretations. The performance of our method on the simulated data under different scenarios demonstrates that the proposed algorithm has better explanatory power and classification accuracy than commonly used machine learning algorithms. The application of our method to time series gene expression profiles measured in peripheral blood from a group of subjects with respiratory viral exposure shows that our method can predict exposure response at early stage (within 24 h) and the informative gene sets are enriched for pathways related to respiratory and influenza virus infection.ConclusionsThe biological hypothesis in this paper is that the dynamic changes of the biological system are related to the clinical response. Our results suggest that when the relationship between the clinical response and a single gene or a gene set is not significant, we may benefit from studying the relationships among genes in gene sets that may lead to novel biological insights.

Time Series Gene Expression Profiles Analysis Identified Several Potential Biomarkers for Sepsis.

Sepsis is a life-threatening disorder and leads to organ dysfunction and death. Therefore, searching for more alternative biomarkers is of great significance for sepsis assessment and surveillance. In our study, the gene expression profiles of 163 samples from healthy controls and septic patients were analyzed and 8 gene co-expression modules were identified by constructing weighted gene co-expression network. The blue and yellow modules showed close correlations with the phenotypic trait "days postsepsis." Besides, differentially expressed genes (DEGs) over time in septic patients were screened using Short Time-series Expression Miner (STEM) program. The intersection of genes in the blue and yellow modules and DEGs, which were significantly enriched in "HTLV-1 infection" pathway, was analyzed with protein-protein interaction network. The logistic regression model based on these eight mRNAs was constructed to determine the type of the sample reliably. Eight vital genes CECR1, ANXA2, ELANE, CTSG, AZU1, PRTN3, LYZ, and DEFA4 presented high scores and may be associated with sepsis, which provided candidate biomarkers for sepsis.

Corrigendum.

Aging CellVolume 19, Issue 5 e13165 CORRIGENDUMOpen Access Corrigendum This article corrects the following: Implications of time-series gene expression profiles of replicative senescence You-Mie Kim, Hae-Ok Byun, Byul A. Jee, Hyunwoo Cho, Yong-Hak Seo, You-Sun Kim, Min Hi Park, Hae-Young Chung, Hyun Goo Woo, Gyesoon Yoon, Volume 12Issue 4Aging Cell pages: 622-634 First Published online: May 7, 2013 First published: 27 May 2020 https://doi.org/10.1111/acel.13165AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat In the original publication of the article “Implications of time-series gene expression profiles of replicative senescence” by Y.-M. Kim et al., the name of the first author, You-Mie Kim, was misspelled as “You-Mi Kim.” The author would like to apologize for the inconvenience caused. Reference Kim, Y.-M., Byun, H.-O., Jee, B. A., Cho, H., Seo, Y.-H., Kim, Y.-S., … Yoon, G. (2013). Implications of time-series gene expression profiles of replicative senescence. Aging Cell, 12, 622– 634. https://doi.org/10.1111/acel.12087. Volume19, Issue5May 2020e13165 ReferencesRelatedInformation

CYC1, SDHA, UQCRC1, UQCRQ, and SDHB might be important biomarkers in kidney transplant rejection

BackgroundKidney transplant rejection is considered as a vital factor of kidney transplant failure. Therefore, it’s necessary to search for effective biomarkers for kidney transplant surveillance. MethodsIn this study, we conducted time-series gene expression profiles analysis of samples from kidney transplant patients with different post-transplant days through weighted gene co-expression network analysis (WGCNA). Associations between gene co-expression modules and days post-transplant were determined through spearman rank correlation analysis. Potential kidney transplant rejection-related modules were subjected to gene functional enrichment analysis through clusterProfiler and protein-protein interaction analysis via STRING database. ResultsA total of 11 gene co-expression modules were identified, and the pink module which was mainly involved in “energy derivation by oxidation of organic compounds” and “Huntington disease” showed significant correlation with the phenotypic trait “days post-transplant”. CYC1, SDHA, UQCRC1, UQCRQ, and SDHB in the pink module exhibited high scores in the protein-protein interaction network analysis. ConclusionsWe reported several potential genes may be associated with the kidney transplant rejection, which should provide novel biomarkers for kidney transplant surveillance.

Time series analysis of gene changes and processes after burn with human gene expression profiles

Severe burns might be followed by severe infection associated with high mortality. In this study, we aimed to identify changes in genes and processes across time points after burn via analyzing time series gene expression profiles in burn patients and control from the Gene Expression Omnibus (GEO). Patients were classified into four groups according to time after burn and weighted gene co-expression network analysis (WGCNA) obtained three gene modules including magenta, yellow and greenyellow modules that significantly correlated positively with time after burn. We also identified four groups of differentially expressed genes (DEGs) in samples at 0–1d, 1–2d, 2–4d, and 4–7d after burn compared with controls. Functional enrichment analysis of those DEGs indicated significant enrichment of inflammatory/immune related processes throughout time points after burn, while, samples at later time points were also closely associated with cell activation regulation related processes. Short time series expression analysis of overlapping genes among the four lists of DEGs screened out two temporal gene expression profiles that exhibited decreasing and increasing expression trend across times after burn, and genes contained in those two profiles might be related to pathologic changes after severe burn.

Gene expression profiling of the early pathogenesis of wooden breast disease in commercial broiler chickens using RNA-sequencing.

Wooden Breast Disease (WBD), a myopathy in commercial broiler chickens characterized by abnormally firm consistency of the pectoral muscle, impacts the poultry industry negatively due to severe reduction in meat quality traits. To unravel the molecular profile associated with the onset and early development of WBD in broiler chickens, we compared time-series gene expression profiles of Pectoralis (P.) major muscles between unaffected and affected birds from a high-breast-muscle-yield, purebred broiler line. P. major biopsy samples were collected from the cranial and caudal aspects of the muscle belly in birds that were raised up to 7 weeks of age (i.e. market age). Three subsets of biopsy samples comprising 6 unaffected (U) and 10 affected (A) from week 2 (cranial) and 4 (caudal), and 4U and 11A from week 3 (cranial) were processed for RNA-sequencing analysis. Sequence reads generated were processed using a suite of bioinformatics programs producing differentially expressed (DE) genes for each dataset at fold-change (A/U or U/A) >1.3 and False Discovery Ratio (FDR) <0.05 (week 2: 41 genes; week 3: 618 genes and week 4: 39 genes). Functional analysis of DE genes using literature mining, BioDBnet and IPA revealed several biological processes and pathways associated with onset and progress of WBD. Top among them were dysregulation of energy metabolism, response to inflammation, vascular disease and remodeling of extracellular matrix. This study reveals that presence of molecular perturbations involving the vasculature, extracellular matrix and metabolism are pertinent to the onset and early pathogenesis of WBD in commercial meat-type chickens.

Automated transition analysis of activated gene regulation during diauxic nutrient shift in Escherichia coli and adipocyte differentiation in mouse cells

BackgroundComprehensively understanding the dynamics of biological systems is among the biggest current challenges in biology and medicine. To acquire this understanding, researchers have measured the time-series expression profiles of cell lines of various organisms. Biological technologies have also drastically improved, providing a huge amount of information with support from bioinformatics and systems biology. However, the transitions between the activation and inactivation of gene regulations, at the temporal resolution of single time points, are difficult to extract from time-course gene expression profiles.ResultsOur proposed method reports the activation period of each gene regulation from gene expression profiles and a gene regulatory network. The correctness and effectiveness of the method were validated by analyzing the diauxic shift from glucose to lactose in Escherichia coli. The method completely detected the three periods of the shift; 1) consumption of glucose as nutrient source, 2) the period of seeking another nutrient source and 3) consumption of lactose as nutrient source. We then applied the method to mouse adipocyte differentiation data. Cell differentiation into adipocytes is known to involve two waves of the gene regulation cascade, and sub-waves are predicted. From the gene expression profiles of the cell differentiation process from ES to adipose cells (62 time points), our method acquired four periods; three periods covering the two known waves of the cascade, and a final period of gene regulations when the differentiation to adipocytes was completed.ConclusionsOur proposed method identifies the transitions of gene regulations from time-series gene expression profiles. Dynamic analyses are essential for deep understanding of biological systems and for identifying the causes of the onset of diseases such as diabetes and osteoporosis. The proposed method can greatly contribute to the progress of biology and medicine.

Network perturbation analysis of gene transcriptional profiles reveals protein targets and mechanism of action of drugs and influenza A viral infection.

Genome-wide transcriptional profiling provides a global view of cellular state and how this state changes under different treatments (e.g. drugs) or conditions (e.g. healthy and diseased). Here, we present ProTINA (Protein Target Inference by Network Analysis), a network perturbation analysis method for inferring protein targets of compounds from gene transcriptional profiles. ProTINA uses a dynamic model of the cell-type specific protein–gene transcriptional regulation to infer network perturbations from steady state and time-series differential gene expression profiles. A candidate protein target is scored based on the gene network's dysregulation, including enhancement and attenuation of transcriptional regulatory activity of the protein on its downstream genes, caused by drug treatments. For benchmark datasets from three drug treatment studies, ProTINA was able to provide highly accurate protein target predictions and to reveal the mechanism of action of compounds with high sensitivity and specificity. Further, an application of ProTINA to gene expression profiles of influenza A viral infection led to new insights of the early events in the infection.

The Ubiquitin-like with PHD and Ring Finger Domains 1 (UHRF1)/DNA Methyltransferase 1 (DNMT1) Axis Is a Primary Regulator of Cell Senescence

As senescence develops, cells sequentially acquire diverse senescent phenotypes along with simultaneous multistage gene reprogramming. It remains unclear what acts as the key regulator of the collective changes in gene expression at initiation of senescent reprogramming. Here we analyzed time series gene expression profiles obtained in two different senescence models in human diploid fibroblasts: replicative senescence and H2O2-induced senescence. Our results demonstrate that suppression of DNA methyltransferase 1 (DNMT1)-mediated DNA methylation activity was an initial event prior to the display of senescent phenotypes. We identified seven DNMT1-interacting proteins, ubiquitin-like with PHD and ring finger domains 1 (UHRF1), EZH2, CHEK1, SUV39H1, CBX5, PARP1, and HELLS (also known as LSH (lymphoid-specific helicase) 1), as being commonly down-regulated at the same time point as DNMT1 in both senescence models. Knockdown experiments revealed that, among the DNMT1-interacting proteins, only UHRF1 knockdown suppressed DNMT1 transcription. However, UHRF1 overexpression alone did not induce DNMT1 expression, indicating that UHRF1 was essential but not sufficient for DNMT1 transcription. Although UHRF1 knockdown effectively induced senescence, this was significantly attenuated by DNMT1 overexpression, clearly implicating the UHRF1/DNMT1 axis in senescence. Bioinformatics analysis further identified WNT5A as a downstream effector of UHRF1/DNMT1-mediated senescence. Senescence-associated hypomethylation was found at base pairs -1569 to -1363 from the transcription start site of the WNT5A gene in senescent human diploid fibroblasts. As expected, WNT5A overexpression induced senescent phenotypes. Overall, our results indicate that decreased UHRF1 expression is a key initial event in the suppression of DNMT1-mediated DNA methylation and in the consequent induction of senescence via increasing WNT5A expression.

Application of dynamic topic models to toxicogenomics data.

BackgroundAll biological processes are inherently dynamic. Biological systems evolve transiently or sustainably according to sequential time points after perturbation by environment insults, drugs and chemicals. Investigating the temporal behavior of molecular events has been an important subject to understand the underlying mechanisms governing the biological system in response to, such as, drug treatment. The intrinsic complexity of time series data requires appropriate computational algorithms for data interpretation. In this study, we propose, for the first time, the application of dynamic topic models (DTM) for analyzing time-series gene expression data.ResultsA large time-series toxicogenomics dataset was studied. It contains over 3144 microarrays of gene expression data corresponding to rat livers treated with 131 compounds (most are drugs) at two doses (control and high dose) in a repeated schedule containing four separate time points (4-, 8-, 15- and 29-day). We analyzed, with DTM, the topics (consisting of a set of genes) and their biological interpretations over these four time points. We identified hidden patterns embedded in this time-series gene expression profiles. From the topic distribution for compound-time condition, a number of drugs were successfully clustered by their shared mode-of-action such as PPARɑ agonists and COX inhibitors. The biological meaning underlying each topic was interpreted using diverse sources of information such as functional analysis of the pathways and therapeutic uses of the drugs. Additionally, we found that sample clusters produced by DTM are much more coherent in terms of functional categories when compared to traditional clustering algorithms.ConclusionsWe demonstrated that DTM, a text mining technique, can be a powerful computational approach for clustering time-series gene expression profiles with the probabilistic representation of their dynamic features along sequential time frames. The method offers an alternative way for uncovering hidden patterns embedded in time series gene expression profiles to gain enhanced understanding of dynamic behavior of gene regulation in the biological system.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1225-0) contains supplementary material, which is available to authorized users.

Plasma miRNAs in predicting radiosensitivity in non-small cell lung cancer.

BackgroundRadioresistance of thoracic radiotherapy is a major bottleneck in the treatment of non-small cell lung cancer (NSCLC). Until now, there have been no effective biomarkers to predict the radiosensitivity.PurposesBased on miRNA profile screened from NSCLC cell lines with different radiosensitivity, this study was conducted to explore the correlation between plasma miRNAs and radiotherapy response in NSCLC patients, and to identify biomarkers of the radiosensitivity in NSCLC.MethodsDifferentially expressed genes were acquired from time-series gene expression profiles of radioresistant H1299 and radiosensitive H460 lung cancer cells (GSE20549). Potential miRNAs were screened from these differentially expressed genes by combining bioinformatics with GO analysis, pathway analysis, and miRNA prediction. A clinical observational study was performed to explore the correlation between candidate miRNAs and radiotherapy response. Stage IIIa–IV NSCLC patients who received two to four cycles of previous chemotherapy and underwent thoracic radiotherapy alone were included. Total RNA was purified from peripheral blood before radiotherapy, and plasma miRNAs were detected by real-time PCR (qRT-PCR). Then, tumor response, progression-free survival (PFS), and overall survival (OS) were acquired. Four miRNAs significantly different between effective and ineffective groups were further analyzed to obtain cutpoints from receiver operating characteristic (ROC) curves and the predictive value of radiosensitivity.ResultsCandidate miRNAs included 14 miRNAs screened from radioresistant genes and five from radiosensitive genes. From Jan., 2013 to Dec., 2014, 54 eligible patients were enrolled with a median follow-up of 15.3 months (range 4.6 to 31.4) by the deadline of Aug. 31, 2015. Totally, there were no case of complete response (CR), 15 of partial response (PR), 35 of stable disease (SD), and 4 of progressive disease (PD). Eight patients had no progression and 19 patients were still alive. The median PFS and OS were 6.6 months (range 2.3 to 29.3) and 15.3 months (range 4.6 to 31.4), respectively. Four miRNAs (hsa-miR-98-5p, hsa-miR-302e, hsa-miR-495-3p, and hsa-miR-613) demonstrated a higher expression in effective group (CR + PR, 15 cases) than in ineffective group (SD + PD, 39 cases). Based on each cutpoint, objective response rate (ORR) was higher in miR-high group than in miR-low group. No miRNA showed correlation with median PFS or OS.ConclusionBioinformatical analysis and clinical verification reveal the correlation between plasma miRNAs and radiosensitivity in NSCLC patients. Plasma miRNAs represent novel biomarkers to predict radiotherapy response clinically.Electronic supplementary materialThe online version of this article (doi:10.1007/s13277-016-5052-8) contains supplementary material, which is available to authorized users.

Mining the key regulatory genes of chicken inosine 5'-monophosphate metabolism based on time series microarray data.

IMP (inosine 5′-monophosphate) is a compound that enhances the flavor of poultry meat. IMP has become a new breeding trait to improve poultry meat quality. We tried to identify several potential regulatory genes, and construct their predicted regulatory relationships. Time series gene expression profiles of thigh muscle tissues of Rugao chicken, a famous indigenous breed in China, were performed for analysis of genes that are co-expressed or correlated with the concentration of IMP. We found 15 crucial co-expression genes, which are Hspa2, Pten, Gabpa, Bpi, Mkl1, Srf, Cd34, Hspa4, Etv6, Bmpr2, Gde1, Igfbp5, Cd28, Pecam1 and Gja1, that may directly or indirectly regulate IMP metabolism. Eventually, we computed the correlation coefficient between 19 IMP Genes and 15 CGs (15 co-expression genes), and we identified and constructed a predicted regulation network. In conclusion, variation of IMP concentration was primarily connected with the muscle development process. During this process, 15 CGs were identified that may have significant influence on IMP metabolism. In particular, Bmpr2, Pten and co-expression genes correlated with Entpd8 might play important roles in regulating IMP de novo synthesis, decomposition and salvage synthesis.Electronic supplementary materialThe online version of this article (doi:10.1186/s40104-015-0022-3) contains supplementary material, which is available to authorized users.

Discovering biological patterns from short time-series gene expression profiles with integrating PPI data

Abstract As genes with similar functions are closely related, cluster analysis becomes an important tool to understand and predicts gene functions (patterns) from gene expression profiles. In many situations, each gene expression profile only contains a few data points. Directly applying traditional clustering algorithms to such short gene expression profiles cannot obtain biological meaningful patterns. In this paper, we propose a novel method to discover biologically meaningful patterns by clustering short time-series gene expression profiles with integrating protein–protein interaction (PPI) data. Numerical experiments are conducted on two sets of Arabidopsis thaliana short time-series gene expression profiles, with treatments of cold stress and drought stress, respectively. The proposed method can effectively assign genes belonging to target functional clusters (patterns), in terms of having small p-value of GO term ‘response to cold’ ( GO:0009409 ) in dataset one, and small p-value of GO term ‘response to water deprivation’ ( GO:0009414 ) in dataset two than those from an existing clustering algorithm (namely STEM) for short time-series gene expression profiles. Additionally, our proposed method is able to identify higher percentage of stress-related genes and un-annotated genes in resultant cluster than STEM for both datasets; which does not only improve gene clustering effectiveness, but also contribute to functional prediction of un-annotated genes.

Gene Expression Change in Human Dental Pulp Cells Exposed to a Low‐Level Toxic Concentration of Triethylene Glycol Dimethacrylate: An RNA‐seq Analysis

Dental composite resin restoration for defective tooth may lead unpolymerized resin monomers to be leached into dental pulp tissue. The aim of this study was to investigate the early gene expression change over time of human dental pulp cells (HDPCs) treated with a low-level toxic concentration of Triethylene Glycol Dimethacrylate (TEGDMA), a common dental resin monomer, by adopting the novel high-throughput transcriptome analysis of RNA-seq. The low-level toxic concentration of TEGDMA was determined through MTT assays with serially diluted concentrations. After the HDPCs were exposed to TEGDMA for 6, 12, 24 or 48hr, the total RNA of the samples was prepared for RNA-seq. qRT-PCR for several genes was performed for validation of RNA-seq results. In the treated group, 1280 genes were differentially expressed compared with the control group. Five patterns of time-series gene expression profiles were identified through k-means clustering analysis. Angiogenesis, cell adhesion and migration, extracellular matrix organization, response to extracellular stimulus, inflammatory response and mineralization-related process were major gene ontology terms in functional annotation clustering. HMOX1, OSGIN1, SMN2, SRXN1 AKR1C1, SPP1 and TOMM40L were highly up-regulated genes, and WRAP53 and CCL2 were highly down-regulated genes over time. qRT-PCR for several genes exhibited a high level of agreement with RNA-seq. TEGDMA induced the HDPCs to show massive and dynamic gene expression changes over time. The previously suggested toxic mechanism of TEGDMA was not only verified, but new genes whose functions have yet to be determined were also found.