A rapid method for computationally inferring transcriptome coverage and microarray sensitivity

An overview of multi-omics technologies in rheumatoid arthritis: applications in biomarker and pathway discovery

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease with a complex pathological mechanism involving autoimmune response, local inflammation and bone destruction. Metabolic pathways play an important role in immune-related diseases and their immune responses. The pathogenesis of rheumatoid arthritis may be related to its metabolic dysregulation. Moreover, histological techniques, including genomics, transcriptomics, proteomics and metabolomics, provide powerful tools for comprehensive analysis of molecular changes in biological systems. The present study explores the molecular and metabolic mechanisms of RA, emphasizing the central role of metabolic dysregulation in the RA disease process and highlighting the complexity of metabolic pathways, particularly metabolic remodeling in synovial tissues and its association with cytokine-mediated inflammation. This paper reveals the potential of histological techniques in identifying metabolically relevant therapeutic targets in RA; specifically, we summarize the genetic basis of RA and the dysregulated metabolic pathways, and explore their functional significance in the context of immune cell activation and differentiation. This study demonstrates the critical role of histological techniques in decoding the complex metabolic network of RA and discusses the integration of histological data with other types of biological data.

Mining the proliferative diabetic retinopathy-associated genes and pathways by integrated bioinformatic analysis

PURPOSE

Diabetic retinopathy (DR) especially proliferative diabetic retinopathy (PDR) is a serious eye disease. We aimed to identify key pathway and hub genes associated with PDR by analyzing the expression of retinal fibrovascular tissue in PDR patients.

METHODS

First raw data were downloaded from the Gene Expression Omnibus database. Median normalization was subsequently applied to preprocess. Differentially expressed genes (DEGs) analyzed with the Limma package. Weighted correlation network analysis (WGCNA) was utilized to build the co-expression network for all genes. Then, we compared the DEGs and modules filtered out by WGCNA. A protein-protein interaction network based on the STRING web site and the Cytoscape software was constructed by the overlapping DEGs. Next, the Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed. Finally, we used the Comparative Toxicogenomics Database to identify some important pathways and hub genes tightly related to PDR.

RESULTS

Functional enrichment analysis showed that the pathway of cytokine-cytokine receptor interaction was significantly related to PDR eight hub genes which were associated with pathway including tumor necrosis factor (TNF), tumor necrosis factor receptor superfamily member 12A (TNFRSF12A), C-C chemokine 20 (CCL20), chemokine (C-X-C motif) ligand 2 (CXCL2), oncostatin M (OSM) interleukin 10 (IL10), interleukin 15 (IL 15), and interleukin 1B (IL1B).

CONCLUSIONS

We identified one pathway and eight hub genes, which were associated with PDR. The pathway provided references that will advance the understanding of mechanisms of PDR. Moreover, the hub genes may serve as therapeutic targets for precise diagnosis and treatment of PDR in the future.

Digital gene expression approach over multiple RNA-Seq data sets to detect neoblast transcriptional changes in Schmidtea mediterranea

BACKGROUND

The freshwater planarian Schmidtea mediterranea is recognised as a valuable model for research into adult stem cells and regeneration. With the advent of the high-throughput sequencing technologies, it has become feasible to undertake detailed transcriptional analysis of its unique stem cell population, the neoblasts. Nonetheless, a reliable reference for this type of studies is still lacking.

RESULTS

Taking advantage of digital gene expression (DGE) sequencing technology we compare all the available transcriptomes for S. mediterranea and improve their annotation. These results are accessible via web for the community of researchers. Using the quantitative nature of DGE, we describe the transcriptional profile of neoblasts and present 42 new neoblast genes, including several cancer-related genes and transcription factors. Furthermore, we describe in detail the Smed-meis-like gene and the three Nuclear Factor Y subunits Smed-nf-YA, Smed-nf-YB-2 and Smed-nf-YC.

CONCLUSIONS

DGE is a valuable tool for gene discovery, quantification and annotation. The application of DGE in S. mediterranea confirms the planarian stem cells or neoblasts as a complex population of pluripotent and multipotent cells regulated by a mixture of transcription factors and cancer-related genes.

Beyond differential expression: the quest for causal mutations and effector molecules

High throughput gene expression technologies are a popular choice for researchers seeking molecular or systems-level explanations of biological phenomena. Nevertheless, there has been a groundswell of opinion that these approaches have not lived up to the hype because the interpretation of the data has lagged behind its generation. In our view a major problem has been an over-reliance on isolated lists of differentially expressed (DE) genes which – by simply comparing genes to themselves – have the pitfall of taking molecular information out of context. Numerous scientists have emphasised the need for better context. This can be achieved through holistic measurements of differential connectivity in addition to, or in replacement, of DE. However, many scientists continue to use isolated lists of DE genes as the major source of input data for common readily available analytical tools. Focussing this opinion article on our own research in skeletal muscle, we outline our resolutions to these problems – particularly a universally powerful way of quantifying differential connectivity. With a well designed experiment, it is now possible to use gene expression to identify causal mutations and the other major effector molecules with whom they cooperate, irrespective of whether they themselves are DE. We explain why, for various reasons, no other currently available experimental techniques or quantitative analyses are capable of reaching these conclusions.

Cytokines and cytokine profiles in human autoimmune diseases and animal models of autoimmunity

The precise pathomechanisms of human autoimmune diseases are still poorly understood. However, a deepened understanding of these is urgently needed to improve disease prevention and early detection and guide more specific treatment approaches. In recent years, many new genes and signalling pathways involved in autoimmunity with often overlapping patterns between different disease entities have been detected. Major contributions were made by experiments using DNA microarray technology, which has been used for the analysis of gene expression patterns in chronic inflammatory and autoimmune diseases, among which were rheumatoid arthritis, systemic lupus erythematosus, psoriasis, systemic sclerosis, multiple sclerosis, and type-1 diabetes. In systemic lupus erythematosus, a so-called interferon signature has been identified. In psoriasis, researchers found a particular immune signalling cluster. Moreover the identification of a new subset of inflammatory T cells, so-called Th17 T cells, secreting interleukin (IL)-17 as one of their major cytokines and the identification of the IL-23/IL-17 axis of inflammation regulation, have significantly improved our understanding of autoimmune diseases. Since a plethora of new treatment approaches using antibodies or small molecule inhibitors specifically targeting cytokines, cellular receptors, or signalling mechanisms has emerged in recent years, more individualized treatment for affected patients may be within reach in the future.

Mining tissue specificity, gene connectivity and disease association to reveal a set of genes that modify the action of disease causing genes

BACKGROUND

The tissue specificity of gene expression has been linked to a number of significant outcomes including level of expression, and differential rates of polymorphism, evolution and disease association. Recent studies have also shown the importance of exploring differential gene connectivity and sequence conservation in the identification of disease-associated genes. However, no study relates gene interactions with tissue specificity and disease association.

METHODS

We adopted an a priori approach making as few assumptions as possible to analyse the interplay among gene-gene interactions with tissue specificity and its subsequent likelihood of association with disease. We mined three large datasets comprising expression data drawn from massively parallel signature sequencing across 32 tissues, describing a set of 55,606 true positive interactions for 7,197 genes, and microarray expression results generated during the profiling of systemic inflammation, from which 126,543 interactions among 7,090 genes were reported.

RESULTS

Amongst the myriad of complex relationships identified between expression, disease, connectivity and tissue specificity, some interesting patterns emerged. These include elevated rates of expression and network connectivity in housekeeping and disease-associated tissue-specific genes. We found that disease-associated genes are more likely to show tissue specific expression and most frequently interact with other disease genes. Using the thresholds defined in these observations, we develop a guilt-by-association algorithm and discover a group of 112 non-disease annotated genes that predominantly interact with disease-associated genes, impacting on disease outcomes.

CONCLUSION

We conclude that parameters such as tissue specificity and network connectivity can be used in combination to identify a group of genes, not previously confirmed as disease causing, that are involved in interactions with disease causing genes. Our guilt-by-association algorithm should be useful for the discovery of additional modifiers of genetic diseases, and more generally, for the ability to associate genes of unknown function to clusters of genes with defined functions allowing for novel biological inference that can be subsequently validated.

Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks

MOTIVATION

We present PCIT, an algorithm for the reconstruction of gene co-expression networks (GCN) that combines the concept partial correlation coefficient with information theory to identify significant gene to gene associations defining edges in the reconstruction of GCN. The properties of PCIT are examined in the context of the topology of the reconstructed network including connectivity structure, clustering coefficient and sensitivity.

RESULTS

We apply PCIT to a series of simulated datasets with varying levels of complexity in terms of number of genes and experimental conditions, as well as to three real datasets. Results show that, as opposed to the constant cutoff approach commonly used in the literature, the PCIT algorithm can identify and allow for more moderate, yet not less significant, estimates of correlation (r) to still establish a connection in the GCN. We show that PCIT is more sensitive than established methods and capable of detecting functionally validated gene-gene interactions coming from absolute r values as low as 0.3. These bona fide associations, which often relate to genes with low variation in expression patterns, are beyond the detection limits of conventional fixed-threshold methods, and would be overlooked by studies relying on those methods.

AVAILABILITY

FORTRAN 90 source code to perform the PCIT algorithm is available as Supplementary File 1.

Deep analysis of cellular transcriptomes - LongSAGE versus classic MPSS

BACKGROUND

Deep transcriptome analysis will underpin a large fraction of post-genomic biology. 'Closed' technologies, such as microarray analysis, only detect the set of transcripts chosen for analysis, whereas 'open' e.g. tag-based technologies are capable of identifying all possible transcripts, including those that were previously uncharacterized. Although new technologies are now emerging, at present the major resources for open-type analysis are the many publicly available SAGE (serial analysis of gene expression) and MPSS (massively parallel signature sequencing) libraries. These technologies have never been compared for their utility in the context of deep transcriptome mining.

RESULTS

We used a single LongSAGE library of 503,431 tags and a "classic" MPSS library of 1,744,173 tags, both prepared from the same T cell-derived RNA sample, to compare the ability of each method to probe, at considerable depth, a human cellular transcriptome. We show that even though LongSAGE is more error-prone than MPSS, our LongSAGE library nevertheless generated 6.3-fold more genome-matching (and therefore likely error-free) tags than the MPSS library. An analysis of a set of 8,132 known genes detectable by both methods, and for which there is no ambiguity about tag matching, shows that MPSS detects only half (54%) the number of transcripts identified by SAGE (3,617 versus 1,955). Analysis of two additional MPSS libraries shows that each library samples a different subset of transcripts, and that in combination the three MPSS libraries (4,274,992 tags in total) still only detect 73% of the genes identified in our test set using SAGE. The fraction of transcripts detected by MPSS is likely to be even lower for uncharacterized transcripts, which tend to be more weakly expressed. The source of the loss of complexity in MPSS libraries compared to SAGE is unclear, but its effects become more severe with each sequencing cycle (i.e. as MPSS tag length increases).

CONCLUSION

We show that MPSS libraries are significantly less complex than much smaller SAGE libraries, revealing a serious bias in the generation of MPSS data unlikely to have been circumvented by later technological improvements. Our results emphasize the need for the rigorous testing of new expression profiling technologies.

Stress-induced gene expression profiling in the black tiger shrimp Penaeus monodon

Cultured shrimp are continuously exposed to variable environmental conditions that have been associated with stress and subsequent outbreaks of disease. To investigate the effect of environmental stress on Penaeus monodon gene expression, a 3,853 random cDNA microarray chip was generated with clones originating from six stress-enriched hemocyte libraries generated by suppression subtractive hybridization and a normal hemocyte cDNA library. Changes in temporal gene expression were analyzed from shrimp exposed to hypoxic, hyperthermic, and hypoosmotic conditions; 3.1% of the cDNAs were differentially expressed in response to at least one of the environmental stressors, and 72% of the differentially expressed clones had no significant sequence similarity to previously known genes. Among those genes with high identity to known sequences, the most common functional groups were immune-related genes and non-long terminal repeat retrotransposons. Hierarchical clustering revealed a set of cDNAs with temporal and stress-specific gene expression profiles as well as a set of cDNAs indicating a common stress response between stressors. Hypoxic and hyperthermic stressors induced the most severe short-term response in terms of gene regulation, and the osmotic stress had the least variation in expression profiles relative to the control. These expression data agree with observed differences in shrimp physical appearance and behavior following exposure to stress conditions.

Gene expression profiling of Hereford Shorthorn cattle following challenge with Boophilus microplus tick larvae

The ability of cattle to resist tick infestations is partly genetically determined. In order to better define the nature of Bos taurus resistance to the cattle tick Boophilus microplus, skin gene expression was studied using a cattle skin derived cDNA microarray. Expression profiles were determined in skin biopsies sampled from three highly tick resistant animals (HR) and two animals with lower tick resistance (LR) at time 0, immediately before challenge, and again 24 h after challenge. The analysis of the resulting expression data addressed two biological questions: first, for any animal exposed to ticks, which genes are differentially expressed in the 24 h following challenge; and second, which genes are differentially expressed between animals of high and low resistance at 24 h after challenge? In total, 214 genes were found to be differentially expressed in response to larval challenge across all the animals. Seventy-two genes were upregulated and 76 were downregulated at 24 h after challenge. Genes with significantly altered gene expression levels following tick infestation were predominantly keratin genes or mitochondrial genes, as well as odorant binding protein (OBP) and Bos taurus major allergen BDA20. In addition, we identified 66 genes with differential expression between HR and LR animals at 24 h. Of these, genes representing the extracellular matrix and immunoglobulin gene expression pathways were overrepresented. Three differentially expressed genes, OBP, Bos taurus major allergen BDA20 and dendritic cell protein HFL-B5 were further analysed by quantitative reverse transcription PCR (qRT-PCR). The qRT-PCR assay results closely mirrored the expression profiles found in the microarray experiment.

Simultaneous identification of differential gene expression and connectivity in inflammation, adipogenesis and cancer

MOTIVATION

Biological differences between classes are reflected in transcriptional changes which in turn affect the levels by which essential genes are individually expressed and collectively connected. The purpose of this communication is to introduce an analytical procedure to simultaneously identify genes that are differentially expressed (DE) as well as differentially connected (DC) in two or more classes of interest.

RESULTS

Our procedure is based on a two-step approach: First, mixed-model equations are applied to obtain the normalized expression levels of each gene in each class treatment. These normalized expressions form the basis to compute a measure of (possible) DE as well as the correlation structure existing among genes. Second, a two-component mixture of bi-variate distributions is fitted to identify the component that encapsulates those genes that are DE and/or DC. We demonstrate our approach using three distinct datasets including a human systemic inflammation oligonucleotide data; a spotted cDNA data dealing with bovine in vitro adipogenesis and SAGE database on cancerous and normal tissue samples.

Innate immune response gene expression profiles of N9 microglia are pathogen-type specific

Glial cells, particularly microglia, are thought to play a pivotal role in initiating and guiding innate immune responses to CNS infections and in perpetuating inflammation and pathology in CNS diseases such as multiple sclerosis and Alzheimer's disease. We describe here the development and use of a new microarray designed to specifically profile transcript expression of innate immunity genes. Microarray analysis validated by quantitative PCR demonstrated an extensive range of pattern recognition receptor gene expression in resting N9 microglia, including Toll-like receptors, scavenger receptors and lectins. Stimulation with LPS or infection with virus modulated pattern recognition receptor, cytokine, chemokine and other innate immune transcripts in a distinct and stimulus-specific manner. This study demonstrates that a single glial cell phenotype has an innate capability to detect infection, determine its form and generate specific responses.

Gene expression-based approaches to beef quality research

Advances in mammalian genomics have permitted the application of gene expression profiling approaches to gene discovery for meat quality traits in cattle. The first custom cDNA microarray based on the transcriptome of bovine muscle and fat tissue was developed and applied to animal experimentation and cell culture experimentation between 1999 and 2005. Complementary DNA microarray tools for beef quality research were developed in parallel with bioinformatics tools that permit the analysis of microarray data obtained from complex experimental designs commonly encountered in large animal research. In addition, tools were designed to link gene expression data with gene function in the bovine, such as in vitro models of bovine adipogenesis and bioinformatics tools to map gene networks from expression data. The application of these genomics tools to the study of beef quality has yielded novel knowledge of genes and molecules involved in the processes of intramuscular adipogenesis and protein turnover. This review summarises the current state of knowledge and important lessons derived from bovine genomics initiatives in Australia and around the world.

Limitations of mRNA amplification from small-size cell samples

Background

Global mRNA amplification has become a widely used approach to obtain gene expression profiles from limited material. An important concern is the reliable reflection of the starting material in the results obtained. This is especially important with extremely low quantities of input RNA where stochastic effects due to template dilution may be present. This aspect remains under-documented in the literature, as quantitative measures of data reliability are most often lacking. To address this issue, we examined the sensitivity levels of each transcript in 3 different cell sample sizes. ANOVA analysis was used to estimate the overall effects of reduced input RNA in our experimental design. In order to estimate the validity of decreasing sample sizes, we examined the sensitivity levels of each transcript by applying a novel model-based method, TransCount.

Results

From expression data, TransCount provided estimates of absolute transcript concentrations in each examined sample. The results from TransCount were used to calculate the Pearson correlation coefficient between transcript concentrations for different sample sizes. The correlations were clearly transcript copy number dependent. A critical level was observed where stochastic fluctuations became significant. The analysis allowed us to pinpoint the gene specific number of transcript templates that defined the limit of reliability with respect to number of cells from that particular source. In the sample amplifying from 1000 cells, transcripts expressed with at least 121 transcripts/cell were statistically reliable and for 250 cells, the limit was 1806 transcripts/cell. Above these thresholds, correlation between our data sets was at acceptable values for reliable interpretation.

Conclusion

These results imply that the reliability of any amplification experiment must be validated empirically to justify that any gene exists in sufficient quantity in the input material. This finding has important implications for any experiment where only extremely small samples such as single cell analyses or laser captured microdissected cells are available.

Construction and validation of a Bovine Innate Immune Microarray

BACKGROUND

Microarray transcript profiling has the potential to illuminate the molecular processes that are involved in the responses of cattle to disease challenges. This knowledge may allow the development of strategies that exploit these genes to enhance resistance to disease in an individual or animal population.

RESULTS

The Bovine Innate Immune Microarray developed in this study consists of 1480 characterised genes identified by literature searches, 31 positive and negative control elements and 5376 cDNAs derived from subtracted and normalised libraries. The cDNA libraries were produced from 'challenged' bovine epithelial and leukocyte cells. The microarray was found to have a limit of detection of 1 pg/microg of total RNA and a mean slide-to-slide correlation co-efficient of 0.88. The profiles of differentially expressed genes from Concanavalin A (ConA) stimulated bovine peripheral blood lymphocytes were determined. Three distinct profiles highlighted 19 genes that were rapidly up-regulated within 30 minutes and returned to basal levels by 24 h; 76 genes that were up-regulated between 2-8 hours and sustained high levels of expression until 24 h and 10 genes that were down-regulated. Quantitative real-time RT-PCR on selected genes was used to confirm the results from the microarray analysis. The results indicate that there is a dynamic process involving gene activation and regulatory mechanisms re-establishing homeostasis in the ConA activated lymphocytes. The Bovine Innate Immune Microarray was also used to determine the cross-species hybridisation capabilities of an ovine PBL sample.

CONCLUSION

The Bovine Innate Immune Microarray has been developed which contains a set of well-characterised genes and anonymous cDNAs from a number of different bovine cell types. The microarray can be used to determine the gene expression profiles underlying innate immune responses in cattle and sheep.

Detecting novel low-abundant transcripts in Drosophila

Increasing evidence suggests that low-abundant transcripts may play fundamental roles in biological processes. In an attempt to estimate the prevalence of low-abundant transcripts in eukaryotic genomes, we performed a transcriptome analysis in Drosophila using the SAGE technique. We collected 244,313 SAGE tags from transcripts expressed in Drosophila embryonic, larval, pupae, adult, and testicular tissue. From these SAGE tags, we identified 40,823 unique SAGE tags. Our analysis showed that 55% of the 40,823 unique SAGE tags are novel without matches in currently known Drosophila transcripts, and most of the novel SAGE tags have low copy numbers. Further analysis indicated that these novel SAGE tags represent novel low-abundant transcripts expressed from loci outside of currently annotated exons including the intergenic and intronic regions, and antisense of the currently annotated exons in the Drosophila genome. Our study reveals the presence of a significant number of novel low-abundant transcripts in Drosophila, and highlights the need to isolate these novel low-abundant transcripts for further biological studies.

Construction of gene interaction and regulatory networks in bovine skeletal muscle from expression data

We propose a data-driven reverse engineering approach to isolate the components of a gene interaction and regulatory network. We apply this method to the construction of a network for bovine skeletal muscle. Key nodes in the network include muscle-specific genes and transcription factors. muscle-specific genes are identified from data mining the USA National Cancer Institute, Cancer Genome Anatomy Project database, while transcription factors are predicted by accurate function annotation. A total of 5 microarray studies spanning 78 hybridisations and 23 different experimental conditions provided raw expression data. A recently-reported analytical method based on multivariate mixed-model equations is used to compute gene co-expression measures across 624 genes. The resulting network included 102 genes (of which 40 were muscle-specific genes and 7 were transcription factors) that clustered in 7 distinct modules with clear biological interpretation.

NCBI GEO: mining millions of expression profiles--database and tools

The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) is the largest fully public repository for high-throughput molecular abundance data, primarily gene expression data. The database has a flexible and open design that allows the submission, storage and retrieval of many data types. These data include microarray-based experiments measuring the abundance of mRNA, genomic DNA and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. GEO currently holds over 30,000 submissions representing approximately half a billion individual molecular abundance measurements, for over 100 organisms. Here, we describe recent database developments that facilitate effective mining and visualization of these data. Features are provided to examine data from both experiment- and gene-centric perspectives using user-friendly Web-based interfaces accessible to those without computational or microarray-related analytical expertise. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.

Gene expression profiling of ovine skin and wool follicle development using a combined ovine - bovine skin cDNA microarray

Low fibre diameter and high fleece weight are important determinants of the economic value of the Merino fleece. The combination of these traits is found in Merino sheep with high follicle densities resulting from a high secondary to primary follicle ratio. Morphological stages in the development of primary and secondary follicles of fetal sheep skin have been well described. We have used gene expression profiling of fetal skin to identify genes that may be important in controlling these follicle developmental processes. A combined ovine (2.3 K) and bovine (6.14 K) cDNA microarray of 2 fetal and 1 adult stage skin tissues was constructed to compare gene expression levels between fetal day 82, day 105, day 120 and adult sheep skin developmental stages. The transcript profile resulted in 238 differentially expressed array elements relative to the adult expression, which represented 132 unique genes. These clustered into 50 up- and 82 down-regulated genes and distinct gene ontologies including structural constituents, phosphate transport, signal transduction and organogenesis. Northern blot analysis of 2 selected genes, S100A7LI and TAGLN, validated the microarray results. This list of genes contains candidates of interest for further investigation into the molecular control of wool follicle development.