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1.  Evaluation of Common Type 2 Diabetes Risk Variants in a South Asian Population of Sri Lankan Descent 
PLoS ONE  2014;9(6):e98608.
Most studies seeking common variant associations with type 2 diabetes (T2D) have focused on individuals of European ancestry. These discoveries need to be evaluated in other major ancestral groups, to understand ethnic differences in predisposition, and establish whether these contribute to variation in T2D prevalence and presentation. This study aims to establish whether common variants conferring T2D-risk in Europeans contribute to T2D-susceptibility in the South Asian population of Sri Lanka.
Lead single nucleotide polymorphism (SNPs) at 37 T2D-risk loci attaining genome-wide significance in Europeans were genotyped in 878 T2D cases and 1523 normoglycaemic controls from Sri Lanka. Association testing was performed by logistic regression adjusting for age and sex and by the Cochran-Mantel-Haenszel test after stratifying according to self-identified ethnolinguistic subgroup. A weighted genetic risk score was generated to examine the combined effect of these SNPs on T2D-risk in the Sri Lankan population.
Of the 36 SNPs passing quality control, sixteen showed nominal (p<0.05) association in Sri Lankan samples, fifteen of those directionally-consistent with the original signal. Overall, these association findings were robust to analyses that accounted for membership of ethnolinguistic subgroups. Overall, the odds ratios for 31 of the 36 SNPs were directionally-consistent with those observed in Europeans (p = 3.2×10−6). Allelic odds ratios and risk allele frequencies in Sri Lankan subjects were not systematically different to those reported in Europeans. Genetic risk score and risk of T2D were strongly related in Sri Lankans (per allele OR 1.10 [95%CI 1.08–1.13], p = 1.2×10−17).
Our data indicate that most T2D-risk variants identified in Europeans have similar effects in South Asians from Sri Lanka, and that systematic difference in common variant associations are unlikely to explain inter-ethnic differences in prevalence or presentation of T2D.
PMCID: PMC4057178  PMID: 24926958
2.  A System for Information Management in BioMedical Studies—SIMBioMS 
Bioinformatics  2009;25(20):2768-2769.
Summary: SIMBioMS is a web-based open source software system for managing data and information in biomedical studies. It provides a solution for the collection, storage, management and retrieval of information about research subjects and biomedical samples, as well as experimental data obtained using a range of high-throughput technologies, including gene expression, genotyping, proteomics and metabonomics. The system can easily be customized and has proven to be successful in several large-scale multi-site collaborative projects. It is compatible with emerging functional genomics data standards and provides data import and export in accepted standard formats. Protocols for transferring data to durable archives at the European Bioinformatics Institute have been implemented.
Availability: The source code, documentation and initialization scripts are available at
PMCID: PMC2759553  PMID: 19633095
3.  Reduced Insulin Exocytosis in Human Pancreatic β-Cells With Gene Variants Linked to Type 2 Diabetes 
Diabetes  2012;61(7):1726-1733.
The majority of genetic risk variants for type 2 diabetes (T2D) affect insulin secretion, but the mechanisms through which they influence pancreatic islet function remain largely unknown. We functionally characterized human islets to determine secretory, biophysical, and ultrastructural features in relation to genetic risk profiles in diabetic and nondiabetic donors. Islets from donors with T2D exhibited impaired insulin secretion, which was more pronounced in lean than obese diabetic donors. We assessed the impact of 14 disease susceptibility variants on measures of glucose sensing, exocytosis, and structure. Variants near TCF7L2 and ADRA2A were associated with reduced glucose-induced insulin secretion, whereas susceptibility variants near ADRA2A, KCNJ11, KCNQ1, and TCF7L2 were associated with reduced depolarization-evoked insulin exocytosis. KCNQ1, ADRA2A, KCNJ11, HHEX/IDE, and SLC2A2 variants affected granule docking. We combined our results to create a novel genetic risk score for β-cell dysfunction that includes aberrant granule docking, decreased Ca2+ sensitivity of exocytosis, and reduced insulin release. Individuals with a high risk score displayed an impaired response to intravenous glucose and deteriorating insulin secretion over time. Our results underscore the importance of defects in β-cell exocytosis in T2D and demonstrate the potential of cellular phenotypic characterization in the elucidation of complex genetic disorders.
PMCID: PMC3379663  PMID: 22492527
4.  Extent, Causes, and Consequences of Small RNA Expression Variation in Human Adipose Tissue 
PLoS Genetics  2012;8(5):e1002704.
Small RNAs are functional molecules that modulate mRNA transcripts and have been implicated in the aetiology of several common diseases. However, little is known about the extent of their variability within the human population. Here, we characterise the extent, causes, and effects of naturally occurring variation in expression and sequence of small RNAs from adipose tissue in relation to genotype, gene expression, and metabolic traits in the MuTHER reference cohort. We profiled the expression of 15 to 30 base pair RNA molecules in subcutaneous adipose tissue from 131 individuals using high-throughput sequencing, and quantified levels of 591 microRNAs and small nucleolar RNAs. We identified three genetic variants and three RNA editing events. Highly expressed small RNAs are more conserved within mammals than average, as are those with highly variable expression. We identified 14 genetic loci significantly associated with nearby small RNA expression levels, seven of which also regulate an mRNA transcript level in the same region. In addition, these loci are enriched for variants significant in genome-wide association studies for body mass index. Contrary to expectation, we found no evidence for negative correlation between expression level of a microRNA and its target mRNAs. Trunk fat mass, body mass index, and fasting insulin were associated with more than twenty small RNA expression levels each, while fasting glucose had no significant associations. This study highlights the similar genetic complexity and shared genetic control of small RNA and mRNA transcripts, and gives a quantitative picture of small RNA expression variation in the human population.
Author Summary
Genetic information is transmitted to the cell only through RNA molecules. A special class of RNAs is comprised of the small (up to 30 nucleotide) ones, known to be potent regulators of various cellular processes. At the same time, they have not been as widely studied as messenger RNAs—we do not know how much variation in their sequence and expression level occurs naturally in human populations or how this variability influences other traits. We measured small RNA levels and genetic variability in fat tissue from 131 individuals by high-throughput sequencing. We could associate the expression levels with genetic background of the individuals, as well as changes in metabolic traits. Surprisingly, we found no large scale influence of small RNA variation on mRNA levels, their main regulatory target. Overall, our study is the first to give a quantitative picture of the naturally occurring variation in these important regulatory molecules in human fat tissue.
PMCID: PMC3349731  PMID: 22589741
5.  Coexpression Network Analysis in Abdominal and Gluteal Adipose Tissue Reveals Regulatory Genetic Loci for Metabolic Syndrome and Related Phenotypes 
PLoS Genetics  2012;8(2):e1002505.
Metabolic Syndrome (MetS) is highly prevalent and has considerable public health impact, but its underlying genetic factors remain elusive. To identify gene networks involved in MetS, we conducted whole-genome expression and genotype profiling on abdominal (ABD) and gluteal (GLU) adipose tissue, and whole blood (WB), from 29 MetS cases and 44 controls. Co-expression network analysis for each tissue independently identified nine, six, and zero MetS–associated modules of coexpressed genes in ABD, GLU, and WB, respectively. Of 8,992 probesets expressed in ABD or GLU, 685 (7.6%) were expressed in ABD and 51 (0.6%) in GLU only. Differential eigengene network analysis of 8,256 shared probesets detected 22 shared modules with high preservation across adipose depots (DABD-GLU = 0.89), seven of which were associated with MetS (FDR P<0.01). The strongest associated module, significantly enriched for immune response–related processes, contained 94/620 (15%) genes with inter-depot differences. In an independent cohort of 145/141 twins with ABD and WB longitudinal expression data, median variability in ABD due to familiality was greater for MetS–associated versus un-associated modules (ABD: 0.48 versus 0.18, P = 0.08; GLU: 0.54 versus 0.20, P = 7.8×10−4). Cis-eQTL analysis of probesets associated with MetS (FDR P<0.01) and/or inter-depot differences (FDR P<0.01) provided evidence for 32 eQTLs. Corresponding eSNPs were tested for association with MetS–related phenotypes in two GWAS of >100,000 individuals; rs10282458, affecting expression of RARRES2 (encoding chemerin), was associated with body mass index (BMI) (P = 6.0×10−4); and rs2395185, affecting inter-depot differences of HLA-DRB1 expression, was associated with high-density lipoprotein (P = 8.7×10−4) and BMI–adjusted waist-to-hip ratio (P = 2.4×10−4). Since many genes and their interactions influence complex traits such as MetS, integrated analysis of genotypes and coexpression networks across multiple tissues relevant to clinical traits is an efficient strategy to identify novel associations.
Author Summary
Metabolic Syndrome (MetS) is a highly prevalent disorder with considerable public health concern, but its underlying genetic factors remain elusive. Given that most cellular components exert their functions through interactions with other cellular components, even the largest of genome-wide association (GWA) studies may often not detect their effects, nor necessarily provide insight into the complex molecular mechanisms of the disease. Rather than focusing on individual genes, the analysis of coexpression networks can be used for finding clusters (modules) of correlated expression levels across samples. In this study, we used a gene network–based approach for integrating clinical MetS, genotypic, and gene expression data from abdominal and gluteal adipose tissue and whole blood. We identified modules of genes related to MetS significantly enriched for immune response and oxidative phosphorylation pathways. We tested SNPs for association with MetS–associated expression (eSNPs), and tested prioritised eSNPs for association with MetS–related phenotypes in two large-scale GWA datasets. We identified two loci, neither of which had reached genome-wide significance levels in GWAs, associated with expression levels of RARRES2 and HLA-DRB1 and with MetS–related phenotypes, demonstrating that the integrated analysis of genotype and expression data from relevant multiple tissues can identify novel associations with complex traits such as MetS.
PMCID: PMC3285582  PMID: 22383892
6.  MicroRNA Expression in Abdominal and Gluteal Adipose Tissue Is Associated with mRNA Expression Levels and Partly Genetically Driven 
PLoS ONE  2011;6(11):e27338.
To understand how miRNAs contribute to the molecular phenotype of adipose tissues and related traits, we performed global miRNA expression profiling in subcutaneous abdominal and gluteal adipose tissue of 70 human subjects and characterised which miRNAs were differentially expressed between these tissues. We found that 12% of the miRNAs were significantly differentially expressed between abdominal and gluteal adipose tissue (FDR adjusted p<0.05) in the primary study, of which 59 replicated in a follow-up study of 40 additional subjects. Further, 14 miRNAs were found to be associated with metabolic syndrome case-control status in abdominal tissue and three of these replicated (primary study: FDR adjusted p<0.05, replication: p<0.05 and directionally consistent effect). Genome-wide genotyping was performed in the 70 subjects to enable miRNA expression quantitative trait loci (eQTL) analysis. Candidate miRNA eQTLs were followed-up in the additional 40 subjects and six significant, independent cis-located miRNA eQTLs (primary study: p<0.001; replication: p<0.05 and directionally consistent effect) were identified. Finally, global mRNA expression profiling was performed in both tissues to enable association analysis between miRNA and target mRNA expression levels. We find 22% miRNAs in abdominal and 9% miRNAs in gluteal adipose tissue with expression levels significantly associated with the expression of corresponding target mRNAs (FDR adjusted p<0.05). Taken together, our results indicate a clear difference in the miRNA molecular phenotypic profile of abdominal and gluteal adipose tissue, that the expressions of some miRNAs are influenced by cis-located genetic variants and that miRNAs are associated with expression levels of their predicted mRNA targets.
PMCID: PMC3216936  PMID: 22102887
7.  A Genome-Wide Metabolic QTL Analysis in Europeans Implicates Two Loci Shaped by Recent Positive Selection 
PLoS Genetics  2011;7(9):e1002270.
We have performed a metabolite quantitative trait locus (mQTL) study of the 1H nuclear magnetic resonance spectroscopy (1H NMR) metabolome in humans, building on recent targeted knowledge of genetic drivers of metabolic regulation. Urine and plasma samples were collected from two cohorts of individuals of European descent, with one cohort comprised of female twins donating samples longitudinally. Sample metabolite concentrations were quantified by 1H NMR and tested for association with genome-wide single-nucleotide polymorphisms (SNPs). Four metabolites' concentrations exhibited significant, replicable association with SNP variation (8.6×10−11
Author Summary
Physiological concentrations of metabolites—small molecules involved in biochemical processes in living systems—can be measured and used to diagnose and predict disease states. A common goal is to detect and clinically exploit statistical differences in metabolite concentrations between diseased and healthy individuals. As a basis for the design and interpretation of case-control studies, it is useful to have a characterization of metabolic diversity amongst healthy individuals, some of which stems from inter-individual genetic variation. When a single genetic locus has a sufficiently strong effect on metabolism, its genomic position can be determined by collecting metabolite concentration data and genome-wide genotype data on a set of individuals and searching for associations between the two data sets—a so-called metabolite quantitative trait locus (mQTL) study. By so tracing mQTLs, we can identify the genetic drivers of metabolism, characterize how the nature or quantity of the corresponding expressed protein(s) feeds forward to influence metabolite levels, and specify disease-predictive models that incorporate mutual dependence amongst genetics, environment, and metabolism.
PMCID: PMC3169529  PMID: 21931564
A comprehensive variation map of the human metabolome identifies genetic and stable-environmental sources as major drivers of metabolite concentrations. The data suggest that sample sizes of a few thousand are sufficient to detect metabolite biomarkers predictive of disease.
We designed a longitudinal twin study to characterize the genetic, stable-environmental, and longitudinally fluctuating influences on metabolite concentrations in two human biofluids—urine and plasma—focusing specifically on the representative subset of metabolites detectable by 1H nuclear magnetic resonance (1H NMR) spectroscopy.We identified widespread genetic and stable-environmental influences on the (urine and plasma) metabolomes, with (30 and 42%) attributable on average to familial sources, and (47 and 60%) attributable to longitudinally stable sources.Ten of the metabolites annotated in the study are estimated to have >60% familial contribution to their variation in concentration.Our findings have implications for the design and interpretation of 1H NMR-based molecular epidemiology studies. On the basis of the stable component of variation quantified in the current paper, we specified a model of disease association under which we inferred that sample sizes of a few thousand should be sufficient to detect disease-predictive metabolite biomarkers.
Metabolites are small molecules involved in biochemical processes in living systems. Their concentration in biofluids, such as urine and plasma, can offer insights into the functional status of biological pathways within an organism, and reflect input from multiple levels of biological organization—genetic, epigenetic, transcriptomic, and proteomic—as well as from environmental and lifestyle factors. Metabolite levels have the potential to indicate a broad variety of deviations from the ‘normal' physiological state, such as those that accompany a disease, or an increased susceptibility to disease. A number of recent studies have demonstrated that metabolite concentrations can be used to diagnose disease states accurately. A more ambitious goal is to identify metabolite biomarkers that are predictive of future disease onset, providing the possibility of intervention in susceptible individuals.
If an extreme concentration of a metabolite is to serve as an indicator of disease status, it is usually important to know the distribution of metabolite levels among healthy individuals. It is also useful to characterize the sources of that observed variation in the healthy population. A proportion of that variation—the heritable component—is attributable to genetic differences between individuals, potentially at many genetic loci. An effective, molecular indicator of a heritable, complex disease is likely to have a substantive heritable component. Non-heritable biological variation in metabolite concentrations can arise from a variety of environmental influences, such as dietary intake, lifestyle choices, general physical condition, composition of gut microflora, and use of medication. Variation across a population in stable-environmental influences leads to long-term differences between individuals in their baseline metabolite levels. Dynamic environmental pressures lead to short-term fluctuations within an individual about their baseline level. A metabolite whose concentration changes substantially in response to short-term pressures is relatively unlikely to offer long-term prediction of disease. In summary, the potential suitability of a metabolite to predict disease is reflected by the relative contributions of heritable and stable/unstable-environmental factors to its variation in concentration across the healthy population.
Studies involving twins are an established technique for quantifying the heritable component of phenotypes in human populations. Monozygotic (MZ) twins share the same DNA genome-wide, while dizygotic (DZ) twins share approximately half their inherited DNA, as do ordinary siblings. By comparing the average extent of phenotypic concordance within MZ pairs to that within DZ pairs, it is possible to quantify the heritability of a trait, and also to quantify the familiality, which refers to the combination of heritable and common-environmental effects (i.e., environmental influences shared by twins in a pair). In addition to incorporating twins into the study design, it is useful to quantify the phenotype in some individuals at multiple time points. The longitudinal aspect of such a study allows environmental effects to be decomposed into those that affect the phenotype over the short term and those that exert stable influence.
For the current study, urine and blood samples were collected from a cohort of MZ and DZ twins, with some twins donating samples on two occasions several months apart. Samples were analysed by 1H nuclear magnetic resonance (1H NMR) spectroscopy—an untargeted, discovery-driven technique for quantifying metabolite concentrations in biological samples. The application of 1H NMR to a biological sample creates a spectrum, made up of multiple peaks, with each peak's size quantitatively representing the concentration of its corresponding hydrogen-containing metabolite.
In each biological sample in our study, we extracted a full set of peaks, and thereby quantified the concentrations of all common plasma and urine metabolites detectable by 1H NMR. We developed bespoke statistical methods to decompose the observed concentration variation at each metabolite peak into that originating from familial, individual-environmental, and unstable-environmental sources.
We quantified the variability landscape across all common metabolite peaks in the urine and plasma 1H NMR metabolomes. We annotated a subset of peaks with a total of 65 metabolites; the variance decompositions for these are shown in Figure 1. Ten metabolites' concentrations were estimated to have familial contributions in excess of 60%. The average proportion of stable variation across all extracted metabolite peaks was estimated to be 47% in the urine samples and 60% in the plasma samples; the average estimated familiality was 30% for urine and 42% for plasma. These results comprise the first quantitative variation map of the 1H NMR metabolome. The identification and quantification of substantive widespread stability provides support for the use of these biofluids in molecular epidemiology studies. On the basis of our findings, we performed power calculations for a hypothetical study searching for predictive disease biomarkers among 1H NMR-detectable urine and plasma metabolites. Our calculations suggest that sample sizes of 2000–5000 should allow reliable identification of disease-predictive metabolite concentrations explaining 5–10% of disease risk, while greater sample sizes of 5000–20 000 would be required to identify metabolite concentrations explaining 1–2% of disease risk.
1H Nuclear Magnetic Resonance spectroscopy (1H NMR) is increasingly used to measure metabolite concentrations in sets of biological samples for top-down systems biology and molecular epidemiology. For such purposes, knowledge of the sources of human variation in metabolite concentrations is valuable, but currently sparse. We conducted and analysed a study to create such a resource. In our unique design, identical and non-identical twin pairs donated plasma and urine samples longitudinally. We acquired 1H NMR spectra on the samples, and statistically decomposed variation in metabolite concentration into familial (genetic and common-environmental), individual-environmental, and longitudinally unstable components. We estimate that stable variation, comprising familial and individual-environmental factors, accounts on average for 60% (plasma) and 47% (urine) of biological variation in 1H NMR-detectable metabolite concentrations. Clinically predictive metabolic variation is likely nested within this stable component, so our results have implications for the effective design of biomarker-discovery studies. We provide a power-calculation method which reveals that sample sizes of a few thousand should offer sufficient statistical precision to detect 1H NMR-based biomarkers quantifying predisposition to disease.
PMCID: PMC3202796  PMID: 21878913
biomarker; 1H nuclear magnetic resonance spectroscopy; metabolome-wide association study; top-down systems biology; variance decomposition
PLoS Genetics  2011;7(2):e1002003.
While there have been studies exploring regulatory variation in one or more tissues, the complexity of tissue-specificity in multiple primary tissues is not yet well understood. We explore in depth the role of cis-regulatory variation in three human tissues: lymphoblastoid cell lines (LCL), skin, and fat. The samples (156 LCL, 160 skin, 166 fat) were derived simultaneously from a subset of well-phenotyped healthy female twins of the MuTHER resource. We discover an abundance of cis-eQTLs in each tissue similar to previous estimates (858 or 4.7% of genes). In addition, we apply factor analysis (FA) to remove effects of latent variables, thus more than doubling the number of our discoveries (1,822 eQTL genes). The unique study design (Matched Co-Twin Analysis—MCTA) permits immediate replication of eQTLs using co-twins (93%–98%) and validation of the considerable gain in eQTL discovery after FA correction. We highlight the challenges of comparing eQTLs between tissues. After verifying previous significance threshold-based estimates of tissue-specificity, we show their limitations given their dependency on statistical power. We propose that continuous estimates of the proportion of tissue-shared signals and direct comparison of the magnitude of effect on the fold change in expression are essential properties that jointly provide a biologically realistic view of tissue-specificity. Under this framework we demonstrate that 30% of eQTLs are shared among the three tissues studied, while another 29% appear exclusively tissue-specific. However, even among the shared eQTLs, a substantial proportion (10%–20%) have significant differences in the magnitude of fold change between genotypic classes across tissues. Our results underline the need to account for the complexity of eQTL tissue-specificity in an effort to assess consequences of such variants for complex traits.
Author Summary
Regulation of gene expression is a fundamental cellular process determining a large proportion of the phenotypic variance. Previous studies have identified genetic loci influencing gene expression levels (eQTLs), but the complexity of their tissue-specific properties has not yet been well-characterized. In this study, we perform cis-eQTL analysis in a unique matched co-twin design for three human tissues derived simultaneously from the same set of individuals. The study design allows validation of the substantial discoveries we make in each tissue. We explore in depth the tissue-dependent features of regulatory variants and estimate the proportions of shared and specific effects. We use continuous measures of eQTL sharing to circumvent the statistical power limitations of comparing direct overlap of eQTLs in multiple tissues. In this framework, we demonstrate that 30% of eQTLs are shared among tissues, while 29% are exclusively tissue-specific. Furthermore, we show that the fold change in expression between eQTL genotypic classes differs between tissues. Even among shared eQTLs, we report a substantial proportion (10%–20%) of significant tissue differences in magnitude of these effects. The complexities we highlight here are essential for understanding the impact of regulatory variants on complex traits.
PMCID: PMC3033383  PMID: 21304890
BMC Genomics  2010;11:96.
Readily accessible samples such as peripheral blood or cell lines are increasingly being used in large cohorts to characterise gene expression differences between a patient group and healthy controls. However, cell and RNA isolation procedures and the variety of cell types that make up whole blood can affect gene expression measurements. We therefore systematically investigated global gene expression profiles in peripheral blood from six individuals collected during two visits by comparing five of the following cell and RNA isolation methods: whole blood (PAXgene), peripheral blood mononuclear cells (PBMCs), lymphoblastoid cell lines (LCLs), CD19 and CD20 specific B-cell subsets.
Gene expression measurements were clearly discriminated by isolation method although the reproducibility was high for all methods (range ρ = 0.90-1.00). The PAXgene samples showed a decrease in the number of expressed genes (P < 1*10-16) with higher variability (P < 1*10-16) compared to the other methods. Differentially expressed probes between PAXgene and PBMCs were correlated with the number of monocytes, lymphocytes, neutrophils or erythrocytes. The correlations (ρ = 0.83; ρ = 0.79) of the expression levels of detected probes between LCLs and B-cell subsets were much lower compared to the two B-cell isolation methods (ρ = 0.98). Gene ontology analysis of detected genes showed that genes involved in inflammatory responses are enriched in B-cells CD19 and CD20 whereas genes involved in alcohol metabolic process and the cell cycle were enriched in LCLs.
Gene expression profiles in blood-based samples are strongly dependent on the predominant constituent cell type(s) and RNA isolation method. It is crucial to understand the differences and variability of gene expression measurements between cell and RNA isolation procedures, and their relevance to disease processes, before application in large clinical studies.
PMCID: PMC2841682  PMID: 20141636
BMC Medical Genomics  2009;2:54.
MicroRNAs (miRNAs) are non-coding RNA molecules involved in post-transcriptional control of gene expression of a wide number of genes, including those involved in glucose homeostasis. Type 2 diabetes (T2D) is characterized by hyperglycaemia and defects in insulin secretion and action at target tissues. We sought to establish differences in global miRNA expression in two insulin-target tissues from inbred rats of spontaneously diabetic and normoglycaemic strains.
We used a miRNA microarray platform to measure global miRNA expression in two insulin-target tissues: liver and adipose tissue from inbred rats of spontaneously diabetic (Goto-Kakizaki [GK]) and normoglycaemic (Brown-Norway [BN]) strains which are extensively used in genetic studies of T2D. MiRNA data were integrated with gene expression data from the same rats to investigate how differentially expressed miRNAs affect the expression of predicted target gene transcripts.
The expression of 170 miRNAs was measured in liver and adipose tissue of GK and BN rats. Based on a p-value for differential expression between GK and BN, the most significant change in expression was observed for miR-125a in liver (FC = 5.61, P = 0.001, Padjusted = 0.10); this overexpression was validated using quantitative RT-PCR (FC = 13.15, P = 0.0005). MiR-125a also showed over-expression in the GK vs. BN analysis within adipose tissue (FC = 1.97, P = 0.078, Padjusted = 0.99), as did the previously reported miR-29a (FC = 1.51, P = 0.05, Padjusted = 0.99). In-silico tools assessing the biological role of predicted miR-125a target genes suggest an over-representation of genes involved in the MAPK signaling pathway. Gene expression analysis identified 1308 genes with significantly different expression between GK and BN rats (Padjusted < 0.05): 233 in liver and 1075 in adipose tissue. Pathways related to glucose and lipid metabolism were significantly over-represented among these genes. Enrichment analysis suggested that differentially expressed genes in GK compared to BN included more predicted miR-125a target genes than would be expected by chance in adipose tissue (FDR = 0.006 for up-regulated genes; FDR = 0.036 for down-regulated genes) but not in liver (FDR = 0.074 for up-regulated genes; FDR = 0.248 for down-regulated genes).
MiR-125a is over-expressed in liver in hyperglycaemic GK rats relative to normoglycaemic BN rats, and our array data also suggest miR-125a is over-expressed in adipose tissue. We demonstrate the use of in-silico tools to provide the basis for further investigation of the potential role of miR-125a in T2D. In particular, the enrichment of predicted miR-125a target genes among differentially expressed genes has identified likely target genes and indicates that integrating global miRNA and mRNA expression data may give further insights into miRNA-mediated regulation of gene expression.
PMCID: PMC2754496  PMID: 19689793
BMC Bioinformatics  2007;8:52.
One of the crucial aspects of day-to-day laboratory information management is collection, storage and retrieval of information about research subjects and biomedical samples. An efficient link between sample data and experiment results is absolutely imperative for a successful outcome of a biomedical study. Currently available software solutions are largely limited to large-scale, expensive commercial Laboratory Information Management Systems (LIMS). Acquiring such LIMS indeed can bring laboratory information management to a higher level, but often implies sufficient investment of time, effort and funds, which are not always available. There is a clear need for lightweight open source systems for patient and sample information management.
We present a web-based tool for submission, management and retrieval of sample and research subject data. The system secures confidentiality by separating anonymized sample information from individuals' records. It is simple and generic, and can be customised for various biomedical studies. Information can be both entered and accessed using the same web interface. User groups and their privileges can be defined. The system is open-source and is supplied with an on-line tutorial and necessary documentation. It has proven to be successful in a large international collaborative project.
The presented system closes the gap between the need and the availability of lightweight software solutions for managing information in biomedical studies involving human research subjects.
PMCID: PMC1803798  PMID: 17291344

Results 1-12 (12)