Many centrality measures have been proposed to mine and characterize the correlations between network topological properties and protein essentiality. However, most of them show limited prediction accuracy, and the number of common predicted essential proteins by different methods is very small.
In this paper, an ensemble framework is proposed which integrates gene expression data and protein-protein interaction networks (PINs). It aims to improve the prediction accuracy of basic centrality measures. The idea behind this ensemble framework is that different protein-protein interactions (PPIs) may show different contributions to protein essentiality. Five standard centrality measures (degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and subgraph centrality) are integrated into the ensemble framework respectively. We evaluated the performance of the proposed ensemble framework using yeast PINs and gene expression data. The results show that it can considerably improve the prediction accuracy of the five centrality measures individually. It can also remarkably increase the number of common predicted essential proteins among those predicted by each centrality measure individually and enable each centrality measure to find more low-degree essential proteins.
This paper demonstrates that it is valuable to differentiate the contributions of different PPIs for identifying essential proteins based on network topological characteristics. The proposed ensemble framework is a successful paradigm to this end.
Electronic supplementary material
The online version of this article (doi:10.1186/s12859-016-1166-7) contains supplementary material, which is available to authorized users.
Essential protein; Protein-protein interaction networks; Centrality measure; Ensemble learning; Gene expression
In this study, the flanking sequence of an inserted fragment conferring glyphosate tolerance on transgenic cotton line BG2-7 was analyzed by thermal asymmetric interlaced polymerase chain reaction (TAIL-PCR) and standard PCR. The results showed apparent insertion of the exogenous gene into chromosome D10 of the Gossypium hirsutum L. genome, as the left and right borders of the inserted fragment are nucleotides 61,962,952 and 61,962,921 of chromosome D10, respectively. In addition, a 31-bp cotton microsatellite sequence was noted between the genome sequence and the 5' end of the exogenous gene. In total, 84 and 298 bp were deleted from the left and right borders of the exogenous gene, respectively, with 30 bp deleted from the cotton chromosome at the insertion site. According to the flanking sequence obtained, several pairs of event-specific detection primers were designed to amplify sequence between the 5' end of the exogenous gene and the cotton genome junction region as well as between the 3' end and the cotton genome junction region. Based on screening tests, the 5'-end primers GTCATAACGTGACTCCCTTAATTCTCC/CCTATTACACGGCTATGC and 3'-end primers TCCTTTCGCTTTCTTCCCTT/ACACTTACATGGCGTCTTCT were used to detect the respective BG2-7 event-specific primers. The limit of detection of the former primers reached 44 copies, and that of the latter primers reached 88 copies. The results of this study provide useful data for assessment of BG2-7 safety and for accelerating its industrialization.
Peritoneal metastasis is a primary cause of mortality in patients with gastric cancer. Urokinase-type plasminogen activator (uPA) has been demonstrated to be associated with tumor cell metastasis through the degradation of the extracellular matrix. The present study aimed to investigate the mechanisms of the uPA system in gastric cancer with peritoneal metastasis. Expression of uPA, uPA receptor (uPAR) and plasminogen activator inhibitor-1 (PAI-1) in four gastric cell lines (AGS, SGC7901, MKN45 and MKN28) was measured by semiquantitative reverse transcription polymerase chain reaction, enzyme-linked immunosorbent assay and western blotting. uPA activity was detected using a uPA activity kit. Peritoneal implantation models of rats were established by injecting four gastric cancer cell lines for the selection of the cancer cells with a high planting potential. Biological behaviors, including adhesion, migration and invasion, were determined using a methyl thiazolyl tetrazolium assay. Expression of the uPA system was observed to be highest in the SGC7901 cells among the four gastric cell lines. uPA activity was observed to be highest in the MKN45 cells and lowest in the AGS cells. Furthermore, peritoneal implantation analysis demonstrated that no peritoneal tumors were identified in the AGS cells, whilst the tumor masses observed in the SGC7901 and MKN45 cells were of different sizes. The survival times of the rats injected with the MKN28 and SGC7901 cells were longer than those of the rats injected with the MKN45 cells. Antibodies for uPA, uPAR and PAI-1 in the uPA system had the ability to inhibit the adhesion, migration and invasion of peritoneal metastasis in the gastric cancer cells. The results of the present study demonstrated that the uPA system was positively associated with peritoneal metastasis in gastric cancer.
urokinase-type plasminogen activator; peritoneal metastasis; gastric cancer
Phytosterols in soybean oil (SO) lipids likely contribute to parenteral nutrition-associated liver disease (PNALD) in infants. No characterization of phytosterol metabolism has been done in infants receiving SO lipids.
In a prospective cohort study, 45 neonates (36 SO lipid vs 9 control) underwent serial blood sample measurements of sitosterol, campesterol, and stigmasterol. Mathematical modeling was used to determine pharmacokinetic parameters of phytosterol metabolism and phytosterol exposure.
Compared to controls, SO lipid-exposed infants had significantly higher levels of sitosterol and campesterol (p<0.01). During SO lipid infusion, sitosterol and campesterol reached half of steady-state plasma levels within 1.5 days and 0.8 days, respectively. Steady-state level was highest for sitosterol (1.68 mg/dL), followed by campesterol (0.98 mg/dL), and lowest for stigmasterol (0.01 mg/dL). Infants born < 28 weeks gestational age had higher sitosterol steady-state levels (p=0.03) and higher area under the curve for sitosterol (p=0.03) during the first 5 days of SO lipid (AUC5) than infants born ≥ 28 weeks gestational age.
Phytosterols in SO lipid accumulate rapidly in neonates. Very preterm infants receiving SO lipid have higher sitosterol exposure, and may have poorly developed mechanisms of eliminating phytosterols that may contribute to their vulnerability to PNALD.
1-Bromopropane (1-BP) has been used as an alternative for fluoride compounds and 1-BP intoxication may involve lung, liver, and central neural system (CNS). Our previous studies showed that 1-BP impaired memory ability by compromising antioxidant cellular defenses. Melatonin is a powerful endogenous
antioxidant, and the objective of this study was to explore the therapeutic role of melatonin in the treatment of 1-BP intoxication. Rats were intragastrically treated with 1-BP with or without melatonin, and then sacrificed on 27th day after 1-BP administration. The Morris water maze (MWM) test was used to evaluate the spatial learning and memory ability of the experimental animals, and NeuN staining was performed to assess neuron loss in hippocampus. We found that rats treated with 1-BP spent more time and swam longer distance before landing on the hidden platform with a comparable swimming speed, which was markedly mitigated by the pretreatment with melatonin in a concentration-dependent manner. In addition, 1-BP-induced notable decrease in neuron population in hippocampus by promoting apoptosis, and melatonin pretreatment attenuated those changes in brain. The GSH/GSSG ratio was proportionately decreased and heme oxygenase 1 was increased in the rats exposed to 1-BP (Figure 6), and administration of melatonin restored them. Meanwhile, MDA, the level of lipid peroxidation product, was significantly increased upon exposed to 1-BP, which was significantly attenuated by melatonin pretreatment, indicating that administration of 1-BP could interfere with redox homeostasis of brain in rat, and such 1-BP-induced biomedical changes were reversed by treatment with melatonin.
We conclude that treatment with melatonin attenuates 1-BP-induced CNS toxicity through its ROS scavenging effect.
Mechanisms associated with type 1 diabetes (T1D) development remain incompletely defined. Using a sensitive array-based bioassay where patient plasma is used to induce transcriptional responses in healthy leukocytes, we previously reported disease-specific, partially interleukin (IL)-1−dependent signatures associated with preonset and recent onset (RO) T1D relative to unrelated healthy control subjects (uHC). To better understand inherited susceptibility in T1D families, we conducted cross-sectional and longitudinal analyses of healthy autoantibody-negative (AA−) high HLA−risk siblings (HRS) (DR3 and/or DR4) and AA− low HLA−risk siblings (LRS) (non-DR3/non-DR4). Signatures, scored with a novel ontology-based algorithm, and confirmatory studies differentiated the RO T1D, uHC, HRS, and LRS plasma milieus. Relative to uHC, T1D family members exhibited an elevated inflammatory state, consistent with innate receptor ligation that was independent of HLA, AA, or disease status and included elevated plasma IL-1α, IL-12p40, CCL2, CCL3, and CCL4 levels. Longitudinally, signatures of T1D progressors exhibited increasing inflammatory bias. Conversely, HRS possessing decreasing AA titers revealed emergence of an IL-10/transforming growth factor-β−mediated regulatory state that paralleled temporal increases in peripheral activated CD4+/CD45RA−/FoxP3high regulatory T-cell frequencies. In AA− HRS, the familial innate inflammatory state also was temporally supplanted by immunoregulatory processes, suggesting a mechanism underlying the decline in T1D susceptibility with age.
The generalized linear mixed model (GLMM) is a useful tool for modeling genetic correlation among family data in genetic association studies. However, when dealing with families of varied sizes and diverse genetic relatedness, the GLMM has a special correlation structure which often makes it difficult to be specified using standard statistical software. In this study, we propose a Cholesky decomposition based re-formulation of the GLMM so that the re-formulated GLMM can be specified conveniently via “proc nlmixed” and “proc glimmix” in SAS, or OpenBUGS via R package BRugs. Performances of these procedures in fitting the re-formulated GLMM are examined through simulation studies. We also apply this re-formulated GLMM to analyze a real data set from Type 1 Diabetes Genetics Consortium (T1DGC).
family data; generalized linear mixed models (GLMM); genetic correlation; genetic variance components; random genetic effects; re-parameterization; Cholesky decomposition; Bayesian methods
The complex milieu of inflammatory mediators associated with many diseases is often too dilute to directly measure in the periphery, necessitating development of more sensitive measurements suitable for mechanistic studies, earlier diagnosis, guiding therapeutic decisions, and monitoring interventions. We previously demonstrated that plasma samples from recent-onset Type 1 diabetes (RO T1D) patients induce a proinflammatory transcriptional signature in freshly drawn peripheral blood mononuclear cells (PBMCs) relative to that of unrelated healthy controls (HC). Here, using cryopreserved PBMC, we analyzed larger RO T1D and HC cohorts, examined T1D progression in pre-onset samples, and compared the RO T1D signature to those associated with three disorders characterized by airway infection and inflammation. The RO T1D signature, consisting of interleukin-1 cytokine family members, chemokines involved in immunocyte chemotaxis, immune receptors, and signaling molecules, was detected during early pre-diabetes and found to resolve post-onset. The signatures associated with cystic fibrosis patients chronically infected with Pseudomonas aeruginosa, patients with confirmed bacterial pneumonia, and subjects with H1N1 influenza all reflected immunological activation, yet each were distinct from one another and negatively correlated with that of T1D. This study highlights the remarkable capacity of cells to serve as biosensors capable of sensitively and comprehensively differentiating immunological states.
Type 1 diabetes; Cystic Fibrosis; Influenza; Gene expression profiling; Biomarker; Inflammation
In many aspects the onset of a chronic disease resembles a phase transition in a complex dynamic system: Quantitative changes accumulate largely unnoticed until a critical threshold is reached, which causes abrupt qualitative changes of the system. In this study we examine a special case, the onset of type-1 diabetes (T1D), a disease that results from loss of the insulin-producing pancreatic islet β cells. Within each islet, the β cells are electrically coupled to each other via gap-junctional channels. This intercellular coupling enables the β cells to synchronize their insulin release, thereby generating the multiscale temporal rhythms in blood insulin that are critical to maintaining blood glucose homeostasis. Using percolation theory we show how normal islet function is intrinsically linked to network connectivity. In particular, the critical amount of β-cell death at which the islet cellular network loses site percolation is consistent with laboratory and clinical observations of the threshold loss of β cells that causes islet functional failure. In addition, numerical simulations confirm that the islet cellular network needs to be percolated for β cells to synchronize. Furthermore, the interplay between site percolation and bond strength predicts the existence of a transient phase of islet functional recovery after onset of T1D and introduction of treatment, potentially explaining the honeymoon phenomenon. Based on these results, we hypothesize that the onset of T1D may be the result of a phase transition of the islet β-cell network.
In this paper we develop a new mathematical model of glucose-induced insulin secretion from pancreatic islet β-cells, and we use this model to investigate the rate limiting factors. We assume that insulin granules reside in different pools inside each β-cell, and that all β-cells respond homogeneously to glucose with the same recruitment thresholds. Consistent with recent experimental observations, our model also accounts for the fusion of newcomer granules that are not pre-docked at the plasma membrane. In response to a single step increase in glucose concentration, our model reproduces the characteristic biphasic insulin release observed in multiple experimental systems, including perfused pancreata and isolated islets of rodent or human origin. From our model analysis we note that first-phase insulin secretion depends on rapid depletion of the primed, release-ready granule pools, while the second phase relies on granule mobilization from the reserve. Moreover, newcomers have the potential to contribute significantly to the second phase. When the glucose protocol consists of multiple changes in sequence (a so-called glucose staircase), our model predicts insulin spikes of increasing height, as has been seen experimentally. This increase stems from the glucose-dependent increase in the fusion rate of insulin granules at the plasma membrane of single β-cells. In contrast, previous mathematical models reproduced the staircase experiment by assuming heterogeneous β-cell activation. In light of experimental data indicating limited heterogeneous activation for β-cells within intact islets, our findings suggest that a graded, dose-dependent cell response to glucose may contribute to insulin secretion patterns observed in multiple experiments, and thus regulate in vivo insulin release. In addition, the strength of insulin granule mobilization, priming and fusion are critical limiting factors in determining the total amount of insulin release.
Insulin secretion; Insulin granule dynamics; Multiple pool; β-cells; Diabetes
The dilute plasma cytokine milieu associated with Type 1 diabetes (T1D), while difficult to measure directly, is sufficient to drive transcription in a bioassay that uses healthy leukocytes as reporters. Previously, we reported disease-associated, partially IL-1 dependent, transcriptional signatures in both T1D patients and the BioBreeding (BB) rat model. Here we examine temporal signatures in congenic BBDR.lyp/lyp rats that develop spontaneous T1D, and BBDR rats where T1D progresses only after immunological perturbation in young animals. After weaning, the BBDR temporal signature showed early coincident induction of transcription related to innate inflammation as well as IL-10- and TGF-β-mediated regulation. BBDR plasma cytokine levels mirrored the signatures showing early inflammation, followed by induction of a regulated state that correlated with failure of virus to induce T1D in older rats. In contrast, the BBDR.lyp/lyp temporal signature exhibited asynchronous dynamics, with delayed induction of inflammatory transcription and later, weaker induction of regulatory transcription, consistent with their deficiency in regulatory T cells. Through longitudinal analyses of plasma induced signatures in BB rats and a human T1D progressor, we have identified changes in immunoregulatory processes that attenuate a preexisting innate inflammatory state in BBDR rats, suggesting a mechanism underlying the decline in T1D susceptibility with age.
Type 1 Diabetes; Inflammation; Gene Expression; Cytokine; Immune Regulation; Virus-induced diabetes
In this study we investigated the advantage of including network information in prioritizing disease genes of type 1 diabetes (T1D). First, a naïve Bayesian network (NBN) model was developed to integrate information from multiple data sources and to define a T1D-involvement probability score (PS) for each individual gene. The algorithm was validated using known functional candidate genes as a benchmark. Genes with higher PS were found to be more likely to appear in T1D-related publications. Next a new network activity metric was proposed to evaluate the T1D relevance of protein-protein interaction (PPI) subnetworks. The metric considered the contribution both from individual genes and from network topological characteristics. The predictions were confirmed by several independent datasets, including a genome wide association study (GWAS), and two large-scale human gene expression studies. We found that novel candidate genes in the T1D subnetworks showed more significant associations with T1D than genes predicted using PS alone. Interestingly, most novel candidates were not encoded within the human leukocyte antigen (HLA) region, and their expression levels showed correlation with disease only in cohorts with low-risk HLA genotypes. The results suggested the importance of mapping disease gene networks in dissecting the genetics of complex diseases, and offered a general approach to network-based disease gene prioritization from multiple data sources.
It has been demonstrated that urokinase-type plasminogen activator (uPA) is involved in tumor cell metastasis by degrading the extracellular matrix. However, there is little direct evidence of clinical uPA system expression in peritoneal metastatic tissues of gastric cancer. The objective of this study was to investigate uPA system expression in peritoneal tissues of peritoneal and nonperitoneal metastasis patients, and to explore the diagnostic value of the uPA system.
Expressions of uPA, uPAR, and PAI-1 were measured by semi-quantitative RT-PCR and ELISA. uPA activity was detected using a uPA activity kit.
There was no significant difference in uPA, uPAR, and PAI-1 expression in two types of peritoneal tissue in seven patients with peritoneal metastasis. However, uPA, uPAR, and PAI-1 expressions in peritoneal metastatic lesions were significantly higher than those in normal peritoneal tissues of 24 nonperitoneal metastasis patients (P <0.05). Moreover, no statistical discrepancy of uPA activity was observed in various different tissues.
The expression of the uPA system positively correlates with peritoneal metastasis of gastric cancer. This expression difference in peritoneal or nonperitoneal metastasis patients may provide a reference for diagnosis of peritoneal metastasis.
Gastric cancer; ELISA; Peritoneal metastasis; RT-PCR; UPA system
There has been a growing interest in identifying context-specific active protein-protein interaction (PPI) subnetworks through integration of PPI and time course gene expression data. However the interaction dynamics during the biological process under study has not been sufficiently considered previously.
Here we propose a topology-phase locking (TopoPL) based scoring metric for identifying active PPI subnetworks from time series expression data. First the temporal coordination in gene expression changes is evaluated through phase locking analysis; The results are subsequently integrated with PPI to define an activity score for each PPI subnetwork, based on individual member expression, as well topological characteristics of the PPI network and of the expression temporal coordination network; Lastly, the subnetworks with the top scores in the whole PPI network are identified through simulated annealing search.
Application of TopoPL to simulated data and to the yeast cell cycle data showed that it can more sensitively identify biologically meaningful subnetworks than the method that only utilizes the static PPI topology, or the additive scoring method. Using TopoPL we identified a core subnetwork with 49 genes important to yeast cell cycle. Interestingly, this core contains a protein complex known to be related to arrangement of ribosome subunits that exhibit extremely high gene expression synchronization.
Inclusion of interaction dynamics is important to the identification of relevant gene networks.
Peritoneal metastasis in gastric cancer represents a ubiquitous human health problem but effective therapies with limited side effects are still lacking. Although previous research suggested that u-PA was involved in some tumor metastasis such as lung-specific metastasis, the role of u-PA for peritoneal metastasis in gastric cancer is still unclear. The aim of this study was to explore whether selective pharmacological blockade of u-PA is able to affect the peritoneal metastasis of gastric cancer both in vivo and in vitro.
In the present study, we evaluated the effects and explored the anti-tumor mechanisms of amiloride, a selective u-PA inhibitor, on a panel of gastric cancer cell lines and in a murine model of human gastric cancer MKN45.
The study showed that amiloride significantly inhibited the tumor growth and prolonged the survival of the tumor-bearing mice. In vitro, compared with controls, amiloride could not only significantly down-regulate the mRNA expression and protein level of u-PA from MKN45 cells with dose dependence but also inhibit the adhesion of HMrSV5 cells, migration and invasion of MKN45 cells.
The findings in our current report provide evidence that selective u-PA inhibitor amiloride has potent effects against peritoneal metastasis in gastric cancer, suggesting its possible therapeutic value for the treatment of gastric cancer.
u-PA inhibitor; Amiloride; Peritoneal metastasis; Gastric cancer
Complex disorders often involve dysfunctions in multiple tissue organs. Elucidating the communication among them is important to understanding disease pathophysiology. In this study we integrate multiple tissue gene expression and quantitative trait measurements of an obesity-induced diabetes mouse model, with databases of molecular interaction networks, to construct a cross tissue trait-pathway network. The animals belong to two strains of mice (BTBR or B6), of two obesity status (obese or lean), and at two different ages (4 weeks and 10 weeks). Only 10 week obese BTBR animals are diabetic. The expression data was first utilized to determine the state of every pathway in each tissue, which is subsequently utilized to construct a pathway co-expression network and to define trait-relevant and trait-linking pathways. Among the six tissues profiled, the adipose contains the largest number of trait-linking pathways. Among the eight traits measured, the body weight and plasma insulin level possess the most number of relevant and linking pathways. Topological analysis of the trait-pathway network revealed that the glycolysis/gluconeogenesis pathway in liver and the insulin signaling pathway in muscle are of top importance to the information flow in the network, with the highest degrees and betweenness centralities. Interestingly, pathways related to metabolism and oxidative stress actively interact with many other pathways in all animals, whereas, among the 10 week animals, the inflammation pathways were preferentially interactive in the diabetic ones only. In summary, our method offers a systems approach to delineate disease trait relevant intra- and cross tissue pathway interactions, and provides insights to the molecular basis of the obesity-induced diabetes.
Inflammatory mediators associated with type 1 diabetes are dilute and difficult to measure in the periphery, necessitating development of more sensitive and informative biomarkers for studying diabetogenic mechanisms, assessing preonset risk, and monitoring therapeutic interventions.
RESEARCH DESIGN AND METHODS
We previously utilized a novel bioassay in which human type 1 diabetes sera were used to induce a disease-specific transcriptional signature in unrelated, healthy peripheral blood mononuclear cells (PBMCs). Here, we apply this strategy to investigate the inflammatory state associated with type 1 diabetes in biobreeding (BB) rats.
Consistent with their common susceptibility, sera of both spontaneously diabetic BB DRlyp/lyp and diabetes inducible BB DR+/+ rats induced transcription of cytokines, immune receptors, and signaling molecules in PBMCs of healthy donor rats compared with control sera. Like the human type 1 diabetes signature, the DRlyp/lyp signature, which is associated with progression to diabetes, was differentiated from that of the DR+/+ by induction of many interleukin (IL)-1–regulated genes. Supplementing cultures with an IL-1 receptor antagonist (IL-1Ra) modulated the DRlyp/lyp signature (P < 10−6), while administration of IL-1Ra to DRlyp/lyp rats delayed onset (P = 0.007), and sera of treated animals did not induce the characteristic signature. Consistent with the presence of immunoregulatory cells in DR+/+ rats was induction of a signature possessing negative regulators of transcription and inflammation.
Paralleling our human studies, serum signatures in BB rats reflect processes associated with progression to type 1 diabetes. Furthermore, these studies support the potential utility of this approach to detect changes in the inflammatory state during therapeutic intervention.
Bayesian Network (BN) is a powerful approach to reconstructing genetic regulatory networks from gene expression data. However, expression data by itself suffers from high noise and lack of power. Incorporating prior biological knowledge can improve the performance. As each type of prior knowledge on its own may be incomplete or limited by quality issues, integrating multiple sources of prior knowledge to utilize their consensus is desirable.
We introduce a new method to incorporate the quantitative information from multiple sources of prior knowledge. It first uses the Naïve Bayesian classifier to assess the likelihood of functional linkage between gene pairs based on prior knowledge. In this study we included cocitation in PubMed and schematic similarity in Gene Ontology annotation. A candidate network edge reservoir is then created in which the copy number of each edge is proportional to the estimated likelihood of linkage between the two corresponding genes. In network simulation the Markov Chain Monte Carlo sampling algorithm is adopted, and samples from this reservoir at each iteration to generate new candidate networks. We evaluated the new algorithm using both simulated and real gene expression data including that from a yeast cell cycle and a mouse pancreas development/growth study. Incorporating prior knowledge led to a ~2 fold increase in the number of known transcription regulations recovered, without significant change in false positive rate. In contrast, without the prior knowledge BN modeling is not always better than a random selection, demonstrating the necessity in network modeling to supplement the gene expression data with additional information.
our new development provides a statistical means to utilize the quantitative information in prior biological knowledge in the BN modeling of gene expression data, which significantly improves the performance.
In nonlinear dynamic systems, synchrony through oscillation and frequency modulation is a general control strategy to coordinate multiple modules in response to external signals. Conversely, the synchrony information can be utilized to infer interaction. Increasing evidence suggests that frequency modulation is also common in transcription regulation.
In this study, we investigate the potential of phase locking analysis, a technique to study the synchrony patterns, in the transcription network modeling of time course gene expression data. Using the yeast cell cycle data, we show that significant phase locking exists between transcription factors and their targets, between gene pairs with prior evidence of physical or genetic interactions, and among cell cycle genes. When compared with simple correlation we found that the phase locking metric can identify gene pairs that interact with each other more efficiently. In addition, it can automatically address issues of arbitrary time lags or different dynamic time scales in different genes, without the need for alignment. Interestingly, many of the phase locked gene pairs exhibit higher order than 1:1 locking, and significant phase lags with respect to each other. Based on these findings we propose a new phase locking metric for network reconstruction using time course gene expression data. We show that it is efficient at identifying network modules of focused biological themes that are important to cell cycle regulation.
Our result demonstrates the potential of phase locking analysis in transcription network modeling. It also suggests the importance of understanding the dynamics underlying the gene expression patterns.
Previously we have reported a microarray image processing and data analysis package Matarray, where quality scores are defined for every spot that reflect the reliability and variability of the data acquired from each spot. In this article we present a new development in Matarray, where the quality scores are incorporated as weights in the statistical evaluation and data mining of microarray data. With this approach filtering of poor quality data is automatically achieved through the reduction in their weights, thereby eliminating the need to manually flag or remove bad data points, as well as the problem of missing values. More significantly, utilizing a set of control clones spiked in at known input ratios ranging from 1:30 to 30:1, we find that the quality-weighted statistics leads to more accurate gene expression measurements and more sensitive detection of their changes with significantly lower type II error rates. Further, we have applied the quality-weighted clustering to a time-course microarray data set, and find that the new algorithm improves grouping accuracy. In summary, incorporating quantitative quality measure of microarray data as weight in complex data analysis leads to improved reliability and convenience. In addition it provides a practical way to deal with the missing value issue in establishing automatic statistical tests.
microarray; quality score; weighted algorithms; accurate expression measurement
Proteins directly interacting with each other tend to have similar functions and be involved in the same cellular processes. Mutations in genes that code for them often lead to the same family of disease phenotypes. Efforts have been made to prioritize positional candidate genes for complex diseases utilize the protein-protein interaction (PPI) information. But such an approach is often considered too general to be practically useful for specific diseases.
In this study we investigate the efficacy of this approach in type 1 diabetes (T1D). 266 known disease genes, and 983 positional candidate genes from the 18 established linkage loci of T1D, are compiled from the T1Dbase (http://t1dbase.org). We found that the PPI network of known T1D genes has distinct topological features from others, with significantly higher number of interactions among themselves even after adjusting for their high network degrees (p<1e-5). We then define those positional candidates that are first degree PPI neighbours of the 266 known disease genes to be new candidate disease genes. This leads to a list of 68 genes for further study. Cross validation using the known disease genes as benchmark reveals that the enrichment is ~17.1 fold over random selection, and ~4 fold better than using the linkage information alone. We find that the citations of the new candidates in T1D-related publications are significantly (p<1e-7) more than random, even after excluding the co-citation with the known disease genes; they are significantly over-represented (p<1e-10) in the top 30 GO terms shared by known disease genes. Furthermore, sequence analysis reveals that they contain significantly (p<0.0004) more protein domains that are known to be relevant to T1D. These findings provide indirect validation of the newly predicted candidates.
Our study demonstrates the potential of the PPI information in prioritizing positional candidate genes for T1D.
Members of the apolipoprotein gene cluster (APOA1/C3/A4/A5) on human chromosome 11q23 play an important role in lipid metabolism. Polymorphisms in both APOA5 and APOC3 are strongly associated with plasma triglyceride concentrations. The close genomic locations of these two genes as well as their functional similarity have hindered efforts to define whether each gene independently influences human triglyceride concentrations. In this study, we examined the linkage disequilibrium and haplotype structure of 49 SNPs in a 150-kb region spanning the gene cluster. We identified a total of five common APOA5 haplotypes with a frequency of greater than 8% in samples of northern European origin. The APOA5 haplotype block did not extend past the 7 SNPs in the gene and was separated from the other apolipoprotein gene in the cluster by a region of significantly increased recombination. Furthermore, one previously identified triglyceride risk haplotype of APOA5 (APOA5*3) showed no association with three APOC3 SNPs previously associated with triglyceride concentrations, in contrast to the other risk haplotype (APOA5*2), which was associated with all three minor APOC3 SNP alleles. These results highlight the complex genetic relationship between APOA5 and APOC3 and support the notion that APOA5 represents an independent risk gene affecting plasma triglyceride concentrations in humans.
Single nucleotide polymorphism; Apolipoprotein A5; Haplotype; Linkage disequilibrium; Recombination; Four-gamete test
Several studies have confirmed the increasing rate of type 1 diabetes mellitus (T1DM) in children and the link with increasing BMI at diagnosis termed the ‘accelerator hypothesis’. Our objective was to assess whether changing incidence of type 1 diabetes in a group of children and adolescent from the Midwest United States was associated with changes in BMI.
Data from 1618 (52.1% M/47.9% F) newly-diagnosed children and adolescents (<19 years) with T1DM, admitted to Children's Hospital of Wisconsin (CHW) between January 1995 and December 2004, was analyzed in relationship to body mass index (BMI) standard deviation score (SDS).
An overall, 10-year cumulative incidence of 27.92 per 100,000 (19.12 to 41.72/100,000) was observed, with an average yearly cumulative incidence of 2.39%. The increase was largest in the younger age groups, 0–4, 5–9, and 10–14 having an average yearly increase of 2.4, 2.3, and 3.0%, respectively, corresponding to a relative 10-year increase of 25.3, 33.8, and 38.0%, respectively. Age at diagnosis was inversely correlated with BMI SDS (p<0.001) and remained significant for both males and females.
Annual incidence of T1DM increased two-fold at CHW over the 10-year study period. The majority of the increase was observed in the youngest age groups, which also appeared to be the heaviest. This research adds to the growing literature supporting the hypothesis that excess weight gain during childhood may be a risk factor for early manifestation of T1DM.
The effects of AC field exposure on the viability and proliferation of mammalian cells under conditions appropriate for their dielectrophoretic manipulation and sorting were investigated using DS19 murine erythroleukemia cells as a model system. The frequency range 100 Hz-10 MHz and medium conductivities of 10 mS/m, 30 mS/m and 56 mS/m were studied for fields generated by applying signals of up to 7V peak to peak (p-p) to a parallel electrode array having equal electrode widths and gaps of 100 μm. Between 1 kHz and 10 MHz, cell viability after up to 40 min of field exposure was found to be above 95% and cells were able to proliferate. However, cell growth lag phase was extended with decreasing field frequency and with increasing voltage, medium conductivity and exposure duration. Modified growth behavior was not passed on to the next cell passage, indicating that field exposure did not cause permanent alterations in cell proliferation characteristics. Cell membrane potentials induced by field exposure were calculated and shown to be well below values typically associated with cell damage. Furthermore, medium treated by field exposure and then added to untreated cells produced the same modifications of growth as exposing cells directly, and these modifications occurred only when the electrode polarization voltage exceeded a threshold of ~0.4 V p-p. These findings suggested that electrochemical products generated during field exposure were responsible for the changes in cell growth. Finally, it was found that hydrogen peroxide was produced when sugar-containing media were exposed to fields and that normal cell growth could be restored by addition of catalase to the medium, whether or not field exposure occurred in the presence of cells. These results show that AC fields typically used for dielectrophoretic manipulation and sorting of cells do not damage DS19 cells and that cell alterations arising from electrochemical effects can be completely mitigated.
AC field exposure; Lag in cell growth; Hydrogen peroxide; Catalase; Dielectrophoresis; Electrode polarization
The specific membrane capacitance and conductivity of mammalian cells, which reflect their surface morphological complexities and membrane barrier functions, respectively, have been shown to respond to cell physiologic and pathologic changes. Here, the effects of induced apoptosis on these membrane properties of cultured human promyelocytic HL-60 cells are reported. Changes in membrane capacitance and conductivity were deduced from measurements of cellular dielectrophoretic crossover frequencies following treatment with genistein (GEN). The apparent specific cell membrane capacitance of HL-60 cells fell from an initial value of 17.6±0.9 to 9.1±0.5 mF/m2 4 h after treatment. Changes began within minutes of treatment and preceded both the externalization of phosphatidylserine (PS), as gauged by the Annexin V assay, and the appearance of a sub-G1 cell subpopulation, as determined through ethidium bromide staining of DNA. Treatment by the broad spectrum caspase inhibitor N-benzyloxycarbony-Val-Ala-Asp(O-methyl)-fluoromethyketone (zVAD-fmk) did not prevent these early cell membrane dielectric responses, suggesting that the caspase system was not involved. Although membrane conductivity did not alter during the first 4 h of GEN treatment, it rose significantly and progressively thereafter. Finally, as the barrier function failed and the cells became necrotic, it increased by many orders of magnitude. The effective membrane capacitance and conductivity findings serve to focus attention on the membrane as a site for early participation in apoptosis. In conjunction with our prior reports of the use of dielectric methods for cell manipulation and separation, these results demonstrate that dielectrophoretic technologies should be applicable to the rapid detection, separation, and quantification of normal, apoptotic, and necrotic cells from cell mixtures.
Apoptosis; Dielectrophoresis; Membrane capacitance; Membrane conductance; DEP crossover method; Detection of apoptotic cells