About 28% of genes appear to have an expression pattern that follows a mixture distribution. We use first- and second-order partial correlation coefficients to identify trios and quartets of non-sex- linked genes that are highly associated and that are also mixtures. We identified 18 trio and 35 quartet mixtures and evaluated their mixture distribution concordance. Concordance was defined as the proportion of observations that simultaneously fall in the component with the higher mean or simultaneously in the component with the lower mean based on their Bayesian posterior probabilities. These trios and quartets have a concordance rate greater than 80%. There are 33 genes involved in these trios and quartets. A factor analysis with varimax rotation identifies three gene groups based on their factor loadings. One group of 18 genes has a concordance rate of 56.7%, another group of 8 genes has a concordance rate of 60.8%, and a third group of 7 genes has a concordance rate of 69.6%. Each of these rates is highly significant, suggesting that there may be strong biological underpinnings for the mixture mechanisms of these genes. Bayesian factor screening confirms this hypothesis by identifying six single-nucleotide polymorphisms that are significantly associated with the expression phenotypes of the five most concordant genes in the first group.
In developing NeuroMMSig, we have established the first draft of a mechanism- based taxonomy for AD and PD, constituting a central part of the AETIONOMY project 1 . On-going work in this project focuses on using the definitions of path- ways for clustering dementia patients. We have also demonstrated how researchers are able to investigate shared mechanisms across diseases thanks to the common schemata and semantic alignment followed through the mechanism inventory . The context of NeuroMMSig has been extended with new diseases such as epilepsy . Further, NeuroMMSig was extended during the Human Brain Pharmacome project 2 with chemogenomic information to support computational prediction of drug repositioning candidates. This work has also been used to sup- port data-driven analysis of differential geneexpression profiles of AD using heat diffusion algorithms . Thus, future efforts could be directed towards enabling the submission of multimodal clinical data as outlined by , or using novel algorithms on the mechanism enrichment server. Ultimately, NeuroMMSig has been a teaching tool that lead clinicians, wet-lab scientists, and bioinformaticians to their first steps into the world of systems and networks biology. In the future, further work can be conducted to transfer the paradigm of NeuroMMSig to other disease domains such as the psychiatric arena. In summary, the vast collection of mechanistic networks is not only instrumental for identifying the mechanisms underlying disparate subtypes of these conditions, but it is also a technology en- abler that can have future implications in designing targets for other mechanisms rather than the ones the pharmaceutical industry have been focused on in the last decades .
suggesting largely transcriptional regulation. In contrast, mRNA accumulation was about 10-20-fold greater than transcriptional increase for serum amyloid A, C3, and factor B, suggesting participation of posttranscriptional mechanisms. Since finding a disparity between the magnitudes of increase in mRNA and transcription does not definitively establish involvement of posttranscriptional mechanisms, we subjected our data to modeling studies and dynamic mathematical analysis to evaluate this possibility more rigorously. In modeling studies, accumulation curves resembling those observed for these three mRNAs could be generated from the nuclear run-on results only if posttranscriptional regulation was assumed. Dynamic mathematical analysis of relative transcription rates and relative mRNA abundance also strongly supported participation of posttranscriptional mechanisms. These observations suggest that posttranscriptional regulation plays a substantial role in induction of some, but not all acute phase proteins.
In this study, we showed by the molecular typing with PCR technique that all Leishmania local isolates were belonging to L.tropica. Such method is widely used for distinguishing the different species of Leishmania, simply by am- plifying highly conserved sequence blocks of kDNA minicircles (19, 20). Recently, PCR is used for visceral and cutaneous leishmaniasis diagnosis (21). In compared studies of PCR assays, PCR based on kDNA was the most sensitive diagnostic assay for CL and recom- mended for routine diagnosis (22). The ge- nomic DNA of one isolate belonging to L.tropica was extracted and LACK gene was amplified, cloned and sequenced. Ltropica LACK nucleotide sequences from the local isolates were identical and manifest a great similarity with L.tropica LACK gene from pub- lished records. This provides further confir- mation of the identity of our local isolates based on the analysis of LACK gene. Fur- thermore, LACK gene from local isolates showed significant homology to LACK se- quence from several other species including L.donovani, L.major, L.amazonensis, L.brazilliensis, L.chagasi, L.infantum and L.mexicana, implying that LACK protein may have an important functions for the parasite life cycle. This high similarity of LACK nucleotide sequences may refer to identical amino acid sequence among the different Leishmania spp. Melby et al. (2001) had shown that L.donovani LACK de- duced amino acid sequence was identical to the published L.chagasi and L.infantum se- quences (GenBank accession numbers U27569 and U49695, respectively) and dif- fered at only 1 or 2 amino acids from the L.major, L.mexicana, L.amazonensis, and L.braziliensis sequences they obtained (10). In this work, LACK protein from our local strains was found to differ in two amino acids from other Leishmania spp. because of nucleo- tides substitution at position 173 and 443.
Polyamines are required at a stage of geneexpression distinct from hypusi- nated eIF5A. We next determined whether blocking polyamine synthesis (upstream of hypusination) provided the same block in EBOV mRNA translation as was seen when hypusination was interrupted. We have previously shown that blocking polyamine synthesis with DFMO results in a strong and signiﬁcant defect in EBOV minigenome expression (Fig. 5A), without having any effect on a GFP control (3). We reasoned that EBOV may also require polyamines for translation of its transcripts, presumably through the requirement of spermidine for hypusination. First, we assessed whether mRNA from cells treated with the ornithine decarboxylase inhibitor DFMO would be translated if transfected into fresh cells containing normal levels of polyamines, similar to RNA transfection experiments conducted with the hypusination inhibitor GC7. Cells were treated with DFMO for 24 h and transfected with the EBOV minigenome components, and then either the cells were subjected to a luciferase assay to verify reduced expression or total RNA was collected and puriﬁed to transfect fresh cells (Fig. 2A). As previously shown, DFMO signiﬁcantly reduces luciferase activity from the minigenome reporter by about 90% (Fig. 5A) but does not affect that from a control ffLuc (Fig. 5B) (3). Unlike what we found with the hypusination inhibitor GC7, we found that RNA samples from lysates with reduced luciferase activity following DFMO treatment still had reduced luciferase activity (reduced by 80%) when transfected into fresh cells (Fig. 5C). This suggests that unlike in the GC7 experiments, where the RNA was present and intact but not translated, in DFMO-treated cells either the EBOV polymerase did not synthesize mRNA or the synthesized mRNA was not translatable. To differentiate between these two possibilities, we conducted Northern blot analysis to measure EBOV-transcribed mRNA levels. We found that the level of EBOV minigenome-derived ffLuc mRNA from DFMO-treated cells was signiﬁcantly reduced compared to that from nontreated cells (Fig. 5D and E), indicating that the block in geneexpression is, at least in part, at the transcriptional level.
The shape issue on unevenly sampled proﬁles was ﬁrst addressed by using linear slopes between time points. 10 The time-series are considered as piecewise linear functions and the slopes, deﬁned as ∆x/∆t, where x is the geneexpression and t is the time, are compared. However, these slopes are proportion- ally inverse to the length of sampling intervals ∆t. Expression levels at long sampling intervals could have too weak impact in the comparison, while those at short sampling intervals could have too strong impact. These intervals vary from some minutes to several hours. By modeling the series it is possible to overcome this drawback. The models will recon- struct the underlying continuous expression proﬁles, which can be resampled at frequent regular intervals for further analysis. 11 The slope idea of the piece- wise linear function between time points has now been extended and generalized to derivatives, quan- tiﬁed by n s slopes. The value of n s corresponds to the ratio of the length of sampling interval and the evenly resampling period,
We then cloned fragments from the ORF 72 coding region into the intergenic region of a bicistronic transcription unit composed of two luciferase coding regions (Fig. 5A). The 5⬘ LUC gene is derived from Renilla, while the 3⬘ LUC is from Photinus. The two enzymes have different cofactor require- ments and therefore can be readily differentiated from one another. All potential splice donor sites were removed from the 5⬘-NTR of this vector, to avoid problems with splicing to the acceptor site within ORF 72. Fragments of 232, 474, 658, or 856 bp from the region upstream of the ORF 71 AUG were inserted between the two LUC genes, and each construct was transfected into SLK cells. Analysis of the resulting RNAs by FIG. 3. A 735-nt segment spanning most of the ORF 72 coding region is flanked by functional splice sites. The location of splice sites was determined by RT-PCR amplification and sequencing of splice products as described in Materials and Methods. The genomic sequences flanking the splice donor and acceptor sites are shown in the left and right text boxes, respectively. Within the text boxes, slashes indicate the exact position of the splice donor and acceptor sites, located at nt 123595 and 122859 of the KSHV genome, respectively. Conserved nucleotides (shown in bold) at both sites and a pyrimidine-rich tract (shown underlined) upstream of the acceptor site identify them as consensus splice sites. The start codon of ORF 72 is shown framed within the left text box. The structures of the bicistronic ORF 72/71 and the spliced monocistronic ORF 71 transcripts are illustrated in the lower part of the figure. The locations of the splice sites are indicated by lines emanating from the text boxes. Simple arrows indicate the start codon and the positions of additional AUG codons within the coding region of ORF 72. Arrows marked with asterisks indicate the Kozak start codons (12) which initiate the 5⬘ uORF and ORF 71. Note that the splice sites are situated to remove the ORF 72 start codon as well as all internal AUG codons, leaving a 66-bp fragment of ORF 72 devoid of potential translation initiation sites.
These studies collectively provide several insights into the mechanisms of BES1- regulated geneexpression. First, cis-DNA elements may help determine the function of BES1. The degenerate E-box element (CANNTG) can have multiple subtypes depending on the central two nucleotides, which are predicted to own different biochemical properties when forming hydrogen bounds with BES1 and therefore likely to affect BES1 function. In our study, BES1 shows higher affinity to the CACGTG motif and, intriguingly, this motif is very dominant in BES1 binding regions of BR-repressed genes but not in BR-activated genes. Though there are no clues to directly link the higher binding affinity to gene repression, it is possible that binding CACGTG somehow changes the structure of BES1 and poises BES1 to recruit certain protein factors and cause gene repression. This can be an interesting question for future study. Other non-E box motifs can also modulate BES1 functions through their cognate transcription factors. For example, the GAMYB binding site is found to be adjacent to BES1 binding sites in many instances. Considering the concept of enhanceosome, it is likely BES1 could form such a complex with other transcription factors to regulate geneexpression cooperatively. Under such circumstances, BES1 might not always be a decisive component, which can help explain why only about one-tenth of BES1 target genes are responsive to BR stimuli.
1 ≤ i ≤ p and 1 ≤ j ≤ k) represents the effect of the jth regulatory factor on the ith gene under N different condi- tions(arrays). If the generative model does hold, based on this information, we can predicate to which extent a spe- cific latent regulatory factor regulates the expression level of a gene under different conditions or whether this factor is (positive or negative)”active” under the conditions. The other aspect is when fixing a specific regulatory factor, the distribution of the elements of matrix A could be a good in- dication for analyzing the behavior of specific genes in dif- ferent regulatory factors. Given a threshold, the distribution of geneexpression profile in a given regulatory factor gener- ally features a small number of significantly over-expressed or under-expressed genes, which kind of ”dominate” this regulator factor.
In study 1 we also examined the relationship between transcriptomic changes and antiretroviral therapy regi- men, or more specifically CPE. The CPE is based on pharmacokinetic and pharmacodynamic characteristics of antiretroviral medications, including their ability to cross the blood brain barrier and eradicate HIV within the CNS . In the current study, using differential expressionanalysis, only a few genes were found to have modest correlations with CPE, and no GO categories were identified. Follow-up analysis with GNCPro in the WM revealed only a single gene with notable connecti- vity; TNPO3. TNPO3 is required for HIV-1 integration into the host DNA, and higher CPE would be expected to decrease its expression [51,52]. Counter intuitively, expression of this gene was positively correlated with CPE in the WM. Even when using WGCNA, no co-expression modules were found to be associated with CPE, suggesting that higher penetrating regimens do not have a significant impact upon geneexpression. While ours is the first study to examine the association between CPE and brain tran- scriptome in HIV, the relationship between combination antiretroviral therapy (cART) use and brain gene expres- sion was recently described by Borjabad et al. . Notably, they found that cART-treated cases had transcriptome sig- natures that more closely resembled those of HIV- seronegative cases. Further, brains of individuals who were taking cART at the time of death had 83-93% fewer dysre- gulated genes compared to untreated individuals. Despite this, in both treated and untreated HIV + brains there were approximately 100 dysregulated genes related to immune functioning, interferon response, cell cycle, and myelin pathways. Perhaps helpful in explaining our findings, geneexpression in the HIV + brains did not correlate with brain viral burden, suggesting that even high CPE regimens, which have been shown to reduce CSF viral load , may not reduce transcriptomic dysregulation. Indeed, the absence of an association between CPE and brain tran- scriptome would help to explain the overall equivocal results thus far of studies examining the relationship between CPE and HIV-related NCI [31-33,85].
Life at the cellular level is based on a complex but robust dynamic network of biochemical reactions, in which thousands of different proteins, small molecules and DNA segments interact selectively and nonlinearly to produce the proper response to changing envi- ronmental and physiological conditions. Thus to respond appropriately to an external stimulus requires that the cell selectively activates a small subset of its interconnected molecular components. To understand how this specificity is achieved requires a systems level approach. Physiologists and biologists have been studying how cells, organs and organisms function as a whole for decades, but the complexity of these systems makes it difficult, if not impossible, to understand them by logical thinking alone. To overcome this difficulty, applied mathematicians turned to the use of computational models to analyze the behavior of complex biological systems. While mathematical modeling has become an accepted tool in the field ecology, similar efforts at the cellular and molecular levels have suffered from a lack of quantitative data to validate and test quantitative models. However, recent progresses in biochemistry, biophysics and molecular biology coupled with emerging high throughput techniques have generated sufficient data to warrant the use of computational modeling to understand the behavior of complex biochemical net- works. The complexity of cellular systems and the relative ease with which mathematical models can be developed and analyzed makes computational modeling an appealing and useful tool for understanding the biochemical mechanisms that underlie cellular behavior (Kitano, 2002).
can conduct inter-specific comparisons and investigate the underlying evolutionary story. For example, Waltman et al. performed biclustering of multiple-species data and then used a conservation score to identify conserved modules among these species . Based on co-regulation modules, Yang et al. derived an expression-based quantity to characterize the functional constraint acting on a gene, and then tested the correlation of those quantities with gene Sequence divergence rate to estimate the evolutionary potential of genes . With temporal modules, the dynamic regulatory interaction can be explored. Gonçalves et al.  ranked TFs targeting the modules at each time point and graphically depicted the regulatory activity in a module at consecutive time points. Other researchers examined the external relationship among modules, e.g., grouped modules of host proteins based on a distance measure to form higher-level subsystems . Table 6 summarized four kinds of modularity analysis applications, including functional module identification, regulatory modules, evolution characteristic, and module subsystem. Module-based network inference, as a higher level of modularity analysis, will be introduced in next section.
With the advances in high-throughput technologies, sev- eral prognostic biomarkers have been revealed previously. Genetically, genome-wide association studies (GWAS) have revealed that genes on chromosome 8q24, particularly the PSCA gene (Prostate Stem Cell Antigen), were associated with increased metastatic potential of bladder cancer [5, 6]. A hypothesis reasons that these genes detected by GWAS may be associated with androgen receptor responsive- ness and inducing androgen-independent pathways, which stimulates tumor growth . The losses of regions on 10q (including PTEN), 16q, and 22q, and gains on 10p, 11q, 12p, 19p, and 19q were positively associated with metastasis in muscle-invasive bladder cancers . With the genome- wide geneexpression data, several studies have identified a combination of gene signatures to predict the prognosis of MIBC. Specifically, four gene signatures, IL1B, S100A8, S100A9 and EGFR, have been reported to have the capa- bility of predicting MIBC progression . The novel com- bination markers of USP18 and DGCR2 can also predict survival in patients with muscle invasive bladder cancer . In addition, NR1H3 expression is identified as a prognostic factor of overall survival for patients with muscle-invasive bladder cancer . However, there are some limitations for these studies. First, the gene signatures identified by these studies were not robust due to lack of validation dataset or small sample size in validation dataset. Second, compara- tive analysis was not conducted on the performance of these gene signatures for MIBC prognostic prediction. Third, the potential mechanism resulting in the worse prognosis has not been thoroughly investigated. In addition, the potential therapeutics for patients with worse prognosis was not pro- posed by these studies. In the present study, to avoid these limitations, we attempted to detect a combination of gene signatures for MIBC prognostic prediction and stratifica- tion. Based on the prognostic stratification, we also inves- tigated the underlying molecular mechanism and potential therapeutic targets associated with worse prognosis of high-risk MIBC, which could improve our understanding of MIBC progression and provide new therapeutic approaches for these high-risk patients.
Basal cells were sorted on the basis of surface levels of (31 integrin. The 20% of cells expressing the highest levels of pi integrin were sorted into a stem cell enriched population, and the 20% of cells expressing the lowest levels of p i integrin were sorted into a transit cell enriched population (Figure 4.1). mRNA was extracted from 1-3 milhon cells from each sorted population, and reverse transcribed in the presence of a^^PdCTP. This produced a probe with a higher specific activity compared to the random hexamer labelled cDNA in the previous experiments. Hybridisations were performed as before, including the neomycin resistance cassette probe, which hybridised to the guide spots. The stem cell enriched probe was synthesised and hybridised three times to the same filter from set 1 (the first 16,000 clones). This process was repeated with a filter from set 2. Duplicate filters from set 1 and set 2 were hybridised with the transit amplifying cell enriched probe. The filters were exposed, scanned and analysed using Xdigitise as described in chapter 3. The filters were additionally hybridised with one of the primers used in the amplification of the PCR products that were spotted on the filter allowing each of the 32,000 signals on the filter to be normalised on the basis DNA content of the spot. This allowed for far more reproducible results, and allowed the detection of smaller differences in expression. Figure 4.2 describes the improved hybridisation method.
A variety of methodologies exist to compare several competing models for a given dataset and to select the one best fits the data. Bayesian model averaging (BMA) is one way of combining models in order to account for the uncertainty. By averaging over many different competing models, BMA incorporates model uncertainty in inference and prediction. Draper (1995) and Raftery (1995) reviewed BMA and the cost of ignoring model uncertainty. Madigan and Raftery (1994) also considered the BMA by using Occam’s razor and Occam’s window approaches to reduce the num- ber of candidate models. Yuan and Yin (2011) used model averaging procedures to make more robust inferences regarding the dose finding design for phase I clinical trials. Pramana et al. (2012) focused on the case in which several parametric models are fitted to geneexpression data and discussed model averaging techniques for the estimation of the dose response model. See Hoeting et al. (1999) for a good tutorial for BMA. There is a large literature on BMA. However, there is limited attention on model uncertainty, e. g. a single Weibull, a mixture of Weibulls and a Weibull cure model.
According to Rui Xu, and Donald Wunsch , data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communi- ties. Cluster analysis is not a one-shot process. It needs a series of trials and repetitions. Moreover, there are no universal and effective criteria to guide the selection of features and clustering schemes. Validation criteria provide some insights on the quality of clus- tering solutions. But how to choose the appropriate criterion is still a problem, which requires more efforts. Clustering has been applied in a wide variety of fields, ranging from engineering (machine learning, artificial intelligence, pattern recognition, mechanical engineering, electrical engineering), computer sciences (web mining, spatial database anal- ysis, textual document collection, image segmentation), life and medical sciences (genetics, biology, micro biology, paleontology, psychiatry, clinic, pathology), to earth sciences (ge- ography. geology, remote sensing), social sciences (sociology, psychology, archeology), and economics (marketing, business).
Following the hybridization step, the spots in the hybridized microarray are excited by a laser and scanned at suitable wavelengths to detect the red and green dyes. The amount of fluorescence emitted upon excitation corresponds to the amount of bound nucleic acid . To ensure data quality, visualization of the data is a vital process. Many methods for visualization, quality assessment, and data normalization have been in existence [15, 16]. Clustering has been known as a way of finding and visualizing patterns in the data.. By this way microarray technology has become a brilliant method to classify types of cells. This ability to differentiate expression patterns has been especially useful in many areas especially cancer research. The activities of genes can even lead to the identification of different treatments to different cancers. In general, mRNA from cells or tissue is extracted, converted to DNA and labeled, hybridized to the DNA elements on the array surface, and detected by phospho-imaging or fluorescence scanning . Thus, what is seen at the end of the experimental stage is an image of the microarray, in which each spot that corresponds to a gene has an associated fluorescence value representing the relative expression level of that gene. Microarray image processing uses differential excitation and emission wavelengths. These images are then analyzed to identify the spots, calculate their associated signal intensities, and assess their local noise . Most image acquisition software contains basic filtering tools to process these spots. These results allow intensity to be calculated for every spot on the chip. The products of the image acquisition are the TIFF image pairing and a non normalized data file [13, 14, 15, 16].
1.2. Analysis of geneexpression data
Large scale geneexpression data sets include thousands of genes measured at dozens of conditions. The number and diversity of genes make manual analysis difficult and automatic analysis methods necessary. Initial efforts to analyze these data sets began with the application of unsupervised machine learning, or clus- tering, to group genes according to similarity in geneexpression . Clustering provides a tool to reduce the size of the dataset to a simpler one that can more easily be manually examined. In typical studies, researchers examine the clusters to find those containing genes with common biological properties, such as the presence of common upstream promoter regions or involvement in the same bi- ological processes. After commonalities have been identified (often manually) it becomes possible to understand the global aspects of the biological phenomena studied. As the community developed an interest in this area, additional novel clustering methods were introduced and evaluated for geneexpression data [1,6]. The analysis of microarray geneexpression data for various tissue samples has enabled researchers to determine geneexpression profiles characteristic of the disease subtypes. The groups of genes involved in these genetic profiles are rather large and a deeper understanding of the functional distinction between the dis- ease subtypes might help not only to select highly accurate ’genetic signatures’ of the various subtypes, but hopefully also to select potential targets for drug design. Most current approaches to microarray data analysis use (supervised or unsupervised) clustering algorithms to deal with the numerical expression data. While a clustering method reduces the dimensionality of the data to a size that a scientist can tackle, it does not identify the critical background biological in- formation that helps the researcher understand the significance of each cluster. However, that biological knowledge in terms of functional annotation of the genes is already available in public databases. Direct inclusion of this knowledge source can greatly improve the analysis, support (in term of user confidence) and explain obtained numerical results.
The data from each of the three transcription factor binding site prediction programs were organized into a chart matrix (Table 2). The JASPAR database detected 119 transcription factor binding sites in the promoter re- gions of the twelve genes that shared upstream regulators. Only 7 of the promoters of the 12 genes contained conserved sequences when analyzed in rVISTA, which reduced the number of transcription factor binding sites to 25. TRED added further stringency as the data from this database relies on published transcription factor binding data, but TRED contained data for only 9 of the 12 genes. TRED identified 25 binding sites within the promoter regions of those genes differentially expressed genes. Of particular interest was the transcription factor binding sites detected by all three programs in the same gene (Table 2). Only four distinct transcription factor binding sites in three differentially expressed genes were detected by all three bioinformatics programs. By combining the data from the three programs, definitive signal transduction pathways were predicted. For the purpose of this analysis, signal transduction pathways are defined as an upstream regulator, a transcription factor, and a differentially expressed gene from the consensus list. The four predicted pathways were
ABSTRACT: Geneexpression indicates the present state of the cell. Samples associated with the normal and cancer stomach tissues in Homo sapiens are collected by providing different stomach tissue query terms from GEO and ArrayExpress database. Manual curation is carried using Standard Operating Procedure (SOP) for both normal and cancerous condition. These samples are submitted to the novel algorithm developed at IBAB, Bangalore, to validate samples and identify stomach cancer specific genes. The study retrieved 1336 samples for stomach cancer condition. From 1336 samples it is known that genes expressed in stomach cancer were 12309; genes not expressed in stomach cancer were 7531. The study also retrieved 434 samples for normal stomach condition, by analyzing it is known that genes expressed in Normal stomach were 10477 and genes not expressed in Normal stomach were 9327. By comparison, 2080 genes were identified which were expressed in stomach cancer and not detected in Normal stomach tissue.