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 Pharmaceuticals , 2011, DOI: 10.3390/ph4091216 Abstract: Aptamers are nucleic acid-based ligands identified through a process of molecular evolution named SELEX (Systematic Evolution of Ligands by Exponential enrichment). During the last 10-15 years, numerous aptamers have been developed specifically against targets present on or associated with the surface of human cells or infectious pathogens such as viruses, bacteria, fungi or parasites. Several of the aptamers have been described as potent probes, rivalling antibodies, for use in flow cytometry or microscopy. Some have also been used as drugs by inhibiting or activating functions of their targets in a manner similar to neutralizing or agonistic antibodies. Additionally, it is straightforward to conjugate aptamers to other agents without losing their affinity and they have successfully been used in vitro and in vivo to deliver drugs, siRNA, nanoparticles or contrast agents to target cells. Hence, aptamers identified against cell surface biomarkers represent a promising class of ligands. This review presents the different strategies of SELEX that have been developed to identify aptamers for cell surface-associated proteins as well as some of the methods that are used to study their binding on living cells.
 PLOS ONE , 2012, DOI: 10.1371/journal.pone.0052319 Abstract: Parkinson disease (PD) progresses relentlessly and affects approximately 4% of the population aged over 80 years old. It is difficult to diagnose in its early stages. The purpose of our study is to identify molecular biomarkers for PD initiation using a computational bioinformatics analysis of gene expression. We downloaded the gene expression profile of PD from Gene Expression Omnibus and identified differentially coexpressed genes (DCGs) and dysfunctional pathways in PD patients compared to controls. Besides, we built a regulatory network by mapping the DCGs to known regulatory data between transcription factors (TFs) and target genes and calculated the regulatory impact factor of each transcription factor. As the results, a total of 1004 genes associated with PD initiation were identified. Pathway enrichment of these genes suggests that biological processes of protein turnover were impaired in PD. In the regulatory network, HLF, E2F1 and STAT4 were found have altered expression levels in PD patients. The expression levels of other transcription factors, NKX3-1, TAL1, RFX1 and EGR3, were not found altered. However, they regulated differentially expressed genes. In conclusion, we suggest that HLF, E2F1 and STAT4 may be used as molecular biomarkers for PD; however, more work is needed to validate our result.
 BMC Systems Biology , 2012, DOI: 10.1186/1752-0509-6-65 Abstract: Here, we present a novel method called Gene Interaction Enrichment and Network Analysis (GIENA) to identify dysregulated gene interactions, i.e., pairs of genes whose relationships differ between disease and control. Four functions are defined to model the biologically relevant gene interactions of cooperation (sum of mRNA expression), competition (difference between mRNA expression), redundancy (maximum of expression), or dependency (minimum of expression) among the expression levels. The proposed framework identifies dysregulated interactions and pathways enriched in dysregulated interactions; points out interactions that are perturbed across pathways; and moreover, based on the biological annotation of each type of dysregulated interaction gives clues about the regulatory logic governing the systems level perturbation. We demonstrated the potential of GIENA using published datasets related to cancer.We showed that GIENA identifies dysregulated pathways that are missed by traditional enrichment methods based on the individual gene properties and that use of traditional methods combined with GIENA provides coverage of the largest number of relevant pathways. In addition, using the interactions detected by GIENA, specific gene networks both within and across pathways associated with the relevant phenotypes are constructed and analyzed.Genome-wide mRNA expression data provide a rich resource for studying the molecular mechanisms of complex diseases. Through comparison of mRNA expression data between case and control samples, biomarkers and functional molecules significant for diagnosis, prognosis, and treatment have been identified for many complex diseases, including cancers [1,2]. Extracting signals while rejecting noise in the data and interpreting the results to elucidate biological mechanisms relevant to disease are however, challenging [3]. Lists of hundreds of mRNAs identified as differentially expressed are interesting but can be difficult to interpret in ter
 Statistics , 2007, DOI: 10.1214/07-AOAS104 Abstract: A prespecified set of genes may be enriched, to varying degrees, for genes that have altered expression levels relative to two or more states of a cell. Knowing the enrichment of gene sets defined by functional categories, such as gene ontology (GO) annotations, is valuable for analyzing the biological signals in microarray expression data. A common approach to measuring enrichment is by cross-classifying genes according to membership in a functional category and membership on a selected list of significantly altered genes. A small Fisher's exact test $p$-value, for example, in this $2\times2$ table is indicative of enrichment. Other category analysis methods retain the quantitative gene-level scores and measure significance by referring a category-level statistic to a permutation distribution associated with the original differential expression problem. We describe a class of random-set scoring methods that measure distinct components of the enrichment signal. The class includes Fisher's test based on selected genes and also tests that average gene-level evidence across the category. Averaging and selection methods are compared empirically using Affymetrix data on expression in nasopharyngeal cancer tissue, and theoretically using a location model of differential expression. We find that each method has a domain of superiority in the state space of enrichment problems, and that both methods have benefits in practice. Our analysis also addresses two problems related to multiple-category inference, namely, that equally enriched categories are not detected with equal probability if they are of different sizes, and also that there is dependence among category statistics owing to shared genes. Random-set enrichment calculations do not require Monte Carlo for implementation. They are made available in the R package allez.
 Journal of Cancer Therapy (JCT) , 2010, DOI: 10.4236/jct.2010.11006 Abstract: A new method for analysis of microarray gene expression experiments referred to as Sum-based Meta-analytical Enrichment？(SME) is proposed in this manuscript. SME is a combined enrichment and meta-analytical approach to infer？on the association of gene sets with particular phenotypes. SME allows enrichment to be performed across datasets,？which to our knowledge was not earlier possible. As a proof of concept study, this technique is applied to datasets from？Oncomine, a publicly available cancer microarray database. The genes that are significantly up-/down-regulated？(p-value ≤ 10-4) in various cancer types in Oncomine were listed. These genes were assigned to biological processes？using GO annotations. The SME algorithm was applied to identify a list of GO processes most deregulated in 4 major？cancer types. For validation we examined whether the processes predicted by SME were already documented in literature.SME method identified several known processes for the 4 cancer types and identified several novel processes？which are biologically plausible. Nearly all the pathways identified by SME as common to the 4 cancers were found to？contribute to processes which are widely regarded as cancer hallmarks. SME provides an intuitive yet objective ‘process-centric’ interpretation of the ‘gene-centric’ output of individual microarray comparison studies. The methods described？here should be applicable in the next-generation sequencing based gene expression analysis as well.
 Global Cardiology Science & Practice , 2012, DOI: 10.5339/gcsp.2013.10 Abstract: Background: Atherosclerotic vascular disease (AVD), a leading cause of morbidity and mortality, is increasing in prevalence in the developing world. We describe an approach to establish a biorepository linked to medical records with the eventual goal of facilitating discovery of biomarkers for AVD.
 Journal of Nucleic Acids , 2014, DOI: 10.1155/2014/214929 Abstract: Infection with Shiga toxin- (Stx-) producing E. coli causes life threatening hemolytic uremic syndrome (HUS), a leading cause of acute renal failure in children. Of the two antigenically distinct toxins, Stx1 and Stx2, Stx2 is more firmly linked with the development of HUS. In the present study, selective evolution of ligands by exponential enrichment (SELEX) was used in an attempt to identify RNA aptamers against Stx1 and Stx2. After 5 rounds of selection, significant enrichment of aptamer pool was obtained against Stx2, but not against Stx1, using a RNA aptamer library containing 56 random nucleotides (N56). Characterization of individual aptamer sequences revealed that six unique RNA aptamers (mA/pC, mB/pA, mC, mD, pB, and pD) recognized Stx2 in a filter binding assay. None of these aptamers bound Stx1. Aptamers mA/pC, mB/pA, mC, and mD, but not pB and pD, partially blocked binding of Alexa 488-labeled Stx2 with HeLa cells in a flow cytometry assay. However, none of the aptamers neutralized Stx2-mediated cytotoxicity and death of HeLa cells. 1. Introduction Infection with Shiga toxin- (Stx-) producing Escherichia coli (STEC) is the most significant cause of hemolytic uremic syndrome (HUS), the leading cause of acute renal failure in children [1–4]. Of the two antigenically distinct toxins, Stx1 and Stx2, Stx2 is more firmly linked with the development of HUS as STEC strains producing this toxin are more frequently associated with HUS than strains that produce both Stx1 and Stx2, while Stx1 alone has rarely been associated with HUS [5–7]. Stx1 and Stx2 are similar in basic structure [8], binding specificity [8], and mode of action. Both toxins consist of an A-subunit monomer and a B-subunit pentamer [8–10]. The pentameric B subunit binds to its cell surface receptor CD77, also called globotriaosylceramide (Gb3; Galα1-4Galβ1-4glucosyl ceramide) [11, 12]. This triggers endocytosis of the holotoxin, mainly through clathrin-coated pits [13]. The internalization of catalytically active A-subunit, delivered to cytosol via retrograde transport, causes shut down of protein synthesis and leads to cell death [14, 15]. Nucleic acid aptamers are being developed as therapeutic and diagnostic reagents against biotoxins [16]. Aptamers are short, synthetic, single stranded nucleic acids with unique three-dimensional conformations that bind strongly and selectively to a target molecule via shape specific recognition [17–20]. Aptamers are similar in many respects to antibodies except that they are cheaper to produce, have extremely long shelf lives, and thus, are far
 Journal of Biomarkers , 2014, DOI: 10.1155/2014/321680 Abstract: Prostate cancer (PCA) is a major health concern in current times. Ever since prostate specific antigen (PSA) was introduced in clinical practice almost three decades ago, the diagnosis and management of PCA have been revolutionized. With time, concerns arose as to the inherent shortcomings of this biomarker and alternatives were actively sought. Over the past decade new PCA biomarkers have been identified in tissue, blood, urine, and other body fluids that offer improved specificity and supplement our knowledge of disease progression. This review focuses on superiority of circulating biomarkers over tissue biomarkers due to the advantages of being more readily accessible, minimally invasive (blood) or noninvasive (urine), accessible for sampling on regular intervals, and easily utilized for follow-up after surgery or other treatment modalities. Some of the circulating biomarkers like PCA3, IL-6, and TMPRSS2-ERG are now detectable by commercially available kits while others like microRNAs (miR-21, -221, -141) and exosomes hold potential to become available as multiplexed assays. In this paper, we will review some of these potential candidate circulating biomarkers that either individually or in combination, once validated with large-scale trials, may eventually get utilized clinically for improved diagnosis, risk stratification, and treatment. 1. Introduction In 2014, more than 200,000 American men will be diagnosed with prostate cancer (PCA). It is the most frequently diagnosed solid tumor and the second leading cause of cancer-related deaths amongst men in the United States. It is estimated to cause 28% of the total number of cancer cases and 10% of the total cancer deaths amongst adult male cancer patients. One in 6 men carries a lifetime risk of a PCA diagnosis [1]. Prostate-specific antigen (PSA) is the biomarker currently being used for PCA. PSA-based screening test has been proved to be a useful prognostic tool. Usually high preoperative values have been related to advanced disease and a poorer clinical outcome. Clinicians initially used PSA for monitoring PCA patients after treatment to detect disease progression, treatment failure, or potential relapse [2] and then subsequently recommended its use for screening purposes [3–5]. Since the late 1980s, the introduction of PSA screening along with digital rectal exam (DRE) and transrectal ultrasound (TRUS) in general clinical practice has led to an increase in the documented incidence of PCA [6, 7]. This trend has been accompanied by an increase in invasive procedures with radical prostatectomy rates
 PLOS Genetics , 2013, DOI: 10.1371/journal.pgen.1003716 Abstract: Strains of Extraintestinal Pathogenic Escherichia coli (ExPEC) exhibit an array of virulence strategies and are a major cause of urinary tract infections, sepsis and meningitis. Efforts to understand ExPEC pathogenesis are challenged by the high degree of genetic and phenotypic variation that exists among isolates. Determining which virulence traits are widespread and which are strain-specific will greatly benefit the design of more effective therapies. Towards this goal, we utilized a quantitative genetic footprinting technique known as transposon insertion sequencing (Tn-seq) in conjunction with comparative pathogenomics to functionally dissect the genetic repertoire of a reference ExPEC isolate. Using Tn-seq and high-throughput zebrafish infection models, we tracked changes in the abundance of ExPEC variants within saturated transposon mutant libraries following selection within distinct host niches. Nine hundred and seventy bacterial genes (18% of the genome) were found to promote pathogen fitness in either a niche-dependent or independent manner. To identify genes with the highest therapeutic and diagnostic potential, a novel Trait Enrichment Analysis (TEA) algorithm was developed to ascertain the phylogenetic distribution of candidate genes. TEA revealed that a significant portion of the 970 genes identified by Tn-seq have homologues more often contained within the genomes of ExPEC and other known pathogens, which, as suggested by the first axiom of molecular Koch's postulates, is considered to be a key feature of true virulence determinants. Three of these Tn-seq-derived pathogen-associated genes—a transcriptional repressor, a putative metalloendopeptidase toxin and a hypothetical DNA binding protein—were deleted and shown to independently affect ExPEC fitness in zebrafish and mouse models of infection. Together, the approaches and observations reported herein provide a resource for future pathogenomics-based research and highlight the diversity of factors required by a single ExPEC isolate to survive within varying host environments.
 Genome Medicine , 2010, DOI: 10.1186/gm122 Abstract: Rectus abdominis muscle biopsies were obtained from 65 upper gastrointestinal (UGI) cancer patients during open surgery and RNA profiling was performed on a subset of this cohort (n = 21) using the Affymetrix U133+2 platform. Quantitative analysis revealed a gene signature, which underwent technical validation and independent confirmation in a separate clinical cohort.Quantitative significance analysis of microarrays produced an 83-gene signature that was able to identify patients with greater than 5% weight loss, while this molecular profile was unrelated to markers of systemic inflammation. Selected genes correlating with weight loss were validated using quantitative real-time PCR and independently studied as general cachexia biomarkers in diaphragm and vastus lateralis from a second cohort (n = 13; UGI cancer patients). CaMKIIβ correlated positively with weight loss in all muscle groups and CaMKII protein levels were elevated in rectus abdominis. TIE1 was also positively associated with weight loss in both rectus abdominis and vastus lateralis muscle groups while other biomarkers demonstrated tissue-specific expression patterns. Candidates selected from the pre-clinical literature, including FOXO protein and ubiquitin E3 ligases, were not related to weight loss in this human clinical study. Furthermore, promoter analysis identified that the 83 weight loss-associated genes had fewer FOXO binding sites than expected by chance.We were able to discover and validate new molecular biomarkers of human cancer cachexia. The exercise activated genes CaMKIIβ and TIE1 related positively to weight-loss across muscle groups, indicating that this cachexia signature is not simply due to patient inactivity. Indeed, excessive CaMKIIβ activation is a potential mechanism for reduced muscle protein synthesis. Our genomics analysis also supports the view that the available preclinical models do not accurately reflect the molecular characteristics of human muscle from cancer cachexia p
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