A score system for quality evaluation of RNA sequence tags: an improvement for gene expression profiling

Background: High-throughput molecular approaches for gene expression profiling, such as Serial Analysis of Gene Expression (SAGE), Massively Parallel Signature Sequencing (MPSS) or Sequencing-by-Synthesis (SBS) represent powerful techniques that provide global transcription profiles of different cell types through sequencing of short fragments of transcripts, denominated sequence tags. These techniques have improved our understanding about the relationships between these expression profiles and cellular phenotypes. Despite this, more reliable datasets are still necessary. In this work, we present a web-based tool named S3T: Score System for Sequence Tags, to index sequenced tags in accordance with their reliability. This is made through a series of evaluations based on a defined rule set. S3T allows the identification/selection of tags, considered more reliable for further gene expression analysis. Results: This methodology was applied to a public SAGE dataset. In order to compare data before and after filtering, a hierarchical clustering analysis was performed in samples from the same type of tissue, in distinct biological conditions, using these two datasets. Our results provide evidences suggesting that it is possible to find more congruous clusters after using S3T scoring system. Conclusion: These results substantiate the proposed application to generate more reliable data. This is a significant contribution for determination of global gene expression profiles. The library analysis with S3T is freely available at http://gdm.fmrp.usp.br/s3t/.S3T source code and datasets can also be downloaded from the aforementioned website.

Ultra-Deep Sequencing Reveals the microRNA Expression Pattern of the Human Stomach

Background: While microRNAs (miRNAs) play important roles in tissue differentiation and in maintaining basal physiology, little is known about the miRNA expression levels in stomach tissue. Alterations in the miRNA profile can lead to cell deregulation, which can induce neoplasia. Methodology/Principal Findings: A small RNA library of stomach tissue was sequenced using high-throughput SOLiD sequencing technology. We obtained 261,274 quality reads with perfect matches to the human miRnome, and 42% of known miRNAs were identified. Digital Gene Expression profiling (DGE) was performed based on read abundance and showed that fifteen miRNAs were highly expressed in gastric tissue. Subsequently, the expression of these miRNAs was validated in 10 healthy individuals by RT-PCR showed a significant correlation of 83.97% (P<0.05). Six miRNAs showed a low variable pattern of expression (miR-29b, miR-29c, miR-19b, miR-31, miR-148a, miR-451) and could be considered part of the expression pattern of the healthy gastric tissue. Conclusions/Significance: This study aimed to validate normal miRNA profiles of human gastric tissue to establish a reference profile for healthy individuals. Determining the regulatory processes acting in the stomach will be important in the fight against gastric cancer, which is the second-leading cause of cancer mortality worldwide.

Automatic Network Fingerprinting through Single-Node Motifs

Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs-a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks.

Gene Expression Complex Networks: Synthesis, Identification, and Analysis

Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erdos-Renyi (ER), the small-world Watts-Strogatz (WS), the scale-free Barabasi-Albert (BA), and geographical networks (GG). The experimental results indicate that the inference method was sensitive to average degree k variation, decreasing its network recovery rate with the increase of k. The signal size was important for the inference method to get better accuracy in the network identification rate, presenting very good results with small expression profiles. However, the adopted inference method was not sensible to recognize distinct structures of interaction among genes, presenting a similar behavior when applied to different network topologies. In summary, the proposed framework, though simple, was adequate for the validation of the inferred networks by identifying some properties of the evaluated method, which can be extended to other inference methods.

Identification of protein-coding and non-coding RNA expression profiles in CD34(+) and in stromal cells in refractory anemia with ringed sideroblasts

Background: Myelodysplastic syndromes (MDS) are a group of clonal hematological disorders characterized by ineffective hematopoiesis with morphological evidence of marrow cell dysplasia resulting in peripheral blood cytopenia. Microarray technology has permitted a refined high-throughput mapping of the transcriptional activity in the human genome. Non-coding RNAs (ncRNAs) transcribed from intronic regions of genes are involved in a number of processes related to post-transcriptional control of gene expression, and in the regulation of exon-skipping and intron retention. Characterization of ncRNAs in progenitor cells and stromal cells of MDS patients could be strategic for understanding gene expression regulation in this disease. Methods: In this study, gene expression profiles of CD34(+) cells of 4 patients with MDS of refractory anemia with ringed sideroblasts (RARS) subgroup and stromal cells of 3 patients with MDS-RARS were compared with healthy individuals using 44 k combined intron-exon oligoarrays, which included probes for exons of protein-coding genes, and for non-coding RNAs transcribed from intronic regions in either the sense or antisense strands. Real-time RT-PCR was performed to confirm the expression levels of selected transcripts. Results: In CD34(+) cells of MDS-RARS patients, 216 genes were significantly differentially expressed (q-value <= 0.01) in comparison to healthy individuals, of which 65 (30%) were non-coding transcripts. In stromal cells of MDS-RARS, 12 genes were significantly differentially expressed (q-value <= 0.05) in comparison to healthy individuals, of which 3 (25%) were non-coding transcripts. Conclusions: These results demonstrated, for the first time, the differential ncRNA expression profile between MDS-RARS and healthy individuals, in CD34(+) cells and stromal cells, suggesting that ncRNAs may play an important role during the development of myelodysplastic syndromes.

GPIomics: global analysis of glycosylphosphatidylinositol-anchored molecules of Trypanosoma cruzi

Glycosylphosphatidylinositol (GPI) anchoring is a common, relevant posttranslational modification of eukaryotic surface proteins. Here, we developed a fast, simple, and highly sensitive (high attomole-low femtomole range) method that uses liquid chromatography-tandem mass spectrometry (LC-MS(n)) for the first large-scale analysis of GPI-anchored molecules (i.e., the GPIome) of a eukaryote, Trypanosoma cruzi, the etiologic agent of Chagas disease. Our genome-wise prediction analysis revealed that approximately 12% of T. cruzi genes possibly encode GPI-anchored proteins. By analyzing the GPIome of T. cruzi insect-dwelling epimastigote stage using LC-MS(n), we identified 90 GPI species, of which 79 were novel. Moreover, we determined that mucins coded by the T. cruzi small mucin-like gene (TcSMUG S) family are the major GPI-anchored proteins expressed on the epimastigote cell surface. TcSMUG S mucin mature sequences are short (56-85 amino acids) and highly O-glycosylated, and contain few proteolytic sites, therefore, less likely susceptible to proteases of the midgut of the insect vector. We propose that our approach could be used for the high throughput GPIomic analysis of other lower and higher eukaryotes. Molecular Systems Biology 7 April 2009; doi:10.1038/msb.2009.13

Evaluation of GFP Tag as a Screening Reporter in Directed Evolution of a Hyperthermophilic beta-Glucosidase

By applying a directed evolution methodology specific enzymatic characteristics can be enhanced, but to select mutants of interest from a large mutant bank, this approach requires high throughput screening and facile selection. To facilitate such primary screening of enhanced clones, an expression system was tested that uses a green fluorescent protein (GFP) tag from Aequorea victoria linked to the enzyme of interest. As GFP`s fluorescence is readily measured, and as there is a 1:1 molar correlation between the target protein and GFP, the concept proposed was to determine whether GFP could facilitate primary screening of error-prone PCR (EPP) clones. For this purpose a thermostable beta-glucosidase (BglA) from Fervidobacterium sp. was used as a model enzyme. A vector expressing the chimeric protein BglA-GFP-6XHis was constructed and the fusion protein purified and characterized. When compared to the native proteins, the components of the fusion displayed modified characteristics, such as enhanced GFP thermostability and a higher BglA optimum temperature. Clones carrying mutant BglA proteins obtained by EPP, were screened based on the BglA/GFP activity ratio. Purified tagged enzymes from selected clones resulted in modified substrate specificity.

Time course proteomic profiling of human myocardial infarction plasma samples: An approach to new biomarker discovery

Background: The aim of this study was to identify novel candidate biomarker proteins differentially expressed in the plasma of patients with early stage acute myocardial infarction (AMI) using SELDI-TOF-MS as a high throughput screening technology. Methods: Ten individuals with recent acute ischemic-type chest pain (< 12 h duration) and ST-segment elevation AMI (1STEMI) and after a second AMI (2STEMI) were selected. Blood samples were drawn at six times after STEMI diagnosis. The first stage (T(0)) was in Emergency Unit before receiving any medication, the second was just after primary angioplasty (T(2)), and the next four stages occurred at 12 h intervals after T(0). Individuals (n = 7) with similar risk factors for cardiovascular disease and normal ergometric test were selected as a control group (CG). Plasma proteomic profiling analysis was performed using the top-down (i.e. intact proteins) SELDI-TOF-MS, after processing in a Multiple Affinity Removal Spin Cartridge System (Agilent). Results: Compared with the CG, the 1STEMI group exhibited 510 differentially expressed protein peaks in the first 48 h after the AMI (p < 0.05). The 2STEMI group, had similar to 85% fewer differently expressed protein peaks than those without previous history of AMI (76, p < 0.05). Among the 16 differentially-regulated protein peaks common to both STEMI cohorts (compared with the CG at T(0)), 6 peaks were persistently down-regulated at more than one time-stage, and also were inversed correlated with serum protein markers (cTnI, CK and CKMB) during 48 h-period after IAM. Conclusions: Proteomic analysis by SELDI-TOF-MS technology combined with bioinformatics tools demonstrated differential expression during a 48 h time course suggests a potential role of some of these proteins as biomarkers for the very early stages of AMI, as well as for monitoring early cardiac ischemic recovery. (C) 2011 Elsevier B.V. All rights reserved.

A rapid single-step multiplex method for discriminating between Trichogramma (Hymenoptera: Trichogrammatidae) species in Australia

Inaccurate species identification confounds insect ecological studies. Examining aspects of Trichogramma ecology pertinent to the novel insect resistance management strategy for future transgenic cotton, Gossypium hirsutum L., production in the Ord River Irrigation Area (ORIA) of Western Australia required accurate differentiation between morphologically similar Trichogramma species. Established molecular diagnostic methods for Trichogramma identification use species-specific sequence difference in the internal transcribed spacer (ITS)-2 chromosomal region; yet, difficulties arise discerning polymerase chain reaction (PCR) fragments of similar base pair length by gel electrophoresis. This necessitates the restriction enzyme digestion of PCR-amplified ITS-2 fragments to readily differentiate Trichogramma australicum Girault and Trichogramma pretiosum Riley. To overcome the time and expense associated with a two-step diagnostic procedure, we developed a “one-step” multiplex PCR technique using species-specific primers designed to the ITS-2 region. This approach allowed for a high-throughput analysis of samples as part of ongoing ecological studies examining Trichogramma biological control potential in the ORIA where these two species occur in sympatry.

Bioinformatics construction of the human cell surfaceome

Cell surface proteins are excellent targets for diagnostic and therapeutic interventions. By using bioinformatics tools, we generated a catalog of 3,702 transmembrane proteins located at the surface of human cells (human cell surfaceome). We explored the genetic diversity of the human cell surfaceome at different levels, including the distribution of polymorphisms, conservation among eukaryotic species, and patterns of gene expression. By integrating expression information from a variety of sources, we were able to identify surfaceome genes with a restricted expression in normal tissues and/or differential expression in tumors, important characteristics for putative tumor targets. A high-throughput and efficient quantitative real-time PCR approach was used to validate 593 surfaceome genes selected on the basis of their expression pattern in normal and tumor samples. A number of candidates were identified as potential diagnostic and therapeutic targets for colorectal tumors and glioblastoma. Several candidate genes were also identified as coding for cell surface cancer/testis antigens. The human cell surfaceome will serve as a reference for further studies aimed at characterizing tumor targets at the surface of human cells.

Meningiomas and schwannomas: Molecular subgroup classification found by expression arrays

Microarray gene expression profiling is a high-throughput system used to identify differentially expressed genes and regulation patterns, and to discover new tumor markers. As the molecular pathogenesis of meningiomas and schwannomas, characterized by NF2 gene alterations, remains unclear and suitable molecular targets need to be identified, we used low density cDNA microarrays to establish expression patterns of 96 cancer-related genes on 23 schwannomas, 42 meningiomas and 3 normal cerebral meninges. We also performed a mutational analysis of the NF2 gene (PCR, dHPLC, Sequencing and MLPA), a search for 22q LOH and an analysis of gene silencing by promoter hypermethylation (MS-MLPA). Results showed a high frequency of NF2 gene mutations (40%), increased 22q LOH as aggressiveness increased, frequent losses and gains by MLPA in benign meningiomas, and gene expression silencing by hypermethylation. Array analysis showed decreased expression of 7 genes in meningiomas. Unsupervised analyses identified 2 molecular subgroups for both meningiomas and schwannomas showing 38 and 20 differentially expressed genes, respectively, and 19 genes differentially expressed between the two tumor types. These findings provide a molecular subgroup classification for meningiomas and schwannomas with possible implications for clinical practice.

Proteomics of breast cancer for marker discovery and signal pathway profiling

Breast cancer is the most common form of cancer among women and the identification of markers to discriminate tumorigenic from normal cells, as well as the different stages of this pathology, is of critical importance. Two-dimensional electrophoresis has been used before for studying breast cancer, but the progressive completion of human genomic sequencing and the introduction of mass spectrometry, combined with advanced bioinformatics for protein identification, have considerably increased the possibilities for characterizing new markers and therapeutic targets. Breast cancer proteomics has already identified markers of potential clinical interest (such as the molecular chaperone 14-3-3 sigma) and technological innovations such as large scale and high throughput analysis are now driving the field. Methods in functional proteomics have also been developed to study the intracellular signaling pathways that underlie the development of breast cancer. As illustrated with fibroblast growth factor-2, a mitogen and motogen factor for breast cancer cells, proteomics is a powerful approach to identify signaling proteins and to decipher the complex signaling circuitry involved in tumor growth. Together with genomics, proteomics is well on the way to molecularly characterizing the different types of breast tumor, and thus defining new therapeutic targets for future treatment.

Using MF-PCR to diagnose multiple defects from single cells: implications for PGD

Single cell genetic analysis is generally performed using PCR and FISH. Until recently, FISH has been the method of choice. FISH however is expensive, has significant misdiagnosis rates, can result in interpretation difficulties and is labour intensive making it unsuitable for high throughput processing. Recently fluorescent PCR reliability has increased to levels at or surpassing FISH whilst maintaining low cost. However, PCR accuracy has been a concern due to allelic dropout. Multiplex PCR can now increase accuracy by using multiple markers for each chromosome to firstly provide diagnosis if markers fail and,or secondly confirm diagnosis. We compare a variety of diagnostic methods and demonstrate for the first time a multiplex PCR system providing simultaneous diagnosis and confirmation of the major aneuploidy chromosomes (21, 18, 13) and sex as well as DNA fingerprint in single cells. We also discuss the implications of using PCR for aneuploidy screening in preimplantation genetic diagnosis. (C) 2001 Elsevier Science Ireland Ltd. All rights reserved.

A new joint coding and modulation scheme for channels with clipping

A major limitation in any high-performance digital communication system is the linearity region of the transmitting amplifier. Nonlinearities typically lead to signal clipping. Efficient communication in such conditions requires maintaining a low peak-to-average power ratio (PAR) in the transmitted signal while achieving a high throughput of data. Excessive PAR leads either to frequent clipping or to inadequate resolution in the analog-to-digital or digital-to-analog converters. Currently proposed signaling schemes for future generation wireless communications suffer from a high PAR. This paper presents a new signaling scheme for channels with clipping which achieves a PAR as low as 3. For a given linear range in the transmitter's digital-to-analog converter, this scheme achieves a lower bit-error rate than existing multicarrier schemes, owing to increased separation between constellation points. We present the theoretical basis for this new scheme, approximations for the expected bit-error rate, and simulation results. (C) 2002 Elsevier Science (USA).

A subset-oriented algorithm for minimizing the number of steps required for synthesis of cyclic-peptide libraries

Libraries of cyclic peptides are being synthesized using combinatorial chemistry for high throughput screening in the drug discovery process. This paper describes the min_syn_steps.cpp program (available at http://www.imb.uq.edu.au/groups/smythe/tran), which after inputting a list of cyclic peptides to be synthesized, removes cyclic redundant sequences and calculates synthetic strategies which minimize the synthetic steps as well as the reagent requirements. The synthetic steps and reagent requirements could be minimized by finding common subsets within the sequences for block synthesis. Since a brute-force approach to search for optimum synthetic strategies is impractically large, a subset-orientated approach is utilized here to limit the size of the search. (C) 2002 Elsevier Science Ltd. All rights reserved.