Genome-wide SNP genotyping highlights the role of natural selection in Plasmodium falciparum population divergence

Background: The malaria parasite Plasmodium falciparum exhibits abundant genetic diversity, and this diversity is key to its success as a pathogen. Previous efforts to study genetic diversity in P. falciparum have begun to elucidate the demographic history of the species, as well as patterns of population structure and patterns of linkage disequilibrium within its genome. Such studies will be greatly enhanced by new genomic tools and recent large-scale efforts to map genomic variation. To that end, we have developed a high throughput single nucleotide polymorphism (SNP) genotyping platform for P. falciparum. Results: Using an Affymetrix 3,000 SNP assay array, we found roughly half the assays (1,638) yielded high quality, 100% accurate genotyping calls for both major and minor SNP alleles. Genotype data from 76 global isolates confirm significant genetic differentiation among continental populations and varying levels of SNP diversity and linkage disequilibrium according to geographic location and local epidemiological factors. We further discovered that nonsynonymous and silent (synonymous or noncoding) SNPs differ with respect to within-population diversity, interpopulation differentiation, and the degree to which allele frequencies are correlated between populations. Conclusions: The distinct population profile of nonsynonymous variants indicates that natural selection has a significant influence on genomic diversity in P. falciparum, and that many of these changes may reflect functional variants deserving of follow-up study. Our analysis demonstrates the potential for new high-throughput genotyping technologies to enhance studies of population structure, natural selection, and ultimately enable genome-wide association studies in P. falciparum to find genes underlying key phenotypic traits.

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

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.

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).

Use of heterologously-expressed cytochomre P450 and glutathione transferase enzymes in toxicity assays

Our groups have had a long-term interest in utilizing bacterial systems in the characterization of bioactivation and detoxication reactions catalyzed by cytochrome P450 (P450) and glutathione transferase (GST) enzymes. Bacterial systems remain the first choice for initial screens with new chemicals and have advantages, including high-throughput capability. Most human P450s of interest in toxicology have been readily expressed in Escherichia coli with only minor sequence modification. These enzymes can be readily purified and used in assays of activation of chemicals. Bicistronic systems have been developed in order to provide the auxiliary NADPH-P450 reductase. Alternative systems involve these enzymes expressed together within bacteria. In one approach, a lac selection system is used with E. coli and has been applied to the characterization of inhibitors of P450s 1A2 and 1131, as well as in basic studies involving random mutagenesis. Another approach utilizes induction of the SOS (umu) response in Salmonella typhimurium, and systems have now been developed with human P450s 1A1, 1A2, 1B1, 2C9, 2D6, 2E1, and 3A4, which have been used to report responses from heterocyclic amines. S. typhimurium his reporter systems have also been used with GSTs, first to demonstrate the role of rat GST 5-5 in the activation of dihalomethanes. These systems have been used to compare these GSTs with regard to activation of dihaloalkanes and potential toxicity. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.

An image processing application for quantification of protein aggregates in Caenorhabditis elegans

Protein aggregation became a widely accepted marker of many polyQ disorders, including Machado-Joseph disease (MJD), and is often used as readout for disease progression and development of therapeutic strategies. The lack of good platforms to rapidly quantify protein aggregates in a wide range of disease animal models prompted us to generate a novel image processing application that automatically identifies and quantifies the aggregates in a standardized and operator-independent manner. We propose here a novel image processing tool to quantify the protein aggregates in a Caenorhabditis elegans (C. elegans) model of MJD. Confocal mi-croscopy images were obtained from animals of different genetic conditions. The image processing application was developed using MeVisLab as a platform to pro-cess, analyse and visualize the images obtained from those animals. All segmenta-tion algorithms were based on intensity pixel levels.The quantification of area or numbers of aggregates per total body area, as well as the number of aggregates per animal were shown to be reliable and reproducible measures of protein aggrega-tion in C. elegans. The results obtained were consistent with the levels of aggrega-tion observed in the images. In conclusion, this novel imaging processing applica-tion allows the non-biased, reliable and high throughput quantification of protein aggregates in a C. elegans model of MJD, which may contribute to a significant improvement on the prognosis of treatment effectiveness for this group of disor-ders

Automatic segmentation and 3D feature extraction of protein aggregates in Caenorhabditis Elegans

In the last years, it has become increasingly clear that neurodegenerative diseases involve protein aggregation, a process often used as disease progression readout and to develop therapeutic strategies. This work presents an image processing tool to automatic segment, classify and quantify these aggregates and the whole 3D body of the nematode Caenorhabditis Elegans. A total of 150 data set images, containing different slices, were captured with a confocal microscope from animals of distinct genetic conditions. Because of the animals’ transparency, most of the slices pixels appeared dark, hampering their body volume direct reconstruction. Therefore, for each data set, all slices were stacked in one single 2D image in order to determine a volume approximation. The gradient of this image was input to an anisotropic diffusion algorithm that uses the Tukey’s biweight as edge-stopping function. The image histogram median of this outcome was used to dynamically determine a thresholding level, which allows the determination of a smoothed exterior contour of the worm and the medial axis of the worm body from thinning its skeleton. Based on this exterior contour diameter and the medial animal axis, random 3D points were then calculated to produce a volume mesh approximation. The protein aggregations were subsequently segmented based on an iso-value and blended with the resulting volume mesh. The results obtained were consistent with qualitative observations in literature, allowing non-biased, reliable and high throughput protein aggregates quantification. This may lead to a significant improvement on neurodegenerative diseases treatment planning and interventions prevention

A Flexible Architecture for the Computation of Direct and Inverse Transforms in H.264/AVC Video Codecs

A new high throughput and scalable architecture for unified transform coding in H.264/AVC is proposed in this paper. Such flexible structure is capable of computing all the 4x4 and 2x2 transforms for Ultra High Definition Video (UHDV) applications (4320x7680@ 30fps) in real-time and with low hardware cost. These significantly high performance levels were proven with the implementation of several different configurations of the proposed structure using both FPGA and ASIC 90 nm technologies. In addition, such experimental evaluation also demonstrated the high area efficiency of theproposed architecture, which in terms of Data Throughput per Unit of Area (DTUA) is at least 1.5 times more efficient than its more prominent related designs(1).

Scalable Unified Transform Architecture for Advanced Video Coding Embedded Systems

A novel high throughput and scalable unified architecture for the computation of the transform operations in video codecs for advanced standards is presented in this paper. This structure can be used as a hardware accelerator in modern embedded systems to efficiently compute all the two-dimensional 4 x 4 and 2 x 2 transforms of the H.264/AVC standard. Moreover, its highly flexible design and hardware efficiency allows it to be easily scaled in terms of performance and hardware cost to meet the specific requirements of any given video coding application. Experimental results obtained using a Xilinx Virtex-5 FPGA demonstrated the superior performance and hardware efficiency levels provided by the proposed structure, which presents a throughput per unit of area relatively higher than other similar recently published designs targeting the H.264/AVC standard. Such results also showed that, when integrated in a multi-core embedded system, this architecture provides speedup factors of about 120x concerning pure software implementations of the transform algorithms, therefore allowing the computation, in real-time, of all the above mentioned transforms for Ultra High Definition Video (UHDV) sequences (4,320 x 7,680 @ 30 fps).