Confocal Laser Scanning Microscopy, a New In Vivo Diagnostic Tool for Schistosomiasis

BACKGROUND: The gold standard for the diagnosis of schistosomiasis is the detection of the parasite's characteristic eggs in urine, stool, or rectal and bladder biopsy specimens. Direct detection of eggs is difficult and not always possible in patients with low egg-shedding rates. Confocal laser scanning microscopy (CLSM) permits non-invasive cell imaging in vivo and is an established way of obtaining high-resolution images and 3-dimensional reconstructions. Recently, CLSM was shown to be a suitable method to visualize Schistosoma mansoni eggs within the mucosa of dissected mouse gut. In this case, we evaluated the suitability of CLSM to detect eggs of Schistosoma haematobium in a patient with urinary schistosomiasis and low egg-shedding rates. METHODOLOGY/PRINCIPAL FINDINGS: The confocal laser scanning microscope used in this study was based on a scanning laser system for imaging the retina of a living eye, the Heidelberg Retina Tomograph II, in combination with a lens system (image modality). Standard light cystoscopy was performed using a rigid cystoscope under general anaesthesia. The CLSM endoscope was then passed through the working channel of the rigid cystoscope. The mucosal tissue of the bladder was scanned using CLSM. Schistoma haematobium eggs appeared as bright structures, with the characteristic egg shape and typical terminal spine. CONCLUSION/SIGNIFICANCE: We were able to detect schistosomal eggs in the urothelium of a patient with urinary schistosomiasis. Thus, CLSM may be a suitable tool for the diagnosis of schistosomiasis in humans, especially in cases where standard diagnostic tools are not suitable

A method of determining where to target surveillance efforts in heterogeneous epidemiological systems

The spread of pathogens into new environments poses a considerable threat to human, animal, and plant health, and by extension, human and animal wellbeing, ecosystem function, and agricultural productivity, worldwide. Early detection through effective surveillance is a key strategy to reduce the risk of their establishment. Whilst it is well established that statistical and economic considerations are of vital importance when planning surveillance efforts, it is also important to consider epidemiological characteristics of the pathogen in question—including heterogeneities within the epidemiological system itself. One of the most pronounced realisations of this heterogeneity is seen in the case of vector-borne pathogens, which spread between ‘hosts’ and ‘vectors’—with each group possessing distinct epidemiological characteristics. As a result, an important question when planning surveillance for emerging vector-borne pathogens is where to place sampling resources in order to detect the pathogen as early as possible. We answer this question by developing a statistical function which describes the probability distributions of the prevalences of infection at first detection in both hosts and vectors. We also show how this method can be adapted in order to maximise the probability of early detection of an emerging pathogen within imposed sample size and/or cost constraints, and demonstrate its application using two simple models of vector-borne citrus pathogens. Under the assumption of a linear cost function, we find that sampling costs are generally minimised when either hosts or vectors, but not both, are sampled

Persistent Infection and Promiscuous Recombination of Multiple Genotypes of an RNA Virus within a Single Host Generate Extensive Diversity

Recombination and reassortment of viral genomes are major processes contributing to the creation of new, emerging viruses. These processes are especially significant in long-term persistent infections where multiple viral genotypes co-replicate in a single host, generating abundant genotypic variants, some of which may possess novel host-colonizing and pathogenicity traits. In some plants, successive vegetative propagation of infected tissues and introduction of new genotypes of a virus by vector transmission allows for viral populations to increase in complexity for hundreds of years allowing co-replication and subsequent recombination of the multiple viral genotypes. Using a resequencing microarray, we examined a persistent infection by a Citrus tristeza virus (CTV) complex in citrus, a vegetatively propagated, globally important fruit crop, and found that the complex comprised three major and a number of minor genotypes. Subsequent deep sequencing analysis of the viral population confirmed the presence of the three major CTV genotypes and, in addition, revealed that the minor genotypes consisted of an extraordinarily large number of genetic variants generated by promiscuous recombination between the major genotypes. Further analysis provided evidence that some of the recombinants underwent subsequent divergence, further increasing the genotypic complexity. These data demonstrate that persistent infection of multiple viral genotypes within a host organism is sufficient to drive the large-scale production of viral genetic variants that may evolve into new and emerging viruses

Deep Annotation of Populus trichocarpa microRNAs from Diverse Tissue Sets

Populus trichocarpa is an important woody model organism whose entire genome has been sequenced. This resource has facilitated the annotation of microRNAs (miRNAs), which are short non-coding RNAs with critical regulatory functions. However, despite their developmental importance, P. trichocarpa miRNAs have yet to be annotated from numerous important tissues. Here we significantly expand the breadth of tissue sampling and sequencing depth for miRNA annotation in P. trichocarpa using high-throughput smallRNA (sRNA) sequencing. miRNA annotation was performed using three individual next-generation sRNA sequencing runs from separate leaves, xylem, and mechanically treated xylem, as well as a fourth run using a pooled sample containing vegetative apices, male flowers, female flowers, female apical buds, and male apical and lateral buds. A total of 276 miRNAs were identified from these datasets, including 155 previously unannotated miRNAs, most of which are P. trichocarpa specific. Importantly, we identified several xylem-enriched miRNAs predicted to target genes known to be important in secondary growth, including the critical reaction wood enzyme xyloglucan endo-transglycosylase/hydrolase and vascular-related transcription factors. This study provides a thorough genome-wide annotation of miRNAs in P. trichocarpa through deep sRNA sequencing from diverse tissue sets. Our data significantly expands the P. trichocarpa miRNA repertoire, which will facilitate a broad range of research in this major model system

Time warping of evolutionary distant temporal gene expression data based on noise suppression

<p>Abstract</p> <p>Background</p> <p>Comparative analysis of genome wide temporal gene expression data has a broad potential area of application, including evolutionary biology, developmental biology, and medicine. However, at large evolutionary distances, the construction of global alignments and the consequent comparison of the time-series data are difficult. The main reason is the accumulation of variability in expression profiles of orthologous genes, in the course of evolution.</p> <p>Results</p> <p>We applied Pearson distance matrices, in combination with other noise-suppression techniques and data filtering to improve alignments. This novel framework enhanced the capacity to capture the similarities between the temporal gene expression datasets separated by large evolutionary distances. We aligned and compared the temporal gene expression data in budding (<it>Saccharomyces cerevisiae</it>) and fission (<it>Schizosaccharomyces pombe</it>) yeast, which are separated by more then ~400 myr of evolution. We found that the global alignment (time warping) properly matched the duration of cell cycle phases in these distant organisms, which was measured in prior studies. At the same time, when applied to individual ortholog pairs, this alignment procedure revealed groups of genes with distinct alignments, different from the global alignment.</p> <p>Conclusion</p> <p>Our alignment-based predictions of differences in the cell cycle phases between the two yeast species were in a good agreement with the existing data, thus supporting the computational strategy adopted in this study. We propose that the existence of the alternative alignments, specific to distinct groups of genes, suggests presence of different synchronization modes between the two organisms and possible functional decoupling of particular physiological gene networks in the course of evolution.</p

Biclustering via optimal re-ordering of data matrices in systems biology: rigorous methods and comparative studies

<p>Abstract</p> <p>Background</p> <p>The analysis of large-scale data sets via clustering techniques is utilized in a number of applications. Biclustering in particular has emerged as an important problem in the analysis of gene expression data since genes may only jointly respond over a subset of conditions. Biclustering algorithms also have important applications in sample classification where, for instance, tissue samples can be classified as cancerous or normal. Many of the methods for biclustering, and clustering algorithms in general, utilize simplified models or heuristic strategies for identifying the "best" grouping of elements according to some metric and cluster definition and thus result in suboptimal clusters.</p> <p>Results</p> <p>In this article, we present a rigorous approach to biclustering, OREO, which is based on the Optimal RE-Ordering of the rows and columns of a data matrix so as to globally minimize the dissimilarity metric. The physical permutations of the rows and columns of the data matrix can be modeled as either a network flow problem or a traveling salesman problem. Cluster boundaries in one dimension are used to partition and re-order the other dimensions of the corresponding submatrices to generate biclusters. The performance of OREO is tested on (a) metabolite concentration data, (b) an image reconstruction matrix, (c) synthetic data with implanted biclusters, and gene expression data for (d) colon cancer data, (e) breast cancer data, as well as (f) yeast segregant data to validate the ability of the proposed method and compare it to existing biclustering and clustering methods.</p> <p>Conclusion</p> <p>We demonstrate that this rigorous global optimization method for biclustering produces clusters with more insightful groupings of similar entities, such as genes or metabolites sharing common functions, than other clustering and biclustering algorithms and can reconstruct underlying fundamental patterns in the data for several distinct sets of data matrices arising in important biological applications.</p

Transcriptional Reprogramming of CD11b+Esamhi Dendritic Cell Identity and Function by Loss of Runx3

Classical dendritic cells (cDC) are specialized antigen-presenting cells mediating immunity and tolerance. cDC cell-lineage decisions are largely controlled by transcriptional factor regulatory cascades. Using an in vivo cell-specific targeting of Runx3 at various stages of DC lineage development we show that Runx3 is required for cell-identity, homeostasis and function of splenic Esamhi DC. Ablation of Runx3 in DC progenitors led to a substantial decrease in splenic CD4+/CD11b+ DC. Combined chromatin immunoprecipitation sequencing and gene expression analysis of purified DC-subsets revealed that Runx3 is a key gene expression regulator that facilitates specification and homeostasis of CD11b+Esamhi DC. Mechanistically, loss of Runx3 alters Esamhi DC gene expression to a signature characteristic of WT Esamlow DC. This transcriptional reprogramming caused a cellular change that diminished phagocytosis and hampered Runx3-/- Esamhi DC capacity to prime CD4+ T cells, attesting to the significant role of Runx3 in specifying Esamhi DC identity and function

Fluorescence activated cell sorting followed by small RNA sequencing reveals stable microRNA expression during cell cycle progression.

BACKGROUND: Previously, drug-based synchronization procedures were used for characterizing the cell cycle dependent transcriptional program. However, these synchronization methods result in growth imbalance and alteration of the cell cycle machinery. DNA content-based fluorescence activated cell sorting (FACS) is able to sort the different cell cycle phases without perturbing the cell cycle. MiRNAs are key transcriptional regulators of the cell cycle, however, their expression dynamics during cell cycle has not been explored. METHODS: Following an optimized FACS, a complex initiative of high throughput platforms (microarray, Taqman Low Density Array, small RNA sequencing) were performed to study gene and miRNA expression profiles of cell cycle sorted human cells originating from different tissues. Validation of high throughput data was performed using quantitative real time PCR. Protein expression was detected by Western blot. Complex statistics and pathway analysis were also applied. RESULTS: Beyond confirming the previously described cell cycle transcriptional program, cell cycle dependently expressed genes showed a higher expression independently from the cell cycle phase and a lower amplitude of dynamic changes in cancer cells as compared to untransformed fibroblasts. Contrary to mRNA changes, miRNA expression was stable throughout the cell cycle. CONCLUSIONS: Cell cycle sorting is a synchronization-free method for the proper analysis of cell cycle dynamics. Altered dynamic expression of universal cell cycle genes in cancer cells reflects the transformed cell cycle machinery. Stable miRNA expression during cell cycle progression may suggest that dynamical miRNA-dependent regulation may be of less importance in short term regulations during the cell cycle

Network deconvolution as a general method to distinguish direct dependencies in networks

Recognizing direct relationships between variables connected in a network is a pervasive problem in biological, social and information sciences as correlation-based networks contain numerous indirect relationships. Here we present a general method for inferring direct effects from an observed correlation matrix containing both direct and indirect effects. We formulate the problem as the inverse of network convolution, and introduce an algorithm that removes the combined effect of all indirect paths of arbitrary length in a closed-form solution by exploiting eigen-decomposition and infinite-series sums. We demonstrate the effectiveness of our approach in several network applications: distinguishing direct targets in gene expression regulatory networks; recognizing directly interacting amino-acid residues for protein structure prediction from sequence alignments; and distinguishing strong collaborations in co-authorship social networks using connectivity information alone. In addition to its theoretical impact as a foundational graph theoretic tool, our results suggest network deconvolution is widely applicable for computing direct dependencies in network science across diverse disciplines.National Institutes of Health (U.S.) (grant R01 HG004037)National Institutes of Health (U.S.) (grant HG005639)Swiss National Science Foundation (Fellowship)National Science Foundation (U.S.) (NSF CAREER Award 0644282

The Annotation, Mapping, Expression and Network (AMEN) suite of tools for molecular systems biology

<p>Abstract</p> <p>Background</p> <p>High-throughput genome biological experiments yield large and multifaceted datasets that require flexible and user-friendly analysis tools to facilitate their interpretation by life scientists. Many solutions currently exist, but they are often limited to specific steps in the complex process of data management and analysis and some require extensive informatics skills to be installed and run efficiently.</p> <p>Results</p> <p>We developed the Annotation, Mapping, Expression and Network (AMEN) software as a stand-alone, unified suite of tools that enables biological and medical researchers with basic bioinformatics training to manage and explore genome annotation, chromosomal mapping, protein-protein interaction, expression profiling and proteomics data. The current version provides modules for (i) uploading and pre-processing data from microarray expression profiling experiments, (ii) detecting groups of significantly co-expressed genes, and (iii) searching for enrichment of functional annotations within those groups. Moreover, the user interface is designed to simultaneously visualize several types of data such as protein-protein interaction networks in conjunction with expression profiles and cellular co-localization patterns. We have successfully applied the program to interpret expression profiling data from budding yeast, rodents and human.</p> <p>Conclusion</p> <p>AMEN is an innovative solution for molecular systems biological data analysis freely available under the GNU license. The program is available via a website at the Sourceforge portal which includes a user guide with concrete examples, links to external databases and helpful comments to implement additional functionalities. We emphasize that AMEN will continue to be developed and maintained by our laboratory because it has proven to be extremely useful for our genome biological research program.</p