Atlas Toolkit: Fast registration of 3D morphological datasets in the absence of landmarks

Image registration is a gateway technology for Developmental Systems Biology, enabling computational analysis of related datasets within a shared coordinate system. Many registration tools rely on landmarks to ensure that datasets are correctly aligned; yet suitable landmarks are not present in many datasets. Atlas Toolkit is a Fiji/ImageJ plugin collection offering elastic group-wise registration of 3D morphological datasets, guided by segmentation of the interesting morphology. We demonstrate the method by combinatorial mapping of cell signalling events in the developing eyes of chick embryos, and use the integrated datasets to predictively enumerate Gene Regulatory Network states

The Life-Cycle of Operons

Operons are a major feature of all prokaryotic genomes, but how and why operon structures vary is not well understood. To elucidate the life-cycle of operons, we compared gene order between Escherichia coli K12 and its relatives and identified the recently formed and destroyed operons in E. coli. This allowed us to determine how operons form, how they become closely spaced, and how they die. Our findings suggest that operon evolution may be driven by selection on gene expression patterns. First, both operon creation and operon destruction lead to large changes in gene expression patterns. For example, the removal of lysA and ruvA from ancestral operons that contained essential genes allowed their expression to respond to lysine levels and DNA damage, respectively. Second, some operons have undergone accelerated evolution, with multiple new genes being added during a brief period. Third, although genes within operons are usually closely spaced because of a neutral bias toward deletion and because of selection against large overlaps, genes in highly expressed operons tend to be widely spaced because of regulatory fine-tuning by intervening sequences. Although operon evolution may be adaptive, it need not be optimal: new operons often comprise functionally unrelated genes that were already in proximity before the operon formed

SILAC-based proteomic quantification of chemoattractant-induced cytoskeleton dynamics on a second to minute timescale

Cytoskeletal dynamics during cell behaviours ranging from endocytosis and exocytosis to cell division and movement is controlled by a complex network of signalling pathways, the full details of which are as yet unresolved. Here we show that SILAC-based proteomic methods can be used to characterize the rapid chemoattractant-induced dynamic changes in the actin–myosin cytoskeleton and regulatory elements on a proteome-wide scale with a second to minute timescale resolution. This approach provides novel insights in the ensemble kinetics of key cytoskeletal constituents and association of known and novel identified binding proteins. We validate the proteomic data by detailed microscopy-based analysis of in vivo translocation dynamics for key signalling factors. This rapid large-scale proteomic approach may be applied to other situations where highly dynamic changes in complex cellular compartments are expected to play a key role

Predicting cell types and genetic variations contributing to disease by combining GWAS and epigenetic data

Genome-wide association studies (GWASs) identify single nucleotide polymorphisms (SNPs) that are enriched in individuals suffering from a given disease. Most disease-associated SNPs fall into non-coding regions, so that it is not straightforward to infer phenotype or function; moreover, many SNPs are in tight genetic linkage, so that a SNP identified as associated with a particular disease may not itself be causal, but rather signify the presence of a linked SNP that is functionally relevant to disease pathogenesis. Here, we present an analysis method that takes advantage of the recent rapid accumulation of epigenomics data to address these problems for some SNPs. Using asthma as a prototypic example; we show that non-coding disease-associated SNPs are enriched in genomic regions that function as regulators of transcription, such as enhancers and promoters. Identifying enhancers based on the presence of the histone modification marks such as H3K4me1 in different cell types, we show that the location of enhancers is highly cell-type specific. We use these findings to predict which SNPs are likely to be directly contributing to disease based on their presence in regulatory regions, and in which cell types their effect is expected to be detectable. Moreover, we can also predict which cell types contribute to a disease based on overlap of the disease-associated SNPs with the locations of enhancers present in a given cell type. Finally, we suggest that it will be possible to re-analyze GWAS studies with much higher power by limiting the SNPs considered to those in coding or regulatory regions of cell types relevant to a given disease

Effect of body fat distribution on the transcription response to dietary fat interventions

Combination of decreased energy expenditure and increased food intake results in fat accumulation either in the abdominal site (upper body obesity, UBO) or on the hips (lower body obesity, LBO). In this study, we used microarray gene expression profiling of adipose tissue biopsies to investigate the effect of body fat distribution on the physiological response to two dietary fat interventions. Mildly obese UBO and LBO male subjects (n = 12, waist-to-hip ratio range 0.93–1.12) were subjected to consumption of diets containing predominantly either long-chain fatty acids (PUFA) or medium-chain fatty acids (MCT). The results revealed (1) a large variation in transcription response to MCT and PUFA diets between UBO and LBO subjects, (2) higher sensitivity of UBO subjects to MCT/PUFA dietary intervention and (3) the upregulation of immune and apoptotic pathways and downregulation of metabolic pathways (oxidative, lipid, carbohydrate and amino acid metabolism) in UBO subjects when consuming MCT compared with PUFA diet. In conclusion, we report that despite the recommendation of MCT-based diet for improving obesity phenotype, this diet may have adverse effect on inflammatory and metabolic status of UBO subjects. The body fat distribution is, therefore, an important parameter to consider when providing personalized dietary recommendation

Difference-based clustering of short time-course microarray data with replicates

<p>Abstract</p> <p>Background</p> <p>There are some limitations associated with conventional clustering methods for short time-course gene expression data. The current algorithms require prior domain knowledge and do not incorporate information from replicates. Moreover, the results are not always easy to interpret biologically.</p> <p>Results</p> <p>We propose a novel algorithm for identifying a subset of genes sharing a significant temporal expression pattern when replicates are used. Our algorithm requires no prior knowledge, instead relying on an observed statistic which is based on the first and second order differences between adjacent time-points. Here, a pattern is predefined as the sequence of symbols indicating direction and the rate of change between time-points, and each gene is assigned to a cluster whose members share a similar pattern. We evaluated the performance of our algorithm to those of K-means, Self-Organizing Map and the Short Time-series Expression Miner methods.</p> <p>Conclusions</p> <p>Assessments using simulated and real data show that our method outperformed aforementioned algorithms. Our approach is an appropriate solution for clustering short time-course microarray data with replicates.</p

The role of PET/CT in detection of gastric cancer recurrence

<p>Abstract</p> <p>Background</p> <p>In the course of surveillance of gastric cancer recurrence after curative resection, contrast CT scan is used in general. However, new findings from CT scan are not always confirmatory for the recurrence. In this case, we usually use short-term follow up strategy or therapeutic intervention with clinical decision. Recently, the use of fusion Positron Emission Tomography/Computed Tomography (PET/CT) is increasing. The purpose of this study is to evaluate the efficacy and usefulness of PET/CT for detecting recurrence of gastric cancer after curative resection.</p> <p>Methods</p> <p>Fifty two patients who received curative resection of gastric cancer and had undergone PET/CT and contrast CT for surveillance of recurrence until Dec 2006 in Seoul National University Hospital were analyzed retrospectively. Recurrence of gastric cancer was validated by histologic confirmation (n = 17) or serial contrast CT follow up with at least 5 month interval (n = 35). McNemar's test and Fisher's exact test were used to evaluate sensitivity and specificity of PET/CT and contrast CT.</p> <p>Results</p> <p>Of 52 patients, 38 patients were confirmed as recurrence. The sensitivity was 68.4% (26/38) for PET/CT and 89.4% (34/38) for contrast CT (p = 0.057). The specificity was 71.4% (10/14) and 64.2% (9/14), respectively (p = 1.0). In terms of the recurred sites, the sensitivity and specificity of PET/CT were similar to those of contrast CT in all sites except peritoneum. Contrast CT was more sensitive than PET/CT (p = 0.039) for detecting peritoneal seeding. Additional PET/CT on contrast CT showed no further increase of positive predictive value regardless of sites. Among 13 patients whose image findings between two methods were discordant and tissue confirmation was difficult, the treatment decision was made in 7 patients based on PET/CT, showing the final diagnostic accuracy of 42.8% (3/7).</p> <p>Conclusion</p> <p>PET/CT was as sensitive and specific as contrast CT in detection of recurred gastric cancer except peritoneal seeding. However, additional PET/CT on contrast CT did not increase diagnostic accuracy in detection of recurred gastric cancer. Further studies are warranted to validate the role of PET/CT in detection of gastric cancer recurrence.</p

Time-Course Analysis of Cyanobacterium Transcriptome: Detecting Oscillatory Genes

The microarray technique allows the simultaneous measurements of the expression levels of thousands of mRNAs. By mining these data one can identify the dynamics of the gene expression time series. The detection of genes that are periodically expressed is an important step that allows us to study the regulatory mechanisms associated with the circadian cycle. The problem of finding periodicity in biological time series poses many challenges. Such challenge occurs due to the fact that the observed time series usually exhibit non-idealities, such as noise, short length, outliers and unevenly sampled time points. Consequently, the method for finding periodicity should preferably be robust against such anomalies in the data. In this paper, we propose a general and robust procedure for identifying genes with a periodic signature at a given significance level. This identification method is based on autoregressive models and the information theory. By using simulated data we show that the suggested method is capable of identifying rhythmic profiles even in the presence of noise and when the number of data points is small. By recourse of our analysis, we uncover the circadian rhythmic patterns underlying the gene expression profiles from Cyanobacterium Synechocystis

Tumor heterogeneity in neoplasms of breast, colon, and skin

<p>Abstract</p> <p>Background</p> <p>Different cell subpopulations in a single tumor may show diverse capacities for growth, differentiation, metastasis formation, and sensitivity to treatments. Thus, heterogeneity is an important feature of tumors. However, due to limitations in experimental and analytical techniques, tumor heterogeneity has rarely been studied in detail.</p> <p>Presentation of the hypothesis</p> <p>Different tumor types have different heterogeneity patterns, thus heterogeneity could be a characteristic feature of a particular tumor type.</p> <p>Testing the hypothesis</p> <p>We applied our previously published mathematical heterogeneity model to decipher tumor heterogeneity through the analysis of genetic copy number aberrations revealed by array CGH data for tumors of three different tissues: breast, colon, and skin. The model estimates the number of subpopulations present in each tumor. The analysis confirms that different tumor types have different heterogeneity patterns. Computationally derived genomic copy number profiles from each subpopulation have also been analyzed and discussed with reference to the multiple hypothetical relationships between subpopulations in origin-related samples.</p> <p>Implications of the hypothesis</p> <p>Our observations imply that tumor heterogeneity could be seen as an independent parameter for determining the characteristics of tumors. In the context of more comprehensive usage of array CGH or genome sequencing in a clinical setting our study provides a new way to realize the full potential of tumor genetic analysis.</p