Geodesic-Based Bayesian Coherent Point Drift.

Coherent point drift is a well-known algorithm for non-rigid registration, i.e., a procedure for deforming a shape to match another shape. Despite its prevalence, the algorithm has a major drawback that remains unsolved: It unnaturally deforms the different parts of a shape, e.g., human legs, when they are neighboring each other. The inappropriate deformations originate from a proximity-based deformation constraint, called motion coherence. This study proposes a non-rigid registration method that addresses the drawback. The key to solving the problem is to redefine the motion coherence using a geodesic, i.e., the shortest route between points on a shape's surface. We also propose the accelerated variant of the registration method. In numerical studies, we demonstrate that the algorithms can circumvent the drawback of coherent point drift. We also show that the accelerated algorithm can be applied to shapes comprising several millions of points.

UVA causes dysfunction of ETBR and BMPR2 in vascular endothelial cells, resulting in structural abnormalities of the skin capillaries.

BACKGROUND: Capillary structural abnormalities cause skin disorders. Mottled redness, i.e., skin redness unevenness, may appear on the sun-exposed skin, suggesting capillary structural abnormalities, although its mechanism remains unclear. OBJECTIVE: To observe the capillary structures in the sun-exposed skin where skin redness unevenness is likely to occur, and clarify the mechanism of capillary structural abnormalities. METHODS: The tissue structures in the skin with skin redness unevenness were observed by LC-OCT. Subsequently, immunostaining of the sun-exposed skin where skin redness unevenness is often observed, was performed. Vascular endothelial cells were UV-irradiated to analyze the expression and functions of genes involved in the capillary structures and morphogenesis. RESULTS: The skin with skin redness unevenness exhibited scattering of dilated tubular tissue and disturbance of distribution uniformity. Immunostaining of the sun-exposed skin that were more likely to be exposed to UV rays also revealed similarly disorder of capillary structures. In addition, UVA-irradiated vascular endothelial cells exhibited increased expression of ETBR, involved in telangiectasia, decreased expression of BMPR2, involved in the morphogenesis and maintenance of the blood vessels, and reduced migration of the capillaries. CONCLUSION: UV rays alter ETBR and BMPR2 expression in the skin capillaries, and cause partial dilation and decreased migration, resulting in capillary structural abnormalities and causing skin redness unevenness.

A Bayesian Formulation of Coherent Point Drift.

Coherent point drift is a well-known algorithm for solving point set registration problems, i.e., finding corresponding points between shapes represented as point sets. Despite its advantages over other state-of-the-art algorithms, theoretical and practical issues remain. Among theoretical issues, (1) it is unknown whether the algorithm always converges, and (2) the meaning of the parameters concerning motion coherence is unclear. Among practical issues, (3) the algorithm is relatively sensitive to target shape rotation, and (4) acceleration of the algorithm is restricted to the use of the Gaussian kernel. To overcome these issues and provide a different and more general perspective to the algorithm, we formulate coherent point drift in a Bayesian setting. The formulation brings the following consequences and advances to the field: convergence of the algorithm is guaranteed by variational Bayesian inference; the definition of motion coherence as a prior distribution provides a basis for interpretation of the parameters; rigid and non-rigid registration can be performed in a single algorithm, enhancing robustness against target rotation. We also propose an acceleration scheme for the algorithm that can be applied to non-Gaussian kernels and that provides greater efficiency than coherent point drift.

Acceleration of Non-Rigid Point Set Registration With Downsampling and Gaussian Process Regression.

Non-rigid point set registration is the process of transforming a shape represented as a point set into a shape matching another shape. In this paper, we propose an acceleration method for solving non-rigid point set registration problems. We accelerate non-rigid registration by dividing it into three steps: i) downsampling of point sets; ii) non-rigid registration of downsampled point sets; and iii) interpolation of shape deformation vectors corresponding to points removed during downsampling. To register downsampled point sets, we use a registration algorithm based on a prior distribution, called motion coherence prior. Using the same prior, we derive an interpolation method interpreted as Gaussian process regression. Through numerical experiments, we demonstrate that our algorithm registers point sets containing over ten million points. We also show that our algorithm reduces computing time more radically than a state-of-the-art acceleration algorithm.

SPF-CellTracker: Tracking Multiple Cells with Strongly-Correlated Moves Using a Spatial Particle Filter.

Tracking many cells in time-lapse 3D image sequences is an important challenging task of bioimage informatics. Motivated by a study of brain-wide 4D imaging of neural activity in C. elegans, we present a new method of multi-cell tracking. Data types to which the method is applicable are characterized as follows: (i) cells are imaged as globular-like objects, (ii) it is difficult to distinguish cells on the basis of shape and size only, (iii) the number of imaged cells in the several-hundred range, (iv) movements of nearly-located cells are strongly correlated, and (v) cells do not divide. We developed a tracking software suite that we call SPF-CellTracker. Incorporating dependency on the cells' movements into the prediction model is the key for reducing the tracking errors: the cell switching and the coalescence of the tracked positions. We model the target cells' correlated movements as a Markov random field and we also derive a fast computation algorithm, which we call spatial particle filter. With the live-imaging data of the nuclei of C. elegans neurons in which approximately 120 nuclei of neurons were imaged, the proposed method demonstrated improved accuracy compared to the standard particle filter and the method developed by Tokunaga et al. (2014).

MicroRNA-140 mediates RB tumor suppressor function to control stem cell-like activity through interleukin-6.

We established an in vitro cell culture system to determine novel activities of the retinoblastoma (Rb) protein during tumor progression. Rb depletion in p53-null mouse-derived soft tissue sarcoma cells induced a spherogenic phenotype. Cells retrieved from Rb-depleted spheres exhibited slower proliferation and less efficient BrdU incorporation, however, much higher spherogenic activity and aggressive behavior. We discovered six miRNAs, including mmu-miR-18a, -25, -29b, -140, -337, and -1839, whose expression levels correlated tightly with the Rb status and spherogenic activity. Among these, mmu-miR-140 appeared to be positively controlled by Rb and to antagonize the effect of Rb depletion on spherogenesis and tumorigenesis. Furthermore, among genes potentially targeted by mmu-miR-140, Il-6 was upregulated by Rb depletion and downregulated by mmu-mir-140 overexpression. Altogether, we demonstrate the possibility that mmu-mir-140 mediates the Rb function to downregulate Il-6 by targeting its 3'-untranslated region. Finally, we detected the same relationship among RB, hsa-miR-140 and IL-6 in a human breast cancer cell line MCF-7. Because IL-6 is a critical modulator of malignant features of cancer cells and the RB pathway is impaired in the majority of cancers, hsa-miR-140 might be a promising therapeutic tool that disrupts linkage between tumor suppressor inactivation and pro-inflammatory cytokine response.

Accurate Automatic Detection of Densely Distributed Cell Nuclei in 3D Space.

To measure the activity of neurons using whole-brain activity imaging, precise detection of each neuron or its nucleus is required. In the head region of the nematode C. elegans, the neuronal cell bodies are distributed densely in three-dimensional (3D) space. However, no existing computational methods of image analysis can separate them with sufficient accuracy. Here we propose a highly accurate segmentation method based on the curvatures of the iso-intensity surfaces. To obtain accurate positions of nuclei, we also developed a new procedure for least squares fitting with a Gaussian mixture model. Combining these methods enables accurate detection of densely distributed cell nuclei in a 3D space. The proposed method was implemented as a graphical user interface program that allows visualization and correction of the results of automatic detection. Additionally, the proposed method was applied to time-lapse 3D calcium imaging data, and most of the nuclei in the images were successfully tracked and measured.

Inflammation in gastric cancer: Interplay of the COX-2/prostaglandin E2 and Toll-like receptor/MyD88 pathways.

Cyclooxygenase-2 (COX-2) and its downstream product prostaglandin E2 (PGE2 ) play a key role in generation of the inflammatory microenvironment in tumor tissues. Gastric cancer is closely associated with Helicobacter pylori infection, which stimulates innate immune responses through Toll-like receptors (TLRs), inducing COX-2/PGE2 pathway through nuclear factor-κB activation. A pathway analysis of human gastric cancer shows that both the COX-2 pathway and Wnt/ß-catenin signaling are significantly activated in tubular-type gastric cancer, and basal levels of these pathways are also increased in other types of gastric cancer. Expression of interleukin-11, chemokine (C-X-C motif) ligand 1 (CXCL1), CXCL2, and CXCL5, which play tumor-promoting roles through a variety of mechanisms, is induced in a COX-2/PGE2 pathway-dependent manner in both human and mouse gastric tumors. Moreover, the COX-2/PGE2 pathway plays an important role in the maintenance of stemness with expression of stem cell markers, including CD44, Prom1, and Sox9, which are induced in both gastritis and gastric tumors through a COX-2/PGE2 -dependent mechanism. In contrast, disruption of Myd88 results in suppression of the inflammatory microenvironment in gastric tumors even when the COX-2/PGE2 pathway is activated, indicating that the interplay of the COX-2/PGE2 and TLR/MyD88 pathways is needed for inflammatory response in tumor tissues. Furthermore, TLR2/MyD88 signaling plays a role in maintenance of stemness in normal stem cells as well as gastric tumor cells. Accordingly, these results suggest that targeting the COX-2/PGE2 pathway together with TLR/MyD88 signaling, which would suppress the inflammatory microenvironment and maintenance of stemness, could be an effective preventive or therapeutic strategy for gastric cancer.

Myeloid Differentiation Factor 88 Signaling in Bone Marrow-Derived Cells Promotes Gastric Tumorigenesis by Generation of Inflammatory Microenvironment.

It has been established that COX-2 and downstream signaling by prostaglandin E2 (PGE2) play a key role in tumorigenesis through generation of inflammatory microenvironment. Toll-like receptor (TLR) signaling through myeloid differentiation factor 88 (MyD88) also regulates inflammatory responses in tumors. However, the relationship between these distinct pathways in tumorigenesis is not yet fully understood. We herein investigated the role of MyD88 in gastric tumorigenesis using Gan mice, which develop inflammation-associated gastric tumors due to the simultaneous activation of the COX-2/PGE2 pathway and Wnt signaling. Notably, the disruption of Myd88 in Gan mice resulted in the significant suppression of gastric tumorigenesis with the inhibition of inflammatory responses, even though COX-2/PGE2 pathway is constitutively activated. Moreover, Myd88 disruption in bone marrow-derived cells (BMDCs) in Gan mice also suppressed inflammation and tumorigenesis, indicating that MyD88 signaling in BMDCs regulates the inflammatory microenvironment. We also found that expression of Tlr2 and its coreceptor Cd14 was induced in tumor epithelial cells in Gan mice, which was suppressed by the disruption of Myd88. It has already been shown that TLR2/CD14 signaling is important for stemness of intestinal epithelial cells. These results indicate that MyD88 in BMDCs, together with COX-2/PGE2 pathway, plays an essential role in the generation of the inflammatory microenvironment, which may promote tumorigenesis through induction of TLR2/CD14 pathway in tumor epithelial cells. These results suggest that inhibition of TLR/MyD88 signaling together with COX-2/PGE2 pathway will be an effective preventive strategy for gastric cancer.

Factors to preserve CpG-rich sequences in methylated CpG islands.

BACKGROUND: Mammalian CpG islands (CGIs) normally escape DNA methylation in all adult tissues and developmental stages. However, in our previous study we unexpectedly identified many methylated CGIs in human peripheral blood leukocytes. Methylated CpG dinucleotides convert to TpG dinucleotides through deaminization of their cytosine bases more frequently than hypomethylated CpG dinucleotides. Therefore, we wondered how methylated CGIs in germline or non-germline cells maintain their CpG-rich sequences. It is known that events such as germline hypomethylation, CpG selection, biased gene conversion (BGC), and frequent CpG fixation can contribute to the maintenance of CpG-rich sequences in methylated CGIs in germline or non-germline cells. However, it has not been investigated which of the processes maintain CpG-rich sequences of methylated CGIs in each genomic position. RESULTS: In this study, we comprehensively examined the contribution of the processes described above to the maintenance of CpG-rich sequences in methylated CGIs in germline and non-germline cells which were classified by genomic positions. Approximately 60-80% of CGIs with high methylation in H1 cell line (H1-HM) in all the genomic positions showed a low average CpG→TpG/CpA substitution rate. In contrast, fewer than half the numbers of CGIs with H1-HM in all the genomic positions showed a low average CpG→TpG/CpA substitution rate and low levels of methylation in sperm cells (SPM-LM). Furthermore, a small fraction of CGIs with a low average CpG→TpG/CpA substitution rate and high levels of methylation in sperm cells (SPM-HM) showed CpG selection. On the other hand, independent of the positions in genes, most CGIs with SPM-HM showed a slightly higher average TpG/CpA→CpG substitution rate compared with those with SPM-LM. CONCLUSIONS: Relatively high numbers (approximately 60-80%) of CGIs with H1-HM in all the genomic positions preserve their CpG-rich sequences by a low CpG→TpG/CpA substitution rate caused mainly by their SPM-LM, and for those with SPM-HM partly by CpG selection and TpG/CpA→CpG fixation. BGC has little contribution to the maintenance of CpG-rich sequences of CGIs with SPM-HM which were classified by genomic positions.

Automated detection and tracking of many cells by using 4D live-cell imaging data.

MOTIVATION: Automated fluorescence microscopes produce massive amounts of images observing cells, often in four dimensions of space and time. This study addresses two tasks of time-lapse imaging analyses; detection and tracking of the many imaged cells, and it is especially intended for 4D live-cell imaging of neuronal nuclei of Caenorhabditis elegans. The cells of interest appear as slightly deformed ellipsoidal forms. They are densely distributed, and move rapidly in a series of 3D images. Thus, existing tracking methods often fail because more than one tracker will follow the same target or a tracker transits from one to other of different targets during rapid moves. RESULTS: The present method begins by performing the kernel density estimation in order to convert each 3D image into a smooth, continuous function. The cell bodies in the image are assumed to lie in the regions near the multiple local maxima of the density function. The tasks of detecting and tracking the cells are then addressed with two hill-climbing algorithms. The positions of the trackers are initialized by applying the cell-detection method to an image in the first frame. The tracking method keeps attacking them to near the local maxima in each subsequent image. To prevent the tracker from following multiple cells, we use a Markov random field (MRF) to model the spatial and temporal covariation of the cells and to maximize the image forces and the MRF-induced constraint on the trackers. The tracking procedure is demonstrated with dynamic 3D images that each contain >100 neurons of C.elegans. AVAILABILITY: http://daweb.ism.ac.jp/yoshidalab/crest/ismb2014 SUPPLEMENTARY INFORMATION: Supplementary data are available at http://daweb.ism.ac.jp/yoshidalab/crest/ismb2014

TIGAR: transcript isoform abundance estimation method with gapped alignment of RNA-Seq data by variational Bayesian inference.

MOTIVATION: Many human genes express multiple transcript isoforms through alternative splicing, which greatly increases diversity of protein function. Although RNA sequencing (RNA-Seq) technologies have been widely used in measuring amounts of transcribed mRNA, accurate estimation of transcript isoform abundances from RNA-Seq data is challenging because reads often map to more than one transcript isoforms or paralogs whose sequences are similar to each other. RESULTS: We propose a statistical method to estimate transcript isoform abundances from RNA-Seq data. Our method can handle gapped alignments of reads against reference sequences so that it allows insertion or deletion errors within reads. The proposed method optimizes the number of transcript isoforms by variational Bayesian inference through an iterative procedure, and its convergence is guaranteed under a stopping criterion. On simulated datasets, our method outperformed the comparable quantification methods in inferring transcript isoform abundances, and at the same time its rate of convergence was faster than that of the expectation maximization algorithm. We also applied our method to RNA-Seq data of human cell line samples, and showed that our prediction result was more consistent among technical replicates than those of other methods. AVAILABILITY: An implementation of our method is available at http://github.com/nariai/tigar CONTACT: nariai@megabank.tohoku.ac.jp SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PAAQD: Predicting immunogenicity of MHC class I binding peptides using amino acid pairwise contact potentials and quantum topological molecular similarity descriptors.

Prediction of peptide immunogenicity is a promising approach for novel vaccine discovery. Conventionally, epitope prediction methods have been developed to accelerate the process of vaccine production by searching for candidate peptides from pathogenic proteins. However, recent studies revealed that peptides with high binding affinity to major histocompatibility complex molecules (MHCs) do not always result in high immunogenicity. Therefore, it is promising to predict the peptide immunogenicity rather than epitopes in order to discover new vaccines more effectively. To this end, we developed a novel T-cell reactivity predictor which we call PAAQD. Nonapeptides were encoded numerically, using combining information of amino acid pairwise contact potentials (AAPPs) and quantum topological molecular similarity (QTMS) descriptors. Encoded data were used in the construction of our classification model. Our numerical experiments suggested that the predictive performance of PAAQD is at least comparable with POPISK, one of the pioneering techniques for T-cell reactivity prediction. Also, our experiment suggested that the first and eighth positions of nonapeptides are the most important for immunogenicity and most of the anchor residues in epitope prediction were not important in T-cell reactivity prediction. The R implementation of PAAQD is available at http://pirun.ku.ac.th/~fsciiok/PAAQD.rar.

EpicCapo: epitope prediction using combined information of amino acid pairwise contact potentials and HLA-peptide contact site information.

BACKGROUND: Epitope identification is an essential step toward synthetic vaccine development since epitopes play an important role in activating immune response. Classical experimental approaches are laborious and time-consuming, and therefore computational methods for generating epitope candidates have been actively studied. Most of these methods, however, are based on sophisticated nonlinear techniques for achieving higher predictive performance. The use of these techniques tend to diminish their interpretability with respect to binding potential: that is, they do not provide much insight into binding mechanisms. RESULTS: We have developed a novel epitope prediction method named EpicCapo and its variants, EpicCapo(+) and EpicCapo(+REF). Nonapeptides were encoded numerically using a novel peptide-encoding scheme for machine learning algorithms by utilizing 40 amino acid pairwise contact potentials (referred to as AAPPs throughout this paper). The predictive performances of EpicCapo(+) and EpicCapo(+REF) outperformed other state-of-the-art methods without losing interpretability. Interestingly, the most informative AAPPs estimated by our study were those developed by Micheletti and Simons while previous studies utilized two AAPPs developed by Miyazawa & Jernigan and Betancourt & Thirumalai. In addition, we found that all amino acid positions in nonapeptides could effect on performances of the predictive models including non-anchor positions. Finally, EpicCapo(+REF) was applied to identify candidates of promiscuous epitopes. As a result, 67.1% of the predicted nonapeptides epitopes were consistent with preceding studies based on immunological experiments. CONCLUSIONS: Our method achieved high performance in testing with benchmark datasets. In addition, our study identified a number of candidates of promiscuous CTL epitopes consistent with previously reported immunological experiments. We speculate that our techniques may be useful in the development of new vaccines. The R implementation of EpicCapo(+REF) is available at http://pirun.ku.ac.th/~fsciiok/EpicCapoREF.zip. Datasets are available at http://pirun.ku.ac.th/~fsciiok/Datasets.zip.

SiGN-SSM: open source parallel software for estimating gene networks with state space models.

UNLABELLED: SiGN-SSM is an open-source gene network estimation software able to run in parallel on PCs and massively parallel supercomputers. The software estimates a state space model (SSM), that is a statistical dynamic model suitable for analyzing short time and/or replicated time series gene expression profiles. SiGN-SSM implements a novel parameter constraint effective to stabilize the estimated models. Also, by using a supercomputer, it is able to determine the gene network structure by a statistical permutation test in a practical time. SiGN-SSM is applicable not only to analyzing temporal regulatory dependencies between genes, but also to extracting the differentially regulated genes from time series expression profiles. AVAILABILITY: SiGN-SSM is distributed under GNU Affero General Public Licence (GNU AGPL) version 3 and can be downloaded at http://sign.hgc.jp/signssm/. The pre-compiled binaries for some architectures are available in addition to the source code. The pre-installed binaries are also available on the Human Genome Center supercomputer system. The online manual and the supplementary information of SiGN-SSM is available on our web site. CONTACT: tamada@ims.u-tokyo.ac.jp.

Prediction of carbohydrate-binding proteins from sequences using support vector machines.

Carbohydrate-binding proteins are proteins that can interact with sugar chains but do not modify them. They are involved in many physiological functions, and we have developed a method for predicting them from their amino acid sequences. Our method is based on support vector machines (SVMs). We first clarified the definition of carbohydrate-binding proteins and then constructed positive and negative datasets with which the SVMs were trained. By applying the leave-one-out test to these datasets, our method delivered 0.92 of the area under the receiver operating characteristic (ROC) curve. We also examined two amino acid grouping methods that enable effective learning of sequence patterns and evaluated the performance of these methods. When we applied our method in combination with the homology-based prediction method to the annotated human genome database, H-invDB, we found that the true positive rate of prediction was improved.

Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models.

MOTIVATION: Statistical inference of gene networks by using time-course microarray gene expression profiles is an essential step towards understanding the temporal structure of gene regulatory mechanisms. Unfortunately, most of the current studies have been limited to analysing a small number of genes because the length of time-course gene expression profiles is fairly short. One promising approach to overcome such a limitation is to infer gene networks by exploring the potential transcriptional modules which are sets of genes sharing a common function or involved in the same pathway. RESULTS: In this article, we present a novel approach based on the state space model to identify the transcriptional modules and module-based gene networks simultaneously. The state space model has the potential to infer large-scale gene networks, e.g. of order 10(3), from time-course gene expression profiles. Particularly, we succeeded in the identification of a cell cycle system by using the gene expression profiles of Saccharomyces cerevisiae in which the length of the time-course and number of genes were 24 and 4382, respectively. However, when analysing shorter time-course data, e.g. of length 10 or less, the parameter estimations of the state space model often fail due to overfitting. To extend the applicability of the state space model, we provide an approach to use the technical replicates of gene expression profiles, which are often measured in duplicate or triplicate. The use of technical replicates is important for achieving highly-efficient inferences of gene networks with short time-course data. The potential of the proposed method has been demonstrated through the time-course analysis of the gene expression profiles of human umbilical vein endothelial cells (HUVECs) undergoing growth factor deprivation-induced apoptosis. AVAILABILITY: Supplementary Information and the software (TRANS-MNET) are available at http://daweb.ism.ac.jp/~yoshidar/software/ssm/.

[A case of hepatocellular carcinoma with paraneoplastic hypercholesterolemia, thrombocytosis and hypoglycemia].

A 57 year-old woman was admitted to our hospital because of large hepatocellular carcinoma (HCC). Laboratory data showed hypercholesterolemia, thrombocytosis and hypoglycemia. Based on several examinations and the clinical course, we diagnosed HCC with paraneoplastic syndrome. It is rare that 3 paraneoplastic phenomena occur in a patient with HCC. In particular, paraneoplastic thrombocytosis is very rare in paraneoplastic syndrome associated with HCC.

Clustering samples characterized by time course gene expression profiles using the mixture of state space models.

We propose a novel method to classify samples where each sample is characterized by a time course gene expression profile. By exploiting the mixture of state space model, the proposed method addresses the following tasks: (1) clustering samples according to temporal patterns of gene expressions, (2) automatic detection of genes that discriminate identified clusters, (3) estimation of a restricted autoregressive coefficient for each cluster. We demonstrate the proposed method along with the cluster analysis of 53 multiple sclerosis patients under recombinant interferon beta therapy with the longitudinal time course expression profiles.