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Agilent Technologies supports Professor's work developing transformative NMR applications for structural biology.

Alchemia announces multi-target drug discovery collaboration with AstraZeneca on VAST discovery platform.

DSM and DecImmune Therapeutics sign agreement to develop N2 pathway blocking antibody.

Novel technology from Columbia University for Neurobiological Research.

A new EU-funded industry-academia drug discovery partnership targets challenging kinases.

Avian Influenza A (H7N9) threatens, GenScript supports researchers in impending battle.

JAPAN – Study notes widening disease transmission by monkeys.

MALAYSIA – Drug-resistant malaria threatens control programs.

SINGAPORE – Novel gene predicts both breast cancer relapse and response to chemotherapy.

SINGAPORE – New drug formulations to boost fight against respiratory illnesses and antibiotic-resistant superbugs.

SINGAPORE – Zebrafish genes linked to human respiratory diseases.

SINGAPORE – Singapore researchers discover a gene that increases incidence of acute myelogenous leukemia.

SINGAPORE – Scientists develop green tea-based ‘missiles’ to kill cancer cells more effectively.

UNITED STATES – Large study reveals new genetic variants that raise risk for prostate cancer.

UNITED STATES – Math model designed to replace invasive kidney biopsy for lupus patients.

UNITED STATES – Mayo Clinic researchers reveal pathway that contributes to Alzheimer's disease.

UNITED STATES – Immunotherapy combination demonstrates superior anti-cancer activity.

SINGAPORE – Singapore eHealth Innovations Summit Announces the First EMRAM Stage 7 Hospital in Singapore and Emphasized Technology as Transformative Agent in Specialty Functions.

TAIWAN – Health2Sync Strategically Partners with Taiwan's Ministry of Health and Welfare in Asia's First Government Supported Online Diabetes Care Program.

UNITED STATES – Scientists Identify Protein Involved in Restoring Effectiveness of Common Treatment for Breast Cancer.

UNITED STATES – Scientists Reveal How Signals from Pathogenic Bacteria Reach Danger Sensors of Cells.

UNITED STATES – Scientists Find New Path in Brain to Ease Depression.

UNITED STATES – Tips for Living a Heart Healthy Lifestyle.

CANADA – Review Suggests Eating Oats Can Lower Cholesterol as Measured by a Variety of Markers.

SOUTH KOREA – CSA Group Opens Highly Advanced Electro - Medical Laboratory in Seoul.

AUSTRALIA – Cynata’s Technology Significant Efficacy in Preclinical Asthma Study.

INDIA – Essilor Launches ‘Love to See Change’ Campaign to Educate People about Need to Preserve Visual Health.

Enzymes catalyze diverse biochemical reactions and are building blocks of cellular and metabolic pathways. Data and metadata of enzymes are distributed across databases and are archived in various formats. The enzyme databases provide utilities for efficient searches and downloading enzyme records in batch mode but do not support organism-specific extraction of subsets of data. Users are required to write scripts for parsing entries for customized data extraction prior to downstream analysis. Integrated Customized Extraction of Enzyme Data (iCEED) has been developed to provide organism-specific customized data extraction utilities for seven commonly used enzyme databases and brings these resources under an integrated portal. iCEED provides dropdown menus and search boxes using typehead utility for submission of queries as well as enzyme class-based browsing utility. A utility to facilitate mapping and visualization of functionally important features on the three-dimensional (3D) structures of enzymes is integrated. The customized data extraction utilities provided in iCEED are expected to be useful for biochemists, biotechnologists, computational biologists, and life science researchers to build curated datasets of their choice through an easy to navigate web-based interface. The integrated feature visualization system is useful for a fine-grained understanding of the enzyme structure–function relationship. Desired subsets of data, extracted and curated using iCEED can be subsequently used for downstream processing, analyses, and knowledge discovery. iCEED can also be used for training and teaching purposes.

In the research category of bio-informatics, gene interaction and biochemical reactions constitute a rather complicated sub-category. This area of research presents a big challenge for biologists. Determining biological pathways, and the complex chemical reactions involved, is very difficult. Nevertheless these research results are very valuable. Harnessing the calculating power of computer networks and freely circulating information, computer-drawn graphs can be used to express biological pathways, and thus provide biologists with a powerful tool to present their research. In this paper, JAVA programming language combined with extendable markup language is used to develop a bio-pathway drawing software tool that is connected to a gene database and gene expression analysis. Two currently available genetic pathway software tools, PathwayEdit and GenMAPP, were compared to the newly developed software. The participants involved in testing the software expressed a highly affirmative response to the new system.

The enterovirus 71 infection is associated with severe neurological disease in several clinical researches; however, the detailed gene network mechanisms of enterovirus 71-infected cells remain unclear at present. We present a new approach integrating microarray expression data, KEGG database, gene ontology (GO), and OMIM information for efficiently deciphering pathways of enterovirus 71-infected cells. This approach includes the following steps: (1) profiling the significant gene-gene interaction through pathway database, (2) utilizing Fisher's exact test to analyze pathway information and to rank the first ten significant pathways, (3) annotating functions of genes in the pathways through gene ontology, (4) investigating related genes and perhaps concern diseases by referring to OMIM information. Our findings illustrate at least three possible pathways in enterovirus 71-infected human neural SF268 cells: Jak-STAT signaling: cell cycle and apoptosis. Furthermore, we show that some genes are associated with neural development and neural apoptosis, such as c-Myc, BAX, NGF, and CPP32. These would be useful for profiling disease mechanisms and host response to virus in future research.

We present a new direction of research, which deploys Text Mining technologies to construct and maintain data bases organized in the form of pathway, by associating parts of papers with relevant portions of a pathway and vice versa. In order to materialize this scenario, we present two annotated corpora. The first, Event Annotation, identifies the spans of text in which biological events are reported, while the other, Pathway Annotation, associates portions of papers with specific parts in a pathway.

Potential biological effects initiated by exposure to ionizing radiation are described in some detail, including the characteristics of radiation sources, radiation fields, dosimetry, and fundamental radiation protection quantities and units. Special attention is paid to exposure pathways and the risk assessment of radiation exposure to the human body in terms of relevant health risks reflecting biological effects resulting from external and internal radiation exposure. Some particulars are presented to illustrate the nature and severity of stochastic and deterministic effects caused by the exposure. The chapter then presents an overview of pertinent radiation protection requirements and monitoring methods for the evaluation of exposure of persons in regular and emergency situations, as well as the assessment of environmental radioactive contamination inflicted by releases of radioactivity from facilities where radioactive materials are used or stored, taking into account their possible damage or destruction resulting from accidents and potential terrorist attacks, sabotage, or other malevolent actions. In all these cases, discharges of radioactive substances from various sources, including those used in medicine, industry, and research facilities, should be considered to obtain reliable information about specific radiation routes. Radioactive materials, in the form of solid particulates, aerosols, and gases, disperse in several ways known as exposure pathways. It is essential that the description of both exposure pathways and their characteristics be expressed in officially recommended and generally accepted quantities and units. In general, the final part of an exposure pathway refers to how a person can come into contact with hazardous substances, including materials containing radionuclides.

Radionuclides are present in the environment or result from human activities, such as the use of radiation or nuclear technologies. The presence of radionuclides in foods can pose certain health risks, and their toxicity depends on several factors, including the intake of radionuclides, the type of radionuclide, its concentration, and the specific characteristics of the food. Under typical situations, the contributions of internal exposure to the human body from food or water, where only trace concentrations of usually natural radionuclides are present, are minor in most cases. Slightly different situations may occur when food, due to radionuclides from man-made sources used in various applications in industry and medicine or radioactivity induced during radiation sterilization of food, shows an increased level compared with the natural background concentrations. But even here, there is no reason to consider the impact on living organisms to be serious since the increase in exposure levels is only a fraction of exposure due to the natural background.

On the other hand, however, the concentration may be higher in some cases when the food is produced from plants or animals that are heavily contaminated. Specific aspects of radioactivity or, in general, ionizing radiation (hereinafter, simply, radiation), which can be detected even at particularly small levels thanks to the availability of very sensitive instrumentation, have to be stressed here. Radiation monitors are able to measure even extremely insignificant changes within the fluctuation level of natural radiation. When the instrument shows some results close to the background level or even up to several times this level, there is no need to be concerned since the impact of such exposure on a person is almost negligible in comparison with other, much more dangerous situations we face daily in our typical living or working environment caused by many other, much more hazardous agents. The chapter will present an overview of the health risks attributed to the toxicity resulting from exposure to foods contaminated by radionuclides of various origins.

Flowering time (heading date) is a major determinant of regional and seasonal adaptation of cultivated rice. A large amount of variation is observed in heading date and photoperiodic response among rice cultivars and strains, including wild relatives. Quantitative trait locus (QTL) analyses of progeny derived from several cross combinations of rice cultivars suggest that more than 15 loci are involved in heading date. Map-based cloning has been performed on several QTLs for photoperiodic response. We have demonstrated that Heading date 1 (Hd1) is an ortholog of CONSTANS (CO) in Arabidopsis and is involved in the promotion of heading under short-day (SD) conditions and inhibition under long-day (LD) conditions. Hd6 is involved in inhibition under LD conditions and encodes the alpha-subunit of protein kinase CK2. Hd3a shows a high level of similarity to Arabidopsis FT (flowering time) and functions as a flowering inducer. Early heading date 1 (Ehd1) is involved in promotion under SD conditions and encodes a B-type response regulator. Hd5 is involved in inhibition under LD conditions and encodes a putative subunit of a CCAAT-box-binding protein. Late heading date 4 (Lhd4) is involved in inhibition under LD conditions and encodes a protein with a CCT motif. The combining of information from genetic and sequencing analyses reveals that the combination of natural alleles with loss or gain of function at particular QTLs, such as Hd1, Hd5, Hd6, Ehd1, and Lhd4, seems to generate a wide range of continuous variation in photoperiodic flowering in rice. These genetic and molecular analyses have allowed us to propose a pathway for the genetic control of photoperiodic flowering in rice, and analysis of the mRNA levels of genes in near-isogenic lines has clearly revealed their hierarchical relationship in the genetic control pathway. Identification and expression analyses of genes suggest the conservation and divergence of various features in the photoperiodic control of flowering in rice, an SD plant, and Arabidopsis, an LD plant.

A pathway is a collection of two or more proteins/molecules connected by their interactions within and around a cell. We study the informatics and evolutionary issues of pathways. Similar to the definition of homology in the comparison of nucleotide and protein sequences, we define homologous pathways as pathways that are evolved from the same ancestral pathway. We first present a survey of existing pathway databases and discuss their format of pathway representation. Then, our pathway representation, the SLIPR format, is presented. It is a semilinear graphic representation of nodes (proteins) and modes (interactions). Pathways in SLIPR format enable pathway comparisons for evolutionary relationship and large-scale pathway database searches. We also discuss how one can map out orthologous pathways, achieving a predictive power on functional assignment of novel genes, once the pathway is understood well-enough in a closely-related species.

Recent precision medicine initiatives have led to the expectation of improved clinical decisionmaking anchored in genomic data science. However, over the last decade, only a handful of new single-gene product biomarkers have been translated to clinical practice (FDA approved) in spite of considerable discovery efforts deployed and a plethora of transcriptomes available in the Gene Expression Omnibus. With this modest outcome of current approaches in mind, we developed a pilot simulation study to demonstrate the untapped benefits of developing disease detection methods for cases where the true signal lies at the pathway level, even if the pathway’s gene expression alterations may be heterogeneous across patients. In other words, we relaxed the crosspatient homogeneity assumption from the transcript level (cohort assumptions of deregulated gene expression) to the pathway level (assumptions of deregulated pathway expression). Furthermore, we have expanded previous single-subject (SS) methods into cohort analyses to illustrate the benefit of accounting for an individual’s variability in cohort scenarios. We compare SS and cohort-based (CB) techniques under 54 distinct scenarios, each with 1,000 simulations, to demonstrate that the emergence of a pathway-level signal occurs through the summative effect of its altered gene expression, heterogeneous across patients. Studied variables include pathway gene set size, fraction of expressed gene responsive within gene set, fraction of expressed gene responsive up- vs down-regulated, and cohort size. We demonstrated that our SS approach was uniquely suited to detect signals in heterogeneous populations in which individuals have varying levels of baseline risks that are simultaneously confounded by patient-specific “genome -by-environment” interactions (G×E). Area under the precision-recall curve of the SS approach far surpassed that of the CB (1^st quartile, median, 3^rd quartile: SS = 0.94, 0.96, 0.99; CB= 0.50, 0.52, 0.65). We conclude that single-subject pathway detection methods are uniquely suited for consistently detecting pathway dysregulation by the inclusion of a patient’s individual variability. http://www.lussiergroup.org/publications/PathwayMarker/

A number of genetic investigations in autism spectrum disorders (ASD) have identified single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) that are associated with this neurodevelopmental disorder. However, as research progresses the evidence points to these genetic alterations likely being confined to specific families or populations with their penetrance in larger populations being somewhat lower. This has led researchers to postulate that the genetics of ASD may consist of an additive collection of common genetic variations that when considered in combination with each other are able to explain more of the heritability of the disorder. In addition to taking a more global view of ASD genetics the contribution of these alterations to the clinical landscape of ASD must also be considered. This chapter begins by summarising non-genetic and genetic findings relating to diagnosing or classifying individuals falling within the ASD continuum. We then discuss the utility of using gene pathway analysis to build a genetic classifier for ASD based on our recent published work including strategies to control for population stratification and inclusion of both risk and protective contributors of ASD status. Finally, we will discuss how this approach may be applied to other neuropsychiatric disorders and ways to improve on classification accuracy.

Enormous efforts of whole exome and genome sequencing from hundreds to thousands of patients have provided the landscape of somatic genomic alterations in many cancer types to distinguish between driver mutations and passenger mutations. Driver mutations show strong associations with cancer clinical outcomes such as survival. However, due to the heterogeneity of tumors, somatic mutation profiles are exceptionally sparse whereas other types of genomic data such as miRNA or gene expression contain much more complete data for all genomic features with quantitative values measured in each patient. To overcome the extreme sparseness of somatic mutation profiles and allow for the discovery of combinations of somatic mutations that may predict cancer clinical outcomes, here we propose a new approach for binning somatic mutations based on existing biological knowledge. Through the analysis using renal cell carcinoma dataset from The Cancer Genome Atlas (TCGA), we identified combinations of somatic mutation burden based on pathways, protein families, evolutionary conversed regions, and regulatory regions associated with survival. Due to the nature of heterogeneity in cancer, using a binning strategy for somatic mutation profiles based on biological knowledge will be valuable for improved prognostic biomarkers and potentially for tailoring therapeutic strategies by identifying combinations of driver mutations.