Project description:Lectins can also be classified according to their binding specificity or selectivity with carbohydrates. This type of classification is helpful for selection of lectins as structural probes in biomedical applications. This chapter summarizes the concept and the updated information regarding the specificity-based lectin classification. Lectins are functionally classified based on their relative binding reactivities with the structural units of carbohydrate or glyco-epitopes. They are grouped according to their monosaccharide specificities and then further sub-grouped based on their reactivities with more complex structures. Carbohydrate specificities of biomedically important lectins are classified into six groups according to their specificities to monosaccharides. The chapter introduces a practical platform of carbohydrate microarrays that is useful for lectin characterization and classification. Finally, the chapter discusses a few examples to illustrate the application of this technology in lectin-related experimental investigations.
Project description:A disease-specific biomarker (or biomarkers) is a characteristic reflecting a pathological condition in human body, which can be used as a diagnostic or prognostic tool for the clinical management. A urine-based biomarker(s) may provide a clinical value as attractive tools for clinicians to utilize in the clinical setting in particular to bladder diseases including bladder cancer and other bladder benign dysfunctions. Urine can be easily obtained by patients with no preparation or painful procedures required from patients' side. Currently advanced omics technologies and computational power identified potential omics-based novel biomarkers. An unbiased profiling based on transcriptomics, proteomics, epigenetics, metabolomics approaches et al. found that expression at RNA, protein, and metabolite levels are linked with specific bladder diseases and outcomes. In this review, we will discuss about the urine-based biomarkers reported by many investigators including us and how these biomarkers can be applied as a diagnostic and prognostic tool in clinical trials and patient care to promote bladder health. Furthermore, we will discuss how these promising biomarkers can be developed into a smart medical device and what we should be cautious about toward being used in real clinical setting.
Project description:Gangliosides in the brain play a crucial role in modulating the integrity of vertebrate central nervous system in a region-specific manner. However, to date, a comprehensive structural elucidation of complex intact ganglioside isomers has not been achieved, resulting in the elusiveness into related molecular mechanism. Here, we present a glycolipidomic approach for isomer-specific and brain region-specific profiling of the mouse brain. Considerable region-specificity and commonality in specific group of regions are highlighted. Notably, we observe a similarity in the abundance of major isomers, GD1a and GD1b, within certain regions, which provides significant biological implications with interpretation through the lens of a theoretical retrosynthetic state-transition network. Furthermore, A glycocentric-omics approaches using gangliosides and N-glycans reveal a remarkable convergence in spatial dynamics, providing valuable insight into molecular interaction network. Collectively, this study uncovers the spatial dynamics of intact glyco-conjugates in the brain, which are relevant to regional function and accelerates the discovery of potential therapeutic targets for brain diseases.
Project description:BackgroundGenuine Chinese medicine is produced from medicinal plant cultivated in a specific region and is of better quality and efficacy, more consistently qualified and famous than that from the same medicinal plant cultivated in other regions. The cultivating region of genuine medicinal plant is known as the genuine producing area. Yangchun City, which is in Guangdong Province of China, is a genuine producing area for the famous Chinese medicine Amomi Fructus (also called Sharen). Amomi Fructus is the ripe and dry fruit of the Zingiberaceae plant A. villosum Lour.. A. villosum was introduced from the Persian Gulf region and has been cultivated in China for over 1000 years. Until now there are no reports on screening for good germplasm of A. villosum.MethodsThe contents of volatile oil and bornyl acetate of Amomi Fructus from 14 populations were determined with GC method, and the relative contents of the main chemical components in the volatile oils were determined with GC-MS method. Evaluation and variance analysis of the comprehensive quality of the 14 samples were conducted by means of a multi-indicator entropy-weight TOPSIS model (Technique for Order Preference by Similarity to an Ideal Solution) combined with OPLS-DA (Orthogonal Partial Least Squares Discrimination Analysis) and HCA (Hierarchical Clustering Analysis). The ISSR (Inter-Simple Sequence Repeat) molecular marker technique and the UPGMA (unweighted pair-group method with arithmetic means) were employed to analyze the genetic relationship among A. villosum populations.ResultsThe contents of volatile oil and bornyl acetate differed significantly among the different populations, but the main chemical component in the volatile oil was the same in all the samples, which was bornyl acetate. OPLS-DA results showed that 9 indicators were the main factors influencing the quality differences among the 14 populations. The entropy-weight TOPSIS results showed that there were significant differences in the comprehensive qualities of the 12 populations from the genuine producing area. The best quality of fruit was found in the genuine producing area of Chunwan Town; the qualities of 33% of genuine fruits were lower than that of non-genuine fruits. Twenty-three DNA fragments were obtained by ISSR-PCR amplification using four ISSR primers, eleven of which were polymorphic loci, which accounted for 47.8%. The similarity coefficients (GS) of different populations of A. villosum ranged from 0.6087 to 0.9565.ConclusionThere are significant differences among different populations of A. villosum in terms of the kinds of major chemical components and their contents, comprehensive quality and genetic diversity. The germplasm resources of A. villosum are rich in the genuine producing area. It means superior germplasm could be selected in the area. The comprehensive quality of the fruit of A. villosum from the non-genuine producing area is better than some of that from genuine producing area, proving that the non-genuine producing area can also produce Amomi Fructus with excellent quality.
Project description:Prostate cancer (PCa) is a common malignant tumor with increasing incidence and high heterogeneity among males worldwide. In the era of big data and artificial intelligence, the paradigm of biomarker discovery is shifting from traditional experimental and small data-based identification toward big data-driven and systems-level screening. Complex interactions between genetic factors and environmental effects provide opportunities for systems modeling of PCa genesis and evolution. We hereby review the current research frontiers in informatics for PCa clinical translation. First, the heterogeneity and complexity in PCa development and clinical theranostics are introduced to raise the concern for PCa systems biology studies. Then biomarkers and risk factors ranging from molecular alternations to clinical phenotype and lifestyle changes are explicated for PCa personalized management. Methodologies and applications for multi-dimensional data integration and computational modeling are discussed. The future perspectives and challenges for PCa systems medicine and holistic healthcare are finally provided.
Project description:This study describes and validates a new method for metagenomic biomarker discovery by way of class comparison, tests of biological consistency and effect size estimation. This addresses the challenge of finding organisms, genes, or pathways that consistently explain the differences between two or more microbial communities, which is a central problem to the study of metagenomics. We extensively validate our method on several microbiomes and a convenient online interface for the method is provided at http://huttenhower.sph.harvard.edu/lefse/.
Project description:As part of the Dystrophia Myotonica Biomarker Discovery Initiative (DMBDI) a dataset was obtained from 35 participants, including 31 Myotonic Dystrophy type 1 (DM1) cases and four unaffected controls. All DM1 cases in this research were heterozygous for the abnormally expanded CTG repeat. The mode of the length of the DM1 CTG expansion (Modal Allele Length, MAL) was determined by small-pool PCR of blood DNA for 35/36 patients. For this work we did not attempt to measure the repeat length from muscle, due to a very high degree of repeat instability in muscle cells, and associated difficulties in its experimental measurement. One patient refused blood donation. For each of the 35 blood-donating patients mRNA expression profiling of blood was performed using Affymetrix GeneChip™ Human Exon 1.0 ST microarray. For 28 of 36 patients a successful quadriceps muscle biopsy was obtained. The muscle tissue was mRNA profiled using the same type of microarray. In total, a complete set of samples (blood and muscle) was obtained for 27 of 36 patients; samples were given a disease staging score based on muscle impairment rating. mRNA profiling was carried out by the GeneLogic service lab (on a fee-for-service basis) using standard Affymetrix hybridisation protocol.
Project description:BackgroundBiomarker identification is one of the major and important goal of functional genomics and translational medicine studies. Large scale -omics data are increasingly being accumulated and can provide vital means for the identification of biomarkers for the early diagnosis of complex disease and/or for advanced patient/diseases stratification. These tasks are clearly interlinked, and it is essential that an unbiased and stable methodology is applied in order to address them. Although, recently, many, primarily machine learning based, biomarker identification approaches have been developed, the exploration of potential associations between biomarker identification and the design of future experiments remains a challenge.MethodsIn this study, using both simulated and published experimentally derived datasets, we assessed the performance of several state-of-the-art Random Forest (RF) based decision approaches, namely the Boruta method, the permutation based feature selection without correction method, the permutation based feature selection with correction method, and the backward elimination based feature selection method. Moreover, we conducted a power analysis to estimate the number of samples required for potential future studies.ResultsWe present a number of different RF based stable feature selection methods and compare their performances using simulated, as well as published, experimentally derived, datasets. Across all of the scenarios considered, we found the Boruta method to be the most stable methodology, whilst the Permutation (Raw) approach offered the largest number of relevant features, when allowed to stabilise over a number of iterations. Finally, we developed and made available a web interface ( https://joelarkman.shinyapps.io/PowerTools/ ) to streamline power calculations thereby aiding the design of potential future studies within a translational medicine context.ConclusionsWe developed a RF-based biomarker discovery framework and provide a web interface for our framework, termed PowerTools, that caters the design of appropriate and cost-effective subsequent future omics study.
Project description:Existing methods for the prediction of immunologically active T-cell epitopes are based on the amino acid sequence or structure of pathogen proteins. Additional information regarding the locations of epitopes may be acquired by considering the evolution of viruses in hosts with different immune backgrounds. In particular, immune-dependent evolutionary patterns at sites within or near T-cell epitopes can be used to enhance epitope identification. We have developed a mutation-selection model of T-cell epitope evolution that allows the human leukocyte antigen (HLA) genotype of the host to influence the evolutionary process. This is one of the first examples of the incorporation of environmental parameters into a phylogenetic model and has many other potential applications where the selection pressures exerted on an organism can be related directly to environmental factors. We combine this novel evolutionary model with a hidden Markov model to identify contiguous amino acid positions that appear to evolve under immune pressure in the presence of specific host immune alleles and that therefore represent potential epitopes. This phylogenetic hidden Markov model provides a rigorous probabilistic framework that can be combined with sequence or structural information to improve epitope prediction. As a demonstration, we apply the model to a data set of HIV-1 protein-coding sequences and host HLA genotypes.
Project description:The management of cancer-associated thrombosis (CAT) is an evolving area. With the use of direct oral anticoagulants as a new option in the management of CAT, clinicians now face several choices for the individual cancer patient with venous thromboembolism. A personalized approach, matching the right drug to the right patient, based on drug properties, efficacy and safety, side effect profile of each drug, and patient values and preference, will probably supplant the one size fits all approach of use of only low-molecular-weight heparin in the near future. We herein present eight translational, clinical research, and review articles on recent advances in the management of CAT published in the Special Issue "Treatment for Cancer-Associated Thrombosis" of Cancers. For now, a multidisciplinary patient-centered approach involving a close cooperation between oncologists and other specialists is warranted to guide clinical decision making and optimize the treatment of VTE in cancer patient.