ABSTRACT: Structural genomics (SG) programs have developed during the last decade many novel methodologies for faster and more accurate structure determination. These new tools and approaches led to the determination of thousands of protein structures. The generation of enormous amounts of experimental data resulted in significant improvements in the understanding of many biological processes at molecular levels. However, the amount of data collected so far is so large that traditional analysis methods are limiting the rate of extraction of biological and biochemical information from 3D models. This situation has prompted us to review the challenges that remain unmet by SG, as well as the areas in which the potential impact of SG could exceed what has been achieved so far.
Project description:While three dimensional structures have long been used to search for new drug targets, only a fraction of new drugs coming to the market has been developed with the use of a structure-based drug discovery approach. However, the recent years have brought not only an avalanche of new macromolecular structures, but also significant advances in the protein structure determination methodology only now making their way into structure-based drug discovery. In this paper, we review recent developments resulting from the Structural Genomics (SG) programs, focusing on the methods and results most likely to improve our understanding of the molecular foundation of human diseases. SG programs have been around for almost a decade, and in that time, have contributed a significant part of the structural coverage of both the genomes of pathogens causing infectious diseases and structurally uncharacterized biological processes in general. Perhaps most importantly, SG programs have developed new methodology at all steps of the structure determination process, not only to determine new structures highly efficiently, but also to screen protein/ligand interactions. We describe the methodologies, experience and technologies developed by SG, which range from improvements to cloning protocols to improved procedures for crystallographic structure solution that may be applied in "traditional" structural biology laboratories particularly those performing drug discovery. We also discuss the conditions that must be met to convert the present high-throughput structure determination pipeline into a high-output structure-based drug discovery system.
Project description:The explosion of the size of the universe of known protein sequences has stimulated two complementary approaches to structural mapping of these sequences: theoretical structure prediction and experimental determination by structural genomics (SG). In this work, we assess the accuracy of structure prediction by two automated template-based structure prediction metaservers (genesilico.pl and bioinfo.pl) by measuring the structural similarity of the predicted models to corresponding experimental models determined a posteriori. Of 199 targets chosen from SG programs, the metaservers predicted the structures of about a fourth of them "correctly." (In this case, "correct" was defined as placing more than 70 % of the alpha carbon atoms in the model within 2 Å of the experimentally determined positions.) Almost all of the targets that could be modeled to this accuracy were those with an available template in the Protein Data Bank (PDB) with more than 25 % sequence identity. The majority of those SG targets with lower sequence identity to structures in the PDB were not predicted by the metaservers with this accuracy. We also compared metaserver results to CASP8 results, finding that the models obtained by participants in the CASP competition were significantly better than those produced by the metaservers.
Project description:Tea is among the world's most widely consumed non-alcoholic beverages and possesses enormous economic, health, and cultural values. It is produced from the cured leaves of tea plants, which are important evergreen crops globally cultivated in over 50?countries. Along with recent innovations and advances in biotechnologies, great progress in tea plant genomics and genetics has been achieved, which has facilitated our understanding of the molecular mechanisms of tea quality and the evolution of the tea plant genome. In this review, we briefly summarize the achievements of the past two decades, which primarily include diverse genome and transcriptome sequencing projects, gene discovery and regulation studies, investigation of the epigenetics and noncoding RNAs, origin and domestication, phylogenetics and germplasm utilization of tea plant as well as newly developed tools/platforms. We also present perspectives and possible challenges for future functional genomic studies that will contribute to the acceleration of breeding programs in tea plants.
Project description:Management and prognosis of disease requires the accurate determination of specific biomarkers indicative of normal or disease-related biological processes or responses to therapy. Moreover since multiple determinations of biomarkers have demonstrated to provide more accurate information than individual determinations to assist the clinician in prognosis and diagnosis, the detection of several clinical biomarkers by using the same analytical device hold enormous potential for early detection and personalized therapy and will simplify the diagnosis providing more information in less time. In this field, electrochemical immunosensors have demonstrated to offer interesting alternatives against conventional strategies due to their simplicity, fast response, low cost, high sensitivity and compatibility with multiplexed determination, microfabrication technology and decentralized determinations, features which made them very attractive for integration in point-of-care (POC) devices. Therefore, in this review, the relevance and current challenges of multiplexed determination of clinical biomarkers are briefly introduced, and an overview of the electrochemical immunosensing platforms developed so far for this purpose is given in order to demonstrate the great potential of these methodologies. After highlighting the main features of the selected examples, the unsolved challenges and future directions in this field are also briefly discussed.
Project description:Yellowfin tuna, Thunnus albacares, is one of the most important seafood commodities in the world. Despite its great biological and economic importance, conflicting evidence arises from classical genetic and tagging studies concerning the yellowfin tuna population structure at local and global oceanic scales. Access to more powerful and cost effective genetic tools would represent the first step towards resolving the population structure of yellowfin tuna across its distribution range. Using a panel of 939 neutral Single Nucleotide Polymorphisms (SNPs), and the most comprehensive data set of yellowfin samples available so far, we found genetic differentiation among the Atlantic, Indian and Pacific oceans. The genetic stock structure analysis carried out with 33 outlier SNPs, putatively under selection, identified discrete populations within the Pacific Ocean and, for the first time, also within the Atlantic Ocean. Stock assessment approaches that consider genetic differences at neutral and adaptive genomic loci should be routinely implemented to check the status of the yellowfin tuna, prevent illegal trade, and develop more sustainable management measures.
Project description:BACKGROUND:One-anastomosis gastric bypass (OAGB) and single-anastomosis duodenal switch (SADS) have become increasingly popular weight loss strategies. However, data directly comparing the effectiveness of these procedures with Roux-en-Y gastric bypass (RYGB) and vertical sleeve gastrectomy (SG) are limited. OBJECTIVES:To examine the metabolic outcomes of OAGB, SADS, RYGB, and SG in a controlled rodent model. SETTING:Academic research laboratory, United States. METHODS:Surgeries were performed in diet-induced obese Long-Evans rats, and metabolic outcomes were monitored before and for 15 weeks after surgery. RESULTS:All bariatric procedures induced weight loss compared with sham that lasted throughout the course of the study. The highest percent fat loss occurred after OAGB and RYGB. All bariatric procedures had improved glucose dynamics associated with an increase in insulin (notably OAGB and SADS) and/or glucagon-like protein-1 secretion. Circulating cholesterol was reduced in OAGB, SG, and RYGB. OAGB and SG additionally decreased circulating triglycerides. Liver triglycerides were most profoundly reduced after OAGB and RYGB. Circulating iron levels were decreased in all surgical groups, associated with a decreased hematocrit value and increased reticulocyte count. The fecal microbiome communities of OAGB, SADS, and RYGB were significantly altered; however, SG exhibited no change in microbiome diversity or composition. CONCLUSIONS:These data support the use of the rat for modeling bariatric surgical procedures and highlight the ability of the OAGB to meet or exceed the metabolic improvements of RYGB. These data point to the likelihood that each surgery accomplishes metabolic improvements through both overlapping and distinct mechanisms and warrants further research.
Project description:As environmental scientists working in countries whose COVID-linked deaths already exceed their military casualties from all campaigns since 1945, we believe there are significant messages from the handling of this horrific disease for efforts addressing the enormous challenges posed by the ongoing extinction and climate emergencies.
Project description:Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains.
Project description:The enormous amount of freely accessible functional genomics data is an invaluable resource for interrogating the biological function of multiple DNA-interacting players and chromatin modifications by large-scale comparative analyses. However, in practice, interrogating large collections of public data requires major efforts for (i) reprocessing available raw reads, (ii) incorporating quality assessments to exclude artefactual and low-quality data, and (iii) processing data by using high-performance computation. Here, we present qcGenomics, a user-friendly online resource for ultrafast retrieval, visualization, and comparative analysis of tens of thousands of genomics datasets to gain new functional insight from global or focused multidimensional data integration.
Project description:Cancer is a multifactorial disease with increasing incidence. There are more than 100 different cancer types, defined by location, cell of origin, and genomic alterations that influence oncogenesis and therapeutic response. This heterogeneity between tumors of different patients and also the heterogeneity within the same patient's tumor pose an enormous challenge to cancer treatment. In this review, we explore tumor heterogeneity on the longitudinal and the latitudinal axis, reviewing current and future approaches to study this heterogeneity and their potential to support oncologists in tailoring a patient's treatment regimen. We highlight how the ideal of precision oncology is reaching far beyond the knowledge of genetic variants to inform clinical practice and discuss the technologies and strategies already available to improve our understanding and management of heterogeneity in cancer treatment. We will focus on integrating multi-omics technologies with suitable in vitro models and their proficiency in mimicking endogenous tumor heterogeneity.