Biological data becomes computer literate: new advances in bioinformatics

A Review of Pharmacological and Toxicological Effects of Sophora tonkinensis with Bioinformatics Prediction

Sophora tonkinensis Gagnep. (ST) (Fabaceae) is distributed chiefly in south-central and southeast China and Vietnam. In traditional Chinese medicine theory, the root and rhizome of ST are toxic and mainly used in the treatment of pharyngeal and laryngeal diseases. Modern studies provide new insights into the pharmacological and toxicological aspects of ST. The pharmacological and toxicological properties of ST were reviewed in this paper based on the literature from Google Scholar and CNKI, and the bioinformatics platforms were applied to explore the pharmacological and toxicological potentials of ST. The results of the literature analysis showed that ST has hepatoprotective, immunomodulatory, and anticancer effects and produces obvious toxicity to the liver and nervous and cardiovascular system. The results of bioinformatics showed that the compounds from ST may be applied to the treatment of cancer and digestive and nervous system diseases and show the possibility to cause hematotoxicity, neurotoxicity, and immunotoxicity. The present review demonstrates that attention should be paid to the potential toxicity of ST in the treatment of diseases and provides the reference for the subsequent pharmacological toxicological studies on the mechanism and chemical basis of ST.

The Research on the Osmotic Stress Gene Mining Model Based on the Arabidopsis Genome

In the field of the bioinformatics, during osmotic stress response genes mining processing, it is also very crucial to verify experimental data obtained in the course of complex experiments by using the computer. Aim of this paper is taking Arabidopsis thaliana as the experimental crop, designing technology roadmap, taking advantage of the skills of function and programming, then designing algorithms. After using the program to predict the transcription start point, the promoter sequence is extracted and simplified. In addition, different alignment methods are classified. Then, comparing the promoter sequence with the cis-element and using the formula for further processing. Finally, get the probability P value, which provide further help to experts and scholars on the basis of probability values to determine the correlation between the osmotic stress. The experimental data source of chromosomal sequences is received from Genbank database files, and cis-element sequence that associated with osmotic stress is collected from TRANSFAC and TRRD database. From this, the authors not only used the Arabidopsis promoter as the experimental data, but also use a variety of eukaryotic promoters include promoters GhNHX1 rice, cotton OsNHX1 promoter, as a comparison. Wherein the data obtained in the biological laboratory, which in the course of running the program, 70% have been verified. P value close to 0.8, this article will be treated as the promoter contains osmotic stress cis-elements, the expression of gene induced by osmotic stress. For thaliana, cotton and rice, programs running average time was 51s, 72s and 114s. Through the use of some commonly used bioinformatics gene mining algorithms, MEME algorithm and BioProspector algorithm for the same data have been processed, the average running time of the system is increasing with the increase of data. Running time of MEME algorithm increases from 60s to reach 198s, BioProspector algorithm increases from 45s to 150s model process used herein were 50s, 75s, 110s, 135s. At the same time, the authors can see in the three algorithms, the model algorithm used herein with respect to the first two more optimized. To ensure the accuracy rate, meanwhile has high speed and stabilization of higher.

Why should we investigate the morphological disparity of plant clades?

BACKGROUND

Disparity refers to the morphological variation in a sample of taxa, and is distinct from diversity or taxonomic richness. Diversity and disparity are fundamentally decoupled; many groups attain high levels of disparity early in their evolution, while diversity is still comparatively low. Diversity may subsequently increase even in the face of static or declining disparity by increasingly fine sub-division of morphological 'design' space (morphospace). Many animal clades reached high levels of disparity early in their evolution, but there have been few comparable studies of plant clades, despite their profound ecological and evolutionary importance. This study offers a prospective and some preliminary macroevolutionary analyses.

METHODS

Classical morphometric methods are most suitable when there is reasonable conservation of form, but lose traction where morphological differences become greater (e.g. in comparisons across higher taxa). Discrete character matrices offer one means to compare a greater diversity of forms. This study explores morphospaces derived from eight discrete data sets for major plant clades, and discusses their macroevolutionary implications.

KEY RESULTS

Most of the plant clades in this study show initial, high levels of disparity that approach or attain the maximum levels reached subsequently. These plant clades are characterized by an initial phase of evolution during which most regions of their empirical morphospaces are colonized. Angiosperms, palms, pines and ferns show remarkably little variation in disparity through time. Conifers furnish the most marked exception, appearing at relatively low disparity in the latest Carboniferous, before expanding incrementally with the radiation of successive, tightly clustered constituent sub-clades.

CONCLUSIONS

Many cladistic data sets can be repurposed for investigating the morphological disparity of plant clades through time, and offer insights that are complementary to more focused morphometric studies. The unique structural and ecological features of plants make them ideally suited to investigating intrinsic and extrinsic constraints on disparity.

An integrated multi-omics study revealed metabolic alterations underlying the effects of coffee consumption

Many epidemiological studies have indicated that coffee consumption may reduce the risks of developing obesity and diabetes, but the underlying mechanisms of these effects are poorly understood. Our previous study revealed the changes on gene expression profiles in the livers of C57BL/6J mice fed a high-fat diet containing three types of coffee (caffeinated, decaffeinated and green unroasted coffee), using DNA microarrays. The results revealed remarkable alterations in lipid metabolism-related molecules which may be involved in the anti-obesity effects of coffee. We conducted the present study to further elucidate the metabolic alterations underlying the effects of coffee consumption through comprehensive proteomic and metabolomic analyses. Proteomics revealed an up-regulation of isocitrate dehydrogenase (a key enzyme in the TCA cycle) and its related proteins, suggesting increased energy generation. The metabolomics showed an up-regulation of metabolites involved in the urea cycle, with which the transcriptome data were highly consistent, indicating accelerated energy expenditure. The TCA cycle and the urea cycle are likely be accelerated in a concerted manner, since they are directly connected by mutually providing each other's intermediates. The up-regulation of these pathways might result in a metabolic shift causing increased ATP turnover, which is related to the alterations of lipid metabolism. This mechanism may play an important part in the suppressive effects of coffee consumption on obesity, inflammation, and hepatosteatosis. This study newly revealed global metabolic alterations induced by coffee intake, providing significant insights into the association between coffee intake and the prevention of type 2 diabetes, utilizing the benefits of multi-omics analyses.

Multipurpose instantaneous microarray detection of acute encephalitis causing viruses and their expression profiles

Detection of multiple viruses is important for global analysis of gene or protein content and expression, opening up new prospects in terms of molecular and physiological systems for pathogenic diagnosis. Early diagnosis is crucial for disease treatment and control as it reduces inappropriate use of antiviral therapy and focuses surveillance activity. This requires the ability to detect and accurately diagnose infection at or close to the source/outbreak with minimum delay and the need for specific, accessible point-of-care diagnosis able to distinguish causative viruses and their subtypes. None of the available viral diagnostic assays combine a point-of-care format with the complex capability to identify a large range of human and animal viruses. Microarray detection provides a useful, labor-saving tool for detection of multiple viruses with several advantages, such as convenience and prevention of cross-contamination of polymerase chain reaction (PCR) products, which is of foremost importance in such applications. Recently, real-time PCR assays with the ability to confirm the amplification product and quantitate the target concentration have been developed. Furthermore, nucleotide sequence analysis of amplification products has facilitated epidemiological studies of infectious disease outbreaks and monitoring of treatment outcomes for infections, in particular for viruses that mutate at high frequency. This review discusses applications of microarray technology as a potential new tool for detection and identification of acute encephalitis-causing viruses in human serum, plasma, and cell cultures.

Methodology for Constructing Problem Definitions in Bioinformatics

Motivation

A recurrent criticism is that certain bioinformatics tools do not account for crucial biology and therefore fail answering the targeted biological question. We posit that the single most important reason for such shortcomings is an inaccurate formulation of the computational problem.

Results

Our paper describes how to define a bioinformatics problem so that it captures both the underlying biology and the computational constraints for a particular problem. The proposed model delineates comprehensively the biological problem and conducts an item-by-item bioinformatics transformation resulting in a germane computational problem. This methodology not only facilitates interdisciplinary information flow but also accommodates emerging knowledge and technologies.

Global approach to perinatal medicine: functional genomics and proteomics

Functional genomics (transcriptomics and proteomics) is a global, systematic and comprehensive approach to the identification and description of the processes and pathways involved in normal and abnormal physiological states. The functional genomics methods most applied today are DNA microarrays and proteomics methods, primarily two-dimensional gel electrophoresis coupled with mass spectrometry. To date, interesting research has been carried out, representing milestones for future implementation of functional genomics/proteomics in perinatal medicine. For instance, possible biomarkers of pre-eclampsia, preterm labor and gestational trophoblastic diseases have been discovered. Further systematic examination of differentially regulated genes and proteins in maternal and fetal tissues and fluids will be required. However, high-throughput technologies reflect biological fluctuations and methodological errors. Large amounts of such different data challenge the performance and capacity of the statistical tools and software available at present. Further major developments in this field are pending and the intellectual investment will certainly result in clinical advances.

Bacterial genomes and infectious diseases

The genome sequencing approach has proved to be highly effective and invaluable for gaining an insight on structure of bacteria genomes and the biology and evolution of bacteria. The diversity of bacteria genomes is beyond expectation. Gaining a full understanding of the biology and pathogenic mechanisms of these pathogens will be a major task because on an average only approximately 69% of the encoded proteins in each genome have known functions. Genome sequence analyses have identified novel putative virulence genes, vaccine candidates, targets for antibacterial drugs, and specific diagnostic probes. Microarray technology that makes use of the genomic sequences of human and bacterial pathogens will be a major tool for gaining full understanding of the complexity of host-pathogen interactions and mechanisms of pathogenesis.

Exploring privileged structures: the combinatorial synthesis of cyclic peptides

Head-to-tail cyclic peptides have been reported to bind to multiple, unrelated classes of receptor with high affinity. They may therefore be considered to be privileged structures. This review outlines the strategies by which both macrocyclic cyclic peptides and cyclic dipeptides or diketopiperazines have been synthesised in combinatorial libraries. It also briefly outlines some of the biological applications of these molecules, thereby justifying their inclusion as privileged structures.

Evaluation of the effects of the dietary intake of proteins and amino acids by DNA microarray technology

The DNA microarray technique has been increasingly utilized in various fields of life sciences. It allows us to analyze the expression levels of thousands of genes simultaneously. The high productivity will facilitate the evaluation of changes in amino acid metabolism and their consequences in response to dietary proteins and amino acids. We compared the expression profiles by the GeneChip system in the liver and other tissues among three groups of rats fed with a 12% casein, a 12% gluten or a protein-free diet. Feeding the gluten or the protein-free diet up- or down-regulated a few hundred genes in the liver compared to the casein diet. Although some of the genes were already known to respond to changes in the protein nutritional state, the majority was newly identified responders. This paper also discusses the possibility of a use this technology for safety evaluation of excessive intake of dietary components, especially of amino acids.

Bioinformatics in neurosurgery

WITH THE COMPLETION of the Human Genome Project, the amount of molecular biological sequence data available in public databases has reached staggering proportions. Data continue to accumulate at an exponential rate in the postgenomic era. Compilation, storage, searching, sharing, studying, and transmitting of all these data present formidable challenges. To keep pace with this extant database, the science of bioinformatics (sometimes called computational biology) has evolved. Bioinformatics is the combination of biology and computers and usually involves the storage or analysis of molecular biological sequence data at either the deoxyribonucleic acid, ribonucleic acid, or protein (amino acid) level. Most bioinformatics tools are freely available on the Internet for use by investigators around the globe. The collective wisdom from bioinformatics databases worldwide will continue to spawn advances in the neurological sciences for generations to come. Neurosurgeons must be aware of the power and potential applications of bioinformatics for the analysis of neurosurgical diseases.

Developmental biology: an array of new possibilities

Microarrays offer biologists comprehensive and powerful tools to analyze the involvement of genes in developmental processes at an unprecedented scale. Microarrays that employ defined sequences will permit us to elucidate genetic relationships and responses, while those that employ undefined DNA sequences (ESTs, cDNA, or genomic libraries) will help us to discover new genes, relate them to documented gene networks, and examine the way in which genes (and the process that they themselves control) are regulated. With access to broad new avenues of research come strategic and logistical headaches, most of which are embodied in the reams of data that are created over the course of an experiment. The solutions to these problems have provided interesting computational tools, which will allow us to compile huge data sets and to construct a genome-wide view of development. We are on the threshold of a new vista of possibilities where we might consider in comprehensive and yet specific detail, for example, the degree to which diverse organisms utilize similar genetic networks to achieve similar ends.

Gene expression studies using microarrays: principles, problems, and prospects

A number of mammalian genomes having been sequenced, an important next step is to catalog the expression patterns of all transcription units in health and disease by use of microarrays. Such discovery programs are crucial to our understanding of the gene networks that control developmental, physiological, and pathological processes. However, despite the excitement, the full promise of microarray technology has yet to be realized, as the superficial simplicity of the concept belies considerable problems. Microarray technology is very new; methodologies are still evolving, common standards have yet to be established, and many problems with experimental design and variability have still to be fully understood and overcome. This review will describe the time course of a microarray experiment-RNA isolation from sample, target preparation, hybridization to the microarray probe, data capture, and bioinformatic analysis. For each stage, the advantages and disadvantages of competing techniques are compared, and inherent sources of error are identified and discussed.

Yellow pages to the transcriptome

Transcriptomics has become an important tool for the large-scale analysis of biological processes. This review aims to provide sufficient criteria to make an appropriate choice among the variety of 'closed' systems, represented by DNA microarrays, and 'open' systems like fragment display, tag sequencing and subtractive hybridization, depending on the biological system under investigation. The most important technologies currently available are presented, their strengths and weaknesses are discussed and companies active in the field are listed. The potential of transcriptomics in the pharmaceutical research and development process is highlighted by applications in oncology, research on neurological diseases, and predictive toxicology. Finally, a prognosis for future developments of the technologies is given.

Clinical proteomics for cancer biomarker discovery and therapeutic targeting

As we emerge into the post-genome era, proteomics finds itself as the driving force field as we translate the nucleic acid information archive into understanding how the cell actually works and how disease processes operate. Even so, the traditionally held view of proteomics as simply cataloging and developing lists of the cellular protein repertoire of a cell are now changing, especially in the sub-discipline of clinical proteomics. The most relevant information archive to clinical applications and drug development involves the elucidation of the information flow of the cell; the "software" of protein pathway networks and circuitry. The deranged circuitry of the cell as the drug target itself as well as the effect of the drug on not just the target, but also the entire network, is what we now are striving towards. Clinical proteomics, as a new and most exciting sub-discipline of proteomics, involves the bench-to-bedside clinical application of proteomic tools. Unlike the genome, there are potentially thousands of proteomes: each cell type has its own unique proteome. Moreover, each cell type can alter its proteome depending on the unique tissue microenvironment in which it resides, giving rise to multiple permutations of a single proteome. Since there is no polymerase chain reaction equivalent to proteomics- identifying and discovering the "wiring diagram" of a human diseased cell in a biopsy specimen remains a daunting challenge. New micro-proteomic technologies are being and still need to be developed to drill down into the proteomes of clinically relevant material. Cancer, as a model disease, provides a fertile environment to study the application of proteomics at the bedside. The promise of clinical proteomics and the new technologies that are developed is that we will detect cancer earlier through discovery of biomarkers, we will discover the next generation of targets and imaging biomarkers, and we can then apply this knowledge to patient-tailored therapy.

Exploring privileged structures: the combinatorial synthesis of cyclic peptides

Head-to-tail cyclic peptides have been reported to bind to multiple, unrelated classes of receptor with high affinity. They may therefore be considered to be privileged structures. This review outlines the strategies by which both macrocyclic cyclic peptides and cyclic dipeptides or diketopiperazines have been synthesised in combinatorial libraries. It also briefly outlines some of the biological applications of these molecules, thereby justifying their inclusion as privileged structures.