Nucleic acid approaches for detection and identification of biological warfare and infectious disease agents

Biological warfare agents are the most problematic of the weapons of mass destruction and terror. Both civilian and military sources predict that over the next decade the threat from proliferation of these agents will increase significantly. In this review we summarize the state of the art in detection and identification of biological threat agents based on PCR technology with emphasis on the new technology of microarrays. The advantages and limitations of real-time PCR technology and a review of the literature as it applies to pathogen and virus detection are presented. The paper covers a number of issues related to the challenges facing biological threat agent detection technologies and identifies critical components that must be overcome for the emergence of reliable PCR-based DNA technologies as bioterrorism countermeasures and for environmental applications. The review evaluates various system components developed for an integrated DNA microchip and the potential applications of the next generation of fully automated DNA analyzers with integrated sample preparation and biosensing elements. The article also reviews promising devices and technologies that are near to being, or have been, commercialized.     

Exact algorithms for planted motif problems.

The problem of identifying meaningful patterns (i.e., motifs) from biological data has been studied extensively due to its paramount importance. Three versions of this problem have been identified in the literature. One of these three problems is the planted (l, d)-motif problem. Several instances of this problem have been posed as a challenge. Numerous algorithms have been proposed in the literature that address this challenge. Many of these algorithms fall under the category of heuristic algorithms. In this paper we present algorithms for the planted (l, d)-motif problem that always find the correct answer(s). Our algorithms are very simple and are based on some ideas that are fundamentally different from the ones employed in the literature. We believe that the techniques we introduce in this paper will find independent applications.     

活体动物体内光学成像技术的研究进展

生物发光和荧光成像作为近年来新兴的活体动物体内光学成像技术,以其操作简便及直观性成为研究小动物活体成像的一种理想方法,在生命科学研究中得以不断发展。利用这种成像技术,可以直接实时观察标记的基因及细胞在活体动物体内的活动及反应。利用光学标记的转基因动物模型可以研究疾病的发生发展过程,进行药物研究及筛选等。本文综述了现有活体动物体内光学成像技术的原理、应用领域及发展前景,比较了生物发光与几种荧光技术的不同特点和应用。     

Catalytic RNA,ribozyme, and its applications in synthetic biology

Ribozymes are functional RNA molecules that can catalyze biochemical reactions. Since the discovery of the first catalytic RNA, various functional ribozymes (e.g., self-cleaving ribozymes, splicing ribozymes, RNase P, etc.) have been uncovered, and their structures and mechanisms have been identified. Ribozymes have the advantage of possessing features of “RNA” molecules; hence, they are highly applicable for manipulating various biological systems. To fully employ ribozymes in a broad range of biological applications in synthetic biology, a variety of ribozymes have been developed and engineered. Here, we summarize the main features of ribozymes and the methods used for engineering their functions. We also describe the past and recent efforts towards exploiting ribozymes for effective and novel applications in synthetic biology. Based on studies on their significance in biological applications till date, ribozymes are expected to advance technologies in artificial biological systems.     

Recent advances in RNA structurome

RNA structures are essential to support RNA functions and regulation in various biological processes. Recently, a range of novel technologies have been developed to decode genome-wide RNA structures and novel modes of functionality across a wide range of species. In this review, we summarize key strategies for probing the RNA structurome and discuss the pros and cons of representative technologies. In particular, these new technologies have been applied to dissect the structural landscape of the SARS-CoV-2 RNA genome. We also summarize the functionalities of RNA structures discovered in different regulatory layers—including RNA processing, transport, localization, and mRNA translation—across viruses, bacteria, animals, and plants. We review many versatile RNA structural elements in the context of different physiological and pathological processes (e.g., cell differentiation, stress response, and viral replication). Finally, we discuss future prospects for RNA structural studies to map the RNA structurome at higher resolution and at the single-molecule and single-cell level, and to decipher novel modes of RNA structures and functions for innovative applications.

     

The end of the microarray Tower of Babel: will universal standards lead the way?

Microarrays are the most common method of studying global gene expression, and may soon enter the realm of FDA-approved clinical/diagnostic testing of cancer and other diseases. However, the acceptance of array data has been made difficult by the proliferation of widely different array platforms with gene probes ranging in size from 25 bases (oligonucleotides) to several kilobases (complementary DNAs or cDNAs). The algorithms applied for image and data analysis are also as varied as the microarray platforms, perhaps more so. In addition, there is a total lack of universally accepted standards for use among the different platforms and even within the same array types. Due to this lack of coherency in array technologies, confusion in interpretation of data within and across platforms has often been the norm, and studies of the same biological phenomena have, in many cases, led to contradictory results. In this commentary/review, some of the causes of this confusion will be summarized, and progress in overcoming these obstacles will be described, with the goal of providing an optimistic view of the future for the use of array technologies in global expression profiling and other applications.     

生物芯片、生物传感器和生物信息学

近年来,在生物技术和医学研究领域涌现出了许多新技术平台,其中就包括生物芯片技术和生物传感器技术。生物芯片和生物传感器的构建都必须以生物信息学为基础,而两种技术平台应用所得出的数据和结果又反过来大大丰富和充实了生物信息学本身。本分析概述了生物芯片和生物传感器两种技术平台以及生物信息学,对三之间的相互关系进行了讨论。     

Integrated molecular imaging technologies for investigation of metals in biological systems: A brief review

Metals play an essential role in biological systems and are required as structural or catalytic co-factors in many proteins. Disruption of the homeostatic control and/or spatial distributions of metals can lead to disease. Imaging technologies have been developed to visualize elemental distributions across a biological sample. Measurement of elemental distributions by imaging mass spectrometry and imaging X-ray fluorescence are increasingly employed with technologies that can assess histological features and molecular compositions. Data from several modalities can be interrogated as multimodal images to correlate morphological, elemental, and molecular properties. Elemental and molecular distributions have also been axially resolved to achieve three-dimensional volumes, dramatically increasing the biological information. In this review, we provide an overview of recent developments in the field of metal imaging with an emphasis on multimodal studies in two and three dimensions. We specifically highlight studies that present technological advancements and biological applications of how metal homeostasis affects human health.     

A new approach to the rapid determination of protein side chain conformations

Two efficient algorithms have been developed which allow amino acid side chain conformations to be optimized rapidly for a given peptide backbone conformation. Both these approaches are based on the assumption that each side chain can be represented by a small number of rotameric states. These states have been obtained by a dynamic cluster analysis of a large data base of known crystallographic structures. Successful applications of these algorithms to the prediction of known protein conformations are presented.     

Fluorescence complementation: an emerging tool for biological research

Numerous technologies based on utilizing fluorescent proteins have been developed for biological research, and fluorescence complementation (FC) is a recent application for visualization of molecular events in living cells and organisms. Currently, ten fluorescent proteins have been demonstrated to support FC. Over the past five years, FC-based technologies have been developed to visualize a variety of molecular events, such as protein-protein interactions, post-translational modifications, protein folding, conformational changes, RNA-protein interactions, mRNA localization and DNA hybridization. In addition, FC has also been used for drug discovery. These applications are providing fascinating insights into many biological processes. Here, we review the principles and applications of FC technologies, discuss their current challenges and examine prospects for future advances.     

Chemistry of phospholipid oxidation

The oxidation of lipids has long been a topic of interest in biological and food sciences, and the fundamental principles of non-enzymatic free radical attack on phospholipids are well established, although questions about detail of the mechanisms remain. The number of end products that are formed following the initiation of phospholipid peroxidation is large, and is continually growing as new structures of oxidized phospholipids are elucidated. Common products are phospholipids with esterified isoprostane-like structures and chain-shortened products containing hydroxy, carbonyl or carboxylic acid groups; the carbonyl-containing compounds are reactive and readily form adducts with proteins and other biomolecules. Phospholipids can also be attacked by reactive nitrogen and chlorine species, further expanding the range of products to nitrated and chlorinated phospholipids. Key to understanding the mechanisms of oxidation is the development of advanced and sensitive technologies that enable structural elucidation. Tandem mass spectrometry has proved invaluable in this respect and is generally the method of choice for structural work. A number of studies have investigated whether individual oxidized phospholipid products occur in vivo, and mass spectrometry techniques have been instrumental in detecting a variety of oxidation products in biological samples such as atherosclerotic plaque material, brain tissue, intestinal tissue and plasma, although relatively few have achieved an absolute quantitative analysis. The levels of oxidized phospholipids in vivo is a critical question, as there is now substantial evidence that many of these compounds are bioactive and could contribute to pathology. The challenges for the future will be to adopt lipidomic approaches to map the profile of oxidized phospholipid formation in different biological conditions, and relate this to their effects in vivo. This article is part of a Special Issue entitled: Oxidized phospholipids-their properties and interactions with proteins.     

Focus: Microbiome: Metagenomic Assembly: Overview,Challenges and Applications

Advances in sequencing technologies have led to the increased use of high throughput sequencing in characterizing the microbial communities associated with our bodies and our environment. Critical to the analysis of the resulting data are sequence assembly algorithms able to reconstruct genes and organisms from complex mixtures. Metagenomic assembly involves new computational challenges due to the specific characteristics of the metagenomic data. In this survey, we focus on major algorithmic approaches for genome and metagenome assembly, and discuss the new challenges and opportunities afforded by this new field. We also review several applications of metagenome assembly in addressing interesting biological problems.     

Deep Learning and Its Applications in Biomedicine

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning.     

Advances in quantitative proteomics

Large-scale protein quantification has become a major proteomics application in many areas of biological and medical research. During the past years, different techniques have been developed, including gel-based such as differential in-gel electrophoresis (DIGE) and liquid chromatography-based such as isotope labeling and label-free quantification. These quantitative proteomics tools hold significant promise for biomarker discovery, diagnostic and therapeutic applications. They are also important for research in functional genomics and systems biology towards basic understanding of molecular networks and pathway interactions. In this review, we summarize current technologies in quantitative proteomics and discuss recent applications of the technologies.     

DNA sequencing: an overview of solid-state and biological nanopore-based methods

The field of sequencing is a topic of significant interest since its emergence and has become increasingly important over time. Impressive achievements have been obtained in this field, especially in relations to DNA and RNA sequencing. Since the first achievements by Sanger and colleagues in the 1950s, many sequencing techniques have been developed, while others have disappeared. DNA sequencing has undergone three generations of major evolution. Each generation has its own specifications that are mentioned briefly. Among these generations, nanopore sequencing has its own exciting characteristics that have been given more attention here. Among pioneer technologies being used by the third-generation techniques, nanopores, either biological or solid-state, have been experimentally or theoretically extensively studied. All sequencing technologies have their own advantages and disadvantages, so nanopores are not free from this general rule. It is also generally pointed out what research has been done to overcome the obstacles. In this review, biological and solid-state nanopores are elaborated on, and applications of them are also discussed briefly.     

A novel approach to structural alignment using realistic structural and environmental information

In the era of structural genomics, it is necessary to generate accurate structural alignments in order to build good templates for homology modeling. Although a great number of structural alignment algorithms have been developed, most of them ignore intermolecular interactions during the alignment procedure. Therefore, structures in different oligomeric states are barely distinguishable, and it is very challenging to find correct alignment in coil regions. Here we present a novel approach to structural alignment using a clique finding algorithm and environmental information (SAUCE). In this approach, we build the alignment based on not only structural coordinate information but also realistic environmental information extracted from biological unit files provided by the Protein Data Bank (PDB). At first, we eliminate all environmentally unfavorable pairings of residues. Then we identify alignments in core regions via a maximal clique finding algorithm. Two extreme value distribution (EVD) form statistics have been developed to evaluate core region alignments. With an optional extension step, global alignment can be derived based on environment-based dynamic programming linking. We show that our method is able to differentiate three-dimensional structures in different oligomeric states, and is able to find flexible alignments between multidomain structures without predetermined hinge regions. The overall performance is also evaluated on a large scale by comparisons to current structural classification databases as well as to other alignment methods.     

Simulation of microarray data with realistic characteristics

Background  

Microarray technologies have become common tools in biological research. As a result, a need for effective computational methods for data analysis has emerged. Numerous different algorithms have been proposed for analyzing the data. However, an objective evaluation of the proposed algorithms is not possible due to the lack of biological ground truth information. To overcome this fundamental problem, the use of simulated microarray data for algorithm validation has been proposed.