The era of 'omics unlimited

Biobanking: Objectives, Requirements, and Future Challenges-Experiences from the Munich Vascular Biobank

Collecting biological tissue samples in a biobank grants a unique opportunity to validate diagnostic and therapeutic strategies for translational and clinical research. In the present work, we provide our long-standing experience in establishing and maintaining a biobank of vascular tissue samples, including the evaluation of tissue quality, especially in formalin-fixed paraffin-embedded specimens (FFPE). Our Munich Vascular Biobank includes, thus far, vascular biomaterial from patients with high-grade carotid artery stenosis (n = 1567), peripheral arterial disease (n = 703), and abdominal aortic aneurysm (n = 481) from our Department of Vascular and Endovascular Surgery (January 2004–December 2018). Vascular tissue samples are continuously processed and characterized to assess tissue morphology, histological quality, cellular composition, inflammation, calcification, neovascularization, and the content of elastin and collagen fibers. Atherosclerotic plaques are further classified in accordance with the American Heart Association (AHA), and plaque stability is determined. In order to assess the quality of RNA from FFPE tissue samples over time (2009–2018), RNA integrity number (RIN) and the extent of RNA fragmentation were evaluated. Expression analysis was performed with two housekeeping genes—glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and beta-actin (ACTB)—using TaqMan-based quantitative reverse-transcription polymerase chain reaction (qRT)-PCR. FFPE biospecimens demonstrated unaltered RNA stability over time for up to 10 years. Furthermore, we provide a protocol for processing tissue samples in our Munich Vascular Biobank. In this work, we demonstrate that biobanking is an important tool not only for scientific research but also for clinical usage and personalized medicine.

Metazoan Parasite Vaccines: Present Status and Future Prospects

Eukaryotic parasites and pathogens continue to cause some of the most detrimental and difficult to treat diseases (or disease states) in both humans and animals, while also continuously expanding into non-endemic countries. Combined with the ever growing number of reports on drug-resistance and the lack of effective treatment programs for many metazoan diseases, the impact that these organisms will have on quality of life remain a global challenge. Vaccination as an effective prophylactic treatment has been demonstrated for well over 200 years for bacterial and viral diseases. From the earliest variolation procedures to the cutting edge technologies employed today, many protective preparations have been successfully developed for use in both medical and veterinary applications. In spite of the successes of these applications in the discovery of subunit vaccines against prokaryotic pathogens, not many targets have been successfully developed into vaccines directed against metazoan parasites. With the current increase in -omics technologies and metadata for eukaryotic parasites, target discovery for vaccine development can be expedited. However, a good understanding of the host/vector/pathogen interface is needed to understand the underlying biological, biochemical and immunological components that will confer a protective response in the host animal. Therefore, systems biology is rapidly coming of age in the pursuit of effective parasite vaccines. Despite the difficulties, a number of approaches have been developed and applied to parasitic helminths and arthropods. This review will focus on key aspects of vaccine development that require attention in the battle against these metazoan parasites, as well as successes in the field of vaccine development for helminthiases and ectoparasites. Lastly, we propose future direction of applying successes in pursuit of next generation vaccines.

The Deep Thioredoxome in Chlamydomonas reinhardtii: New Insights into Redox Regulation

Thiol-based redox post-translational modifications have emerged as important mechanisms of signaling and regulation in all organisms, and thioredoxin plays a key role by controlling the thiol-disulfide status of target proteins. Recent redox proteomic studies revealed hundreds of proteins regulated by glutathionylation and nitrosylation in the unicellular green alga Chlamydomonas reinhardtii, while much less is known about the thioredoxin interactome in this organism. By combining qualitative and quantitative proteomic analyses, we have comprehensively investigated the Chlamydomonas thioredoxome and 1188 targets have been identified. They participate in a wide range of metabolic pathways and cellular processes. This study broadens not only the redox regulation to new enzymes involved in well-known thioredoxin-regulated metabolic pathways but also sheds light on cellular processes for which data supporting redox regulation are scarce (aromatic amino acid biosynthesis, nuclear transport, etc). Moreover, we characterized 1052 thioredoxin-dependent regulatory sites and showed that these data constitute a valuable resource for future functional studies in Chlamydomonas. By comparing this thioredoxome with proteomic data for glutathionylation and nitrosylation at the protein and cysteine levels, this work confirms the existence of a complex redox regulation network in Chlamydomonas and provides evidence of a tremendous selectivity of redox post-translational modifications for specific cysteine residues.

Monochromosomal Hybrids and Chromosome Transfer: A Functional Approach for Gene Identification

Functional complementation of cellular defects has been a valuable approach for localizing causative genes to specific chromosomes. The complementation strategy was followed by positional cloning and characterization of genes for their biological relevance. We herein describe strategies used for the construction of monochromosomal hybrids and their applications for cloning and characterization of genes related to cell growth, cell senescence and DNA repair. We have cloned RNaseT2, GluR6 (glutamate ionotropic receptor kainate type subunit 2-GRIK2) and protein tyrosine phosphatase, receptor type K (PTPRK) genes using these strategies.

Biological Activity of Ionic Liquids and Their Application in Pharmaceutics and Medicine

Ionic liquids are remarkable chemical compounds, which find applications in many areas of modern science. Because of their highly tunable nature and exceptional properties, ionic liquids have become essential players in the fields of synthesis and catalysis, extraction, electrochemistry, analytics, biotechnology, etc. Apart from physical and chemical features of ionic liquids, their high biological activity has been attracting significant attention from biochemists, ecologists, and medical scientists. This Review is dedicated to biological activities of ionic liquids, with a special emphasis on their potential employment in pharmaceutics and medicine. The accumulated data on the biological activity of ionic liquids, including their antimicrobial and cytotoxic properties, are discussed in view of possible applications in drug synthesis and drug delivery systems. Dedicated attention is given to a novel active pharmaceutical ingredient-ionic liquid (API-IL) concept, which suggests using traditional drugs in the form of ionic liquid species. The main aim of this Review is to attract a broad audience of chemical, biological, and medical scientists to study advantages of ionic liquid pharmaceutics. Overall, the discussed data highlight the importance of the research direction defined as "Ioliomics", studies of ions in liquids in modern chemistry, biology, and medicine.

A Cell Microarray Format: A Peptide Release System Using a Photo-Cleavable Linker for Cell Toxicity and Cell Uptake Analysis

There has been increasing interest in the potential use of microarray technologies to perform systematic and high-throughput cell-based assays. We are currently focused on developing more practical array formats and detection methods that will enable researchers to conduct more detailed analyses in cell microarray studies. In this chapter, we describe the construction of a novel peptide-array format system for analyzing cellular toxicity and cellular uptake. In this system, a peptide is immobilized at the bottom of a conventional 96-well plate using a photo-cleavable linker. The peptide can then be released from the bottom by irradiating the desired wells with UV light, thus allowing the cytotoxicity or cellular uptake of the peptide to be monitored. This system will facilitate the realization of high-throughput cell arrays for cellomics analyses and cell-based phenotypic drug screens.

Integrated Analysis of Transcriptomic and Proteomic Datasets Reveals Information on Protein Expressivity and Factors Affecting Translational Efficiency

Integrated analysis of large-scale transcriptomic and proteomic data can provide important insights into the metabolic mechanisms underlying complex biological systems. In this chapter, we present methods to address two aspects of issues related to integrated transcriptomic and proteomic analysis. First, due to the fact that proteomic datasets are often incomplete, and integrated analysis of partial proteomic data may introduce significant bias. To address these issues, we describe a zero-inflated Poisson (ZIP)-based model to uncover the complicated relationships between protein abundances and mRNA expression levels, and then apply them to predict protein abundance for the proteins not experimentally detected. The ZIP model takes into consideration the undetected proteins by assuming that there is a probability mass at zero representing expressed proteins that were undetected owing to technical limitations. The model validity is demonstrated using biological information of operons, regulons, and pathways. Second, weak correlation between transcriptomic and proteomic datasets is often due to biological factors affecting translational processes. To quantify the effects of these factors, we describe a multiple regression-based statistical framework to quantitatively examine the effects of various translational efficiency-related sequence features on mRNA-protein correlation. Using the datasets from sulfate-reducing bacteria Desulfovibrio vulgaris, the analysis shows that translation-related sequence features can contribute up to 15.2-26.2% of the total variation of the correlation between transcriptomic and proteomic datasets, and also reveals the relative importance of various features in translation process.

"Omics" in pharmaceutical research: overview, applications, challenges, and future perspectives

In the post-genomic era, biological studies are characterized by the rapid development and wide application of a series of "omics" technologies, including genomics, proteomics, metabolomics, transcriptomics, lipidomics, cytomics, metallomics, ionomics, interactomics, and phenomics. These "omics" are often based on global analyses of biological samples using high through-put analytical approaches and bioinformatics and may provide new insights into biological phenomena. In this paper, the development and advances in these omics made in the past decades are reviewed, especially genomics, transcriptomics, proteomics and metabolomics; the applications of omics technologies in pharmaceutical research are then summarized in the fields of drug target discovery, toxicity evaluation, personalized medicine, and traditional Chinese medicine; and finally, the limitations of omics are discussed, along with the future challenges associated with the multi-omics data processing, dynamics omics analysis, and analytical approaches, as well as amenable solutions and future prospects.

The synthetic biology future

Herein, I track the evolution of synthetic biology from its earliest incarnations more than 50 years ago, through the DIYbio revolution, to the next 50 years.

Drug resistance to Mycobacterium tuberculosis: from the traditional Chinese view to modern systems biology

The pathogen, Mycobacterium tuberculosis (M. tuberculosis) is a well-evolved, organized pathogen that has developed drug resistance, specifically multidrug resistance (MDR) and extensive drug resistance (XDR). This review primarily summarizes the mechanisms of drug resistance by M. tuberculosis according to the traditional Chinese view. The traditional Chinese view of drug resistance includes: the physical barrier of the cell wall; mutations relating to current anti-TB agents; drug efflux pumps; and drug stress, including the SOS response systems, the mismatch repair systems and the toxin-antitoxin systems. In addition, this review addresses the integrated systems biology of genomics, transcriptomics, proteomics, metabolomics and interactomics. Development of the various levels of systems biology has enabled determination of the anatomy of bacteria. Finally, the current review proposes that further investigation regarding the population of individuals with a high drug metabolic speed is vital to further understand drug resistance in M. tuberculosis.

Alzheimer's disease drug development: translational neuroscience strategies

Alzheimer's disease (AD) is an urgent public health challenge that is rapidly approaching epidemic proportions. New therapies that defer or prevent the onset, delay the decline, or improve the symptoms are urgently needed. All phase 3 drug development programs for disease-modifying agents have failed thus far. New approaches to drug development are needed. Translational neuroscience focuses on the linkages between basic neuroscience and the development of new diagnostic and therapeutic products that will improve the lives of patients or prevent the occurrence of brain disorders. Translational neuroscience includes new preclinical models that may better predict human efficacy and safety, improved clinical trial designs and outcomes that will accelerate drug development, and the use of biomarkers to more rapidly provide information regarding the effects of drugs on the underlying disease biology. Early translational research is complemented by later stage translational approaches regarding how best to use evidence to impact clinical practice and to assess the influence of new treatments on the public health. Funding of translational research is evolving with an increased emphasis on academic and NIH involvement in drug development. Translational neuroscience provides a framework for advancing development of new therapies for AD patients.

[Protocols of proteins interactomics: molecular fishing on optical chips and magnetic nanoparticles]

Now it is absolutely clear, that the majority of proteins in living systems function due to interaction with each other in stable or dynamic proteins complexes. Therefore necessity of deeper studies of proteins functions causes expansion of protein-protein interaction research. In the present review the brief description and comparative estimation of experimental methods and protocols of protein interactomics, based on technology of molecular fishing on an optical chips and paramagnetic nanoparticles is given.

Systematic screening of the cellular uptake of designed alpha-helix peptides

The cellular penetration (CP) activity of functional molecules has attracted significant attention as one of the most promising new approaches for drug delivery. In particular, cell-penetrating peptides (CPPs) have been studied extensively in cellular engineering. Because there have been few large-scale systematic studies to identify peptide sequences with optimal CP activity or that are suitable for further applications in cell engineering, such as cell-specific penetration and cell-selective culture, we screened and compared the cellular uptake (CU) activity of 54 systematically designed α-helical peptides in HeLa cells. Furthermore, the CU activity of 24 designed peptides was examined in four cell lines using a cell fingerprinting technique and statistical approaches. The CU activities in various cells depended on amino acid residues of peptide sequences as well as charge, α-helical content and hydrophobicity of the peptides. Notably, the mutation of a single residue significantly altered the CU ability of a peptide, highlighting the variability of cell uptake mechanisms. Moreover, these results demonstrated the feasibility of cell-selective culture by conducting cell-selective permeation and death in cultures containing two cell types. These studies may lead to further peptide library design and screening for new classes of CPPs with useful functions.

Chemocentric informatics approach to drug discovery: identification and experimental validation of selective estrogen receptor modulators as ligands of 5-hydroxytryptamine-6 receptors and as potential cognition enhancers

We have devised a chemocentric informatics methodology for drug discovery integrating independent approaches to mining biomolecular databases. As a proof of concept, we have searched for novel putative cognition enhancers. First, we generated Quantitative Structure-Activity Relationship (QSAR) models of compounds binding to 5-hydroxytryptamine-6 receptor (5-HT(6)R), a known target for cognition enhancers, and employed these models for virtual screening to identify putative 5-HT(6)R actives. Second, we queried chemogenomics data from the Connectivity Map ( http://www.broad.mit.edu/cmap/ ) with the gene expression profile signatures of Alzheimer's disease patients to identify compounds putatively linked to the disease. Thirteen common hits were tested in 5-HT(6)R radioligand binding assays and ten were confirmed as actives. Four of them were known selective estrogen receptor modulators that were never reported as 5-HT(6)R ligands. Furthermore, nine of the confirmed actives were reported elsewhere to have memory-enhancing effects. The approaches discussed herein can be used broadly to identify novel drug-target-disease associations.

Visualization and curve-parameter estimation strategies for efficient exploration of phenotype microarray kinetics

Background

The Phenotype MicroArray (OmniLog® PM) system is able to simultaneously capture a large number of phenotypes by recording an organism's respiration over time on distinct substrates. This technique targets the object of natural selection itself, the phenotype, whereas previously addressed ‘-omics’ techniques merely study components that finally contribute to it. The recording of respiration over time, however, adds a longitudinal dimension to the data. To optimally exploit this information, it must be extracted from the shapes of the recorded curves and displayed in analogy to conventional growth curves.

Methodology

The free software environment R was explored for both visualizing and fitting of PM respiration curves. Approaches using either a model fit (and commonly applied growth models) or a smoothing spline were evaluated. Their reliability in inferring curve parameters and confidence intervals was compared to the native OmniLog® PM analysis software. We consider the post-processing of the estimated parameters, the optimal classification of curve shapes and the detection of significant differences between them, as well as practically relevant questions such as detecting the impact of cultivation times and the minimum required number of experimental repeats.

Conclusions

We provide a comprehensive framework for data visualization and parameter estimation according to user choices. A flexible graphical representation strategy for displaying the results is proposed, including 95% confidence intervals for the estimated parameters. The spline approach is less prone to irregular curve shapes than fitting any of the considered models or using the native PM software for calculating both point estimates and confidence intervals. These can serve as a starting point for the automated post-processing of PM data, providing much more information than the strict dichotomization into positive and negative reactions. Our results form the basis for a freely available R package for the analysis of PM data.

Omic techniques in systems biology approaches to traditional Chinese medicine research: present and future

Omic techniques have become key tools in the development of systems biology. As the holistic approaches underlying the practice of traditional Chinese medicine (TCM) and new tendencies in Western medicine towards personalised medicine require in-depth knowledge of mechanisms of action and active compounds, the use of omic techniques is crucial for understanding and interpretation of TCM development, especially in view of its expansion in Western countries. In this short review, omic applications in TCM research are reviewed which has allowed some speculation regarding future perspectives for these approaches in TCM modernisation and standardisation. Guidelines for good practice for the application of omics in TCM research are also proposed.

The Bridge Helix of RNA polymerase acts as a central nanomechanical switchboard for coordinating catalysis and substrate movement

The availability of in vitro assembly systems to produce recombinant archaeal RNA polymerases (RNAPs) offers one of the most powerful experimental tools for investigating the still relatively poorly understood molecular mechanisms underlying RNAP function. Over the last few years, we pioneered new robot-based high-throughput mutagenesis approaches to study structure/function relationships within various domains surrounding the catalytic center. The Bridge Helix domain, which appears in numerous X-ray structures as a 35-amino-acid-long alpha helix, coordinates the concerted movement of several other domains during catalysis through kinking of two discrete molecular hinges. Mutations affecting these kinking mechanisms have a direct effect on the specific catalytic activity of RNAP and can in some instances more than double it. Molecular dynamics simulations have established themselves as exceptionally useful for providing additional insights and detailed models to explain the underlying structural motions.

Cell fingerprint patterns using designed α-helical peptides to screen for cell-specific toxicity

We conducted cell-based cytotoxicity screening of a 101-membered α-helical peptide library using cell fingerprints (CFPs). The CFP data suggested that there is a relationship between cytotoxicity and peptide characteristics, such as hydrophobicity, charge, and amino acid composition. In spite of the small size of the library used in this study, several peptides demonstrated cell-specific toxicity. The strategy of combining a designed peptide library with CFP thus shows real promise for peptide-based screening with cells.

Prediction and Characterization of Missing Proteomic Data in Desulfovibrio vulgaris

Proteomic datasets are often incomplete due to identification range and sensitivity issues. It becomes important to develop methodologies to estimate missing proteomic data, allowing better interpretation of proteomic datasets and metabolic mechanisms underlying complex biological systems. In this study, we applied an artificial neural network to approximate the relationships between cognate transcriptomic and proteomic datasets of Desulfovibrio vulgaris, and to predict protein abundance for the proteins not experimentally detected, based on several relevant predictors, such as mRNA abundance, cellular role and triple codon counts. The results showed that the coefficients of determination for the trained neural network models ranged from 0.47 to 0.68, providing better modeling than several previous regression models. The validity of the trained neural network model was evaluated using biological information (i.e. operons). To seek understanding of mechanisms causing missing proteomic data, we used a multivariate logistic regression analysis and the result suggested that some key factors, such as protein instability index, aliphatic index, mRNA abundance, effective number of codons (N(c)) and codon adaptation index (CAI) values may be ascribed to whether a given expressed protein can be detected. In addition, we demonstrated that biological interpretation can be improved by use of imputed proteomic datasets.

Genetic-based biomarkers and next-generation sequencing: the future of personalized care in colorectal cancer

The past 5 years have witnessed extraordinary advances in the field of DNA sequencing technology. What once took years to accomplish with Sanger sequencing can now be accomplished in a matter of days with next-generation sequencing (NGS) technology. This has allowed researchers to sequence individual genomes and match combinations of mutations with specific diseases. As cancer is inherently a disease of the genome, it is not surprising to see NGS technology already being applied to cancer research with promises of greater understanding of carcinogenesis. While the task of deciphering the cancer genomic code remains ongoing, we are already beginning to see the application of genetic-based testing in the area of colorectal cancer. In this article we will provide an overview of current colorectal cancer genetic-based biomarkers, namely mutations and other genetic alterations in cancer genome DNA, discuss recent advances in NGS technology and speculate on future directions for the application of NGS technology to colorectal cancer diagnosis and treatment.

What determines age-related disease: do we know all the right questions?

The average human lifespan has increased throughout the last century due to the mitigation of many infectious diseases. More people now die of age-related diseases than ever before, but these diseases have been resistant to elimination. Progress has been made in treatments and preventative measures to delay the onsets of these diseases, but most cancers and vascular diseases are still with us and they kill about the same fraction of the population year after year. For example, US Caucasian female deaths from breast plus genital cancers have remained a fairly constant approximately 7% of the age-related disease deaths from 1938 to 1998 and have been consistently approximately 2-fold greater than female colon plus rectal cancer deaths over that span. This type of stability pattern pervades the age-related diseases and suggests that intrinsic properties within populations determine these fractions. Recognizing this pattern and deciphering its origin will be necessary for the complete understanding of these major causes of death. It would appear that more than the random processes of aging drive this effect. The question is how to meaningfully approach this problem. This commentary discusses the epidemiological and aging perspectives and their current limitations in providing an explanation. The age of bioinformatics offers hope, but only if creative systems approaches are forthcoming.

Integrating multiple 'omics' analysis for microbial biology: application and methodologies

Recent advances in various 'omics' technologies enable quantitative monitoring of the abundance of various biological molecules in a high-throughput manner, and thus allow determination of their variation between different biological states on a genomic scale. Several popular 'omics' platforms that have been used in microbial systems biology include transcriptomics, which measures mRNA transcript levels; proteomics, which quantifies protein abundance; metabolomics, which determines abundance of small cellular metabolites; interactomics, which resolves the whole set of molecular interactions in cells; and fluxomics, which establishes dynamic changes of molecules within a cell over time. However, no single 'omics' analysis can fully unravel the complexities of fundamental microbial biology. Therefore, integration of multiple layers of information, the multi-'omics' approach, is required to acquire a precise picture of living micro-organisms. In spite of this being a challenging task, some attempts have been made recently to integrate heterogeneous 'omics' datasets in various microbial systems and the results have demonstrated that the multi-'omics' approach is a powerful tool for understanding the functional principles and dynamics of total cellular systems. This article reviews some basic concepts of various experimental 'omics' approaches, recent application of the integrated 'omics' for exploring metabolic and regulatory mechanisms in microbes, and advances in computational and statistical methodologies associated with integrated 'omics' analyses. Online databases and bioinformatic infrastructure available for integrated 'omics' analyses are also briefly discussed.