Biology-based Approaches Research Articles

NGS-QC Generator: A Quality Control System for ChIP-Seq and Related Deep Sequencing-Generated Datasets.

The combination of massive parallel sequencing with a variety of modern DNA/RNA enrichment technologies provides means for interrogating functional protein-genome interactions (ChIP-seq), genome-wide transcriptional activity (RNA-seq; GRO-seq), chromatin accessibility (DNase-seq, FAIRE-seq, MNase-seq), and more recently the three-dimensional organization of chromatin (Hi-C, ChIA-PET). In systems biology-based approaches several of these readouts are generally cumulated with the aim of describing living systems through a reconstitution of the genome-regulatory functions. However, an issue that is often underestimated is that conclusions drawn from such multidimensional analyses of NGS-derived datasets critically depend on the quality of the compared datasets. To address this problem, we have developed the NGS-QC Generator, a quality control system that infers quality descriptors for any kind of ChIP-sequencing and related datasets. In this chapter we provide a detailed protocol for (1) assessing quality descriptors with the NGS-QC Generator; (2) to interpret the generated reports; and (3) to explore the database of QC indicators (www.ngs-qc.org) for >21,000 publicly available datasets.

Forward Individualized Medicine from Personal Genomes to Interactomes.

When considering the variation in the genome, transcriptome, proteome and metabolome, and their interaction with the environment, every individual can be rightfully considered as a unique biological entity. Individualized medicine promises to take this uniqueness into account to optimize disease treatment and thereby improve health benefits for every patient. The success of individualized medicine relies on a precise understanding of the genotype-phenotype relationship. Although omics technologies advance rapidly, there are several challenges that need to be overcome: Next generation sequencing can efficiently decipher genomic sequences, epigenetic changes, and transcriptomic variation in patients, but it does not automatically indicate how or whether the identified variation will cause pathological changes. This is likely due to the inability to account for (1) the consequences of gene-gene and gene-environment interactions, and (2) (post)transcriptional as well as (post)translational processes that eventually determine the concentration of key metabolites. The technologies to accurately measure changes in these latter layers are still under development, and such measurements in humans are also mainly restricted to blood and circulating cells. Despite these challenges, it is already possible to track dynamic changes in the human interactome in healthy and diseased states by using the integration of multi-omics data. In this review, we evaluate the potential value of current major bioinformatics and systems biology-based approaches, including genome wide association studies, epigenetics, gene regulatory and protein-protein interaction networks, and genome-scale metabolic modeling. Moreover, we address the question whether integrative analysis of personal multi-omics data will help understanding of personal genotype-phenotype relationships.

Nature Inspired Computing: An Overview and Some Future Directions.

This paper presents an overview of significant advances made in the emerging field of nature-inspired computing (NIC) with a focus on the physics- and biology-based approaches and algorithms. A parallel development in the past two decades has been the emergence of the field of computational intelligence (CI) consisting primarily of the three fields of neural networks, evolutionary computing and fuzzy logic. It is observed that NIC and CI intersect. The authors advocate and foresee more cross-fertilisation of the two emerging fields.

Use of a mutant OGA for detecting O-GlcNAc modified proteins.

In the previous issue of Biochemical Journal Mariappa et al. [(2015) Biochem J. 470,: 255-262] demonstrate a new method for visualizing O-linked N-acetylglucosamine (O-GlcNAc) modified proteins by making use of a catalytically dead version of the enzyme that normally removes this modification. They show their approach has broader specificity than current antibody-based techniques and higher specificity than lectin and chemical biology-based labelling approaches. This commentary discusses methods for O-GlcNAc detection and the significance of this work for characterizing this common, but currently poorly understood regulatory modification.

Novel Tools for the Study of Cell Type-Specific Exosomes and Microvesicles

Many cell types release exosomes and microvesicles (EMVs) with a size range from 0.05-5 μm, harbouring receptors, bioactive and signaling proteins, molecular mediators and nucleic acids for cell-to-cell communication. Microvesicles bud directly from the plasma membrane, in contrast to exosomes which are derived from multivesicular bodies within the cell. Increased levels of EMVs have been observed in plasma, urine and other body fluids in patients suffering from a wide range of common complex diseases, including vascular, metabolic, lung, autoimmune and neurodegenerative diseases, chronic inflammation and cancer. EMVs may affect target cells directly by surface-bound ligands, transferred surface receptors and membrane-associated enzymes, such as glycosylphosphatidylinositolanchored proteins, or delivered cytoplasmic or membrane-associated constituents, such as cytosolic proteins, micro/mRNAs, bioactive lipids and even mitochondria. The use of EMVs as diagnostic markers for the prediction, diagnosis, therapy monitoring and prognosis of complex diseases is becoming increasingly attractive. Novel technologies for analysis of the size, density and molecular composition of EMVs are currently emerging together with methods for their improved isolation and purification out of heterogenous vesicle populations. In addition, the recent revolution in mass-spectroscopy, (micro-) flow cytometry, atomic force microscopy, nanoparticle tracking and biosensing will considerably facilitate the quantitative and qualitative analysis of all the constituents assembled in EMVs. Technologies will be preferred that provide signatures specific for EMV subsets rather than a single or a few parameter(s) averaged for the total EMV population. Those EMV signatures have to be correlated to specific disease states along cross-sectional and longitudinal clinical studies. Moreover, it has to be tested which signatures and molecular components, i.e. EMV subspecies, are most informative to obtain actionable disease information. Ultimately, the reliable, rapid and low-cost analysis of EMVs will support systems biology-based approaches for the diagnosis and therapy of complex diseases and supplements the analytical power of conventional biomarkers.

Expression Profiles and Clinicopathologic Features in Early Resected Non-small-cell Lung Cancer

Expression Profiles and Clinicopathologic Features in Early Resected Non-small-cell Lung Cancer

Biosynthesis of the Amaryllidaceae alkaloids

Amaryllidaceae alkaloids (AAs) are a structurally diverse group of plant specialized metabolites with powerful biological activities. The medicinal properties of many AAs have been identified including the antitumor agent narciclasine and galanthamine, used for Alzheimer’s disease. Tracer studies have led to proposed pathways but AA biosynthesis remains molecularly uncharacterized. The use of systems biology-based approaches could lead to the unraveling of AA metabolic pathways. The elucidation of AA biosynthesis will provide necessary tools required to enhance AA production in plants as well as the development of microbial production platforms as an alternative to plants as a commercial source of valuable AAs.

Systems biology approaches to develop innovative strategies for lung cancer therapy.

Lung cancer (LC) is a number one killer of cancer-related death among men and women worldwide. Major advances have been made in the diagnosis, staging and use of surgery for LC, but systemic chemotherapy and radiotherapy alone or in combination with some targeted agents remains the core treatment of advanced LC. Unfortunately, in spite of improved diagnosis, surgical methods and new treatments, mortality is still extremely high among LC patients. To understand the precise functioning of signaling pathways associated with resistance to current treatments in LC, as well as to identify novel treatment regimens, a holistic approach to analyze signaling networks should be applied. Here, we describe systems biology-based approaches to generate biomarkers and novel therapeutic targets in LC, as well as how this may contribute to personalized treatment for this malignancy.

Endosperm transfer cell-specific genes and proteins: structure, function and applications in biotechnology

Endosperm transfer cells (ETC) are one of four main types of cells in endosperm. A characteristic feature of ETC is the presence of cell wall in-growths that create an enlarged plasma membrane surface area. This specialized cell structure is important for the specific function of ETC, which is to transfer nutrients from maternal vascular tissue to endosperm. ETC-specific genes are of particular interest to plant biotechnologists, who use genetic engineering to improve grain quality and yield characteristics of important field crops. The success of molecular biology-based approaches to manipulating ETC function is dependent on a thorough understanding of the functions of ETC-specific genes and ETC-specific promoters. The aim of this review is to summarize the existing data on structure and function of ETC-specific genes and their products. Potential applications of ETC-specific genes, and in particular their promoters for biotechnology will be discussed.

DNA substrate preparation for atomic force microscopy studies of protein–DNA interactions

Protein-DNA interactions provide fundamental control mechanisms over biologically essential processes such as DNA replication, transcription, and repair. However, many details of these mechanisms still remain unclear. Atomic force microscopy (AFM) analyses provide unique and important structural and functional information on such protein-DNA interactions at the level of the individual molecules. The high sensitivity of the method with topographical visualization of all sample components also demands for extremely clean and pure materials. Here, we provide an overview of molecular biology-based approaches to produce DNA substrates for AFM imaging as well as other types of experiments, such as optical or magnetic tweezers, that profit from controllable substrate properties in long DNA fragments. We present detailed strategies to produce different types of motifs in DNA that are frequently employed targets of protein interactions. Importantly, the presented preparation techniques imply exact knowledge of the location of the introduced specific target sites within the DNA fragments, allowing for a distinction between specific and non-specific protein-DNA interactions in the AFM images and for separate conformational analyses of the different types of protein-DNA complexes.

Identifying driver mutations from sequencing data of heterogeneous tumors in the era of personalized genome sequencing

Distinguishing driver mutations from passenger mutations is critical to the understanding of the molecular mechanisms of carcinogenesis and for identifying prognostic and diagnostic markers as well as therapeutic targets. We reviewed the current approaches and software for identifying driver mutations from passenger mutations including both biology-based approaches and machine-learning-based approaches. We also reviewed approaches to identify driver mutations in the context of pathways or gene sets. Finally, we discussed the challenges of predicting driver mutations considering the complexities of inter- and intra-tumor heterogeneity as well as the evolution and progression of tumors.

Multitask learning for host–pathogen protein interactions

Motivation: An important aspect of infectious disease research involves understanding the differences and commonalities in the infection mechanisms underlying various diseases. Systems biology-based approaches study infectious diseases by analyzing the interactions between the host species and the pathogen organisms. This work aims to combine the knowledge from experimental studies of host–pathogen interactions in several diseases to build stronger predictive models. Our approach is based on a formalism from machine learning called ‘multitask learning’, which considers the problem of building models across tasks that are related to each other. A ‘task’ in our scenario is the set of host–pathogen protein interactions involved in one disease. To integrate interactions from several tasks (i.e. diseases), our method exploits the similarity in the infection process across the diseases. In particular, we use the biological hypothesis that similar pathogens target the same critical biological processes in the host, in defining a common structure across the tasks.Results: Our current work on host–pathogen protein interaction prediction focuses on human as the host, and four bacterial species as pathogens. The multitask learning technique we develop uses a task-based regularization approach. We find that the resulting optimization problem is a difference of convex (DC) functions. To optimize, we implement a Convex–Concave procedure-based algorithm. We compare our integrative approach to baseline methods that build models on a single host–pathogen protein interaction dataset. Our results show that our approach outperforms the baselines on the training data. We further analyze the protein interaction predictions generated by the models, and find some interesting insights.Availability: The predictions and code are available at: http://www.cs.cmu.edu/∼mkshirsa/ismb2013_paper320.htmlContact: j.klein-seetharaman@warwick.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

Chemical biology-based approaches on fluorescent labeling of proteins in live cells

Recently, significant advances have been made in live cell imaging owing to the rapid development of selective labeling of proteins in vivo. Green fluorescent protein (GFP) was the first example of fluorescent reporters genetically introduced to protein of interest (POI). While GFP and various types of engineered fluorescent proteins (FPs) have been actively used for live cell imaging for many years, the size and the limited windows of fluorescent spectra of GFP and its variants set limits on possible applications. In order to complement FP-based labeling methods, alternative approaches that allow incorporation of synthetic fluorescent probes to target POIs were developed. Synthetic fluorescent probes are smaller than fluorescent proteins, often have improved photochemical properties, and offer a larger variety of colors. These synthetic probes can be introduced to POIs selectively by numerous approaches that can be largely categorized into chemical recognition-based labeling, which utilizes metal-chelating peptide tags and fluorophore-carrying metal complexes, and biological recognition-based labeling, such as (1) specific non-covalent binding between an enzyme tag and its fluorophore-carrying substrate, (2) self-modification of protein tags using substrate variants conjugated to fluorophores, (3) enzymatic reaction to generate a covalent binding between a small molecule substrate and a peptide tag, and (4) split-intein-based C-terminal labeling of target proteins. The chemical recognition-based labeling reaction often suffers from compromised selectivity of metal-ligand interaction in the cytosolic environment, consequently producing high background signals. Use of protein-substrate interactions or enzyme-mediated reactions generally shows improved specificity but each method has its limitations. Some examples are the presence of large linker protein, restriction on the choice of introducible probes due to the substrate specificity of enzymes, and competitive reaction mediated by an endogenous analogue of the introduced protein tag. These limitations have been addressed, in part, by the split-intein-based labeling approach, which introduces fluorescent probes with a minimal size (~4 amino acids) peptide tag. In this review, the advantages and the limitations of each labeling method are discussed.

Traceless Affinity Labeling of Endogenous Proteins for Functional Analysis in Living Cells

Protein labeling and imaging techniques have provided tremendous opportunities to study the structure, function, dynamics, and localization of individual proteins in the complex environment of living cells. Molecular biology-based approaches, such as GFP-fusion tags and monoclonal antibodies, have served as important tools for the visualization of individual proteins in cells. Although these techniques continue to be valuable for live cell imaging, they have a number of limitations that have only been addressed by recent progress in chemistry-based approaches. These chemical approaches benefit greatly from the smaller probe sizes that should result in fewer perturbations to proteins and to biological systems as a whole. Despite the research in this area, so far none of these labeling techniques permit labeling and imaging of selected endogenous proteins in living cells. Researchers have widely used affinity labeling, in which the protein of interest is labeled by a reactive group attached to a ligand, to identify and characterize proteins. Since the first report of affinity labeling in the early 1960s, efforts to fine-tune the chemical structures of both the reactive group and ligand have led to protein labeling with excellent target selectivity in the whole proteome of living cells. Although the chemical probes used for affinity labeling generally inactivate target proteins, this strategy holds promise as a valuable tool for the labeling and imaging of endogenous proteins in living cells and by extension in living animals. In this Account, we summarize traceless affinity labeling, a technique explored mainly in our laboratory. In our overview of the different labeling techniques, we emphasize the challenge of designing chemical probes that allow for dissociation of the affinity module (often a ligand) after the labeling reaction so that the labeled protein retains its native function. This feature distinguishes the traceless labeling approach from the traditional affinity labeling method and allows for real-time monitoring of protein activity. With the high target specificity and biocompatibility of this technique, we have achieved individual labeling and imaging of endogenously expressed proteins in samples of high biological complexity. We also highlight applications in which our current approach enabled the monitoring of important biological events, such as ligand binding, in living cells. These novel chemical labeling techniques are expected to provide a molecular toolbox for studying a wide variety of proteins and beyond in living cells.

Systems biology-based approaches toward understanding drought tolerance in food crops

Economically important crops, such as maize, wheat, rice, barley, and other food crops are affected by even small changes in water potential at important growth stages. Developing a comprehensive understanding of host response to drought requires a global view of the complex mechanisms involved. Research on drought tolerance has generally been conducted using discipline-specific approaches. However, plant stress response is complex and interlinked to a point where discipline-specific approaches do not give a complete global analysis of all the interlinked mechanisms. Systems biology perspective is needed to understand genome-scale networks required for building long-lasting drought resistance. Network maps have been constructed by integrating multiple functional genomics data with both model plants, such as Arabidopsis thaliana, Lotus japonicus, and Medicago truncatula, and various food crops, such as rice and soybean. Useful functional genomics data have been obtained from genome-wide comparative transcriptome and proteome analyses of drought responses from different crops. This integrative approach used by many groups has led to identification of commonly regulated signaling pathways and genes following exposure to drought. Combination of functional genomics and systems biology is very useful for comparative analysis of other food crops and has the ability to develop stable food systems worldwide. In addition, studying desiccation tolerance in resurrection plants will unravel how combination of molecular genetic and metabolic processes interacts to produce a resurrection phenotype. Systems biology-based approaches have helped in understanding how these individual factors and mechanisms (biochemical, molecular, and metabolic) “interact” spatially and temporally. Signaling network maps of such interactions are needed that can be used to design better engineering strategies for improving drought tolerance of important crop species.

Clinical application of a systems model of apoptosis execution for the prediction of colorectal cancer therapy responses and personalisation of therapy

ObjectiveKey to the clinical management of colorectal cancer is identifying tools which aid in assessing patient prognosis and determining more effective and personalised treatment strategies. We evaluated whether an experimental...

ChemInform Abstract: Chemoenzymatic and Bioenzymatic Synthesis of Carbohydrate Containing Natural Products

AbstractReview: 221 refs.

Systems biology of the autophagy-lysosomal pathway

The mechanisms of the control and activity of the autophagy-lysosomal protein degradation machinery are emerging as an important theme for neurodevelopment and neurodegeneration. However, the underlying regulatory and functional networks of known genes controlling autophagy and lysosomal function and their role in disease are relatively unexplored. We performed a systems biology-based integrative computational analysis to study the interactions between molecular components and to develop models for regulation and function of genes involved in autophagy and lysosomal function. Specifically, we analyzed transcriptional and microRNA-based post-transcriptional regulation of these genes and performed functional enrichment analyses to understand their involvement in nervous system-related diseases and phenotypes. Transcriptional regulatory network analysis showed that binding sites for transcription factors, SREBP1, USF, AP-1 and NFE2, are common among autophagy and lysosomal genes. MicroRNA enrichment analysis revealed miR-130, 98, 124, 204 and 142 as the putative post-transcriptional regulators of the autophagy-lysosomal pathway genes. Pathway enrichment analyses revealed that the mTOR and insulin signaling pathways are important in the regulation of genes involved in autophagy. In addition, we found that glycosaminoglycan and glycosphingolipid pathways also make a major contribution to lysosomal gene regulation. The analysis confirmed the known contribution of the autophagy-lysosomal genes to Alzheimer and Parkinson diseases and also revealed potential involvement in tuberous sclerosis, neuronal ceroidlipofuscinoses, sepsis and lung, liver and prostatic neoplasms. To further probe the impact of autophagy-lysosomal gene deficits on neurologically-linked phenotypes, we also mined the mouse knockout phenotype data for the autophagylysosomal genes and found them to be highly predictive of nervous system dysfunction. Overall this study demonstrates the utility of systems biology-based approaches for understanding the autophagy-lysosomal pathways and gaining additional insights into the potential impact of defects in these complex biological processes.

Health technology assessment in the era of personalized health care

This article examines the challenges for health technology assessment (HTA) in the light of new developments of personalized health care, focusing on European HTA perspectives. Using the example of the Integrated Genome Research Network - Mutanom (IG Mutanom) project, with focus on personalized cancer diagnostics and treatment, we assess the scope of current HTA and examine it prospectively in the context of the translation of basic and clinical research into public health genomics and personalized health care. The approaches developed within the IG-Mutanom project are based on innovative technology potentially providing targeted therapies for cancer; making translation into clinical practice requires a novel course of action, however. New models of HTA are needed that can account for the unique types of evidence inherent to individualized targeted therapies. Using constructive health technology assessment (CTA) models is an option, but further suitable models should be developed. Integrative, systems biology-based approaches toward personalized medicine call for novel assessment methods. The translation of their highly innovative technologies into the practice of health care requires the development of new HTA concepts.

Arabidopsis NDR1 Is an Integrin-Like Protein with a Role in Fluid Loss and Plasma Membrane-Cell Wall Adhesion

Arabidopsis (Arabidopsis thaliana) NON-RACE-SPECIFIC DISEASE RESISTANCE1 (NDR1), a plasma membrane-localized protein, plays an essential role in resistance mediated by the coiled-coil-nucleotide-binding site-leucine-rich repeat class of resistance (R) proteins, which includes RESISTANCE TO PSEUDOMONAS SYRINGAE2 (RPS2), RESISTANCE TO PSEUDOMONAS SYRINGAE PV MACULICOLA1, and RPS5. Infection with Pseudomonas syringae pv tomato DC3000 expressing the bacterial effector proteins AvrRpt2, AvrB, and AvrPphB activates resistance by the aforementioned R proteins. Whereas the genetic requirement for NDR1 in plant disease resistance signaling has been detailed, our study focuses on determining a global, physiological role for NDR1. Through the use of homology modeling and structure threading, NDR1 was predicted to have a high degree of structural similarity to Arabidopsis LATE EMBRYOGENESIS ABUNDANT14, a protein implicated in abiotic stress responses. Specific protein motifs also point to a degree of homology with mammalian integrins, well-characterized proteins involved in adhesion and signaling. This structural homology led us to examine a physiological role for NDR1 in preventing fluid loss and maintaining cell integrity through plasma membrane-cell wall adhesions. Our results show a substantial alteration in induced (i.e. pathogen-inoculated) electrolyte leakage and a compromised pathogen-associated molecular pattern-triggered immune response in ndr1-1 mutant plants. As an extension of these analyses, using a combination of genetic and cell biology-based approaches, we have identified a role for NDR1 in mediating plasma membrane-cell wall adhesions. Taken together, our data point to a broad role for NDR1 both in mediating primary cellular functions in Arabidopsis through maintaining the integrity of the cell wall-plasma membrane connection and as a key signaling component of these responses during pathogen infection.