Showing papers in "Computational and structural biotechnology journal in 2015"
TL;DR: Given the growing trend on the application of ML methods in cancer research, this work presents here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.
Abstract: Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.
1,991 citations
TL;DR: A novel reference model, named zero interaction potency (ZIP), is proposed, which captures the drug interaction relationships by comparing the change in the potency of the dose–response curves between individual drugs and their combinations, and utilizes a delta score to quantify the deviation from the expectation of zero interaction.
Abstract: Rational design of multi-targeted drug combinations is a promising strategy to tackle the drug resistance problem for many complex disorders. A drug combination is usually classified as synergistic or antagonistic, depending on the deviation of the observed combination response from the expected effect calculated based on a reference model of non-interaction. The existing reference models were proposed originally for low-throughput drug combination experiments, which make the model assumptions often incompatible with the complex drug interaction patterns across various dose pairs that are typically observed in large-scale dose–response matrix experiments. To address these limitations, we proposed a novel reference model, named zero interaction potency (ZIP), which captures the drug interaction relationships by comparing the change in the potency of the dose–response curves between individual drugs and their combinations. We utilized a delta score to quantify the deviation from the expectation of zero interaction, and proved that a delta score value of zero implies both probabilistic independence and dose additivity. Using data from a large-scale anticancer drug combination experiment, we demonstrated empirically how the ZIP scoring approach captures the experimentally confirmed drug synergy while keeping the false positive rate at a low level. Further, rather than relying on a single parameter to assess drug interaction, we proposed the use of an interaction landscape over the full dose–response matrix to identify and quantify synergistic and antagonistic dose regions. The interaction landscape offers an increased power to differentiate between various classes of drug combinations, and may therefore provide an improved means for understanding their mechanisms of action toward clinical translation.
433 citations
TL;DR: This review will present data demonstrating the ability of A2a receptor blockade to enhance tumor vaccines, checkpoint blockade and adoptive T cell therapy, and explore the complexities of adenosine signaling in the immune response.
Abstract: The last several years have witnessed exciting progress in the development of immunotherapy for the treatment of cancer. This has been due in great part to the development of so-called checkpoint blockade. That is, antibodies that block inhibitory receptors such as CTLA-4 and PD-1 and thus unleash antigen-specific immune responses against tumors. It is clear that tumors evade the immune response by usurping pathways that play a role in negatively regulating normal immune responses. In this regard, adenosine in the immune microenvironment leading to the activation of the A2a receptor has been shown to represent one such negative feedback loop. Indeed, the tumor microenvironment has relatively high concentrations of adenosine. To this end, blocking A2a receptor activation has the potential to markedly enhance anti-tumor immunity in mouse models. This review will present data demonstrating the ability of A2a receptor blockade to enhance tumor vaccines, checkpoint blockade and adoptive T cell therapy. Also, as several recent studies have demonstrated that under certain conditions A2a receptor blockade can enhance tumor progression, we will also explore the complexities of adenosine signaling in the immune response. Despite important nuances to the A2a receptor pathway that require further elucidation, studies to date strongly support the development of A2a receptor antagonists (some of which have already been tested in phase III clinical trials for Parkinson Disease) as novel modalities in the immunotherapy armamentarium.
197 citations
TL;DR: Some of the different experimental and bioinformatic strategies applied to analyze the 16S rRNA gene profiling and shotgun sequencing data of the human microbiome are described and how some of the recent insights can provide more detailed description on the interactions between microorganisms and viruses in oral and gut microbiomes are discussed.
Abstract: The advances in experimental methods and the development of high performance bioinformatic tools have substantially improved our understanding of microbial communities associated with human niches Many studies have documented that changes in microbial abundance and composition of the human microbiome is associated with human health and diseased state The majority of research on human microbiome is typically focused in the analysis of one level of biological information, ie, metagenomics or metatranscriptomics In this review, we describe some of the different experimental and bioinformatic strategies applied to analyze the 16S rRNA gene profiling and shotgun sequencing data of the human microbiome We also discuss how some of the recent insights in the combination of metagenomics, metatranscriptomics and viromics can provide more detailed description on the interactions between microorganisms and viruses in oral and gut microbiomes Recent studies on viromics have begun to gain importance due to the potential involvement of viruses in microbial dysbiosis In addition, metatranscriptomic combined with metagenomic analysis have shown that a substantial fraction of microbial transcripts can be differentially regulated relative to their microbial genomic abundances Thus, understanding the molecular interactions in the microbiome using the combination of metagenomics, metatranscriptomics and viromics is one of the main challenges towards a system level understanding of human microbiome
168 citations
TL;DR: This work identified several novel clusters of PKs and LPs from genomes deposited in the database and suggests that a substantial fraction of predicted LPs and type I PKs are uncharacterized, and their functions remain to be studied.
Abstract: Bacillus and related genera in the Bacillales within the Firmicutes harbor a variety of secondary metabolite gene clusters encoding polyketide synthases and non-ribosomal peptide synthetases responsible for remarkable diverse number of polyketides (PKs) and lipopeptides (LPs). These compounds may be utilized for medical and agricultural applications. Here, we summarize the knowledge on structural diversity and underlying gene clusters of LPs and PKs in the Bacillales. Moreover, we evaluate by using published prediction tools the potential metabolic capacity of these bacteria to produce type I PKs or LPs. The huge sequence repository of bacterial genomes and metagenomes provides the basis for such genome-mining to reveal the potential for novel structurally diverse secondary metabolites. The otherwise cumbersome task to isolate often unstable PKs and deduce their structure can be streamlined. Using web based prediction tools, we identified here several novel clusters of PKs and LPs from genomes deposited in the database. Our analysis suggests that a substantial fraction of predicted LPs and type I PKs are uncharacterized, and their functions remain to be studied. Known and predicted LPs and PKs occurred in the majority of the plant associated genera, predominantly in Bacillus and Paenibacillus. Surprisingly, many genera from other environments contain no or few of such compounds indicating the role of these secondary metabolites in plant-associated niches.
125 citations
TL;DR: The result of these processes is neuronal necrosis, which is a programmed cell death that is the basis of all acute neuronal injury in the adult brain.
Abstract: Excitotoxicity involves the excessive release of glutamate from presynaptic nerve terminals and from reversal of astrocytic glutamate uptake, when there is excessive neuronal depolarization. N-methyl-d-aspartate (NMDA) receptors, a subtype of glutamate receptor, are activated in postsynaptic neurons, opening their receptor-operated cation channels to allow Ca2 + influx. The Ca2 + influx activates two enzymes, calpain I and neuronal nitric oxide synthase (nNOS). Calpain I activation produces mitochondrial release of cytochrome c (cyt c), truncated apoptosis-inducing factor (tAIF) and endonuclease G (endoG), the lysosomal release of cathepsins B and D and DNase II, and inactivation of the plasma membrane Na+–Ca2 + exchanger, which add to the buildup of intracellular Ca2 +. tAIF is involved in large-scale DNA cleavage and cyt c may be involved in chromatin condensation; endoG produces internucleosomal DNA cleavage. The nuclear actions of the other proteins have not been determined. nNOS forms nitric oxide (NO), which reacts with superoxide (O2−) to form peroxynitrite (ONOO−). These free radicals damage cellular membranes, intracellular proteins and DNA. DNA damage activates poly(ADP-ribose) polymerase-1 (PARP-1), which produces poly(ADP-ribose) (PAR) polymers that exit nuclei and translocate to mitochondrial membranes, also releasing AIF. Poly(ADP-ribose) glycohydrolase hydrolyzes PAR polymers into ADP-ribose molecules, which translocate to plasma membranes, activating melastatin-like transient receptor potential 2 (TRPM-2) channels, which open, allowing Ca2 + influx into neurons. NADPH oxidase (NOX1) transfers electrons across cellular membranes, producing O2−. The result of these processes is neuronal necrosis, which is a programmed cell death that is the basis of all acute neuronal injury in the adult brain.
91 citations
TL;DR: This review will provide a general overview about the recent Omics-based research of the microbiota in livestock including its major findings.
Abstract: Technical progress in the field of next-generation sequencing, mass spectrometry and bioinformatics facilitates the study of highly complex biological samples such as taxonomic and functional characterization of microbial communities that virtually colonize all present ecological niches. Compared to the structural information obtained by metagenomic analyses, metaproteomic approaches provide, in addition, functional data about the investigated microbiota. In general, integration of the main Omics-technologies (genomics, transcriptomics, proteomics and metabolomics) in live science promises highly detailed information about the specific research object and helps to understand molecular changes in response to internal and external environmental factors.
The microbial communities settled in the mammalian gastrointestinal tract are essential for the host metabolism and have a major impact on its physiology and health. The microbiotas of livestock like chicken, pig and ruminants are becoming a focus of interest for veterinaries, animal nutritionists and microbiologists. While pig is more often used as an animal model for human-related studies, the rumen microbiota harbors a diversity of enzymes converting complex carbohydrates into monomers which bears high potential for biotechnological applications.
This review will provide a general overview about the recent Omics-based research of the microbiota in livestock including its major findings. Differences concerning the results of pre-Omics-approaches in livestock as well as the perspectives of this relatively new Omics-platform will be highlighted.
89 citations
TL;DR: This review addresses current challenges on RNA-seq analysis and specifically focuses on new bioinformatics tools developed for time series experiments and possible improvements in analysis, data integration as well as future applications of differential expression analysis.
Abstract: Analysis of gene expression has contributed to a plethora of biological and medical research studies. Microarrays have been intensively used for the profiling of gene expression during diverse developmental processes, treatments and diseases. New massively parallel sequencing methods, often named as RNA-sequencing (RNA-seq) are extensively improving our understanding of gene regulation and signaling networks. Computational methods developed originally for microarrays analysis can now be optimized and applied to genome-wide studies in order to have access to a better comprehension of the whole transcriptome. This review addresses current challenges on RNA-seq analysis and specifically focuses on new bioinformatics tools developed for time series experiments. Furthermore, possible improvements in analysis, data integration as well as future applications of differential expression analysis are discussed.
88 citations
TL;DR: The importance of understanding the specific methodological biases that can contribute to improve the universality of metagenomic approaches is highlighted, as removing a specific bias in a molecular workflow improves detection in amplicon sequencing, but it was insufficient to overcome the limitations for detecting endospore-forming Firmicutes in whole-genome metagenomics.
Abstract: Microbial diversity studies based on metagenomic sequencing have greatly enhanced our knowledge of the microbial world. However, one caveat is the fact that not all microorganisms are equally well detected, questioning the universality of this approach. Firmicutes are known to be a dominant bacterial group. Several Firmicutes species are endospore formers and this property makes them hardy in potentially harsh conditions, and thus likely to be present in a wide variety of environments, even as residents and not functional players. While metagenomic libraries can be expected to contain endospore formers, endospores are known to be resilient to many traditional methods of DNA isolation and thus potentially undetectable. In this study we evaluated the representation of endospore-forming Firmicutes in 73 published metagenomic datasets using two molecular markers unique to this bacterial group (spo0A and gpr). Both markers were notably absent in well-known habitats of Firmicutes such as soil, with spo0A found only in three mammalian gut microbiomes. A tailored DNA extraction method resulted in the detection of a large diversity of endospore-formers in amplicon sequencing of the 16S rRNA and spo0A genes. However, shotgun classification was still poor with only a minor fraction of the community assigned to Firmicutes. Thus, removing a specific bias in a molecular workflow improves detection in amplicon sequencing, but it was insufficient to overcome the limitations for detecting endospore-forming Firmicutes in whole-genome metagenomics. In conclusion, this study highlights the importance of understanding the specific methodological biases that can contribute to improve the universality of metagenomic approaches.
84 citations
TL;DR: Omics approaches are enabled by high-throughput 21st century technologies and this review will discuss how their implementation has revolutionised the authors' understanding of microbial communities.
Abstract: Some of the most transformative discoveries promising to enable the resolution of this century's grand societal challenges will most likely arise from environmental science and particularly environmental microbiology and biotechnology. Understanding how microbes interact in situ, and how microbial communities respond to environmental changes remains an enormous challenge for science. Systems biology offers a powerful experimental strategy to tackle the exciting task of deciphering microbial interactions. In this framework, entire microbial communities are considered as metaorganisms and each level of biological information (DNA, RNA, proteins and metabolites) is investigated along with in situ environmental characteristics. In this way, systems biology can help unravel the interactions between the different parts of an ecosystem ultimately responsible for its emergent properties. Indeed each level of biological information provides a different level of characterisation of the microbial communities. Metagenomics, metatranscriptomics, metaproteomics, metabolomics and SIP-omics can be employed to investigate collectively microbial community structure, potential, function, activity and interactions. Omics approaches are enabled by high-throughput 21st century technologies and this review will discuss how their implementation has revolutionised our understanding of microbial communities.
79 citations
TL;DR: The efforts to address the challenge of annotating the function at the molecular level of uncharacterized proteins by evaluating and testing the different approaches to predicting the function of proteins of unknown function are reviewed.
Abstract: With the exponential growth in the determination of protein sequences and structures via genome sequencing and structural genomics efforts, there is a growing need for reliable computational methods to determine the biochemical function of these proteins. This paper reviews the efforts to address the challenge of annotating the function at the molecular level of uncharacterized proteins. While sequence- and three-dimensional-structure-based methods for protein function prediction have been reviewed previously, the recent trends in local structure-based methods have received less attention. These local structure-based methods are the primary focus of this review. Computational methods have been developed to predict the residues important for catalysis and the local spatial arrangements of these residues can be used to identify protein function. In addition, the combination of different types of methods can help obtain more information and better predictions of function for proteins of unknown function. Global initiatives, including the Enzyme Function Initiative (EFI), COMputational BRidges to EXperiments (COMBREX), and the Critical Assessment of Function Annotation (CAFA), are evaluating and testing the different approaches to predicting the function of proteins of unknown function. These initiatives and global collaborations will increase the capability and reliability of methods to predict biochemical function computationally and will add substantial value to the current volume of structural genomics data by reducing the number of absent or inaccurate functional annotations.
TL;DR: A collection of simulation results are reviewed that allow outlining the current status of the field of knotted proteins, and discuss directions for future research.
Abstract: Knotted proteins have their native structures arranged in the form of an open knot. In the last ten years researchers have been making significant efforts to reveal their folding mechanism and understand which functional advantage(s) knots convey to their carriers. Molecular simulations have been playing a fundamental role in this endeavor, and early computational predictions about the knotting mechanism have just been confirmed in wet lab experiments. Here we review a collection of simulation results that allow outlining the current status of the field of knotted proteins, and discuss directions for future research.
TL;DR: It is found that the accuracy of SQM-DH methods for non-covalent interactions is very often reported to be comparable to dispersion-corrected density functional theory (DFT-D), while computation times are about three orders of magnitude lower.
Abstract: Recent successes and failures of the application of ‘enhanced’ semiempirical QM (SQM) methods are reviewed in the light of the benefits and backdraws of adding dispersion (D) and hydrogen-bond (H) correction terms. We find that the accuracy of SQM-DH methods for non-covalent interactions is very often reported to be comparable to dispersion-corrected density functional theory (DFT-D), while computation times are about three orders of magnitude lower. SQM-DH methods thus open up a possibility to simulate realistically large model systems for problems both in life and materials science with comparably high accuracy.
TL;DR: How FSCV can be used to detect adenosine release, compared with other techniques used to measureadenosine, and an overview of adenoine signaling that has been characterized using F SCV are outlined.
Abstract: Adenosine is a signaling molecule and downstream product of ATP that acts as a neuromodulator. Adenosine regulates physiological processes, such as neurotransmission and blood flow, on a time scale of minutes to hours. Recent developments in electrochemical techniques, including fast-scan cyclic voltammetry (FSCV), have allowed direct detection of adenosine with sub-second temporal resolution. FSCV studies have revealed a novel mode of rapid signaling that lasts only a few seconds. This rapid release of adenosine can be evoked by electrical or mechanical stimulations or it can be observed spontaneously without stimulation. Adenosine signaling on this time scale is activity dependent; however, the mode of release is not fully understood. Rapid adenosine release modulates oxygen levels and evoked dopamine release, indicating that adenosine may have a rapid modulatory role. In this review, we outline how FSCV can be used to detect adenosine release, compare FSCV with other techniques used to measure adenosine, and present an overview of adenosine signaling that has been characterized using FSCV. These studies point to a rapid mode of adenosine modulation, whose mechanism and function will continue to be characterized in the future.
TL;DR: The data suggest that the hydrogen bond coupling between hydration water and protein hydrophilic groups is crucial in triggering the main mechanisms that define the enzymatic activity of proteins.
Abstract: The role the solvent plays in determining the biological activity of proteins is of primary importance. Water is the solvent of life and proteins need at least a water monolayer covering their surface in order to become biologically active. We study how the properties of water and the effect of its coupling with the hydrophilic moieties of proteins govern the regime of protein activity. In particular we follow, by means of Fourier Transform Infrared spectroscopy, the thermal evolution of the amide vibrational modes of hydrated lysozyme in the temperature interval 180 K < T < 350 K. In such a way we are able to observe the thermal limit of biological activity characterizing hydrated lysozyme. Finally we focus on the region of lysozyme thermal denaturation by following the evolution of the proton Nuclear Magnetic Resonance (NMR) spectra for 298 K < T < 366 K with the High-Resolution Magic Angle Spinning probe. Our data suggest that the hydrogen bond coupling between hydration water and protein hydrophilic groups is crucial in triggering the main mechanisms that define the enzymatic activity of proteins.
TL;DR: An integrative and interdisciplinary approach may provide new strategies with regard to chronotherapeutic treatment and new insights concerning the restoration of the circadian timing in clock-associated diseases.
Abstract: The circadian clock is a powerful endogenous timing system, which allows organisms to fine-tune their physiology and behaviour to the geophysical time. The interplay of a distinct set of core-clock genes and proteins generates oscillations in expression of output target genes which temporally regulate numerous molecular and cellular processes. The study of the circadian timing at the organismal as well as at the cellular level outlines the field of chronobiology, which has been highly interdisciplinary ever since its origins. The development of high-throughput approaches enables the study of the clock at a systems level. In addition to experimental approaches, computational clock models exist which allow the analysis of rhythmic properties of the clock network. Such mathematical models aid mechanistic understanding and can be used to predict outcomes of distinct perturbations in clock components, thereby generating new hypotheses regarding the putative function of particular clock genes. Perturbations in the circadian timing system are linked to numerous molecular dysfunctions and may result in severe pathologies including cancer. A comprehensive knowledge regarding the mechanistic of the circadian system is crucial to develop new procedures to investigate pathologies associated with a deregulated clock.
In this manuscript we review the combination of experimental methodologies, bioinformatics and theoretical models that have been essential to explore this remarkable timing-system. Such an integrative and interdisciplinary approach may provide new strategies with regard to chronotherapeutic treatment and new insights concerning the restoration of the circadian timing in clock-associated diseases.
TL;DR: A major focus will be placed on recent findings of purinergic signaling in mesenchymal stem cells addressed in in vitro and in vivo studies, since stem cell fate might be manipulated by this system guiding differentiation towards the desired lineage in the future.
Abstract: A major challenge modern society has to face is the increasing need for tissue regeneration due to degenerative diseases or tumors, but also accidents or warlike conflicts. There is great hope that stem cell-based therapies might improve current treatments of cardiovascular diseases, osteochondral defects or nerve injury due to the unique properties of stem cells such as their self-renewal and differentiation potential. Since embryonic stem cells raise severe ethical concerns and are prone to teratoma formation, adult stem cells are still in the focus of research. Emphasis is placed on cellular signaling within these cells and in between them for a better understanding of the complex processes regulating stem cell fate. One of the oldest signaling systems is based on nucleotides as ligands for purinergic receptors playing an important role in a huge variety of cellular processes such as proliferation, migration and differentiation. Besides their natural ligands, several artificial agonists and antagonists have been identified for P1 and P2 receptors and are already used as drugs. This review outlines purinergic receptor expression and signaling in stem cells metabolism. We will briefly describe current findings in embryonic and induced pluripotent stem cells as well as in cancer-, hematopoietic-, and neural crest-derived stem cells. The major focus will be placed on recent findings of purinergic signaling in mesenchymal stem cells addressed in in vitro and in vivo studies, since stem cell fate might be manipulated by this system guiding differentiation towards the desired lineage in the future.
TL;DR: This review will address the current biological understanding of various SENP isoforms and their role in the pathogenesis of different cancers and other diseases, and summarize successful examples of computational screening that allowed the identification of SENP inhibitors with therapeutic potential.
Abstract: Sumoylation is a reversible post-translational modification that involves the covalent attachment of small ubiquitin-like modifier (SUMO) proteins to their substrate proteins. Prior to their conjugation, SUMO proteins need to be proteolytically processed from its precursor form to mature or active form. SUMO specific proteases (SENPs) are cysteine proteases that cleave the pro or inactive form of SUMO at C-terminus using its hydrolase activity to expose two glycine residues. SENPs also catalyze the de-conjugation of SUMO proteins using their isopeptidase activity, which is crucial for recycling of SUMO from substrate proteins. SENPs are important for maintaining the balance between sumoylated and unsumoylated proteins required for normal cellular physiology. Several studies reported the overexpression of SENPs in disease conditions and highlighted their role in the development of various diseases, especially cancer. In this review, we will address the current biological understanding of various SENP isoforms and their role in the pathogenesis of different cancers and other diseases. We will then discuss the advances in the development of protein-based, peptidyl and small molecule inhibitors of various SENP isoforms. Finally, we will summarize successful examples of computational screening that allowed the identification of SENP inhibitors with therapeutic potential.
TL;DR: Understanding of how genetic recombination drives pneumococcal adaptation and evolution has been transformed by the rapid influx of whole genome sequence data and the advent of novel analysis methods and powerful computational tools for population genetics and evolution studies.
Abstract: Streptococcus pneumoniae (the pneumococcus) is a highly recombinogenic bacterium responsible for a high burden of human disease globally. Genetic recombination, a process in which exogenous DNA is acquired and incorporated into its genome, is a key evolutionary mechanism employed by the pneumococcus to rapidly adapt to selective pressures. The rate at which the pneumococcus acquires genetic variation through recombination is much higher than the rate at which the organism acquires variation through spontaneous mutations. This higher rate of variation allows the pneumococcus to circumvent the host innate and adaptive immune responses, escape clinical interventions, including antibiotic therapy and vaccine introduction. The rapid influx of whole genome sequence (WGS) data and the advent of novel analysis methods and powerful computational tools for population genetics and evolution studies has transformed our understanding of how genetic recombination drives pneumococcal adaptation and evolution. Here we discuss how genetic recombination has impacted upon the evolution of the pneumococcus.
TL;DR: The identification of molecular subtyping of gastric adenocarcinoma and the development of integrated genomics approaches for clinical applications such as prediction of clinical intervening emerge as an essential phase toward personalized medicine in treating gastric cancer.
Abstract: Gastric cancer, a highly heterogeneous disease, is the second leading cause of cancer death and the fourth most common cancer globally, with East Asia accounting for more than half of cases annually. Alongside TNM staging, gastric cancer clinic has two well-recognized classification systems, the Lauren classification that subdivides gastric adenocarcinoma into intestinal and diffuse types and the alternative World Health Organization system that divides gastric cancer into papillary, tubular, mucinous (colloid), and poorly cohesive carcinomas. Both classification systems enable a better understanding of the histogenesis and the biology of gastric cancer yet have a limited clinical utility in guiding patient therapy due to the molecular heterogeneity of gastric cancer. Unprecedented whole-genome-scale data have been catalyzing and advancing the molecular subtyping approach. Here we cataloged and compared those published gene expression profiling signatures in gastric cancer. We summarized recent integrated genomic characterization of gastric cancer based on additional data of somatic mutation, chromosomal instability, EBV virus infection, and DNA methylation. We identified the consensus patterns across these signatures and identified the underlying molecular pathways and biological functions. The identification of molecular subtyping of gastric adenocarcinoma and the development of integrated genomics approaches for clinical applications such as prediction of clinical intervening emerge as an essential phase toward personalized medicine in treating gastric cancer.
TL;DR: The role that TNAP plays as an ectonucleotidase in CNS regulating the levels of extracellular ATP and consequently purinergic signaling is focused on.
Abstract: Tissue-nonspecific alkaline phosphatase (TNAP) is one of the four isozymes in humans and mice that have the capacity to hydrolyze phosphate groups from a wide spectrum of physiological substrates. Among these, TNAP degrades substrates implicated in neurotransmission. Transgenic mice lacking TNAP activity display the characteristic skeletal and dental phenotype of infantile hypophosphatasia, as well as spontaneous epileptic seizures and die around 10 days after birth. This physiopathology, linked to the expression pattern of TNAP in the central nervous system (CNS) during embryonic stages, suggests an important role for TNAP in neuronal development and synaptic function, situating it as a good target to be explored for the treatment of neurological diseases. In this review, we will focus mainly on the role that TNAP plays as an ectonucleotidase in CNS regulating the levels of extracellular ATP and consequently purinergic signaling.
TL;DR: Typical application scenarios of MD simulations in GPCR drug discovery programs and their scope and limitations will be described on the basis of two selected case studies, namely the binding of small molecule antagonists to the human CC chemokine receptor 3 (CCR3) and a detailed investigation of the interplay between receptor dynamics and solvation.
Abstract: Recent years have seen a tremendous progress in the elucidation of experimental structural information for G-protein coupled receptors (GPCRs). Although for the vast majority of pharmaceutically relevant GPCRs structural information is still accessible only by homology models the steadily increasing amount of structural information fosters the application of structure-based drug design tools for this important class of drug targets. In this article we focus on the application of molecular dynamics (MD) simulations in GPCR drug discovery programs. Typical application scenarios of MD simulations and their scope and limitations will be described on the basis of two selected case studies, namely the binding of small molecule antagonists to the human CC chemokine receptor 3 (CCR3) and a detailed investigation of the interplay between receptor dynamics and solvation for the binding of small molecules to the human muscarinic acetylcholine receptor 3 (hM3R).
TL;DR: This mini-review briefly outlines the key regulators of lipid metabolism, their dysregulation, and how computational modeling is being used to gain an increased insight into this system.
Abstract: One of the greatest challenges in biology is to improve the understanding of the mechanisms which underpin aging and how these affect health. The need to better understand aging is amplified by demographic changes, which have caused a gradual increase in the global population of older people. Aging western populations have resulted in a rise in the prevalence of age-related pathologies. Of these diseases, cardiovascular disease is the most common underlying condition in older people. The dysregulation of lipid metabolism due to aging impinges significantly on cardiovascular health. However, the multifaceted nature of lipid metabolism and the complexities of its interaction with aging make it challenging to understand by conventional means. To address this challenge computational modeling, a key component of the systems biology paradigm is being used to study the dynamics of lipid metabolism. This mini-review briefly outlines the key regulators of lipid metabolism, their dysregulation, and how computational modeling is being used to gain an increased insight into this system.
TL;DR: Microorganisms able to degrade gaseous alkanes were recently obtained from deep-sea and terrestrial sediments around hydrocarbon seeps and an additional mechanism of activation at the terminal carbon atoms was demonstrated for propane, which could in principle be employed also for the activation of ethane.
Abstract: The short chain, gaseous alkanes ethane, propane, n - and iso -butane are released in significant amounts into the atmosphere, where they contribute to tropospheric chemistry and ozone formation. Biodegradation of gaseous alkanes by aerobic microorganisms, mostly bacteria and fungi isolated from terrestrial environments, has been known for several decades. The first indications for short chain alkane anaerobic degradation were provided by geochemical studies of deep-sea environments around hydrocarbon seeps, and included the uncoupling of the sulfate-reduction and anaerobic oxidation of methane rates, the consumption of gaseous alkanes in anoxic sediments, or the enrichment in 13 C of gases in interstitial water vs. the source gas. Microorganisms able to degrade gaseous alkanes were recently obtained from deep-sea and terrestrial sediments around hydrocarbon seeps. Up to date, only sulfate-reducing pure or enriched cultures with ethane, propane and n -butane have been reported. The only pure culture presently available, strain BuS5, is affiliated to the Desulfosarcina – Desulfococcus cluster of the Deltaproteobacteria . Other phylotypes involved in gaseous alkane degradation have been identified based on stable-isotope labeling and whole-cell hybridization. Under anoxic conditions, propane and n -butane are activated similar to the higher alkanes, by homolytic cleavage of the C H bond of a subterminal carbon atom, and addition of the ensuing radical to fumarate, yielding methylalkylsuccinates. An additional mechanism of activation at the terminal carbon atoms was demonstrated for propane, which could in principle be employed also for the activation of ethane.
TL;DR: In pediatric and adult patients after stem cell transplantation (SCT) disseminated infections caused by human cytomegalovirus (HCMV) can cause life threatening diseases, and resistance to all of these antiviral drugs may induce a severe problem in this patient cohort.
Abstract: In pediatric and adult patients after stem cell transplantation (SCT) disseminated infections caused by human cytomegalovirus (HCMV) can cause life threatening diseases. For treatment, the three antivirals ganciclovir (GCV), foscarnet (PFA) and cidofovir (CDV) are approved and most frequently used. Resistance to all of these antiviral drugs may induce a severe problem in this patient cohort. Responsible for resistance phenomena are mutations in the HCMV phosphotransferase-gene (UL97) and the polymerase-gene (UL54). Most frequently mutations in the UL97-gene are associated with resistance to GCV. Resistance against all three drugs is associated to mutations in the UL54-gene. Monitoring of drug resistance by genotyping is mostly done by PCR-based Sanger sequencing. For phenotyping with cell culture the isolation of HCMV is a prerequisite. The development of multidrug resistance with mutation in both genes is rare, but it is often associated with a fatal outcome. The manifestation of multidrug resistance is mostly associated with combined UL97/UL54-mutations. Normally, mutations in the UL97 gene occur initially followed by UL54 mutation after therapy switch. The appearance of UL54-mutation alone without any detection of UL97-mutation is rare. Interestingly, in a number of patients the UL97 mutation could be detected in specific compartments exclusively and not in blood.
TL;DR: A new paradigm is proposed for assessing confidence in the identification of known metabolites in metabonomics studies using NMR spectroscopy approaches based upon the analysis of the amount of metabolite identification information retrieved from N MR spectra relative to the molecular size of the metabolite.
Abstract: A new paradigm is proposed for assessing confidence in the identification of known metabolites in metabonomics studies using NMR spectroscopy approaches. This new paradigm is based upon the analysis of the amount of metabolite identification information retrieved from NMR spectra relative to the molecular size of the metabolite. Several new indices are proposed including: metabolite identification efficiency (MIE) and metabolite identification carbon efficiency (MICE), both of which can be easily calculated. These indices, together with some guidelines, can be used to provide a better indication of known metabolite identification confidence in metabonomics studies than existing methods. Since known metabolite identification in untargeted metabonomics studies is one of the key bottlenecks facing the science currently, it is hoped that these concepts based on molecular spectroscopic informatics, will find utility in the field.
TL;DR: A case study of a practical solution that simplifies terabyte scale data handling and provides advanced tools for NGS data analysis using the Globus Genomics system, which is an enhanced Galaxy workflow system made available as a service.
Abstract: Next generation sequencing (NGS) technologies produce massive amounts of data requiring a powerful computational infrastructure, high quality bioinformatics software, and skilled personnel to operate the tools. We present a case study of a practical solution to this data management and analysis challenge that simplifies terabyte scale data handling and provides advanced tools for NGS data analysis. These capabilities are implemented using the “Globus Genomics” system, which is an enhanced Galaxy workflow system made available as a service that offers users the capability to process and transfer data easily, reliably and quickly to address end-to-endNGS analysis requirements. The Globus Genomics system is built on Amazon 's cloud computing infrastructure. The system takes advantage of elastic scaling of compute resources to run multiple workflows in parallel and it also helps meet the scale-out analysis needs of modern translational genomics research.
TL;DR: This review highlights the complexities of drivers of antigenic variation, in particular extending evaluation beyond the commonly cited theory of immune evasion, to contribute to greater insight into bacterial pathogenesis, ecology and coevolution with hosts.
Abstract: Across the eubacteria, antigenic variation has emerged as a strategy to evade host immunity. However, phenotypic variation in some of these antigens also allows the bacteria to exploit variable host niches as well. The specific mechanisms are not shared-derived characters although there is considerable convergent evolution and numerous commonalities reflecting considerations of natural selection and biochemical restraints. Unlike in viruses, mechanisms of antigenic variation in most bacteria involve larger DNA movement such as gene conversion or DNA rearrangement, although some antigens vary due to point mutations or modified transcriptional regulation. The convergent evolution that promotes antigenic variation integrates various evolutionary forces: these include mutations underlying variant production; drift which could remove alleles especially early in infection or during life history phases in arthropod vectors (when the bacterial population size goes through a bottleneck); selection not only for any particular variant but also for the mechanism for the production of variants (i.e., selection for mutability); and overcoming negative selection against variant production. This review highlights the complexities of drivers of antigenic variation, in particular extending evaluation beyond the commonly cited theory of immune evasion. A deeper understanding of the diversity of purpose and mechanisms of antigenic variation in bacteria will contribute to greater insight into bacterial pathogenesis, ecology and coevolution with hosts.
TL;DR: In vivo inhibition of the P2X7 receptor induced a significant decrease in the number and size of hippocampal amyloid plaques, corroborate the therapeutic potential of P2 X7 antagonists in the treatment of FAD and are made possible by the use of J20 mice.
Abstract: Amyloid precursor protein (APP) is expressed in a large variety of neural and non-neural cells. The balance between non-pathogenic and pathologic forms of APP processing, mediated by α-secretase and β-secretase respectively, remains a crucial step to understand β-amyloid, Aβ42 peptide, formation and aggregation that are at the origin of the senile plaques in the brain, a characteristic hallmark of Alzheimer's disease (AD). In Neuro-2a, a neuroblastoma cell line that constitutively expresses APP, activation of the P2X7 receptor leads to reduction of α-secretase activity, the opposite effect being obtained by P2Y2 receptor activation.
The in vivo approach was made possible by the use of J20 mice, a transgenic mouse model of familial Alzheimer's disease (FAD) expressing human APP mutant protein. This animal exhibits prominent amyloid plaques by six months of age. In vivo inhibition of the P2X7 receptor induced a significant decrease in the number and size of hippocampal amyloid plaques. This reduction is mediated by an increase in the proteolytic processing of APP through α-secretase activity, which correlates with an increase in the phosphorylated form of GSK-3, a less active form of this enzyme. The in vivo findings corroborate the therapeutic potential of P2X7 antagonists in the treatment of FAD.
TL;DR: Some examples of the ways in which IPSCs have been used to model diseases of the outer retina including retinitis pigmentosa (RP), Usher syndrome (USH), Leber congenital amaurosis (LCA), gyrate atrophy (GA), juvenile neuronal ceroid lipofuscinosis (NCL), Best vitelliform macular dystrophy (BVMD) and age related macular degeneration (AMD).
Abstract: Retinal degeneration arises from the loss of photoreceptors or retinal pigment epithelium (RPE). It is one of the leading causes of irreversible blindness worldwide with limited effective treatment options. Generation of induced pluripotent stem cell (IPSC)-derived retinal cells and tissues from individuals with retinal degeneration is a rapidly evolving technology that holds a great potential for its use in disease modelling. IPSCs provide an ideal platform to investigate normal and pathological retinogenesis, but also deliver a valuable source of retinal cell types for drug screening and cell therapy. In this review, we will provide some examples of the ways in which IPSCs have been used to model diseases of the outer retina including retinitis pigmentosa (RP), Usher syndrome (USH), Leber congenital amaurosis (LCA), gyrate atrophy (GA), juvenile neuronal ceroid lipofuscinosis (NCL), Best vitelliform macular dystrophy (BVMD) and age related macular degeneration (AMD).