In vivo clonal expansion and phenotypes of hypocretin-specific CD4 + T cells in narcolepsy patients and controls
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Science & Technology - Other Topics↗
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Structural characterization of alkaline hydrogen peroxide pretreated grasses exhibiting diverse lignin phenotypes
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Muyang, Li↗
Towards a standard benchmark for phenotype-driven variant and gene prioritisation algorithms: PhEval - Phenotypic inference Evaluation framework
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Biochemistry & Molecular Biology↗
CSGID Solves Structures and Identifies Phenotypes for Five Enzymes in Toxoplasma gondii
Toxoplasma gondii, an Apicomplexan parasite, causes significant morbidity and mortality, including severe disease in immunocompromised hosts and devastating congenital disease, with no effective treatment for the bradyzoite stage. To address this, we used the Tropical Disease Research database, crystallography, molecular modeling, and antisense to identify and characterize a range of potential therapeutic targets for toxoplasmosis. Phosphoglycerate mutase II (PGMII), nucleoside diphosphate kinase (NDK), ribulose phosphate 3-epimerase (RPE), ribose-5-phosphate isomerase (RPI), and ornithine aminotransferase (OAT) were structurally characterized. Crystallography revealed insights into the overall structure, protein oligomeric states and molecular details of active sites important for ligand recognition. Literature and molecular modeling suggested potential inhibitors and druggability. The targets were further studied with vivoPMO to interrupt enzyme synthesis, identifying the targets as potentially important to parasitic replication and, therefore, of therapeutic interest. Targeted vivoPMO resulted in statistically significant perturbation of parasite replication without concomitant host cell toxicity, consistent with a previous CRISPR/Cas9 screen showing PGM, RPE, and RPI contribute to parasite fitness. PGM, RPE, and RPI have the greatest promise for affecting replication in tachyzoites. These targets are shared between other medically important parasites and may have wider therapeutic potential.
59 BASIC BIOLOGICAL SCIENCES↗
In-silico identification of phenotype-biased functional modules
Phenotypes exhibited by microorganisms can be useful for several purposes, e.g., ethanol as an alternate fuel. Sometimes, the target phenotype maybe required in combination with other phenotypes, in order to be useful, for e.g., an industrial process may require that the organism survive in an anaerobic, alcohol rich environment and be able to feed on both hexose and pentose sugars to produce ethanol. This combination of traits may not be available in any existing organism or if they do exist, the mechanisms involved in the phenotype-expression may not be efficient enough to be useful. Thus, it may be required to genetically modify microorganisms. However, before any genetic modification can take place, it is important to identify the underlying cellular subsystems responsible for the expression of the target phenotype. In this paper, we develop a method to identify statistically significant and phenotypically-biased functional modules. The method can compare the organismal network information from hundreds of phenotype expressing and phenotype non-expressing organisms to identify cellular subsystems that are more prone to occur in phenotype-expressing organisms than in phenotype non-expressing organisms. We have provided literature evidence that the phenotype-biased modules identified for phenotypes such as hydrogen production (dark and light fermentation), respiration, gram-positive, gram-negative and motility, are indeed phenotype-related. Thus we have proposed a methodology to identify phenotype-biased cellular subsystems. We have shown the effectiveness of our methodology by applying it to several target phenotypes. The code and all supplemental files can be downloaded from (http://freescience.org/cs/phenotype-biased-biclusters/).
59 BASIC BIOLOGICAL SCIENCES↗
Root phenotypes for improved nitrogen capture
Abstract Background Suboptimal nitrogen availability is a primary constraint for crop production in low-input agroecosystems, while nitrogen fertilization is a primary contributor to the energy, economic, and environmental costs of crop production in high-input agroecosystems. In this article we consider avenues to develop crops with improved nitrogen capture and reduced requirement for nitrogen fertilizer. Scope Intraspecific variation for an array of root phenotypes has been associated with improved nitrogen capture in cereal crops, including architectural phenotypes that colocalize root foraging with nitrogen availability in the soil; anatomical phenotypes that reduce the metabolic costs of soil exploration, improve penetration of hard soil, and exploit the rhizosphere; subcellular phenotypes that reduce the nitrogen requirement of plant tissue; molecular phenotypes exhibiting optimized nitrate uptake kinetics; and rhizosphere phenotypes that optimize associations with the rhizosphere microbiome. For each of these topics we provide examples of root phenotypes which merit attention as potential selection targets for crop improvement. Several cross-cutting issues are addressed including the importance of soil hydrology and impedance, phenotypic plasticity, integrated phenotypes, in silico modeling, and breeding strategies using high throughput phenotyping for co-optimization of multiple phenes. Conclusions Substantial phenotypic variation exists in crop germplasm for an array of root phenotypes that improve nitrogen capture. Although this topic merits greater research attention than it currently receives, we have adequate understanding and tools to develop crops with improved nitrogen capture. Root phenotypes are underutilized yet attractive breeding targets for the development of the nitrogen efficient crops urgently needed in global agriculture.
60 APPLIED LIFE SCIENCES↗
NIBBS-Search for Fast and Accurate Prediction of Phenotype-Biased Metabolic Systems
Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism’s genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotypeexpressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to the set of phenotype-biased subgraphs output by an exact maximally-biased subgraph enumeration algorithm ( MBS-Enum ). The code (NIBBS and the module to visualize the identified subsystems) is available at http:// freescience.org/cs/NIBBS.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Comparative functional pan-genome analyses to build connections between genomic dynamics and phenotypic evolution in polycyclic aromatic hydrocarbon metabolism in the genus Mycobacterium
Background: The bacterial genus Mycobacterium is of great interest in the medical and biotechnological fields. Despite a flood of genome sequencing and functional genomics data, significant gaps in knowledge between genome and phenome seriously hinder efforts toward the treatment of mycobacterial diseases and practical biotechnological applications. In this study, we propose the use of systematic, comparative functional pan-genomic analysis to build connections between genomic dynamics and phenotypic evolution in polycyclic aromatic hydrocarbon (PAH) metabolism in the genus Mycobacterium. Results: Phylogenetic, phenotypic, and genomic information for 27 completely genome-sequenced mycobacteria was systematically integrated to reconstruct a mycobacterial phenotype network (MPN) with a pan-genomic concept at a network level. In the MPN, mycobacterial phenotypes show typical scale-free relationships. PAH degradation is an isolated phenotype with the lowest connection degree, consistent with phylogenetic and environmental isolation of PAH degraders. A series of functional pan-genomic analyses provide conserved and unique types of genomic evidence for strong epistatic and pleiotropic impacts on evolutionary trajectories of the PAH-degrading phenotype. Under strong natural selection, the detailed gene gain/loss patterns from horizontal gene transfer (HGT)/deletion events hypothesize a plausible evolutionary path, an epistasis-based birth and pleiotropy-dependent death, for PAH metabolism in the genus Mycobacterium. This study generated a practical mycobacterial compendium of phenotypic and genomic changes, focusing on the PAH-degrading phenotype, with a pan-genomic perspective of the evolutionary events and the environmental challenges. Conclusions: Our findings suggest that when selection acts on PAH metabolism, only a small fraction of possible trajectories is likely to be observed, owing mainly to a combination of the ambiguous phenotypic effects of PAHs and the corresponding pleiotropy- and epistasis-dependent evolutionary adaptation. Evolutionary constraints on the selection of trajectories, like those seen in PAH-degrading phenotypes, are likely to apply to the evolution of other phenotypes in the genus Mycobacterium.
59 BASIC BIOLOGICAL SCIENCES↗
Phenotypic overlap in the contribution of individual genes to CNV pathogenicity revealed by cross-species computational analysis of single-gene mutations in humans, mice and zebrafish
Numerous disease syndromes are associated with regions of copy number variation (CNV) in the human genome and, in most cases, the pathogenicity of the CNV is thought to be related to altered dosage of the genes contained within the affected segment. However, establishing the contribution of individual genes to the overall pathogenicity of CNV syndromes is difficult and often relies on the identification of potential candidates through manual searches of the literature and online resources. We describe here the development of a computational framework to comprehensively search phenotypic information from model organisms and single-gene human hereditary disorders, and thus speed the interpretation of the complex phenotypes of CNV disorders. There are currently more than 5000 human genes about which nothing is known phenotypically but for which detailed phenotypic information for the mouse and/or zebrafish orthologs is available. Here, we present an ontology-based approach to identify similarities between human disease manifestations and the mutational phenotypes in characterized model organism genes; this approach can therefore be used even in cases where there is little or no information about the function of the human genes. We applied this algorithm to detect candidate genes for 27 recurrent CNV disorders and identified 802 gene-phenotype associations, approximately half of which involved genes that were previously reported to be associated with individual phenotypic features and half of which were novel candidates. A total of 431 associations were made solely on the basis of model organism phenotype data. Additionally, we observed a striking, statistically significant tendency for individual disease phenotypes to be associated with multiple genes located within a single CNV region, a phenomenon that we denote as pheno-clustering. Many of the clusters also display statistically significant similarities in protein function or vicinity within the protein-protein interaction network. Our results provide a basis for understanding previously un-interpretable genotype-phenotype correlations in pathogenic CNVs and for mobilizing the large amount of model organism phenotype data to provide insights into human genetic disorders.
59 BASIC BIOLOGICAL SCIENCES↗
The Pheno-Evo Model: Evolution of Microbial Phenotypic Diversity in 2D Space
Biologists appreciate microbes for their simplicity and predictability: we assume that a population of genetically identical cells in a uniform environment will all behave the same way. While this assumption is often useful, it is also often wrong. Not only might microbes in a clonal population act differently from one another, the differences may be categorical (growing v. non-growing; toxin-sensitive v. tolerant), and this diversity might be an evolved trait conferring increased fitness on the population. How does such phenotypic heterogeneity evolve? And how does a population find the optimal distribution of phenotypes for a given environment? Efforts at modeling microbial phenotypic heterogeneity often focus on populations with two discrete phenotypic types; phenotypes in continuous distributions remain poorly explored. To address this gap and to explore the role of spatial relationships, we use agent-based modeling to simulate a phenotypically diverse population of microbial cells evolving in the presence of periodic toxic stress. Cells on patches in a 2D grid may degrade toxin, suffer damage from toxin, switch phenotype, and reproduce. An individual's phenotype its toxin degradation rate-- for which there is a tradeoff with reproduction-- and the genotype encodes the distribution of phenotypic values in the population. The rate of toxin diffusion determines how individuals influence their neighbors' environments. We examine the effects of toxin concentration, diffusion rate, and environmental predictability on the survival success of populations with different phenotype distributions, and populations' evolutionary trajectories when phenotype distribution is allowed to evolve. We conduct all simulations on the platform NetLogo, which provides a friendly interface allowing users of any experience level to tweak parameters and run their own simulations. We have also created custom tools in R for analyzing and visualizing the results of multiple runs.
microbial evolution↗
Irisin reverses platelet derived growth factor-BB-induced vascular smooth muscle cells phenotype modulation through STAT3 signaling pathway
Vascular smooth muscle cells (VSMCs) phenotype modulation toward a synthetic phenotype is the main cause of cardiovascular disease. As a newly discovered myokine, Irisin is thought to be a promising candidate for the treatment of metabolic disturbances, as well as cardiovascular disease. However, no evidence has been shown for the direct effect of Irisin on VSMCs phenotype modulation and its underling mechanisms. The aim of this study was to explore the effect of Irisin on VSMCs phenotype modulation and the mechanisms involved. In the present study, it was found that Irisin restored the PDGF-BB-induced VSMCs phenotype modulation which exhibited down-regulation of smooth muscle cells (SMC) expression and up-regulation of matrix synthesis related marker expression, as well as proliferative phenotype. Moreover, our research demonstrated that Irisin further activated STAT3 signaling pathways. Finally, by applying an STAT3 inhibitor, WP1066, we revealed the roles of STAT3 in the PDGF-BB-induced VSMCs phenotype modulation when they were treated with Irisin. Taken together, these results demonstrated that Irisin may play a crucial role in regulating VSMCs phenotype modulation via the STAT3 signaling pathway. - Highlights: • Irisin prevents PDGF-BB-stimulated VSMCs dedifferentiation. • Irisin inhibits VSMCs towards a proliferative synthetic phenotype induced by PDGF-BB stimulation. • Irisin treatment further activates STAT3 in VSMCs. • The effect of Irisin on VSMCs phenotype modulation might be STAT3-dependent.
60 APPLIED LIFE SCIENCES↗
SPICE: discovery of phenotype-determining component interplays
Background: A latent behavior of a biological cell is complex. Deriving the underlying simplicity, or the fundamental rules governing this behavior has been the Holy Grail of systems biology. Data-driven prediction of the system components and their component interplays that are responsible for the target system’s phenotype is a key and challenging step in this endeavor. Results: The proposed approach, which we call System Phenotype-related Interplaying Components Enumerator (SPICE), iteratively enumerates statistically significant system components that are hypothesized (1) to play an important role in defining the specificity of the target system’s phenotype(s); (2) to exhibit a functionally coherent behavior, namely, act in a coordinated manner to perform the phenotype-specific function; and (3) to improve the predictive skill of the system’s phenotype(s) when used collectively in the ensemble of predictive models. SPICE can be applied to both instance-based data and network-based data. When validated, SPICE effectively identified system components related to three target phenotypes: biohydrogen production, motility, and cancer. Manual results curation agreed with the known phenotype-related system components reported in literature. Additionally, using the identified system components as discriminatory features improved the prediction accuracy by 10% on the phenotype-classification task when compared to a number of state-of-the-art methods applied to eight benchmark microarray data sets. Conclusion: We formulate a problem—enumeration of phenotype-determining system component interplays—and propose an effective methodology (SPICE) to address this problem. SPICE improved identification of cancer-related groups of genes from various microarray data sets and detected groups of genes associated with microbial biohydrogen production and motility, many of which were reported in literature. SPICE also improved the predictive skill of the system’s phenotype determination compared to individual classifiers and/or other ensemble methods, such as bagging, boosting, random forest, nearest shrunken centroid, and random forest variable selection method.
59 BASIC BIOLOGICAL SCIENCES↗
Construction and accessibility of a cross-species phenotype ontology along with gene annotations for biomedical research [v2; ref status: indexed, http://f1000r.es/2td]
Phenotype analyses, e.g. investigating metabolic processes, tissue formation, or organism behavior, are an important element of most biological and medical research activities. Biomedical researchers are making increased use of ontological standards and methods to capture the results of such analyses, with one focus being the comparison and analysis of phenotype information between species. We have generated a cross-species phenotype ontology for human, mouse and zebrafish that contains classes from the Human Phenotype Ontology, Mammalian Phenotype Ontology, and generated classes for zebrafish phenotypes. We also provide up-to-date annotation data connecting human genes to phenotype classes from the generated ontology. We have included the data generation pipeline into our continuous integration system ensuring stable and up-to-date releases. This article describes the data generation process and is intended to help interested researchers access both the phenotype annotation data and the associated cross-species phenotype ontology. The resource described here can be used in sophisticated semantic similarity and gene set enrichment analyses for phenotype data across species. The stable releases of this resource can be obtained from http://purl.obolibrary.org/obo/hp/uberpheno/ .
59 BASIC BIOLOGICAL SCIENCES↗
Phenotype anchoring in zebrafish reveals a potential role for matrix metalloproteinases (MMPs) in tamoxifen's effects on skin epithelium
The zebrafish is a powerful alternative model used to link phenotypes with molecular effects to discover drug mode of action. Using a zebrafish embryo-larval toxicity bioassay, we evaluated the effects of tamoxifen — a widely used anti-estrogen chemotherapeutic. Zebrafish exposed to ≥ 10 μM tamoxifen exhibited a unique necrotic caudal fin phenotype that was rapidly induced regardless of developmental life-stage when treatment was applied. To define tamoxifen's bioactivity resulting in this phenotype, targeted gene expression was used to evaluate 100 transcripts involved in tissue remodeling, calcium signaling, cell cycle and cell death, growth factors, angiogenesis and hypoxia. The most robustly misregulated transcripts in the tail were matrix metalloproteinases mmp9 and mmp13a, induced 127 and 1145 fold, respectively. Expression of c-fos, c-jun, and ap1s1 were also moderately elevated (3–7 fold), consistent with AP-1 activity — a transcription factor that regulates MMP expression. Immunohistochemistry confirmed high levels of induction for MMP13a in affected caudal fin skin epithelial tissue. The necrotic caudal fin phenotype was significantly attenuated or prevented by three functionally unique MMP inhibitors: EDTA (metal chelator), GM 6001 (broad MMP inhibitor), and SR 11302 (AP-1 transcription factor inhibitor), suggesting MMP-dependence. SR 11302 also inhibited induction of mmp9, mmp13a, and a putative MMP target, igfbp1a. Overall, our studies suggest that tamoxifen's effect is the result of perturbation of the MMP system in the skin leading to ectopic expression, cytotoxicity, and the necrotic caudal fin phenotype. These studies help advance our understanding of tamoxifen's non-classical mode of action and implicate a possible role for MMPs in tissues such as skin. - Highlights: • Tamoxifen rapidly induced a unique necrotic caudal fin phenotype in zebrafish. • Apoptosis co-localized temporally and spatially in the necrotic tail. • The necrotic fin phenotype was p53, GPER and ER independent. • The necrotic fin phenotype was dependent on ectopic MMP induction and activity in the skin. • The necrotic fin phenotype occurred at concentrations exceeding anti-estrogenic effects.
60 APPLIED LIFE SCIENCES↗
In-depth evaluation of commercially available human vascular smooth muscle cells phenotype: Implications for vascular tissue engineering
In vitro research on vascular tissue engineering has extensively used isolated primary human or animal smooth muscle cells (SMC). Research programs that lack such facilities tend towards commercially available primary cells sources. Here, we aim to evaluate the capacity of commercially available human SMC to maintain their contractile phenotype, and determine if dedifferentiation towards the synthetic phenotype occurs in response to conventional cell culture and passaging without any external biochemical or mechanical stimuli. Lower passage SMC adopted a contractile phenotype marked by a relatively slower proliferation rate, higher expression of proteins of the contractile apparatus and smoothelin, elongated morphology, and reduced deposition of collagen types I and III. As the passage number increased, migratory capacity was enhanced, average cell speed, total distance and net distance travelled increased up to passage 8. Through the various assays, corroborative evidence pinpoints SMC at passage 7 as the transition point between the contractile and synthetic phenotypes, while passage 8 distinctly and consistently exhibited characteristics of synthetic phenotype. This knowledge is particularly useful in selecting SMC of appropriate passage number for the target vascular tissue engineering application, for example, a homeostatic vascular graft for blood vessel replacement versus recreating atherosclerotic blood vessel model in vitro. - Highlights: • Ability of human smooth muscle cells to alter phenotype in culture is evaluated. • Examined the effect of passaging human smooth muscle cells on phenotype. • Phenotype is assessed based on morphology, proliferation, markers, and migration. • Multi-resolution assessment methodology, single-cell and cell-population. • Lower and higher passages than P7 adopted a contractile and synthetic phenotype respectively.
60 APPLIED LIFE SCIENCES↗
Identifying emerging phenomenon in long temporal phenotyping experiments
Abstract Motivation The rapid improvement of phenotyping capability, accuracy and throughput have greatly increased the volume and diversity of phenomics data. A remaining challenge is an efficient way to identify phenotypic patterns to improve our understanding of the quantitative variation of complex phenotypes, and to attribute gene functions. To address this challenge, we developed a new algorithm to identify emerging phenomena from large-scale temporal plant phenotyping experiments. An emerging phenomenon is defined as a group of genotypes who exhibit a coherent phenotype pattern during a relatively short time. Emerging phenomena are highly transient and diverse, and are dependent in complex ways on both environmental conditions and development. Identifying emerging phenomena may help biologists to examine potential relationships among phenotypes and genotypes in a genetically diverse population and to associate such relationships with the change of environments or development. Results We present an emerging phenomenon identification tool called Temporal Emerging Phenomenon Finder (TEP-Finder). Using large-scale longitudinal phenomics data as input, TEP-Finder first encodes the complicated phenotypic patterns into a dynamic phenotype network. Then, emerging phenomena in different temporal scales are identified from dynamic phenotype network using a maximal clique based approach. Meanwhile, a directed acyclic network of emerging phenomena is composed to model the relationships among the emerging phenomena. The experiment that compares TEP-Finder with two state-of-art algorithms shows that the emerging phenomena identified by TEP-Finder are more functionally specific, robust and biologically significant. Availability and implementation The source code, manual and sample data of TEP-Finder are all available at: http://phenomics.uky.edu/TEP-Finder/. Supplementary information Supplementary data are available at Bioinformatics online.
Biochemistry & Molecular Biology↗
The Human Phenotype Ontology in 2017
Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human PhenotypeOntology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology.
59 BASIC BIOLOGICAL SCIENCES↗
The Unified Phenotype Ontology : a framework for cross-species integrative phenomics
Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization.
59 BASIC BIOLOGICAL SCIENCES↗