A Novel Drug-Mouse Phenotypic Similarity Method Detects Molecular Determinants of Drug Effects.
Ontology highlight
ABSTRACT:
SUBMITTER: Prinz J
PROVIDER: S-EPMC5038975 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Bats emit biosonar pulses in complex temporal patterns that change to accommodate dynamic surroundings. Efforts to quantify these patterns have included analyses of inter-pulse intervals, sonar sound groups, and changes in individual signal parameters such as duration or frequency. Here, the similarity in temporal structure between trains of biosonar pulses is assessed. The spike train similarity space (SSIMS) algorithm, originally designed for neural activity pattern analysis, was applied to determine which features of the environment influence temporal patterning of pulses emitted by flying big brown bats, Eptesicus fuscus. In these laboratory experiments, bats flew down a flight corridor through an obstacle array. The corridor varied in width (100, 70, or 40 cm) and shape (straight or curved). Using a relational point-process framework, SSIMS was able to discriminate between echolocation call sequences recorded from flights in each of the corridor widths. SSIMS was also able to tell the difference between pulse trains recorded during flights where corridor shape through the obstacle array matched the previous trials (fixed, or expected) as opposed to those recorded from flights with randomized corridor shape (variable, or unexpected), but only for the flight path shape in which the bats had previous training. The results show that experience influences the temporal patterns with which bats emit their echolocation calls. It is demonstrated that obstacle proximity to the bat affects call patterns more dramatically than flight path shape.
| S-EPMC5809465 | biostudies-literature
Project description:BackgroundDrug-drug interactions (DDIs) are responsible for many serious adverse events; their detection is crucial for patient safety but is very challenging. Currently, the US Food and Drug Administration and pharmaceutical companies are showing great interest in the development of improved tools for identifying DDIs.MethodsWe present a new methodology applicable on a large scale that identifies novel DDIs based on molecular structural similarity to drugs involved in established DDIs. The underlying assumption is that if drug A and drug B interact to produce a specific biological effect, then drugs similar to drug A (or drug B) are likely to interact with drug B (or drug A) to produce the same effect. DrugBank was used as a resource for collecting 9454 established DDIs. The structural similarity of all pairs of drugs in DrugBank was computed to identify DDI candidates.ResultsThe methodology was evaluated using as a gold standard the interactions retrieved from the initial DrugBank database. Results demonstrated an overall sensitivity of 0.68, specificity of 0.96, and precision of 0.26. Additionally, the methodology was also evaluated in an independent test using the Micromedex/Drugdex database.ConclusionThe proposed methodology is simple, efficient, allows the investigation of large numbers of drugs, and helps highlight the etiology of DDI. A database of 58 403 predicted DDIs with structural evidence is provided as an open resource for investigators seeking to analyze DDIs.
| S-EPMC3534468 | biostudies-literature
Project description:The traditional de novo drug discovery is known as a high cost and high risk process. In response, recently there is an increasing interest in discovering new indications for known drugs-a process known as drug repositioning-using computational methods. In this study, we present a new systematic approach for identifying potential new indications of an existing drug through its relation to similar drugs. Different from the previous similarity-based methods, we adapted a novel bipartite-graph based method when considering common drug targets and their interaction information. Furthermore, we added drug structure information into the calculation of drug pairwise similarity. In cross-validation experiments, our method achieved a sensitivity of 0.77 and specificity of 0.92 (AUC = 0.888) and compared favorably to the state of the art. When compared with a control group of drug uses, our drug repositioning results were found to be significantly enriched in both the biomedical literature and clinical trials. Our results indicate that combining chemical structure and drug target information results in better prediction performance and that the proposed approach successfully captures the implicit information between drug targets.
| S-EPMC4175719 | biostudies-literature
Project description:Aromatase excess syndrome (AEXS) is a rare autosomal dominant disorder characterized by gynecomastia. This condition is caused by overexpression of CYP19A1 encoding aromatase, and three types of cryptic genomic rearrangement around CYP19A1, that is, duplications, deletions, and inversions, have been identified in AEXS. Duplications appear to have caused CYP19A1 overexpression because of an increased number of physiological promoters, whereas deletions and inversions would have induced wide CYP19A1 expression due to the formation of chimeric genes consisting of a noncoding exon(s) of a neighboring gene and CYP19A1 coding exons. Genotype-phenotype analysis implies that phenotypic severity of AEXS is primarily determined by the expression pattern of CYP19A1 and the chimeric genes and by the structural property of the fused exons with a promoter function (i.e., the presence or the absence of a natural translation start codon). These results provide novel information about molecular mechanisms of human genetic disorders and biological function of estrogens.
| S-EPMC3272822 | biostudies-literature
Project description:The discovery of effective senolytics offers a promising approach for treating many age-related diseases. In this study, we employed a phenotypic drug discovery approach, combining drug screening and drug design, to identify and develop novel senolytic agents based on the flavonoid fisetin. We successfully developed two novel flavonoid analogs, SR29384 and SR31133, which demonstrated significantly enhanced senolytic activities compared to fisetin. These analogs showed broad-spectrum efficacy in eliminating various senescent cell types, reducing tissue senescence, extending healthspan in mice, and prolonging lifespan in Drosophila. Through RNA sequencing, machine learning, and computational screening, our mechanistic studies suggest that these novel flavonoid senolytics may target PARP1, BCL2L1, and CDK2 to induce senescent cell death.
2024-10-27 | GSE279349 | GEO
Project description:The endocannabinoids anandamide and 2-arachidonoylglycerol, as well as several anandamide-related N-acylethanolamines, belong to a family of lipid transmitter that regulate fundamental physiological processes, including neurotransmission and neuroinflammation. Their precise quantification in biological matrices can be achieved by gas chromatography-mass spectrometry (GC-MS), but this method typically requires multiple time-consuming purification steps such as solid-phase extraction followed by HPLC. Here we report a novel solid-phase extraction procedure allowing for single-step, and thus higher throughput, purification of endocannabinoids and N-acylethanolamines before GC-MS quantification. We determined the minimal amount of mouse brain tissue required to reliably detect endocannabinoids and N-acylethanolamines when using this approach and provide direct evidence for quantification accuracy by using radioactive and deuterated standards spiked into mouse brain samples. Using this method, we found that mouse brain contains much higher levels of anandamide (>1 nmol/g tissue) than previously reported, whereas levels of 2-arachidonoylglycerol and other N-acylethanolamines are well within the range of previous reports. In addition, we show that mouse brain amounts of endocannabinoids and N-acylethanolamines differ depending on animal gender as well as on whether the tissue was fixed or not. Our study shows that endocannabinoid and N-acylethanolamine levels quantified in mouse brain by GC-MS depend closely on tissue amount and preparation as well as on animal gender and that, depending on such parameters, anandamide levels could be underestimated.
| S-EPMC2344129 | biostudies-literature
Project description:BACKGROUND:Microglial cells are tissue-resident macrophages of the central nervous system. They are extremely dynamic, sensitive to their microenvironment and present a characteristic complex and heterogeneous morphology and distribution within the brain tissue. Many experimental clues highlight a strong link between their morphology and their function in response to aggression. However, due to their complex "dendritic-like" aspect that constitutes the major pool of murine microglial cells and their dense network, precise and powerful morphological studies are not easy to realize and complicate correlation with molecular or clinical parameters. METHODS:Using the knock-in mouse model CX3CR1(GFP/+), we developed a 3D automated confocal tissue imaging system coupled with morphological modelling of many thousands of microglial cells revealing precise and quantitative assessment of major cell features: cell density, cell body area, cytoplasm area and number of primary, secondary and tertiary processes. We determined two morphological criteria that are the complexity index (CI) and the covered environment area (CEA) allowing an innovative approach lying in (i) an accurate and objective study of morphological changes in healthy or pathological condition, (ii) an in situ mapping of the microglial distribution in different neuroanatomical regions and (iii) a study of the clustering of numerous cells, allowing us to discriminate different sub-populations. RESULTS:Our results on more than 20,000 cells by condition confirm at baseline a regional heterogeneity of the microglial distribution and phenotype that persists after induction of neuroinflammation by systemic injection of lipopolysaccharide (LPS). Using clustering analysis, we highlight that, at resting state, microglial cells are distributed in four microglial sub-populations defined by their CI and CEA with a regional pattern and a specific behaviour after challenge. CONCLUSIONS:Our results counteract the classical view of a homogenous regional resting state of the microglial cells within the brain. Microglial cells are distributed in different defined sub-populations that present specific behaviour after pathological challenge, allowing postulating for a cellular and functional specialization. Moreover, this new experimental approach will provide a support not only to neuropathological diagnosis but also to study microglial function in various disease models while reducing the number of animals needed to approach the international ethical statements.
| S-EPMC4912769 | biostudies-other
Project description:The emergence of cooperation in populations of selfish individuals is a fascinating topic that has inspired much work in theoretical biology. Here, we study the evolution of cooperation in a model where individuals are characterized by phenotypic properties that are visible to others. The population is well mixed in the sense that everyone is equally likely to interact with everyone else, but the behavioral strategies can depend on distance in phenotype space. We study the interaction of cooperators and defectors. In our model, cooperators cooperate with those who are similar and defect otherwise. Defectors always defect. Individuals mutate to nearby phenotypes, which generates a random walk of the population in phenotype space. Our analysis brings together ideas from coalescence theory and evolutionary game dynamics. We obtain a precise condition for natural selection to favor cooperators over defectors. Cooperation is favored when the phenotypic mutation rate is large and the strategy mutation rate is small. In the optimal case for cooperators, in a one-dimensional phenotype space and for large population size, the critical benefit-to-cost ratio is given by b/c = 1 + 2/square root(3). We also derive the fundamental condition for any two-strategy symmetric game and consider high-dimensional phenotype spaces.
| S-EPMC2688992 | biostudies-literature
Project description:The key to drug discovery is the identification of a target and a corresponding drug compound. Effective identification of drug-target interactions facilitates the development of drug discovery. In this paper, drug similarity and target similarity are considered, and graphical representations are used to extract internal structural information and intermolecular interaction information about drugs and targets. First, drug similarity and target similarity are fused using the similarity network fusion (SNF) method. Then, the graph isomorphic network (GIN) is used to extract the features with information about the internal structure of drug molecules. For target proteins, feature extraction is carried out using TextCNN to efficiently capture the features of target protein sequences. Three different divisions (CVD, CVP, CVT) are used on the standard dataset, and experiments are carried out separately to validate the performance of the model for drug-target interaction prediction. The experimental results show that our method achieves better results on AUC and AUPR. The docking results also show the superiority of the proposed model in predicting drug-target interactions.
| S-EPMC9312204 | biostudies-literature
Project description:The lack of robust immunocompetent animal models for hepatitis C virus (HCV) impedes vaccine development and studies of immune responses. Norway rat hepacivirus (NrHV) infection in rats shares HCV-defining characteristics, including hepatotropism, chronicity, immune responses, and aspects of liver pathology. To exploit genetic variants and research tools, we previously adapted NrHV to prolonged infection in laboratory mice. Through intrahepatic RNA inoculation of molecular clones of the identified variants, we here characterized four mutations in the envelope proteins responsible for mouse adaptation, including one disrupting a glycosylation site. These mutations led to high-titer viremia, similar to that observed in rats. In 4-week-old mice, infection was cleared after around 5 weeks compared to 2 to 3 weeks for nonadapted virus. In contrast, the mutations led to persistent but attenuated infection in rats, and they partially reverted, accompanied by an increase in viremia. Attenuated infection in rat but not mouse hepatoma cells demonstrated that the characterized mutations were indeed mouse adaptive rather than generally adaptive across species and that species determinants and not immune interactions were responsible for attenuation in rats. Unlike persistent NrHV infection in rats, acute resolving infection in mice was not associated with the development of neutralizing antibodies. Finally, infection of scavenger receptor B-I (SR-BI) knockout mice suggested that adaptation to mouse SR-BI was not a primary function of the identified mutations. Rather, the virus may have adapted to lower dependency on SR-BI, thereby potentially surpassing species-specific differences. In conclusion, we identified specific determinants of NrHV mouse adaptation, suggesting species-specific interactions during entry. IMPORTANCE A prophylactic vaccine is required to achieve the World Health Organization's objective for hepatitis C virus elimination as a serious public health threat. However, the lack of robust immunocompetent animal models supporting hepatitis C virus infection impedes vaccine development as well as studies of immune responses and viral evasion. Hepatitis C virus-related hepaciviruses were discovered in a number of animal species and provide useful surrogate infection models. Norway rat hepacivirus is of particular interest, as it enables studies in rats, an immunocompetent and widely used small laboratory animal model. Its adaptation to robust infection also in laboratory mice provides access to a broader set of mouse genetic lines and comprehensive research tools. The presented mouse-adapted infectious clones will be of utility for reverse genetic studies, and the Norway rat hepacivirus mouse model will facilitate studies of hepacivirus infection for in-depth characterization of virus-host interactions, immune responses, and liver pathology.
| S-EPMC10134885 | biostudies-literature