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In vitro and in vivo toxicity assessment of the senotherapeutic Peptide 14

Toxicology Reports 2022年 9卷 1632-1638页

作者： Zonari, Alessandra Brace, Lear E. Alencar-Silva, Thuany Porto, William F. Foyt, Daniel Guiang, Mylieneth Cruz, Edgar Andres Ochoa Franco, Octavio L. Oliveira, Carolina R. Boroni, Mariana Carvalho, Juliana L. OneSkin Inc San Francisco 94107 CA United States Genomic Sciences and Biotechnology Program Catholic University of Brasilia DF Brasília 70790-160 Brazil DF Brasília 72236–011 Brazil Center of Proteomic Analyses and Biochemistry Genomic Sciences and Biotechnology Program Catholic University of Brasilia DF Brasilia 70790-160 Brazil S-Inova Biotech Biotechnology Program Catholic University Dom Bosco MS Campo Grande 79117-010 Brazil Molecular Pathology Program University of Brasilia DF Brasilia 70.910-900 Brazil Bioinformatics and Computational Biology Lab Brazilian National Cancer Institute (INCA) RJ Rio de Janeiro 20231-050 Brazil Interdisciplinary Biosciences Laboratory Faculty of Medicine University of Brasília DF Brasília 70.910–900 Brazil

Senotherapeutic molecules decrease cellular senescence burden, constituting promising approaches to combat the accumulation of senescent cells observed in chronological aging and age-related diseases. Numerous molecules have displayed senotherapeutic potential, but toxicity has been frequently observed. Recently, a new senotherapeutic compound, Peptide 14, was developed to modulate cellular senescence in the skin. In order to assess the potential toxic and genotoxic effects of the peptide, we observed the viability of human primary dermal fibroblasts and epidermal keratinocytes with Peptide 14 treatment, and show that it is mostly non-toxic in concentrations up to 100 μM. Cancer lines were also used to investigate its potential of modulating proliferation. Different concentrations of the peptide promoted a discrete reduction in the proliferation of cancerous cells of the MeWo and HeLa lineages. In full-thickness human skin equivalents, topically formulated Peptide 14 also failed to exert any significant irritation, nor cellular toxicity when added to the culture media. Genotoxic assays including the Ames, micronucleus, and karyotyping tests also indicate the safety of the peptide. Finally, the irritative potential of the peptide was assessed in human subjects in a repeated insult patch test executed using 1 mM peptide. No visible skin reactions were observed in any of the 54 participants. Taken together, the present data support that Peptide 14 is a senotherapeutic molecule with a positive safety profile as tested with cruelty-free models, justifying further studies involving the peptide. © 2022 The Authors

关键词： 3D skin equivalents Aging Cellular senescence In-vitro toxicology Peptide 14 RIPT

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AlzPharm: Integration of neurodegeneration data using RDF

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BMC bioinformatics 2007年第SUPPL. 3期8卷 S4-S4页

作者： Lam, Hugo Y.K. Marenco, Luis Clark, Tim Gao, Yong Kinoshita, June Shepherd, Gordon Miller, Perry Wu, Elizabeth Wong, Gwendolyn T. Liu, Nian Crasto, Chiquito Morse, Thomas Stephens, Susie Cheung, Kei-Hoi Interdepartmental Program in Computational Biology and Bioinformatics Yale University New Haven CT United States Center for Medical Informatics Yale University New Haven CT United States Department of Anesthesiology Yale University New Haven CT United States Department of Neurobiology Yale University New Haven CT United States Department of Molecular Cellular and Developmental Biology Yale University New Haven CT United States Department of Genetics Yale University New Haven CT United States Department of Computer Science Yale University New Haven CT United States Initiative in Innovative Computing Harvard University Cambridge MA United States Massachusetts General Hospital Boston MA United States Alzheimer Research Forum Burlington MA United States Oracle Burlington MA United States

Background: Neuroscientists often need to access a wide range of data sets distributed over the Internet. These data sets, however, are typically neither integrated nor interoperable, resulting in a barrier to answering complex neuroscience research questions. Domain ontologies can enable the querying heterogeneous data sets, but they are not sufficient for neuroscience since the data of interest commonly span multiple research domains. To this end, e-Neuroscience seeks to provide an integrated platform for neuroscientists to discover new knowledge through seamless integration of the very diverse types of neuroscience data. Here we present a Semantic Web approach to building this e-Neuroscience framework by using the Resource Description Framework (RDF) and its vocabulary description language, RDF Schema (RDFS), as a standard data model to facilitate both representation and integration of the data. Results: We have constructed a pilot ontology for BrainPharm (a subset of SenseLab) using RDFS and then converted a subset of the BrainPharm data into RDF according to the ontological structure. We have also integrated the converted BrainPharm data with existing RDF hypothesis and publication data from a pilot version of SWAN (Semantic Web Applications in Neuromedicine). Our implementation uses the RDF Data Model in Oracle Database 10g release 2 for data integration, query, and inference, while our Web interface allows users to query the data and retrieve the results in a convenient fashion. Conclusion: Accessing and integrating biomedical data which cuts across multiple disciplines will be increasingly indispensable and beneficial to neuroscience researchers. The Semantic Web approach we undertook has demonstrated a promising way to semantically integrate data sets created independently. It also shows how advanced queries and inferences can be performed over the integrated data, which are hard to achieve using traditional data integration approaches. Our pilot results su

关键词： Data integration

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SYSTEMS biology IN BARLEY POWDERY MILDEW INTERACTIONS

SYSTEMS BIOLOGY IN BARLEY POWDERY MILDEW INTERACTIONS

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The 13th International Cereal Rusts and Powdery Mildews Conference(第13届国际禾谷类锈病与白粉病大会)

作者： Roger WISE Priyanka SURANA Greg FUERST Ruo XU Dan NETTLETON Divya MISTRY Julie DICKERSON Lin WANG Tom BRUTNELL Department of Plant Pathology and Microbiology Center for Plant Responses to Environmental StressesIowa State UniversityAmesIowa 50011USA Department of Statistics Iowa State UniversityAmesIowa 50011USA Bioinformatics and Computational Biology Graduate ProgramIowa State UniversityAmesIowa 50011USA Bioinformatics and Computational Biology Graduate ProgramIowa State UniversityAmesIowa 50011USA Bioinformatics and Computational Biology Graduate Program Iowa State UniversityAmesIowa 50011USA Department of Plant Pathology and MicrobiologyCenter for Plant Responses to Environmental StressesIowa State UniversityAmesIowa 50011USA Crop and Insect Genetics Genomicsand Informatics Research UnitU.S.Department of Agriculture-Agricultural Research ServiceIowa State UniversityAmesIowa 50011USA Department of Plant Pathology and Microbiology Center for Plant Responses to Environmental StressesIowa State UniversityAmesIowa 50011USA Department of StatisticsIowa State UniversityAmesIowa 50011USA Bioinformatics and Computational Biology Graduate ProgramIowa State UniversityAmesIowa 50011USA Bioinformatics and Computational Biology Graduate Program Iowa State UniversityAmesIowa 50011USA Crop and Insect GeneticsGenomicsand Informatics Research UnitU.S.Department of Agriculture-Agricultural Research ServiceIowa State UniversityAmesIowa 50011USA Department of Electrical and Computer Engineering Iowa State UniversityAmesIowa 50011USA Boyce Thompson Institute for Plant ResearchCornell UniversityIthacaNY

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Geometry-based data generation

arXiv

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arXiv 2018年

作者： Lindenbaum, Ofir Stanley, Jay S. Wolf, Guy Krishnaswamy, Smita Department of Genetics Yale University New HavenCT United States Applied Mathematics Program Yale University New HavenCT United States Computational Biology & Bioinformatics Program Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States

We propose a new type of generative model of high-dimensional data that learns a manifold geometry of the data, rather than density, and can generate points evenly along this manifold. This is in contrast to existing generative models that represent data density, and are strongly affected by noise and other artifacts of data collection. We demonstrate how this approach corrects sampling biases and artifacts, and improves several downstream data analysis tasks such as clustering and classification. Finally, we demonstrate that this approach is especially useful in biology where, despite the advent of single-cell technologies, rare subpopulations and gene-interaction relationships are affected by biased sampling. We show that SUGAR can generate hypothetical populations and reveal intrinsic patterns and mutual-information relationships between genes on a single cell dataset of hematopoiesis. Copyright © 2018, The Authors. All rights reserved.

关键词： Clustering algorithms

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Continuity Equation of Left Invariant Gauge Field in Quantum Genotype Of Deltacron

SSRN

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SSRN 2022年

作者： Hannongbua, Supot Kanjamapornkul, Kabin Rungrotmongkol, Thanyada Department of Chemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand Biocatalyst and Environmental Biotechnology Research Unit Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok10330 Thailand

The continuity equation for the invariant gauge field of Deltacron was investigated from the replication and recombination of Delta with Omicron. The new definition of left translation of the invariant gauge field in the quantum genotype of virus and host cell was introduced. The Yang-Mills field in genetic code is an induced curvature in the parallel transport of a unified field between hydrogen bonding between three types of biomolecules. where we defined the invariant gauge field in quantum genotype. This issue allows us to identify the Poincare duality between the nteraction between the behavior field of gene expression in parasitism state and in the curvature of connection in fiber space of Spike protein folding molecule. The Chern Simons current is the anti-self-dual state of mutation between two types of interactive behavior fields in The recombination process of Deltacron was used to plot the Yang-Mills curvature directly from the underlying genetic codes. Further analysis shows that the difference between the tensor correlation with various times of Deltacron the change slope of Omicron BA.1 to the flat curve of the network correlation and closedness centrality of amino acids in receptor binding domain of Spike protein. © 2022, The Authors. All rights reserved.

关键词： Gene expression

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A general kernel boosting framework integrating pathways for predictive modeling based on genomic data

arXiv

引用

arXiv 2020年

作者： Zeng, Li Yu, Zhaolong Zhang, Yiliang Zhao, Hongyu Department of Biostatistics Yale University New HavenCT06511 United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States Department of Genetics Yale School of Medicine New HavenCT06510 United States

Predictive modeling based on genomic data has gained popularity in biomedical research and clinical practice by allowing researchers and clinicians to identify biomarkers and tailor treatment decisions more efficiently. Analysis incorporating pathway information can boost discovery power and better connect new findings with biological mechanisms. In this article, we propose a general framework, Pathway-based Kernel Boosting (PKB), which incorporates clinical information and prior knowledge about pathways for prediction of binary, continuous and survival outcomes. We introduce appropriate loss functions and optimization procedures for different outcome types. Our prediction algorithm incorporates pathway knowledge by constructing kernel function spaces from the pathways and use them as base learners in the boosting procedure. Through extensive simulations and case studies in drug response and cancer survival datasets, we demonstrate that PKB can substantially outperform other competing methods, better identify biological pathways related to drug response and patient survival, and provide novel insights into cancer pathogenesis and treatment response. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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EQTL AND MUTATIONAL ANALYSIS OF NB-LRR MEDIATED IMMUNITY TO POWDERY MILDEW

EQTL AND MUTATIONAL ANALYSIS OF NB-LRR MEDIATED IMMUNITY TO ...

引用

The 13th International Cereal Rusts and Powdery Mildews Conference(第13届国际禾谷类锈病与白粉病大会)

作者： Roger WISE Priyanka SURANA Greg FUERST Ruo XU Divya MISTRY Julie DICKERSON Dan NETTLETON Department of Statistics Iowa State UniversityAmesIowa 50011 Department of Plant Pathology and Microbiology Center for Plant Responses to Environmental StressesIowa State UniversityAmesIowa 50011 Department of StatisticsIowa State UniversityAmesIowa 50011 Bioinformatics and Computational Biology Graduate ProgramIowa State UniversityAmesIowa 50011 Department of Electrical and Computer Engineering Iowa State UniversityAmesIowa 50011USA Bioinformatics and Computational Biology Graduate ProgramIowa State UniversityAmesIowa 50011 Bioinformatics and Computational Biology Graduate Program Iowa State UniversityAmesIowa 50011 Department of Plant Pathology and MicrobiologyCenter for Plant Responses to Environmental StressesIowa State UniversityAmesIowa 50011 Department of Plant Pathology and Microbiology Center for Plant Responses to Environmental StressesIowa State UniversityAmesIowa 50011 Bioinformatics and Computational Biology Graduate Program Iowa State UniversityAmesIowa 50011 Crop and Insect GeneticsGenomicsand Informatics Research UnitU.S.Department of Agriculture-Agricultural Research ServiceIowa State UniversityAmesIowa 50011 Crop and Insect Genetics Genomicsand Informatics Research UnitU.S.Department of Agriculture-Agricultural Research ServiceIowa State UniversityAmesIowa 50011

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Integration of Protein Structure and Population-Scale DNA Sequence Data for Disease Gene Discovery and Variant Interpretation

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Biomedical Data Science 1000年第1期5卷 141-161页

作者： Bian Li Bowen Jin John A. Capra William S. Bush 1Department of Biological Sciences and Center for Structural Biology Vanderbilt University Nashville Tennessee USA 2Graduate Program in Systems Biology and Bioinformatics Department of Nutrition School of Medicine Case Western Reserve University Cleveland Ohio USA 3Bakar Computational Health Sciences Institute and Department of Epidemiology and Biostatistics University of California San Francisco California USA email: tony@*** 4Cleveland Institute for Computational Biology Department of Population and Quantitative Health Sciences Case Western Reserve University Cleveland Ohio USA email: wsb36@case.edu

The experimental and computational techniques for capturing information about protein structures and genetic variation within the human genome have advanced dramatically in the past 20 years, generating extensive new data resources. In this review, we discuss these advances, along with new approaches for determining the impact a genetic variant has on protein function. We focus on the potential of new methods that integrate human genetic variation into protein structures to discover relationships to disease, including the discovery of mutational hotspots in cancer-related proteins, the localization of protein-altering variants within protein regions for common complex diseases, and the assessment of variants of unknown significance for Mendelian traits. We expect that approaches that integratethese data sources will play increasingly important roles in disease gene discovery and variant interpretation.

关键词： protein 3D structure population genetics variant interpretation disease gene discovery data integration

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Transductive and inductive outlier detection with robust autoencoders 24

Transductive and inductive outlier detection with robust aut...

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Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Ofir Lindenbaum Yariv Aizenbud Yuval Kluger Faculty of Engineering Bar-Ilan University Ramat Gan Israel School of Mathematical Sciences Tel Aviv University Tel Aviv Israel Program in Applied Mathematics Yale University New Haven CT and Department of Pathology Yale University School of Medicine New Haven CT and Computational Biology & Bioinformatics Program Yale University New Haven CT

Accurate detection of outliers is crucial for the success of numerous data analysis tasks. In this context, we propose the Probabilistic Robust AutoEncoder (PRAE) that can simultaneously remove outliers during training (transductive) and learn a mapping that can be used to detect outliers in new data (inductive). We first present the Robust AutoEncoder (RAE) objective that excludes outliers while including a subset of samples (inliers) that can be effectively reconstructed using an AutoEncoder (AE). RAE minimizes the autoencoder's reconstruction error while incorporating as many samples as possible. This could be formulated via regularization by subtracting an ℓ0 norm, counting the number of selected samples from the reconstruction term. As this leads to an intractable combinatorial problem, we propose two probabilistic relaxations of RAE, which are differentiable and alleviate the need for a combinatorial search. We prove that the solution to the PRAE problem is equivalent to the solution of RAE. We then use synthetic data to demonstrate that PRAE can accurately remove outliers in various contamination levels. Finally, we show that using PRAE for outlier detection leads to state-of-the-art results for inductive and transductive outlier detection.

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Evolution at the edge of expanding populations

arXiv

引用

arXiv 2017年

作者： Deforet, Maxime Carmona-Fontaine, Carlos Korolev, Kirill S. Xavier, Joao B. Sorbonne Université Centre National de la Recherche Rcientifique Laboratoire Jean Perrin LJP Paris75005 France Center for Genomics and Systems Biology Department of Biology New York University New York CityNY10003 United States Department of Physics Graduate Program in Bioinformatics Boston University BostonMA02215 Program in Computational Biology Memorial Sloan-Kettering Cancer Center New York CityNY10065 United States

Predicting evolution of expanding populations is critical to control biological threats such as invasive species and cancer metastasis. Expansion is primarily driven by reproduction and dispersal, but nature abounds with examples of evolution where organisms pay a reproductive cost to disperse faster. When does selection favor this 'survival of the fastest?' We searched for a simple rule, motivated by evolution experiments where swarming bacteria evolved into an hyperswarmer mutant which disperses ∼ 100% faster but pays a growth cost of ∼ 10% to make many copies of its flagellum. We analyzed a two-species model based on the Fisher equation to explain this observation: the population expansion rate (v) results from an interplay of growth (r) and dispersal (D) and is independent of the carrying capacity: v = 2 √rD. A mutant can take over the edge only if its expansion rate (v2) exceeds the expansion rate of the established species' (v1);this simple condition (v2> v1) determines the maximum cost in slower growth that a faster mutant can pay and still be able to take over. Numerical simulations and time-course experiments where we tracked evolution by imaging bacteria suggest that our findings are general: less favorable conditions delay but do not entirely prevent the success of the fastest. Thus, the expansion rate defines a traveling wave fitness, which could be combined with trade-offs to predict evolution of expanding populations. Copyright © 2017, The Authors. All rights reserved.

关键词： Bacteria

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