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Integrated Regulation of HuR by Translation Repression and Protein Degradation Determines Pulsatile Expression of p53 Under DNA Damage

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ISCIENCE 2019年第May 31, 2019期15卷 342-+页

作者： Guha, Abhishek Ahuja, Deepika Das Mandal, Sukhen Parasar, Bibudha Deyasi, Krishanu Roy, Debadrita Sharma, Vasundhara Willard, Belinda Ghosh, Anandamohan Ray, Partho Sarothi Indian Inst Sci Educ & Res Kolkata Dept Biol Sci Mohanpur 741246 W Bengal India Indian Inst Sci Educ & Res Kolkata Dept Chem Sci Mohanpur 741246 W Bengal India Indian Inst Sci Educ & Res Kolkata Dept Math & Stat Mohanpur 741246 W Bengal India Cleveland Clin Lerner Res Inst Prote & Metabol Core 9500 Euclid Ave Cleveland OH 44195 USA Indian Inst Sci Educ & Res Kolkata Dept Phys Sci Mohanpur 741246 W Bengal India Cornell Univ Dept Chem & Chem Biol Ithaca NY 14853 USA Inst Engn & Management Dept Basic Sci & Humanities Salt Lake Elect Complex Kolkata 700091 W Bengal India Indian Inst Sci Dept Biochem Bangalore 560012 Karnataka India H Lee Moffitt Canc Ctr & Res Inst Dept Immunol MRC 12902 Magnolia Dr Tampa FL 33612 USA

Expression of tumor suppressor p53 is regulated at multiple levels, disruption of which often leads to cancer. We have adopted an approach combining computational systems modeling with experimental validation to elucidate the translation regulatory network that controls p53 expression post DNA damage. The RNA-binding protein HuR activates p53 mRNA translation in response to UVC-induced DNA damage in breast carcinoma cells. p53 and HuR levels show pulsatile change post UV irradiation. The computed model fitted with the observed pulse of p53 and HuR only when hypothetical regulators of synthesis and degradation of HuR were incorporated. miR-125b, a UV-responsive microRNA, was found to represses the translation of HuR mRNA. Furthermore, UV irradiation triggered proteasomal degradation of HuR mediated by an E3-ubiquitin ligase tripartite motif-containing 21 (TRIM21). The integrated action of miR-125b and TRIM21 constitutes an intricate control system that regulates pulsatile expression of HuR and p53 and determines cell viability in response to DNA damage.

关键词： bioinformatics Biological Sciences computational bioinformatics Molecular Biology

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A Learning-Based Method for LncRNA-Disease Association Identification Combing Similarity Information and Rotation Forest

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ISCIENCE 2019年第September 27, 2019期19卷 786-+页

作者： Guo, Zhen-Hao You, Zhu-Hong Wang, Yan-Bin Yi, Hai-Cheng Chen, Zhan-Heng Chinese Acad Sci Xinjiang Tech Inst Phys & Chem Urumqi 830011 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Long non-coding RNA (IncRNA) play critical roles in the occurrence and development of various diseases. The determination of the IncRNA-disease associations thus would contribute to provide new insights into the pathogenesis of the disease, the diagnosis, and the gene treatments. Considering that traditional experimental approaches are difficult to detect potential human IncRNA-disease associations from the vast amount of biological data, developing computational method could be of significantvalue. In this paper, we proposed a novel computational method named LDASR to identify associations between IncRNA and disease by analyzing known IncRNA-disease associations. First, the feature vectors of the IncRNA-disease pairs were obtained by integrating IncRNA Gaussian interaction profile kernel similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. Second, autoencoder neural network was employed to reduce the feature dimension and get the optimal feature subspace from the original feature set. Finally, Rotating Forest was used to carry out prediction of IncRNA-disease association. The proposed method achieves an excellent preference with 0.9502 AUC in leave-one-out cross-validations (LOOCV) and 0.9428 AUC in 5-fold cross-validation, which significantly outperformed previous methods. Moreover, two kinds of case studies on identifying IncRNAs associated with colorectal cancer and glioma further proves the capability of LDASR in identifying novel IncRNA-disease associations. The promising experimental results show that the LDASR can be an excellent addition to the biomedical research in the future.

关键词： Biocomputational Method bioinformatics computational bioinformatics

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Are we closer to the vision? A proposed framework for incorporating omics into environmental assessments

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ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2018年 59卷 87-93页

作者： Martyniuk, Christopher J. Univ Florida Coll Vet Med Canadian Rivers Inst UF Genet Inst Gainesville FL 32611 USA Univ Florida Coll Vet Med Dept Physiol Sci Ctr Environm & Human ToxicolUF Genet Inst Gainesville FL 32611 USA

Environmental science has benefited a great deal from omics-based technologies. High-throughput toxicology has defined adverse outcome pathways (AOPs), prioritized chemicals of concern, and identified novel actions of environmental chemicals. While many of these approaches are conducted under rigorous laboratory conditions, a significant challenge has been the interpretation of omics data in "real-world" exposure scenarios. Clarity in the interpretation of these data limits their use in environmental monitoring programs. In recent years, one overarching objective of many has been to address fundamental questions concerning experimental design and the robustness of data collected under the broad umbrella of environmental genomics. These questions include: (1) the likelihood that molecular profiles return to a predefined baseline level following remediation efforts, (2) how reference site selection in an urban environment influences interpretation of omics data and (3) what is the most appropriate species to monitor in the environment from an omics point of view. In addition, inter-genomics studies have been conducted to assess transcriptome reproducibility in toxicology studies. One lesson learned from inter-genomics studies is that there are core molecular networks that can be identified by multiple laboratories using the same platform. This supports the idea that "omics-networks" defined a priori may be a viable approach moving forward for evaluating environmental impacts over time. Both spatial and temporal variability in ecosystem structure is expected to influence molecular responses to environmental stressors, and it is important to recognize how these variables, as well as individual factor (i.e. sex, age, maturation), may confound interpretation of network responses to chemicals. This mini-review synthesizes the progress made towards adopting these tools into environmental monitoring and identifies future challenges to be addressed, as we move into the next e

关键词： computational bioinformatics Recovery Environmental monitoring Reference site Scientific rigor Ecotoxicology

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Non-linear Deep Neural Network for Rapid and Accurate Prediction of Phenotypic Responses to Kinase Inhibitors

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ISCIENCE 2020年第5期23卷 101129-101129页

作者： Vijay, Siddharth Gujral, Taranjit S. Fred Hutchinson Canc Res Ctr Div Human Biol 1124 Columbia St Seattle WA 98104 USA Univ Washington Dept Pharmacol Seattle WA 98195 USA

Protein kinase inhibitors are one of the most successful targeted therapies to date. Despite this progress, additional kinase inhibitors are needed to expand the target space as well as overcome drug resistance that has emerged in clinical setting. Here, we developed KiDNN (Kinase inhibitor prediction using Deep Neural Networks). KiDNN utilizes non-linear, multilayer feedforward network that mimics complex and dynamic kinase-driven signaling pathways. We used KiDNN to predict the effect of similar to 200 kinase inhibitors on migration of breast and liver cancer cells. We show that the prediction accuracy of KiDNN outperformed other prediction tools based on linear models. We validated that an inhibitor of tyrosine kinase receptors, and an inhibitor of Src family kinases, decreased migration of triple-negative breast cancer cells, consistent with the role of these kinases in driving motility. Overall, we show that non-linear, DNN-based models provide a powerful approach to in silico screen hundreds of kinase inhibitors.

关键词： bioinformatics Cancer computational bioinformatics Neural Networks

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Functional Connectivity Fingerprints at Rest Are Similar across Youths and Adults and Vary with Genetic Similarity

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ISCIENCE 2020年第1期23卷 100801-100801页

作者： Demeter, Damion, V Engelhardt, Laura E. Mallett, Remington Gordon, Evan M. Nugiel, Tehila Harden, K. Paige Tucker-Drob, Elliot M. Lewis-Peacock, Jarrod A. Church, Jessica A. Univ Texas Austin Dept Psychol Austin TX 78712 USA Univ Texas Austin Populat Res Ctr Austin TX 78712 USA Univ Texas Austin Biomed Imaging Ctr Austin TX 78712 USA VISN 17 Ctr Excellence Res Returning War Vet Waco TX 76711 USA Univ Texas Dallas Ctr Vital Longev Sch Behav & Brain Sci Dallas TX 75235 USA Baylor Univ Dept Psychol & Neurosci Waco TX 76789 USA

Distinguishing individuals from brain connectivity, and studying the genetic influences on that identification across different ages, improves our basic understanding of functional brain network organization. We applied support vector machine classifiers to two datasets of twins (adult, pediatric) and two datasets of repeat-scan individuals (adult, pediatric). Classifiers were trained on resting state functional connectivity magnetic resonance imaging (rs-fcMRI) data and used to predict individuals and co-twin pairs from independent data. The classifiers successfully identified individuals from a previous scan with 100% accuracy, even when scans were separated by months. In twin samples, classifier accuracy decreased as genetic similarity decreased. Our results demonstrate that classification is stable within individuals, similar within families, and contains similar representations of functional connections over a few decades of life. Moreover, the degree to which these patterns of connections predict siblings' data varied by genetic relatedness, suggesting that genetic influences on rs-fcMRI connectivity are established early in life.

关键词： Biological Sciences computational bioinformatics Neuroscience

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Optimizing high performance computing workflow for protein functional annotation

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2014年第13期26卷 2112-2121页

作者： Stanberry, Larissa Rekepalli, Bhanu Liu, Yuan Giblock, Paul Higdon, Roger Montague, Elizabeth Broomall, William Kolker, Natali Kolker, Eugene Seattle Childrens Res Inst SCRI DELSA Global Bioinformat & High Throughput Anal Lab Seattle WA 98101 USA Seattle Childrens Res Inst SCRI DELSA Global High Throughput Anal Core Seattle WA 98101 USA Univ Tennessee Joint Inst Computat Sci Oak Ridge Natl Lab DELSA Global Oak Ridge TN USA Cisco Syst San Jose CA 95134 USA Univ Washington Bioinformat & High Throughput Anal Lab Dept PediatSeattle Childrens Hosp High Throughput Anal CoreSCRIDELSA Global Seattle WA 98195 USA Univ Washington Bioinformat & High Throughput Anal Lab Dept Biomed Informat & Med EducSeattle Childrens High Throughput Anal CoreSCRIDELSA Global Seattle WA 98195 USA

Functional annotation of newly sequenced genomes is one of the major challenges in modern biology. With modern sequencing technologies, the protein sequence universe is rapidly expanding. Newly sequenced bacterial genomes alone contain over 7.5 million proteins. The rate of data generation has far surpassed that of protein annotation. The volume of protein data makes manual curationinfeasible, whereas a high compute cost limits the utility of existing automated approaches. In this work, we present an improved and optmized automated workflow to enable large-scale protein annotation. The workflow uses high performance computing architectures and a low complexity classification algorithm to assign proteins into existing clusters of orthologous groups of proteins. On the basis of the Position-Specific Iterative Basic Local Alignment Search Tool the algorithm ensures at least 80% specificity and sensitivity of the resulting classifications. The workflow utilizes highly scalable parallel applications for classification and sequence alignment. Using Extreme Science and Engineering Discovery Environment supercomputers, the workflow processed 1,200,000 newly sequenced bacterial proteins. With the rapid expansion of the protein sequence universe, the proposed workflow will enable scientists to annotate big genome data. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： science gateways petascale data-enabled life sciences sequence similarity computational bioinformatics protein annotation protein sequence universe COG BLAST PSI-BLAST HSPp-BLAST XSEDE PSU

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Discriminatory mining of gene expression microarray data

Discriminatory mining of gene expression microarray data

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12th IEEE Workshop on Neural Networks for Signal Processing

作者： Wang, ZY Wang, Y Lu, JP Kung, SY Zhang, JY Lee, R Xuan, JH Khan, JV Catholic Univ Amer Dept Elect Engn & Comp Sci Washington DC 20064 USA Virginia Polytech Inst & State Univ Dept Elect & Comp Engn Alexandria VA 22314 USA Princeton Univ Dept Elect Engn Princeton NJ 08544 USA Georgetown Univ Vincent T Lombardi Canc Res Ctr Washington DC 20007 USA NHGRI NIH Bethesda MD 20892 USA

Recent advances in machine learning and pattern recognition methods provide new analytical tools to explore high dimensional. gene expression microarray data. Our data mining software, VISual Data Analyzer for cluster discovery (VISDA), reveals many distinguishing patterns among gene expression profiles, which are responsible for the cell's phenotypes. The model-supported exploration of high-dimensional data space is achieved through two complementary schemes: dimensionality reduction by discriminatory data projection and cluster decomposition by soft data clustering. Reducing dimensionality generates the visualization of the complete data set at the top level. This data set is then partitioned into subclusters that can consequently be visualized at lower levels and if necessary partitioned again. In this paper, three different algorithms are evaluated in their abilities to reduce dimensionality and to visualize data sets: Principal Component Analysis (PCA), Discriminatory Component Analysis (DCA), and Projection Pursuit Method (PPM). The partitioning into subclusters uses the Expectation-Maximization (EM) algorithm and the hierarchical normal mixture model that is selected by the user and verified "optimally" by the Minimum Description Length (MDL) criterion. These approaches produce different visualizations that are compared against known phenotypes from the microarray experiments. Overall, these algorithms and user-selected models explore the high dimensional data where standard analyses may not be sufficient.

关键词： computational bioinformatics gene microarrays finite normal mixture cluster visualization and selection machine learning

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ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data

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ISCIENCE 2024年第3期27卷 109198页

作者： Teng, Pang-ning Schaaf, Joshua P. Abulez, Tamara Hood, Brian L. Wilson, Katlin N. Litzi, Tracy J. Mitchell, David Conrads, Kelly A. Hunt, Allison L. Olowu, Victoria Oliver, Julie Park, Fred S. Edwards, Marshe Chiang, Aichun Wilkerson, Matthew D. Raj-Kumar, Praveen-Kumar Tarney, Christopher M. Darcy, Kathleen M. Phippen, Neil T. Maxwell, G. Larry Conrads, Thomas P. Bateman, Nicholas W. Gynecol Canc Ctr Excellence Annandale VA 22003 USA Womens Hlth Integrated Res Ctr Annandale VA 22003 USA Henry M Jackson Fdn Adv Mil Med Inc Bethesda MD 20817 USA Inova Hlth Syst Womens Hlth Integrated Res Ctr Womens Serv Line Falls Church VA 22042 USA Uniformed Serv Univ Hlth Sci Ctr Mil Precis Hlth Dept Anat Physiol & Genet Bethesda MD 20814 USA Chan Soon Shiong Inst Mol Med Windber PA 15963 USA Uniformed Serv Univ Hlth Sci John P Murtha Canc Ctr Dept Surg Bethesda MD 20889 USA Walter Reed Natl Mil Med Ctr Bethesda MD 20889 USA

Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations, and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript -level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome and transcriptome data generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co -quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture (r R 0.63) in two commonly used deconvolution algorithms, ESTIMATE or ConsensusTME. We further developed and optimized protein -based signatures estimating cell admixture proportions and benchmarked these using bulk tumor proteomic data from over 150 patients with HGSOC. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://***/ProteoMixture/.

关键词： computational bioinformatics Proteomics Transcriptomics

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Modularization of grid cells constrained by the pyramidal patch lattice

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ISCIENCE 2021年第4期24卷 102301页

作者： Wang, Tao Yang, Fan Wang, Ziqun Zhang, Bing Wang, Wei Liu, Feng Nanjing Univ Collaborat Innovat Ctr Adv Microstruct Dept Phys Natl Lab Solid State Microstruct Nanjing 210093 Peoples R China Nanjing Univ Med Sch Drum Tower Hosp Dept Radiol Nanjing 210008 Peoples R China Nanjing Univ Inst Brain Sci Nanjing 210023 Peoples R China

Grid cells provide a metric representation of self-location. They are organized into modules, showing discretized scales of grid spacing, but the underlying mechanism remains elusive. In this modeling study, we propose that the hexagonal lattice of pyramidal cell patches may underlie the discretization of grid spacing and orientation. In the continuous attractor network composed of interneurons, stellate and pyramidal cells, the hexagonal lattice of bump attractors is specifically aligned to the patch lattice under 22 conditions determined by the geometry of the patch lattice, while pyramidal cells exhibit synchrony to diverse extents. Given the bump attractor lattice in each module originates from those 22 scenarios, the experimental data on the grid spacing ratio and orientation difference between modules can be reproduced. This work recapitulates the patterns of grid spacing versus orientation in individual animals and reveals the correlation between microstructures and firing fields, providing a systems-level mechanism for grid modularity.

关键词： Biophysics computational bioinformatics Neuroscience

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Predicting Human Olfactory Perception from Activities of Odorant Receptors

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ISCIENCE 2020年第8期23卷 101361页

作者： Kowalewski, Joel Ray, Anandasankar Univ Calif Riverside Interdept Neurosci Program Riverside CA 92521 USA Univ Calif Riverside Dept Mol Cell & Syst Biol 3401 Watkins Dr Riverside CA 92521 USA

Odor perception in humans is initiated by activation of odorant receptors (ORs) in the nose. However, the ORs linked to specific olfactory percepts are unknown, unlike in vision or taste where receptors are linked to perception of different colors and tastes. The large family of ORs (similar to 400) and multiple receptors activated by an odorant present serious challenges. Here, we first use machine learning to screen similar to 0.5 million compounds for new ligands and identify enriched structural motifs for ligands of 34 human ORs. We next demonstrate that the activity of ORs successfully predicts many of the 146 different perceptual qualities of chem-icals. Although chemical features have been used to model odor percepts, we show that biologically relevant OR activity is often superior. Interestingly, each odor percept could be predicted with very few ORs, implying they contribute more to each olfactory percept. A similar model is observed in Drosophila where comprehensive OR-neuron data are available.

关键词： Sensory Neuroscience bioinformatics Automation in bioinformatics computational bioinformatics

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