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Construction and Characterization of a Novel Recombinant Attenuated and Replication-Deficient Candidate Human Adenovirus Type 3 Vaccine:"Adenovirus Vaccine Within an Adenovirus Vector"

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Virologica Sinica 2021年第3期36卷 354-364页

作者： Yuqian Yan Shuping Jing Liqiang Feng Jing Zhang Zhiwei Zeng Min Li Shan Zhao Junxian Ou Wendong Lan Wenyi Guan Xiaowei Wu Jianguo Wu Donald Seto Qiwei Zhang Guangdong Provincial Key Laboratory of Tropical Disease Research School of Public HealthSouthern Medical UniversityGuangzhou 510515China Microbiological Laboratory Zhuhai Center for Disease Control and PreventionZhuhai 519000China Guangzhou Institutes of Biomedicine and Health Chinese Academy of SciencesGuangzhou 510530China Guangdong Provincial Key Laboratory of Virology Institute of Medical MicrobiologyJinan UniversityGuangzhou 510632China Bioinformatics and Computational Biology Program School of Systems BiologyGeorge Mason UniversityManassasVA 20110USA

Human adenoviruses(HAd Vs)are highly contagious and result in large number of acute respiratory disease(ARD)cases with severe morbidity and *** adenovirus type 3(HAd V-3)is the most common type that causes ARD outbreaks in Asia,Europe,and the ***,there is currently no vaccine approved for its general *** hexon protein contains the main neutralizing epitopes,provoking strong and lasting *** this study,a novel recombinant and attenuated adenovirus vaccine candidate against HAd V-3 was constructed based on a commercially-available replication-defective HAd V-5 gene therapy and vaccine *** entire HAd V-3 hexon gene was integrated into the E1 region of the vector by homologous recombination using a bacterial *** resultant recombinants expressing the HAd V-3 hexon protein were rescued in AD293 cells,identified and characterized by RT-PCR,Western blots,indirect immunofluorescence,and electron *** potential vaccine candidate had a similar replicative efficacy as the wild-type HAd V-3 ***,and importantly,the vaccine strain had been rendered replication-defective and was incapable of replication in A549 cells after more than twentygeneration passages in AD293 *** represents a significant safety *** mice immunized both intranasally and intramuscularly by this vaccine candidate raised significant neutralizing antibodies against HAd ***,this recombinant,attenuated,and safe adenovirus vaccine is a promising HAd V-3 vaccine *** strategy of using a clinically approved and replication-defective HAd V-5 vector provides a novel approach to develop universal adenovirus vaccine candidates against all the other types of adenoviruses causing ARDs and perhaps other adenovirus-associated diseases.

关键词： Adenovirus vaccine Human adenovirus type 3(HAdV-3) Replication-deficient adenovirus vector Immunity in BALB/c mice Recombination

Developmental systems biology flourishing on new technologies

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Journal of Genetics and Genomics 2008年第10期35卷 577-584页

作者： Jing-Dong J. Han Yi Liu Huiling Xue Kai Xia Hong Yu Shanshan Zhu Zhang Chen Wei Zhang Zheng Huang Chunyu Jin Bo Xian Jing Li Lei Hou Yixing Han Chaoqun Niu Timothy C. Alcon Chinese Academy of Sciences Key Laboratory of Molecular Developmental Biology Center for Molecular Systems Biology Institute of Genetics and Developmental Biology Chinese Academy of Sciences Beijing 100101 China Key Laboratory of Machine Perception （Ministry of Education） Peking University Beijing 100871 China Bioinformatics and Computational Biology Graduate Program Iowa State University Ames IA 50011-1040 USA

Organism development is a systems level process. It has benefited greatly from the recent technological advances in the field of systems biology. DNA microarray, phenome, interactome and transcriptome mapping, the new generation of deep sequencing technologies, and faster and better computational and modeling approaches have opened new frontiers for both systems biologists and developmental biologists to reexamine the old developmental biology questions, such as pattern formation, and to tackle new problems, such as stem cell reprogramming. As showcased in the International Developmental Systems biology Symposium organized by Chinese Academy of Sciences, developmental systems biology is flourishing in many perspectives, from the evolution of developmental systems, to the underlying genetic and molecular pathways and networks, to the genomic, epigenomic and noncoding levels, to the computational analysis and modeling. We believe that the field will continue to reap rewards into the future with these new approaches.

关键词： developmental systems biology new technologies symposium

Higher-Order Generalization Bounds: Learning Deep Probabilistic programs via PAC-Bayes Objectives

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arXiv 2022年

作者： Warrell, Jonathan Gerstein, Mark Program in Computational Biology and Bioinformatics Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States

Deep Probabilistic programming (DPP) allows powerful models based on recursive computation to be learned using efficient deep-learning optimization techniques. Additionally, DPP offers a unified perspective, where inference and learning algorithms are treated on a par with models as stochastic programs. Here, we offer a framework for representing and learning flexible PAC-Bayes bounds as stochastic programs using DPP-based methods. In particular, we show that DPP techniques may be leveraged to derive generalization bounds that draw on the compositionality of DPP representations. In turn, the bounds we introduce offer principled training objectives for higher-order probabilistic programs. We offer a definition of a higher-order generalization bound, which naturally encompasses single- and multi-task generalization perspectives (including transfer- and meta-learning) and a novel class of bound based on a learned measure of model complexity. Further, we show how modified forms of all higher-order bounds can be efficiently optimized as objectives for DPP training, using variational techniques. We test our framework using single- and multi-task generalization settings on synthetic and biological data, showing improved performance and generalization prediction using flexible DPP model representations and learned complexity measures. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

High torsional energy disulfides: Relationship between cross-strand disulfides and right-handed staples

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Journal of bioinformatics and computational biology 2006年第1期4卷 155-168页

作者： Haworth, Naomi L. Feng, Lina L. Wouters, Merridee A. Computational Biology and Bioinformatics Program Victor Chang Cardiac Research Institute Darlinghurst NSW 2010 384 Victoria Rd. Australia School of Chemistry University of Sydney Sydney NSW 2006 Australia School of Biotechnology and Biomolecular Sciences University of New South Wales Sydney Sydney NSW 2052 Australia

Redox-active disulfides are capable of being oxidized and reduced under physiological conditions. The enzymatic role of redox-active disulfides in thiol-disulfide reductases is well-known, but redox-active disulfides are also present in non-enzymatic protein structures where they may act as switches of protein function. Here, we examine disulfides linking adjacent β-strands (cross-strand disulfides), which have been reported to be redox-active. Our previous work has established that these cross-strand disulfides have high torsional energies, a quantity likely to be related to the ease with which the disulfide is reduced. We examine the relationship between conformations of disulfides and their location in protein secondary structures. By identifying the overlap between cross-strand disulfides and various conformations, we wish to address whether the high torsional energy of a cross-strand disulfide is sufficient to confer redox activity or whether other factors, such as the presence of the cross-strand disulfide in a strained β-sheet, are required. © Imperial College Press.

关键词： Cross-strand disulfide Disulfide Redox signaling Right-handed staple

Reliable uncertainty estimate for antibiotic resistance classification with Stochastic Gradient Langevin Dynamics

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arXiv 2018年

作者： Hamid, Md-Nafiz Friedberg, Iddo Bioinformatics and Computational Biology Program Department of Veterinary Microbiology and Preventive Medicine Iowa State University AmesIA United States

Antibiotic resistance monitoring is of paramount importance in the face of this ongoing global epidemic. Deep learning models trained with traditional optimization algorithms (e.g. Adam, SGD) provide poor posterior estimates when tested against out-of-distribution (OoD) antibiotic resistant/non-resistant genes. In this paper, we introduce a deep learning model trained with Stochastic Gradient Langevin Dynamics (SGLD) to classify antibiotic resistant genes. The model provides better uncertainty estimates when tested against OoD data compared to traditional optimization methods such as Adam. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

Rank Projection Trees for Multilevel Neural Network Interpretation

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arXiv 2018年

作者： Warrell, Jonathan Mohsen, Hussein Gerstein, Mark Program in Computational Biology and Bioinformatics Yale University Molecular Biophysics and Biochemistry Yale University Department of Computer Science Yale University

A variety of methods have been proposed for interpreting nodes in deep neural networks, which typically involve scoring nodes at lower layers with respect to their effects on the output of higher-layer nodes (where lower and higher layers are closer to the input and output layers, respectively). However, we may be interested in picking out a prioritized collection of subsets of the inputs across a range of scales according to their importance for an output node, and not simply a prioritized ranking across the inputs as singletons. Such a situation may arise in biological applications, for instance, where we are interested in epistatic effects between groups of genes in determining a trait of interest. Here, we outline a flexible framework which may be used to generate multiscale network interpretations, using any previously defined scoring function. We demonstrate the ability of our method to pick out biologically important genes and gene sets in the domains of cancer and psychiatric genomics. Copyright © 2018, The Authors. All rights reserved.

关键词： Genes

Gene Co-expression Network analysis of Lung Squamous Cell Carcinoma data

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arXiv 2018年

作者： Hamid, Md-Nafiz Bioinformatics and Computational Biology program Department of Veterinary Microbiology and Preventive Medicine Iowa State University AmesIA United States

We performed a gene co-expression analysis on Lung Squamous Cell Carcinoma data to find modules (groups) of genes that may highly impact the growth of these type of tumors. Additionally, we used cancer survival data to relate modules to prognostic significance in terms of survival time. Analysis on RNA-seq data revealed modules which are significant in gene enrichment analysis. Specifically, two genes - RFC4 and ECT2 - have been found to be significant in terms of survival time. We also performed a second gene co-expression analysis on a second dataset of microarray data, and many significant genes found in this analysis could also be found in the RNA-seq data implying that these genes might indeed play a crucial role in Lung Squamous Cancer. All the R code for the analysis can be found at: https://***/nafizh/Gene_ coexpression_analysis_lung_cancer Copyright © 2018, The Authors. All rights reserved.

关键词： Genes

An approach to comparing tiling array and high throughput sequencing technologies for genomic transcript mapping

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BMC Research Notes 2009年第1期2卷 1-7页

作者： Sasidharan, Rajkumar Agarwal, Ashish Rozowsky, Joel Gerstein, Mark Molecular Biophysics and Biochemistry Department Yale University New Haven CT 06520 United States Department of Plant Biology Carnegie Institution for Science Stanford CA 94305 United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 United States Department of Computer Science Yale University New Haven CT 06520 United States

Background. There are two main technologies for transcriptome profiling, namely, tiling microarrays and high-throughput sequencing. Recently there has been a tremendous amount of excitement about the latter because of the advent of next-generation sequencing technologies and its promises. Consequently, the question of the moment is how these two technologies compare. Here we attempt to develop an approach to do a fair comparison of transcripts identified from tiling microarray and MPSS sequencing data. Findings. This comparison is a challenging task because the sequencing data is discrete while the tiling array data is continuous. We use the published rice and Arabidopsis datasets which provide currently best matched sets of arrays and sequencing experiments using a slightly earlier generation of sequencing, the MPSS tag sequencing technology. After scoring the arrays consistently in both the organisms, a first pass comparison reveals a surprisingly small overlap in transcripts of 22% and 66% respectively, in rice and Arabidopsis. However, when we do the analysis in detail, we find that this is an underestimate. In particular, when we map the probe intensities onto the sequencing tags and then look at their intensity distribution, we see that they are very similar to exons. Furthermore, restricting our comparison to only protein-coding gene loci revealed a very good overlap between the two technologies. Conclusion. Our approach to compare genome tiling microarray and MPSS sequencing data suggests that there is actually a reasonable overlap in transcripts identified by the two technologies. This overlap is distorted by the scoring and thresholding in the tiling array scoring procedure. © 2009 Sasidharan et al;licensee BioMed Central Ltd.

关键词： Tiling Array Transcript Mapping Tile Path Tiling Microarray Intergenic Transcript

Characterizing the tumor microenvironmental changes in a network perspective

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Characterizing the tumor microenvironmental changes in a net...

第四届全国生物信息学与系统生物学学术大会

作者：李文婷王瑞孙之荣 Institute of Bioinformatics School of Life SciencesTsinghua University Computational Biology and Bioinformatics Program Institute for Genome Science and PolicyDuke University Medical CenterDurhamNC27705

Alternations in tumor microenvironment are critical in driving tumor development. However,it proves difficult to characterize their molecular components and further relate to important pathological *** using a network approach,we characterized systematic expression changes at multi-gene module levels between colorectal tumors with and without recurrence after surgery and confirmed their association with two critical

关键词： Characterizing the tumor microenvironmental changes in a network perspective