1
|
Zhang L, Xiong Z, Xiao M. A Review of the Application of Spatial Transcriptomics in Neuroscience. Interdiscip Sci 2024:10.1007/s12539-024-00603-4. [PMID: 38374297 DOI: 10.1007/s12539-024-00603-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/29/2023] [Accepted: 01/02/2024] [Indexed: 02/21/2024]
Abstract
Since spatial transcriptomics can locate and distinguish the gene expression of functional genes in special regions and tissue, it is important for us to investigate the brain development, the development mechanism of brain diseases, and the relationship between brain structure and function in Neuroscience (or Brain science). While previous studies have introduced the crucial spatial transcriptomic techniques and data analysis methods, there are few studies to comprehensively overview the key methods, data resources, and technological applications of spatial transcriptomics in Neuroscience. For these reasons, we first investigate several common spatial transcriptomic data analysis approaches and data resources. Second, we introduce the applications of the spatial transcriptomic data analysis approaches in Neuroscience. Third, we summarize the integrating spatial transcriptomics with other technologies in Neuroscience. Finally, we discuss the challenges and future research directions of spatial transcriptomics in Neuroscience.
Collapse
Affiliation(s)
- Le Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Zhenqi Xiong
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Ming Xiao
- College of Computer Science, Sichuan University, Chengdu, 610065, China.
| |
Collapse
|
2
|
Nait Saada J, Tsangalidou Z, Stricker M, Palamara PF. Inference of Coalescence Times and Variant Ages Using Convolutional Neural Networks. Mol Biol Evol 2023; 40:msad211. [PMID: 37738175 PMCID: PMC10581698 DOI: 10.1093/molbev/msad211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 09/11/2023] [Accepted: 09/18/2023] [Indexed: 09/24/2023] Open
Abstract
Accurate inference of the time to the most recent common ancestor (TMRCA) between pairs of individuals and of the age of genomic variants is key in several population genetic analyses. We developed a likelihood-free approach, called CoalNN, which uses a convolutional neural network to predict pairwise TMRCAs and allele ages from sequencing or SNP array data. CoalNN is trained through simulation and can be adapted to varying parameters, such as demographic history, using transfer learning. Across several simulated scenarios, CoalNN matched or outperformed the accuracy of model-based approaches for pairwise TMRCA and allele age prediction. We applied CoalNN to settings for which model-based approaches are under-developed and performed analyses to gain insights into the set of features it uses to perform TMRCA prediction. We next used CoalNN to analyze 2,504 samples from 26 populations in the 1,000 Genome Project data set, inferring the age of ∼80 million variants. We observed substantial variation across populations and for variants predicted to be pathogenic, reflecting heterogeneous demographic histories and the action of negative selection. We used CoalNN's predicted allele ages to construct genome-wide annotations capturing the signature of past negative selection. We performed LD-score regression analysis of heritability using summary association statistics from 63 independent complex traits and diseases (average N=314k), observing increased annotation-specific effects on heritability compared to a previous allele age annotation. These results highlight the effectiveness of using likelihood-free, simulation-trained models to infer properties of gene genealogies in large genomic data sets.
Collapse
Affiliation(s)
| | | | | | - Pier Francesco Palamara
- Department of Statistics, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| |
Collapse
|
3
|
Zhang L, Badai J, Wang G, Ru X, Song W, You Y, He J, Huang S, Feng H, Chen R, Zhao Y, Chen Y. Discovering hematoma-stimulated circuits for secondary brain injury after intraventricular hemorrhage by spatial transcriptome analysis. Front Immunol 2023; 14:1123652. [PMID: 36825001 PMCID: PMC9941151 DOI: 10.3389/fimmu.2023.1123652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 01/11/2023] [Indexed: 02/09/2023] Open
Abstract
Introduction Central nervous system (CNS) diseases, such as neurodegenerative disorders and brain diseases caused by acute injuries, are important, yet challenging to study due to disease lesion locations and other complexities. Methods Utilizing the powerful method of spatial transcriptome analysis together with novel algorithms we developed for the study, we report here for the first time a 3D trajectory map of gene expression changes in the brain following acute neural injury using a mouse model of intraventricular hemorrhage (IVH). IVH is a common and representative complication after various acute brain injuries with severe mortality and mobility implications. Results Our data identified three main 3D global pseudospace-time trajectory bundles that represent the main neural circuits from the lateral ventricle to the hippocampus and primary cortex affected by experimental IVH stimulation. Further analysis indicated a rapid response in the primary cortex, as well as a direct and integrated effect on the hippocampus after IVH stimulation. Discussion These results are informative for understanding the pathophysiological changes, including the spatial and temporal patterns of gene expression changes, in IVH patients after acute brain injury, strategizing more effective clinical management regimens, and developing novel bioinformatics strategies for the study of other CNS diseases. The algorithm strategies used in this study are searchable via a web service (www.combio-lezhang.online/3dstivh/home).
Collapse
Affiliation(s)
- Le Zhang
- College of Computer Science, Sichuan University, Chengdu, China,Innovation Center of Nursing Research, West China Hospital, Sichuan University, Chengdu, China
| | - Jiayidaer Badai
- College of Computer Science, Sichuan University, Chengdu, China
| | - Guan Wang
- College of Computer Science, Sichuan University, Chengdu, China,Innovation Center of Nursing Research, West China Hospital, Sichuan University, Chengdu, China
| | - Xufang Ru
- Chinese Academy of Sciences (CAS) Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China,Department of Neurosurgery and State Key Laboratory of Trauma, Burn and Combined Injury, Southwest Hospital, Army Medical University, Chongqing, China
| | - Wenkai Song
- College of Computer Science, Sichuan University, Chengdu, China
| | - Yujie You
- College of Computer Science, Sichuan University, Chengdu, China
| | - Jiaojiao He
- College of Computer Science, Sichuan University, Chengdu, China
| | - Suna Huang
- Department of Neurosurgery and State Key Laboratory of Trauma, Burn and Combined Injury, Southwest Hospital, Army Medical University, Chongqing, China
| | - Hua Feng
- Department of Neurosurgery and State Key Laboratory of Trauma, Burn and Combined Injury, Southwest Hospital, Army Medical University, Chongqing, China
| | - Runsheng Chen
- College of Computer Science, Sichuan University, Chengdu, China,Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China,West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China,*Correspondence: Runsheng Chen, ; Yi Zhao, ; Yujie Chen, ;
| | - Yi Zhao
- College of Computer Science, Sichuan University, Chengdu, China,West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China,Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China,*Correspondence: Runsheng Chen, ; Yi Zhao, ; Yujie Chen, ;
| | - Yujie Chen
- Chinese Academy of Sciences (CAS) Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China,Department of Neurosurgery and State Key Laboratory of Trauma, Burn and Combined Injury, Southwest Hospital, Army Medical University, Chongqing, China,*Correspondence: Runsheng Chen, ; Yi Zhao, ; Yujie Chen, ;
| |
Collapse
|
4
|
Yu S, Cao S, He S, Zhang K. Locus-Specific Detection of DNA Methylation: The Advance, Challenge, and Perspective of CRISPR-Cas Assisted Biosensors. SMALL METHODS 2023; 7:e2201624. [PMID: 36609885 DOI: 10.1002/smtd.202201624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Indexed: 06/17/2023]
Abstract
Deoxyribonucleic acid (DNA) methylation is one of the epigenetic characteristics that result in heritable and revisable phenotype changes but without sequence changes in DNA. Aberrant methylation occurring at a specific locus was reported to be associated with cancers, insulin resistance, obesity, Alzheimer's disease, Parkinson's disease, etc. Therefore, locus-specific DNA methylation can serve as a valuable biomarker for disease diagnosis and therapy. Recently, Clustered regularly interspaced short palindromic repeats (CRISPR)-Cas systems are applied to develop biosensors for DNA, ribonucleic acid, proteins, and small molecules detection. Because of their highly specific binding ability and signal amplification capacity, CRISPR-Cas assisted biosensor also serve as a potential tool for locus-specific detection of DNA methylation. In this perspective, based on the detection principle, a detailed classification and comprehensive discussion of recent works about the latest advances in locus-specific detection of DNA methylation using CRISPR-Cas systems are provided. Furthermore, current challenges and future perspectives of CRISPR-based locus-specific detection of DNA methylation are outlined.
Collapse
Affiliation(s)
- Songcheng Yu
- College of Public Health, Zhengzhou University, No.100 Science Avenue, Zhengzhou City, 450001, P. R. China
| | - Shengnan Cao
- College of Public Health, Zhengzhou University, No.100 Science Avenue, Zhengzhou City, 450001, P. R. China
| | - Sitian He
- College of Public Health, Zhengzhou University, No.100 Science Avenue, Zhengzhou City, 450001, P. R. China
| | - Kaixiang Zhang
- School of Pharmaceutical Sciences, Zhengzhou University, No.100 Science Avenue, Zhengzhou City, 450001, P. R. China
| |
Collapse
|
5
|
Tan DS, Cheung SL, Gao Y, Weinbuch M, Hu H, Shi L, Ti SC, Hutchins AP, Cojocaru V, Jauch R. The homeodomain of Oct4 is a dimeric binder of methylated CpG elements. Nucleic Acids Res 2023; 51:1120-1138. [PMID: 36631980 PMCID: PMC9943670 DOI: 10.1093/nar/gkac1262] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 12/14/2022] [Accepted: 12/19/2022] [Indexed: 01/13/2023] Open
Abstract
Oct4 is essential to maintain pluripotency and has a pivotal role in establishing the germline. Its DNA-binding POU domain was recently found to bind motifs with methylated CpG elements normally associated with epigenetic silencing. However, the mode of binding and the consequences of this capability has remained unclear. Here, we show that Oct4 binds to a compact palindromic DNA element with a methylated CpG core (CpGpal) in alternative states of pluripotency and during cellular reprogramming towards induced pluripotent stem cells (iPSCs). During cellular reprogramming, typical Oct4 bound enhancers are uniformly demethylated, with the prominent exception of the CpGpal sites where DNA methylation is often maintained. We demonstrate that Oct4 cooperatively binds the CpGpal element as a homodimer, which contrasts with the ectoderm-expressed POU factor Brn2. Indeed, binding to CpGpal is Oct4-specific as other POU factors expressed in somatic cells avoid this element. Binding assays combined with structural analyses and molecular dynamic simulations show that dimeric Oct4-binding to CpGpal is driven by the POU-homeodomain whilst the POU-specific domain is detached from DNA. Collectively, we report that Oct4 exerts parts of its regulatory function in the context of methylated DNA through a DNA recognition mechanism that solely relies on its homeodomain.
Collapse
Affiliation(s)
- Daisylyn Senna Tan
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Shun Lai Cheung
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Ya Gao
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Maike Weinbuch
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China,Institute for Molecular Medicine, Ulm University, Ulm, Germany
| | - Haoqing Hu
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Liyang Shi
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Shih-Chieh Ti
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Andrew P Hutchins
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Vlad Cojocaru
- STAR-UBB Institute, Babeş-Bolyai University, Cluj-Napoca, Romania,Computational Structural Biology Group, Utrecht University, The Netherlands,Max Planck Institute for Molecular Biomedicine, Münster, Germany
| | - Ralf Jauch
- To whom correspondence should be addressed. Tel: +852 3917 9511; Fax: +852 28559730;
| |
Collapse
|
6
|
A Computer Simulation of SARS-CoV-2 Mutation Spectra for Empirical Data Characterization and Analysis. Biomolecules 2022; 13:biom13010063. [PMID: 36671448 PMCID: PMC9855923 DOI: 10.3390/biom13010063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/21/2022] [Accepted: 12/23/2022] [Indexed: 12/31/2022] Open
Abstract
It is very important to compute the mutation spectra, and simulate the intra-host mutation processes by sequencing data, which is not only for the understanding of SARS-CoV-2 genetic mechanism, but also for epidemic prediction, vaccine, and drug design. However, the current intra-host mutation analysis algorithms are not only inaccurate, but also the simulation methods are unable to quickly and precisely predict new SARS-CoV-2 variants generated from the accumulation of mutations. Therefore, this study proposes a novel accurate strand-specific SARS-CoV-2 intra-host mutation spectra computation method, develops an efficient and fast SARS-CoV-2 intra-host mutation simulation method based on mutation spectra, and establishes an online analysis and visualization platform. Our main results include: (1) There is a significant variability in the SARS-CoV-2 intra-host mutation spectra across different lineages, with the major mutations from G- > A, G- > C, G- > U on the positive-sense strand and C- > U, C- > G, C- > A on the negative-sense strand; (2) our mutation simulation reveals the simulation sequence starts to deviate from the base content percentage of Alpha-CoV/Delta-CoV after approximately 620 mutation steps; (3) 2019-NCSS provides an easy-to-use and visualized online platform for SARS-Cov-2 online analysis and mutation simulation.
Collapse
|
7
|
You Y, Zhang L, Tao P, Liu S, Chen L. Spatiotemporal Transformer Neural Network for Time-Series Forecasting. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1651. [PMID: 36421506 PMCID: PMC9689721 DOI: 10.3390/e24111651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 11/05/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
Predicting high-dimensional short-term time-series is a difficult task due to the lack of sufficient information and the curse of dimensionality. To overcome these problems, this study proposes a novel spatiotemporal transformer neural network (STNN) for efficient prediction of short-term time-series with three major features. Firstly, the STNN can accurately and robustly predict a high-dimensional short-term time-series in a multi-step-ahead manner by exploiting high-dimensional/spatial information based on the spatiotemporal information (STI) transformation equation. Secondly, the continuous attention mechanism makes the prediction results more accurate than those of previous studies. Thirdly, we developed continuous spatial self-attention, temporal self-attention, and transformation attention mechanisms to create a bridge between effective spatial information and future temporal evolution information. Fourthly, we show that the STNN model can reconstruct the phase space of the dynamical system, which is explored in the time-series prediction. The experimental results demonstrate that the STNN significantly outperforms the existing methods on various benchmarks and real-world systems in the multi-step-ahead prediction of a short-term time-series.
Collapse
Affiliation(s)
- Yujie You
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu 610065, China
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
| | - Peng Tao
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
| | - Suran Liu
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Luonan Chen
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
- State Key Laboratory of Cell Biology, Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
- Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai 519031, China
- West China Biomedical Big Data Center, Med-X Center for Informatics, West China Hospital, Sichuan University, Chengdu 610041, China
| |
Collapse
|
8
|
Wu M, Huang W, Yang N, Liu Y. Learn from antibody–drug conjugates: consideration in the future construction of peptide-drug conjugates for cancer therapy. Exp Hematol Oncol 2022; 11:93. [DOI: 10.1186/s40164-022-00347-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 10/17/2022] [Indexed: 11/09/2022] Open
Abstract
AbstractCancer is one of the leading causes of death worldwide due to high heterogeneity. Although chemotherapy remains the mainstay of cancer therapy, non-selective toxicity and drug resistance of mono-chemotherapy incur broad criticisms. Subsequently, various combination strategies have been developed to improve clinical efficacy, also known as cocktail therapy. However, conventional “cocktail administration” is just passable, due to the potential toxicities to normal tissues and unsatisfactory synergistic effects, especially for the combined drugs with different pharmacokinetic properties. The drug conjugates through coupling the conventional chemotherapeutics to a carrier (such as antibody and peptide) provide an alternative strategy to improve therapeutic efficacy and simultaneously reduce the unspecific toxicities, by virtue of the advantages of highly specific targeting ability and potent killing effect. Although 14 antibody–drug conjugates (ADCs) have been approved worldwide and more are being investigated in clinical trials so far, several limitations have been disclosed during clinical application. Compared with ADCs, peptide-drug conjugates (PDCs) possess several advantages, including easy industrial synthesis, low cost, high tissue penetration and fast clearance. So far, only a handful of PDCs have been approved, highlighting tremendous development potential. Herein, we discuss the progress and pitfalls in the development of ADCs and underline what can learn from ADCs for the better construction of PDCs in the future.
Collapse
|
9
|
Ma F, Xiao M, Zhu L, Jiang W, Jiang J, Zhang PF, Li K, Yue M, Zhang L. An integrated platform for Brucella with knowledge graph technology: From genomic analysis to epidemiological projection. Front Genet 2022; 13:981633. [PMID: 36186430 PMCID: PMC9516312 DOI: 10.3389/fgene.2022.981633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/30/2022] [Indexed: 11/20/2022] Open
Abstract
Motivation:Brucella, the causative agent of brucellosis, is a global zoonotic pathogen that threatens both veterinary and human health. The main sources of brucellosis are farm animals. Importantly, the bacteria can be used for biological warfare purposes, requiring source tracking and routine surveillance in an integrated manner. Additionally, brucellosis is classified among group B infectious diseases in China and has been reported in 31 Chinese provinces to varying degrees in urban areas. From a national biosecurity perspective, research on brucellosis surveillance has garnered considerable attention and requires an integrated platform to provide researchers with easy access to genomic analysis and provide policymakers with an improved understanding of both reported patients and detected cases for the purpose of precision public health interventions. Results: For the first time in China, we have developed a comprehensive information platform for Brucella based on dynamic visualization of the incidence (reported patients) and prevalence (detected cases) of brucellosis in mainland China. Especially, our study establishes a knowledge graph for the literature sources of Brucella data so that it can be expanded, queried, and analyzed. When similar “epidemiological comprehensive platforms” are established in the distant future, we can use knowledge graph to share its information. Additionally, we propose a software package for genomic sequence analysis. This platform provides a specialized, dynamic, and visual point-and-click interface for studying brucellosis in mainland China and improving the exploration of Brucella in the fields of bioinformatics and disease prevention for both human and veterinary medicine.
Collapse
Affiliation(s)
- Fubo Ma
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Ming Xiao
- College of Computer Science, Sichuan University, Chengdu, China
| | - Lin Zhu
- China Animal Health and Epidemiology Center, Qingdao, Shandong, China
| | - Wen Jiang
- College of Computer Science, Sichuan University, Chengdu, China
| | - Jizhe Jiang
- College of Computer Science, Sichuan University, Chengdu, China
| | - Peng-Fei Zhang
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Kang Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Min Yue
- Hainan Institute of Zhejiang University, Sanya, China
- *Correspondence: Le Zhang, ; Min Yue,
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, China
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China
- *Correspondence: Le Zhang, ; Min Yue,
| |
Collapse
|
10
|
Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther 2022; 7:156. [PMID: 35538061 PMCID: PMC9090746 DOI: 10.1038/s41392-022-00994-0] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 03/14/2022] [Accepted: 04/05/2022] [Indexed: 02/08/2023] Open
Abstract
Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.
Collapse
|
11
|
ASTM: Developing the web service for anthrax related spatiotemporal characteristics and meteorology study. QUANTITATIVE BIOLOGY 2022. [DOI: 10.15302/j-qb-022-0288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
12
|
Liu S, You Y, Tong Z, Zhang L. Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research. Front Genet 2021; 12:761629. [PMID: 34764986 PMCID: PMC8576451 DOI: 10.3389/fgene.2021.761629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 09/27/2021] [Indexed: 11/13/2022] Open
Abstract
It is very important for systems biologists to predict the state of the multi-omics time series for disease occurrence and health detection. However, it is difficult to make the prediction due to the high-dimensional, nonlinear and noisy characteristics of the multi-omics time series data. For this reason, this study innovatively proposes an Embedding, Koopman and Autoencoder technologies-based multi-omics time series predictive model (EKATP) to predict the future state of a high-dimensional nonlinear multi-omics time series. We evaluate this EKATP by using a genomics time series with chaotic behavior, a proteomics time series with oscillating behavior and a metabolomics time series with flow behavior. The computational experiments demonstrate that our proposed EKATP can substantially improve the accuracy, robustness and generalizability to predict the future state of a time series for multi-omics data.
Collapse
Affiliation(s)
- Suran Liu
- College of Computer Science, Sichuan University, Chengdu, China
| | - Yujie You
- College of Computer Science, Sichuan University, Chengdu, China
| | - Zhaoqi Tong
- College of Software Engineering, Sichuan University, Chengdu, China
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, China
| |
Collapse
|
13
|
MCDB: A comprehensive curated mitotic catastrophe database for retrieval, protein sequence alignment, and target prediction. Acta Pharm Sin B 2021; 11:3092-3104. [PMID: 34729303 PMCID: PMC8546929 DOI: 10.1016/j.apsb.2021.05.032] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 03/12/2021] [Accepted: 05/06/2021] [Indexed: 02/05/2023] Open
Abstract
Mitotic catastrophe (MC) is a form of programmed cell death induced by mitotic process disorders, which is very important in tumor prevention, development, and drug resistance. Because rapidly increased data for MC is vigorously promoting the tumor-related biomedical and clinical study, it is urgent for us to develop a professional and comprehensive database to curate MC-related data. Mitotic Catastrophe Database (MCDB) consists of 1214 genes/proteins and 5014 compounds collected and organized from more than 8000 research articles. Also, MCDB defines the confidence level, classification criteria, and uniform naming rules for MC-related data, which greatly improves data reliability and retrieval convenience. Moreover, MCDB develops protein sequence alignment and target prediction functions. The former can be used to predict new potential MC-related genes and proteins, and the latter can facilitate the identification of potential target proteins of unknown MC-related compounds. In short, MCDB is such a proprietary, standard, and comprehensive database for MC-relate data that will facilitate the exploration of MC from chemists to biologists in the fields of medicinal chemistry, molecular biology, bioinformatics, oncology and so on. The MCDB is distributed on http://www.combio-lezhang.online/MCDB/index_html/.
Collapse
Key Words
- Data mining
- Database
- GO, Gene Ontology
- IUPAC, International Union of Pure and Applied Chemistry
- InChI Key, International Chemical Identifier hash
- InChI, International Chemical Identifier
- MC, Mitotic Catastrophe
- MCDB, Mitotic Catastrophe Database
- Mitotic catastrophe
- PDB, Protein Data Bank
- PMID, PubMed identifier
- Protein sequence analysis
- PubChem, Public Chemistry
- PubMed, Public Medicine
- SMILES, Simplified Molecular Input Line Entry Specification
- Target prediction
- UniProt, Universal Protein Resource
Collapse
|
14
|
Xiao M, Liu G, Xie J, Dai Z, Wei Z, Ren Z, Yu J, Zhang L. 2019nCoVAS: Developing the Web Service for Epidemic Transmission Prediction, Genome Analysis, and Psychological Stress Assessment for 2019-nCoV. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1250-1261. [PMID: 33406042 PMCID: PMC8769043 DOI: 10.1109/tcbb.2021.3049617] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 10/02/2020] [Accepted: 01/03/2021] [Indexed: 06/12/2023]
Abstract
Since the COVID-19 epidemic is still expanding around the world and poses a serious threat to human life and health, it is necessary for us to carry out epidemic transmission prediction, whole genome sequence analysis, and public psychological stress assessment for 2019-nCoV. However, transmission prediction models are insufficiently accurate and genome sequence characteristics are not clear, and it is difficult to dynamically assess the public psychological stress state under the 2019-nCoV epidemic. Therefore, this study develops a 2019nCoVAS web service (http://www.combio-lezhang.online/2019ncov/home.html) that not only offers online epidemic transmission prediction and lineage-associated underrepresented permutation (LAUP) analysis services to investigate the spreading trends and genome sequence characteristics, but also provides psychological stress assessments based on such an emotional dictionary that we built for 2019-nCoV. Finally, we discuss the shortcomings and further study of the 2019nCoVAS web service.
Collapse
Affiliation(s)
- Ming Xiao
- College of Computer ScienceSichuan UniversityChengdu610065PR China
| | - Guangdi Liu
- College of Computer and Information ScienceSouthwest UniversityChong-Qing400715PR China
| | - Jianghang Xie
- College of Computer ScienceSichuan UniversityChengdu610065PR China
| | - Zichun Dai
- College of Computer ScienceSichuan UniversityChengdu610065PR China
| | - Zihao Wei
- College of Computer ScienceSichuan UniversityChengdu610065PR China
| | - Ziyao Ren
- College of Computer ScienceSichuan UniversityChengdu610065PR China
| | - Jun Yu
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of Genomics, Chinese Academy of SciencesBeijing100101PR China
| | - Le Zhang
- College of Computer ScienceSichuan UniversityChengdu610065PR China
| |
Collapse
|
15
|
Bernardi G. The "Genomic Code": DNA Pervasively Moulds Chromatin Structures Leaving no Room for "Junk". Life (Basel) 2021; 11:342. [PMID: 33924668 PMCID: PMC8070607 DOI: 10.3390/life11040342] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/06/2021] [Accepted: 04/07/2021] [Indexed: 02/07/2023] Open
Abstract
The chromatin of the human genome was analyzed at three DNA size levels. At the first, compartment level, two "gene spaces" were found many years ago: A GC-rich, gene-rich "genome core" and a GC-poor, gene-poor "genome desert", the former corresponding to open chromatin centrally located in the interphase nucleus, the latter to closed chromatin located peripherally. This bimodality was later confirmed and extended by the discoveries (1) of LADs, the Lamina-Associated Domains, and InterLADs; (2) of two "spatial compartments", A and B, identified on the basis of chromatin interactions; and (3) of "forests and prairies" characterized by high and low CpG islands densities. Chromatin compartments were shown to be associated with the compositionally different, flat and single- or multi-peak DNA structures of the two, GC-poor and GC-rich, "super-families" of isochores. At the second, sub-compartment, level, chromatin corresponds to flat isochores and to isochore loops (due to compositional DNA gradients) that are susceptible to extrusion. Finally, at the short-sequence level, two sets of sequences, GC-poor and GC-rich, define two different nucleosome spacings, a short one and a long one. In conclusion, chromatin structures are moulded according to a "genomic code" by DNA sequences that pervade the genome and leave no room for "junk".
Collapse
Affiliation(s)
- Giorgio Bernardi
- Science Department, Roma Tre University, Viale Marconi 446, 00146 Rome, Italy; ; Tel.: +39-33-540-5892
- Stazione Zoologica Anton Dohrn, Villa Comunale, 80121 Naples, Italy
| |
Collapse
|
16
|
Badai J, Bu Q, Zhang L. Review of Artificial Intelligence Applications and Algorithms for Brain Organoid Research. Interdiscip Sci 2020; 12:383-394. [PMID: 32833194 DOI: 10.1007/s12539-020-00386-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 08/04/2020] [Accepted: 08/12/2020] [Indexed: 02/07/2023]
Abstract
The human brain organoid is a miniature three-dimensional tissue culture that can simulate the structure and function of the brain in an in vitro culture environment. Although we consider that human brain organoids could be used to understand brain development and diseases, experimental models of human brain organoids are so highly variable that we apply artificial intelligence (AI) techniques to investigate the development mechanism of the human brain. Therefore, this study briefly reviewed commonly used AI applications for human brain organoid-magnetic resonance imaging, electroencephalography, and gene editing techniques, as well as related AI algorithms. Finally, we discussed the limitations, challenges, and future study direction of AI-based technology for human brain organoids.
Collapse
Affiliation(s)
- Jiayidaer Badai
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Qian Bu
- Department of Food Engineering, College of Biomass Science and Engineering, Sichuan University, Chengdu, 610065, China
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, China. .,Medical Big Data Center of Sichuan University, Chengdu, 610065, China. .,PERA Corporation Ltd., Beijing, 100025, China.
| |
Collapse
|
17
|
Zhang J, Deng C, Li J, Zhao Y. Transcriptome-based selection and validation of optimal house-keeping genes for skin research in goats (Capra hircus). BMC Genomics 2020; 21:493. [PMID: 32682387 PMCID: PMC7368715 DOI: 10.1186/s12864-020-06912-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 07/13/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In quantitative real-time polymerase chain reaction (qRT-PCR) experiments, accurate and reliable target gene expression results are dependent on optimal amplification of house-keeping genes (HKGs). RNA-seq technology offers a novel approach to detect new HKGs with improved stability. Goat (Capra hircus) is an economically important livestock species and plays an indispensable role in the world animal fiber and meat industry. Unfortunately, uniform and reliable HKGs for skin research have not been identified in goat. Therefore, this study seeks to identify a set of stable HKGs for the skin tissue of C. hircus using high-throughput sequencing technology. RESULTS Based on the transcriptome dataset of 39 goat skin tissue samples, 8 genes (SRP68, NCBP3, RRAGA, EIF4H, CTBP2, PTPRA, CNBP, and EEF2) with relatively stable expression levels were identified and selected as new candidate HKGs. Commonly used HKGs including SDHA and YWHAZ from a previous study, and 2 conventional genes (ACTB and GAPDH) were also examined. Four different experimental variables: (1) different development stages, (2) hair follicle cycle stages, (3) breeds, and (4) sampling sites were used for determination and validation. Four algorithms (geNorm, NormFinder, BestKeeper, and ΔCt method) and a comprehensive algorithm (ComprFinder, developed in-house) were used to assess the stability of each HKG. It was shown that NCBP3 + SDHA + PTPRA were more stably expressed than previously used genes in all conditions analysis, and that this combination was effective at normalizing target gene expression. Moreover, a new algorithm for comprehensive analysis, ComprFinder, was developed and released. CONCLUSION This study presents the first list of candidate HKGs for C. hircus skin tissues based on an RNA-seq dataset. We propose that the NCBP3 + SDHA + PTPRA combination could be regarded as a triplet set of HKGs in skin molecular biology experiments in C. hircus and other closely related species. In addition, we also encourage researchers who perform candidate HKG evaluations and who require comprehensive analysis to adopt our new algorithm, ComprFinder.
Collapse
Affiliation(s)
- Jipan Zhang
- College of Animal Science and Technology, Southwest University, Chongqing Key Laboratory of Forage & Herbivore, Chongqing Engineering Research Center for Herbivores Resource Protection and Utilization, Chongqing, 400715, P. R. China
| | - Chengchen Deng
- College of Animal Science and Technology, Southwest University, Chongqing Key Laboratory of Forage & Herbivore, Chongqing Engineering Research Center for Herbivores Resource Protection and Utilization, Chongqing, 400715, P. R. China
| | - Jialu Li
- College of Animal Science and Technology, Southwest University, Chongqing Key Laboratory of Forage & Herbivore, Chongqing Engineering Research Center for Herbivores Resource Protection and Utilization, Chongqing, 400715, P. R. China
| | - Yongju Zhao
- College of Animal Science and Technology, Southwest University, Chongqing Key Laboratory of Forage & Herbivore, Chongqing Engineering Research Center for Herbivores Resource Protection and Utilization, Chongqing, 400715, P. R. China.
| |
Collapse
|
18
|
Lei W, Zeng H, Feng H, Ru X, Li Q, Xiao M, Zheng H, Chen Y, Zhang L. Development of an Early Prediction Model for Subarachnoid Hemorrhage With Genetic and Signaling Pathway Analysis. Front Genet 2020; 11:391. [PMID: 32373167 PMCID: PMC7186496 DOI: 10.3389/fgene.2020.00391] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Accepted: 03/30/2020] [Indexed: 01/15/2023] Open
Abstract
Subarachnoid hemorrhage (SAH) is devastating disease with high mortality, high disability rate, and poor clinical prognosis. It has drawn great attentions in both basic and clinical medicine. Therefore, it is necessary to explore the therapeutic drugs and effective targets for early prediction of SAH. Firstly, we demonstrate that LCN2 can effectively intervene or treat SAH from the perspective of cell signaling pathway. Next, three potential genes that we explored have been validated by manually reviewed experimental evidences. Finally, we turn out that the SAH early ensemble learning predictive model performs better than the classical LR, SVM, and Naïve-Bayes models.
Collapse
Affiliation(s)
- Wanjing Lei
- College of Computer Science, Sichuan University, Chengdu, China
| | - Han Zeng
- College of Computer and Information Science, Southwest University, Chongqing, China
| | - Hua Feng
- Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing, China
- State Key Laboratory of Trauma, Burn and Combined Injury, Third Military Medical University, Chongqing, China
| | - Xufang Ru
- Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing, China
- State Key Laboratory of Trauma, Burn and Combined Injury, Third Military Medical University, Chongqing, China
| | - Qiang Li
- Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing, China
- State Key Laboratory of Trauma, Burn and Combined Injury, Third Military Medical University, Chongqing, China
| | - Ming Xiao
- College of Computer Science, Sichuan University, Chengdu, China
| | - Huiru Zheng
- School of Computing, Ulster University, Coleraine, United Kingdom
| | - Yujie Chen
- Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing, China
- State Key Laboratory of Trauma, Burn and Combined Injury, Third Military Medical University, Chongqing, China
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, China
- College of Computer and Information Science, Southwest University, Chongqing, China
| |
Collapse
|
19
|
Zhao J, Cao Y, Zhang L. Exploring the computational methods for protein-ligand binding site prediction. Comput Struct Biotechnol J 2020; 18:417-426. [PMID: 32140203 PMCID: PMC7049599 DOI: 10.1016/j.csbj.2020.02.008] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 01/23/2020] [Accepted: 02/11/2020] [Indexed: 12/21/2022] Open
Abstract
Proteins participate in various essential processes in vivo via interactions with other molecules. Identifying the residues participating in these interactions not only provides biological insights for protein function studies but also has great significance for drug discoveries. Therefore, predicting protein-ligand binding sites has long been under intense research in the fields of bioinformatics and computer aided drug discovery. In this review, we first introduce the research background of predicting protein-ligand binding sites and then classify the methods into four categories, namely, 3D structure-based, template similarity-based, traditional machine learning-based and deep learning-based methods. We describe representative algorithms in each category and elaborate on machine learning and deep learning-based prediction methods in more detail. Finally, we discuss the trends and challenges of the current research such as molecular dynamics simulation based cryptic binding sites prediction, and highlight prospective directions for the near future.
Collapse
Affiliation(s)
- Jingtian Zhao
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Yang Cao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu 610065, China
| |
Collapse
|