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Castaneda EU, Baker EJ. KNeXT: a NetworkX-based topologically relevant KEGG parser. Front Genet 2024; 15:1292394. [PMID: 38415058 PMCID: PMC10896898 DOI: 10.3389/fgene.2024.1292394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 01/25/2024] [Indexed: 02/29/2024] Open
Abstract
Automating the recreation of gene and mixed gene-compound networks from Kyoto Encyclopedia of Genes and Genomes (KEGG) Markup Language (KGML) files is challenging because the data structure does not preserve the independent or loosely connected neighborhoods in which they were originally derived, referred to here as its topological environment. Identical accession numbers may overlap, causing neighborhoods to artificially collapse based on duplicated identifiers. This causes current parsers to create misleading or erroneous graphical representations when mixed gene networks are converted to gene-only networks. To overcome these challenges we created a python-based KEGG NetworkX Topological (KNeXT) parser that allows users to accurately recapitulate genetic networks and mixed networks from KGML map data. The software, archived as a python package index (PyPI) file to ensure broad application, is designed to ingest KGML files through built-in APIs and dynamically create high-fidelity topological representations. The utilization of NetworkX's framework to generate tab-separated files additionally ensures that KNeXT results may be imported into other graph frameworks and maintain programmatic access to the original x-y axis positions to each node in the KEGG pathway. KNeXT is a well-described Python 3 package that allows users to rapidly download and aggregate specific KGML files and recreate KEGG pathways based on a range of user-defined settings. KNeXT is platform-independent, distinctive, and it is not written on top of other Python parsers. Furthermore, KNeXT enables users to parse entire local folders or single files through command line scripts and convert the output into NCBI or UniProt IDs. KNeXT provides an ability for researchers to generate pathway visualizations while persevering the original context of a KEGG pathway. Source code is freely available at https://github.com/everest-castaneda/knext.
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Affiliation(s)
- Everest Uriel Castaneda
- Department of Biology, Baylor University, Waco, TX, United States
- School of Engineering and Computer Science, Baylor University, Waco, TX, United States
| | - Erich J Baker
- Department of Mathematics and Computer Science, Belmont University, Nashville, TN, United States
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Vadnala RN, Hannenhalli S, Narlikar L, Siddharthan R. Transcription factors organize into functional groups on the linear genome and in 3D chromatin. Heliyon 2023; 9:e18211. [PMID: 37520992 PMCID: PMC10382302 DOI: 10.1016/j.heliyon.2023.e18211] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 07/11/2023] [Accepted: 07/11/2023] [Indexed: 08/01/2023] Open
Abstract
Transcription factors (TFs) and their binding sites have evolved to interact cooperatively or competitively with each other. Here we examine in detail, across multiple cell lines, such cooperation or competition among TFs both in sequential and spatial proximity (using chromatin conformation capture assays), considering in vivo binding data as well as TF binding motifs in DNA. We ascertain significantly co-occurring ("attractive") or avoiding ("repulsive") TF pairs using robust randomized models that retain the essential characteristics of the experimental data. Across human cell lines TFs organize into two groups, with intra-group attraction and inter-group repulsion. This is true for both sequential and spatial proximity, and for both in vivo binding and sequence motifs. Attractive TF pairs exhibit significantly more physical interactions suggesting an underlying mechanism. The two TF groups differ significantly in their genomic and network properties, as well in their function-while one group regulates housekeeping function, the other potentially regulates lineage-specific functions, that are disrupted in cancer. Weaker binding sites tend to occur in spatially interacting regions of the genome. Our results suggest that a complex pattern of spatial cooperativity of TFs and chromatin has evolved with the genome to support housekeeping and lineage-specific functions.
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Affiliation(s)
- Rakesh Netha Vadnala
- The Institute of Mathematical Sciences, Chennai, India
- Homi Bhabha National Institute, Mumbai, India
| | | | - Leelavati Narlikar
- Department of Data Science, Indian Institute of Science Education and Research, Pune, India
| | - Rahul Siddharthan
- The Institute of Mathematical Sciences, Chennai, India
- Homi Bhabha National Institute, Mumbai, India
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Auboeuf D. Physicochemical Foundations of Life that Direct Evolution: Chance and Natural Selection are not Evolutionary Driving Forces. Life (Basel) 2020; 10:life10020007. [PMID: 31973071 PMCID: PMC7175370 DOI: 10.3390/life10020007] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 01/15/2020] [Accepted: 01/16/2020] [Indexed: 12/11/2022] Open
Abstract
The current framework of evolutionary theory postulates that evolution relies on random mutations generating a diversity of phenotypes on which natural selection acts. This framework was established using a top-down approach as it originated from Darwinism, which is based on observations made of complex multicellular organisms and, then, modified to fit a DNA-centric view. In this article, it is argued that based on a bottom-up approach starting from the physicochemical properties of nucleic and amino acid polymers, we should reject the facts that (i) natural selection plays a dominant role in evolution and (ii) the probability of mutations is independent of the generated phenotype. It is shown that the adaptation of a phenotype to an environment does not correspond to organism fitness, but rather corresponds to maintaining the genome stability and integrity. In a stable environment, the phenotype maintains the stability of its originating genome and both (genome and phenotype) are reproduced identically. In an unstable environment (i.e., corresponding to variations in physicochemical parameters above a physiological range), the phenotype no longer maintains the stability of its originating genome, but instead influences its variations. Indeed, environment- and cellular-dependent physicochemical parameters define the probability of mutations in terms of frequency, nature, and location in a genome. Evolution is non-deterministic because it relies on probabilistic physicochemical rules, and evolution is driven by a bidirectional interplay between genome and phenotype in which the phenotype ensures the stability of its originating genome in a cellular and environmental physicochemical parameter-depending manner.
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Affiliation(s)
- Didier Auboeuf
- Laboratory of Biology and Modelling of the Cell, Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, 46 Allée d'Italie, Site Jacques Monod, F-69007, Lyon, France
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4
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Diament A, Tuller T. Modeling three-dimensional genomic organization in evolution and pathogenesis. Semin Cell Dev Biol 2018; 90:78-93. [PMID: 30030143 DOI: 10.1016/j.semcdb.2018.07.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Accepted: 07/08/2018] [Indexed: 12/17/2022]
Abstract
The regulation of gene expression is mediated via the complex three-dimensional (3D) conformation of the genetic material and its interactions with various intracellular factors. Various experimental and computational approaches have been developed in recent years for understating the relation between the 3D conformation of the genome and the phenotypes of cells in normal condition and diseases. In this review, we will discuss novel approaches for analyzing and modeling the 3D genomic conformation, focusing on deciphering disease-causing mutations that affect gene expression. We conclude that as this is a very challenging mission, an important direction should involve the comparative analysis of various 3D models from various organisms or cells.
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Affiliation(s)
- Alon Diament
- Dept. of Biomedical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Tamir Tuller
- Dept. of Biomedical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel; The Sagol School of Neuroscience, Tel-Aviv University, Tel Aviv 6997801, Israel.
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Chen Z, Quan L, Huang A, Zhao Q, Yuan Y, Yuan X, Shen Q, Shang J, Ben Y, Qin FXF, Wu A. seq-ImmuCC: Cell-Centric View of Tissue Transcriptome Measuring Cellular Compositions of Immune Microenvironment From Mouse RNA-Seq Data. Front Immunol 2018; 9:1286. [PMID: 29922297 PMCID: PMC5996037 DOI: 10.3389/fimmu.2018.01286] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Accepted: 05/22/2018] [Indexed: 12/02/2022] Open
Abstract
The RNA sequencing approach has been broadly used to provide gene-, pathway-, and network-centric analyses for various cell and tissue samples. However, thus far, rich cellular information carried in tissue samples has not been thoroughly characterized from RNA-Seq data. Therefore, it would expand our horizons to better understand the biological processes of the body by incorporating a cell-centric view of tissue transcriptome. Here, a computational model named seq-ImmuCC was developed to infer the relative proportions of 10 major immune cells in mouse tissues from RNA-Seq data. The performance of seq-ImmuCC was evaluated among multiple computational algorithms, transcriptional platforms, and simulated and experimental datasets. The test results showed its stable performance and superb consistency with experimental observations under different conditions. With seq-ImmuCC, we generated the comprehensive landscape of immune cell compositions in 27 normal mouse tissues and extracted the distinct signatures of immune cell proportion among various tissue types. Furthermore, we quantitatively characterized and compared 18 different types of mouse tumor tissues of distinct cell origins with their immune cell compositions, which provided a comprehensive and informative measurement for the immune microenvironment inside tumor tissues. The online server of seq-ImmuCC are freely available at http://wap-lab.org:3200/immune/.
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Affiliation(s)
- Ziyi Chen
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Suzhou Institute of Systems Medicine, Suzhou, Jiangsu, China
| | - Lijun Quan
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Suzhou Institute of Systems Medicine, Suzhou, Jiangsu, China
| | - Anfei Huang
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Suzhou Institute of Systems Medicine, Suzhou, Jiangsu, China
| | - Qiang Zhao
- Suzhou Institute of Systems Medicine, Suzhou, Jiangsu, China.,School of Life Science and Technology, China Pharmaceutical University, Nanjing, China
| | - Yao Yuan
- Suzhou Institute of Systems Medicine, Suzhou, Jiangsu, China.,School of Pharmacy, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Xuye Yuan
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Suzhou Institute of Systems Medicine, Suzhou, Jiangsu, China
| | - Qin Shen
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Suzhou Institute of Systems Medicine, Suzhou, Jiangsu, China
| | - Jingzhe Shang
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Suzhou Institute of Systems Medicine, Suzhou, Jiangsu, China
| | - Yinyin Ben
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Suzhou Institute of Systems Medicine, Suzhou, Jiangsu, China
| | - F Xiao-Feng Qin
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Suzhou Institute of Systems Medicine, Suzhou, Jiangsu, China
| | - Aiping Wu
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Suzhou Institute of Systems Medicine, Suzhou, Jiangsu, China
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Patange S, Girvan M, Larson DR. Single-cell systems biology: probing the basic unit of information flow. ACTA ACUST UNITED AC 2017; 8:7-15. [PMID: 29552672 DOI: 10.1016/j.coisb.2017.11.011] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Gene expression varies across cells in a population or a tissue. This heterogeneity has come into sharp focus in recent years through developments in new imaging and sequencing technologies. However, our ability to measure variation has outpaced our ability to interpret it. Much of the variability may arise from random effects occurring in the processes of gene expression (transcription, RNA processing and decay, translation). The molecular basis of these effects is largely unknown. Likewise, a functional role of this variability in growth, differentiation and disease has only been elucidated in a few cases. In this review, we highlight recent experimental and theoretical advances for measuring and analyzing stochastic variation.
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Affiliation(s)
- Simona Patange
- Laboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute. Bethesda, MD 20892
- Institute for Physical Science and Technology, University of Maryland, College Park, MD
| | - Michelle Girvan
- Institute for Physical Science and Technology, University of Maryland, College Park, MD
- Department of Physics, University of Maryland. College Park, MD
| | - Daniel R Larson
- Laboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute. Bethesda, MD 20892
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