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Guo Y, Yan S, Zhang W. Translatomics to explore dynamic differences in immunocytes in the tumor microenvironment. MOLECULAR THERAPY. NUCLEIC ACIDS 2023; 34:102037. [PMID: 37808922 PMCID: PMC10551571 DOI: 10.1016/j.omtn.2023.102037] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Protein is an essential component of all living organisms and is primarily responsible for life activities; furthermore, its synthesis depends on a highly complex and accurate translation system. For proteins, the regulation at the translation level exceeds the sum of that during transcription, mRNA degradation, and protein degradation. Therefore, it is necessary to study regulation at the translation level. Imbalance in the translation process may change the cellular landscape, which not only leads to the occurrence, maintenance, progression, invasion, and metastasis of cancer but also affects the function of immune cells and changes the tumor microenvironment. Detailed analysis of transcriptional and protein atlases is needed to better understand how gene translation occurs. However, a more rigorous direct correlation between mRNA and protein levels is needed, which somewhat limits further studies. Translatomics is a technique for capturing and sequencing ribosome-related mRNAs that can effectively identify translation changes caused by ribosome stagnation and local translation abnormalities during cancer occurrence to further understand the changes in the translation landscape of cancer cells themselves and immune cells in the tumor microenvironment, which can provide new strategies and directions for tumor treatment.
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Affiliation(s)
- Yilin Guo
- Department of Laboratory Medicine, The Third Xiangya Hospital, Central South University, Changsha, Hunan 410013, P.R. China
- Department of Laboratory Medicine, Xiangya School of Medicine, Central South University, Changsha, Hunan 410013, P.R. China
| | - Shiqi Yan
- Department of Laboratory Medicine, The Third Xiangya Hospital, Central South University, Changsha, Hunan 410013, P.R. China
- Department of Laboratory Medicine, Xiangya School of Medicine, Central South University, Changsha, Hunan 410013, P.R. China
| | - Wenling Zhang
- Department of Laboratory Medicine, The Third Xiangya Hospital, Central South University, Changsha, Hunan 410013, P.R. China
- Department of Laboratory Medicine, Xiangya School of Medicine, Central South University, Changsha, Hunan 410013, P.R. China
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Yang TH, Lin YC, Hsia M, Liao ZY. SSRTool: a web tool for evaluating RNA secondary structure predictions based on species-specific functional interpretability. Comput Struct Biotechnol J 2022; 20:2473-2483. [PMID: 35664227 PMCID: PMC9136272 DOI: 10.1016/j.csbj.2022.05.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 05/13/2022] [Accepted: 05/13/2022] [Indexed: 01/02/2023] Open
Abstract
RNA secondary structures can carry out essential cellular functions alone or interact with one another to form the hierarchical tertiary structures. Experimental structure identification approa ches can show the in vitro structures of RNA molecules. However, they usually have limits in the resolution and are costly. In silico structure prediction tools are thus primarily relied on for pre-experiment analysis. Various structure prediction models have been developed over the decades. Since these tools are usually used before knowing the actual RNA structures, evaluating and ranking the pile of secondary structure predictions of a given sequence is essential in computational analysis. In this research, we implemented a web service called SSRTool (RNA Secondary Structure prediction Ranking Tool) to assist in the ranking and evaluation of the generated predicted structures of a given sequence. Based on the computed species-specific interpretability significance in four common RNA structure–function aspects, SSRTool provides three functions along with visualization interfaces: (1) Rank user-generated predictions. (2) Provide an automated streamline of structure prediction and ranking for a given sequence. (3) Infer the functional aspects of a given structure. We demonstrated the applicability of SSRTool via real case studies and reported the similar trends between computed species-specific rankings and the corresponding prediction F1 values. The SSRTool web service is available online at https://cobisHSS0.im.nuk.edu.tw/SSRTool/, http://cosbi3.ee.ncku.edu.tw/SSRTool/, or the redirecting site https://github.com/cobisLab/SSRTool/.
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Affiliation(s)
- Tzu-Hsien Yang
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan
- Corresponding author.
| | - Yu-Cian Lin
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan
| | - Min Hsia
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan
| | - Zhan-Yi Liao
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan
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Yang TH. An Aggregation Method to Identify the RNA Meta-Stable Secondary Structure and its Functionally Interpretable Structure Ensemble. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:75-86. [PMID: 34014829 DOI: 10.1109/tcbb.2021.3082396] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
RNA can provide vital cellular functions through its secondary or tertiary structure. Due to the low-throughput nature of experimental approaches, studies on RNA structures mainly resort to computational methods. However, current existing tools fail to consider RNA structure ensembles and do not provide ways to decipher functional hypotheses for the new predictions. In this research, a novel method was proposed to identify the functionally interpretable structure ensemble of a given RNA sequence and provide the meta-stable structure, or the most frequently observed functional RNA cellular conformation, based on the ensemble. In the prediction of meta-stable structures, the proposed method outperformed existing tools on a yeast test set. The inferred functional aspects were then manually checked and demonstrated a micro-averaging F1 value of 0.92. Further, a biological example of the yeast ASH1-E1 element was discussed to articulate that these functional aspects can also suggest testable hypotheses. Then the proposed method was verified to be well applicable to other species through a human test set. Finally, the proposed method was demonstrated to show resistance to sequence length-dependent performance deterioration.
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Kiniry SJ, Judge CE, Michel AM, Baranov PV. Trips-Viz: an environment for the analysis of public and user-generated ribosome profiling data. Nucleic Acids Res 2021; 49:W662-W670. [PMID: 33950201 PMCID: PMC8262740 DOI: 10.1093/nar/gkab323] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 04/11/2021] [Accepted: 04/20/2021] [Indexed: 02/07/2023] Open
Abstract
Trips-Viz (https://trips.ucc.ie/) is an interactive platform for the analysis and visualization of ribosome profiling (Ribo-Seq) and shotgun RNA sequencing (RNA-seq) data. This includes publicly available and user generated data, hence Trips-Viz can be classified as a database and as a server. As a database it provides access to many processed Ribo-Seq and RNA-seq data aligned to reference transcriptomes which has been expanded considerably since its inception. Here, we focus on the server functionality of Trips-viz which also has been greatly improved. Trips-viz now enables visualisation of proteomics data from a large number of processed mass spectrometry datasets. It can be used to support translation inferred from Ribo-Seq data. Users are now able to upload a custom reference transcriptome as well as data types other than Ribo-Seq/RNA-Seq. Incorporating custom data has been streamlined with RiboGalaxy (https://ribogalaxy.ucc.ie/) integration. The other new functionality is the rapid detection of translated open reading frames (ORFs) through a simple easy to use interface. The analysis of differential expression has been also improved via integration of DESeq2 and Anota2seq in addition to a number of other improvements of existing Trips-viz features.
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Affiliation(s)
- Stephen J Kiniry
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
| | - Ciara E Judge
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
| | - Audrey M Michel
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
- Ribomaps Ltd, Western Gateway Bld, Western Rd, Cork, Ireland
| | - Pavel V Baranov
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, RAS, Moscow, Russia
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Wu WS, Tsao YH, Shiue SC, Chen TY, Tseng YY, Tseng JT. A tool for analyzing and visualizing ribo-seq data at the isoform level. BMC Bioinformatics 2021; 22:271. [PMID: 34058988 PMCID: PMC8323483 DOI: 10.1186/s12859-021-04192-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 04/18/2021] [Accepted: 05/12/2021] [Indexed: 11/18/2022] Open
Abstract
Background Translational regulation is one important aspect of gene expression regulation. Dysregulation of translation results in abnormal cell physiology and leads to diseases. Ribosome profiling (RP), also called ribo-seq, is a powerful experimental technique to study translational regulation. It can capture a snapshot of translation by deep sequencing of ribosome-protected mRNA fragments. Many ribosome profiling data processing tools have been developed. However, almost all tools analyze ribosome profiling data at the gene level. Since different isoforms of a gene may produce different proteins with distinct biological functions, it is advantageous to analyze ribosome profiling data at the isoform level. To meet this need, previously we developed a pipeline to analyze 610 public human ribosome profiling data at the isoform level and constructed HRPDviewer database. Results To allow other researchers to use our pipeline as well, here we implement our pipeline as an easy-to-use software tool called RPiso. Compared to Ribomap (a widely used tool which provides isoform-level ribosome profiling analyses), our RPiso (1) estimates isoform abundance more accurately, (2) supports analyses on more species, and (3) provides a web-based viewer for interactively visualizing ribosome profiling data on the selected mRNA isoforms. Conclusions In this study, we developed RPiso software tool (http://cosbi7.ee.ncku.edu.tw/RPiso/) to provide isoform-level ribosome profiling analyses. RPiso is very easy to install and execute. RPiso also provides a web-based viewer for interactively visualizing ribosome profiling data on the selected mRNA isoforms. We believe that RPiso is a useful tool for researchers to analyze and visualize their own ribosome profiling data at the isoform level. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04192-7.
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Affiliation(s)
- Wei-Sheng Wu
- Department of Electrical Engineering, National Cheng Kung University, Tainan, 701, Taiwan.
| | - Yi-Hong Tsao
- Department of Biotechnology and Bioindustry Sciences, National Cheng Kung University, Tainan, 701, Taiwan
| | - Sheng-Cian Shiue
- Department of Electrical Engineering, National Cheng Kung University, Tainan, 701, Taiwan
| | - Ting-Yu Chen
- Department of Electrical Engineering, National Cheng Kung University, Tainan, 701, Taiwan
| | - Yan-Yuan Tseng
- Center for Molecular Medicine and Genetics, School of Medicine, Wayne State University, Detroit, MI, 48201, USA
| | - Joseph T Tseng
- Department of Biotechnology and Bioindustry Sciences, National Cheng Kung University, Tainan, 701, Taiwan.
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Yang TH, Wang CY, Tsai HC, Liu CT. Human IRES Atlas: an integrative platform for studying IRES-driven translational regulation in humans. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2021:6263636. [PMID: 33942874 PMCID: PMC8094437 DOI: 10.1093/database/baab025] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 04/16/2021] [Accepted: 04/23/2021] [Indexed: 11/13/2022]
Abstract
It is now known that cap-independent translation initiation facilitated by internal ribosome entry sites (IRESs) is vital in selective cellular protein synthesis under stress and different physiological conditions. However, three problems make it hard to understand transcriptome-wide cellular IRES-mediated translation initiation mechanisms: (i) complex interplay between IRESs and other translation initiation–related information, (ii) reliability issue of in silico cellular IRES investigation and (iii) labor-intensive in vivo IRES identification. In this research, we constructed the Human IRES Atlas database for a comprehensive understanding of cellular IRESs in humans. First, currently available and suitable IRES prediction tools (IRESfinder, PatSearch and IRESpy) were used to obtain transcriptome-wide human IRESs. Then, we collected eight genres of translation initiation–related features to help study the potential molecular mechanisms of each of the putative IRESs. Three functional tests (conservation, structural RNA–protein scores and conditional translation efficiency) were devised to evaluate the functionality of the identified putative IRESs. Moreover, an easy-to-use interface and an IRES–translation initiation interaction map for each gene transcript were implemented to help understand the interactions between IRESs and translation initiation–related features. Researchers can easily search/browse an IRES of interest using the web interface and deduce testable mechanism hypotheses of human IRES-driven translation initiation based on the integrated results. In summary, Human IRES Atlas integrates putative IRES elements and translation initiation–related experiments for better usage of these data and deduction of mechanism hypotheses. Database URL: http://cobishss0.im.nuk.edu.tw/Human_IRES_Atlas/
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Affiliation(s)
- Tzu-Hsien Yang
- Department of Information Management, National University of Kaohsiung, 700, Kaohsiung University Rd., Nanzih District, Kaohsiung, Taiwan 811, Republic of China
| | - Chung-Yu Wang
- Department of Information Management, National University of Kaohsiung, 700, Kaohsiung University Rd., Nanzih District, Kaohsiung, Taiwan 811, Republic of China
| | - Hsiu-Chun Tsai
- Department of Information Management, National University of Kaohsiung, 700, Kaohsiung University Rd., Nanzih District, Kaohsiung, Taiwan 811, Republic of China
| | - Cheng-Tse Liu
- Department of Information Management, National University of Kaohsiung, 700, Kaohsiung University Rd., Nanzih District, Kaohsiung, Taiwan 811, Republic of China
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Panda A, Yadav A, Yeerna H, Singh A, Biehl M, Lux M, Schulz A, Klecha T, Doniach S, Khiabanian H, Ganesan S, Tamayo P, Bhanot G. Tissue- and development-stage-specific mRNA and heterogeneous CNV signatures of human ribosomal proteins in normal and cancer samples. Nucleic Acids Res 2020; 48:7079-7098. [PMID: 32525984 PMCID: PMC7367157 DOI: 10.1093/nar/gkaa485] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 05/20/2020] [Accepted: 05/28/2020] [Indexed: 12/26/2022] Open
Abstract
We give results from a detailed analysis of human Ribosomal Protein (RP) levels in normal and cancer samples and cell lines from large mRNA, copy number variation and ribosome profiling datasets. After normalizing total RP mRNA levels per sample, we find highly consistent tissue specific RP mRNA signatures in normal and tumor samples. Multiple RP mRNA-subtypes exist in several cancers, with significant survival and genomic differences. Some RP mRNA variations among subtypes correlate with copy number loss of RP genes. In kidney cancer, RP subtypes map to molecular subtypes related to cell-of-origin. Pan-cancer analysis of TCGA data showed widespread single/double copy loss of RP genes, without significantly affecting survival. In several cancer cell lines, CRISPR-Cas9 knockout of RP genes did not affect cell viability. Matched RP ribosome profiling and mRNA data in humans and rodents stratified by tissue and development stage and were strongly correlated, showing that RP translation rates were proportional to mRNA levels. In a small dataset of human adult and fetal tissues, RP protein levels showed development stage and tissue specific heterogeneity of RP levels. Our results suggest that heterogeneous RP levels play a significant functional role in cellular physiology, in both normal and disease states.
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Affiliation(s)
- Anshuman Panda
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08903, USA
| | - Anupama Yadav
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Huwate Yeerna
- Moores Cancer Center, University of California San Diego, La Jolla, CA 92103, USA
| | - Amartya Singh
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08903, USA
| | - Michael Biehl
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Nijenborgh 9, NL-9747 AG Groningen, The Netherlands
| | - Markus Lux
- Cognitive Interaction Technology (CITEC), Bielefeld University, Inspiration 1, D-33619 Bielefeld, Germany
| | - Alexander Schulz
- Cognitive Interaction Technology (CITEC), Bielefeld University, Inspiration 1, D-33619 Bielefeld, Germany
| | - Tyler Klecha
- Department of Molecular Biology and Biochemistry, Rutgers University, Piscataway, NJ,08854, USA
| | - Sebastian Doniach
- Department of Applied Physics, Stanford University, Palo Alto, CA 94305, USA
| | | | - Shridar Ganesan
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08903, USA
| | - Pablo Tamayo
- Moores Cancer Center, University of California San Diego, La Jolla, CA 92103, USA
- School of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Gyan Bhanot
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08903, USA
- Moores Cancer Center, University of California San Diego, La Jolla, CA 92103, USA
- Department of Molecular Biology and Biochemistry, Rutgers University, Piscataway, NJ,08854, USA
- Department of Physics and Astronomy, Rutgers University, Piscataway, NJ 08854, USA
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Kiniry SJ, Michel AM, Baranov PV. Computational methods for ribosome profiling data analysis. WILEY INTERDISCIPLINARY REVIEWS. RNA 2020; 11:e1577. [PMID: 31760685 DOI: 10.1002/wrna.1577] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 10/12/2019] [Accepted: 10/16/2019] [Indexed: 12/15/2022]
Abstract
Since the introduction of the ribosome profiling technique in 2009 its popularity has greatly increased. It is widely used for the comprehensive assessment of gene expression and for studying the mechanisms of regulation at the translational level. As the number of ribosome profiling datasets being produced continues to grow, so too does the need for reliable software that can provide answers to the biological questions it can address. This review describes the computational methods and tools that have been developed to analyze ribosome profiling data at the different stages of the process. It starts with initial routine processing of raw data and follows with more specific tasks such as the identification of translated open reading frames, differential gene expression analysis, or evaluation of local or global codon decoding rates. The review pinpoints challenges associated with each step and explains the ways in which they are currently addressed. In addition it provides a comprehensive, albeit incomplete, list of publicly available software applicable to each step, which may be a beneficial starting point to those unexposed to ribosome profiling analysis. The outline of current challenges in ribosome profiling data analysis may inspire computational biologists to search for novel, potentially superior, solutions that will improve and expand the bioinformatician's toolbox for ribosome profiling data analysis. This article is characterized under: Translation > Ribosome Structure/Function RNA Evolution and Genomics > Computational Analyses of RNA Translation > Translation Mechanisms Translation > Translation Regulation.
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Affiliation(s)
- Stephen J Kiniry
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
| | - Audrey M Michel
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
| | - Pavel V Baranov
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, RAS, Moscow, Russia
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