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Sahoo K, Sundararajan V. Methods in DNA methylation array dataset analysis: A review. Comput Struct Biotechnol J 2024; 23:2304-2325. [PMID: 38845821 PMCID: PMC11153885 DOI: 10.1016/j.csbj.2024.05.015] [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: 12/18/2023] [Revised: 04/25/2024] [Accepted: 05/08/2024] [Indexed: 06/09/2024] Open
Abstract
Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.
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Affiliation(s)
| | - Vino Sundararajan
- Correspondence to: Department of Bio Sciences, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore 632 014, Tamil Nadu, India.
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Gioacchino E, Vandelannoote K, Ruberto AA, Popovici J, Cantaert T. Unraveling the intricacies of host-pathogen interaction through single-cell genomics. Microbes Infect 2024; 26:105313. [PMID: 38369008 DOI: 10.1016/j.micinf.2024.105313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 11/23/2023] [Accepted: 02/13/2024] [Indexed: 02/20/2024]
Abstract
Single-cell genomics provide researchers with tools to assess host-pathogen interactions at a resolution previously inaccessible. Transcriptome analysis, epigenome analysis, and immune profiling techniques allow for a better comprehension of the heterogeneity underlying both the host response and infectious agents. Here, we highlight technological advancements and data analysis workflows that increase our understanding of host-pathogen interactions at the single-cell level. We review various studies that have used these tools to better understand host-pathogen dynamics in a variety of infectious disease contexts, including viral, bacterial, and parasitic diseases. We conclude by discussing how single-cell genomics can advance our understanding of host-pathogen interactions.
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Affiliation(s)
- Emanuele Gioacchino
- Immunology Unit, Institut Pasteur du Cambodge, The Pasteur Network, Phnom Penh, Cambodia
| | - Koen Vandelannoote
- Bacterial Phylogenomics Group, Institut Pasteur du Cambodge, The Pasteur Network, Phnom Penh, Cambodia
| | - Anthony A Ruberto
- Center for Tropical and Emerging Global Diseases, University of Georgia, Athens, GA, USA; Institute of Bioinformatics, University of Georgia, Athens, GA, USA
| | - Jean Popovici
- Malaria Research Unit, Institut Pasteur du Cambodge, The Pasteur Network, Phnom Penh, Cambodia; Infectious Disease Epidemiology and Analytics, Institut Pasteur, Paris, France
| | - Tineke Cantaert
- Immunology Unit, Institut Pasteur du Cambodge, The Pasteur Network, Phnom Penh, Cambodia.
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Schiebout C, Frost HR. CAraCAl: CAMML with the integration of chromatin accessibility. BMC Bioinformatics 2024; 25:212. [PMID: 38872103 PMCID: PMC11170880 DOI: 10.1186/s12859-024-05833-3] [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: 03/07/2024] [Accepted: 06/10/2024] [Indexed: 06/15/2024] Open
Abstract
BACKGROUND A vital step in analyzing single-cell data is ascertaining which cell types are present in a dataset, and at what abundance. In many diseases, the proportions of varying cell types can have important implications for health and prognosis. Most approaches for cell type annotation have centered around cell typing for single-cell RNA-sequencing (scRNA-seq) and have had promising success. However, reliable methods are lacking for many other single-cell modalities such as single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), which quantifies the extent to which genes of interest in each cell are epigenetically "open" for expression. RESULTS To leverage the informative potential of scATAC-seq data, we developed CAMML with the integration of chromatin accessibility (CAraCAl), a bioinformatic method that performs cell typing on scATAC-seq data. CAraCAl performs cell typing by scoring each cell for its enrichment of cell type-specific gene sets. These gene sets are composed of the most upregulated or downregulated genes present in each cell type according to projected gene activity. CONCLUSIONS We found that CAraCAl does not improve performance beyond CAMML when scRNA-seq is present, but if only scATAC-seq is available, CAraCAl performs cell typing relatively successfully. As such, we also discuss best practices for cell typing and the strengths and weaknesses of various cell annotation options.
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Affiliation(s)
- Courtney Schiebout
- Department of Biomedical Data Science, Dartmouth College, Hanover, NH, 03766, USA.
| | - H Robert Frost
- Department of Biomedical Data Science, Dartmouth College, Hanover, NH, 03766, USA
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Qiao M. Deciphering the genetic code of neuronal type connectivity through bilinear modeling. eLife 2024; 12:RP91532. [PMID: 38857169 PMCID: PMC11164534 DOI: 10.7554/elife.91532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024] Open
Abstract
Understanding how different neuronal types connect and communicate is critical to interpreting brain function and behavior. However, it has remained a formidable challenge to decipher the genetic underpinnings that dictate the specific connections formed between neuronal types. To address this, we propose a novel bilinear modeling approach that leverages the architecture similar to that of recommendation systems. Our model transforms the gene expressions of presynaptic and postsynaptic neuronal types, obtained from single-cell transcriptomics, into a covariance matrix. The objective is to construct this covariance matrix that closely mirrors a connectivity matrix, derived from connectomic data, reflecting the known anatomical connections between these neuronal types. When tested on a dataset of Caenorhabditis elegans, our model achieved a performance comparable to, if slightly better than, the previously proposed spatial connectome model (SCM) in reconstructing electrical synaptic connectivity based on gene expressions. Through a comparative analysis, our model not only captured all genetic interactions identified by the SCM but also inferred additional ones. Applied to a mouse retinal neuronal dataset, the bilinear model successfully recapitulated recognized connectivity motifs between bipolar cells and retinal ganglion cells, and provided interpretable insights into genetic interactions shaping the connectivity. Specifically, it identified unique genetic signatures associated with different connectivity motifs, including genes important to cell-cell adhesion and synapse formation, highlighting their role in orchestrating specific synaptic connections between these neurons. Our work establishes an innovative computational strategy for decoding the genetic programming of neuronal type connectivity. It not only sets a new benchmark for single-cell transcriptomic analysis of synaptic connections but also paves the way for mechanistic studies of neural circuit assembly and genetic manipulation of circuit wiring.
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Affiliation(s)
- Mu Qiao
- LinkedInMountain ViewUnited States
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Köhler AR, Haußer J, Harsch A, Bernhardt S, Häußermann L, Brenner LM, Lungu C, Olayioye MA, Bashtrykov P, Jeltsch A. Modular dual-color BiAD sensors for locus-specific readout of epigenome modifications in single cells. CELL REPORTS METHODS 2024; 4:100739. [PMID: 38554702 PMCID: PMC11045877 DOI: 10.1016/j.crmeth.2024.100739] [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: 10/10/2023] [Revised: 02/01/2024] [Accepted: 02/28/2024] [Indexed: 04/02/2024]
Abstract
Dynamic changes in the epigenome at defined genomic loci play crucial roles during cellular differentiation and disease development. Here, we developed dual-color bimolecular anchor detector (BiAD) sensors for high-sensitivity readout of locus-specific epigenome modifications by fluorescence microscopy. Our BiAD sensors comprise an sgRNA/dCas9 complex as anchor and double chromatin reader domains as detector modules, both fused to complementary parts of a split IFP2.0 fluorophore, enabling its reconstitution upon binding of both parts in close proximity. In addition, a YPet fluorophore is recruited to the sgRNA to mark the genomic locus of interest. With these dual-color BiAD sensors, we detected H3K9me2/3 and DNA methylation and their dynamic changes upon RNAi or inhibitor treatment with high sensitivity at endogenous genomic regions. Furthermore, we showcased locus-specific H3K36me2/3 readout as well as H3K27me3 and H3K9me2/3 enrichment on the inactive X chromosome, highlighting the broad applicability of our dual-color BiAD sensors for single-cell epigenome studies.
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Affiliation(s)
- Anja R Köhler
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
| | - Johannes Haußer
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
| | - Annika Harsch
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
| | - Steffen Bernhardt
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
| | - Lilia Häußermann
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
| | - Lisa-Marie Brenner
- Institute of Cell Biology and Immunology, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
| | - Cristiana Lungu
- Institute of Cell Biology and Immunology, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
| | - Monilola A Olayioye
- Institute of Cell Biology and Immunology, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
| | - Pavel Bashtrykov
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
| | - Albert Jeltsch
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany.
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Islam MT, Liu Y, Hassan MM, Abraham PE, Merlet J, Townsend A, Jacobson D, Buell CR, Tuskan GA, Yang X. Advances in the Application of Single-Cell Transcriptomics in Plant Systems and Synthetic Biology. BIODESIGN RESEARCH 2024; 6:0029. [PMID: 38435807 PMCID: PMC10905259 DOI: 10.34133/bdr.0029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 01/28/2024] [Indexed: 03/05/2024] Open
Abstract
Plants are complex systems hierarchically organized and composed of various cell types. To understand the molecular underpinnings of complex plant systems, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for revealing high resolution of gene expression patterns at the cellular level and investigating the cell-type heterogeneity. Furthermore, scRNA-seq analysis of plant biosystems has great potential for generating new knowledge to inform plant biosystems design and synthetic biology, which aims to modify plants genetically/epigenetically through genome editing, engineering, or re-writing based on rational design for increasing crop yield and quality, promoting the bioeconomy and enhancing environmental sustainability. In particular, data from scRNA-seq studies can be utilized to facilitate the development of high-precision Build-Design-Test-Learn capabilities for maximizing the targeted performance of engineered plant biosystems while minimizing unintended side effects. To date, scRNA-seq has been demonstrated in a limited number of plant species, including model plants (e.g., Arabidopsis thaliana), agricultural crops (e.g., Oryza sativa), and bioenergy crops (e.g., Populus spp.). It is expected that future technical advancements will reduce the cost of scRNA-seq and consequently accelerate the application of this emerging technology in plants. In this review, we summarize current technical advancements in plant scRNA-seq, including sample preparation, sequencing, and data analysis, to provide guidance on how to choose the appropriate scRNA-seq methods for different types of plant samples. We then highlight various applications of scRNA-seq in both plant systems biology and plant synthetic biology research. Finally, we discuss the challenges and opportunities for the application of scRNA-seq in plants.
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Affiliation(s)
- Md Torikul Islam
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Yang Liu
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Md Mahmudul Hassan
- Department of Genetics and Plant Breeding,
Patuakhali Science and Technology University, Dumki, Patuakhali 8602, Bangladesh
| | - Paul E. Abraham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Jean Merlet
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research and Graduate Education,
University of Tennessee Knoxville, Knoxville, TN 37996, USA
| | - Alice Townsend
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research and Graduate Education,
University of Tennessee Knoxville, Knoxville, TN 37996, USA
| | - Daniel Jacobson
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - C. Robin Buell
- Center for Applied Genetic Technologies,
University of Georgia, Athens, GA 30602, USA
- Department of Crop and Soil Sciences,
University of Georgia, Athens, GA 30602, USA
- Institute of Plant Breeding, Genetics, and Genomics,
University of Georgia, Athens, GA 30602, USA
| | - Gerald A. Tuskan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Xiaohan Yang
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
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Stabile AM, Pistilli A, Mariangela R, Rende M, Bartolini D, Di Sante G. New Challenges for Anatomists in the Era of Omics. Diagnostics (Basel) 2023; 13:2963. [PMID: 37761332 PMCID: PMC10529314 DOI: 10.3390/diagnostics13182963] [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: 07/31/2023] [Revised: 09/08/2023] [Accepted: 09/10/2023] [Indexed: 09/29/2023] Open
Abstract
Anatomic studies have traditionally relied on macroscopic, microscopic, and histological techniques to investigate the structure of tissues and organs. Anatomic studies are essential in many fields, including medicine, biology, and veterinary science. Advances in technology, such as imaging techniques and molecular biology, continue to provide new insights into the anatomy of living organisms. Therefore, anatomy remains an active and important area in the scientific field. The consolidation in recent years of some omics technologies such as genomics, transcriptomics, proteomics, and metabolomics allows for a more complete and detailed understanding of the structure and function of cells, tissues, and organs. These have been joined more recently by "omics" such as radiomics, pathomics, and connectomics, supported by computer-assisted technologies such as neural networks, 3D bioprinting, and artificial intelligence. All these new tools, although some are still in the early stages of development, have the potential to strongly contribute to the macroscopic and microscopic characterization in medicine. For anatomists, it is time to hitch a ride and get on board omics technologies to sail to new frontiers and to explore novel scenarios in anatomy.
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Affiliation(s)
- Anna Maria Stabile
- Department of Medicine and Surgery, Section of Human, Clinical and Forensic Anatomy, University of Perugia, 60132 Perugia, Italy; (A.M.S.); (A.P.); (R.M.); (M.R.)
| | - Alessandra Pistilli
- Department of Medicine and Surgery, Section of Human, Clinical and Forensic Anatomy, University of Perugia, 60132 Perugia, Italy; (A.M.S.); (A.P.); (R.M.); (M.R.)
| | - Ruggirello Mariangela
- Department of Medicine and Surgery, Section of Human, Clinical and Forensic Anatomy, University of Perugia, 60132 Perugia, Italy; (A.M.S.); (A.P.); (R.M.); (M.R.)
| | - Mario Rende
- Department of Medicine and Surgery, Section of Human, Clinical and Forensic Anatomy, University of Perugia, 60132 Perugia, Italy; (A.M.S.); (A.P.); (R.M.); (M.R.)
| | - Desirée Bartolini
- Department of Medicine and Surgery, Section of Human, Clinical and Forensic Anatomy, University of Perugia, 60132 Perugia, Italy; (A.M.S.); (A.P.); (R.M.); (M.R.)
- Department of Pharmaceutical Sciences, University of Perugia, 06126 Perugia, Italy
| | - Gabriele Di Sante
- Department of Medicine and Surgery, Section of Human, Clinical and Forensic Anatomy, University of Perugia, 60132 Perugia, Italy; (A.M.S.); (A.P.); (R.M.); (M.R.)
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Mazzeo F, Meccariello R. Cannabis and Paternal Epigenetic Inheritance. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:ijerph20095663. [PMID: 37174181 PMCID: PMC10177768 DOI: 10.3390/ijerph20095663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 04/19/2023] [Accepted: 04/23/2023] [Indexed: 05/15/2023]
Abstract
Cannabis is the most widely used illicit drug in Western counties and its abuse is particularly high in male adolescents and young adults. Its main psychotropic component, the cannabinoid delta-9-tetrahydrocannabinol (Δ9-THC), interferes in the endogenous endocannabinoid system. This signaling system is involved in the control of many biological activities, including the formation of high-quality male gametes. Direct adverse effects of Δ9-THC in male reproduction are well known in both animal models and humans. Nevertheless, the possibility of long-term effects due to epigenetic mechanisms has recently been reported. In this review, we summarize the main advances in the field suggesting the need to pay attention to the possible long-term epigenetic risks for the reproductive health of cannabis users and the health of their offspring.
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Affiliation(s)
- Filomena Mazzeo
- Dipartimento di Scienze Economiche, Giuridiche, Informatiche e Motorie, Università di Napoli Parthenope, Nola, 80035 Naples, Italy
- Department of Economics, Law, Cybersecurity and Sports Sciences, University of Naples "Parthenope", Nola, 80133 Naples, Italy
| | - Rosaria Meccariello
- Dipartimento di Scienze Motorie e del Benessere, Università di Napoli Parthenope, 80133 Napoli, Italy
- Department of Movement Sciences and Wellbeing, University "Parthenope", 80133 Naples, Italy
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