Publications by category
Journal articles
Jeffries AR, Leung SK, Castanho I, Moore K, Davies JP, Dempster EL, Bray NJ, O‘Neill P, Tseng E, Ahmed Z, et al (In Press). Full-length transcript sequencing of human and mouse identifies widespread isoform diversity and alternative splicing in the cerebral cortex.
Abstract:
Full-length transcript sequencing of human and mouse identifies widespread isoform diversity and alternative splicing in the cerebral cortex
AbstractAlternative splicing is a post-transcriptional regulatory mechanism producing multiple distinct mRNA molecules from a single pre-mRNA. Alternative splicing has a prominent role in the central nervous system, impacting neurodevelopment and various neuronal functions as well as being increasingly implicated in brain disorders including autism, schizophrenia and Alzheimer’s disease. Standard short-read RNA-Seq approaches only sequence fragments of the mRNA molecule, making it difficult to accurately characterize the true nature of RNA isoform diversity. In this study, we used long-read isoform sequencing (Iso-Seq) to generate full-length cDNA sequences and map transcript diversity in the human and mouse cerebral cortex. We identify widespread RNA isoform diversity amongst expressed genes in the cortex, including many novel transcripts not present in existing genome annotations. Alternative splicing events were found to make a major contribution to RNA isoform diversity in the cortex, with intron retention being a relatively common event associated with nonsense-mediated decay and reduced transcript expression. of note, we found evidence for transcription from novel (unannotated genes) and fusion events between neighbouring genes. Although global patterns of RNA isoform diversity were found to be generally similar between human and mouse cortex, we identified some notable exceptions. We also identified striking developmental changes in transcript diversity, with differential transcript usage between human adult and fetal cerebral cortex. Finally, we found evidence for extensive isoform diversity in genes associated with autism, schizophrenia and Alzheimer’s disease. Our data confirm the importance of alternative splicing in the cerebral cortex, dramatically increasing transcriptional diversity and representing an important mechanism underpinning gene regulation in the brain. We provide this transcript level data as a resource to the scientific community.
Abstract.
Steg LC, Shireby GL, Imm J, Davies JP, Franklin A, Flynn R, Namboori SC, Bhinge A, Jeffries AR, Burrage J, et al (In Press). Novel epigenetic clock for fetal brain development predicts prenatal age for cellular stem cell models and derived neurons.
Abstract:
Novel epigenetic clock for fetal brain development predicts prenatal age for cellular stem cell models and derived neurons
AbstractInduced pluripotent stem cells (iPSCs) and their differentiated neurons (iPSC-neurons) are a widely used cellular model in the research of the central nervous system. However, it is unknown how well they capture age-associated processes, particularly given that pluripotent cells are only present during the earliest stages of mammalian development. Epigenetic clocks utilize coordinated age-associated changes in DNA methylation to make predictions that correlate strongly with chronological age. It has been shown that the induction of pluripotency rejuvenates predicted epigenetic age. As existing clocks are not optimized for the study of brain development, we developed the fetal brain clock (FBC), a bespoke epigenetic clock trained in human prenatal brain samples in order to investigate more precisely the epigenetic age of iPSCs and iPSC-neurons. The FBC was tested in two independent validation cohorts across a total of 194 samples, confirming that the FBC outperforms other established epigenetic clocks in fetal brain cohorts. We applied the FBC to DNA methylation data from iPSCs and iPSC-derived neuronal precursor cells and neurons, finding that these cell types are epigenetically characterized as having an early fetal age. Furthermore, while differentiation from iPSCs to neurons significantly increases epigenetic age, iPSC-neurons are still predicted as being fetal. Together our findings reiterate the need to better understand the limitations of existing epigenetic clocks for answering biological research questions and highlight a limitation of iPSC-neurons as a cellular model of age-related diseases.
Abstract.
Shireby GL, Davies JP, Francis PT, Burrage J, Walker EM, Neilson GWA, Dahir A, Thomas AJ, Love S, Smith RG, et al (In Press). Recalibrating the Epigenetic Clock: Implications for Assessing Biological Age in the Human Cortex.
Abstract:
Recalibrating the Epigenetic Clock: Implications for Assessing Biological Age in the Human Cortex
AbstractHuman DNA-methylation data have been used to develop biomarkers of ageing - referred to ‘epigenetic clocks’ - that have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks are highly accurate in blood but are less precise when used in older samples or on brain tissue. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life-course (n = 1,397, ages = 1 to 104 years). This dataset was split into ‘training’ and ‘testing’ samples (training: n = 1,047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel human cortex dataset (n = 1,221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1,175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically out-performed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts.
Abstract.
Davies J, Franklin A, Walker E, Owens N, Bray N, Bamford RA, Commin G, Chioza B, Burrage J, Dempster E, et al (2022). 1. DEVELOPMENTAL TRAJECTORIES OF DNA METHYLATION IN NEURAL CELL POPULATIONS IN HUMAN CORTEX AND LINKS TO NEURODEVELOPMENTAL DISORDERS. European Neuropsychopharmacology, 63
Bamford R, Jeffries AR, Walker E, Leung SK, Commin G, Davies JP, Dempster E, Hannon E, Mill J (2022). 67. LONG READ TRANSCRIPTOME SEQUENCING REVEALS ISOFORM DIVERSITY ACROSS HUMAN NEURODEVELOPMENT. European Neuropsychopharmacology, 63, e81-e82.
Hannon E, Davies J, Chioza B, Policicchio S, Burrage J, Commin G, Jeffries AR, Schalkwyk L, Dempster E, Mill J, et al (2022). 89. IDENTIFYING CELL-TYPE-SPECIFIC EPIGENETIC VARIATION IN THE CORTEX ASSOCIATED WITH SCHIZOPHRENIA. European Neuropsychopharmacology, 63, e93-e94.
Shireby G, Dempster EL, Policicchio S, Smith RG, Pishva E, Chioza B, Davies JP, Burrage J, Lunnon K, Seiler Vellame D, et al (2022). DNA methylation signatures of Alzheimer’s disease neuropathology in the cortex are primarily driven by variation in non-neuronal cell-types.
Nature Communications,
13(1).
Abstract:
DNA methylation signatures of Alzheimer’s disease neuropathology in the cortex are primarily driven by variation in non-neuronal cell-types
AbstractAlzheimer’s disease (AD) is a chronic neurodegenerative disease characterized by the progressive accumulation of amyloid-beta and neurofibrillary tangles of tau in the neocortex. We profiled DNA methylation in two regions of the cortex from 631 donors, performing an epigenome-wide association study of multiple measures of AD neuropathology. We meta-analyzed our results with those from previous studies of DNA methylation in AD cortex (total n = 2013 donors), identifying 334 cortical differentially methylated positions (DMPs) associated with AD pathology including methylomic variation at loci not previously implicated in dementia. We subsequently profiled DNA methylation in NeuN+ (neuronal-enriched), SOX10+ (oligodendrocyte-enriched) and NeuN–/SOX10– (microglia- and astrocyte-enriched) nuclei, finding that the majority of DMPs identified in ‘bulk’ cortex tissue reflect DNA methylation differences occurring in non-neuronal cells. Our study highlights the power of utilizing multiple measures of neuropathology to identify epigenetic signatures of AD and the importance of characterizing disease-associated variation in purified cell-types.
Abstract.
Leung SK, Jeffries AR, Castanho I, Jordan BT, Moore K, Davies JP, Dempster EL, Bray NJ, O’Neill P, Tseng E, et al (2021). Full-length transcript sequencing of human and mouse cerebral cortex identifies widespread isoform diversity and alternative splicing. Cell Reports, 37(7), 110022-110022.
Steg LC, Shireby GL, Imm J, Davies JP, Franklin A, Flynn R, Namboori SC, Bhinge A, Jeffries AR, Burrage J, et al (2021). Novel epigenetic clock for fetal brain development predicts prenatal age for cellular stem cell models and derived neurons.
Mol Brain,
14(1).
Abstract:
Novel epigenetic clock for fetal brain development predicts prenatal age for cellular stem cell models and derived neurons.
Induced pluripotent stem cells (iPSCs) and their differentiated neurons (iPSC-neurons) are a widely used cellular model in the research of the central nervous system. However, it is unknown how well they capture age-associated processes, particularly given that pluripotent cells are only present during the earliest stages of mammalian development. Epigenetic clocks utilize coordinated age-associated changes in DNA methylation to make predictions that correlate strongly with chronological age. It has been shown that the induction of pluripotency rejuvenates predicted epigenetic age. As existing clocks are not optimized for the study of brain development, we developed the fetal brain clock (FBC), a bespoke epigenetic clock trained in human prenatal brain samples in order to investigate more precisely the epigenetic age of iPSCs and iPSC-neurons. The FBC was tested in two independent validation cohorts across a total of 194 samples, confirming that the FBC outperforms other established epigenetic clocks in fetal brain cohorts. We applied the FBC to DNA methylation data from iPSCs and embryonic stem cells and their derived neuronal precursor cells and neurons, finding that these cell types are epigenetically characterized as having an early fetal age. Furthermore, while differentiation from iPSCs to neurons significantly increases epigenetic age, iPSC-neurons are still predicted as being fetal. Together our findings reiterate the need to better understand the limitations of existing epigenetic clocks for answering biological research questions and highlight a limitation of iPSC-neurons as a cellular model of age-related diseases.
Abstract.
Author URL.
Shireby GL, Davies JP, Francis PT, Burrage J, Walker EM, Neilson GWA, Dahir A, Thomas AJ, Love S, Smith RG, et al (2020). Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex.
Brain,
143(12), 3763-3775.
Abstract:
Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex.
Human DNA methylation data have been used to develop biomarkers of ageing, referred to as 'epigenetic clocks', which have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks have been shown to be highly accurate in blood but are less precise when used in older samples or in tissue types not included in training the model, including brain. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life course (n = 1397, ages = 1 to 108 years). This dataset was split into 'training' and 'testing' samples (training: n = 1047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel independent human cortex dataset (n = 1221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination, optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically outperformed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts.
Abstract.
Author URL.
Conferences
Davies J, Franklin A, Commin G, Walker E, Policicchio S, Jeffries A, Burrage J, Chioza B, Liu J, Bray N, et al (2021). CELL-TYPE-SPECIFIC PATTERNS OF DNA METHYLATION IN THE DEVELOPING HUMAN BRAIN.
Author URL.
Shireby G, Hannon E, Commin G, Burrage J, Davies J, Policicchio S, Schalkwyk L, Dempster E, Mill J (2021). LEVERAGING DNA METHYLATION QUANTITATIVE-TRAIT LOCI TO CHARACTERIZE THE RELATIONSHIP BETWEEN METHYLOMIC VARIATION, GENE EXPRESSION, AND PSYCHIATRIC DISEASE.
Author URL.
Policicchio S, Davies J, Choiza B, Hannon E, Burrage J, Dempster E, Mill J (2019). MAPPING CELL-TYPE SPECIFIC MARKERS OF GENOMIC REGULATION IN THE HUMAN BRAIN.
Author URL.
Publications by year
In Press
Jeffries AR, Leung SK, Castanho I, Moore K, Davies JP, Dempster EL, Bray NJ, O‘Neill P, Tseng E, Ahmed Z, et al (In Press). Full-length transcript sequencing of human and mouse identifies widespread isoform diversity and alternative splicing in the cerebral cortex.
Abstract:
Full-length transcript sequencing of human and mouse identifies widespread isoform diversity and alternative splicing in the cerebral cortex
AbstractAlternative splicing is a post-transcriptional regulatory mechanism producing multiple distinct mRNA molecules from a single pre-mRNA. Alternative splicing has a prominent role in the central nervous system, impacting neurodevelopment and various neuronal functions as well as being increasingly implicated in brain disorders including autism, schizophrenia and Alzheimer’s disease. Standard short-read RNA-Seq approaches only sequence fragments of the mRNA molecule, making it difficult to accurately characterize the true nature of RNA isoform diversity. In this study, we used long-read isoform sequencing (Iso-Seq) to generate full-length cDNA sequences and map transcript diversity in the human and mouse cerebral cortex. We identify widespread RNA isoform diversity amongst expressed genes in the cortex, including many novel transcripts not present in existing genome annotations. Alternative splicing events were found to make a major contribution to RNA isoform diversity in the cortex, with intron retention being a relatively common event associated with nonsense-mediated decay and reduced transcript expression. of note, we found evidence for transcription from novel (unannotated genes) and fusion events between neighbouring genes. Although global patterns of RNA isoform diversity were found to be generally similar between human and mouse cortex, we identified some notable exceptions. We also identified striking developmental changes in transcript diversity, with differential transcript usage between human adult and fetal cerebral cortex. Finally, we found evidence for extensive isoform diversity in genes associated with autism, schizophrenia and Alzheimer’s disease. Our data confirm the importance of alternative splicing in the cerebral cortex, dramatically increasing transcriptional diversity and representing an important mechanism underpinning gene regulation in the brain. We provide this transcript level data as a resource to the scientific community.
Abstract.
Steg LC, Shireby GL, Imm J, Davies JP, Franklin A, Flynn R, Namboori SC, Bhinge A, Jeffries AR, Burrage J, et al (In Press). Novel epigenetic clock for fetal brain development predicts prenatal age for cellular stem cell models and derived neurons.
Abstract:
Novel epigenetic clock for fetal brain development predicts prenatal age for cellular stem cell models and derived neurons
AbstractInduced pluripotent stem cells (iPSCs) and their differentiated neurons (iPSC-neurons) are a widely used cellular model in the research of the central nervous system. However, it is unknown how well they capture age-associated processes, particularly given that pluripotent cells are only present during the earliest stages of mammalian development. Epigenetic clocks utilize coordinated age-associated changes in DNA methylation to make predictions that correlate strongly with chronological age. It has been shown that the induction of pluripotency rejuvenates predicted epigenetic age. As existing clocks are not optimized for the study of brain development, we developed the fetal brain clock (FBC), a bespoke epigenetic clock trained in human prenatal brain samples in order to investigate more precisely the epigenetic age of iPSCs and iPSC-neurons. The FBC was tested in two independent validation cohorts across a total of 194 samples, confirming that the FBC outperforms other established epigenetic clocks in fetal brain cohorts. We applied the FBC to DNA methylation data from iPSCs and iPSC-derived neuronal precursor cells and neurons, finding that these cell types are epigenetically characterized as having an early fetal age. Furthermore, while differentiation from iPSCs to neurons significantly increases epigenetic age, iPSC-neurons are still predicted as being fetal. Together our findings reiterate the need to better understand the limitations of existing epigenetic clocks for answering biological research questions and highlight a limitation of iPSC-neurons as a cellular model of age-related diseases.
Abstract.
Shireby GL, Davies JP, Francis PT, Burrage J, Walker EM, Neilson GWA, Dahir A, Thomas AJ, Love S, Smith RG, et al (In Press). Recalibrating the Epigenetic Clock: Implications for Assessing Biological Age in the Human Cortex.
Abstract:
Recalibrating the Epigenetic Clock: Implications for Assessing Biological Age in the Human Cortex
AbstractHuman DNA-methylation data have been used to develop biomarkers of ageing - referred to ‘epigenetic clocks’ - that have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks are highly accurate in blood but are less precise when used in older samples or on brain tissue. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life-course (n = 1,397, ages = 1 to 104 years). This dataset was split into ‘training’ and ‘testing’ samples (training: n = 1,047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel human cortex dataset (n = 1,221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1,175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically out-performed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts.
Abstract.
2022
Davies J, Franklin A, Walker E, Owens N, Bray N, Bamford RA, Commin G, Chioza B, Burrage J, Dempster E, et al (2022). 1. DEVELOPMENTAL TRAJECTORIES OF DNA METHYLATION IN NEURAL CELL POPULATIONS IN HUMAN CORTEX AND LINKS TO NEURODEVELOPMENTAL DISORDERS. European Neuropsychopharmacology, 63
Bamford R, Jeffries AR, Walker E, Leung SK, Commin G, Davies JP, Dempster E, Hannon E, Mill J (2022). 67. LONG READ TRANSCRIPTOME SEQUENCING REVEALS ISOFORM DIVERSITY ACROSS HUMAN NEURODEVELOPMENT. European Neuropsychopharmacology, 63, e81-e82.
Hannon E, Davies J, Chioza B, Policicchio S, Burrage J, Commin G, Jeffries AR, Schalkwyk L, Dempster E, Mill J, et al (2022). 89. IDENTIFYING CELL-TYPE-SPECIFIC EPIGENETIC VARIATION IN THE CORTEX ASSOCIATED WITH SCHIZOPHRENIA. European Neuropsychopharmacology, 63, e93-e94.
Shireby G, Dempster EL, Policicchio S, Smith RG, Pishva E, Chioza B, Davies JP, Burrage J, Lunnon K, Seiler Vellame D, et al (2022). DNA methylation signatures of Alzheimer’s disease neuropathology in the cortex are primarily driven by variation in non-neuronal cell-types.
Nature Communications,
13(1).
Abstract:
DNA methylation signatures of Alzheimer’s disease neuropathology in the cortex are primarily driven by variation in non-neuronal cell-types
AbstractAlzheimer’s disease (AD) is a chronic neurodegenerative disease characterized by the progressive accumulation of amyloid-beta and neurofibrillary tangles of tau in the neocortex. We profiled DNA methylation in two regions of the cortex from 631 donors, performing an epigenome-wide association study of multiple measures of AD neuropathology. We meta-analyzed our results with those from previous studies of DNA methylation in AD cortex (total n = 2013 donors), identifying 334 cortical differentially methylated positions (DMPs) associated with AD pathology including methylomic variation at loci not previously implicated in dementia. We subsequently profiled DNA methylation in NeuN+ (neuronal-enriched), SOX10+ (oligodendrocyte-enriched) and NeuN–/SOX10– (microglia- and astrocyte-enriched) nuclei, finding that the majority of DMPs identified in ‘bulk’ cortex tissue reflect DNA methylation differences occurring in non-neuronal cells. Our study highlights the power of utilizing multiple measures of neuropathology to identify epigenetic signatures of AD and the importance of characterizing disease-associated variation in purified cell-types.
Abstract.
Shireby G, Dempster E, Policicchio S, Smith RG, Pishva E, Chioza B, Davies JP, Burrage J, Lunnon K, Seiler-Vellame D, et al (2022). DNA methylation signatures of Alzheimer’s disease neuropathology in the cortex are primarily driven by variation in non-neuronal cell-types.
2021
Davies J, Franklin A, Commin G, Walker E, Policicchio S, Jeffries A, Burrage J, Chioza B, Liu J, Bray N, et al (2021). CELL-TYPE-SPECIFIC PATTERNS OF DNA METHYLATION IN THE DEVELOPING HUMAN BRAIN.
Author URL.
Leung SK, Jeffries AR, Castanho I, Jordan BT, Moore K, Davies JP, Dempster EL, Bray NJ, O’Neill P, Tseng E, et al (2021). Full-length transcript sequencing of human and mouse cerebral cortex identifies widespread isoform diversity and alternative splicing. Cell Reports, 37(7), 110022-110022.
Shireby G, Hannon E, Commin G, Burrage J, Davies J, Policicchio S, Schalkwyk L, Dempster E, Mill J (2021). LEVERAGING DNA METHYLATION QUANTITATIVE-TRAIT LOCI TO CHARACTERIZE THE RELATIONSHIP BETWEEN METHYLOMIC VARIATION, GENE EXPRESSION, AND PSYCHIATRIC DISEASE.
Author URL.
Steg LC, Shireby GL, Imm J, Davies JP, Franklin A, Flynn R, Namboori SC, Bhinge A, Jeffries AR, Burrage J, et al (2021). Novel epigenetic clock for fetal brain development predicts prenatal age for cellular stem cell models and derived neurons.
Mol Brain,
14(1).
Abstract:
Novel epigenetic clock for fetal brain development predicts prenatal age for cellular stem cell models and derived neurons.
Induced pluripotent stem cells (iPSCs) and their differentiated neurons (iPSC-neurons) are a widely used cellular model in the research of the central nervous system. However, it is unknown how well they capture age-associated processes, particularly given that pluripotent cells are only present during the earliest stages of mammalian development. Epigenetic clocks utilize coordinated age-associated changes in DNA methylation to make predictions that correlate strongly with chronological age. It has been shown that the induction of pluripotency rejuvenates predicted epigenetic age. As existing clocks are not optimized for the study of brain development, we developed the fetal brain clock (FBC), a bespoke epigenetic clock trained in human prenatal brain samples in order to investigate more precisely the epigenetic age of iPSCs and iPSC-neurons. The FBC was tested in two independent validation cohorts across a total of 194 samples, confirming that the FBC outperforms other established epigenetic clocks in fetal brain cohorts. We applied the FBC to DNA methylation data from iPSCs and embryonic stem cells and their derived neuronal precursor cells and neurons, finding that these cell types are epigenetically characterized as having an early fetal age. Furthermore, while differentiation from iPSCs to neurons significantly increases epigenetic age, iPSC-neurons are still predicted as being fetal. Together our findings reiterate the need to better understand the limitations of existing epigenetic clocks for answering biological research questions and highlight a limitation of iPSC-neurons as a cellular model of age-related diseases.
Abstract.
Author URL.
2020
Steg LC, Shireby GL, Imm J, Davies JP, Flynn R, Namboori SC, Bhinge A, Jeffries AR, Burrage J, Neilson GWA, et al (2020). Novel Epigenetic Clock for Fetal Brain Development Predicts Fetal Epigenetic Age for iPSCs and iPSC-Derived Neurons.
Shireby GL, Davies JP, Francis PT, Burrage J, Walker EM, Neilson GWA, Dahir A, Thomas AJ, Love S, Smith RG, et al (2020). Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex.
Brain,
143(12), 3763-3775.
Abstract:
Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex.
Human DNA methylation data have been used to develop biomarkers of ageing, referred to as 'epigenetic clocks', which have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks have been shown to be highly accurate in blood but are less precise when used in older samples or in tissue types not included in training the model, including brain. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life course (n = 1397, ages = 1 to 108 years). This dataset was split into 'training' and 'testing' samples (training: n = 1047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel independent human cortex dataset (n = 1221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination, optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically outperformed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts.
Abstract.
Author URL.
2019
Policicchio S, Davies J, Choiza B, Hannon E, Burrage J, Dempster E, Mill J (2019). MAPPING CELL-TYPE SPECIFIC MARKERS OF GENOMIC REGULATION IN THE HUMAN BRAIN.
Author URL.
