Publications
103 results found
Alhathli E, Julian T, Girach ZUA, et al., 2024, Mendelian randomization study with clinical follow-up links metabolites to risk and severity of pulmonary arterial hypertension, Journal of the American Heart Association, Vol: 13, ISSN: 2047-9980
BACKGROUND: Pulmonary arterial hypertension (PAH) exhibits phenotypic heterogeneity and variable response to therapy. The metabolome has been implicated in the pathogenesis of PAH, but previous works have lacked power to implicate specific metabolites. Mendelian randomization (MR) is a method for causal inference between exposures and outcomes. METHODS AND RESULTS: Using genome-wide association study summary statistics, we implemented MR analysis to test for potential causal relationships between serum concentration of 575 metabolites and PAH. Five metabolites were causally associated with the risk of PAH after multiple testing correction. Next, we measured serum concentration of candidate metabolites in an independent clinical cohort of 449 patients with PAH to check whether metabolite concentrations are correlated with markers of disease severity. Of the 5 candidates nominated by our MR work, serine was negatively associated and homostachydrine was positively associated with clinical severity of PAH via direct measurement in this independent clinical cohort. Finally we used conditional and orthogonal approaches to explore the biology underlying our lead metabolites. Rare variant burden testing was carried out using whole exome sequencing data from 578 PAH cases and 361 675 controls. Multivariable MR is an extension of MR that uses a single set of instrumental single-nucleotide polymorphisms to measure multiple exposures; multivariable MR is used to determine interdependence between the effects of different exposures on a single outcome. Rare variant analysis demonstrated that loss-of-function mutations within activating transcription factor 4, a transcription factor responsible for upregulation of serine synthesis under conditions of serine starvation, are associated with higher risk for PAH. Homostachydrine is a xenobiotic metabolite that is structurally related to l-proline betaine, which has previously been linked to modulation of inflammation and tissue
Rajab MD, Taketa T, Wharton SB, et al., 2024, Ranking and filtering of neuropathology features in the machine learning evaluation of dementia studies., Brain Pathology, ISSN: 1015-6305
Early diagnosis of dementia diseases, such as Alzheimer's disease, is difficult because of the time and resources needed to perform neuropsychological and pathological assessments. Given the increasing use of machine learning methods to evaluate neuropathology features in the brains of dementia patients, it is important to investigate how the selection of features may be impacted and which features are most important for the classification of dementia. We objectively assessed neuropathology features using machine learning techniques for filtering features in two independent ageing cohorts, the Cognitive Function and Aging Studies (CFAS) and Alzheimer's Disease Neuroimaging Initiative (ADNI). The reliefF and least loss methods were most consistent with their rankings between ADNI and CFAS; however, reliefF was most biassed by feature–feature correlations. Braak stage was consistently the highest ranked feature and its ranking was not correlated with other features, highlighting its unique importance. Using a smaller set of highly ranked features, rather than all features, can achieve a similar or better dementia classification performance in CFAS (60%–70% accuracy with Naïve Bayes). This study showed that specific neuropathology features can be prioritised by feature filtering methods, but they are impacted by feature–feature correlations and their results can vary between cohort studies. By understanding these biases, we can reduce discrepancies in feature ranking and identify a minimal set of features needed for accurate classification of dementia.
Gupta G, Kariotis S, Rajab M, et al., 2023, Unsupervised machine learning to investigate trajectory patterns of COVID-19 symptoms and physical activity measured via the MyHeart Counts App and smart devices, npj Digital Medicine, Vol: 6, ISSN: 2398-6352
Previous studies have associated COVID-19 symptoms severity with levels of physical activity. We therefore investigated longitudinal trajectories of COVID-19 symptoms in a cohort of healthcare workers (HCWs) with non-hospitalised COVID-19 and their real-world physical activity. 121 HCWs with a history of COVID-19 infection who had symptoms monitored through at least two research clinic visits, and via smartphone were examined. HCWs with a compatible smartphone were provided with an Apple Watch Series 4 and were asked to install the MyHeart Counts Study App to collect COVID-19 symptom data and multiple physical activity parameters. Unsupervised classification analysis of symptoms identified two trajectory patterns of long and short symptom duration. The prevalence for longitudinal persistence of any COVID-19 symptom was 36% with fatigue and loss of smell being the two most prevalent individual symptom trajectories (24.8% and 21.5%, respectively). 8 physical activity features obtained via the MyHeart Counts App identified two groups of trajectories for high and low activity. Of these 8 parameters only ‘distance moved walking or running’ was associated with COVID-19 symptom trajectories. We report a high prevalence of long-term symptoms of COVID-19 in a non-hospitalised cohort of HCWs, a method to identify physical activity trends, and investigate their association. These data highlight the importance of tracking symptoms from onset to recovery even in non-hospitalised COVID-19 individuals. The increasing ease in collecting real-world physical activity data non-invasively from wearable devices provides opportunity to investigate the association of physical activity to symptoms of COVID-19 and other cardio-respiratory diseases.
Wang D, Rajab M, Jammeh E, et al., 2023, Assessment of Alzheimer-related pathologies of dementia using machine learning feature selection, Alzheimer's Research and Therapy, Vol: 15, Pages: 1-17, ISSN: 1758-9193
Although a variety of brain lesions may contribute to the pathological assessment of dementia, the relationship of these lesions to dementia, how they interact and how to quantify them remains uncertain. Systematically assessing neuropathological measures by their degree of association with dementia may lead to better diagnostic systems and treatment targets. This study aims to apply machine learning approaches to feature selection in order to identify critical features of Alzheimer-related pathologies associated with dementia. We applied machine learning techniques for feature ranking and classification to objectively compare neuropathological features and their relationship to dementia status during life using a cohort (n=186) from the Cognitive Function and Ageing Study (CFAS). We first tested Alzheimer’s Disease and tau markers and then other neuropathologies associated with dementia. Seven feature ranking methods using different information criteria consistently ranked 22 out of the 34 neuropathology features for importance to dementia classification. Although highly correlated, Braak neurofibrillary tangle stage, beta-amyloid and cerebral amyloid angiopathy features were ranked the highest. The best-performing dementia classifier using the top eight neuropathological features achieved 79% sensitivity, 69% specificity and 75% precision. However, when assessing all seven classifiers and the 22 ranked features, a substantial proportion (40.4%) of dementia cases was consistently misclassified. These results highlight the benefits of using machine learning to identify critical indices of plaque, tangle and cerebral amyloid angiopathy burdens that may be useful for classifying dementia.
Kariotis S, Jammeh E, Swietlik EM, et al., 2022, Biological heterogeneity in idiopathic pulmonary arterial hypertension identified through unsupervised transcriptomic profiling of whole blood, Nature Communications, Vol: 13, Pages: 1-1, ISSN: 2041-1723
Willett BJ, Grove J, MacLean OA, et al., 2022, SARS-CoV-2 Omicron is an immune escape variant with an altered cell entry pathway (vol 7, pg 1161, 2022), Nature Microbiology, Vol: 7, Pages: 1709-1709, ISSN: 2058-5276
Wilkinson SAJ, Richter A, Casey A, et al., 2022, Recurrent SARS-CoV-2 mutations in immunodeficient patients, VIRUS EVOLUTION, Vol: 8
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- Citations: 16
Kemp SA, Collier DA, Datir RP, et al., 2022, SARS-CoV-2 evolution during treatment of chronic infection (vol 592, pg 297, 2021), NATURE, Vol: 608, Pages: E23-E23, ISSN: 0028-0836
Collier DA, De Marco A, Ferreira IATM, et al., 2022, Sensitivity of SARS-CoV-2 B.1.1.7 to mRNA vaccine-elicited antibodies (vol 593, pg 136, 2022), NATURE, Vol: 608, Pages: E24-E24, ISSN: 0028-0836
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- Citations: 1
Willett BJ, Grove J, MacLean OA, et al., 2022, SARS-CoV-2 Omicron is an immune escape variant with an altered cell entry pathway, NATURE MICROBIOLOGY, Vol: 7, Pages: 1161-+, ISSN: 2058-5276
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- Citations: 169
Eales O, Wang H, Bodinier B, et al., 2022, SARS-CoV-2 lineage dynamics in England from September to November 2021: high diversity of Delta sub-lineages and increased transmissibility of AY.4.2, BMC Infectious Diseases, Vol: 22, ISSN: 1471-2334
Background: Since the emergence of SARS-CoV-2, evolutionary pressure has driven large increases in the transmissibility of the virus. However, with increasing levels of immunity through vaccination and natural infection the evolutionary pressure will switch towards immune escape. Genomic surveillance in regions of high immunity is crucial in detecting emerging variants that can more successfully navigate the immune landscape. Methods: We present phylogenetic relationships and lineage dynamics within England (a country with high levels of immunity), as inferred from a random community sample of individuals who provided a self-administered throat and nose swab for rt-PCR testing as part of the REal-time Assessment of Community Transmission-1 (REACT-1) study. During round 14 (9 September - 27 September 2021) and 15 (19 October - 5 November 2021) lineages were determined for 1322 positive individuals, with 27.1% of those which reported their symptom status reporting no symptoms in the previous month.Results: We identified 44 unique lineages, all of which were Delta or Delta sub-lineages, and found a reduction in their mutation rate over the study period. The proportion of the Delta sub-lineage AY.4.2 was increasing, with a reproduction number 15% (95% CI, 8%-23%) greater than the most prevalent lineage, AY.4. Further, AY.4.2 was less associated with the most predictive COVID-19 symptoms (p = 0.029) and had a reduced mutation rate (p = 0.050). Both AY.4.2 and AY.4 were found to be geographically clustered in September but this was no longer the case by late October/early November, with only the lineage AY.6 exhibiting clustering towards the South of England.Conclusions: As SARS-CoV-2 moves towards endemicity and new variants emerge, genomic data obtained from random community samples can augment routine surveillance data without the potential biases introduced due to higher sampling rates of symptomatic individuals.
Nickbakhsh S, Hughes J, Christofidis N, et al., 2022, Genomic epidemiology of SARS-CoV-2 in a university outbreak setting and implications for public health planning, SCIENTIFIC REPORTS, Vol: 12, ISSN: 2045-2322
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- Citations: 3
Parker MD, Stewart H, Shehata OM, et al., 2022, Altered subgenomic RNA abundance provides unique insight into SARS-CoV-2 B.1.1.7/Alpha variant infections, COMMUNICATIONS BIOLOGY, Vol: 5
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- Citations: 5
Baranasic D, Hortenhuber M, Balwierz PJ, et al., 2022, Multiomic atlas with functional stratification and developmental dynamics of zebrafish <i>cis</i>-regulatory elements, NATURE GENETICS, Vol: 54, Pages: 1037-+, ISSN: 1061-4036
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- Citations: 14
Klaser K, Molteni E, Graham M, et al., 2022, COVID-19 due to the B.1.617.2 (Delta) variant compared to B.1.1.7 (Alpha) variant of SARS-CoV-2: a prospective observational cohort study, SCIENTIFIC REPORTS, Vol: 12, ISSN: 2045-2322
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- Citations: 18
Vohringer HS, Sanderson T, Sinnott M, et al., 2022, Genomic reconstruction of the SARS CoV-2 epidemic in England (vol 600, pg 506, 2021), NATURE, Vol: 606, Pages: E18-E18, ISSN: 0028-0836
Rajab MD, Jammeh E, Taketa T, et al., 2022, Assessment of Alzheimer-related Pathologies of Dementia Using Machine Learning Feature Selection
<jats:title>Abstract</jats:title> <jats:p>Although a variety of brain lesions may contribute to the pathological diagnosis of dementia, the relationship of these lesions to dementia, how they interact and how to quantify them remain uncertain. Systematically assessing neuropathological measures in relation to the cognitive and functional definitions of dementia may enable the development of better diagnostic systems and treatment targets. The objective of this study is to apply machine learning approaches for feature selection to identify key features of Alzheimer-related pathologies associated with dementia. We applied machine learning techniques for feature ranking and classification as an unbiased comparison of neuropathological features and assessment of their diagnostic performance using a cohort (n = 186) from the Cognitive Function and Ageing Study (CFAS). Seven feature ranking methods using different information criteria consistently ranked 22 out of the 34 neuropathology features for importance to dementia classification. Braak neurofibrillary tangle stage, Beta-amyloid and cerebral amyloid angiopathy features were the most highly ranked, although were highly correlated with each other. The best performing dementia classifier using the top eight ranked neuropathology features achieved 79% sensitivity, 69% specificity, and 75% precision. A substantial proportion (40.4%) of dementia cases was consistently misclassified by all seven algorithms and any combination of the 22 ranked features. These results highlight the potential of using machine learning to identify key indices of plaque, tangle and cerebral amyloid angiopathy burdens that may be useful for the classification of dementia.</jats:p>
Rajab MD, Jammeh E, Taketa T, et al., 2022, Assessment of Alzheimer-related Pathologies of Dementia Using Machine Learning Feature Selection
<jats:title>Abstract</jats:title><jats:p>Although a variety of brain lesions may contribute to the pathological diagnosis of dementia, the relationship of these lesions to dementia, how they interact and how to quantify them remain uncertain. Systematically assessing neuropathological measures in relation to the cognitive and functional definitions of dementia may enable the development of better diagnostic systems and treatment targets. The objective of this study is to apply machine learning approaches for feature selection to identify key features of Alzheimer-related pathologies associated with dementia. We applied machine learning techniques for feature ranking and classification as an unbiased comparison of neuropathological features and assessment of their diagnostic performance using a cohort (n=186) from the Cognitive Function and Ageing Study (CFAS). Seven feature ranking methods using different information criteria consistently ranked 22 out of the 34 neuropathology features for importance to dementia classification. Braak neurofibrillary tangle stage, Beta-amyloid and cerebral amyloid angiopathy features were the most highly ranked, although were highly correlated with each other. The best performing dementia classifier using the top eight ranked neuropathology features achieved 79% sensitivity, 69% specificity, and 75% precision. A substantial proportion (40.4%) of dementia cases was consistently misclassified by all seven algorithms and any combination of the 22 ranked features. These results highlight the potential of using machine learning to identify key indices of plaque, tangle and cerebral amyloid angiopathy burdens that may be useful for the classification of dementia.</jats:p>
Errington N, Kariotis S, Jammeh E, et al., 2022, Unsupervised Clustering of PH Using Circulating miRNA - Towards Molecular Classification of PH?, International Conference of the American-Thoracic-Society, Publisher: AMER THORACIC SOC, ISSN: 1073-449X
Kariotis S, Jammeh E, Swietlik EM, et al., 2022, Longitudinal Analysis of Three Major Risk-Associated Transcriptomic Subgroups Within the IPAH Classification, International Conference of the American-Thoracic-Society, Publisher: AMER THORACIC SOC, ISSN: 1073-449X
Aggarwal D, Page AJ, Schaefer U, et al., 2022, Genomic assessment of quarantine measures to prevent SARS-CoV-2 importation and transmission, Nature Communications, Vol: 13, ISSN: 2041-1723
Mitigation of SARS-CoV-2 transmission from international travel is a priority. We evaluated the effectiveness of travellers being required to quarantine for 14-days on return to England in Summer 2020. We identified 4,207 travel-related SARS-CoV-2 cases and their contacts, and identified 827 associated SARS-CoV-2 genomes. Overall, quarantine was associated with a lower rate of contacts, and the impact of quarantine was greatest in the 16–20 age-group. 186 SARS-CoV-2 genomes were sufficiently unique to identify travel-related clusters. Fewer genomically-linked cases were observed for index cases who returned from countries with quarantine requirement compared to countries with no quarantine requirement. This difference was explained by fewer importation events per identified genome for these cases, as opposed to fewer onward contacts per case. Overall, our study demonstrates that a 14-day quarantine period reduces, but does not completely eliminate, the onward transmission of imported cases, mainly by dissuading travel to countries with a quarantine requirement.
Lindsey BB, Villabona-Arenas CJ, Campbell F, et al., 2022, Characterising within-hospital SARS-CoV-2 transmission events using epidemiological and viral genomic data across two pandemic waves (vol 13, pg 1013, 2022), NATURE COMMUNICATIONS, Vol: 13
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- Citations: 3
Aggarwal D, Warne B, Jahun AS, et al., 2022, Genomic epidemiology of SARS-CoV-2 in a UK university identifies dynamics of transmission, Nat Commun, Vol: 13
Understanding SARS-CoV-2 transmission in higher education settings is important to limit spread between students, and into at-risk populations. In this study, we sequenced 482 SARS-CoV-2 isolates from the University of Cambridge from 5 October to 6 December 2020. We perform a detailed phylogenetic comparison with 972 isolates from the surrounding community, complemented with epidemiological and contact tracing data, to determine transmission dynamics. We observe limited viral introductions into the university; the majority of student cases were linked to a single genetic cluster, likely following social gatherings at a venue outside the university. We identify considerable onward transmission associated with student accommodation and courses; this was effectively contained using local infection control measures and following a national lockdown. Transmission clusters were largely segregated within the university or the community. Our study highlights key determinants of SARS-CoV-2 transmission and effective interventions in a higher education setting that will inform public health policy during pandemics.
Cottrell CM, Myers K, Dunning M, et al., 2022, Development of unique ex vivo models of post-surgical residual glioblastoma, WFNOS 2022 Seoul, Publisher: The Korean Brain Tumor Society; The Korean Society for Neuro-Oncology; The Korean Society for Pediatric Neuro-Oncology
Vohringer HS, Sanderson T, Sinnott M, et al., 2021, Genomic reconstruction of the SARS-CoV-2 epidemic in England, NATURE, Vol: 600, Pages: 506-+, ISSN: 0028-0836
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- Citations: 2
Kariotis S, Jammeh E, Swietlik EM, et al., 2021, Biological heterogeneity in idiopathic pulmonary arterial hypertension identified through unsupervised transcriptomic profiling of whole blood, Nature Communications, Vol: 12, Pages: 1-14, ISSN: 2041-1723
Idiopathic pulmonary arterial hypertension (IPAH) is a rare but fatal disease diagnosed by right heart catheterisation and the exclusion of other forms of pulmonary arterial hypertension, producing a heterogeneous population with varied treatment response. Here we show unsupervised machine learning identification of three major patient subgroups that account for 92% of the cohort, each with unique whole blood transcriptomic and clinical feature signatures. These subgroups are associated with poor, moderate, and good prognosis. The poor prognosis subgroup is associated with upregulation of the ALAS2 and downregulation of several immunoglobulin genes, while the good prognosis subgroup is defined by upregulation of the bone morphogenetic protein signalling regulator NOG, and the C/C variant of HLA-DPA1/DPB1 (independently associated with survival). These findings independently validated provide evidence for the existence of 3 major subgroups (endophenotypes) within the IPAH classification, could improve risk stratification and provide molecular insights into the pathogenesis of IPAH.
de Silva TI, Liu G, Lindsey BB, et al., 2021, The impact of viral mutations on recognition by SARS-CoV-2 specific T cells., iScience, Vol: 24, Pages: 103353-103353, ISSN: 2589-0042
We identify amino acid variants within dominant SARS-CoV-2 T cell epitopes by interrogating global sequence data. Several variants within nucleocapsid and ORF3a epitopes have arisen independently in multiple lineages and result in loss of recognition by epitope-specific T cells assessed by IFN-γ and cytotoxic killing assays. Complete loss of T cell responsiveness was seen due to Q213K in the A∗01:01-restricted CD8+ ORF3a epitope FTSDYYQLY207-215; due to P13L, P13S, and P13T in the B∗27:05-restricted CD8+ nucleocapsid epitope QRNAPRITF9-17; and due to T362I and P365S in the A∗03:01/A∗11:01-restricted CD8+ nucleocapsid epitope KTFPPTEPK361-369. CD8+ T cell lines unable to recognize variant epitopes have diverse T cell receptor repertoires. These data demonstrate the potential for T cell evasion and highlight the need for ongoing surveillance for variants capable of escaping T cell as well as humoral immunity.
Baranasic D, Hörtenhuber M, Balwierz P, et al., 2021, Integrated annotation and analysis of genomic features reveal new types of functional elements and large-scale epigenetic phenomena in the developing zebrafish
<jats:title>Abstract</jats:title><jats:p>Zebrafish, a popular model for embryonic development and for modelling human diseases, has so far lacked a systematic functional annotation programme akin to those in other animal models. To address this, we formed the international DANIO-CODE consortium and created the first central repository to store and process zebrafish developmental functional genomic data. Our Data Coordination Center (<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://danio-code.zfin.org">https://danio-code.zfin.org</jats:ext-link>) combines a total of 1,802 sets of unpublished and reanalysed published genomics data, which we used to improve existing annotations and show its utility in experimental design. We identified over 140,000 cis-regulatory elements in development, including novel classes with distinct features dependent on their activity in time and space. We delineated the distinction between regulatory elements active during zygotic genome activation and those active during organogenesis, identifying new aspects of how they relate to each other. Finally, we matched regulatory elements and epigenomic landscapes between zebrafish and mouse and predict functional relationships between them beyond sequence similarity, extending the utility of zebrafish developmental genomics to mammals.</jats:p>
Errington N, Iremonger J, Pickworth JA, et al., 2021, A diagnostic miRNA signature for pulmonary arterial hypertension using a consensus machine learning approach, EBioMedicine, Vol: 69, ISSN: 2352-3964
BACKGROUND: Pulmonary arterial hypertension (PAH) is a rare but life shortening disease, the diagnosis of which is often delayed, and requires an invasive right heart catheterisation. Identifying diagnostic biomarkers may improve screening to identify patients at risk of PAH earlier and provide new insights into disease pathogenesis. MicroRNAs are small, non-coding molecules of RNA, previously shown to be dysregulated in PAH, and contribute to the disease process in animal models. METHODS: Plasma from 64 treatment naïve patients with PAH and 43 disease and healthy controls were profiled for microRNA expression by Agilent Microarray. Following quality control and normalisation, the cohort was split into training and validation sets. Four separate machine learning feature selection methods were applied to the training set, along with a univariate analysis. FINDINGS: 20 microRNAs were identified as putative biomarkers by consensus feature selection from all four methods. Two microRNAs (miR-636 and miR-187-5p) were selected by all methods and used to predict PAH diagnosis with high accuracy. Integrating microRNA expression profiles with their associated target mRNA revealed 61 differentially expressed genes verified in two independent, publicly available PAH lung tissue data sets. Two of seven potentially novel gene targets were validated as differentially expressed in vitro in human pulmonary artery smooth muscle cells. INTERPRETATION: This consensus of multiple machine learning approaches identified two miRNAs that were able to distinguish PAH from both disease and healthy controls. These circulating miRNA, and their target genes may provide insight into PAH pathogenesis and reveal novel regulators of disease and putative drug targets.
Kaplan EH, Wang D, Wang M, et al., 2021, Aligning SARS-CoV-2 indicators via an epidemic model: application to hospital admissions and RNA detection in sewage sludge., Health Care Manag Sci, Vol: 24, Pages: 320-329
Ascertaining the state of coronavirus outbreaks is crucial for public health decision-making. Absent repeated representative viral test samples in the population, public health officials and researchers alike have relied on lagging indicators of infection to make inferences about the direction of the outbreak and attendant policy decisions. Recently researchers have shown that SARS-CoV-2 RNA can be detected in municipal sewage sludge with measured RNA concentrations rising and falling suggestively in the shape of an epidemic curve while providing an earlier signal of infection than hospital admissions data. The present paper presents a SARS-CoV-2 epidemic model to serve as a basis for estimating the incidence of infection, and shows mathematically how modeled transmission dynamics translate into infection indicators by incorporating probability distributions for indicator-specific time lags from infection. Hospital admissions and SARS-CoV-2 RNA in municipal sewage sludge are simultaneously modeled via maximum likelihood scaling to the underlying transmission model. The results demonstrate that both data series plausibly follow from the transmission model specified and provide a 95% confidence interval estimate of the reproductive number R0 ≈ 2.4 ± 0.2. Sensitivity analysis accounting for alternative lag distributions from infection until hospitalization and sludge RNA concentration respectively suggests that the detection of viral RNA in sewage sludge leads hospital admissions by 3 to 5 days on average. The analysis suggests that stay-at-home restrictions plausibly removed 89% of the population from the risk of infection with the remaining 11% exposed to an unmitigated outbreak that infected 9.3% of the total population.
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