Using systems biology approaches to elucidate gene regulatory networks controlling the plant defence response

Transcriptional regulation controlling pathogen-responsive gene expression in Arabidopsis is believed to underlie the plant defence response, which confers partial immunity of Arabidopsis to infection by Botrytis cinerea. In this thesis networks of transcriptional regulation mediating the defence response are studied in various ways. First transcriptional regulation was predicted for all genes differentially expressed during B. cinerea infection by development of a novel clustering approach, Temporal Clustering by Affinity Propagation (TCAP). This approach finds groups of genes whose expression profile time series have strong time-delayed correlation, a measure that is demonstrated to be more predictive of transcriptional regulation than conventionally used similarity measures. TCAP predicts the known regulation of GI by LHY, and co-clusters ORA59 and some of its downstream targets. Predicted novel regulators of pathogen-responsive gene expression were then studied in a reverse genetics screen, which discovered several novel but weakly altered susceptibility phenotypes. Comparison of predicted targets to known targets was complicated by the sparsity of mutant versus wildtype gene expression experiments performed during B. cinerea infections in the literature. To explore the context-dependence of transcriptional regulation, evidence of transcriptional regulation in different contexts was collected. This was compiled to generate a qualitative model of transcriptional regulation during the defence response. This model was validated and extended by experimental analysis of transcription factor-promoter binding in Yeast and transcriptional activation in planta. Comparative transcriptomics showed that downstream genes of some of these regulators | TGA3, ARF2, ERF1 and ANAC072 | are over-represented in the list of genes differentially expressed during B. cinerea infection, which is consistent with these targets being regulated by them during B. cinerea infection. Finally this qualitative model was used as prior information and was used along with gene expression time series to infer quantitative models of the gene regulatory network mediating the defence response. Some known regulation was predicted, and additionally ANAC055 was predicted to be a central regulator of pathogenresponsive gene expression

Caught in the act: Implications for the increasing abundance of mafic enclaves during the eruption of the Soufriere Hills Volcano, Montserrat

An exceptional opportunity to sample several large blocks sourced from the same region of the growing Soufrière Hills lava dome has documented a significant increase in the presence of mafic enclaves in the host andesite during the course of a long-lived eruptive episode with several phases. In 1997 (Phase I) mafic inclusions comprised ~1 volume percent of erupted material; in 2007 (Phase III) deposits their volumetric abundance increased to 5–7 percent. A broader range of geochemically distinctive types occurs amongst the 2007 enclaves. Crystal-poor enclaves generally have the least evolved (basaltic) compositions; porphyritic enclaves represent compositions intermediate between basaltic and andesitic compositions. The absence of porphyritic enclaves prior to Phase III magmatism at Soufrière Hills Volcano suggests that a mixing event occurred during the course of the current eruptive episode, providing direct evidence consistent with geophysical observations that the system is continuously re-invigorated from depth

Challenges and Pitfalls of Using Repeat Spirometry Recordings in Routine Primary Care Data to Measure FEV1 Decline in a COPD Population.

BACKGROUND: Electronic healthcare records (EHR) are increasingly used in epidemiological studies but are often viewed as lacking quality compared to randomised control trials and prospective cohorts. Studies of patients with chronic obstructive pulmonary disease (COPD) often use the rate of forced expiratory volume in 1 second (FEV1) decline as an outcome; however, its definition and robustness in EHR have not been investigated. We aimed to investigate how the rate of FEV1 decline differs by the criteria used in an EHR database. METHODS: Clinical Practice Research Datalink and Hospital Episode Statistics were used. Patient populations were defined using 8 sets of criteria around repeated FEV1 measurements. At a minimum, patients had a diagnosis of COPD, were ≥35 years old, were current or ex-smokers, and had data recorded from 2004. FEV1 measurements recorded during follow-up were identified. Thereafter, eight populations were defined based on criteria around: i) the exclusion of patients or individual measurements with potential measurement error; ii) minimum number of FEV1 measurements; iii) minimum time interval between measurements; iv) specific timing of measurements; v) minimum follow-up time; and vi) the use of linked data. For each population, the rate of FEV1 decline was estimated using mixed linear regression. RESULTS: For 7/8 patient populations, rates of FEV1 decline (age and sex adjusted) were similar and ranged from -18.7mL/year (95% CI -19.2 to -18.2) to -16.5mL/year (95% CI -17.3 to -15.7). Rates of FEV1 decline in populations that excluded patients with potential measurement error ranged from -79.4mL/year (95% CI -80.7 to -78.2) to -46.8mL/year (95% CI -47.6 to -46.0). CONCLUSION: FEV1 decline remained similar in a COPD population regardless of number of FEV1 measurements, time intervals between measurements, follow-up period, exclusion of specific FEV1 measurements, and linkage to HES. However, exclusion of individuals with questionable data led to selection bias and faster rates of decline

Prediction of five-year mortality after COPD diagnosis using primary care records

Accurate prognosis information after a diagnosis of chronic obstructive pulmonary disease (COPD) would facilitate earlier and better informed decisions about the use of prevention strategies and advanced care plans. We therefore aimed to develop and validate an accurate prognosis model for incident COPD cases using only information present in general practitioner (GP) records at the point of diagnosis. Incident COPD patients between 2004–2012 over the age of 35 were studied using records from 396 general practices in England. We developed a model to predict all-cause five-year mortality at the point of COPD diagnosis, using 47,964 English patients. Our model uses age, gender, smoking status, body mass index, forced expiratory volume in 1-second (FEV1) % predicted and 16 co-morbidities (the same number as the Charlson Co-morbidity Index). The performance of our chosen model was validated in all countries of the UK (N = 48,304). Our model performed well, and performed consistently in validation data. The validation area under the curves in each country varied between 0.783–0.809 and the calibration slopes between 0.911–1.04. Our model performed better in this context than models based on the Charlson Co-morbidity Index or Cambridge Multimorbidity Score. We have developed and validated a model that outperforms general multimorbidity scores at predicting five-year mortality after COPD diagnosis. Our model includes only data routinely collected before COPD diagnosis, allowing it to be readily translated into clinical practice, and has been made available through an online risk calculator (https://skiddle.shinyapps.io/incidentcopdsurvival/)

High-resolution temporal profiling of transcripts during Arabidopsis leaf senescence reveals a distinct chronology of processes and regulation

Leaf senescence is an essential developmental process that impacts dramatically on crop yields and involves altered regulation of thousands of genes and many metabolic and signaling pathways, resulting in major changes in the leaf. The regulation of senescence is complex, and although senescence regulatory genes have been characterized, there is little information on how these function in the global control of the process. We used microarray analysis to obtain a highresolution time-course profile of gene expression during development of a single leaf over a 3-week period to senescence. A complex experimental design approach and a combination of methods were used to extract high-quality replicated data and to identify differentially expressed genes. The multiple time points enable the use of highly informative clustering to reveal distinct time points at which signaling and metabolic pathways change. Analysis of motif enrichment, as well as comparison of transcription factor (TF) families showing altered expression over the time course, identify clear groups of TFs active at different stages of leaf development and senescence. These data enable connection of metabolic processes, signaling pathways, and specific TF activity, which will underpin the development of network models to elucidate the process of senescence

Inhaled corticosteroids and FEV 1 decline in chronic obstructive pulmonary disease: a systematic review

Abstract: Rate of FEV1 decline in COPD is heterogeneous and the extent to which inhaled corticosteroids (ICS) influence the rate of decline is unclear. The majority of previous reviews have investigated specific ICS and non-ICS inhalers and have consisted of randomised control trials (RCTs), which have specific inclusion and exclusion criteria and short follow up times. We aimed to investigate the association between change in FEV1 and ICS-containing medications in COPD patients over longer follow up times. MEDLINE and EMBASE were searched and literature comparing change in FEV1 in COPD patients taking ICS-containing medications with patients taking non-ICS-containing medications were identified. Titles, abstract, and full texts were screened and information extracted using the PICO checklist. Risk of bias was assessed using the Cochrane Risk of Bias tool and a descriptive synthesis of the literature was carried out due to high heterogeneity of included studies. Seventeen studies met our inclusion criteria. We found that the difference in change in FEV1 in people using ICS and non-ICS containing medications depended on the study follow-up time. Shorter follow-up studies (1 year or less) were more likely to report an increase in FEV1 from baseline in both patients on ICS and in patients on non-ICS-containing medications, with the majority of these studies showing a greater increase in FEV1 in patients on ICS-containing medications. Longer follow-up studies (greater than 1 year) were more likely to report a decline in FEV1 from baseline in patients on ICS and in patients on non-ICS containing medications but rates of FEV1 decline were similar. Further studies are needed to better understand changes in FEV1 when ICS-containing medications are prescribed and to determine whether ICS-containing medications influence rate of decline in FEV1 in the long term. Results from inclusive trials and observational patient cohorts may provide information more generalisable to a population of COPD patients

Genetic Risk as a Marker of Amyloid-β and Tau Burden in Cerebrospinal Fluid.

BACKGROUND: The search for a biomarker of Alzheimer's disease (AD) pathology (amyloid-β (Aβ) and tau) is ongoing, with the best markers currently being measurements of Aβ and tau in cerebrospinal fluid (CSF) and via positron emission tomography (PET) scanning. These methods are relatively invasive, costly, and often have high screening failure rates. Consequently, research is aiming to elucidate blood biomarkers of Aβ and tau. OBJECTIVE: This study aims to investigate a case/control polygenic risk score (PGRS) as a marker of tau and investigate blood markers of a combined Aβ and tau outcome for the first time. A sub-study also considers plasma tau as markers of Aβ and tau pathology in CSF. METHODS: We used data from the EDAR*, DESCRIPA**, and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts in a logistic regression analysis to investigate blood markers of Aβ and tau in CSF. In particular, we investigated the extent to which a case/control PGRS is predictive of CSF tau, CSF amyloid, and a combined amyloid and tau outcome. The predictive ability of models was compared to that of age, gender, and APOE genotype ('basic model'). RESULTS: In EDAR and DESCRIPA test data, inclusion of a case/control PGRS was no more predictive of Aβ, and a combined Aβ and tau endpoint than the basic models (accuracies of 66.0%, and 73.3% respectively). The tau model saw a small increase in accuracy compared to basic models (59.6%). ADNI 2 test data also showed a slight increase in accuracy for the Aβ model when compared to the basic models (61.4%). CONCLUSION: We see some evidence that a case/control PGRS is marginally more predictive of Aβ and tau pathology than the basic models. The search for predictive factors of Aβ and tau pathologies, above and beyond demographic information, is still ongoing. Better understanding of AD risk alleles, development of more sensitive assays, and studies of larger sample size are three avenues that may provide such factors. However, the clinical utility of possible predictors of brain Aβ and tau pathologies must also be investigated.*'Beta amyloid oligomers in the early diagnosis of AD and as marker for treatment response'**'Development of screening guidelines and criteria for pre-dementia Alzheimer's disease'.Multiple funders listed on paper