Steven John Kiddle

High-Resolution Temporal Profiling of Transcripts during Arabidopsis Leaf Senescence Reveals a Distinct Chronology of Processes and Regulation

This work presents a high-resolution time-course analysis of gene expression during development of a leaf from expansion through senescence. Enrichment in ontologies, sequence motifs, and transcription factor families within genes showing altered expression over time identified both metabolic pathways and potential regulators active at different stages of leaf development and senescence. 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 high-resolution 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.

Arabidopsis defense against Botrytis cinerea: chronology and regulation deciphered by high-resolution temporal transcriptomic analysis

The authors generated a high-resolution time series of Arabidopsis thaliana gene expression following infection with the fungal pathogen Botrytis cinerea. Computational analysis of this large data set identified the timing of specific processes and regulatory events in the host plant and showed a role for the transcription factor TGA3 in the defense response against the fungal pathogen. Transcriptional reprogramming forms a major part of a plant’s response to pathogen infection. Many individual components and pathways operating during plant defense have been identified, but our knowledge of how these different components interact is still rudimentary. We generated a high-resolution time series of gene expression profiles from a single Arabidopsis thaliana leaf during infection by the necrotrophic fungal pathogen Botrytis cinerea. Approximately one-third of the Arabidopsis genome is differentially expressed during the first 48 h after infection, with the majority of changes in gene expression occurring before significant lesion development. We used computational tools to obtain a detailed chronology of the defense response against B. cinerea, highlighting the times at which signaling and metabolic processes change, and identify transcription factor families operating at different times after infection. Motif enrichment and network inference predicted regulatory interactions, and testing of one such prediction identified a role for TGA3 in defense against necrotrophic pathogens. These data provide an unprecedented level of detail about transcriptional changes during a defense response and are suited to systems biology analyses to generate predictive models of the gene regulatory networks mediating the Arabidopsis response to B. cinerea.

Candidate Blood Proteome Markers of Alzheimer's Disease Onset and Progression: A Systematic Review and Replication Study

A blood-based protein biomarker, or set of protein biomarkers, that could predict onset and progression of Alzheimers disease (AD) would have great utility; potentially clinically, but also for clinical trials and especially in the selection of subjects for preventative trials. We reviewed a comprehensive list of 21 published discovery or panel-based (> 100 proteins) blood proteomics studies of AD, which had identified a total of 163 candidate biomarkers. Few putative blood-based protein biomarkers replicate in independent studies but we found that some proteins do appear in multiple studies; for example, four candidate biomarkers are found to associate with AD-related phenotypes in five independent research cohorts in these 21 studies: α-1-antitrypsin, α-2-macroglobulin, apolipoprotein E, and complement C3. Using SomaLogics SOMAscan proteomics technology, we were able to conduct a large-scale replication study for 94 of the 163 candidate biomarkers from these 21 published studies in plasma samples from 677 subjects from the AddNeuroMed (ANM) and the Alzheimers Research UK/Maudsley BRC Dementia Case Registry at Kings Health Partners (ARUK/DCR) research cohorts. Nine of the 94 previously reported candidates were found to associate with AD-related phenotypes (False Discovery Rate (FDR) q-value < 0.1). These proteins show sufficient replication to be considered for further investigation as a biomarker set. Overall, we show that there are some signs of a replicable signal in the range of proteins identified in previous studies and we are able to further replicate some of these. This suggests that AD pathology does affect the blood proteome with some consistency.

Alzheimer's disease biomarker discovery using SOMAscan multiplexed protein technology

Blood proteins and their complexes have become the focus of a great deal of interest in the context of their potential as biomarkers of Alzheimers disease (AD). We used a SOMAscan assay for quantifying 1001 proteins in blood samples from 331 AD, 211 controls, and 149 mild cognitive impaired (MCI) subjects. The strongest associations of protein levels with AD outcomes were prostate‐specific antigen complexed to α1‐antichymotrypsin (AD diagnosis), pancreatic prohormone (AD diagnosis, left entorhinal cortex atrophy, and left hippocampus atrophy), clusterin (rate of cognitive decline), and fetuin B (left entorhinal atrophy). Multivariate analysis found that a subset of 13 proteins predicted AD with an accuracy of area under the curve of 0.70. Our replication of previous findings provides further evidence that levels of these proteins in plasma are truly associated with AD. The newly identified proteins could be potential biomarkers and are worthy of further investigation.

Plasma based markers of [11C] PiB-PET brain amyloid burden.

Changes in brain amyloid burden have been shown to relate to Alzheimers disease pathology, and are believed to precede the development of cognitive decline. There is thus a need for inexpensive and non-invasive screening methods that are able to accurately estimate brain amyloid burden as a marker of Alzheimers disease. One potential method would involve using demographic information and measurements on plasma samples to establish biomarkers of brain amyloid burden; in this study data from the Alzheimers Disease Neuroimaging Initiative was used to explore this possibility. Sixteen of the analytes on the Rules Based Medicine Human Discovery Multi-Analyte Profile 1.0 panel were found to associate with [11C]-PiB PET measurements. Some of these markers of brain amyloid burden were also found to associate with other AD related phenotypes. Thirteen of these markers of brain amyloid burden – c-peptide, fibrinogen, alpha-1-antitrypsin, pancreatic polypeptide, complement C3, vitronectin, cortisol, AXL receptor kinase, interleukin-3, interleukin-13, matrix metalloproteinase-9 total, apolipoprotein E and immunoglobulin E – were used along with co-variates in multiple linear regression, and were shown by cross-validation to explain >30% of the variance of brain amyloid burden. When a threshold was used to classify subjects as PiB positive, the regression model was found to predict actual PiB positive individuals with a sensitivity of 0.918 and a specificity of 0.545. The number of APOE ϵ 4 alleles and plasma apolipoprotein E level were found to contribute most to this model, and the relationship between these variables and brain amyloid burden was explored.

Temporal clustering by affinity propagation reveals transcriptional modules in Arabidopsis thaliana

MOTIVATION Identifying regulatory modules is an important task in the exploratory analysis of gene expression time series data. Clustering algorithms are often used for this purpose. However, gene regulatory events may induce complex temporal features in a gene expression profile, including time delays, inversions and transient correlations, which are not well accounted for by current clustering methods. As the cost of microarray experiments continues to fall, the temporal resolution of time course studies is increasing. This has led to a need to take account of detailed temporal features of this kind. Thus, while standard clustering methods are both widely used and much studied, their shared shortcomings with respect to such temporal features motivates the work presented here. RESULTS Here, we introduce a temporal clustering approach for high-dimensional gene expression data which takes account of time delays, inversions and transient correlations. We do so by exploiting a recently introduced, message-passing-based algorithm called Affinity Propagation (AP). We take account of temporal features of interest following an approximate but efficient dynamic programming approach due to Qian et al. The resulting approach is demonstrably effective in its ability to discern non-obvious temporal features, yet efficient and robust enough for routine use as an exploratory tool. We show results on validated transcription factor-target pairs in yeast and on gene expression data from a study of Arabidopsis thaliana under pathogen infection. The latter reveals a number of biologically striking findings. AVAILABILITY Matlab code for our method is available at http://www.wsbc.warwick.ac.uk/stevenkiddle/tcap.html.

Circulating Proteomic Signatures of Chronological Age

To elucidate the proteomic features of aging in plasma, the subproteome targeted by the SOMAscan assay was profiled in blood samples from 202 females from the TwinsUK cohort. Findings were replicated in 677 independent individuals from the AddNeuroMed, Alzheimer’s Research UK, and Dementia Case Registry cohorts. Results were further validated using RNAseq data from whole blood in TwinsUK and the most significant proteins were tested for association with aging-related phenotypes after adjustment for age. Eleven proteins were associated with chronological age and were replicated at protein level in an independent population. These were further investigated at gene expression level in 384 females from the TwinsUK cohort. The two most strongly associated proteins were chordin-like protein 1 (meta-analysis β [SE] = 0.013 [0.001], p = 3.66 × 10−46) and pleiotrophin (0.012 [0.005], p = 3.88 × 10−41). Chordin-like protein 1 was also significantly correlated with birthweight (0.06 [0.02], p = 0.005) and with the individual Framingham 10-years cardiovascular risk scores in TwinsUK (0.71 [0.18], p = 9.9 × 10−5). Pleiotrophin is a secreted growth factor with a plethora of functions in multiple tissues and known to be a marker for cardiovascular risk and osteoporosis. Our study highlights the importance of proteomics to identify some molecular mechanisms involved in human health and aging.

Are Blood-Based Protein Biomarkers for Alzheimer's Disease also Involved in Other Brain Disorders?: A Systematic Review

BACKGROUND Alzheimers disease (AD) biomarkers are urgently needed for both early and accurate diagnosis and prediction of disease progression. Past research has studied blood-based proteins as potential AD biomarkers, revealing many candidate proteins. To date only limited effort has been made to investigate the disease specificity of AD candidate proteins and whether these proteins are also involved in other neurodegenerative or psychiatric conditions. OBJECTIVE This review seeks to determine if blood-based AD candidate protein biomarkers are disease specific. METHODS A two-stage systematic literature search was conducted. Firstly, the most consistently identified AD protein biomarkers in blood were determined from a list of published discovery or panel-based (>100 proteins) blood proteomics studies in AD. Secondly, an online database search was conducted using the 10 most consistently identified proteins to determine if they were involved in other brain disorders, namely frontotemporal lobe dementia, vascular dementia, Lewy body disease, Parkinsons disease, schizophrenia, depression, and autism. RESULTS Among the reviewed candidate proteins, plasma protease C1 inhibitor, pancreatic prohormone, and fibrinogen γ chain were found to have the least evidence for non-specificity to AD. All other candidates were found to be affected by other brain disorders. CONCLUSION Since we found evidence that the majority of AD candidate proteins might also be involved in other brain disorders, more research into the disease specificity of AD protein biomarkers is required.

Blood protein predictors of brain amyloid for enrichment in clinical trials

Measures of neocortical amyloid burden (NAB) identify individuals who are at substantially greater risk of developing Alzheimers disease (AD). Blood‐based biomarkers predicting NAB would have great utility for the enrichment of AD clinical trials, including large‐scale prevention trials.

Wigwams: identifying gene modules co-regulated across multiple biological conditions

Motivation: Identification of modules of co-regulated genes is a crucial first step towards dissecting the regulatory circuitry underlying biological processes. Co-regulated genes are likely to reveal themselves by showing tight co-expression, e.g. high correlation of expression profiles across multiple time series datasets. However, numbers of up- or downregulated genes are often large, making it difficult to discriminate between dependent co-expression resulting from co-regulation and independent co-expression. Furthermore, modules of co-regulated genes may only show tight co-expression across a subset of the time series, i.e. show condition-dependent regulation. Results: Wigwams is a simple and efficient method to identify gene modules showing evidence for co-regulation in multiple time series of gene expression data. Wigwams analyzes similarities of gene expression patterns within each time series (condition) and directly tests the dependence or independence of these across different conditions. The expression pattern of each gene in each subset of conditions is tested statistically as a potential signature of a condition-dependent regulatory mechanism regulating multiple genes. Wigwams does not require particular time points and can process datasets that are on different time scales. Differential expression relative to control conditions can be taken into account. The output is succinct and non-redundant, enabling gene network reconstruction to be focused on those gene modules and combinations of conditions that show evidence for shared regulatory mechanisms. Wigwams was run using six Arabidopsis time series expression datasets, producing a set of biologically significant modules spanning different combinations of conditions. Availability and implementation: A Matlab implementation of Wigwams, complete with graphical user interfaces and documentation, is available at: warwick.ac.uk/wigwams. Contact: k.j.denby@warwick.ac.uk Supplementary Data: Supplementary data are available at Bioinformatics online.