A comprehensive multi-omics analysis reveals unique signatures to predict Alzheimer's disease.

Background: Complex disorders, such as Alzheimer's disease (AD), result from the combined influence of multiple biological and environmental factors. The integration of high-throughput data from multiple omics platforms can provide system overviews, improving our understanding of complex biological processes underlying human disease. In this study, integrated data from four omics platforms were used to characterise biological signatures of AD. Method: The study cohort consists of 455 participants (Control:148, Cases:307) from the Religious Orders Study and Memory and Aging Project (ROSMAP). Genotype (SNP), methylation (CpG), RNA and proteomics data were collected, quality-controlled and pre-processed (SNP = 130; CpG = 83; RNA = 91; Proteomics = 119). Using a diagnosis of Mild Cognitive Impairment (MCI)/AD combined as the target phenotype, we first used Partial Least Squares Regression as an unsupervised classification framework to assess the prediction capabilities for each omics dataset individually. We then used a variation of the sparse generalized canonical correlation analysis (sGCCA) to assess predictions of the combined datasets and identify multi-omics signatures characterising each group of participants. Results: Analysing datasets individually we found methylation data provided the best predictions with an accuracy of 0.63 (95%CI = [0.54-0.71]), followed by RNA, 0.61 (95%CI = [0.52-0.69]), SNP, 0.59 (95%CI = [0.51-0.68]) and proteomics, 0.58 (95%CI = [0.51-0.67]). After integration of the four datasets, predictions were dramatically improved with a resulting accuracy of 0.95 (95% CI = [0.89-0.98]). Conclusion: The integration of data from multiple platforms is a powerful approach to explore biological systems and better characterise the biological signatures of AD. The results suggest that integrative methods can identify biomarker panels with improved predictive performance compared to individual platforms alone. Further validation in independent cohorts is required to validate and refine the results presented in this study.

Global gene expression profile of proliferative verrucous leukoplakia and its underlying biological disease mechanisms.

BACKGROUND: Proliferative verrucous leukoplakia (PVL) is a rare and enigmatic oral potentially malignant disorder which almost invariably results in oral squamous cell carcinoma (OSCC). The aims of this project were to use transcriptome profiling to characterise PVL gene expression patterns for biomarker identification and gain insight into the molecular aetiopathogenesis of PVL. METHODS: Forty-three oral cavity mucosal biopsies from 32 patients with oral lesions clinically compatible with either PVL or non-PVL conventional oral leukoplakia (OLK) underwent transcriptome profiling by RNA sequencing. Data was analysed by hierarchical clustering, differential gene expression, functional enrichment and network analysis, sparse partial least squares discriminant analysis sPLS-DA, and immune cell phenotypic estimation. RESULTS: We found 464 genes significantly differentially expressed at least 2-fold between PVL and non-PVL OLK (193 up and 271 down). HOX genes, including HOXA1 and HOXB7, keratin-associated proteins (KRTAPs) and olfactory receptor G proteins (OR) were significantly upregulated in PVL. Other upregulated genes in PVL included FOS, WNT16 and IFNA1. Pathway analysis showed that there was a significant downregulation of connective tissue signalling in PVL. Classifying multivariate models based upon 22 genes discriminated PVL from non-PVL OLK. Bioinformatic profiling showed that immune cell profiles in PVL and OLK were similar except that fibroblast markers were reduced in PVL. CONCLUSION: These results demonstrate that PVL and conventional OLK are molecularly distinct with upregulation of many cancer-associated genes. They provide insight into the pathogenesis of PVL and show that biomarker based molecular diagnostics is feasible to discriminate and inform diagnosis and management.

Utility of DNA Methylation as a Biomarker in Aging and Alzheimer's Disease.

Epigenetic mechanisms such as DNA methylation have been implicated in a number of diseases including cancer, heart disease, autoimmune disorders, and neurodegenerative diseases. While it is recognized that DNA methylation is tissue-specific, a limitation for many studies is the ability to sample the tissue of interest, which is why there is a need for a proxy tissue such as blood, that is reflective of the methylation state of the target tissue. In the last decade, DNA methylation has been utilized in the design of epigenetic clocks, which aim to predict an individual's biological age based on an algorithmically defined set of CpGs. A number of studies have found associations between disease and/or disease risk with increased biological age, adding weight to the theory of increased biological age being linked with disease processes. Hence, this review takes a closer look at the utility of DNA methylation as a biomarker in aging and disease, with a particular focus on Alzheimer's disease.

Comprehensive genetic analysis of the human lipidome identifies loci associated with lipid homeostasis with links to coronary artery disease.

We integrated lipidomics and genomics to unravel the genetic architecture of lipid metabolism and identify genetic variants associated with lipid species putatively in the mechanistic pathway for coronary artery disease (CAD). We quantified 596 lipid species in serum from 4,492 individuals from the Busselton Health Study. The discovery GWAS identified 3,361 independent lipid-loci associations, involving 667 genomic regions (479 previously unreported), with validation in two independent cohorts. A meta-analysis revealed an additional 70 independent genomic regions associated with lipid species. We identified 134 lipid endophenotypes for CAD associated with 186 genomic loci. Associations between independent lipid-loci with coronary atherosclerosis were assessed in ∼456,000 individuals from the UK Biobank. Of the 53 lipid-loci that showed evidence of association (P < 1 × 10-3), 43 loci were associated with at least one lipid endophenotype. These findings illustrate the value of integrative biology to investigate the aetiology of atherosclerosis and CAD, with implications for other complex diseases.

Non-Modifiable Factors as Moderators of the Relationship Between Physical Activity and Brain Volume: A Cross-Sectional UK Biobank Study.

BACKGROUND: Previous research suggests physical activity attenuates grey and white matter loss; however, there appears to be individual variability in this effect. Understanding factors that can influence the relationship between physical activity and brain volume may enable prediction of individual response. OBJECTIVE: The current study examined the relationship between objectively-measured physical activity and brain volume; and whether this relationship is moderated by age, sex, or a priori candidate genetic factors, brain-derived neurotrophic factor (BDNF) Val66Met, or apolipoprotein (APOE) ɛ4 allele carriage. METHODS: Data from 10,083 men and women (50 years and over) of the UK Biobank were used to examine the study objectives. All participants underwent a magnetic resonance imaging scan to quantify grey and white matter volumes, physical activity monitoring via actigraphy, and genotyping. RESULTS: Physical activity was associated with total grey matter volume, total white matter volume, and right hippocampal volume. Only males had an association between higher physical activity levels and greater cortical grey matter volume, total grey matter volume, and right hippocampal volume. Age moderated the relationship between physical activity and white matter volume. CONCLUSION: Our results indicate that in males, but not females, an association exists between objectively-measured physical activity and grey matter volume. Age may also play a role in impacting the relationship between physical activity and brain volume. Future research should evaluate longitudinal brain volumetrics to better understand the nature of age and sex-effects on the physical activity and brain volume relationship.

Assessment of a polygenic hazard score for the onset of pre-clinical Alzheimer's disease.

BACKGROUND: With a growing number of loci associated with late-onset (sporadic) Alzheimer's disease (AD), the polygenic contribution to AD is now well established. The development of polygenic risk score approaches have shown promising results for identifying individuals at higher risk of developing AD, thereby facilitating the development of preventative and therapeutic strategies. A polygenic hazard score (PHS) has been proposed to quantify age-specific genetic risk for AD. In this study, we assessed the predictive power and transferability of this PHS in an independent cohort, to support its clinical utility. RESULTS: Using genotype and imaging data from 780 individuals enrolled in the Australian Imaging, Biomarkers and Lifestyle (AIBL) study, we investigated associations between the PHS and several AD-related traits, including 1) cross-sectional Aß-amyloid (Aß) deposition, 2) longitudinal brain atrophy, 3) longitudinal cognitive decline, 4) age of onset. Except in the cognitive domain, we obtained results that were consistent with previously published findings. The PHS was associated with increased Aß burden, faster regional brain atrophy and an earlier age of onset. CONCLUSION: Overall, the results support the predictive power of a PHS, however, with only marginal improvement compared to apolipoprotein E alone.

Comprehensive analysis of epigenetic clocks reveals associations between disproportionate biological ageing and hippocampal volume.

The concept of age acceleration, the difference between biological age and chronological age, is of growing interest, particularly with respect to age-related disorders, such as Alzheimer's Disease (AD). Whilst studies have reported associations with AD risk and related phenotypes, there remains a lack of consensus on these associations. Here we aimed to comprehensively investigate the relationship between five recognised measures of age acceleration, based on DNA methylation patterns (DNAm age), and cross-sectional and longitudinal cognition and AD-related neuroimaging phenotypes (volumetric MRI and Amyloid-ß PET) in the Australian Imaging, Biomarkers and Lifestyle (AIBL) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). Significant associations were observed between age acceleration using the Hannum epigenetic clock and cross-sectional hippocampal volume in AIBL and replicated in ADNI. In AIBL, several other findings were observed cross-sectionally, including a significant association between hippocampal volume and the Hannum and Phenoage epigenetic clocks. Further, significant associations were also observed between hippocampal volume and the Zhang and Phenoage epigenetic clocks within Amyloid-ß positive individuals. However, these were not validated within the ADNI cohort. No associations between age acceleration and other Alzheimer's disease-related phenotypes, including measures of cognition or brain Amyloid-ß burden, were observed, and there was no association with longitudinal change in any phenotype. This study presents a link between age acceleration, as determined using DNA methylation, and hippocampal volume that was statistically significant across two highly characterised cohorts. The results presented in this study contribute to a growing literature that supports the role of epigenetic modifications in ageing and AD-related phenotypes.

A Targeted Association Study of Blood-Brain Barrier Gene SNPs and Brain Atrophy.

BACKGROUND: The blood-brain barrier (BBB) is formed by a high-density lining of endothelial cells, providing a border between circulating blood and the brain interstitial fluid. This structure plays a key role in protecting the brain microenvironment by restricting passage of certain molecules and circulating pathogens. OBJECTIVE: To identify associations between brain volumetric changes and a set of 355 BBB-related single nucleotide polymorphisms (SNP). METHOD: In a population of 721 unrelated individuals, linear mixed effect models were used to assess if specific variants were linked to regional rates of atrophy over a 12-year time span. Four brain regions were investigated, including cortical grey matter, cortical white matter, ventricle, and hippocampus. Further, we also investigated the potential impact of history of hypertension, diabetes, and the incidence of stroke on regional brain volume change. RESULTS: History of hypertension, diabetes, and stroke was not associated with longitudinal brain volume change. However, we identified a series of genetic variants associated with regional brain volume changes. The associations were independent of variation due to the APOEɛ4 allele and were significant post correction for multiple comparisons. CONCLUSION: This study suggests that key genes involved in the regulation of BBB integrity may be associated with longitudinal changes in specific brain regions. The derived polygenic risk scores indicate that these interactions are multigenic. Further research needs to be conducted to investigate how BBB functions maybe compromised by genetic variation.

Transcriptomic Biomarker Signatures for Discrimination of Oral Cancer Surgical Margins.

Relapse after surgery for oral squamous cell carcinoma (OSCC) contributes significantly to morbidity, mortality and poor outcomes. The current histopathological diagnostic techniques are insufficiently sensitive for the detection of oral cancer and minimal residual disease in surgical margins. We used whole-transcriptome gene expression and small noncoding RNA profiles from tumour, close margin and distant margin biopsies from 18 patients undergoing surgical resection for OSCC. By applying multivariate regression algorithms (sPLS-DA) suitable for higher dimension data, we objectively identified biomarker signatures for tumour and marginal tissue zones. We were able to define molecular signatures that discriminated tumours from the marginal zones and between the close and distant margins. These signatures included genes not previously associated with OSCC, such as MAMDC2, SYNPO2 and ARMH4. For discrimination of the normal and tumour sampling zones, we were able to derive an effective gene-based classifying model for molecular abnormality based on a panel of eight genes (MMP1, MMP12, MYO1B, TNFRSF12A, WDR66, LAMC2, SLC16A1 and PLAU). We demonstrated the classification performance of these gene signatures in an independent validation dataset of OSCC tumour and marginal gene expression profiles. These biomarker signatures may contribute to the earlier detection of tumour cells and complement existing surgical and histopathological techniques used to determine clear surgical margins.

Lichenoid dysplasia is not a distinct pathological entity.

OBJECTIVES: Oral Lichenoid Dysplasia (OLD) is a controversial histological term applied to lesions that display features of oral lichen planus (OLP) and oral epithelial dysplasia (OED). In this study we investigated the molecular profiles of OLD, OLP and OED to determine whether OLD exists as a distinct pathological entity. MATERIALS AND METHODS: Samples from patients presenting with lesions diagnosed histologically as OLP, OLD or OED underwent RNA sequencing followed by differential gene expression, functional enrichment and network analysis, sparse partial least squares discriminant analysis, and immune cell phenotypic estimation. RESULTS: Unsupervised clustering demonstrated a group of genes with high expression in OLP and OLD, and low expression in OED, predominantly involved in inflammatory processes. Many genes were significantly differentially expressed between either OLD or OLP and OED, but few between OLD and OLP. Functional enrichment showed significant pathways and ontologies related to inflammatory signalling and immune response between OLD or OLP and OED. Broad commonality was found between OLP and OLD in upregulation of specific immune system pathways. Classifying models discriminated histologically diagnosed OLD from OED based upon molecular data alone. Bioinformatic profiling showed that immune cell populations in OLP and OLD were consistent, and distinct from OED. CONCLUSION: Molecular data shows that OLD is not a distinct pathological entity. Its transcriptomic and immunophenotypic profile is similar to OLP and distinct from OED. We recommend that oral lichenoid dysplasia not be used as a distinct pathological entity. Our data further supports exclusion of dysplasia in diagnosis of OLP.

The Expansion and Diversification of Pentatricopeptide Repeat RNA-Editing Factors in Plants.

The RNA-binding pentatricopeptide repeat (PPR) family comprises hundreds to thousands of genes in most plants, but only a few dozen in algae, indicating massive gene expansions during land plant evolution. The nature and timing of these expansions has not been well defined due to the sparse sequence data available from early-diverging land plant lineages. In this study, we exploit the comprehensive OneKP datasets of over 1000 transcriptomes from diverse plants and algae toward establishing a clear picture of the evolution of this massive gene family, focusing on the proteins typically associated with RNA editing, which show the most spectacular variation in numbers and domain composition across the plant kingdom. We characterize over 2 250 000 PPR motifs in over 400 000 proteins. In lycophytes, polypod ferns, and hornworts, nearly 10% of expressed protein-coding genes encode putative PPR editing factors, whereas they are absent from algae and complex-thalloid liverworts. We show that rather than a single expansion, most land plant lineages with high numbers of editing factors have continued to generate novel sequence diversity. We identify sequence variations that imply functional differences between PPR proteins in seed plants versus non-seed plants and variations we propose to be linked to seed-plant-specific editing co-factors. Finally, using the sequence variations across the datasets, we develop a structural model of the catalytic DYW domain associated with C-to-U editing and identify a clade of unique DYW variants that are strong candidates as U-to-C RNA-editing factors, given their phylogenetic distribution and sequence characteristics.

Validation of a priori candidate Alzheimer's disease SNPs with brain amyloid-beta deposition.

The accumulation of brain amyloid ß (Aß) is one of the main pathological hallmarks of Alzheimer's disease (AD). However, the role of brain amyloid deposition in the development of AD and the genetic variants associated with this process remain unclear. In this study, we sought to identify associations between Aß deposition and an a priori evidence based set of 1610 genetic markers, genotyped from 505 unrelated individuals (258 Aß+ and 247 Aß-) enrolled in the Australian Imaging, Biomarker & Lifestyle (AIBL) study. We found statistically significant associations for 6 markers located within intronic regions of 6 genes, including AC103796.1-BDNF, PPP3R1, NGFR, KL, ABCA7 & CALHM1. Although functional studies are required to elucidate the role of these genes in the accumulation of Aß and their potential implication in AD pathophysiology, our findings are consistent with results obtained in previous GWAS efforts.

A flood of information: Using Sentinel-3 water colour products to assure continuity in the monitoring of water quality trends in the Great Barrier Reef (Australia).

An operational method to assess trends in marine water composition and ecosystem health during flood periods has been developed for the Great Barrier Reef (GBR), Queensland, Australia. This method integrates satellite water colour data with field water quality and ecosystem monitoring data and involves the classification of Moderate-Resolution Imaging Spectroradiometer (MODIS satellite) pixels into six distinct water bodies using a "wet season" colour scale developed specifically for the GBR. Using this information, several monitoring and reporting products have been derived and are operationally implemented into a long-term water quality monitoring program for the GBR. However, MODIS sensors are aging and a long-term monitoring solution is needed. This study reviewed the water colour monitoring products currently used in the GBR. It tested the feasibility to transition these methods from historical MODIS satellite imagery to the new Sentinel-3 satellite of the European Space Agency and from the wet season colour scale to the historical Forel-Ule colour scale, using a freely-distributed Forel Ule (FU) Satellite Toolbox. Monitoring products derived from both satellites and colour scales showed very similar patterns across two case study regions of the GBR, the Wet Tropics and Burdekin marine regions, over the 2017-18 wet season. The results obtained in this study highlighted the potential of using FU Sentinel-3 imagery for the mapping of GBR marine water bodies, including flood conditions. Furthermore, the operational monitoring products and frameworks developed for the GBR are likely to provide valuable foundations for analysis of FU Sentinel-3 data in the future. Such satellite water colour datasets and frameworks will be instrumental to better understand the impact of floods and reduced water clarity on marine ecosystems, as well as to support water quality management and facilitate catchment management policy in the GBR and worldwide.

Simulation of heterosis in a genome-scale metabolic network provides mechanistic explanations for increased biomass production rates in hybrid plants.

Heterosis, or hybrid vigour, is said to occur when F1 individuals exhibit increased performance for a number of traits compared to their parental lines. Improved traits can include increased size, better yield, faster development and a higher tolerance to pathogens or adverse conditions. The molecular basis for the phenomenon remains disputed, despite many decades of theorising and experimentation. In this study, we add a genetics layer to a constraint-based model of plant (Arabidopsis) primary metabolism and show that we can realistically reproduce and quantify heterosis in a highly complex trait (the rate of biomass production). The results demonstrate that additive effects coupled to the complex patterns of epistasis generated by a large metabolic network are sufficient to explain most or all the heterosis seen in typical F1 hybrids. Such models provide a simple approach to exploring and understanding heterosis and should assist in designing breeding strategies to exploit this phenomenon in the future.

DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays.

MOTIVATION: In the continuously expanding omics era, novel computational and statistical strategies are needed for data integration and identification of biomarkers and molecular signatures. We present Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO), a multi-omics integrative method that seeks for common information across different data types through the selection of a subset of molecular features, while discriminating between multiple phenotypic groups. RESULTS: Using simulations and benchmark multi-omics studies, we show that DIABLO identifies features with superior biological relevance compared with existing unsupervised integrative methods, while achieving predictive performance comparable to state-of-the-art supervised approaches. DIABLO is versatile, allowing for modular-based analyses and cross-over study designs. In two case studies, DIABLO identified both known and novel multi-omics biomarkers consisting of mRNAs, miRNAs, CpGs, proteins and metabolites. AVAILABILITY AND IMPLEMENTATION: DIABLO is implemented in the mixOmics R Bioconductor package with functions for parameters' choice and visualization to assist in the interpretation of the integrative analyses, along with tutorials on http://mixomics.org and in our Bioconductor vignette. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUBAcon: a consensus algorithm for unifying the subcellular localization data of the Arabidopsis proteome.

MOTIVATION: Knowing the subcellular location of proteins is critical for understanding their function and developing accurate networks representing eukaryotic biological processes. Many computational tools have been developed to predict proteome-wide subcellular location, and abundant experimental data from green fluorescent protein (GFP) tagging or mass spectrometry (MS) are available in the model plant, Arabidopsis. None of these approaches is error-free, and thus, results are often contradictory. RESULTS: To help unify these multiple data sources, we have developed the SUBcellular Arabidopsis consensus (SUBAcon) algorithm, a naive Bayes classifier that integrates 22 computational prediction algorithms, experimental GFP and MS localizations, protein-protein interaction and co-expression data to derive a consensus call and probability. SUBAcon classifies protein location in Arabidopsis more accurately than single predictors. AVAILABILITY: SUBAcon is a useful tool for recovering proteome-wide subcellular locations of Arabidopsis proteins and is displayed in the SUBA3 database (http://suba.plantenergy.uwa.edu.au). The source code and input data is available through the SUBA3 server (http://suba.plantenergy.uwa.edu.au//SUBAcon.html) and the Arabidopsis SUbproteome REference (ASURE) training set can be accessed using the ASURE web portal (http://suba.plantenergy.uwa.edu.au/ASURE).

Characterizing microbial communities and processes in a modern stromatolite (Shark Bay) using lipid biomarkers and two-dimensional distributions of porewater solutes.

Modern microbial mats are highly complex and dynamic ecosystems. Diffusive equilibration in thin films (DET) and diffusive gradients in thin films (DGT) samplers were deployed in a modern smooth microbial mat from Shark Bay in order to observe, for the first time, two-dimensional distributions of porewater solutes during day and night time. Two-dimensional sulfide and alkalinity distributions revealed a strong spatial heterogeneity and a minor contribution of sulfide to alkalinity. Phosphate distributions were also very heterogeneous, while iron(II) distributions were quite similar during day and night with a few hotspots of mobilization. Lipid biomarkers from the three successive layers of the mat were also analysed in order to characterize the microbial communities regulating analyte distributions. The major hydrocarbon products detected in all layers included n-alkanes and isoprenoids, whilst other important biomarkers included hopanoids. Phospholipid fatty acid profiles revealed a decrease in cyanobacterial markers with depth, whereas sulfate-reducing bacteria markers increased in abundance in accordance with rising sulfide concentrations with depth. Despite the general depth trends in community structure and physiochemical conditions within the mat, two-dimensional solute distributions showed considerable small-scale lateral variability, indicating that the distributions and activities of the microbial communities regulating these solute distributions were equally heterogeneous and complex.

SUBA3: a database for integrating experimentation and prediction to define the SUBcellular location of proteins in Arabidopsis.

The subcellular location database for Arabidopsis proteins (SUBA3, http://suba.plantenergy.uwa.edu.au) combines manual literature curation of large-scale subcellular proteomics, fluorescent protein visualization and protein-protein interaction (PPI) datasets with subcellular targeting calls from 22 prediction programs. More than 14 500 new experimental locations have been added since its first release in 2007. Overall, nearly 650 000 new calls of subcellular location for 35 388 non-redundant Arabidopsis proteins are included (almost six times the information in the previous SUBA version). A re-designed interface makes the SUBA3 site more intuitive and easier to use than earlier versions and provides powerful options to search for PPIs within the context of cell compartmentation. SUBA3 also includes detailed localization information for reference organelle datasets and incorporates green fluorescent protein (GFP) images for many proteins. To determine as objectively as possible where a particular protein is located, we have developed SUBAcon, a Bayesian approach that incorporates experimental localization and targeting prediction data to best estimate a protein's location in the cell. The probabilities of subcellular location for each protein are provided and displayed as a pictographic heat map of a plant cell in SUBA3.