The advent of high throughput technologies has led to a wealth of publicly available ‘omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions, but mainly for a single type of ‘omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce , an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a systems biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous ‘omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple ‘omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest integrative frameworks for the multivariate analyses of ‘omics data available from the package.

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.

The advent of high throughput technologies has led to a wealth of publicly available 'omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such largescale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a 'molecular signature') to explain or predict biological conditions, but mainly for a single type of 'omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a system biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous 'omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis,

Alzheimer's disease (AD) is characterized by both amyloid and Tau pathologies. The amyloid component and altered cholesterol metabolism are closely linked, but the relationship between Tau pathology and cholesterol is currently unclear. Brain cholesterol is synthesized in situ and cannot cross the blood-brain barrier: to be exported from the central nervous system into the blood circuit, excess cholesterol must be converted to 24S-hydroxycholesterol by the cholesterol 24-hydroxylase encoded by the CYP46A1 gene. In AD patients, the concentration of 24S-hydroxycholesterol in the plasma and the cerebrospinal fluid are lower than in healthy controls. The THY-Tau22 mouse is a model of AD-like Tau pathology without amyloid pathology. We used this model to investigate the potential association between Tau pathology and CYP46A1 modulation. The amounts of CYP46A1 and 24S-hydroxycholesterol in the hippocampus were lower in THY-Tau22 than control mice. We used an adeno-associated virus (AAV) gene transfer strategy to increase CYP46A1 expression in order to investigate the consequences on THY-Tau22 mouse phenotype. Injection of the AAV-CYP46A1 vector into the hippocampus of THY-Tau22 mice led to CYP46A1 and 24S-hydroxycholesterol content normalization. The cognitive deficits, impaired long-term depression and spine defects that characterize the THY-Tau22 model were completely rescued, whereas Tau hyperphosphorylation and associated gliosis were unaffected. These results argue for a causal link between CYP46A1 protein content and memory impairments that result from Tau pathology. Therefore, CYP46A1 may be a relevant therapeutic target for Tauopathies and especially for AD.

We report an integrated pipeline for efficient serum glycoprotein biomarker candidate discovery and qualification that may be used to facilitate cancer diagnosis and management. The discovery phase used semi-automated lectin magnetic bead array (LeMBA)-coupled tandem mass spectrometry with a dedicated data-housing and analysis pipeline; GlycoSelector (http://glycoselector.di. uq.edu.au). The qualification phase used lectin magnetic bead array-multiple reaction monitoring-mass spectrometry incorporating an interactive web-interface, Shiny mixOmics (http://mixomics-projects.di.uq.edu.au/Shiny), for univariate and multivariate statistical analysis. Relative quantitation was performed by referencing to a spiked-in glycoprotein, chicken ovalbumin. We applied this workflow to identify diagnostic biomarkers for esophageal adenocarcinoma (EAC), a life threatening malignancy with poor prognosis in the advanced setting. EAC develops from metaplastic condition Barrett's esophagus (BE). Currently diagnosis and monitoring of at-risk patients is through endoscopy and biopsy, which is expensive and requires hospital admission. Hence there is a clinical need for a noninvasive diagnostic biomarker of EAC. In total 89 patient samples from healthy controls, and patients with BE or EAC were screened in discovery and qualification stages. Of the 246 glycoforms measured in the qualification stage, 40 glycoforms (as measured by lectin affinity) qualified as candidate serum markers. The top candidate for distinguishing healthy from BE patients' group was Narcissus pseudonarcissus lectin (

Angiogenesis is the development of a novel vascular network from a pre-existing structure. Blocking angiogenesis is an attractive strategy to inhibit tumor growth and metastasis formation. Based on structural and mutagenesis data, we have developed novel cyclic peptides that mimic, simultaneously, two regions of the VEGF crucial for the interaction with the VEGF receptors. The peptides, displaying the best affinity for VEGF receptor 1 on a competition assay, inhibited endothelial cell transduction pathway, migration, and capillary-like tubes formation. The specificity of these peptides for VEGF receptors was demonstrated by microscopy using a fluorescent peptide derivative. The resolution of the structure of some cyclic peptides by NMR and molecular modeling has allowed the identification of various factors accounting for their inhibitory activity. Taken together, these results validate the selection of these two regions as targets to develop molecules able to disturb the development of cancer and angiogenesis-associated diseases.

No treatment is available for early-onset forms of metachromatic leukodystrophy (MLD), a lysosomal storage disease caused by autosomal recessive defect in arylsulfatase A (ARSA) gene causing severe demyelination in central and peripheral nervous systems. We have developed a gene therapy approach, based on intracerebral administration of AAVrh.10-hARSA vector, coding for human ARSA enzyme. We have previously demonstrated potency of this approach in MLD mice lacking ARSA expression. We describe herein the preclinical efficacy, safety, and biodistribution profile of intracerebral administration of AAVrh.10-hARSA to nonhuman primates (NHPs). NHPs received either the dose planned for patients adjusted to the brain volume ratio between child and NHP (1×dose, 1.1×10(11) vg/hemisphere, unilateral or bilateral injection) or 5-fold this dose (5×dose, 5.5×10(11) vg/hemisphere, bilateral injection). NHPs were subjected to clinical, biological, and brain imaging observations and were euthanized 7 or 90 days after injection. There was no toxicity based on clinical and biological parameters, nor treatment-related histological findings in peripheral organs. A neuroinflammatory process correlating with brain MRI T2 hypersignals was observed in the brain 90 days after administration of the 5×dose, but was absent or minimal after administration of the 1×dose. Antibody response to AAVrh.10 and hARSA was detected, without correlation with brain lesions. After injection of the 1×dose, AAVrh.10-hARSA vector was detected in a large part of the injected hemisphere, while ARSA activity exceeded the normal endogenous activity level by 14-31%. Consistently with other reports, vector genome was detected in off-target organs such as liver, spleen, lymph nodes, or blood, but not in gonads. Importantly, AAVrh.10-hARSA vector was no longer detectable in urine at day 7. Our data demonstrate requisite safe and effective profile for intracerebral AAVrh.10-hARSA delivery in NHPs, supporting its clinical use in children affected with MLD.

DWI is informative for diagnosis of AS and nr-axSpA, and has moderate utility in assessment of disease activity or treatment response, with performance similar to that of the SPARCC MRI score.