Carlos Riveros

The multiple sclerosis whole blood mRNA transcriptome and genetic associations indicate dysregulation of specific T cell pathways in pathogenesis

Multiple sclerosis (MS) is an autoimmune disease with a genetic component, caused at least in part by aberrant lymphocyte activity. The whole blood mRNA transcriptome was measured for 99 untreated MS patients: 43 primary progressive MS, 20 secondary progressive MS, 36 relapsing remitting MS and 45 age-matched healthy controls. The ANZgene Multiple Sclerosis Genetics Consortium genotyped more than 300 000 SNPs for 115 of these samples. Transcription from genes on translational regulation, oxidative phosphorylation, immune synapse and antigen presentation pathways was markedly increased in all forms of MS. Expression of genes tagging T cells was also upregulated (P < 10(-12)) in MS. A T cell gene signature predicts disease state with a concordance index of 0.79 with age and gender as co-variables, but the signature is not associated with clinical course or disability. The ANZgene genome wide association screen identified two novel regions with genome wide significance: one encoding the T cell co-stimulatory molecule, CD40; the other a region on chromosome 12q13-14. The CD40 haplotype associated with increased MS susceptibility has decreased gene expression in MS (P < 0.0007). The second MS susceptibility region includes 17 genes on 12q13-14 in tight linkage disequilibrium. Of these, only 13 are expressed in leukocytes, and of these the expression of one, FAM119B, is much lower in the susceptibility haplotype (P < 10(-14)). Overall, these data indicate dysregulation of T cells can be detected in the whole blood of untreated MS patients, and supports targeting of activated T cells in therapy for all forms of MS.

A Transcription Factor Map as Revealed by a Genome-Wide Gene Expression Analysis of Whole-Blood mRNA Transcriptome in Multiple Sclerosis

BACKGROUND Several lines of evidence suggest that transcription factors are involved in the pathogenesis of Multiple Sclerosis (MS) but complete mapping of the whole network has been elusive. One of the reasons is that there are several clinical subtypes of MS and transcription factors that may be involved in one subtype may not be in others. We investigate the possibility that this network could be mapped using microarray technologies and contemporary bioinformatics methods on a dataset derived from whole blood in 99 untreated MS patients (36 Relapse Remitting MS, 43 Primary Progressive MS, and 20 Secondary Progressive MS) and 45 age-matched healthy controls. METHODOLOGY/PRINCIPAL FINDINGS We have used two different analytical methodologies: a non-standard differential expression analysis and a differential co-expression analysis, which have converged on a significant number of regulatory motifs that are statistically overrepresented in genes that are either differentially expressed (or differentially co-expressed) in cases and controls (e.g., V

Blood metabolite markers of preclinical Alzheimer's disease in two longitudinally followed cohorts of older individuals.

KROX_Q6, p-value <3.31E-6; V

GPU-FS-kNN: A Software Tool for Fast and Scalable kNN Computation Using GPUs

CREBP1_Q2, p-value <9.93E-6, V

Switch and Tap-Changer Reconfiguration of Distribution Networks Using Evolutionary Algorithms

YY1_02, p-value <1.65E-5). CONCLUSIONS/SIGNIFICANCE Our analysis uncovered a network of transcription factors that potentially dysregulate several genes in MS or one or more of its disease subtypes. The most significant transcription factor motifs were for the Early Growth Response EGR/KROX family, ATF2, YY1 (Yin and Yang 1), E2F-1/DP-1 and E2F-4/DP-2 heterodimers, SOX5, and CREB and ATF families. These transcription factors are involved in early T-lymphocyte specification and commitment as well as in oligodendrocyte dedifferentiation and development, both pathways that have significant biological plausibility in MS causation.

Brain transcriptome perturbations in the Hfe−/− mouse model of genetic iron loading

Recently, quantitative metabolomics identified a panel of 10 plasma lipids that were highly predictive of conversion to Alzheimers disease (AD) in cognitively normal older individuals (n = 28, area under the curve [AUC] = 0.92, sensitivity/specificity of 90%/90%).

Identification of Differentially Expressed Genes through Integrated Study of Alzheimer’s Disease Affected Brain Regions

Background The analysis of biological networks has become a major challenge due to the recent development of high-throughput techniques that are rapidly producing very large data sets. The exploding volumes of biological data are craving for extreme computational power and special computing facilities (i.e. super-computers). An inexpensive solution, such as General Purpose computation based on Graphics Processing Units (GPGPU), can be adapted to tackle this challenge, but the limitation of the device internal memory can pose a new problem of scalability. An efficient data and computational parallelism with partitioning is required to provide a fast and scalable solution to this problem. Results We propose an efficient parallel formulation of the k-Nearest Neighbour (kNN) search problem, which is a popular method for classifying objects in several fields of research, such as pattern recognition, machine learning and bioinformatics. Being very simple and straightforward, the performance of the kNN search degrades dramatically for large data sets, since the task is computationally intensive. The proposed approach is not only fast but also scalable to large-scale instances. Based on our approach, we implemented a software tool GPU-FS-kNN (GPU-based Fast and Scalable k-Nearest Neighbour) for CUDA enabled GPUs. The basic approach is simple and adaptable to other available GPU architectures. We observed speed-ups of 50–60 times compared with CPU implementation on a well-known breast microarray study and its associated data sets. Conclusion Our GPU-based Fast and Scalable k-Nearest Neighbour search technique (GPU-FS-kNN) provides a significant performance improvement for nearest neighbour computation in large-scale networks. Source code and the software tool is available under GNU Public License (GPL) at https://sourceforge.net/p/gpufsknn/.

kNN-MST-Agglomerative: A fast and scalable graph-based data clustering approach on GPU

The reconfiguration of distribution networks is an important combinatorial problem. This work addresses the particular case of reconfiguration after an outage caused by the loss of a single branch of the network. The reconfiguration is carried out over two domains simultaneously: re-switching strategies and transformer tap-changer adjustments. The approach was tested using a real large-scale network within the concession area of Energy Australia. The model considers four operational elements: an AC power flow model, the networks radial topology when operating, voltage limits and load limits. Two evolutionary algorithms were implemented and tested. The first was a genetic algorithm, applied over the space of possible re-switching strategies, and for both re-switching and tap-changer adjustments, simultaneously. The second was a memetic algorithm, applied over the same two variations of the reconfiguration problem. Computational tests consider the evaluation of the loss of every branch, reporting the number of buses affected, and the number of overloaded branches after the reconfiguration.

The Discovery of Novel Biomarkers Improves Breast Cancer Intrinsic Subtype Prediction and Reconciles the Labels in the METABRIC Data Set

Severe disruption of brain iron homeostasis can cause fatal neurodegenerative disease, however debate surrounds the neurologic effects of milder, more common iron loading disorders such as hereditary hemochromatosis, which is usually caused by loss-of-function polymorphisms in the HFE gene. There is evidence from both human and animal studies that HFE gene variants may affect brain function and modify risks of brain disease. To investigate how disruption of HFE influences brain transcript levels, we used microarray and real-time reverse transcription polymerase chain reaction to assess the brain transcriptome in Hfe(-/-) mice relative to wildtype AKR controls (age 10 weeks, n≥4/group). The Hfe(-/-) mouse brain showed numerous significant changes in transcript levels (p<0.05) although few of these related to proteins directly involved in iron homeostasis. There were robust changes of at least 2-fold in levels of transcripts for prominent genes relating to transcriptional regulation (FBJ osteosarcoma oncogene Fos, early growth response genes), neurotransmission (glutamate NMDA receptor Grin1, GABA receptor Gabbr1) and synaptic plasticity and memory (calcium/calmodulin-dependent protein kinase IIα Camk2a). As previously reported for dietary iron-supplemented mice, there were altered levels of transcripts for genes linked to neuronal ceroid lipofuscinosis, a disease characterized by excessive lipofuscin deposition. Labile iron is known to enhance lipofuscin generation which may accelerate brain aging. The findings provide evidence that iron loading disorders can considerably perturb levels of transcripts for genes essential for normal brain function and may help explain some of the neurologic signs and symptoms reported in hemochromatosis patients.

The Gene Interaction Miner

Background Alzheimer’s disease (AD) is the most common form of dementia in older adults that damages the brain and results in impaired memory, thinking and behaviour. The identification of differentially expressed genes and related pathways among affected brain regions can provide more information on the mechanisms of AD. In the past decade, several studies have reported many genes that are associated with AD. This wealth of information has become difficult to follow and interpret as most of the results are conflicting. In that case, it is worth doing an integrated study of multiple datasets that helps to increase the total number of samples and the statistical power in detecting biomarkers. In this study, we present an integrated analysis of five different brain region datasets and introduce new genes that warrant further investigation. Methods The aim of our study is to apply a novel combinatorial optimisation based meta-analysis approach to identify differentially expressed genes that are associated to AD across brain regions. In this study, microarray gene expression data from 161 samples (74 non-demented controls, 87 AD) from the Entorhinal Cortex (EC), Hippocampus (HIP), Middle temporal gyrus (MTG), Posterior cingulate cortex (PC), Superior frontal gyrus (SFG) and visual cortex (VCX) brain regions were integrated and analysed using our method. The results are then compared to two popular meta-analysis methods, RankProd and GeneMeta, and to what can be obtained by analysing the individual datasets. Results We find genes related with AD that are consistent with existing studies, and new candidate genes not previously related with AD. Our study confirms the up-regualtion of INFAR2 and PTMA along with the down regulation of GPHN, RAB2A, PSMD14 and FGF. Novel genes PSMB2, WNK1, RPL15, SEMA4C, RWDD2A and LARGE are found to be differentially expressed across all brain regions. Further investigation on these genes may provide new insights into the development of AD. In addition, we identified the presence of 23 non-coding features, including four miRNA precursors (miR-7, miR570, miR-1229 and miR-6821), dysregulated across the brain regions. Furthermore, we compared our results with two popular meta-analysis methods RankProd and GeneMeta to validate our findings and performed a sensitivity analysis by removing one dataset at a time to assess the robustness of our results. These new findings may provide new insights into the disease mechanisms and thus make a significant contribution in the near future towards understanding, prevention and cure of AD.