Noha A. Yousri

Comprehensive transcriptomic and proteomic characterization of human mesenchymal stem cells reveals source specific cellular markers

Mesenchymal stem cells (MSC) are multipotent cells with great potential in therapy, reflected by more than 500 MSC-based clinical trials registered with the NIH. MSC are derived from multiple tissues but require invasive harvesting and imply donor-to-donor variability. Embryonic stem cell-derived MSC (ESC-MSC) may provide an alternative, but how similar they are to ex vivo MSC is unknown. Here we performed an in depth characterization of human ESC-MSC, comparing them to human bone marrow-derived MSC (BM-MSC) as well as human embryonic stem cells (hESC) by transcriptomics (RNA-seq) and quantitative proteomics (nanoLC-MS/MS using SILAC). Data integration highlighted and validated a central role of vesicle-mediated transport and exosomes in MSC biology and also demonstrated, through enrichment analysis, their versatility and broad application potential. Particular emphasis was placed on comparing profiles between ESC-MSC and BM-MSC and assessing their equivalency. Data presented here shows that differences between ESC-MSC and BM-MSC are similar in magnitude to those reported for MSC of different origin and the former may thus represent an alternative source for therapeutic applications. Finally, we report an unprecedented coverage of MSC CD markers, as well as membrane associated proteins which may benefit immunofluorescence-based applications and contribute to a refined molecular description of MSC.

A systems view of type 2 diabetes-associated metabolic perturbations in saliva, blood and urine at different timescales of glycaemic control

Aims/hypothesisMetabolomics has opened new avenues for studying metabolic alterations in type 2 diabetes. While many urine and blood metabolites have been associated individually with diabetes, a complete systems view analysis of metabolic dysregulations across multiple biofluids and over varying timescales of glycaemic control is still lacking.MethodsHere we report a broad metabolomics study in a clinical setting, covering 2,178 metabolite measures in saliva, blood plasma and urine from 188 individuals with diabetes and 181 controls of Arab and Asian descent. Using multivariate linear regression we identified metabolites associated with diabetes and markers of acute, short-term and long-term glycaemic control.ResultsNinety-four metabolite associations with diabetes were identified at a Bonferroni level of significance (p < 2.3 × 10−5), 16 of which have never been reported. Sixty-five of these diabetes-associated metabolites were associated with at least one marker of glycaemic control in the diabetes group. Using Gaussian graphical modelling, we constructed a metabolic network that links diabetes-associated metabolites from three biofluids across three different timescales of glycaemic control.Conclusions/interpretationOur study reveals a complex network of biochemical dysregulation involving metabolites from different pathways of diabetes pathology, and provides a reference framework for future diabetes studies with metabolic endpoints.

Network-based approach for analyzing intra- and interfluid metabolite associations in human blood, urine, and saliva.

Most studies investigating human metabolomics measurements are limited to a single biofluid, most often blood or urine. An organisms biochemical pool, however, comprises complex transboundary relationships, which can only be understood by investigating metabolic interactions and physiological processes spanning multiple parts of the human body. Therefore, we here propose a data-driven network-based approach to generate an integrated picture of metabolomics associations over multiple fluids. We performed an analysis of 2251 metabolites measured in plasma, urine, and saliva, from 374 participants of the Qatar Metabolomics Study on Diabetes (QMDiab). Gaussian graphical models (GGMs) were used to estimate metabolite-metabolite interactions on different subsets of the data set. First, we compared similarities and differences of the metabolome and the association networks between the three fluids. Second, we investigated the cross-talk between the fluids by analyzing correlations occurring between them. Third, we propose a framework for the analysis of medically relevant phenotypes by integrating type 2 diabetes, sex, age, and body mass index into our networks. In conclusion, we present a generic, data-driven network-based approach for structuring and visualizing metabolite correlations within and between multiple body fluids, enabling unbiased interpretation of metabolomics multifluid data.

Discovering Connected Patterns in Gene Expression Arrays

Clustering methods have been extensively used for gene expression data analysis to detect groups of related genes. The clusters provide useful information to analyze gene function, gene regulation and cellular patterns. Most existing clustering algorithms, though, discover only coherent gene expression patterns, and do not handle connected patterns. Coherent and connected patterns correspond to globular and arbitrary shaped clusters, respectively, in low dimensional spaces. For high dimensional gene expression data, two connected patterns can be two similar patterns with time lags in a time series data, or in general, two different patterns that are connected by an intermediate pattern that is related to both of them. Discovering such connected patterns has important biological implications not revealed by groups of coherent patterns. In this paper, a novel algorithm that finds connected patterns, in gene expression data, is proposed. Using a novel merge criterion, it can distinguish clusters based on distances between patterns, thus avoiding the effect of noise and outliers. Moreover, the algorithm uses a metric based on Pearson correlation to find neighbours, which renders it a lower complexity than related algorithms. Both time series and non temporal gene expression data sets are used to illustrate the efficiency of the proposed algorithm. Results on the serum and the leukaemia data sets reveal interesting biologically significant information

Pattern Cores And Connectedness in Cancer Gene Expression

The huge number of gene expressions resulting from a single microarray experiment, together with the large number of tumor samples, needs efficient methods that can extract hidden information and structure in such data sets. Clustering is a common analysis tool used to find groups of gene expression patterns. However, analysis of large clusters can be an infeasible task in large sets. In this work, a method is proposed to capture the main structure of the data by identifying core gene expressions. This reduces the data to only a subset of representatives used to grasp the main behavior of gene expression. When integrated with clustering, it becomes feasible to analyze clusters of large sizes, and to identify main expression patterns and relations between them. The importance of using a connected-based clustering is emphasized in order to reveal the gradual change between core gene expressions, something which cannot be achieved using traditional clustering algorithms. Analysis is done on breast cancer data to illustrate the significance of the proposed methodology.

Multi-level gene/MiRNA feature selection using deep belief nets and active learning.

Selecting the most discriminative genes/miRNAs has been raised as an important task in bioinformatics to enhance disease classifiers and to mitigate the dimensionality curse problem. Original feature selection methods choose genes/miRNAs based on their individual features regardless of how they perform together. Considering group features instead of individual ones provides a better view for selecting the most informative genes/miRNAs. Recently, deep learning has proven its ability in representing the data in multiple levels of abstraction, allowing for better discrimination between different classes. However, the idea of using deep learning for feature selection is not widely used in the bioinformatics field yet. In this paper, a novel multi-level feature selection approach named MLFS is proposed for selecting genes/miRNAs based on expression profiles. The approach is based on both deep and active learning. Moreover, an extension to use the technique for miRNAs is presented by considering the biological relation between miRNAs and genes. Experimental results show that the approach was able to outperform classical feature selection methods in hepatocellular carcinoma (HCC) by 9%, lung cancer by 6% and breast cancer by around 10% in F1-measure. Results also show the enhancement in F1-measure of our approach over recently related work in [1] and [2].

A genome-wide association meta-analysis on lipoprotein(a) concentrations adjusted for apolipoprotein(a) isoforms

High lipoprotein (a) [Lp(a)] concentrations are an independent risk factor for cardiovascular outcomes. Concentrations are strongly influenced by apo(a) kringle IV repeat isoforms. We aimed to identify genetic loci associated with Lp(a) concentrations using data from five genome-wide association studies (n = 13,781). We identified 48 independent SNPs in the LPA and 1 SNP in the APOE gene region to be significantly associated with Lp(a) concentrations. We also adjusted for apo(a) isoforms to identify loci affecting Lp(a) levels independently from them, which resulted in 31 SNPs (30 in the LPA, 1 in the APOE gene region). Seven SNPs showed a genome-wide significant association with coronary artery disease (CAD) risk. A rare SNP (rs186696265; MAF ∼1%) showed the highest effect on Lp(a) and was also associated with increased risk of CAD (odds ratio = 1.73, P = 3.35 × 10−30). Median Lp(a) values increased from 2.1 to 91.1 mg/dl with increasing number of Lp(a)-increasing alleles. We found the APOE2-determining allele of rs7412 to be significantly associated with Lp(a) concentrations (P = 3.47 × 10−10). Each APOE2 allele decreased Lp(a) by 3.34 mg/dl corresponding to ∼15% of the population’s mean values. Performing a gene-based test of association, including suspected Lp(a) receptors and regulators, resulted in one significant association of the TLR2 gene with Lp(a) (P = 3.4 × 10−4). In summary, we identified a large number of independent SNPs in the LPA gene region, as well as the APOE2 allele, to be significantly associated with Lp(a) concentrations.

A novel validity measure for clusters of arbitrary shapes and densities

Several validity indices have been designed to evaluate solutions obtained by clustering algorithms. Traditional indices are generally designed to evaluate center-based clustering, where clusters are assumed to be of globular shapes with defined centers or representatives. Therefore they are not suitable to evaluate clusters of arbitrary shapes and densities, where clusters have no defined centers or representatives, but formed based on the connectivity of patterns to their neighbours. In this work, a novel validity measure based on a density-based criterion is proposed. It is based on the concept that densities of clusters can be distinguished by the neighbourhood distances between patterns. It is suitable for clusters of any shapes and of different densities. The main concepts of the proposed measure are explained and experimental results that support the proposed measure are given.

Algorithms for selecting materialized views in a data warehouse

Summary form only given. A data warehouse stores materialized views of data from one or more sources, for the purpose of efficiently implementing decision-support or OLAP queries. One of the most important decisions in designing a DW is the selection of materialized views to be maintained at the warehouse. The goal is to select an appropriate set of views so that the sum cost of processing set of queries and maintaining the materialized views is minimized. In this paper, new algorithms are proposed for selecting materialized views in a data warehouse. Two targets of research are considered. The first target is to propose an approach to solve the problem considering both multi-query optimization, and the maintenance process optimization. The other target considers using a simple search strategy that reduces the search space for the view selection problem, and reduces the time complexity to a linear instead of a quadratic one.

A genome-wide association meta-analysis on apolipoprotein A-IV concentrations

Apolipoprotein A-IV (apoA-IV) is a major component of HDL and chylomicron particles and is involved in reverse cholesterol transport. It is an early marker of impaired renal function. We aimed to identify genetic loci associated with apoA-IV concentrations and to investigate relationships with known susceptibility loci for kidney function and lipids. A genome-wide association meta-analysis on apoA-IV concentrations was conducted in five population-based cohorts (n = 13,813) followed by two additional replication studies (n = 2,267) including approximately 10 M SNPs. Three independent SNPs from two genomic regions were significantly associated with apoA-IV concentrations: rs1729407 near APOA4 (P = 6.77 × 10 − 44), rs5104 in APOA4 (P = 1.79 × 10−24) and rs4241819 in KLKB1 (P = 5.6 × 10−14). Additionally, a look-up of the replicated SNPs in downloadable GWAS meta-analysis results was performed on kidney function (defined by eGFR), HDL-cholesterol and triglycerides. From these three SNPs mentioned above, only rs1729407 showed an association with HDL-cholesterol (P = 7.1 × 10 − 07). Moreover, weighted SNP-scores were built involving known susceptibility loci for the aforementioned traits (53, 70 and 38 SNPs, respectively) and were associated with apoA-IV concentrations. This analysis revealed a significant and an inverse association for kidney function with apoA-IV concentrations (P = 5.5 × 10−05). Furthermore, an increase of triglyceride-increasing alleles was found to decrease apoA-IV concentrations (P = 0.0078). In summary, we identified two independent SNPs located in or next the APOA4 gene and one SNP in KLKB1. The association of KLKB1 with apoA-IV suggests an involvement of apoA-IV in renal metabolism and/or an interaction within HDL particles. Analyses of SNP-scores indicate potential causal effects of kidney function and by lesser extent triglycerides on apoA-IV concentrations.