Role of sex and time of blood sampling in SOD1 and SOD2 expression variability

Brain-body mechanisms contribute to sexual dimorphism in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is the most common form of human motor neuron disease. It is characterized by the progressive degeneration of upper and lower motor neurons, leading to generalized motor weakness and, ultimately, respiratory paralysis and death within 3-5 years. The disease is shaped by genetics, age, sex and environmental stressors, but no cure or routine biomarkers exist for the disease. Male individuals have a higher propensity to develop ALS, and a different manifestation of the disease phenotype, than female individuals. However, the mechanisms underlying these sex differences remain a mystery. In this Review, we summarize the epidemiology of ALS, examine the sexually dimorphic presentation of the disease and highlight the genetic variants and molecular pathways that might contribute to sex differences in humans and animal models of ALS. We advance the idea that sexual dimorphism in ALS arises from the interactions between the CNS and peripheral organs, involving vascular, metabolic, endocrine, musculoskeletal and immune systems, which are strikingly different between male and female individuals. Finally, we review the response to treatments in ALS and discuss the potential to implement future personalized therapeutic strategies for the disease.

Activity of Antioxidant Enzymes and Their Association with Lipid Profile in Mexican People without Cardiovascular Disease: An Analysis of Interactions

Dyslipidemia and oxidative stress are both considered to be factors involved in cardiovascular disease; however, the relationship between them has been little explored. In this work, we studied the association between the lipid profile and the activity of antioxidant enzymes such as paraoxonase-1 (PON1), superoxide dismutase 1 (SOD1), ceruloplasmin, and catalase, as well as total antioxidant capacity (the ferric-reducing ability of plasma (FRAP)), in 626 volunteers without cardiovascular disease. Their lipid profile was evaluated, and they were classified as having or not having high triglycerides (↑TG), high low-density cholesterol (↑LDLC), and low high-density cholesterol (↓HDLC), resulting in eight groups: Without dyslipidemia, ↑TG, ↑LDLC, ↓HDLC, ↑TG↑LDLC, ↑TG↓HDLC, ↑LDLC↓HDLC, and ↑TG↑LDLC↓HDLC. When comparisons by group were made, no significant differences in the activity of antioxidant enzymes were obtained. However, the linear regression analysis considering the potential interactions between ↑TG, ↑LDLC, and ↓HDLC suggested a triple interaction between the three lipid profile alterations on the activity of PON1 and a double interaction between ↑TG and ↑LDLC on ferroxidase-ceruloplasmin activity. The analysis presented in this work showed an association between the lipid profile and antioxidant-enzyme activity and highlighted the importance of considering the interactions between the components of a phenomenon instead of studying them individually. Longitudinal studies are needed to elucidate the nature of these associations.

Multiple testing for gene sets from microarray experiments

Background

A key objective in many microarray association studies is the identification of individual genes associated with clinical outcome. It is often of additional interest to identify sets of genes, known a priori to have similar biologic function, associated with the outcome.

Results

In this paper, we propose a general permutation-based framework for gene set testing that controls the false discovery rate (FDR) while accounting for the dependency among the genes within and across each gene set. The application of the proposed method is demonstrated using three public microarray data sets. The performance of our proposed method is contrasted to two other existing Gene Set Enrichment Analysis (GSEA) and Gene Set Analysis (GSA) methods.

Conclusions

Our simulations show that the proposed method controls the FDR at the desired level. Through simulations and case studies, we observe that our method performs better than GSEA and GSA, especially when the number of prognostic gene sets is large.

Validation and characterization of DNA microarray gene expression data distribution and associated moments

Background

The data from DNA microarrays are increasingly being used in order to understand effects of different conditions, exposures or diseases on the modulation of the expression of various genes in a biological system. This knowledge is then further used in order to generate molecular mechanistic hypotheses for an organism when it is exposed to different conditions. Several different methods have been proposed to analyze these data under different distributional assumptions on gene expression. However, the empirical validation of these assumptions is lacking.

Results

Best fit hypotheses tests, moment-ratio diagrams and relationships between the different moments of the distribution of the gene expression was used to characterize the observed distributions. The data are obtained from the publicly available gene expression database, Gene Expression Omnibus (GEO) to characterize the empirical distributions of gene expressions obtained under varying experimental situations each of which providing relatively large number of samples for hypothesis testing. All data were obtained from either of two microarray platforms - the commercial Affymetrix mouse 430.2 platform and a non-commercial Rosetta/Merck one. The data from each platform were preprocessed in the same manner.

Conclusions

The null hypotheses for goodness of fit for all considered univariate theoretical probability distributions (including the Normal distribution) are rejected for more than 50% of probe sets on the Affymetrix microarray platform at a 95% confidence level, suggesting that under the tested conditions a priori assumption of any of these distributions across all probe sets is not valid. The pattern of null hypotheses rejection was different for the data from Rosetta/Merck platform with only around 20% of the probe sets failing the logistic distribution goodness-of-fit test. We find that there are statistically significant (at 95% confidence level based on the F-test for the fitted linear model) relationships between the mean and the logarithm of the coefficient of variation of the distributions of the logarithm of gene expressions. An additional novel statistically significant quadratic relationship between the skewness and kurtosis is identified. Data from both microarray platforms fail to identify with any one of the chosen theoretical probability distributions from an analysis of the l-moment ratio diagram.