Correction to: Assessment of cetacean–fishery interactions in the marine food web of the Gulf of Taranto (Northern Ionian Sea,Central Mediterranean Sea)

Reviews in Fish Biology and Fisheries - In the original publication of the article, the given name and surname of the authors are inverted in the author’s affiliation and in the citation of...     

The Role of Anabolic Androgenic Steroids in Disruption of the Physiological Function in Discrete Areas of the Central Nervous System

Anabolic-androgenic steroids (AAS) abuse is often associated with a wide spectrum of adverse effects. These drugs are frequently abused by adolescents and athletes for esthetic purposes, as well as for improvement of their endurance and performances. In this literature review, we evaluated the correlation between AAS and anxiety or aggression. Two pathways are thought to be involved in AAS-induced behavioral disorders. Direct pathway via the amygdalo-fugal pathway, which connects the central nucleus of the amygdala to the brainstem, is involved in cognitive-emotive and homeostatic processes. The latter is modified by chronic AAS use, which subsequently leads to increased anxiety. Indirect pathways via the serotonergic, dopaminergic, and glutamatergic signals which are modified by AAS abuse in latero-anterior hypothalamus and can mediate the aggressive behavior. In conclusion, the molecular mechanisms underlying the behavioral alterations following AAS abuse is unclear and remains ambiguous as additional long-term studies aimed to understand the precise mechanisms are required.     

Effects of Nandrolone Stimulation on Testosterone Biosynthesis in Leydig Cells

Anabolic androgenic steroids (AAS) are among the drugs most used by athletes for improving physical performance, as well as for aesthetic purposes. A number of papers have showed the side effects of AAS in different organs and tissues. For example, AAS are known to suppress gonadotropin‐releasing hormone, luteinizing hormone, and follicle‐stimulating hormone. This study investigates the effects of nandrolone on testosterone biosynthesis in Leydig cells using various methods, including mass spectrometry, western blotting, confocal microscopy and quantitative real‐time PCR. The results obtained show that testosterone levels increase at a 3.9 μM concentration of nandrolone and return to the basal level a 15.6 μM dose of nandrolone. Nandrolone‐induced testosterone increment was associated with upregulation of the steroidogenic acute regulatory protein (StAR) and downregulation of 17a‐hydroxylase/17, 20 lyase (CYP17A1). Instead, a 15.6 µM dose of nandrolone induced a down‐regulation of CYP17A1. Further in vivo studies based on these data are needed to better understand the relationship between disturbed testosterone homeostasis and reproductive system impairment in male subjects. J. Cell. Physiol. 231: 1385–1391, 2016. © 2015 The Authors. Journal of Cellular Physiology Published by Wiley Periodicals, Inc.     

Comparative study of gene set enrichment methods

Background  

The analysis of high-throughput gene expression data with respect to sets of genes rather than individual genes has many advantages. A variety of methods have been developed for assessing the enrichment of sets of genes with respect to differential expression. In this paper we provide a comparative study of four of these methods: Fisher's exact test, Gene Set Enrichment Analysis (GSEA), Random-Sets (RS), and Gene List Analysis with Prediction Accuracy (GLAPA). The first three methods use associative statistics, while the fourth uses predictive statistics. We first compare all four methods on simulated data sets to verify that Fisher's exact test is markedly worse than the other three approaches. We then validate the other three methods on seven real data sets with known genetic perturbations and then compare the methods on two cancer data sets where our a priori knowledge is limited.     

Biological and functional analysis of statistically significant pathways deregulated in colon cancer by using gene expression profiles

Gene expression profiling offers a great opportunity for studying multi-factor diseases and for understanding the key role of genes in mechanisms which drive a normal cell to a cancer state. Single gene analysis is insufficient to describe the complex perturbations responsible for cancer onset, progression and invasion. A deeper understanding of the mechanisms of tumorigenesis can be reached focusing on deregulation of gene sets or pathways rather than on individual genes. We apply two known and statistically well founded methods for finding pathways and biological processes deregulated in pathological conditions by analyzing gene expression profiles. In particular, we measure the amount of deregulation and assess the statistical significance of predefined pathways belonging to a curated collection (Molecular Signature Database) in a colon cancer data set. We find that pathways strongly involved in different tumors are strictly connected with colon cancer. Moreover, our experimental results show that the study of complex diseases through pathway analysis is able to highlight genes weakly connected to the phenotype which may be difficult to detect by using classical univariate statistics. Our study shows the importance of using gene sets rather than single genes for understanding the main biological processes and pathways involved in colorectal cancer. Our analysis evidences that many of the genes involved in these pathways are strongly associated to colorectal tumorigenesis. In this new perspective, the focus shifts from finding differentially expressed genes to identifying biological processes, cellular functions and pathways perturbed in the phenotypic conditions by analyzing genes co-expressed in a given pathway as a whole, taking into account the possible interactions among them and, more importantly, the correlation of their expression with the phenotypical conditions.     

On the statistical assessment of classifiers using DNA microarray data

Background

In this paper we present a method for the statistical assessment of cancer predictors which make use of gene expression profiles. The methodology is applied to a new data set of microarray gene expression data collected in Casa Sollievo della Sofferenza Hospital, Foggia – Italy. The data set is made up of normal (22) and tumor (25) specimens extracted from 25 patients affected by colon cancer. We propose to give answers to some questions which are relevant for the automatic diagnosis of cancer such as: Is the size of the available data set sufficient to build accurate classifiers? What is the statistical significance of the associated error rates? In what ways can accuracy be considered dependant on the adopted classification scheme? How many genes are correlated with the pathology and how many are sufficient for an accurate colon cancer classification? The method we propose answers these questions whilst avoiding the potential pitfalls hidden in the analysis and interpretation of microarray data.

Results

We estimate the generalization error, evaluated through the Leave-K-Out Cross Validation error, for three different classification schemes by varying the number of training examples and the number of the genes used. The statistical significance of the error rate is measured by using a permutation test. We provide a statistical analysis in terms of the frequencies of the genes involved in the classification. Using the whole set of genes, we found that the Weighted Voting Algorithm (WVA) classifier learns the distinction between normal and tumor specimens with 25 training examples, providing e = 21% (p = 0.045) as an error rate. This remains constant even when the number of examples increases. Moreover, Regularized Least Squares (RLS) and Support Vector Machines (SVM) classifiers can learn with only 15 training examples, with an error rate of e = 19% (p = 0.035) and e = 18% (p = 0.037) respectively. Moreover, the error rate decreases as the training set size increases, reaching its best performances with 35 training examples. In this case, RLS and SVM have error rates of e = 14% (p = 0.027) and e = 11% (p = 0.019). Concerning the number of genes, we found about 6000 genes (p < 0.05) correlated with the pathology, resulting from the signal-to-noise statistic. Moreover the performances of RLS and SVM classifiers do not change when 74% of genes is used. They progressively reduce up to e = 16% (p < 0.05) when only 2 genes are employed. The biological relevance of a set of genes determined by our statistical analysis and the major roles they play in colorectal tumorigenesis is discussed.

Conclusions

The method proposed provides statistically significant answers to precise questions relevant for the diagnosis and prognosis of cancer. We found that, with as few as 15 examples, it is possible to train statistically significant classifiers for colon cancer diagnosis. As for the definition of the number of genes sufficient for a reliable classification of colon cancer, our results suggest that it depends on the accuracy required.     

Assessment of cetacean–fishery interactions in the marine food web of the Gulf of Taranto (Northern Ionian Sea,Central Mediterranean Sea)

Reviews in Fish Biology and Fisheries - The exploitation of fishery resources acts as a driving force on cetaceans both directly, by determining their fishing mortality or injury as by-catch...