ABSTRACT:The caput epididymis of some mammals contains large quantities of serotonin whose origin, targets, and physiological variations have been poorly studied. We combined morphological and biochemical techniques to begin approaching these aspects of serotonin in the rat caput epididymis. Serotonin immunostaining was detected in mast, epithelial, and neuroendocrine cells. Epithelial cells displayed immunoreactivity to 5HT 1A , 5HT 2A, and 5HT 3 serotonin receptors. Endothelial and mast cells labeled positive for 5HT 1B serotonin receptors and spermatozoa displayed 5HT 2A and 5HT 3 serotonin receptor immunoreactivity. Epithelial, endothelial, and mast cells stained positive for serotonin transporters. Only epithelial cells showed tryptophan hydroxylase immunoreactivity; this enzyme catalyzes the limiting step in the serotonin synthetic pathway. In addition, Western blot analyses of caput homogenates documented the presence of 2 protein bands (<51 kd and <48 kd) that were immunoreactive for tryptophan hydroxylase. Chromatographic analyses documented the presence of tryptophan hydroxylase in the caput, and showed that both its activity and serotonin availability increased with sexual maturation and decreased following p-chlorophenylalanine treatment, an inhibitor of tryptophan hydroxylase activity. Interestingly, serotonin concentration and tryptophan hydroxylase activity tended to be higher in breeding males than in those with no mating experience. We think that these results support the existence of a local serotoninergic system in the rat caput epididymis that might regulate some aspects of male reproductive function.

Serotonin (5-hydroxytryptamine; C 10 H 12 N 2 O (5-HT)) is produced in the CNS and in some cells of peripheral tissues. In the mammalian male reproductive system, both 5-HT and tryptophan hydroxylase (TPH) have been described in Leydig cells of the testis and in principal cells of the caput epididymis. In capacitated hamster sperm, it has been shown that 5-HT promotes the acrosomal reaction. The aim of this work was to explore the existence of components of the serotoninergic system and their relevance in human sperm physiology. We used both immunocytochemistry and western blot to detect serotoninergic markers such as 5-HT, TPH1, MAO A , 5-HT 1B , 5-HT 3 , and 5HT T ; HPLC for TPH enzymatic activity; Computer Assisted Semen Analysis assays to measure sperm motility parameters and pharmacological approaches to show the effect of 5-HT in sperm motility and tyrosine phosphorylation was assessed by western blot. We found the presence of serotoninergic markers (5-HT, TPH1, MAO A , 5-HT 1B , 5-HT 2A , 5-HT 3 , 5-HT T , and TPH enzymatic activity) in human sperm. In addition, we observed a significant increase in tyrosine phosphorylation and changes in sperm motility after 5-HT treatment. In conclusion, our data demonstrate the existence of components of a serotoninergic system in human sperm and support the notion for a functional role of 5-HT in mammalian sperm physiology, which can be modulated pharmacologically.

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Prostate cancer is the most common cancer in men and the second leading cause of cancer-related deaths. The most used biomarker to detect prostate cancer is Prostate Specific Antigen (PSA), whose levels are measured in serum. However, it has been recently established that molecular markers of cancer should not be based solely on genes and proteins but should also reflect other genomic traits; long non-coding RNAs (lncRNAs) serve this purpose. lncRNAs are transcripts of >200 bases that do not encode proteins and that have been shown to display abnormal expression profiles in different types of cancer. Experimental studies have highlighted lncRNAs as potential biomarkers for prognoses and treatments in patients with different types of cancer, including prostate cancer, where the PCA3 lncRNA is currently used as a diagnostic tool and management strategy. With the development of genomic technologies, particularly next-generation sequencing (NGS), several other lncRNAs have been linked to prostate cancer and are currently under validation for their medical use. In this review, we will discuss different strategies for the discovery of novel lncRNAs that can be evaluated as prognostic biomarkers, the clinical impact of these lncRNAs and how lncRNAs can be used as potential therapeutic targets.

Background Risk stratification or progression in prostate cancer is performed with the support of clinical-pathological data such as the sum of the Gleason score and serum levels PSA. For several decades, methods aimed at the early detection of prostate cancer have included the determination of PSA serum levels. The aim of this systematic review is to provide an overview about recent advances in the discovery of new molecular biomarkers through transcriptomics, genomics and artificial intelligence that are expected to improve clinical management of the prostate cancer patient. Methods An exhaustive search was conducted by Pubmed, Google Scholar and Connected Papers using keywords relating to the genetics, genomics and artificial intelligence in prostate cancer, it includes “biomarkers”, “non-coding RNAs”, “lncRNAs”, “microRNAs”, “repetitive sequence”, “prognosis”, “prediction”, “whole-genome sequencing”, “RNA-Seq”, “transcriptome”, “machine learning”, and “deep learning”. Results New advances, including the search for changes in novel biomarkers such as mRNAs, microRNAs, lncRNAs, and repetitive sequences, are expected to contribute to an earlier and accurate diagnosis for each patient in the context of precision medicine, thus improving the prognosis and quality of life of patients. We analyze several aspects that are relevant for prostate cancer including its new molecular markers associated with diagnosis, prognosis, and prediction to therapy and how bioinformatic approaches such as machine learning and deep learning can contribute to clinic. Furthermore, we also include current techniques that will allow an earlier diagnosis, such as Spatial Transcriptomics, Exome Sequencing, and Whole-Genome Sequencing. Conclusion Transcriptomic and genomic analysis have contributed to generate knowledge in the field of prostate carcinogenesis, new information about coding and non-coding genes as biomarkers has emerged. Synergies created by the implementation of artificial intelligence to analyze and understand sequencing data have allowed the development of clinical strategies that facilitate decision-making and improve personalized management in prostate cancer.