MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression in both animals and plants. By pairing to microRNA responsive elements (mREs) on target mRNAs, miRNAs play gene-regulatory roles, producing remarkable changes in several physiological and pathological processes. Thus, the identification of miRNA-mRNA target interactions is fundamental for discovering the regulatory network governed by miRNAs. The best way to achieve this goal is usually by computational prediction followed by experimental validation of these miRNA-mRNA interactions. This review summarizes the key strategies for miRNA target identification. Several tools for computational analysis exist, each with different approaches to predict miRNA targets, and their number is constantly increasing. The major algorithms available for this aim, including Machine Learning methods, are discussed, to provide practical tips for familiarizing with their assumptions and understanding how to interpret the results. Then, all the experimental procedures for verifying the authenticity of the identified miRNA-mRNA target pairs are described, including High-Throughput technologies, in order to find the best approach for miRNA validation. For each strategy, strengths and weaknesses are discussed, to enable users to evaluate and select the right approach for their interests.

A large 10-mer phage peptide library was panned against whole Escherichia coli cells, and an antimicrobial peptide (QEKIRVRLSA) was selected. The peptide was synthesized in monomeric and dendrimeric tetrabranched form (multiple antigen peptide [MAP]), which generally allows a dramatic increase of peptide stability to peptidases and proteases. The antibacterial activity of the dendrimeric peptide against E. coli was much higher than that of the monomeric form. Modification of the original sequence, by residue substitution or sequence shortening, produced three different MAPs, M4 (QAKIRVRLSA), M5 (KIRVRLSA), and M6 (QKKIRVRLSA) with enhanced stability to natural degradation and antimicrobial activity against a large panel of gram-negative bacteria. The MICs of the most potent peptide, M6, were as low as 4 to 8 μg/ml against recent clinical isolates of multidrug-resistant Pseudomonas aeruginosa and members of the Enterobacteriaceae . The same dendrimeric peptides showed high stability to blood proteases, low hemolytic activity, and low cytotoxic effects on eukaryotic cells, making them promising candidates for the development of new antibacterial drugs.

Amyotrophic lateral sclerosis (ALS) is an incurable and fatal disorder characterized by the progressive loss of motor neurons in the cerebral cortex, brain stem, and spinal cord. Sporadic ALS form accounts for the majority of patients, but in 1–13.5% of cases the disease is inherited. The diagnosis of ALS is mainly based on clinical assessment and electrophysiological examinations with a history of symptom progression and is then made with a significant delay from symptom onset. Thus, the identification of biomarkers specific for ALS could be of a fundamental importance in the clinical practice. An ideal biomarker should display high specificity and sensitivity for discriminating ALS from control subjects and from ALS-mimics and other neurological diseases, and should then monitor disease progression within individual patients. microRNAs (miRNAs) are considered promising biomarkers for neurodegenerative diseases, since they are remarkably stable in human body fluids and can reflect physiological and pathological processes relevant for ALS. Here, we review the state of the art of miRNA biomarker identification for ALS in cerebrospinal fluid (CSF), blood and muscle tissue; we discuss advantages and disadvantages of different approaches, and underline the limits but also the great potential of this research for future practical applications.

Macrophage migration inhibitory factor (MIF) was discovered as an activated T-lymphocyte-derived protein that inhibits the random migration of macrop