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.

Introduction: Antibiotic-resistant bacteria kill 25,000 people every year in the EU. Patients subject to recurrent lung infections are the most vulnerable to severe or even lethal infections. For these patients, pulmonary delivery of antibiotics would be advantageous, since inhalation can achieve higher concentration in the lungs than iv administration and can provide a faster onset of action. This would allow for the delivery of higher doses and hence reduce the number of treatments required. We report here about a new nanosystem (M33-NS) obtained by capturing SET-M33 peptide on single-chain dextran nanoparticles. SET-M33 is a non-natural antimicrobial peptide synthesized in branched form. This form gives the peptide resistance to degradation in biological fluids. SET-M33 has previously shown efficacy in vitro against about one hundred of Gram-negative multidrug and extensively drug-resistant clinical isolates and was also active in preclinical infection models of pneumonia, sepsis and skin infections. Methods: The new nanosystem was evaluated for its efficacy in bacteria cells and in a mouse model of pneumonia. Toxicity and genotoxicity were also tested in vitro. Biodistribution and pharmacokinetic studies in healthy rats were carried out using a radiolabeled derivative of the nanosystem. Results: The M33-nanosystem, studied here, showed to be effective against Pseudomonas aeruginosa in time-kill kinetic experiments. Cytotoxicity towards different animal cell lines was acceptable. Lung residence time of the antimicrobial peptide, administered via aerosol in healthy rats, was markedly improved by capturing SET-M33 on dextran nanoparticles. M33-NS was also efficient in eradicating pulmonary infection in a BALB/c mouse model of pneumonia caused by P. aeruginosa. Discussion: This study revealed that the encapsulation of the antimicrobial peptide in dextran nanoparticles markedly improved lung residence time of the peptide administered via aerosol. The result has to be considered among the aims of the development of a new therapeutic option for patients suffering recurrent infections, that will benefit from high local doses of persistent antimicrobials.

Near-infrared quantum dots (NIR QDs) are a new class of fluorescent labels with excellent bioimaging features, such as high fluorescence intensity, good fluorescence stability, sufficient electron density, and strong tissue-penetrating ability. For all such features, NIR QDs have great potential for early cancer diagnosis, in vivo tumor imaging and high resolution electron microscopy studies on cancer cells. In the present study we constructed NIR QDs functionalized with the NT4 cancer-selective tetrabranched peptides (NT4-QDs). We observed specific uptake of NT4-QDs in human cancer cells in in vitro experiments and a much higher selective accumulation and retention of targeted QDs at the tumor site, compared to not targeted QDs, in a colon cancer mouse model. NIR QDs labelled with the tetrabranched NT4 peptide have very promising performance for selective addressing of tumor cells in vitro and in vivo, proving rising features of NT4-QDs as theranostics.

Alternative splicing (AS) is a crucial process to enhance gene expression driving organism development. Interestingly, more than 95% of human genes undergo AS, producing multiple protein isoforms from the same transcript. Any alteration (e.g., nucleotide substitutions, insertions, and deletions) involving consensus splicing regulatory sequences in a specific gene may result in the production of aberrant and not properly working proteins. In this review, we introduce the key steps of splicing mechanism and describe all different types of genomic variants affecting this process (splicing variants in acceptor/donor sites or branch point or polypyrimidine tract, exonic, and deep intronic changes). Then, we provide an updated approach to improve splice variants detection. First, we review the main computational tools, including the recent Machine Learning-based algorithms, for the prediction of splice site variants, in order to characterize how a genomic variant interferes with splicing process. Next, we report the experimental methods to validate the predictive analyses are defined, distinguishing between methods testing RNA (transcriptomics analysis) or proteins (proteomics experiments). For both prediction and validation steps, benefits and weaknesses of each tool/procedure are accurately reported, as well as suggestions on which approaches are more suitable in diagnostic rather than in clinical research.

Cerebral cavernous malformations (CCMs) are vascular lesions that affect predominantly microvasculature in the brain and spinal cord. CCM can occur either in sporadic or familial form, characterized by autosomal dominant inheritance and development of multiple lesions throughout the patient’s life. Three genes associated with CCM are known: CCM1/KRIT1 (krev interaction trapped 1), CCM2/MGC4607 (encoding a protein named malcavernin), and CCM3/PDCD10 (programmed cell death 10). All the mutations identified in these genes cause a loss of function and compromise the protein functions needed for maintaining the vascular barrier integrity. Loss of function of CCM proteins causes molecular disorganization and dysfunction of endothelial adherens junctions. In this review, we provide an overall vision of the CCM pathology, starting with the genetic bases of the disease, describing the role of the proteins, until we reach the cellular level. Thus, we summarize the genetics of CCM, providing a description of CCM genes and mutation features, provided an updated knowledge of the CCM protein structure and function, and discuss the molecular mechanisms through which CCM proteins may act within endothelial cells, particularly in endothelial barrier maintenance/regulation and in cellular signaling.

Amyotrophic Lateral Sclerosis (ALS) is characterized by the progressive degeneration of upper or lower motor neurons, leading to muscle wasting and paralysis, resulting in respiratory failure and death. The precise ALS aetiology is poorly understood, mainly due to clinical and genetic heterogeneity. Thus, the identification of reliable biomarkers of disease could be helpful in clinical practice. In this study, we investigated whether the levels of brain-derived neurotrophic factor (BDNF) and its precursor Pro-BDNF in serum and cerebrospinal fluid (CSF) may reflect the pathological changes related to ALS. We found higher BDNF and lower Pro-BDNF levels in ALS sera compared to healthy controls. BDNF/Pro-BDNF ratio turned out to be accurate in distinguishing ALS patients from controls. Then, the correlations of these markers with several ALS clinical variables were evaluated. This analysis revealed three statistically significant associations: (1) Patients carrying the C9orf72 expansion significantly differed from non-carrier patients and showed serum BDNF levels comparable to control subjects; (2) BDNF levels in CSF were significantly higher in ALS patients with faster disease progression; (3) lower serum levels of Pro-BDNF were associated with a shorter survival. Therefore, we suggest that BDNF and Pro-BDNF, alone or in combination, might be used as ALS prognostic biomarkers.