Transcriptomic Characterization of Cow, Donkey and Goat Milk Extracellular Vesicles Reveals Their Anti-Inflammatory and Immunomodulatory Potential.

Milk extracellular vesicles (mEVs) seem to be one of the main maternal messages delivery systems. Extracellular vesicles (EVs) are micro/nano-sized membrane-bound structures enclosing signaling molecules and thus acting as signal mediators between distant cells and/or tissues, exerting biological effects such as immune modulation and pro-regenerative activity. Milk is also a unique, scalable, and reliable source of EVs. Our aim was to characterize the RNA content of cow, donkey, and goat mEVs through transcriptomic analysis of mRNA and small RNA libraries. Over 10,000 transcripts and 2000 small RNAs were expressed in mEVs of each species. Among the most represented transcripts, 110 mRNAs were common between the species with cow acting as the most divergent. The most represented small RNA class was miRNA in all the species, with 10 shared miRNAs having high impact on the immune regulatory function. Functional analysis for the most abundant mRNAs shows epigenetic functions such as histone modification, telomere maintenance, and chromatin remodeling for cow; lipid catabolism, oxidative stress, and vitamin metabolism for donkey; and terms related to chemokine receptor interaction, leukocytes migration, and transcriptional regulation in response to stress for goat. For miRNA targets, shared terms emerged as the main functions for all the species: immunity modulation, protein synthesis, cellular cycle regulation, transmembrane exchanges, and ion channels. Moreover, donkey and goat showed additional terms related to epigenetic modification and DNA maintenance. Our results showed a potential mEVs immune regulatory purpose through their RNA cargo, although in vivo validation studies are necessary.

Atomic-scale modeling of the interaction between short polypeptides and carbon surfaces.

We performed a comparative study of the adsorption of an in vitro selected peptide on two different carbon surfaces: a flat graphene and a curved (0,15) nanotube. The sequence was selected from recent experiments, as the one giving the highest carbon affinity for carbon nanotubes. Rigid docking of the molecule on the two surfaces by a genetic algorithm was followed by molecular dynamics simulations with empirical force fields (OPLS-AA) in water at finite temperature. The total free energies of folding and adhesion and the quality of surface binding were determined, based on a combination of solvation energy, formation of hydrogen bonds, and amount of the apolar (hydrophobic) contact surface between peptide and carbon surface. For both cases, we find a strong adhesion energy and large nonpolar contact surface. Isoleucines and tryptophans are the most strongly bound residues to the two carbon surfaces, the latter one largely dominating. In the case of the carbon nanotube, the peptide shows several competing stable structures, corresponding to different possible molecular foldings, and a propensity to enhance the intramolecular stability by forming new hydrogen bonds. In both systems, different arrangements of the histidine and tryptophan residues enable a better adaptation to the carbon surfaces. These findings suggest that the experimentally observed surface specificity of the peptide on nanotubes may depend on its capability to support multiple strongly bound configurations.

Structure and dynamics of the anti-AMCV scFv(F8): effects of selected mutations on the antigen combining site.

The recombinant antibody fragment scFv(F8), which recognizes the coat protein of the plant virus AMCV, is characterized by peculiar high in vitro stability and functional folding even in reducing environments, making it fit for designing stable antibodies with desired properties. Mutagenesis and functional analysis evidenced two residues, at positions 47 and 58 of the V(H) chain, playing a crucial role in the antigen binding recognition. Here, we used a computational procedure to assess the effects of these mutations on the stability, structure and dynamics of the antigen-binding site. Structural models of the wild type scFv(F8) and of its H47 and H58 mutants were built by homology modelling and assessed by multiple 15.5ns of molecular dynamics simulations. Computational results indicate that the 47H substitution strongly affects the CDR-H(2) conformation, destabilizes the V(H)/V(L) interface and confers high conformational flexibility to the antigen-binding site, leading the mutant to functional loss. The mutation at position H58 strenghtens the binding site, bestowing a high antigen specificity on the mutant. The essential dynamics and the analysis of the protein-solvent interface further corroborate the correspondence between the extent of the structurally-determined flexibility of the binding site with the different functional behaviours proved by the wild-type and its mutants. These results may have useful implications for structure-based design of antibody combining site.

Asymptotic states and topological structure of an activation-deactivation chemical network.

The influence of the topology on the asymptotic states of a network of interacting chemical species has been studied by simulating its time evolution. Random and scale-free networks have been designed to support relevant features of activation-deactivation reactions networks (mapping signal transduction networks) and the system of ordinary differential equations associated to the dynamics has been numerically solved. We analysed stationary states of the dynamics as a function of the network's connectivity and of the distribution of the chemical species on the network; we found important differences between the two topologies in the regime of low connectivity. In particular, only for low connected scale-free networks it is possible to find zero activity patterns as stationary states of the dynamics which work as signal off-states. Asymptotic features of random and scale-free networks become similar as the connectivity increases.

Parameter estimate of signal transduction pathways.

BACKGROUND: The "inverse" problem is related to the determination of unknown causes on the bases of the observation of their effects. This is the opposite of the corresponding "direct" problem, which relates to the prediction of the effects generated by a complete description of some agencies. The solution of an inverse problem entails the construction of a mathematical model and takes the moves from a number of experimental data. In this respect, inverse problems are often ill-conditioned as the amount of experimental conditions available are often insufficient to unambiguously solve the mathematical model. Several approaches to solving inverse problems are possible, both computational and experimental, some of which are mentioned in this article. In this work, we will describe in details the attempt to solve an inverse problem which arose in the study of an intracellular signaling pathway. RESULTS: Using the Genetic Algorithm to find the sub-optimal solution to the optimization problem, we have estimated a set of unknown parameters describing a kinetic model of a signaling pathway in the neuronal cell. The model is composed of mass action ordinary differential equations, where the kinetic parameters describe protein-protein interactions, protein synthesis and degradation. The algorithm has been implemented on a parallel platform. Several potential solutions of the problem have been computed, each solution being a set of model parameters. A sub-set of parameters has been selected on the basis on their small coefficient of variation across the ensemble of solutions. CONCLUSION: Despite the lack of sufficiently reliable and homogeneous experimental data, the genetic algorithm approach has allowed to estimate the approximate value of a number of model parameters in a kinetic model of a signaling pathway: these parameters have been assessed to be relevant for the reproduction of the available experimental data.

Designing hardware for protein sequence analysis.

UNLABELLED: We present the architecture of PROSIDIS, a special purpose co-processor designed to search for the occurrence of substrings similar to a given 'template string' within a proteome. Actual tests show speed up figures ranging from 5 to 50 with respect to conventional general-purpose processors. AVAILABILITY: the PROSIDIS configuration file and the c code are available at http://www.enea.it/hpcn/php/rosato/

Evidence for cysteine clustering in thermophilic proteomes.

Through linguistic analysis, we show that the presence of an amino acid at a given position within a proteome positively influences the presence of identical amino acids at nearby positions. We call this phenomenon 'amino acid clustering'. Clustering extends well beyond the closest neighbouring sites and is particularly pronounced for cysteine and tryptophan. Cysteine clusters preferentially form CXXC structures, and they are often involved in metal coordination or disulfide bond formation. Cysteine clustering shows a clear correlation with the growth temperature of the organism. This seems to be a general property of living organisms.