Genomic and proteomic approaches in pig meat quality research field

Pig meat and carcass quality is a complex concept determined by environmental and genetic factors concurring to the phenotypic variation in qualitative characteristics of meat (fat content, tenderness, juiciness, flavor,etc). This thesis shows the results of different investigations to study and to analyze pig meat and carcass quality focusing mainly on genomic; moreover proteomic approach has been also used. The aim was to analyze data from association studies between genes considered as candidate and meat and carcass quality in different pig breeds. The approach was used to detect new SNP in genes functionally associated to the studied traits and to confirm as candidate other genes already known. Five polymorphisms (one new SNP in Calponin 1 gene and four additional polymorphism already known in other genes) were considered on chromosome 2 (SSC2). Calponin 1 (CNN1) was associated to the studied traits and furthermore the results reported confirmed the data already known for Lactate dehydrogenase A (LDHA), Low density lipoprotein receptor (LDLR), Myogenic differentiation 1 (MYOD1) e Ubiquitin-like 5 (UBL5), in Italian Large White pigs. Using an in silico search it was possible to detect on SSC2 a new SNP of Deoxyhypusine synthase (DHPS) gene partially overlapping with WD repeat domain 83 (WDR83) gene and significant for the meat pH variation in Italian Large White (ILW) pigs. Perilipin 1 (PLIN1) mapping on chromosome 7 and Perilipin 2 (PLIN2) mapping on chromosome 1 were studied and the results obtained in Duroc breed have shown significant associations with carcass traits. Moreover a study of protein composition of porcine LD muscle, indicated an effect of temperature treatment of carcass, on proteins of the sarcoplasmic fraction and in particular on PGM1 phosphorylation. Future studies on pig meat quality should be based on the integration of different experimental approaches (genomics, proteomics, transcriptomics, etc).

Secondary Microseisms Characterization and Green’s Function Extraction at the Larderello-Travale Geothermal Field (Italy)

Over the past ten years, the cross-correlation of long-time series of ambient seismic noise (ASN) has been widely adopted to extract the surface-wave part of the Green’s Functions (GF). This stochastic procedure relies on the assumption that ASN wave-field is diffuse and stationary. At frequencies <1Hz, the ASN is mainly composed by surface-waves, whose origin is attributed to the sea-wave climate. Consequently, marked directional properties may be observed, which call for accurate investigation about location and temporal evolution of the ASN-sources before attempting any GF retrieval. Within this general context, this thesis is aimed at a thorough investigation about feasibility and robustness of the noise-based methods toward the imaging of complex geological structures at the local (∼10-50km) scale. The study focused on the analysis of an extended (11 months) seismological data set collected at the Larderello-Travale geothermal field (Italy), an area for which the underground geological structures are well-constrained thanks to decades of geothermal exploration. Focusing on the secondary microseism band (SM;f>0.1Hz), I first investigate the spectral features and the kinematic properties of the noise wavefield using beamforming analysis, highlighting a marked variability with time and frequency. For the 0.1-0.3Hz frequency band and during Spring- Summer-time, the SMs waves propagate with high apparent velocities and from well-defined directions, likely associated with ocean-storms in the south- ern hemisphere. Conversely, at frequencies >0.3Hz the distribution of back- azimuths is more scattered, thus indicating that this frequency-band is the most appropriate for the application of stochastic techniques. For this latter frequency interval, I tested two correlation-based methods, acting in the time (NCF) and frequency (modified-SPAC) domains, respectively yielding esti- mates of the group- and phase-velocity dispersions. Velocity data provided by the two methods are markedly discordant; comparison with independent geological and geophysical constraints suggests that NCF results are more robust and reliable.