A candidate gene approach to identify DNA markers associated with meat quality and production traits: investigation of several cathepsin genes in Italian heavy pigs.

The cathepsin enzymes represent an important family of lysosomal proteinases with a broad spectrum of functions in many, if not in all, tissues and cell types. In addition to their primary role during the normal protein turnover, they possess highly specific proteolytic activities, including antigen processing in the immune response and a direct role in the development of obesity and tumours. In pigs, the involvement of cathepsin enzymes in proteolytic processes have important effects during the conversion of muscle to meat, due to their influence on meat texture and sensory characteristics, mainly in seasoned products. Their contribution is fundamental in flavour development of dry-curing hams. However, several authors have demonstrated that high cathepsin activity, in particular of cathepsin B, is correlated to defects of these products, such as an excessive meat softness together with abnormal free tyrosine content, astringent or metallic aftertastes and formation of a white film on the cut surface. Thus, investigation of their genetic variability could be useful to identify DNA markers associated with these dry cured hams parameters, but also with meat quality, production and carcass traits in Italian heavy pigs. Unfortunately, no association has been found between cathepsin markers and meat quality traits so far, in particular with cathepsin B activity, suggesting that other genes, besides these, affect meat quality parameters. Nevertheless, significant associations were observed with several carcass and production traits in pigs. A recent study has demonstrated that different single nucleotide polymorphisms (SNPs) localized in cathepsin D (CTSD), F (CTSF), H and Z genes were highly associated with growth, fat deposition and production traits in an Italian Large White pig population. The aim of this thesis was to confirm some of these results in other pig populations and identify new cathepsin markers in order to evaluate their effects on cathepsin activity and other production traits. Furthermore, starting from the data obtained in previous studies on CTSD gene, we also analyzed the known polymorphism located in the insulin-like growth factor 2 gene (IGF2 intron3-g.3072G>A). This marker is considered the causative mutation for the quantitative trait loci (QTL) affecting muscle mass and fat deposition in pigs. Since IGF2 maps very close to CTSD on porcine chromosome (SSC) 2, we wanted to clarify if the effects of the CTSD marker were due to linkage disequilibrium with the IGF2 intron3-g.3072G>A mutation or not. In the first chapter, we reported the results from these two SSC2 gene markers. First of all, we evaluated the effects of the IGF2 intron3-g.3072G>A polymorphism in the Italian Large White breed, for which no previous studies have analysed this marker. Highly significant associations were identified with all estimated breeding values for production and carcass traits (P<0.00001), while no effects were observed for meat quality traits. Instead, the IGF2 intron3-g.3072G>A mutation did not show any associations with the analyzed traits in the Italian Duroc pigs, probably due to the low level of variability at this polymorphic site for this breed. In the same Duroc pig population, significant associations were obtained for the CTSD marker for all production and carcass traits (P < 0.001), after excluding possible confounding effects of the IGF2 mutation. The effects of the CTSD g.70G>A polymorphism were also confirmed in a group of Italian Large White pigs homozygous for the IGF2 intron3-g.3072G allele G (IGF2 intron3-g.3072GG) and by haplotype analysis between the markers of the two considered genes. Taken together, all these data indicated that the IGF2 intron3-g.3072G>A mutation is not the only polymorphism affecting fatness and muscle deposition in pigs. In the second chapter, we reported the analysis of two new SNPs identified in cathepsin L (CTSL) and cathepsin S (CTSS) genes and the association results with meat quality parameters (including cathepsin B activity) and several production traits in an Italian Large White pig population. Allele frequencies of these two markers were evaluated in 7 different pig breeds. Furthermore, we mapped using a radiation hybrid panel the CTSS gene on SSC4. Association studies with several production traits, carried out in 268 Italian Large White pigs, indicated positive effects of the CTSL polymorphism on average daily gain, weight of lean cuts and backfat thickness (P<0.05). The results for these latter traits were also confirmed using a selective genotype approach in other Italian Large White pigs (P<0.01). In the 268 pig group, the CTSS polymorphism was associated with feed:gain ratio and average daily gain (P<0.05). Instead, no association was observed between the analysed markers and meat quality parameters. Finally, we wanted to verify if the positive results obtained for the cathepsin L and S markers and for other previous identified SNPs (cathepsin F, cathepsin Z and their inhibitor cystatin B) were confirmed in the Italian Duroc pig breed (third chapter). We analysed them in two groups of Duroc pigs: the first group was made of 218 performance-tested pigs not selected by any phenotypic criteria, the second group was made of 100 Italian Duroc pigs extreme and divergent for visible intermuscular fat trait. In the first group, the CTSL polymorphism was associated with weight of lean cuts (P<0.05), while suggestive associations were obtained for average daily gain and backfat thickness (P<0.10). Allele frequencies of the CTSL gene marker also differed positively among the visible intermuscular extreme tails. Instead, no positive effects were observed for the other DNA markers on the analysed traits. In conclusion, in agreement with the present data and for the biological role of these enzymes, the porcine CTSD and CTSL markers: a) may have a direct effect in the biological mechanisms involved in determining fat and lean meat content in pigs, or b) these markers could be very close to the putative functional mutation(s) present in other genes. These findings have important practical applications, in particular the CTSD and CTSL mutations could be applied in a marker assisted selection (MAS) both in the Italian Large White and Italian Duroc breeds. Marker assisted selection could also increase in efficiency by adding information from the cathepsin S genotype, but only in the Italian Large White breed.

Different approaches to identify markers for meat quality and production traits in pigs: analysis of the transcriptome in post mortem skeletal muscle and investigation of candidate genes

Due to the growing attention of consumers towards their food, improvement of quality of animal products has become one of the main focus of research. To this aim, the application of modern molecular genetics approaches has been proved extremely useful and effective. This innovative drive includes all livestock species productions, including pork. The Italian pig breeding industry is unique because needs heavy pigs slaughtered at about 160 kg for the production of high quality processed products. For this reason, it requires precise meat quality and carcass characteristics. Two aspects have been considered in this thesis: the application of the transcriptome analysis in post mortem pig muscles as a possible method to evaluate meat quality parameters related to the pre mortem status of the animals, including health, nutrition, welfare, and with potential applications for product traceability (chapters 3 and 4); the study of candidate genes for obesity related traits in order to identify markers associated with fatness in pigs that could be applied to improve carcass quality (chapters 5, 6, and 7). Chapter three addresses the first issue from a methodological point of view. When we considered this issue, it was not obvious that post mortem skeletal muscle could be useful for transcriptomic analysis. Therefore we demonstrated that the quality of RNA extracted from skeletal muscle of pigs sampled at different post mortem intervals (20 minutes, 2 hours, 6 hours, and 24 hours) is good for downstream applications. Degradation occurred starting from 48 h post mortem even if at this time it is still possible to use some RNA products. In the fourth chapter, in order to demonstrate the potential use of RNA obtained up to 24 hours post mortem, we present the results of RNA analysis with the Affymetrix microarray platform that made it possible to assess the level of expression of more of 24000 mRNAs. We did not identify any significant differences between the different post mortem times suggesting that this technique could be applied to retrieve information coming from the transcriptome of skeletal muscle samples not collected just after slaughtering. This study represents the first contribution of this kind applied to pork. In the fifth chapter, we investigated as candidate for fat deposition the TBC1D1 [TBC1 (tre-2/USP6, BUB2, cdc16) gene. This gene is involved in mechanisms regulating energy homeostasis in skeletal muscle and is associated with predisposition to obesity in humans. By resequencing a fragment of the TBC1D1 gene we identified three synonymous mutations localized in exon 2 (g.40A>G, g.151C>T, and g.172T>C) and 2 polymorphisms localized in intron 2 (g.219G>A and g.252G>A). One of these polymorphisms (g.219G>A) was genotyped by high resolution melting (HRM) analysis and PCR-RFLP. Moreover, this gene sequence was mapped by radiation hybrid analysis on porcine chromosome 8. The association study was conducted in 756 performance tested pigs of Italian Large White and Italian Duroc breeds. Significant results were obtained for lean meat content, back fat thickness, visible intermuscular fat and ham weight. In chapter six, a second candidate gene (tribbles homolog 3, TRIB3) is analyzed in a study of association with carcass and meat quality traits. The TRIB3 gene is involved in energy metabolism of skeletal muscle and plays a role as suppressor of adipocyte differentiation. We identified two polymorphisms in the first coding exon of the porcine TRIB3 gene, one is a synonymous SNP (c.132T> C), a second is a missense mutation (c.146C> T, p.P49L). The two polymorphisms appear to be in complete linkage disequilibrium between and within breeds. The in silico analysis of the p.P49L substitution suggests that it might have a functional effect. The association study in about 650 pigs indicates that this marker is associated with back fat thickness in Italian Large White and Italian Duroc breeds in two different experimental designs. This polymorphisms is also associated with lactate content of muscle semimembranosus in Italian Large White pigs. Expression analysis indicated that this gene is transcribed in skeletal muscle and adipose tissue as well as in other tissues. In the seventh chapter, we reported the genotyping results for of 677 SNPs in extreme divergent groups of pigs chosen according to the extreme estimated breeding values for back fat thickness. SNPs were identified by resequencing, literature mining and in silico database mining. analysis, data reported in the literature of 60 candidates genes for obesity. Genotyping was carried out using the GoldenGate (Illumina) platform. Of the analyzed SNPs more that 300 were polymorphic in the genotyped population and had minor allele frequency (MAF) >0.05. Of these SNPs, 65 were associated (P<0.10) with back fat thickness. One of the most significant gene marker was the same TBC1D1 SNPs reported in chapter 5, confirming the role of this gene in fat deposition in pig. These results could be important to better define the pig as a model for human obesity other than for marker assisted selection to improve carcass characteristics.

Glycomics as an innovative technology to identify biomarkers of aging

Introduction. Glycomic analysis allows investigating on the global glycome within body fluids (as serum/plasma), this could eventually lead to identify new types of disease biomarkers, or as in this study, biomarkers of human aging studying specific aging models. Recent studies demonstrated that the plasma N-glycome is modified during human aging, suggesting that measurements of log-ratio of two serum/plasma N-glycans (NGA2F and NA2F), named GlycoAge test could provide a non-invasive biomarker of aging. Down syndrome (DS) is a genetic disorder in which multiple major aspects of senescent phenotype occur much earlier than in healthy age-matched subjects and has been often defined as an accelerated aging syndrome. The aim of this study was to compare plasma N-glycome of patients affected by DS with age- and sex matched non-affected controls, represented by their siblings (DSS), in order to assess if DS is characterized by a specific N-glycomic pattern. Therefore, in order to investigate if N-glycans changes that occur in DS were able to reveal an accelerated aging in DS patients, we enrolled the mothers (DSM) of the DS and DSS, representing the non-affected control group with a different chronological age respect to DS. We applied two different N-glycomics approaches on the same samples: first, in order to study the complete plasma N-glycome we applied a new high-sensitive protocol based on a MALDI-TOF-MS approach, second, we used DSA-FACE technology. Results: MALDI-TOF/MS analysis detected a specific N-glycomics signature for DS, characterized by an increase of fucosylated and bisecting species. Moreover, in DS the abundance of agalactosylated (as NA2F) species was similar or higher than their mothers. The measurement of GlycoAge test with DSA-FACE, validated also by MALDI-TOF, demonstrated a strongly association with age, moreover in DS, it’s value was similar to their mothers, and significantly higher than their age- and sex matched not-affected siblings

Exploring the biofilm of Streptococcus agalactiae to identify virulence factors

Streptococcus agalactiae, also known as Group B Streptococcus (GBS) is the primary colonizer of the anogenital mucosa of up to 40% of healthy women and an important cause of invasive neonatal infections worldwide. Among the 10 known capsular serotypes, GBS type III accounts for 30-76% of the cases of neonatal meningitis. Biofilms are dense aggregates of surface-adherent microorganisms embedded in an exopolysaccharide matrix. Centers for Disease Control and Prevention estimate that 65% of human bacterial infections involve biofilms (Post et al., 2004). In recent years, the ability of GBS to form biofilm attracted attention for its possible role in fitness and/or virulence. Here, a new in vitro biofilm formation protocol was developed to guarantee more stringent conditions, to better discriminate between strong-, low- and non- biofilm forming strains and reduce ambiguous data interpretation. This protocol was applied to screen the in vitro biofilm formation ability of more than 350 GBS clinical isolates from pregnant women and neonatal infections belonging to different serotype, in relation to media composition and pH. The results showed the enhancement of GBS biofilm formation in acidic condition and identified a subset of isolates belonging to serotypes III and V that forms strong biofilms in these conditions. Interestingly, the best biofilm formers belonged to the serotype III hypervirulent clone ST-17.It was also found that pH 5.0 induces down-regulation of the capsule but that this reduction is not enough by itself to ensure biofilm formation. Moreover, the ability of proteinase K to strongly inhibit biofilm formation and to disaggregate mature biofilms suggested that proteins play an essential role in promoting GBS biofilm formation and contribute to the biofilm structural stability. Finally, a set of proteins potentially expressed during the GBS in vitro biofilm formation were identified by mass spectrometry.

Interrogation of human monoclonal antibodies induced by 4C-MenB to identify protective antigens contained in the OMV component

Neisseria meningitidis is a gram negative human obligated pathogen, mostly found as a commensal in the oropharyngeal mucosa of healthy individuals. It can invade this epithelium determining rare but devastating and fast progressing outcomes, such as meningococcal meningitidis and septicemia, leading to death (about 135000 per year worldwide). Conjugated vaccines for serogroups A, C, W135, X and Y were developed, while for N. meningitidis serogroup B (MenB) the vaccines were based on Outern Membrane Vesicles (OMV). One of them is the 4C-MenB (Bexsero). The antigens included in this vaccine’s formulation are, in addition to the OMV from New Zeland epidemic strain 98/254, three recombinant proteins: NadA, NHBA and fHbp. While the role of these recombinant components was deeply characterized, the vesicular contribution in 4C-MenB elicited protection is mediated mainly by porin A and other unidentified antigens. To unravel the relative contribution of these different antigens in eliciting protective antibody responses, we isolated human monoclonal antibodies (mAbs) from single-cell sorted plasmablasts of 3 adult vaccinees peripheral blood. mAbs have been screened for binding to 4C-MenB components by Luminex bead-based assay. OMV-specific mAbs were purified and tested for functionality by serum bactericidal assay (SBA) on 18 different MenB strains and characterized in a protein microarray containing a panel of prioritized meningococcal proteins. The bactericidal mAbs identified to recognize the outer membrane proteins PorA and PorB, stating the importance of PorB in cross-strain protection. In addition, RmpM, BamE, Hyp1065 and ComL were found as immunogenic components of the 4C-MenB vaccine.

Developing a radiomic based machine learning model to identify patients with resected non-small-cell lung cancer at high risk of relapse

Background There is a wide variation of recurrence risk of Non-small-cell lung cancer (NSCLC) within the same Tumor Node Metastasis (TNM) stage, suggesting that other parameters are involved in determining this probability. Radiomics allows extraction of quantitative information from images that can be used for clinical purposes. The primary objective of this study is to develop a radiomic prognostic model that predicts a 3 year disease free-survival (DFS) of resected Early Stage (ES) NSCLC patients. Material and Methods 56 pre-surgery non contrast Computed Tomography (CT) scans were retrieved from the PACS of our institution and anonymized. Then they were automatically segmented with an open access deep learning pipeline and reviewed by an experienced radiologist to obtain 3D masks of the NSCLC. Images and masks underwent to resampling normalization and discretization. From the masks hundreds Radiomic Features (RF) were extracted using Py-Radiomics. Hence, RF were reduced to select the most representative features. The remaining RF were used in combination with Clinical parameters to build a DFS prediction model using Leave-one-out cross-validation (LOOCV) with Random Forest. Results and Conclusion A poor agreement between the radiologist and the automatic segmentation algorithm (DICE score of 0.37) was found. Therefore, another experienced radiologist manually segmented the lesions and only stable and reproducible RF were kept. 50 RF demonstrated a high correlation with the DFS but only one was confirmed when clinicopathological covariates were added: Busyness a Neighbouring Gray Tone Difference Matrix (HR 9.610). 16 clinical variables (which comprised TNM) were used to build the LOOCV model demonstrating a higher Area Under the Curve (AUC) when RF were included in the analysis (0.67 vs 0.60) but the difference was not statistically significant (p=0,5147).