Exploring host-pathogen interactions through protein microarray. Large-scale protein microarray analysis revealed novel human receptors for the staphylococcal immune evasion protein FLIPr and for the neisserial adhesin NadA

Adhesion, immune evasion and invasion are key determinants during bacterial pathogenesis. Pathogenic bacteria possess a wide variety of surface exposed and secreted proteins which allow them to adhere to tissues, escape the immune system and spread throughout the human body. Therefore, extensive contacts between the human and the bacterial extracellular proteomes take place at the host-pathogen interface at the protein level. Recent researches emphasized the importance of a global and deeper understanding of the molecular mechanisms which underlie bacterial immune evasion and pathogenesis. Through the use of a large-scale, unbiased, protein microarray-based approach and of wide libraries of human and bacterial purified proteins, novel host-pathogen interactions were identified. This approach was first applied to Staphylococcus aureus, cause of a wide variety of diseases ranging from skin infections to endocarditis and sepsis. The screening led to the identification of several novel interactions between the human and the S. aureus extracellular proteomes. The interaction between the S. aureus immune evasion protein FLIPr (formyl-peptide receptor like-1 inhibitory protein) and the human complement component C1q, key players of the offense-defense fighting, was characterized using label-free techniques and functional assays. The same approach was also applied to Neisseria meningitidis, major cause of bacterial meningitis and fulminant sepsis worldwide. The screening led to the identification of several potential human receptors for the neisserial adhesin A (NadA), an important adhesion protein and key determinant of meningococcal interactions with the human host at various stages. The interaction between NadA and human LOX-1 (low-density oxidized lipoprotein receptor) was confirmed using label-free technologies and cell binding experiments in vitro. Taken together, these two examples provided concrete insights into S. aureus and N. meningitidis pathogenesis, and identified protein microarray coupled with appropriate validation methodologies as a powerful large scale tool for host-pathogen interactions studies.

Analisi di “Copy Number Variants” ed identificazione di nuovi geni candidati per l’Autismo e Ritardo Mentale

I disturbi dello spettro autistico (DSA) ed il ritardo mentale (RM) sono caratterizzati da un’eziologia genetica complessa ed eterogenea. Grazie ai recenti sviluppi nella ricerca genomica, è stato possibile dimostrare il ruolo di numerose copy number variants (CNVs) nella patogenesi di questi disturbi, anche se nella maggior parte dei casi l’eziologia rimane ancora sconosciuta. Questo lavoro riguarda l’identificazione e la caratterizzazione dei CNVs in famiglie con DSA e RM. E’ stata studiata una microdelezione in 7q31 che coinvolge i geni IMMP2L e DOCK4, trasmessa dalla madre con dislessia a due figli con autismo ed una figlia con dislessia. Nella stessa famiglia segrega una seconda microdelezione in 2q14 che inattiva il gene CNTNAP5 ed è trasmessa dal padre (con tratti autistici) ai due figli con autismo. Abbiamo quindi ipotizzato che i geni DOCK4 e CNTNAP5 potessero essere implicati, rispettivamente, nella suscettibilità a dislessia e DSA. Lo screening di numerosi individui affetti ha supportato la nostra ipotesi, con l’identificazione di una nuova microdelezione di DOCK4 che segrega con la dislessia, e 3 nuove varianti missenso in CNTNAP5 in individui con autismo. Dall’analisi genomica comparativa su array (aCGH) di individui con RM, è stata identificata una delezione nella regione 7q31.32, che coinvolge il gene CADPS2, in due fratelli con RM e tratti autistici, probabilmente ereditata dalla madre. Lo screening di mutazione di questo gene in individui con autismo o RM, ha portato all’identificazione di 3 varianti non sinonime, assenti nei controlli, ed ereditate per via materna. Poiché CADPS2 risiede in una regione genomica che contiene loci soggetti ad imprinting, abbiamo ipotizzato che il gene CADPS2 possa essere anch’esso caratterizzato da imprinting, con espressione monoallelica materna. Lo studio di espressione di CADPS2 in cellule del sangue ha avvalorato questa ipotesi, implicando perciò CADPS2 come un nuovo gene di suscettibilità per il RM e DSA.

New approaches to open problems in gene expression microarray data

In the past decade, the advent of efficient genome sequencing tools and high-throughput experimental biotechnology has lead to enormous progress in the life science. Among the most important innovations is the microarray tecnology. It allows to quantify the expression for thousands of genes simultaneously by measurin the hybridization from a tissue of interest to probes on a small glass or plastic slide. The characteristics of these data include a fair amount of random noise, a predictor dimension in the thousand, and a sample noise in the dozens. One of the most exciting areas to which microarray technology has been applied is the challenge of deciphering complex disease such as cancer. In these studies, samples are taken from two or more groups of individuals with heterogeneous phenotypes, pathologies, or clinical outcomes. these samples are hybridized to microarrays in an effort to find a small number of genes which are strongly correlated with the group of individuals. Eventhough today methods to analyse the data are welle developed and close to reach a standard organization (through the effort of preposed International project like Microarray Gene Expression Data -MGED- Society [1]) it is not unfrequant to stumble in a clinician's question that do not have a compelling statistical method that could permit to answer it.The contribution of this dissertation in deciphering disease regards the development of new approaches aiming at handle open problems posed by clinicians in handle specific experimental designs. In Chapter 1 starting from a biological necessary introduction, we revise the microarray tecnologies and all the important steps that involve an experiment from the production of the array, to the quality controls ending with preprocessing steps that will be used into the data analysis in the rest of the dissertation. While in Chapter 2 a critical review of standard analysis methods are provided stressing most of problems that In Chapter 3 is introduced a method to adress the issue of unbalanced design of miacroarray experiments. In microarray experiments, experimental design is a crucial starting-point for obtaining reasonable results. In a two-class problem, an equal or similar number of samples it should be collected between the two classes. However in some cases, e.g. rare pathologies, the approach to be taken is less evident. We propose to address this issue by applying a modified version of SAM [2]. MultiSAM consists in a reiterated application of a SAM analysis, comparing the less populated class (LPC) with 1,000 random samplings of the same size from the more populated class (MPC) A list of the differentially expressed genes is generated for each SAM application. After 1,000 reiterations, each single probe given a "score" ranging from 0 to 1,000 based on its recurrence in the 1,000 lists as differentially expressed. The performance of MultiSAM was compared to the performance of SAM and LIMMA [3] over two simulated data sets via beta and exponential distribution. The results of all three algorithms over low- noise data sets seems acceptable However, on a real unbalanced two-channel data set reagardin Chronic Lymphocitic Leukemia, LIMMA finds no significant probe, SAM finds 23 significantly changed probes but cannot separate the two classes, while MultiSAM finds 122 probes with score >300 and separates the data into two clusters by hierarchical clustering. We also report extra-assay validation in terms of differentially expressed genes Although standard algorithms perform well over low-noise simulated data sets, multi-SAM seems to be the only one able to reveal subtle differences in gene expression profiles on real unbalanced data. In Chapter 4 a method to adress similarities evaluation in a three-class prblem by means of Relevance Vector Machine [4] is described. In fact, looking at microarray data in a prognostic and diagnostic clinical framework, not only differences could have a crucial role. In some cases similarities can give useful and, sometimes even more, important information. The goal, given three classes, could be to establish, with a certain level of confidence, if the third one is similar to the first or the second one. In this work we show that Relevance Vector Machine (RVM) [2] could be a possible solutions to the limitation of standard supervised classification. In fact, RVM offers many advantages compared, for example, with his well-known precursor (Support Vector Machine - SVM [3]). Among these advantages, the estimate of posterior probability of class membership represents a key feature to address the similarity issue. This is a highly important, but often overlooked, option of any practical pattern recognition system. We focused on Tumor-Grade-three-class problem, so we have 67 samples of grade I (G1), 54 samples of grade 3 (G3) and 100 samples of grade 2 (G2). The goal is to find a model able to separate G1 from G3, then evaluate the third class G2 as test-set to obtain the probability for samples of G2 to be member of class G1 or class G3. The analysis showed that breast cancer samples of grade II have a molecular profile more similar to breast cancer samples of grade I. Looking at the literature this result have been guessed, but no measure of significance was gived before.

Genomic characterization of the italian wolf (Canis lupus): the genes involved in black coat colour determination and application of microarray technique for snps detection.

This study provides a comprehensive genetic overview on the endangered Italian wolf population. In particular, it focuses on two research lines. On one hand, we focalised on melanism in wolf in order to isolate a mutation related with black coat colour in canids. With several reported black individuals (an exception at European level), the Italian wolf population constituted a challenging research field posing many unanswered questions. As found in North American wolf, we reported that melanism in the Italian population is caused by a different melanocortin pathway component, the K locus, in which a beta-defensin protein acts as an alternative ligand for the Mc1r. This research project was conducted in collaboration with Prof. Gregory Barsh, Department of Genetics and Paediatrics, Stanford University. On the other hand, we performed analysis on a high number of SNPs thanks to a customized Canine microarray useful to integrate or substitute the STR markers for genotyping individuals and detecting wolf-dog hybrids. Thanks to DNA microchip technology, we obtained an impressive amount of genetic data which provides a solid base for future functional genomic studies. This study was undertaken in collaboration with Prof. Robert K. Wayne, Department of Ecology and Evolutionary Biology, University of California, Los Angeles (UCLA).

Bioinformatic methods in applied genomic research

Here I will focus on three main topics that best address and include the projects I have been working in during my three year PhD period that I have spent in different research laboratories addressing both computationally and practically important problems all related to modern molecular genomics. The first topic is the use of livestock species (pigs) as a model of obesity, a complex human dysfunction. My efforts here concern the detection and annotation of Single Nucleotide Polymorphisms. I developed a pipeline for mining human and porcine sequences. Starting from a set of human genes related with obesity the platform returns a list of annotated porcine SNPs extracted from a new set of potential obesity-genes. 565 of these SNPs were analyzed on an Illumina chip to test the involvement in obesity on a population composed by more than 500 pigs. Results will be discussed. All the computational analysis and experiments were done in collaboration with the Biocomputing group and Dr.Luca Fontanesi, respectively, under the direction of prof. Rita Casadio at the Bologna University, Italy. The second topic concerns developing a methodology, based on Factor Analysis, to simultaneously mine information from different levels of biological organization. With specific test cases we develop models of the complexity of the mRNA-miRNA molecular interaction in brain tumors measured indirectly by microarray and quantitative PCR. This work was done under the supervision of Prof. Christine Nardini, at the “CAS-MPG Partner Institute for Computational Biology” of Shangai, China (co-founded by the Max Planck Society and the Chinese Academy of Sciences jointly) The third topic concerns the development of a new method to overcome the variety of PCR technologies routinely adopted to characterize unknown flanking DNA regions of a viral integration locus of the human genome after clinical gene therapy. This new method is entirely based on next generation sequencing and it reduces the time required to detect insertion sites, decreasing the complexity of the procedure. This work was done in collaboration with the group of Dr. Manfred Schmidt at the Nationales Centrum für Tumorerkrankungen (Heidelberg, Germany) supervised by Dr. Annette Deichmann and Dr. Ali Nowrouzi. Furthermore I add as an Appendix the description of a R package for gene network reconstruction that I helped to develop for scientific usage (http://www.bioconductor.org/help/bioc-views/release/bioc/html/BUS.html).

A machine learning based method to detect genomic imbalances exploiting X chromosome exome reads

Whole Exome Sequencing (WES) is rapidly becoming the first-tier test in clinics, both thanks to its declining costs and the development of new platforms that help clinicians in the analysis and interpretation of SNV and InDels. However, we still know very little on how CNV detection could increase WES diagnostic yield. A plethora of exome CNV callers have been published over the years, all showing good performances towards specific CNV classes and sizes, suggesting that the combination of multiple tools is needed to obtain an overall good detection performance. Here we present TrainX, a ML-based method for calling heterozygous CNVs in WES data using EXCAVATOR2 Normalized Read Counts. We select males and females’ non pseudo-autosomal chromosome X alignments to construct our dataset and train our model, make predictions on autosomes target regions and use HMM to call CNVs. We compared TrainX against a set of CNV tools differing for the detection method (GATK4 gCNV, ExomeDepth, DECoN, CNVkit and EXCAVATOR2) and found that our algorithm outperformed them in terms of stability, as we identified both deletions and duplications with good scores (0.87 and 0.82 F1-scores respectively) and for sizes reaching the minimum resolution of 2 target regions. We also evaluated the method robustness using a set of WES and SNP array data (n=251), part of the Italian cohort of Epi25 collaborative, and were able to retrieve all clinical CNVs previously identified by the SNP array. TrainX showed good accuracy in detecting heterozygous CNVs of different sizes, making it a promising tool to use in a diagnostic setting.

Analyzing the dependence structure of microarray data: a copula–based approach

The main aim of this Ph.D. dissertation is the study of clustering dependent data by means of copula functions with particular emphasis on microarray data. Copula functions are a popular multivariate modeling tool in each field where the multivariate dependence is of great interest and their use in clustering has not been still investigated. The first part of this work contains the review of the literature of clustering methods, copula functions and microarray experiments. The attention focuses on the K–means (Hartigan, 1975; Hartigan and Wong, 1979), the hierarchical (Everitt, 1974) and the model–based (Fraley and Raftery, 1998, 1999, 2000, 2007) clustering techniques because their performance is compared. Then, the probabilistic interpretation of the Sklar’s theorem (Sklar’s, 1959), the estimation methods for copulas like the Inference for Margins (Joe and Xu, 1996) and the Archimedean and Elliptical copula families are presented. In the end, applications of clustering methods and copulas to the genetic and microarray experiments are highlighted. The second part contains the original contribution proposed. A simulation study is performed in order to evaluate the performance of the K–means and the hierarchical bottom–up clustering methods in identifying clusters according to the dependence structure of the data generating process. Different simulations are performed by varying different conditions (e.g., the kind of margins (distinct, overlapping and nested) and the value of the dependence parameter ) and the results are evaluated by means of different measures of performance. In light of the simulation results and of the limits of the two investigated clustering methods, a new clustering algorithm based on copula functions (‘CoClust’ in brief) is proposed. The basic idea, the iterative procedure of the CoClust and the description of the written R functions with their output are given. The CoClust algorithm is tested on simulated data (by varying the number of clusters, the copula models, the dependence parameter value and the degree of overlap of margins) and is compared with the performance of model–based clustering by using different measures of performance, like the percentage of well–identified number of clusters and the not rejection percentage of H0 on . It is shown that the CoClust algorithm allows to overcome all observed limits of the other investigated clustering techniques and is able to identify clusters according to the dependence structure of the data independently of the degree of overlap of margins and the strength of the dependence. The CoClust uses a criterion based on the maximized log–likelihood function of the copula and can virtually account for any possible dependence relationship between observations. Many peculiar characteristics are shown for the CoClust, e.g. its capability of identifying the true number of clusters and the fact that it does not require a starting classification. Finally, the CoClust algorithm is applied to the real microarray data of Hedenfalk et al. (2001) both to the gene expressions observed in three different cancer samples and to the columns (tumor samples) of the whole data matrix.

Synthesis, multiscale-multiphase characterization and applications of thiophene-based biohybrids

Biohybrid derivatives of π-conjugated materials are emerging as powerful tools to study biological events through the (opto)electronic variations of the π-conjugated moieties, as well as to direct and govern the self-assembly properties of the organic materials through the organization principles of the bio component. So far, very few examples of thiophene-based biohybrids have been reported. The aim of this Ph. D thesis has been the development of oligothiophene-oligonucleotide hybrid derivatives as tools, on one side, to detect DNA hybridisation events and, on the other, as model compounds to investigate thiophene-nucleobase interactions in the solid state. To obtain oligothiophene bioconjugates with the required high level of purity, we first developed new synthetic ecofriendly protocols for the synthesis of thiophene oligomers. Our innovative heterogeneous Suzuki coupling methodology, carried out in EtOH/water or isopropanol under microwave irradiation, allowed us to obtain alkyl substituted oligothiophenes and thiophene based co-oligomers in high yields and very short reaction times, free from residual metals and with improved film forming properties. These methodologies were subsequently applied in the synthesis of oligothiophene-oligonucleotide conjugates. Oligothiophene-5-labeled deoxyuridines were synthesized and incorporated into 19-meric oligonucletide sequences. We showed that the oligothiophene-labeled oligonucletide sequences obtained can be used as probes to detect a single nucleotide polymorphism (SNP) in complementary DNA target sequences. In fact, all the probes showed marked variations in emission intensity upon hybridization with a complementary target sequence. The observed variations in emitted light were comparable or even superior to those reported in similar studies, showing that the biohybrids can potentially be useful to develop biosensors for the detection of DNA mismatches. Finally, water-soluble, photoluminescent and electroactive dinucleotide-hybrid derivatives of quaterthiophene and quinquethiophene were synthesized. By means of a combination of spectroscopy and microscopy techniques, electrical characterizations, microfluidic measurements and theoretical calculations, we were able to demonstrate that the self-assembly modalities of the biohybrids in thin films are driven by the interplay of intra and intermolecular interactions in which the π-stacking between the oligothiophene and nucleotide bases plays a major role.

Identification of surface protein complexes of Streptococcus pyogenes through protein microarray technology

A systematic characterization of the composition and structure of the bacterial cell-surface proteome and its complexes can provide an invaluable tool for its comprehensive understanding. The knowledge of protein complexes composition and structure could offer new, more effective targets for a more specific and consequently effective immune response against a complex instead of a single protein. Large-scale protein-protein interaction screens are the first step towards the identification of complexes and their attribution to specific pathways. Currently, several methods exist for identifying protein interactions and protein microarrays provide the most appealing alternative to existing techniques for a high throughput screening of protein-protein interactions in vitro under reasonably straightforward conditions. In this study approximately 100 proteins of Group A Streptococcus (GAS) predicted to be secreted or surface exposed by genomic and proteomic approaches were purified in a His-tagged form and used to generate protein microarrays on nitrocellulose-coated slides. To identify protein-protein interactions each purified protein was then labeled with biotin, hybridized to the microarray and interactions were detected with Cy3-labelled streptavidin. Only reciprocal interactions, i. e. binding of the same two interactors irrespective of which of the two partners is in solid-phase or in solution, were taken as bona fide protein-protein interactions. Using this approach, we have identified 20 interactors of one of the potent toxins secreted by GAS and known as superantigens. Several of these interactors belong to the molecular chaperone or protein folding catalyst families and presumably are involved in the secretion and folding of the superantigen. In addition, a very interesting interaction was found between the superantigen and the substrate binding subunit of a well characterized ABC transporter. This finding opens a new perspective on the current understanding of how superantigens are modified by the bacterial cell in order to become major players in causing disease.

Analisi fosfo-proteomica di campioni di tumore: il problema della stratificazione dei pazienti ed eterogeneità intra-tumorale nell'era della terapia personalizzata

Nella presente tesi indaghiamo la potenzialità di LCM e Reverse Phase Protein microarray negli studi clinici. Si analizza la possibilità di creare una bio banca con line cellular primarie, al fine di conseguire drug test di sensibilità prima di decidere il trattamento da somministrare ai singoli pazienti. Sono stati ottenuti profili proteomici da biopsie pre e post terapia. I risultati dimostrano che questa piattaforma mostra il meccanismo di resistenza acquisito durante la terapia biologica. Questo ci ha portato ad analizzare una possibile stratificazione per pazienti con mCRC . I dati hanno rivelato distinti pathway di attivazione tra metastasi resecabile e non resecabili. I risultati mostrano inoltre due potenziali bersagli farmacologici. Ma la valutazione dell'intero tumore tramite singole biopsie sembra essere un problema a causa dell’eterogeneità intratumorale a livello genomico. Abbiamo indagato questo problema a livello dell'architettura del segnale in campioni di mCRC e ccRCC . I risultati indicano una somiglianza complessiva nei profili proteomici all'interno dello stesso tumore. Considerando che una singola biopsia è rappresentativa di un intera lesione , abbiamo studiato la possibilità di creare linee di cellule primarie, per valutare il profilo molecolare di ogni paziente. Fino ad oggi non c'era un protocollo per creare linee cellulari immortalizzate senza alcuna variazione genetica . abbiamo cosiderato, però, l'approccio innovativo delle CRCs. Ad oggi , non è ancora chiaro se tali cellule mimino il profilo dei tessuti oppure I passaggi in vitro modifichino i loro pathways . Sulla base di un modello di topo , i nostri dati mostrano un profilo di proteomica simile tra le linee di cellule e tessuti di topo LCM. In conclusione, i nostri dati dimostrano l'utilità della piattaforma LCM / RPPA nella sperimentazione clinica e la possibilità di creare una bio - banca di linee cellulari primarie, per migliorare la decisione del trattamento.

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).

Ultrasensitive chemiluminescence bioassays based on microfluidics in miniaturized analytical devices

The activity carried out during my PhD was principally addressed to the development of portable microfluidic analytical devices based on biospecific molecular recognition reactions and CL detection. In particular, the development of biosensors required the study of different materials and procedures for their construction, with particular attention to the development of suitable immobilization procedures, fluidic systems and the selection of the suitable detectors. Different methods were exploited, such as gene probe hybridization assay or immunoassay, based on different platform (functionalized glass slide or nitrocellulose membrane) trying to improve the simplicity of the assay procedure. Different CL detectors were also employed and compared with each other in the search for the best compromise between portability and sensitivity. The work was therefore aimed at miniaturization and simplification of analytical devices and the study involved all aspects of the system, from the analytical methodology to the type of detector, in order to combine high sensitivity with easiness-of-use and rapidity. The latest development involving the use of smartphone as chemiluminescent detector paves the way for a new generation of analytical devices in the clinical diagnostic field thanks to the ideal combination of sensibility a simplicity of the CL with the day-by-day increase in the performance of the new generation smartphone camera. Moreover, the connectivity and data processing offered by smartphones can be exploited to perform analysis directly at home with simple procedures. The system could eventually be used to monitor patient health and directly notify the physician of the analysis results allowing a decrease in costs and an increase in the healthcare availability and accessibility.

Exploring novel targeted therapeutic strategies against Acute Myeloid Leukemia cells based on inhibition of bromodomain proteins and CDC20 activity

Acute myeloid leukemia (AML) is a haematological malignancies arising from the accumulation of undifferentiated myeloid progenitors with an uncontrolled proliferation. The genomic landscape of AML revealed that the disease is characterized by high level of heterogeneity and is subjected to clonal evolution driven by selective pressure of chemotherapy. In this study, we investigated the therapeutic effects of the inhibition of BRD4 and CDC20 in vitro and ex vivo. We demonstrated that inhibition of BRD4 with GSK1215101A in AML cell lines was effective under hypoxia. It induced the activation of antioxidant response both, at transcriptomic and metabolomic levels, driven by enrichment of NRF2 pathway under normoxic and hypoxic condition. Moreover, the combined treatment with Omaveloxolone, a drug inducing NRF2 activation and NF-κB inhibition, potentiated the effects on apoptosis and colony forming capacity of stem progenitor cells. Lastly, gene expression profiling data revealed that combination treatment induced major changes in genes related to cell cycle, together with enrichment of cell differentiation pathways and negative regulation of WNT, in normoxia and hypoxia. Regarding CDC20, we observed its up-regulation in AML patients. Treatment with two different inhibitors, Apcin and proTAME, was effective in primary AML cells and in AML cell lines, through induction of apoptosis and mitotic arrest. The lack of correlation between proliferation markers and CDC20 levels in AML cell subpopulations supports the idea of alternative CDC20 functions, independent from its essential role during mitosis. CDC20-KD experiments conducted in AML cell lines revealed a mild effect on apoptosis induction, but no significant change in cell cycle progression. In summary, these results allowed the identification of a new strategy combination to improve the effects of BRD4 inhibition on LSC residing in the BM hypoxic niche, and provide some new evidence regarding the potential role of CDC20 as a new target for AML treatment.

Moving toward stock units identification based on spatial population structure of marine species in the Mediterranean Sea and adjacent waters through multidisciplinary and holistic approaches for the sustainability of fisheries resources

Investigating stock identity of marine species in a multidisciplinary holistic approach can reveal patterns of complex spatial population structure and signatures of potential local adaptation. The population structure of common sole (Solea solea) in the Mediterranean Sea was delineated using genomic and otolith data, including single nucleotide polymorphisms (SNPs) markers and otolith data. SNPs were correlated with environmental and spatial variables to evaluate the impact of these features on the actual genetic population structure. Integrated holistic approach was applied to combine the tracers with different spatio-temporal scales. SNPs data was also used to illustrate the population structure of European hake (Merluccius merluccius) within the Alboran Sea, extending into the neighboring Mediterranean Sea and Atlantic Ocean. The aim was to identify patterns of neutral and potential adaptive genetic variation by applying seascape genomic framework. Results from both genetic and otolith data suggested significant divergence among putative populations of common sole, confirming a clear separation between Western, Adriatic Sea and Eastern Mediterranean Sea. Evidence of fine-scale population structure in the Western Mediterranean Sea was observed at outlier loci level and in the Adriatic. Our study not only indicates that separation among Mediterranean sole population is led primarily by neutral processes, but it also suggests the presence of local adaptation influenced by environmental and spatial factors. The holistic approach by considering the spatio-temporal scales of variation confirmed that the same pattern of separation between these geographical sites is currently occurring and has occurred for many generations. Results showed the occurrence of population structure in Merluccius merluccius by detecting westward–eastward differentiation among populations and distinct subgroups at a fine geographical scale using outlier SNPs. These results enhance the knowledge of the population structure of commercially relevant species to support the application of spatial stock assessment models, including a redefinition of fishery management units.

Applied genomics to monitor the genetic diversity and integrity of honey bee (Apis mellifera) populations and define sustainable strategies to benefit the beekeeping sector

Honey bees are considered keystone species in ecosystem, the effect of harmful pesticides for the honey bees, the action of extreme climatic waves and their consequence on honey bees health can cause the loss of many colonies which could contribute to the reduction of the effective population size and incentive the use of non-autochthonous queens to replace dead colonies. Over the last decades, the use of non-ligustica bee subspecies in Italy has increased and together with the mentioned phenomena exposed native honey bees to hybridization, laeding to a dramatic loss of genetic erosion and admixture. Healthy genetic diversity within honey bee populations is critical to provide tolerance and resistance to current and future threatening. Nowadays it is urgent to design strategies for the conservation of local subspecies and their valorisation on a productive scale. In this Thesis we applied genomics tool for the analysis of the genetic diversity and the genomic integrity of honey bee populations in Italy are described. In this work mtDNA based methods are presented using honey bee DNA or honey eDNA as source of information of the genetic diversity of A. mellifera at different level. Taken together, the results derived from these studies should enlarge the knowledge of the genetic diversity and integrity of the honey bee populations in Italy, filling the gap of information necessary to design efficient conservation programmes. Furthermore, the methods presented in these works will provide a tool for the honey authentication to sustain and valorise beekeeping products and sector against frauds.