Ruolo dell'elastometria pulsata monodimensionale ad ultrasuoni (fibroscan) nella stadiazione della fibrosi epatica in pazienti sottoposti a trapianto ortotopico di fegato con recidiva di epatite C

PREMESSA: La progressione della recidiva d’epatite C è accelerata nei pazienti sottoposti a trapianto di fegato e ciò ha portato alla necessità di sviluppare nuove e validate metodiche non invasive per la quantificazione e la misura della fibrosi epatica. SCOPI: Stabilire l’efficacia dell’elastometria epatica (Fibroscan®) e dei parametri sierici di fibrosi, attualmente disponibili nella pratica clinica, per predire il grado di fibrosi nei pazienti sottoposti a trapianto epatico. METODI: La correlazione fra fibrosi epatica, determinata mediante biopsia epatica ed esame istologico, e Fibroscan® o indici clinico-sierologici di fibrosi (Benlloch, Apri, Forns, Fibrotest and Doppler resistance index), è stata studiata in pazienti che avevano ricevuto un trapianto ortotopico di fegato con evidenza di recidiva d’epatite da HCV. Un totale di 36 pazienti, con la seguente classificazione istologica: fibrosi secondom METAVIR F1=24, F2=8, F3=3, F4=1, sono stati arruolati nella popolazione oggetto di studio. Un totale di 29 individui volontari sani sono serviti come controllo. Le differenze fra gli stadi di fibrosi sono state calcolate mediante analisi statistica non parametrica. Il miglior cut-off per la differenziazione di fibrosi significativa (F2-F4) è stato identificato mediante l’analisi delle curve ROC. RISULTATI: La rigidità epatica ha presentato valori di 4.4 KPa (2.7-6.9) nei controlli (mediane e ranges), con valori in tutti i soggeti <7.0 KPa; 7.75 KPa (4.2-28.0) negli F1; 16.95 KPa (10.2-31.6) negli F2; 21.10 KPa nell’unico paziente F4 cirrotico. Le differenze sono state statisticamente significative per i soggetti controllo versus F1 e F2 (p<0.0001) e per F1 versus F2 (p<0.0001). Un cut-off elastografico di 11.2 KPagarantisce 88% di Sensibilità, 90% di Specificità, 79% di PPV e 95% di NPV nel differenziare i soggetti F1 dagli F2-F4. Le AUROC, relativamente alla capacità di discriminare fra i differenti gradi di fibrosi, evidenziavano un netto vantaggio per il Fibroscan® rispetto ad ognuno degli indici non invasivi di fibrosi. CONCLUSIONI: L’elastometria epatica presenta una buona accuratezza diagnostica nell’identificare pazienti con fibrosi epatica di grado significativo, superiore a quella di tutti gli altri test non invasivi al momento disponibili nella clinica, nei pazienti portatori di trapianto epatico ortotopico da cadavere con recidiva di HCV.

Validazione dell'accuratezza di diagnostica di una rete neurale artificiale nella predizione della fibrosi epatica da epatite HCV stadiata con biopsia epatica ecoguidata

Nelle epatopatie croniche l’estensione della fibrosi è il principale determinante della prognosi. Sebbene la biopsia epatica rimanga il gold standard ai fini di una stadiazione, il crescente interesse nei confronti di metodi diagnostici non invasivi di fibrosi ha portato allo sviluppo di diversi modelli predittivi basati su parametri clinicolaboratoristici quali Fibrotest, indice APRI, indice Forns. Gli scopi dello studio sono: di stabilire l’accuratezza di un’analisi con rete neurale artificiale (ANN), tecnica di cui è stata dimostrata l’efficacia predittiva in situazioni biologiche complesse nell’identificare lo stadio di fibrosi, di confrontarne i risultati con quelli ottenuti dal calcolo degli indici APRI e Forns sullo stesso gruppo di pazienti e infine di validarne l’efficacia diagnostica in gruppi esterni.

Biomechanical modelling of human knee during living activities

The knee joint is a key structure of the human locomotor system. The knowledge of how each single anatomical structure of the knee contributes to determine the physiological function of the knee, is of fundamental importance for the development of new prostheses and novel clinical, surgical, and rehabilitative procedures. In this context, a modelling approach is necessary to estimate the biomechanic function of each anatomical structure during daily living activities. The main aim of this study was to obtain a subject-specific model of the knee joint of a selected healthy subject. In particular, 3D models of the cruciate ligaments and of the tibio-femoral articular contact were proposed and developed using accurate bony geometries and kinematics reliably recorded by means of nuclear magnetic resonance and 3D video-fluoroscopy from the selected subject. Regarding the model of the cruciate ligaments, each ligament was modelled with 25 linear-elastic elements paying particular attention to the anatomical twisting of the fibres. The devised model was as subject-specific as possible. The geometrical parameters were directly estimated from the experimental measurements, whereas the only mechanical parameter of the model, the elastic modulus, had to be considered from the literature because of the invasiveness of the needed measurements. Thus, the developed model was employed for simulations of stability tests and during living activities. Physiologically meaningful results were always obtained. Nevertheless, the lack of subject-specific mechanical characterization induced to design and partially develop a novel experimental method to characterize the mechanics of the human cruciate ligaments in living healthy subjects. Moreover, using the same subject-specific data, the tibio-femoral articular interaction was modelled investigating the location of the contact point during the execution of daily motor tasks and the contact area at the full extension with and without the whole body weight of the subject. Two different approaches were implemented and their efficiency was evaluated. Thus, pros and cons of each approach were discussed in order to suggest future improvements of this methodologies. The final results of this study will contribute to produce useful methodologies for the investigation of the in-vivo function and pathology of the knee joint during the execution of daily living activities. Thus, the developed methodologies will be useful tools for the development of new prostheses, tools and procedures both in research field and in diagnostic, surgical and rehabilitative fields.

Regulation of SRC-3 localization and dynamics by phosphorylation during ERα-dependent transcriptional activation

Transcription is controlled by promoter-selective transcriptional factors (TFs), which bind to cis-regulatory enhancers elements, termed hormone response elements (HREs), in a specific subset of genes. Regulation by these factors involves either the recruitment of coactivators or corepressors and direct interaction with the basal transcriptional machinery (1). Hormone-activated nuclear receptors (NRs) are well characterized transcriptional factors (2) that bind to the promoters of their target genes and recruit primary and secondary coactivator proteins which possess many enzymatic activities required for gene expression (1,3,4). In the present study, using single-cell high-resolution fluorescent microscopy and high throughput microscopy (HTM) coupled to computational imaging analysis, we investigated transcriptional regulation controlled by the estrogen receptor alpha (ERalpha), in terms of large scale chromatin remodeling and interaction with the associated coactivator SRC-3 (Steroid Receptor Coactivator-3), a member of p160 family (28) primary coactivators. ERalpha is a steroid-dependent transcriptional factor (16) that belongs to the NRs superfamily (2,3) and, in response to the hormone 17-ß estradiol (E2), regulates transcription of distinct target genes involved in development, puberty, and homeostasis (8,16). ERalpha spends most of its lifetime in the nucleus and undergoes a rapid (within minutes) intranuclear redistribution following the addition of either agonist or antagonist (17,18,19). We designed a HeLa cell line (PRL-HeLa), engineered with a chromosomeintegrated reporter gene array (PRL-array) containing multicopy hormone response-binding elements for ERalpha that are derived from the physiological enhancer/promoter region of the prolactin gene. Following GFP-ER transfection of PRL-HeLa cells, we were able to observe in situ ligand dependent (i) recruitment to the array of the receptor and associated coregulators, (ii) chromatin remodeling, and (iii) direct transcriptional readout of the reporter gene. Addition of E2 causes a visible opening (decondensation) of the PRL-array, colocalization of RNA Polymerase II, and transcriptional readout of the reporter gene, detected by mRNA FISH. On the contrary, when cells were treated with an ERalpha antagonist (Tamoxifen or ICI), a dramatic condensation of the PRL-array was observed, displacement of RNA Polymerase II, and complete decreasing in the transcriptional FISH signal. All p160 family coactivators (28) colocalize with ERalpha at the PRL-array. Steroid Receptor Coactivator-3 (SRC-3/AIB1/ACTR/pCIP/RAC3/TRAM1) is a p160 family member and a known oncogenic protein (4,34). SRC-3 is regulated by a variety of posttranslational modifications, including methylation, phosphorylation, acetylation, ubiquitination and sumoylation (4,35). These events have been shown to be important for its interaction with other coactivator proteins and NRs and for its oncogenic potential (37,39). A number of extracellular signaling molecules, like steroid hormones, growth factors and cytokines, induce SRC-3 phosphorylation (40). These actions are mediated by a wide range of kinases, including extracellular-regulated kinase 1 and 2 (ERK1-2), c-Jun N-terminal kinase, p38 MAPK, and IkB kinases (IKKs) (41,42,43). Here, we report SRC-3 to be a nucleocytoplasmic shuttling protein, whose cellular localization is regulated by phosphorylation and interaction with ERalpha. Using a combination of high throughput and fluorescence microscopy, we show that both chemical inhibition (with U0126) and siRNA downregulation of the MAP/ERK1/2 kinase (MEK1/2) pathway induce a cytoplasmic shift in SRC-3 localization, whereas stimulation by EGF signaling enhances its nuclear localization by inducing phosphorylation at T24, S857, and S860, known partecipants in the regulation of SRC-3 activity (39). Accordingly, the cytoplasmic localization of a non-phosphorylatable SRC-3 mutant further supports these results. In the presence of ERalpha, U0126 also dramatically reduces: hormone-dependent colocalization of ERalpha and SRC-3 in the nucleus; formation of ER-SRC-3 coimmunoprecipitation complex in cell lysates; localization of SRC-3 at the ER-targeted prolactin promoter array (PRL-array) and transcriptional activity. Finally, we show that SRC-3 can also function as a cotransporter, facilitating the nuclear-cytoplasmic shuttling of estrogen receptor. While a wealth of studies have revealed the molecular functions of NRs and coregulators, there is a paucity of data on how these functions are spatiotemporally organized in the cellular context. Technically and conceptually, our findings have a new impact upon evaluating gene transcriptional control and mechanisms of action of gene regulators.

Design and implementation of bioinformatics tools for large scale genome annotation

The continuous increase of genome sequencing projects produced a huge amount of data in the last 10 years: currently more than 600 prokaryotic and 80 eukaryotic genomes are fully sequenced and publically available. However the sole sequencing process of a genome is able to determine just raw nucleotide sequences. This is only the first step of the genome annotation process that will deal with the issue of assigning biological information to each sequence. The annotation process is done at each different level of the biological information processing mechanism, from DNA to protein, and cannot be accomplished only by in vitro analysis procedures resulting extremely expensive and time consuming when applied at a this large scale level. Thus, in silico methods need to be used to accomplish the task. The aim of this work was the implementation of predictive computational methods to allow a fast, reliable, and automated annotation of genomes and proteins starting from aminoacidic sequences. The first part of the work was focused on the implementation of a new machine learning based method for the prediction of the subcellular localization of soluble eukaryotic proteins. The method is called BaCelLo, and was developed in 2006. The main peculiarity of the method is to be independent from biases present in the training dataset, which causes the over‐prediction of the most represented examples in all the other available predictors developed so far. This important result was achieved by a modification, made by myself, to the standard Support Vector Machine (SVM) algorithm with the creation of the so called Balanced SVM. BaCelLo is able to predict the most important subcellular localizations in eukaryotic cells and three, kingdom‐specific, predictors were implemented. In two extensive comparisons, carried out in 2006 and 2008, BaCelLo reported to outperform all the currently available state‐of‐the‐art methods for this prediction task. BaCelLo was subsequently used to completely annotate 5 eukaryotic genomes, by integrating it in a pipeline of predictors developed at the Bologna Biocomputing group by Dr. Pier Luigi Martelli and Dr. Piero Fariselli. An online database, called eSLDB, was developed by integrating, for each aminoacidic sequence extracted from the genome, the predicted subcellular localization merged with experimental and similarity‐based annotations. In the second part of the work a new, machine learning based, method was implemented for the prediction of GPI‐anchored proteins. Basically the method is able to efficiently predict from the raw aminoacidic sequence both the presence of the GPI‐anchor (by means of an SVM), and the position in the sequence of the post‐translational modification event, the so called ω‐site (by means of an Hidden Markov Model (HMM)). The method is called GPIPE and reported to greatly enhance the prediction performances of GPI‐anchored proteins over all the previously developed methods. GPIPE was able to predict up to 88% of the experimentally annotated GPI‐anchored proteins by maintaining a rate of false positive prediction as low as 0.1%. GPIPE was used to completely annotate 81 eukaryotic genomes, and more than 15000 putative GPI‐anchored proteins were predicted, 561 of which are found in H. sapiens. In average 1% of a proteome is predicted as GPI‐anchored. A statistical analysis was performed onto the composition of the regions surrounding the ω‐site that allowed the definition of specific aminoacidic abundances in the different considered regions. Furthermore the hypothesis that compositional biases are present among the four major eukaryotic kingdoms, proposed in literature, was tested and rejected. All the developed predictors and databases are freely available at: BaCelLo http://gpcr.biocomp.unibo.it/bacello eSLDB http://gpcr.biocomp.unibo.it/esldb GPIPE http://gpcr.biocomp.unibo.it/gpipe