Study of apoptotic deletion mediated by Bifidobacterium longum with construction of recombinant strains for Serpin encoding gene and phenotypes comparison in a pig cell model

The first part of the research project of the Co-Advisorship Ph.D Thesis was aimed to select the best Bifidobacterium longum strains suitable to set the basis of our study. We were looking for strains with the abilities to colonize the intestinal mucosa and with good adhesion capacities, so that we can test these strains to investigate their ability to induce apoptosis in “damaged” intestinal cells. Adhesion and apoptosis are the two process that we want to study to better understand the role of an adhesion protein that we have previously identified and that have top scores homologies with the recent serpin encoding gene identified in B. longum by Nestlè researchers. Bifidobacterium longum is a probiotic, known for its beneficial effects to the human gut and even for its immunomodulatory and antitumor activities. Recently, many studies have stressed out the intimate relation between probiotic bacteria and the GIT mucosa and their influence on human cellular homeostasis. We focused on the apoptotic deletion of cancer cells induced by B. longum. This has been valued in vitro, performing the incubation of three B.longum strains with enterocyte-like Caco- 2 cells, to evidence DNA fragmentation, a cornerstone of apoptosis. The three strains tested were defined for their adhesion properties using adhesion and autoaggregation assays. These features are considered necessary to select a probiotic strain. The three strains named B12, B18 and B2990 resulted respectively: “strong adherent”, “adherent” and “non adherent”. Then, bacteria were incubated with Caco-2 cells to investigate apoptotic deletion. Cocultures of Caco-2 cells with B. longum resulted positive in DNA fragmentation test, only when adherent strains were used (B12 and B18). These results indicate that the interaction with adherent B. longum can induce apoptotic deletion of Caco-2 cells, suggesting a role in cellular homeostasis of the gastrointestinal tract and in restoring the ecology of damaged colon tissues. These results were used to keep on researching and the strains tested were used as recipient of recombinant techniques aimed to originate new B.longum strains with enhanced capacity of apoptotic induction in “damaged” intestinal cells. To achieve this new goal it was decided to clone the serpin encoding gene of B. longum, so that we can understand its role in adhesion and apoptosis induction. Bifidobacterium longum has immunostimulant activity that in vitro can lead to apoptotic response of Caco-2 cell line. It secretes a hypothetical eukaryotic type serpin protein, which could be involved in this kind of deletion of damaged cells. We had previously characterised a protein that has homologies with the hypothetical serpin of B. longum (DD087853). In order to create Bifidobacterium serpin transformants, a B. longum cosmid library was screened with a PCR protocol using specific primers for serpin gene. After fragment extraction, the insert named S1 was sub-cloned into pRM2, an Escherichia coli - Bifidobacterium shuttle vector, to construct pRM3. Several protocols for B. longum transformation were performed and the best efficiency was obtained using MRS medium and raffinose. Finally bacterial cell supernatants were tested in a dotblot assay to detect antigens presence against anti-antitrypsin polyclonal antibody. The best signal was produced by one starin that has been renamed B. longum BLKS 7. Our research study was aimed to generate transformants able to over express serpin encoding gene, so that we can have the tools for a further study on bacterial apoptotic induction of Caco-2 cell line. After that we have originated new trasformants the next step to do was to test transformants abilities when exposed to an intestinal cell model. In fact, this part of the project was achieved in the Department of Biochemistry of the Medical Faculty of the University of Maribor, guest of the abroad supervisor of the Co-Advisorship Doctoral Thesis: Prof. Avrelija Cencic. In this study we examined the probiotic ability of some bacterial strains using intestinal cells from a 6 years old pig. The use of intestinal mammalian cells is essential to study this symbiosis and a functional cell model mimics a polarised epithelium in which enterocytes are separated by tight junctions. In this list of strains we have included the Bifidobacterium longum BKS7 transformant strain that we have previously originated; in order to compare its abilities. B. longum B12 wild type and B. longum BKS7 transformant and eight Lactobacillus strains of different sources were co-cultured with porcine small intestine epithelial cells (PSI C1) and porcine blood monocytes (PoM2) in Transwell filter inserts. The strains, including Lb. gasseri, Lb. fermentum, Lb. reuterii, Lb. plantarum and unidentified Lactobacillus from kenyan maasai milk and tanzanian coffee, were assayed for activation of cell lines, measuring nitric oxide by Griess reaction, H202 by tetramethylbenzidine reaction and O2 - by cytochrome C reduction. Cytotoxic effect by crystal violet staining and induction on metabolic activity by MTT cell proliferation assay were tested too. Transepithelial electrical resistance (TER) of polarised PSI C1 was measured during 48 hours co-culture. TER, used to observe epithelium permeability, decrease during pathogenesis and tissue becomes permeable to ion passive flow lowering epithelial barrier function. Probiotics can prevent or restore increased permeability. Lastly, dot-blot was achieved against Interleukin-6 of treated cells supernatants. The metabolic activity of PoM2 and PSI C1 increased slightly after co-culture not affecting mitochondrial functions. No strain was cytotoxic over PSI C1 and PoM2 and no cell activation was observed, as measured by the release of NO2, H202 and O2 - by PoM2 and PSI C1. During coculture TER of polarised PSI C1 was two-fold higher comparing with constant TER (~3000 ) of untreated cells. TER raise generated by bacteria maintains a low permeability of the epithelium. During treatment Interleukin-6 was detected in cell supernatants at several time points, confirming immunostimulant activity. All results were obtained using Lactobacillus paracasei Shirota e Carnobacterium divergens as controls. In conclusion we can state that both the list of putative probiotic bacteria and our new transformant strain of B. longum are not harmful when exposed to intestinal cells and could be selected as probiotics, because can strengthen epithelial barrier function and stimulate nonspecific immunity of intestinal cells on a pig cell model. Indeed, we have found out that none of the strains tested that have good adhesion abilities presents citotoxicity to the intestinal cells and that non of the strains tested can induce cell lines to produce high level of ROS, neither NO2. Moreover we have assayed even the capacity of producing certain citokynes that are correlated with immune response. The detection of Interleukin-6 was assayed in all our samples, including B.longum transformant BKS 7 strain, this result indicates that these bacteria can induce a non specific immune response in the intestinal cells. In fact, when we assayed the presence of Interferon-gamma in cells supernatant after bacterial exposure, we have no positive signals, that means that there is no activation of a specific immune response, thus confirming that these bacteria are not recognize as pathogen by the intestinal cells and are certainly not harmful for intestinal cells. The most important result is the measure of Trans Epithelial Electric Resistance that have shown how the intestinal barrier function get strengthen when cells are exposed to bacteria, due to a reduction of the epithelium permeability. We have now a new strain of B. longum that will be used for further studies above the mechanism of apoptotic induction to “damaged cells” and above the process of “restoring ecology”. This strain will be the basis to originate new transformant strains for Serpin encoding gene that must have better performance and shall be used one day even in clinical cases as in “gene therapy” for cancer treatment and prevention.

Pervasive Business Intelligence

In the last few years, a new generation of Business Intelligence (BI) tools called BI 2.0 has emerged to meet the new and ambitious requirements of business users. BI 2.0 not only introduces brand new topics, but in some cases it re-examines past challenges according to new perspectives depending on the market changes and needs. In this context, the term pervasive BI has gained increasing interest as an innovative and forward-looking perspective. This thesis investigates three different aspects of pervasive BI: personalization, timeliness, and integration. Personalization refers to the capacity of BI tools to customize the query result according to the user who takes advantage of it, facilitating the fruition of BI information by different type of users (e.g., front-line employees, suppliers, customers, or business partners). In this direction, the thesis proposes a model for On-Line Analytical Process (OLAP) query personalization to reduce the query result to the most relevant information for the specific user. Timeliness refers to the timely provision of business information for decision-making. In this direction, this thesis defines a new Data Warehuose (DW) methodology, Four-Wheel-Drive (4WD), that combines traditional development approaches with agile methods; the aim is to accelerate the project development and reduce the software costs, so as to decrease the number of DW project failures and favour the BI tool penetration even in small and medium companies. Integration refers to the ability of BI tools to allow users to access information anywhere it can be found, by using the device they prefer. To this end, this thesis proposes Business Intelligence Network (BIN), a peer-to-peer data warehousing architecture, where a user can formulate an OLAP query on its own system and retrieve relevant information from both its local system and the DWs of the net, preserving its autonomy and independency.

Model selection and the vectorial misspecification-resistant information criterion in multivariate time series

The thesis deals with the problem of Model Selection (MS) motivated by information and prediction theory, focusing on parametric time series (TS) models. The main contribution of the thesis is the extension to the multivariate case of the Misspecification-Resistant Information Criterion (MRIC), a criterion introduced recently that solves Akaike’s original research problem posed 50 years ago, which led to the definition of the AIC. The importance of MS is witnessed by the huge amount of literature devoted to it and published in scientific journals of many different disciplines. Despite such a widespread treatment, the contributions that adopt a mathematically rigorous approach are not so numerous and one of the aims of this project is to review and assess them. Chapter 2 discusses methodological aspects of MS from information theory. Information criteria (IC) for the i.i.d. setting are surveyed along with their asymptotic properties; and the cases of small samples, misspecification, further estimators. Chapter 3 surveys criteria for TS. IC and prediction criteria are considered for: univariate models (AR, ARMA) in the time and frequency domain, parametric multivariate (VARMA, VAR); nonparametric nonlinear (NAR); and high-dimensional models. The MRIC answers Akaike’s original question on efficient criteria, for possibly-misspecified (PM) univariate TS models in multi-step prediction with high-dimensional data and nonlinear models. Chapter 4 extends the MRIC to PM multivariate TS models for multi-step prediction introducing the Vectorial MRIC (VMRIC). We show that the VMRIC is asymptotically efficient by proving the decomposition of the MSPE matrix and the consistency of its Method-of-Moments Estimator (MoME), for Least Squares multi-step prediction with univariate regressor. Chapter 5 extends the VMRIC to the general multiple regressor case, by showing that the MSPE matrix decomposition holds, obtaining consistency for its MoME, and proving its efficiency. The chapter concludes with a digression on the conditions for PM VARX models.

Development of a porcine lactation model for the evaluation of mammary transfer of exogenous molecules - A contribution from the conception project

Around 5 million women give birth each year in Europe and, while breastfeeding, the majority of them may need to take medications, either occasionally or continuously. Unfortunately, there is often scarce evidence of trustworthy information about how a specific molecule might affect the physiology of lactation. This is the reason that brought a European public-private partnership to fund the development of a reliable platform to provide women and health-care professionals a helpful instrument to reduce uncertainty about the effects of medication used during breastfeeding. On April 1st 2019, the ConcePTION project (Grant Agreement n°821520) started to develop such envisaged platform. The 3rd Work Package was in charge of the validation of in vitro, in vivo and in silico lactation models. Between the numerous species currently used in preclinical studies, pigs’ similarities with humans’ anatomy, physiology and genomics make them extremely useful as translational models, when proper veterinary expertise is applied. The ASA team from the University of Bologna, went first to characterize the translational lactation model using the swine species, chosen upon literature review. The aim of this work was to lay the foundations of a porcine lactation model that could be suitable for application within pharmaceutical tests, to study drug transfer through milk prior approval and commercialization. The obtained results highlighted both strengths and critical points of the study design, allowing a significant improvement in the knowledge of pharmacokinetic physiology in lactating mammals. Lastly, this project allowed the assessment of microbial changes in gut resident bacteria of newborns through an innovative in vitro colonic model. Indeed, even if there were no evident adverse effects determined by drug residues in milk, possible alterations in the delicate microbial ecology of newborns’ gastrointestinal tract was considered pivotal, giving its possible impact on the individual health and growth.

The RAS-Lung project: implementing blood and tissue genotyping in KRAS-Positive non-small cell lung cancer treatment

Background: The frontline management of non-oncogene addicted non-small cell lung cancer (NSCLC) involves immunotherapy (ICI) alone or combined with chemotherapy (CT-ICI). As therapeutic options expand, refining NSCLC genotyping gains paramount importance. The dynamic landscape of KRAS-positive NSCLC presents a spectrum of treatment options, including ICI, targeted therapy, and combination strategies currently under investigation. Methods: The two-year RASLUNG project, featuring both retrospective and prospective cohorts, aimed to analyze the predictive and prognostic impact of KRAS mutations on tumor tissue and circulating DNA (ctDNA). Secondary objectives included assessing the roles of co-mutations and longitudinal changes in KRAS mutant copies concerning treatment response and survival outcomes. An external validation study confirmed the prognostic or predictive significance of co-mutations. Results: In the prospective cohort (n=24), patients with liver metastases exhibited significantly elevated ctDNA levels(p=0.01), while those with >3 metastatic sites showed increased Allele Frequency (AF) (P=0.002). Median overall survival (OS) was 7.5 months, progression-free survival (PFS) was 4.0 months, and the objective response rate (ORR) was 33.3%. Higher AF correlated with an increased risk of death (HR 1.04, p = 0.03), though not progression. Notably, a reduction in plasma DNA levels was significantly associated with objective response(p=0.01). In the retrospective cohort, KRAS and STK11 mutations co-occurred in 14/21 patients (p=0.053). STK11 mutations were independently detrimental to OS (HR 1.97, p=0.025) after adjusting for various factors. KRAS tissue AF did not correlate with OS or PFS. Within the validation dataset, STK11 mutations were significantly associated with an increased risk of death in univariate (HR 2.01, p<0.001) and multivariate models (HR 1.66, p=0.001) after adjustments. Conclusion: The RAS-Lung Project, employing innovative genotyping techniques, underscores the significance of comprehensive NSCLC genotyping. Tailored next-generation sequencing (NGS) and ctDNA monitoring may offer potential benefits in navigating the evolving landscape of KRAS-positive NSCLC treatment.

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).