The BAG3- and SIRPα- mediated pathways trigger distinct cellular targets and signaling mechanisms in pancreatic cancer microenvironment. To explore their functional connection, we investigated the effects of their combined blockade on cancer growth in orthotopic allografts of pancreatic cancer mt4–2D cells in immunocompetent mice. The anti-BAG3 + anti-SIRPα mAbs treatment inhibited (p = 0.007) tumor growth by about the 70%; also the number of metastatic lesions was decreased, mostly by the effect of the anti-BAG3 mAb. Fibrosis and the expression of the CAF activation marker α-SMA were reduced by about the 30% in animals treated with anti-BAG3 mAb compared to untreated animals, and appeared unaffected by treatment with the anti-SIRPα mAb alone; however, the addition of anti-SIRPα to anti-BAG3 mAb in the combined treatment resulted in a > 60% (p < 0.0001) reduction of the fibrotic area and a 70% (p < 0.0001) inhibition of CAF α-SMA positivity. Dendritic cells (DCs) and CD8+ lymphocytes, hardly detectable in the tumors of untreated animals, were modestly increased by single treatments, while were much more clearly observable (p < 0.0001) in the tumors of the animals subjected to the combined treatment. The effects of BAG3 and SIRPα blockade do not simply reflect the sum of the effects of the single blockades, indicating that the two pathways are connected by regulatory interactions and suggesting, as a proof of principle, the potential therapeutic efficacy of a combined BAG3 and SIRPα blockade in pancreatic cancer.
Climate change and globalization have raised the risk of vector-borne disease (VBD) introduction and spread in various European nations in recent years. In Italy, viruses carried by tropical vectors have been shown to cause viral encephalitis, one of the symptoms of arbovirosis, a spectrum of viral disorders spread by arthropods such as mosquitoes and ticks. Arbovirosis are currently causing alarm and attention, and the World Health Organization (WHO) has released recommendations to adopt essential measures, particularly during the hot season, to restrict the spreading of the infectious agents among breeding stocks. In this scenario, rapid analysis systems are required, because they can quickly provide information on potential virus-host interactions, the evolution of the infection, and the onset of disabling clinical symptoms, or serious illnesses. Such systems include bioinformatics approaches integrated with molecular evaluation. Viruses have co-evolved different strategies to transcribe their own genetic material, by changing the host's transcriptional machinery, even in short periods of time. The introduction of genetic alterations, particularly in RNA viruses, results in a continuous adaptive fight against the host's immune system. We suggest an in silico pipeline method to unravel viral sequences that may interact with host RNA binding proteins (RBPs), which play important roles in RNA metabolism and its several related biological processes. Indeed, viral RNA sequences, able to bind host RBPs may compete with cellular RNAs, altering important metabolic processes. Our findings suggest that the proposed in silico approach, could be a useful and promising tool to investigate the complex and multiform clinical manifestations of viral encephalitis, and possibly identify altered metabolic pathways as targets of pharmacological treatments and innovative therapeutic protocols.
Ductal adenocarcinoma of the pancreas is a cancer with a high mortality rate. Among the main reasons for this baleful prognosis is that, in most patients, this neoplasm is diagnosed at a too advanced stage. Clinical oncology research is now particularly focused on decoding the cancer molecular onset by understanding the complex biological architecture of tumor cell proliferation. In this direction, machine learning has proved to be a valid solution in many sectors of the biomedical field, thanks to its ability to mine useful knowledge by biological and genetic data. Since the major risk factor is represented by genetic predisposition, the aim of this study is to find a mathematical model describing the complex relationship existing between genetic mutations of the involved genes and the onset of the disease. To this end, an approach based on evolutionary algorithms is proposed. In particular, genetic programming is used, which allows solving a symbolic regression problem through the use of genetic algorithms. The identification of these correlations is a typical objective of the diagnostic approach and is one of the most critical and complex activities in the presence of large amounts of data that are difficult to correlate through traditional statistical techniques. The mathematical model obtained highlights the importance of the complex relationship existing between the different gene’s mutations present in the tumor tissue of the group of patients considered.
Ductal adenocarcinoma of the pancreas is a cancer with a high mortality rate. Among the main reasons for this baleful prognosis is that, in most patients, this neoplasm is diagnosed at a too advanced stage. Clinical oncology research is now particularly focused on decoding the cancer molecular onset by understanding the complex biological architecture of tumor cell proliferation. In this direction, Machine-Learning has proved to be a valid solution in many sectors of the biomedical field, thanks to its ability to mine useful knowledge by biological and genetic data. Since the major risk factor is represented by genetic predisposition, the aim of this study is to find a mathematical model describing the complex relationship existing between genetic mutations of the involved genes and the onset of the disease. To this end, an approach based on evolutionary algorithms (Evolutionary Algorithm) is proposed. In particular, Genetic Programming is used, which allows solving a Symbolic Regression problem through the use of Genetic Algorithms. The identification of these correlations is a typical objective of the diagnostic approach, and is one of the most critical and complex activities in the presence of large amounts of data that are difficult to correlate through traditional statistical techniques. The formula obtained highlights the importance of the complex relationship existing between the different gene's mutations present in the tumor tissue of the group of patients considered.
Climate change and globalization have raised the risk of vector-borne disease (VBD) introduction and spread in various European nations in recent years. In Italy, viruses carried by tropical vectors have been shown to cause viral encephalitis, one of the symptoms of arboviruses, a spectrum of viral disorders spread by arthropods such as mosquitoes and ticks. Arboviruses are currently causing alarm and attention, and the World Health Organization (WHO) has released recommendations to adopt essential measures, particularly during the hot season, to restrict the spreading of the infectious agents among breeding stocks. In this scenario, rapid analysis systems are required, because they can quickly provide information on potential virus–host interactions, the evolution of the infection, and the onset of disabling clinical symptoms, or serious illnesses. Such systems include bioinformatics approaches integrated with molecular evaluation. Viruses have co-evolved different strategies to transcribe their own genetic material, by changing the host’s transcriptional machinery, even in short periods of time. The introduction of genetic alterations, particularly in RNA viruses, results in a continuous adaptive fight against the host’s immune system. We propose an in silico pipeline method for performing a comprehensive motif analysis (including motif discovery) on entire genome sequences to uncover viral sequences that may interact with host RNA binding proteins (RBPs) by interrogating the database of known RNA binding proteins, which play important roles in RNA metabolism and biological processes. Indeed, viral RNA sequences, able to bind host RBPs, may compete with cellular RNAs, altering important metabolic processes. Our findings suggest that the proposed in silico approach could be a useful and promising tool to investigate the complex and multiform clinical manifestations of viral encephalitis, and possibly identify altered metabolic pathways as targets of pharmacological treatments and innovative therapeutic protocols.
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