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Milanesi, M.

Publications and source records attributed to Milanesi, M..

3 recordsLinked to original sources

Combining landscape genomics and ecological modelling to investigate local adaptation of indigenous Ugandan cattle to East Coast fever

East Coast fever (ECF) is a fatal sickness affecting cattle populations of eastern, central, and southern Africa. The disease is transmitted by the tick Rhipicephalus appendiculatus, and caused by the protozoan Theileria parva parva, which invades host lymphocytes and promotes their clonal expansion. Importantly, indigenous cattle show tolerance to infection in ECF-endemically stable areas. Here, the putative genetic bases underlying ECF-tolerance were investigated using molecular data and epidemiological information from 823 indigenous cattle from Uganda. Vector distribution and host infection risk were estimated over the study area and subsequently tested as triggers of local adaptation by means of landscape genomics analysis. We identified 41 and seven candidate adaptive loci for tick resistance and infection tolerance, respectively. Among the genes associated with the candidate adaptive loci are PRKG1 and SLA2. PRKG1 was already described as associated with tick resistance in indigenous South African cattle, due to its role into inflammatory response. SLA2 is part of the regulatory pathways involved into lymphocytes proliferation. Additionally, local ancestry analysis suggested the zebuine origin of the genomic region candidate for tick resistance.\n\nAuthor summaryThe tick-borne parasite Theileria parva parva infects cattle populations of eastern, central and southern Africa, by causing a highly fatal pathology called \"East Coast fever\". The disease is especially severe for the exotic breeds imported to Africa, as well as outside the endemic areas of East Africa. In these regions, indigenous cattle populations can survive to infection, and this tolerance might result from unique adaptations evolved to fight the disease. We investigated this hypothesis by using a method named \"landscape genomics\", with which we compared the genetic characteristics of indigenous Ugandan cattle coming from areas at different infection risk, and located genomic sites potentially attributable to tolerance. In particular, the method pinpointed two genes, one (PRKG1) involved into inflammatory response and potentially affecting East Coast fever vector attachment, the other (SLA2) involved into lymphocytes proliferation, a process activated by T. parva parva infection. Our findings can orientate future research on the genetic basis of East Coast fever-tolerance, and derive from a general method that can be applied to investigate adaptation in analogous host-vector-parasite systems. Characterization of the genetic factors underlying East Coast-fever-tolerance represents an essential step towards enhancing sustainability and productivity of local agroecosystems.

evolutionary biology

Non-invasive Assessment of Liver Disease in Rats Using Multiparametric Magnetic Resonance Imaging: A Feasibility Study

Background & AimsNon-invasive quantitation of chronic liver disease using multiparametric MRI has the potential to refine clinical care pathways, trial design and preclinical drug development. The aim of the study was to evaluate the use of multiparametric liver MRI in experimental rat models of chronic liver disease. Methods: Liver injury was induced in male wild-type Sprague-Dawley rats using 4 or 12 weeks carbon tetrachloride (CCl4) intoxication and 4 or 8 weeks methionine and choline deficient (MCD) diet. Liver MRI was performed using a 7.0 Tesla small animal scanner at baseline and specified timepoints after liver injury. Multiparametric liver MRI parameters (T1 mapping, T2* mapping and proton density fat fraction (PDFF)) were correlated with gold standard histopathological measures, assessed by a blinded expert adjudicator. One-way analysis of variance with Tukeys post-hoc test was used to assess differences between groups and Spearmans rank-order correlation to examine associations.\n\nResultsMean hepatic T1 increased significantly in rats treated with CCl4 for 12 weeks compared to controls (1122{+/-}78 ms vs. 959{+/-}114 ms; d=162.7, 95% CI (11.92, 313.4), P=0.038) and correlated strongly with histological collagen content (rs=0.717, P=0.037). In MCD diet-treated rats, hepatic PDFF correlated strongly with histological fat content (rs=0.819, P<0.0001), steatosis grade (rs=0.850, P<0.0001) and steatohepatitis score (rs=0.818, P<0.0001). Although there was minimal histological iron, progressive fat accumulation in MCD diet-treated liver significantly shortened T2*.\n\nConclusionsIn preclinical models, quantitative MRI markers correlated with histopathological assessments, especially for fatty liver disease. Validation in longitudinal studies is required.\n\nKey pointsO_LINon-invasive quantitative measures of chronic liver injury are needed for stratification and monitoring of disease in clinical practice and in drug development\nC_LIO_LIWe adapted and applied a clinically-relevant non-contrast multiparametric MRI protocol in well-established preclinical liver disease models, for simultaneous quantitation of hepatic fat, fibro-inflammatory injury and iron\nC_LIO_LIMultiparametric liver MRI was feasible at 7.0 Tesla in experimental rat models and imaging parameters correlated with gold standard histopathological assessments, especially characteristics of fatty liver disease\nC_LIO_LIFurther development of multiparametric liver MRI could refine drug development strategies and impact upon trial design and clinical care pathways\nC_LI

pathology

BITE: an R package for biodiversity analyses

Nowadays, molecular data analyses for biodiversity studies often require advanced bioinformatics skills, preventing many life scientists from analyzing their own data autonomously. BITE R package provides complete and user-friendly functions to handle SNP data and third-party software results (i.e. Admixture, TreeMix), facilitating their visualization, interpretation and use. Furthermore, BITE implements additional useful procedures, such as representative sampling and bootstrap for TreeMix, filling the gap in existing biodiversity data analysis tools.\n\nAvailabilityhttps://github.com/marcomilanesi/BITE

bioinformatics