Genetic diversity and multiplicity of infection in Fasciola gigantica isolates of Pakistani livestock

Fasciola spp. are responsible for over 3 billion US dollars of production loss annually in livestock and cause widespread zoonotic disease. Nevertheless, understating of the emergence and spread of the trematode species is poor. The multiplicity of F. gigantica infection and its spread is potentially influenced by multiple factors, including the abundance of suitable intermediate hosts, climatic conditions favouring the completion of the parasite's lifecycle, and translocation of infected animals, or free-living parasite stages between regions. Here we describe the development of a ‘tremabiome’ metabarcoding sequencing method to explore the numbers of F. gigantica genotypes per infection and patterns of parasite spread, based on genetic characteristics of the mitochondrial NADH dehydrogenase 1 (mt-ND-1) locus. We collected F. gigantica from three abattoirs in the Punjab and Balochistan provinces of Pakistan, and our results show a high level of genetic diversity in 20 F. gigantica populations derived from small and large ruminants consigned to slaughter in both provinces. This implies that F. gigantica can reproduce in its definitive hosts through meiosis involving cross- and self-breeding, as described in the closely related species, Fasciola hepatica. The genetic diversity between the 20 populations derived from different locations also illustrates the impact of animal movements on gene flow. Our results demonstrate the predominance of single haplotypes, consistent with a single introduction of F. gigantica infection in 85% of the hosts from which the parasite populations were derived. This is consistent with clonal reproduction in the intermediate snail hosts.[Display omitted]•To confirm the species identity of recovered Fasciola spp.•To identify the presence of single or multiple genotypes per infection (multiplicity of infection)•Demonstrate the spread of F. gigantica mt-ND-1 haplotype

Validation of miRNAs as Breast Cancer Biomarkers with a Machine Learning Approach

Certain small noncoding microRNAs (miRNAs) are differentially expressed in normal tissues and cancers, which makes them great candidates for biomarkers for cancer. Previously, a selected subset of miRNAs has been experimentally verified to be linked to breast cancer. In this paper, we validated the importance of these miRNAs using a machine learning approach on miRNA expression data. We performed feature selection, using Information Gain (IG), Chi-Squared (CHI2) and Least Absolute Shrinkage and Selection Operation (LASSO), on the set of these relevant miRNAs to rank them by importance. We then performed cancer classification using these miRNAs as features using Random Forest (RF) and Support Vector Machine (SVM) classifiers. Our results demonstrated that the miRNAs ranked higher by our analysis had higher classifier performance. Performance becomes lower as the rank of the miRNA decreases, confirming that these miRNAs had different degrees of importance as biomarkers. Furthermore, we discovered that using a minimum of three miRNAs as biomarkers for breast cancers can be as effective as using the entire set of 1800 miRNAs. This work suggests that machine learning is a useful tool for functional studies of miRNAs for cancer detection and diagnosis

A Machine Learning Approach for the Classification of Kidney Cancer Subtypes Using miRNA Genome Data

Kidney cancer is one of the deadliest diseases and its diagnosis and subtype classification are crucial for patients’ survival. Thus, developing automated tools that can accurately determine kidney cancer subtypes is an urgent challenge. It has been confirmed by researchers in the biomedical field that miRNA dysregulation can cause cancer. In this paper, we propose a machine learning approach for the classification of kidney cancer subtypes using miRNA genome data. Through empirical studies we found 35 miRNAs that possess distinct key features that aid in kidney cancer subtype diagnosis. In the proposed method, Neighbourhood Component Analysis (NCA) is employed to extract discriminative features from miRNAs and Long Short Term Memory (LSTM), a type of Recurrent Neural Network, is adopted to classify a given miRNA sample into kidney cancer subtypes. In the literature, only a couple of kidney subtypes have been considered for classification. In the experimental study, we used the miRNA quantitative read counts data, which was provided by The Cancer Genome Atlas data repository (TCGA). The NCA procedure selected 35 of the most discriminative miRNAs. With this subset of miRNAs, the LSTM algorithm was able to group kidney cancer miRNAs into five subtypes with average accuracy around 95% and Matthews Correlation Coefficient value around 0.92 under 10 runs of randomly grouped 5-fold cross-validation, which were very close to the average performance of using all miRNAs for classification

Preservation of Endothelium-Dependent Relaxation in Atherosclerotic Mice with Endothelium-Restricted Endothelin-1 Overexpression s

ABSTRACT In human atherosclerosis, which is associated with elevated plasma and coronary endothelin (ET)-1 levels, ET A receptor antagonists improve coronary endothelial function. Mice overexpressing ET-1 specifically in the endothelium (eET-1) crossed with atherosclerosis-prone apolipoprotein E knockout mice (Apoe 2/2 ) exhibit exaggerated high-fat diet (HFD)-induced atherosclerosis. Since endothelial dysfunction often precedes atherosclerosis development, we hypothesized that mice overexpressing endothelial ET-1 on a genetic background deficient in apolipoprotein E (eET-1/Apoe 2/2 ) would have severe endothelial dysfunction. To test this hypothesis, we investigated endothelium-dependent relaxation (EDR) to acetylcholine in eET-1/Apoe 2/2 mice. EDR in mesenteric resistance arteries from 8-and 16-week-old mice fed a normal diet or HFD was improved in eET-1/Apoe 2/2 compared with Apoe 2/2 mice. Nitric oxide synthase (NOS) inhibition abolished EDR in Apoe 2/2 . EDR in eET-1/Apoe 2/2 mice was resistant to NOS inhibition irrespective of age or diet. Inhibition of cyclooxygenase, the cytochrome P450 pathway, and endothelium-dependent hyperpolarization (EDH) resulted in little or no inhibition of EDR in eET-1/Apoe 2/2 compared with wild-type (WT) mice. In eET-1/Apoe 2/2 mice, blocking of EDH or soluble guanylate cyclase (sGC), in addition to NOS inhibition, decreased EDR by 36 and 30%, respectively. The activation of 4-aminopyridine-sensitive voltage-dependent potassium channels (K v ) during EDR was increased in eET-1/Apoe 2/2 compared with WT mice. We conclude that increasing eET-1 in mice that develop atherosclerosis results in decreased mutual dependence of endothelial signaling pathways responsible for EDR, and that NOS-independent activation of sGC and increased activation of K v are responsible for enhanced EDR in this model of atherosclerosis associated with elevated endothelial and circulating ET-1

Across the globe in 4 months: two international society of hypertension new investigator symposia in 2014

No abstract available