Background: The complex balance between environmental and host factors is an important determinant of susceptibility to infection. Disturbances of this equilibrium may result in multifactorial diseases as illustrated by the summer mortality syndrome, a worldwide and complex phenomenon that affects the oysters, Crassostrea gigas. The summer mortality syndrome reveals a physiological intolerance making this oyster species susceptible to diseases. Exploration of genetic basis governing the oyster resistance or susceptibility to infections is thus a major goal for understanding field mortality events. In this context, we used high-throughput genomic approaches to identify genetic traits that may characterize inherent survival capacities in C. gigas. Results: Using digital gene expression (DGE), we analyzed the transcriptomes of hemocytes (immunocompetent cells) of oysters able or not able to survive infections by Vibrio species shown to be involved in summer mortalities. Hemocytes were nonlethally collected from oysters before Vibrio experimental infection, and two DGE libraries were generated from individuals that survived or did not survive. Exploration of DGE data and microfluidic qPCR analyses at individual level showed an extraordinary polymorphism in gene expressions, but also a set of hemocyte-expressed genes whose basal mRNA levels discriminate oyster capacity to survive infections by the pathogenic V. splendidus LGP32. Finally, we identified a signature of 14 genes that predicted oyster survival capacity. Their expressions are likely driven by distinct transcriptional regulation processes associated or not associated to gene copy number variation (CNV). Conclusions: We provide here for the first time in oyster a gene expression survival signature that represents a useful tool for understanding mortality events and for assessing genetic traits of interest for disease resistance selection programs.

Introduction: Osteochondrosis (OC) is a developmental disease in horses which has a significant impact on the horse's welfare and performance. The early disturbance in the process of endochondral ossification progresses to inflammatory and repair processes in older horses. Previously, differentially expressed genes in leukocytes of OC-affected horses have been identified. The aim of the present study is to detect age-related changes in these differentially expressed genes. Materials and Methods: The expression of OC-related genes was analysed by real-time PCR and subsequent statistical analysis (ΔΔCT) in the leukocytes of 135 Belgian Warmblood horses divided into three different age groups: 30 months (n=38). Results: Relative expression of genes of horses less than 12 months of age showed significant induction of the genes MGAT4A, PRKCG, MHCI, ApoB, ApoB3G, B4GALT6 and a significantly lower expression of the genes OAS3. Horses of 18–24 months of age, showed a significantly higher expression of the genes TBC1D9, MGAT4A, IFIH1, MHCIIa and MMP1. Horses of more than 30 months of age showed a significantly higher expression of the genes MGAT4A, HP, SECTM1 compared with their age-matched control groups. Conclusions: The study demonstrates that OC-related genes are differentially expressed in horses of different ages compared with their age-matched controls. Some of the genes may be implicated in cell signalling and differentiation as well as carbohydrate and lipid metabolism and inflammation. However, the causal relationship between the differentially expressed genes and the development and progression of the OC lesions needs to be determined.

Pancreatic ductal adenocarcinoma (PDAC) is expected to be the second cause of cancer death by 2022. For nearly 80% of patients, diagnosis occurs at an advanced, nonsurgical stage, making such patients incurable. Gemcitabine is still an important component in PDAC treatment and is most often used as a backbone to test new targeted therapies and there is, to date, no routine biomarker to predict its efficacy. Samples from a phase III randomized trial were used to develop through a large approach based on blood-based liquid biopsy, transcriptome profiling, and machine learning, a nine gene predictive signature for gemcitabine sensitivity. Patients with a positive test (41.6%) had a significantly longer progression free survival (PFS) (3.8 months vs. 1.9 months p = 0.03) and a longer overall survival (OS) (14.5 months vs. 5.1, p < 0.0001). In multivariate analyses, this signature was independently associated with PFS (HR = 0.5 (0.28–0.9) p = 0.025) and OS (HR = 0.39 (0.21–0.7) p = 0.002).

Half of the patients with heart failure (HF) have preserved ejection fraction (HFpEF). To date, there are no specific markers to distinguish this subgroup. The main objective of this work was to stratify HF patients using current biochemical markers coupled with clinical data. The cohort study included HFpEF (n = 24) and heart failure with reduced ejection fraction (HFrEF) (n = 34) patients as usually considered in clinical practice based on cardiac imaging (EF ≥ 50% for HFpEF; EF < 50% for HFrEF). Routine blood tests consisted of measuring biomarkers of renal and heart functions, inflammation, and iron metabolism. A multi-test approach and analysis of peripheral blood samples aimed to establish a computerized Machine Learning strategy to provide a blood signature to distinguish HFpEF and HFrEF. Based on logistic regression, demographic characteristics and clinical biomarkers showed no statistical significance to differentiate the HFpEF and HFrEF patient subgroups. Hence a multivariate factorial discriminant analysis, performed blindly using the data set, allowed us to stratify the two HF groups. Consequently, a Machine Learning (ML) strategy was developed using the same variables in a genetic algorithm approach. ML provided very encouraging explorative results when considering the small size of the samples applied. The accuracy and the sensitivity were high for both validation and test groups (69% and 100%, 64% and 75%, respectively). Sensitivity was 100% for the validation and 75% for the test group, whereas specificity was 44% and 55% for the validation and test groups because of the small number of samples. Lastly, the precision was acceptable, with 58% in the validation and 60% in the test group. Combining biochemical and clinical markers is an excellent entry to develop a computer classification tool to diagnose HFpEF. This translational approach is a springboard for improving new personalized treatment methods and identifying “high-yield” populations for clinical trials.